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Empirical Bayesian Approaches for Robust Constraint-based Causal Discovery under Insufficient Data | null | Causal discovery is to learn cause-effect relationships among variables given observational data and is important for many applications. Existing causal discovery methods assume data sufficiency, which may not be the case in many real world datasets. As a result, many existing causal discovery methods can fail under limited data. In this work, we propose Bayesian-augmented frequentist independence tests to improve the performance of constraint-based causal discovery methods under insufficient data: 1) We firstly introduce a Bayesian method to estimate mutual information (MI), based on which we propose a robust MI based independence test; 2) Secondly, we consider the Bayesian estimation of hypothesis likelihood and incorporate it into a well-defined statistical test, resulting in a robust statistical testing based independence test. We apply proposed independence tests to constraint-based causal discovery methods and evaluate the performance on benchmark datasets with insufficient samples. Experiments show significant performance improvement in terms of both accuracy and efficiency over SOTA methods. | Zijun Cui, Naiyu Yin, Yuru Wang, Qiang Ji | null | null | 2,022 | ijcai |
A Weighting-Based Tabu Search Algorithm for the p-Next Center Problem | null | The p-next center problem (pNCP) is an extension of the classical p-center problem. It consists of locating p centers from a set of candidate centers and allocating both a reference and a backup center to each client, to minimize the maximum cost, which is the length of the path from a client to its reference center and then to its backup center. Among them, the reference center is the closest center to a client and serves it under normal circumstances, while the backup center is the closest center to the reference center and serves the client when the reference center is out of service. In this paper, we propose a weighting-based tabu search algorithm called WTS for solving pNCP. WTS optimizes the pNCP by solving its decision subproblems with given assignment costs with an efficient swap-based neighborhood structure and a hierarchical penalty strategy for neighborhood evaluation. Extensive experimental studies on 413 benchmark instances demonstrate that WTS outperforms the state-of-the-art methods in the literature. Specifically, WTS improves 12 previous best known results and matches the optimal results for all remaining 401 ones in a much shorter time than other algorithms. More importantly, WTS reaches the lower bounds for 10 instances for the first time. | Qingyun Zhang, Zhouxing Su, Zhipeng Lü, Lingxiao Yang | null | null | 2,022 | ijcai |
On Attacking Out-Domain Uncertainty Estimation in Deep Neural Networks | null | In many applications with real-world consequences, it is crucial to develop reliable uncertainty estimation for the predictions made by the AI decision systems. Targeting at the goal of estimating uncertainty, various deep neural network (DNN) based uncertainty estimation algorithms have been proposed. However, the robustness of the uncertainty returned by these algorithms has not been systematically explored. In this work, to raise the awareness of the research community on robust uncertainty estimation, we show that state-of-the-art uncertainty estimation algorithms could fail catastrophically under our proposed adversarial attack despite their impressive performance on uncertainty estimation. In particular, we aim at attacking out-domain uncertainty estimation: under our attack, the uncertainty model would be fooled to make high-confident predictions for the out-domain data, which they originally would have rejected. Extensive experimental results on various benchmark image datasets show that the uncertainty estimated by state-of-the-art methods could be easily corrupted by our attack. | Huimin Zeng, Zhenrui Yue, Yang Zhang, Ziyi Kou, Lanyu Shang, Dong Wang | null | null | 2,022 | ijcai |
Deep Interactive Surface Creation from 3D Sketch Strokes | null | We present a deep neural framework that allows users to create surfaces from a stream of sparse 3D sketch strokes. Our network consists of a global surface estimation module followed by a local surface refinement. This facilitates in the incremental prediction of surfaces. Thus, our proposed method works with 3D sketch strokes and estimate a surface interactively in real time. We compare the proposed method with various state-of-the-art methods and show its efficacy for surface fitting. Further, we integrate our method into an existing Blender based 3D content creation pipeline to show its usefulness in 3D modelling. | Sukanya Bhattacharjee, Parag Chaudhuri | null | null | 2,022 | ijcai |
Learning Cluster Causal Diagrams: An Information-Theoretic Approach | null | Many real-world phenomena arise from causal relationships among a set of variables. As a powerful tool, Bayesian Network (BN) has been successful in describing high-dimensional distributions. However, the faithfulness condition, enforced in most BN learning algorithms, is violated in the settings where multiple variables synergistically affect the outcome (i.e., with polyadic dependencies). Building upon recent development in cluster causal diagrams (C-DAGs), we initiate the formal study of learning C-DAGs from observational data to relax the faithfulness condition. We propose a new scoring function, the Clustering Information Criterion (CIC), based on information-theoretic measures that represent various complex interactions among variables. The CIC score also contains a penalization of the model complexity under the minimum description length principle. We further provide a searching strategy to learn structures of high scores. Experiments on both synthetic and real data support the effectiveness of the proposed method. | Xueyan Niu, Xiaoyun Li, Ping Li | null | null | 2,022 | ijcai |
Linear Combinatorial Semi-Bandit with Causally Related Rewards | null | In a sequential decision-making problem, having a structural dependency amongst the reward distributions associated with the arms makes it challenging to identify a subset of alternatives that guarantees the optimal collective outcome. Thus, besides individual actions' reward, learning the causal relations is essential to improve the decision-making strategy. To solve the two-fold learning problem described above, we develop the 'combinatorial semi-bandit framework with causally related rewards', where we model the causal relations by a directed graph in a stationary structural equation model. The nodal observation in the graph signal comprises the corresponding base arm's instantaneous reward and an additional term resulting from the causal influences of other base arms' rewards. The objective is to maximize the long-term average payoff, which is a linear function of the base arms' rewards and depends strongly on the network topology. To achieve this objective, we propose a policy that determines the causal relations by learning the network's topology and simultaneously exploits this knowledge to optimize the decision-making process. We establish a sublinear regret bound for the proposed algorithm. Numerical experiments using synthetic and real-world datasets demonstrate the superior performance of our proposed method compared to several benchmarks. | Behzad Nourani-Koliji, Saeed Ghoorchian, Setareh Maghsudi | null | null | 2,022 | ijcai |
Automated Sifting of Stories from Simulated Storyworlds | null | Story sifting (or story recognition) allows for the exploration of events, stories, and patterns that emerge from simulated storyworlds. The goal of this work is to reduce the authoring burden for creating sifting queries. In this paper, we use the event traces of simulated storyworlds to create Dynamic Character Networks that track the changing relationship scores between characters in a simulation. These networks allow for the fortunes between any two characters to be plotted against time as a story arc. Similarity scores between story arcs from the simulation and a user’s query arc can be calculated using the Dynamic Time Warping algorithm. Events corresponding to the story arc that best matches the query arc can then be returned to the user, thus providing an intuitive means for users to sift a variety of stories without coding a search query. These components are implemented in our experimental prototype ARC SIFT. The results of a user study support our expectation that ARC SIFT is an intuitive and accurate tool that allows human users to sift stories out from a larger chronicle of events emerging from a simulated story world. | Wilkins Leong, Julie Porteous, John Thangarajah | null | null | 2,022 | ijcai |
Threshold Designer Adaptation: Improved Adaptation for Designers in Co-creative Systems | null | To best assist human designers with different styles, Machine Learning (ML) systems need to be able to adapt to them. However, there has been relatively little prior work on how and when to best adapt an ML system to a co-designer. In this paper we present threshold designer adaptation: a novel method for adapting a creative ML model to an individual designer. We evaluate our approach with a human subject study using a co-creative rhythm game design tool. We find that designers prefer our proposed method and produce higher quality content in comparison to an existing baseline. | Emily Halina, Matthew Guzdial | null | null | 2,022 | ijcai |
High-Resolution and Arbitrary-Sized Chinese Landscape Painting Creation Based on Generative Adversarial Networks | null | This paper outlines an automated creation system for Chinese landscape paintings based on generative adversarial networks. The system consists of three cascaded modules: generation, resizing, and super-resolution. The generation module first generates a square-shaped painting, then the resizing module predicts an appropriate aspect ratio for it and performs resizing, and finally the super-resolution module is used to increase the resolution and improve the quality. After training each module with the images we collected from the web, our system can create high-resolution landscape paintings in arbitrary sizes. | Peixiang Luo, Jinchao Zhang, Jie Zhou | null | null | 2,022 | ijcai |
Universal Video Style Transfer via Crystallization, Separation, and Blending | null | Universal video style transfer aims to migrate arbitrary styles to input videos. However, how to maintain the temporal consistency of videos while achieving high-quality arbitrary style transfer is still a hard nut to crack. To resolve this dilemma, in this paper, we propose the CSBNet which involves three key modules: 1) the Crystallization (Cr) Module that generates several orthogonal crystal nuclei, representing hierarchical stability-aware content and style components, from raw VGG features; 2) the Separation (Sp) Module that separates these crystal nuclei to generate the stability-enhanced content and style features; 3) the Blending (Bd) Module to cross-blend these stability-enhanced content and style features, producing more stable and higher-quality stylized videos. Moreover, we also introduce a new pair of component enhancement losses to improve network performance. Extensive qualitative and quantitative experiments are conducted to demonstrate the effectiveness and superiority of our CSBNet. Compared with the state-of-the-art models, it not only produces temporally more consistent and stable results for arbitrary videos but also achieves higher-quality stylizations for arbitrary images. | Haofei Lu, Zhizhong Wang | null | null | 2,022 | ijcai |
Sound2Synth: Interpreting Sound via FM Synthesizer Parameters Estimation | null | Synthesizer is a type of electronic musical instrument that is now widely used in modern music production and sound design. Each parameters configuration of a synthesizer produces a unique timbre and can be viewed as a unique instrument. The problem of estimating a set of parameters configuration that best restore a sound timbre is an important yet complicated problem, i.e.: the synthesizer parameters estimation problem. We proposed a multi-modal deep-learning-based pipeline Sound2Synth, together with a network structure Prime-Dilated Convolution (PDC) specially designed to solve this problem. Our method achieved not only SOTA but also the first real-world applicable results on Dexed synthesizer, a popular FM synthesizer. | Zui Chen, Yansen Jing, Shengcheng Yuan, Yifei Xu, Jian Wu, Hang Zhao | null | null | 2,022 | ijcai |
StyleCLIPDraw: Coupling Content and Style in Text-to-Drawing Translation | null | Generating images that fit a given text description using machine learning has improved greatly with the release of technologies such as the CLIP image-text encoder model; however, current methods lack artistic control of the style of image to be generated. We present an approach for generating styled drawings for a given text description where a user can specify a desired drawing style using a sample image. Inspired by a theory in art that style and content are generally inseparable during the creative process, we propose a coupled approach, known here as StyleCLIPDraw, whereby the drawing is generated by optimizing for style and content simultaneously throughout the process as opposed to applying style transfer after creating content in a sequence. Based on human evaluation, the styles of images generated by StyleCLIPDraw are strongly preferred to those by the sequential approach. Although the quality of content generation degrades for certain styles, overall considering both content and style, StyleCLIPDraw is found far more preferred, indicating the importance of style, look, and feel of machine generated images to people as well as indicating that style is coupled in the drawing process itself. Our code, a demonstration, and style evaluation data are publicly available. | Peter Schaldenbrand, Zhixuan Liu, Jean Oh | null | null | 2,022 | ijcai |
Towards Creativity Characterization of Generative Models via Group-Based Subset Scanning | null | Deep generative models, such as Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs), have been employed widely in computational creativity research. However, such models discourage out-of-distribution generation to avoid spurious sample generation, thereby limiting their creativity. Thus, incorporating research on human creativity into generative deep learning techniques presents an opportunity to make their outputs more compelling and human-like. As we see the emergence of generative models directed toward creativity research, a need for machine learning-based surrogate metrics to characterize creative output from these models is imperative.
We propose group-based subset scanning to identify, quantify, and characterize creative processes by detecting a subset of anomalous node-activations in the hidden layers of the generative models.
Our experiments on the standard image benchmarks and their ``creatively generated'' variants reveal that the proposed subset scores distribution is more useful for detecting novelty in creative processes in the activation space rather than the pixel space. Further, we found that creative samples generate larger subsets of anomalies than normal or non-creative samples across datasets. The node activations highlighted during the creative decoding process are different from those responsible for the normal sample generation. Lastly, we assess if the images from the subsets selected by our method were also found creative by human evaluators, presenting a link between creativity perception in humans and node activations within deep neural nets. | Celia Cintas, Payel Das, Brian Quanz, Girmaw Abebe Tadesse, Skyler Speakman, Pin-Yu Chen | null | null | 2,022 | ijcai |
Tradformer: A Transformer Model of Traditional Music Transcriptions | null | We explore the transformer neural network architecture for modeling music,
specifically Irish and Swedish traditional dance music.
Given the repetitive structures of these kinds of music, the transformer should be as successful with fewer parameters and complexity as the hitherto most successful model, a vanilla long short-term memory network.
We find that achieving good performance with the transformer is not straightforward,
and careful consideration is needed for the sampling strategy,
evaluating intermediate outputs in relation to engineering choices,
and finally analyzing what the model learns.
We discuss these points with several illustrations,
providing reusable insights for engineering other music generation systems.
We also report the high performance of our final transformer model
in a competition of music generation systems
focused on a type of Swedish dance. | Luca Casini, Bob L. T. Sturm | null | null | 2,022 | ijcai |
Captioning Bosch: A Twitter Bot | null | The artworks by Dutch painter Hieronymus Bosch are well known for their incredible wealth of details. The popular BoschBot regularly posts small segments of the digitized paintings on Twitter, thus relieving their density and making them more accessible. CaptioningBoschBot, the Twitter bot presented in this demo, reverses the creative process of the artist: It uses the out-of-context painting segments as input for an encoder-decoder model to generate captions that interpret the painted objects. As the model was only trained on realistic, photographic images, curious interpretations of the otherworldly details can be observed. The generated captions are again posted on Twitter to encourage discussions about Bosch's masterpieces and the AI technology in general. | Cornelia Ferner | null | null | 2,022 | ijcai |
Composition-aware Graphic Layout GAN for Visual-Textual Presentation Designs | null | In this paper, we study the graphic layout generation problem of producing high-quality visual-textual presentation designs for given images. We note that image compositions, which contain not only global semantics but also spatial information, would largely affect layout results. Hence, we propose a deep generative model, dubbed as composition-aware graphic layout GAN (CGL-GAN), to synthesize layouts based on the global and spatial visual contents of input images. To obtain training images from images that already contain manually designed graphic layout data, previous work suggests masking design elements (e.g., texts and embellishments) as model inputs, which inevitably leaves hint of the ground truth. We study the misalignment between the training inputs (with hint masks) and test inputs (without masks), and design a novel domain alignment module (DAM) to narrow this gap. For training, we built a large-scale layout dataset which consists of 60,548 advertising posters with annotated layout information. To evaluate the generated layouts, we propose three novel metrics according to aesthetic intuitions. Through both quantitative and qualitative evaluations, we demonstrate that the proposed model can synthesize high-quality graphic layouts according to image compositions. The data and code will be available at https://github.com/minzhouGithub/CGL-GAN. | Min Zhou, Chenchen Xu, Ye Ma, Tiezheng Ge, Yuning Jiang, Weiwei Xu | null | null | 2,022 | ijcai |
Art Creation with Multi-Conditional StyleGANs | null | Creating art is often viewed as a uniquely human endeavor. In this paper, we introduce a multi-conditional Generative Adversarial Network (GAN) approach trained on large amounts of human paintings to synthesize realistic-looking paintings that emulate human art. Our approach is based on the StyleGAN neural network architecture, but incorporates a custom multi-conditional control mechanism that provides fine-granular control over characteristics of the generated paintings, e.g., with regard to the perceived emotion evoked in a spectator. We also investigate several evaluation techniques tailored to multi-conditional generation. | Konstantin Dobler, Florian Hübscher, Jan Westphal, Alejandro Sierra-Múnera, Gerard de Melo, Ralf Krestel | null | null | 2,022 | ijcai |
Dataset Augmentation in Papyrology with Generative Models: A Study of Synthetic Ancient Greek Character Images | null | Character recognition models rely substantially on image datasets that maintain a balance of class samples. However, achieving a balance of classes is particularly challenging for ancient manuscript contexts as character instances may be significantly limited. In this paper, we present findings from a study that assess the efficacy of using synthetically generated character instances to augment an existing dataset of ancient Greek character images for use in machine learning models. We complement our model exploration by engaging professional papyrologists to better understand the practical opportunities afforded by synthetic instances. Our results suggest that synthetic instances improve model performance for limited character classes, and may have unexplored effects on character classes more generally. We also find that trained papyrologists are unable to distinguish between synthetic and non-synthetic images and regard synthetic instances as valuable assets for professional and educational contexts. We conclude by discussing the practical implications of our research. | Matthew I. Swindall, Timothy Player, Ben Keener, Alex C. Williams, James H. Brusuelas, Federica Nicolardi, Marzia D'Angelo, Claudio Vergara, Michael McOsker, John F. Wallin | null | null | 2,022 | ijcai |
Style Fader Generative Adversarial Networks for Style Degree Controllable Artistic Style Transfer | null | Artistic style transfer is the task of synthesizing content images with learned artistic styles. Recent studies have shown the potential of Generative Adversarial Networks (GANs) for producing artistically rich stylizations. Despite the promising results, they usually fail to control the generated images' style degree, which is inflexible and limits their applicability for practical use. To address the issue, in this paper, we propose a novel method that for the first time allows adjusting the style degree for existing GAN-based artistic style transfer frameworks in real time after training. Our method introduces two novel modules into existing GAN-based artistic style transfer frameworks: a Style Scaling Injection (SSI) module and a Style Degree Interpretation (SDI) module. The SSI module accepts the value of Style Degree Factor (SDF) as the input and outputs parameters that scale the feature activations in existing models, offering control signals to alter the style degrees of the stylizations. And the SDI module interprets the output probabilities of a multi-scale content-style binary classifier as the style degrees, providing a mechanism to parameterize the style degree of the stylizations. Moreover, we show that after training our method can enable existing GAN-based frameworks to produce over-stylizations. The proposed method can facilitate many existing GAN-based artistic style transfer frameworks with marginal extra training overheads and modifications. Extensive qualitative evaluations on two typical GAN-based style transfer models demonstrate the effectiveness of the proposed method for gaining style degree control for them. | Zhiwen Zuo, Lei Zhao, Shuobin Lian, Haibo Chen, Zhizhong Wang, Ailin Li, Wei Xing, Dongming Lu | null | null | 2,022 | ijcai |
Learning Pollution Maps from Mobile Phone Images | null | Air pollution monitoring and management is one of the key challenges for urban sectors, especially in developing countries. Measuring pollution levels requires significant investment in reliable and durable instrumentation and subsequent maintenance. On the other hand, there have been many attempts by researchers to develop image-based pollution measurement models which have shown significant results and established the feasibility of the idea. But, taking image-level models to a city-level system presents new challenges, which include scarcity of high-quality annotated data and a high amount of label noise. In this paper, we present a low-cost, end-to-end system for learning pollution maps using images captured through a mobile phone. We demonstrate our system for parts of New Delhi and Ghaziabad. We use transfer learning to overcome the problem of data scarcity. We investigate the effects of label noise in detail and introduce the metric of in-interval accuracy to evaluate our models in presence of noise. We use distributed averaging to learn pollution maps and mitigate the effects of noise to some extent. We also develop haze-based interpretable models which have comparable performance to mainstream models. With only 382 images from Delhi and Ghaziabad and single-scene dataset from Beijing and Shanghai, we are able to achieve a mean absolute error of 44 ug/m^3 in PM2.5 concentration on a test set of 267 images and an in-interval accuracy of 67% on predictions. Going further, we learn pollution maps with a mean absolute error as low as 35 ug/m^3 and in-interval accuracy as high as 74% significantly mitigating the image models' error. We also show that the noise in pollution labels emerging from unreliable sensing instrumentation forms a significant barrier to the realization of an ideal air pollution monitoring system. Our codebase can be found at https://github.com/ankitbha/pollution_with_images. | Ankit Bhardwaj, Shiva Iyer, Yash Jalan, Lakshminarayanan Subramanian | null | null | 2,022 | ijcai |
Chronic Disease Management with Personalized Lab Test Response Prediction | null | Chronic disease management involves frequent administration of invasive lab procedures in order for
clinicians to determine the best course of treatment
regimes for these patients. However, patients are
often put off by these invasive lab procedures and
do not follow the appointment schedules. This has
resulted in poor management of their chronic conditions leading to unnecessary disease complications. An AI system that is able to personalize the
prediction of individual patient lab test responses
will enable clinicians to titrate the medications to
achieve the desired therapeutic outcome. Accurate prediction of lab test response is a challenge because these patients typically have co-morbidities and their treatments might influence the target lab test response. To address this,
we model the complex interactions among different medications, diseases, lab test response, and
fine-grained dosage information to learn a strong
patient representation. Together with information
from similar patients and external knowledge such
as drug-lab interactions and diagnosis-lab interaction, we design a system called KALP to perform
personalized prediction of patients’ response for a
target lab result and identify the top influencing
factors for the prediction. Experiment results on
real-world datasets demonstrate the effectiveness of
KALP in reducing prediction errors by a significant
margin. Case studies show that the identified factors are consistent with clinicians’ understanding. | Suman Bhoi, Mong Li Lee, Wynne Hsu, Hao Sen Andrew Fang, Ngiap Chuan Tan | null | null | 2,022 | ijcai |
Forecasting Patient Outcomes in Kidney Exchange | null | Kidney exchanges allow patients with end-stage renal disease to find a lifesaving living donor by way of an organized market. However, not all patients are equally easy to match, nor are all donor organs of equal quality---some patients are matched within weeks, while others may wait for years with no match offers at all. We propose the first decision-support tool for kidney exchange that takes as input the biological features of a patient-donor pair, and returns (i) the probability of being matched prior to expiry, and (conditioned on a match outcome), (ii) the waiting time for and (iii) the organ quality of the matched transplant. This information may be used to inform medical and insurance decisions. We predict all quantities (i, ii, iii) exclusively from match records that are readily available in any kidney exchange using a quantile random forest approach. To evaluate our approach, we developed two state-of-the-art realistic simulators based on data from the United Network for Organ Sharing that sample from the training and test distribution for these learning tasks---in our application these distributions are distinct. We analyze distributional shift through a theoretical lens, and show that the two distributions converge as the kidney exchange nears steady-state. We then show that our approach produces clinically-promising estimates using simulated data. Finally, we show how our approach, in conjunction with tools from the model explainability literature, can be used to calibrate and detect bias in matching policies. | Naveen Durvasula, Aravind Srinivasan, John Dickerson | null | null | 2,022 | ijcai |
A Murder and Protests, the Capitol Riot, and the Chauvin Trial: Estimating Disparate News Media Stance | null | In this paper, we analyze the responses of three major US cable news networks to three seminal policing events in the US spanning a thirteen month period--the murder of George Floyd by police officer Derek Chauvin, the Capitol riot, Chauvin's conviction, and his sentencing. We cast the problem of aggregate stance mining as a natural language inference task and construct an active learning pipeline for robust textual entailment prediction. Via a substantial corpus of 34,710 news transcripts, our analyses reveal that the partisan divide in viewership of these three outlets reflects on the network's news coverage of these momentous events. In addition, we release a sentence-level, domain-specific text entailment data set on policing consisting of 2,276 annotated instances. | Sujan Dutta, Beibei Li, Daniel S. Nagin, Ashiqur R. KhudaBukhsh | null | null | 2,022 | ijcai |
AggPose: Deep Aggregation Vision Transformer for Infant Pose Estimation | null | Movement and pose assessment of newborns lets experienced pediatricians predict neurodevelopmental disorders, allowing early intervention for related diseases. However, most of the newest AI approaches for human pose estimation methods focus on adults, lacking publicly benchmark for infant pose estimation. In this paper, we fill this gap by proposing infant pose dataset and Deep Aggregation Vision Transformer for human pose estimation, which introduces a fast trained full transformer framework without using convolution operations to extract features in the early stages. It generalizes Transformer + MLP to high-resolution deep layer aggregation within feature maps, thus enabling information fusion between different vision levels. We pre-train AggPose on COCO pose dataset and apply it on our newly released large-scale infant pose estimation dataset. The results show that AggPose could effectively learn the multi-scale features among different resolutions and significantly improve the performance of infant pose estimation. We show that AggPose outperforms hybrid model HRFormer and TokenPose in the infant pose estimation dataset. Moreover, our AggPose outperforms HRFormer by 0.8 AP on COCO val pose estimation on average. Our code is available at github.com/SZAR-LAB/AggPose. | Xu Cao, Xiaoye Li, Liya Ma, Yi Huang, Xuan Feng, Zening Chen, Hongwu Zeng, Jianguo Cao | null | null | 2,022 | ijcai |
AI Facilitated Isolations? The Impact of Recommendation-based Influence Diffusion in Human Society | null | AI recommendation techniques provide users with personalized services, feeding them the information they may be interested in. The increasing personalization raises the hypotheses of the "filter bubble" and "echo chamber" effects. To investigate these hypotheses, in this paper, we inspect the impact of recommendation algorithms on forming two types of ideological isolation, i.e., the individual isolation and the topological isolation, in terms of the filter bubble and echo chamber effects, respectively. Simulation results show that AI recommendation strategies severely facilitate the evolution of the filter bubble effect, leading users to become ideologically isolated at an individual level. Whereas, at a topological level, recommendation algorithms show eligibility in connecting individuals with dissimilar users or recommending diverse topics to receive more diverse viewpoints. This research sheds light on the ability of AI recommendation strategies to temper ideological isolation at a topological level. | Yuxuan Hu, Shiqing Wu, Chenting Jiang, Weihua Li, Quan Bai, Erin Roehrer | null | null | 2,022 | ijcai |
Crowd, Expert & AI: A Human-AI Interactive Approach Towards Natural Language Explanation Based COVID-19 Misinformation Detection | null | In this paper, we study an explainable COVID-19 misinformation detection problem where the goal is to accurately identify COVID-19 misleading posts on social media and explain the posts with natural language explanations (NLEs). Our problem is motivated by the limitations of current explainable misinformation detection approaches that cannot provide NLEs for COVID-19 posts due to the lack of sufficient professional COVID-19 knowledge for supervision. To address such a limitation, we develop CEA-COVID, a crowd-expert-AI framework that jointly exploits the common logical reasoning ability of online crowd workers and the professional knowledge of COVID-19 experts to effectively generate NLEs for detecting and explaining COVID-19 misinformation. We evaluate CEA-COVID using two public COVID-19 misinformation datasets on social media. Results demonstrate that CEA-COVID outperforms existing explainable misinformation detection models in terms of both explainability and detection accuracy. | Ziyi Kou, Lanyu Shang, Yang Zhang, Zhenrui Yue, Huimin Zeng, Dong Wang | null | null | 2,022 | ijcai |
Am I No Good? Towards Detecting Perceived Burdensomeness and Thwarted Belongingness from Suicide Notes | null | The World Health Organization (WHO) has emphasized the importance of significantly accelerating suicide prevention efforts to fulfill the United Nations' Sustainable Development Goal (SDG) objective of 2030. In this paper, we present an end-to-end multitask system to address a novel task of detection of two interpersonal risk factors of suicide, Perceived Burdensomeness (PB) and Thwarted Belongingness (TB) from suicide notes. We also introduce a manually translated code-mixed suicide notes corpus, CoMCEASE-v2.0, based on the benchmark CEASE-v2.0 dataset, annotated with temporal orientation, PB and TB labels. We exploit the temporal orientation and emotion information in the suicide notes to boost overall performance. For comprehensive evaluation of our proposed method, we compare it to several state-of-the-art approaches on the existing CEASE-v2.0 dataset and the newly announced CoMCEASE-v2.0 dataset. Empirical evaluation suggests that temporal and emotional information can substantially improve the detection of PB and TB. | Soumitra Ghosh, Asif Ekbal, Pushpak Bhattacharyya | null | null | 2,022 | ijcai |
S2SNet: A Pretrained Neural Network for Superconductivity Discovery | null | Superconductivity allows electrical current to flow without any energy loss, and thus making solids superconducting is a grand goal of physics, material science, and electrical engineering. More than 16 Nobel Laureates have been awarded for their contribution in superconductivity research. Superconductors are valuable for sustainable development goals (SDGs), such as climate change mitigation, affordable and clean energy, industry, innovation and infrastructure, and so on. However, a unified physics theory explaining all superconductivity mechanism is still unknown. It is believed that superconductivity is microscopically due to not only molecular compositions but also the geometric crystal structure. Hence a new dataset, S2S, containing both crystal structures and superconducting critical temperature, is built upon SuperCon and Material Project. Based on this new dataset, we propose a novel model, S2SNet, which utilizes the attention mechanism for superconductivity prediction. To overcome the shortage of data, S2SNet is pre-trained on the whole Material Project dataset with Masked-Language Modeling (MLM). S2SNet makes a new state-of-the-art, with out-of-sample accuracy of 92% and Area Under Curve (AUC) of 0.92. To the best of our knowledge, S2SNet is the first work to predict superconductivity with only information of crystal structures. This work is beneficial to superconductivity discovery and further SDGs. The code and datasets are available at https://github.com/supercond/S2SNet | Ke Liu, Kaifan Yang, Jiahong Zhang, Renjun Xu | null | null | 2,022 | ijcai |
Towards the Quantitative Interpretability Analysis of Citizens Happiness Prediction | null | Evaluating the high-effect factors of citizens' happiness is beneficial to a wide range of policy-making for economics and politics in most countries. Benefiting from the high-efficiency of regression models, previous efforts by sociology scholars have analyzed the effect of happiness factors with high interpretability. However, restricted to their research concerns, they are specifically interested in some subset of factors modeled as linear functions. Recently, deep learning shows promising prediction accuracy while addressing challenges in interpretability. To this end, we introduce Shapley value that is inherent in solid theory for factor contribution interpretability to work with deep learning models by taking into account interactions between multiple factors. The proposed solution computes the Shapley value of a factor, i.e., its average contribution to the prediction in different coalitions based on coalitional game theory. Aiming to evaluate the interpretability quality of our solution, experiments are conducted on a Chinese General Social Survey (CGSS) questionnaire dataset. Through systematic reviews, the experimental results of Shapley value are highly consistent with academic studies in social science, which implies our solution for citizens' happiness prediction has 2-fold implications, theoretically and practically. | Lin Li, Xiaohua Wu, Miao Kong, Dong Zhou, Xiaohui Tao | null | null | 2,022 | ijcai |
Learning to Generate Poetic Chinese Landscape Painting with Calligraphy | null | In this paper, we present a novel system (denoted as Polaca) to generate poetic Chinese landscape painting with calligraphy. Unlike previous single image-to-image painting generation, Polaca takes the classic poetry as input and outputs the artistic landscape painting image with the corresponding calligraphy. It is equipped with three different modules to complete the whole piece of landscape painting artwork: the first one is a text-to-image module to generate landscape painting image, the second one is an image-to-image module to generate stylistic calligraphy image, and the third one is an image fusion module to fuse the two images into a whole piece of aesthetic artwork. | Shaozu Yuan, Aijun Dai, Zhiling Yan, Ruixue Liu, Meng Chen, Baoyang Chen, Zhijie Qiu, Xiaodong He | null | null | 2,022 | ijcai |
Creating Dynamic Checklists via Bayesian Case-Based Reasoning: Towards Decent Working Conditions for All | null | Every year there are 1.9 million deaths world-wide attributed to occupational health and safety risk factors. To address poor working conditions and fulfill UN's SDG 8, "protect labour rights and promote safe working environments for all workers", governmental agencies conduct labour inspections, using checklists to survey individual organisations for working environment violations. Recent research highlights the benefits of using machine learning for creating checklists. However, the current methods only create static checklists and do not adapt them to new information that surfaces during use. In contrast, we propose a new method called Context-aware Bayesian Case-Based Reasoning (CBCBR) that creates dynamic checklists. These checklists are continuously adapted as the inspections progress, based on how they are answered. Our evaluations show that CBCBR's dynamic checklists outperform static checklists created via the current state-of-the-art methods, increasing the expected number of working environment violations found in the labour inspections. | Eirik Lund Flogard, Ole Jakob Mengshoel, Kerstin Bach | null | null | 2,022 | ijcai |
Ownership Concentration and Wealth Inequality in Russia | null | Knowledge of beneficial owners of companies is key to monitoring and managing wealth inequality in any country. Here we propose a robust and scalable network-based algorithm to reveal hidden ultimate owners in public ownership data. Our approach is based on the idea of Katz centrality in complex networks and circumvents the problem of cyclic ownership used to obscure effective control
through closed chains of intermediaries. When applied to a country-scale directed ownership network with 6 million nodes, the algorithm identifies ultimate holders of every organisation in 2021’s Russia. The distribution of asset ownership in the country follows a power law, indicating strong wealth inequality with Gini index of 0.93. 51.7% of net assets of non-financial companies are ultimately held by the state and state-owned enterprises, 25.0% — by individuals (incl. 3.4% held by Forbes–200-listed individuals), and 11.3% are owned by foreign entities (incl. 5.7% in tax havens). | Kirill Polovnikov, Nikita Pospelov, Dmitriy Skougarevskiy | null | null | 2,022 | ijcai |
Sequential Vaccine Allocation with Delayed Feedback | null | In this work we consider the problem of how to best allocate a limited supply of vaccines in the aftermath of an infectious disease outbreak by viewing the problem as a sequential game between a learner and an environment (specifically, a bandit problem). The difficulty of this problem lies in the fact that the payoff of vaccination cannot be directly observed, making it difficult to compare the relative effectiveness of vaccination on different population groups. Currently used vaccination policies make recommendations based on mathematical modelling and ethical considerations. These policies are static, and do not adapt as conditions change. Our aim is to design and evaluate an algorithm which can make use of routine surveillance data to dynamically adjust its recommendation. We evaluate the performance of our approach by applying it to a simulated epidemic of a disease based on real-world COVID-19 data, and show that our vaccination policy was able to perform better than existing vaccine allocation policies.
In particular, we show that with our allocation method, we can reduce the number of required vaccination by at least 50% in order to keep the peak number of hospitalised patients below a certain threshold. Also, when the same batch sizes are used, our method can reduce the peak number of hospitalisation by up to 20%.
We also demonstrate that our vaccine allocation does not vary the number of batches per group much, making it socially more acceptable (as it reduces uncertainty, hence results in better and more interpretable communication). | Yichen Xiao, Han-Ching Ou, Haipeng Chen, Van Thieu Nguyen, Long Tran-Thanh | null | null | 2,022 | ijcai |
ADVISER: AI-Driven Vaccination Intervention Optimiser for Increasing Vaccine Uptake in Nigeria | null | More than 5 million children under five years die from largely preventable or treatable medical conditions every year, with an overwhelmingly large proportion of deaths occurring in under-developed countries with low vaccination uptake. One of the United Nations' sustainable development goals (SDG 3) aims to end preventable deaths of newborns and children under five years of age. We focus on Nigeria, where the rate of infant mortality is appalling. We collaborate with HelpMum, a large non-profit organization in Nigeria, to design and optimize the allocation of heterogeneous health interventions under uncertainty to increase vaccination uptake, the first such collaboration in Nigeria. Our framework, ADVISER: AI-Driven Vaccination Intervention Optimiser, is based on an integer linear program that seeks to maximize the cumulative probability of successful vaccination. Our optimization formulation is intractable in practice. We present a heuristic approach that enables us to solve the problem for real-world use-cases. We also present theoretical bounds for the heuristic method. Finally, we show that the proposed approach outperforms baseline methods in terms of vaccination uptake through experimental evaluation. HelpMum is currently planning a pilot program based on our approach to be deployed in the largest city of Nigeria, which would be the first deployment of an AI-driven vaccination uptake program in the country and hopefully, pave the way for other data-driven programs to improve health outcomes in Nigeria. | Vineet Nair, Kritika Prakash, Michael Wilbur, Aparna Taneja, Corrine Namblard, Oyindamola Adeyemo, Abhishek Dubey, Abiodun Adereni, Milind Tambe, Ayan Mukhopadhyay | null | null | 2,022 | ijcai |
A Reliability-aware Distributed Framework to Schedule Residential Charging of Electric Vehicles | null | Residential consumers have become active participants in the power distribution network after being equipped with residential EV charging provisions. This creates a challenge for the network operator tasked with dispatching electric power to the residential consumers through the existing distribution network infrastructure in a reliable manner. In this paper, we address the problem of scheduling residential EV charging for multiple consumers while maintaining network reliability. An additional challenge is the restricted exchange of information: where the consumers do not have access to network information and the network operator does not have access to consumer load parameters. We propose a distributed framework which generates an optimal EV charging schedule for individual residential consumers based on their preferences and iteratively updates it until the network reliability constraints set by the operator are satisfied. We validate the proposed approach for different EV adoption levels in a synthetically created digital twin of an actual power distribution network. The results demonstrate that the new approach can achieve a higher level of network reliability compared to the case where residential consumers charge EVs based solely on their individual preferences, thus providing a solution for the existing grid to keep up with increased adoption rates without significant investments in increasing grid capacity. | Rounak Meyur, Swapna Thorve, Madhav Marathe, Anil Vullikanti, Samarth Swarup, Henning Mortveit | null | null | 2,022 | ijcai |
DiRe Committee : Diversity and Representation Constraints in Multiwinner Elections | null | The study of fairness in multiwinner elections focuses on settings where candidates have attributes. However, voters may also be divided into predefined populations under one or more attributes. The models that focus on candidate attributes alone may systematically under-represent smaller voter populations. Hence, we develop a model, DiRe Committee Winner Determination (DRCWD), which delineates candidate and voter attributes to select a committee by specifying diversity and representation constraints and a voting rule. We analyze its computational complexity and develop a heuristic algorithm, which finds the winning DiRe committee in under two minutes on 63% of the instances of synthetic datasets and on 100% of instances of real-world datasets. We also present an empirical analysis of feasibility and utility traded-off.
Moreover, even when the attributes of candidates and voters coincide, it is important to treat them separately as diversity does not imply representation and vice versa. This is to say that having a female candidate on the committee, for example, is different from having a candidate on the committee who is preferred by the female voters, and who themselves may or may not be female. | Kunal Relia | null | null | 2,022 | ijcai |
Monitoring Vegetation From Space at Extremely Fine Resolutions via Coarsely-Supervised Smooth U-Net | null | Monitoring vegetation productivity at extremely fine resolutions is valuable for real-world agricultural applications, such as detecting crop stress and providing early warning of food insecurity. Solar-Induced Chlorophyll Fluorescence (SIF) provides a promising way to directly measure plant productivity from space. However, satellite SIF observations are only available at a coarse spatial resolution, making it impossible to monitor how individual crop types or farms are doing. This poses a challenging coarsely-supervised regression (or downscaling) task; at training time, we only have SIF labels at a coarse resolution (3km), but we want to predict SIF at much finer spatial resolutions (e.g. 30m, a 100x increase). We also have additional fine-resolution input features, but the relationship between these features and SIF is unknown. To address this, we propose Coarsely-Supervised Smooth U-Net (CS-SUNet), a novel method for this coarse supervision setting. CS-SUNet combines the expressive power of deep convolutional networks with novel regularization methods based on prior knowledge (such as a smoothness loss) that are crucial for preventing overfitting. Experiments show that CS-SUNet resolves fine-grained variations in SIF more accurately than existing methods. | Joshua Fan, Di Chen, Jiaming Wen, Ying Sun, Carla Gomes | null | null | 2,022 | ijcai |
Gigs with Guarantees: Achieving Fair Wage for Food Delivery Workers | null | With the increasing popularity of food delivery platforms, it has become pertinent to look into the working conditions of the `gig' workers in these platforms, especially providing them fair wages, reasonable working hours, and transparency on work availability. However, any solution to these problems must not degrade customer experience and be cost-effective to ensure that platforms are willing to adopt them. We propose Work4Food, which provides income guarantees to delivery agents, while minimizing platform costs and ensuring customer satisfaction. Work4Food ensures that the income guarantees are met in such a way that it does not lead to increased working hours or degrade environmental impact. To incorporate these objectives, Work4Food balances supply and demand by controlling the number of agents in the system and providing dynamic payment guarantees to agents based on factors such as agent location, ratings, etc. We evaluate Work4Food on a real-world dataset from a leading food delivery platform and establish its advantages over the state of the art in terms of the multi-dimensional objectives at hand. | Ashish Nair, Rahul Yadav, Anjali Gupta, Abhijnan Chakraborty, Sayan Ranu, Amitabha Bagchi | null | null | 2,022 | ijcai |
Dynamic Structure Learning through Graph Neural Network for Forecasting Soil Moisture in Precision Agriculture | null | Soil moisture is an important component of precision agriculture as it directly impacts the growth and quality of vegetation. Forecasting soil moisture is essential to schedule the irrigation and optimize the use of water. Physics based soil moisture models need rich features and heavy computation which is not scalable. In recent literature, conventional machine learning models have been applied for this problem. These models are fast and simple, but they often fail to capture the spatio-temporal correlation that soil moisture exhibits over a region. In this work, we propose a novel graph neural network based solution that learns temporal graph structures and forecast soil moisture in an end-to-end framework. Our solution is able to handle the problem of missing ground truth soil moisture which is common in practice. We show the merit of our algorithm on real-world soil moisture data. | Anoushka Vyas, Sambaran Bandyopadhyay | null | null | 2,022 | ijcai |
Forecasting the Number of Tenants At-Risk of Formal Eviction: A Machine Learning Approach to Inform Public Policy | null | Eviction of tenants has reached a crisis level in the U.S. and its consequences pose significant challenges to society. To tackle this eviction crisis, policymakers have been allocating financial resources but a more efficient resource allocation would need an accurate forecast of the number of tenants at-risk of evictions ahead of time. To help enhance the existing eviction prevention/diversion programs, in this work, we propose a multi-view deep neural network model, named as MARTIAN, that forecasts the number of tenants at-risk of getting formally evicted (at the census tract level) n months into the future. Then, we evaluate MARTIAN’s predictive performance under various conditions using real-world eviction cases filed across Dallas County, TX. The results of empirical evaluation show that MARTIAN outperforms an extensive set of baseline models in terms of predictive performance. Additionally, MARTIAN’s superior predictive performance is generalizable to unseen census tracts, for which no labeled data is available in the training set. This research has been done in collaboration with Child Poverty Action Lab (CPAL), which is a pioneering non-governmental organization (NGO) working for tackling poverty-related issues across Dallas County, TX. The usability of MARTIAN is under review by subject matter experts. We release our codebase at https://github.com/maryam-tabar/MARTIAN. | Maryam Tabar, Wooyong Jung, Amulya Yadav, Owen Wilson Chavez, Ashley Flores, Dongwon Lee | null | null | 2,022 | ijcai |
AgriBERT: Knowledge-Infused Agricultural Language Models for Matching Food and Nutrition | null | Pretraining domain-specific language models remains an important challenge which limits their applicability in various areas such as agriculture. This paper investigates the effectiveness of leveraging food related text corpora (e.g., food and agricultural literature) in pretraining transformer-based language models. We evaluate our trained language model, called AgriBERT, on the task of semantic matching, i.e., establishing mapping between food descriptions and nutrition data, which is a long-standing challenge in the agricultural domain. In particular, we formulate the task as an answer selection problem, fine-tune the trained language model with the help of an external source of knowledge (e.g., FoodOn ontology), and establish a baseline for this task. The experimental results reveal that our language model substantially outperforms other language models and baselines in the task of matching food description and nutrition. | Saed Rezayi, Zhengliang Liu, Zihao Wu, Chandra Dhakal, Bao Ge, Chen Zhen, Tianming Liu, Sheng Li | null | null | 2,022 | ijcai |
Revealing the Excitation Causality between Climate and Political Violence via a Neural Forward-Intensity Poisson Process | null | The causal mechanism between climate and political violence is fraught with complex mechanisms. Current quantitative causal models rely on one or more assumptions: (1) the climate drivers persistently generate conflict, (2) the causal mechanisms have a linear relationship with the conflict generation parameter, and/or (3) there is sufficient data to inform the prior distribution. Yet, we know conflict drivers often excite a social transformation process which leads to violence (e.g., drought forces agricultural producers to join urban militia), but further climate effects do not necessarily contribute to further violence. Therefore, not only is this bifurcation relationship highly non-linear, there is also often a lack of data to support prior assumptions for high resolution modeling.
Here, we aim to overcome the aforementioned causal modeling challenges by proposing a neural forward-intensity Poisson process (NFIPP) model. The NFIPP is designed to capture the potential non-linear causal mechanism in climate induced political violence, whilst being robust to sparse and timing-uncertain data. Our results span 20 recent years and reveal an excitation-based causal link between extreme climate events and political violence across diverse countries. Our climate-induced conflict model results are cross-validated against qualitative climate vulnerability indices. Furthermore, we label historical events that either improve or reduce our predictability gain, demonstrating the importance of domain expertise in informing interpretation. | Schyler C. Sun, Bailu Jin, Zhuangkun Wei, Weisi Guo | null | null | 2,022 | ijcai |
Quantifying Health Inequalities Induced by Data and AI Models | null | AI technologies are being increasingly tested and applied in critical environments including healthcare. Without an effective way to detect and mitigate AI induced inequalities, AI might do more harm than good, potentially leading to the widening of underlying inequalities. This paper proposes a generic allocation-deterioration framework for detecting and quantifying AI induced inequality. Specifically, AI induced inequalities are quantified as the area between two allocation-deterioration curves. To assess the framework’s performance, experiments were conducted on ten synthetic datasets (N>33,000) generated from HiRID - a real-world Intensive Care Unit (ICU) dataset, showing its ability to accurately detect and quantify inequality proportionally to controlled inequalities. Extensive analyses were carried out to quantify health inequalities (a) embedded in two real-world ICU datasets; (b) induced by AI models trained for two resource allocation scenarios. Results showed that compared to men, women had up to 33% poorer deterioration in markers of prognosis when admitted to HiRID ICUs. All four AI models assessed were shown to induce significant inequalities (2.45% to 43.2%) for non-White compared to White patients. The models exacerbated data embedded inequalities significantly in 3 out of 8 assessments, one of which was >9 times worse. | Honghan Wu, Aneeta Sylolypavan, Minhong Wang, Sarah Wild | null | null | 2,022 | ijcai |
Ranked Prioritization of Groups in Combinatorial Bandit Allocation | null | Preventing poaching through ranger patrols protects endangered wildlife, directly contributing to the UN Sustainable Development Goal 15 of life on land. Combinatorial bandits have been used to allocate limited patrol resources, but existing approaches overlook the fact that each location is home to multiple species in varying proportions, so a patrol benefits each species to differing degrees. When some species are more vulnerable, we ought to offer more protection to these animals; unfortunately, existing combinatorial bandit approaches do not offer a way to prioritize important species. To bridge this gap, (1) We propose a novel combinatorial bandit objective that trades off between reward maximization and also accounts for prioritization over species, which we call ranked prioritization. We show this objective can be expressed as a weighted linear sum of Lipschitz-continuous reward functions. (2) We provide RankedCUCB, an algorithm to select combinatorial actions that optimize our prioritization-based objective, and prove that it achieves asymptotic no-regret. (3) We demonstrate empirically that RankedCUCB leads to up to 38% improvement in outcomes for endangered species using real-world wildlife conservation data. Along with adapting to other challenges such as preventing illegal logging and overfishing, our no-regret algorithm addresses the general combinatorial bandit problem with a weighted linear objective. | Lily Xu, Arpita Biswas, Fei Fang, Milind Tambe | null | null | 2,022 | ijcai |
Scalable and Memory-Efficient Algorithms for Controlling Networked Epidemic Processes Using Multiplicative Weights Update Method | null | We study the problem of designing scalable algorithms to find effective intervention strategies for controlling stochastic epidemic processes on networks. This is a common problem arising in agent based models for epidemic spread.
Previous approaches to this problem focus on either heuristics with no guarantees or approximation algorithms that scale only to networks corresponding to county-sized populations, typically, with less than a million nodes. In particular, the mathematical-programming based approaches need to solve the Linear Program (LP) relaxation of the problem using an LP solver, which restricts the scalability of this approach. In this work, we overcome this restriction by designing an algorithm that adapts the multiplicative weights update (MWU) framework, along with the sample average approximation (SAA) technique, to approximately solve the linear program (LP) relaxation for the problem. To scale this approach further, we provide a memory-efficient algorithm that enables scaling to large networks, corresponding to country-size populations, with over 300 million nodes and 30 billion edges. Furthermore, we show that this approach provides near-optimal solutions to the LP in practice. | Prathyush Sambaturu, Marco Minutoli, Mahantesh Halappanavar, Ananth Kalyanaraman, Anil Vullikanti | null | null | 2,022 | ijcai |
Interactive concept-map based summaries for SEND children | null | Equitable and inclusive quality education is a human right. It is crucial to provide for the learning needs of every child, especially those with learning disabilities. Traditional approaches to learning propose education paths performed with speech therapists. One of the most efficient strategies to help children with reading comprehension difficulties is the creation of a ``concept map'', a structured summary of the written text in a graph structure. Online tools that offer students the possibility to manually create or automatically extract concept maps from text have been created over the years. However, there is still a shortage of software that are specifically designed for children at risk and which produce a concept map that is tailored to the clinical profiles of individuals. In this Project Collaboration, we want to tackle this gap by implementing a multi-modal, online and open-access Artificial-Intelligence powered tool that could help these children to make sense of written text by enabling them to interactively create concept maps. The expected output is threefold. We will implement a new model for concept-map-based document summarization and a clinically appropriate web interface. We will evaluate them in real-world settings through user studies performed by speech therapists. | Martina Galletti, Michael Anslow, Francesca Bianchi, Manuela Calanca, Donatella Tomaiuoli, Remi Van Trijp, Diletta Vedovelli, Eleonora Pasqua | null | null | 2,022 | ijcai |
Climate Bot: A Machine Reading Comprehension System for Climate Change Question Answering | null | Climate change has a severe impact on the overall ecosystem of the whole world, including humankind. This demo paper presents Climate Bot - a machine reading comprehension system for question answering over documents about climate change. The proposed Climate Bot provides an interface for users to ask questions in natural language and get answers from reliable data sources. The purpose of the climate bot is to spread awareness about climate change and help individuals and communities to learn about the impact and challenges of climate change. Additionally, we open-sourced an annotated climate change dataset CCMRC to promote further research on the topic. This paper describes the dataset collection, annotation, system design, and evaluation. | Md Rashad Al Hasan Rony, Ying Zuo, Liubov Kovriguina, Roman Teucher, Jens Lehmann | null | null | 2,022 | ijcai |
Argo: Towards Small Vessel Detection for Humanitarian Purposes | null | Refugees trying to get to Europe via the Mediterranean often face human rights violations. The present situation is not in line with the UN's SDG's 10 and 16. We present Argo: a semi-automatically created vessel classification dataset focused on small boats, with the aim to enable NGOs and the public to detect refugee boats in satellite imagery. We achieve a classification recall of 91% on small ships. With a tool developed on top of the results presented here, NGOs could collect information and hold institutions participating in illegal activities accountable. | Elisabeth Moser, Selina Meyer, Maximilian Schmidhuber, Daniel Ketterer, Matthias Eberhardt | null | null | 2,022 | ijcai |
Conversational Inequality Through the Lens of Political Interruption | null | We present a novel dataset of dialogues containing interruption with an aim to conduct a large-scale analysis of interruption patterns of people from diverse backgrounds in terms of gender, race/ethnicity, occupation, and political orientation. Our dataset includes 625,409 dialogues containing interruptions found in 275,420 transcripts from CNN, Fox News, and MSNBC spanning between January 2000 and July 2021. From this large, unlabeled pool of interruptions, we release an annotated dataset consisting of 2,000 dialogues with fine-grained interruption labels. We use this dataset to train an interruption classifier and predict the interruption type of a given dialogue. Our results reveal that male speakers (in our collected samples) tend to talk more than female speakers, while female speakers interrupt more. Moreover, people tend to use less intrusive interruptions when talking to others sharing the same political belief. This pattern becomes more pronounced among news media with stronger political bias. | Clay H. Yoo, Jiachen Wang, Yuxi Luo, Kunal Khadilkar, Ashiqur R. KhudaBukhsh | null | null | 2,022 | ijcai |
Psychiatric Scale Guided Risky Post Screening for Early Detection of Depression | null | Depression is a prominent health challenge to the world, and early risk detection (ERD) of depression from online posts can be a promising technique for combating the threat. Early depression detection faces the challenge of efficiently tackling streaming data, balancing the tradeoff between timeliness, accuracy and explainability. To tackle these challenges, we propose a psychiatric scale guided risky post screening method that can capture risky posts related to the dimensions defined in clinical depression scales, and providing interpretable diagnostic basis. A Hierarchical Attentional Network equipped with BERT (HAN-BERT) is proposed to further advance explainable predictions. For ERD, we propose an online algorithm based on an evolving queue of risky posts that can significantly reduce the number of model inferences to boost efficiency. Experiments show that our method outperforms the competitive feature-based and neural models under conventional depression detection settings, and achieves simultaneous improvement in both efficacy and efficiency for ERD. | Zhiling Zhang, Siyuan Chen, Mengyue Wu, Kenny Q. Zhu | null | null | 2,022 | ijcai |
On the Expressivity of Markov Reward (Extended Abstract) | null | Reward is the driving force for reinforcement-learning agents. We here set out to understand the expressivity of Markov reward as a way to capture tasks that we would want an agent to perform. We frame this study around three new abstract notions of "task": (1) a set of acceptable behaviors, (2) a partial ordering over behaviors, or (3) a partial ordering over trajectories. Our main results prove that while reward can express many of these tasks, there exist instances of each task type that no Markov reward function can capture. We then provide a set of polynomial-time algorithms that construct a Markov reward function that allows an agent to perform each task type, and correctly determine when no such reward function exists. | David Abel, Will Dabney, Anna Harutyunyan, Mark K. Ho, Michael L. Littman, Doina Precup, Satinder Singh | null | null | 2,022 | ijcai |
CounterGeDi: A Controllable Approach to Generate Polite, Detoxified and Emotional Counterspeech | null | Recently, many studies have tried to create generation models to assist counter speakers by providing counterspeech suggestions for combating the explosive proliferation of online hate. However, since these suggestions are from a vanilla generation model, they might not include the appropriate properties required to counter a particular hate speech instance. In this paper, we propose CounterGeDi - an ensemble of generative discriminators (GeDi) to guide the generation of a DialoGPT model toward more polite, detoxified, and emotionally laden counterspeech. We generate counterspeech using three datasets and observe significant improvement across different attribute scores. The politeness and detoxification scores increased by around 15% and 6% respectively, while the emotion in the counterspeech increased by at least 10% across all the datasets. We also experiment with triple-attribute control and observe significant improvement over single attribute results when combining complementing attributes, e.g., politeness, joyfulness and detoxification. In all these experiments, the relevancy of the generated text does not deteriorate due to the application of these controls. | Punyajoy Saha, Kanishk Singh, Adarsh Kumar, Binny Mathew, Animesh Mukherjee | null | null | 2,022 | ijcai |
Improving Inductive Link Prediction Using Hyper-Relational Facts (Extended Abstract) | null | For many years, link prediction on knowledge. graphs has been a purely transductive task, not allowing for reasoning on unseen entities.
Recently, increasing efforts are put into exploring semi- and fully inductive scenarios, enabling inference over unseen and emerging entities.
Still, all these approaches only consider triple-based KGs, whereas their richer counterparts, hyper-relational KGs (e.g., Wikidata), have not yet been properly studied.
In this work, we classify different inductive settings and study the benefits of employing hyper-relational KGs on a wide range of semi- and fully inductive link prediction tasks powered by recent advancements in graph neural networks.
Our experiments on a novel set of benchmarks show that qualifiers over typed edges can lead to performance improvements of 6% of absolute gains (for the Hits@10 metric) compared to triple-only baselines.
Our code is available at https://github.com/mali-git/hyper_relational_ilp. | Mehdi Ali, Max Berrendorf, Mikhail Galkin, Veronika Thost, Tengfei Ma, Volker Tresp, Jens Lehmann | null | null | 2,022 | ijcai |
Measuring Data Leakage in Machine-Learning Models with Fisher Information (Extended Abstract) | null | Machine-learning models contain information about the data they were trained on. This information leaks either through the model itself or through predictions made by the model. Consequently, when the training data contains sensitive attributes, assessing the amount of information leakage is paramount. We propose a method to quantify this leakage using the Fisher information of the model about the data. Unlike the worst-case a priori guarantees of differential privacy, Fisher information loss measures leakage with respect to specific examples, attributes, or sub-populations within the dataset. We motivate Fisher information loss through the Cramer-Rao bound and delineate the implied threat model. We provide efficient methods to compute Fisher information loss for output-perturbed generalized linear models. Finally, we empirically validate Fisher information loss as a useful measure of information leakage. | Awni Hannun, Chuan Guo, Laurens van der Maaten | null | null | 2,022 | ijcai |
Utilizing Treewidth for Quantitative Reasoning on Epistemic Logic Programs (Extended Abstract) | null | Extending the popular Answer Set Programming (ASP) paradigm by introspective reasoning capacities has received increasing interest within the last years. Particular attention is given to the formalism of epistemic logic programs (ELPs) where standard rules are equipped with modal operators which allow to express conditions on literals for being known or possible, i.e., contained in all or some answer sets, respectively. ELPs thus deliver multiple collections of answer sets, known as world views. Employing ELPs for reasoning problems so far has mainly been restricted to standard deci- sion problems (complexity analysis) and enumeration (development of systems) of world views. In this paper, we first establish quantitative reasoning for ELPs, where the acceptance of a certain set of literals depends on the number (proportion) of world views that are compatible with the set. Second, we present a novel system capable of efficiently solving the underlying counting problems required for quantitative reasoning. Our system exploits the graph-based measure treewidth by iteratively finding (graph) abstractions of ELPs. | Viktor Besin, Markus Hecher, Stefan Woltran | null | null | 2,022 | ijcai |
Combining Clause Learning and Branch and Bound for MaxSAT (Extended Abstract) | null | Branch and Bound (BnB) has been successfully used to solve many combinatorial optimization problems. However, BnB MaxSAT solvers perform poorly when solving real-world and academic optimization problems. They are only competitive for random and some crafted instances. Thus, it is a prevailing opinion in the community that BnB is not really useful for practical MaxSAT solving.
We refute this opinion by presenting a new BnB MaxSAT solver, called MaxCDCL, which combines clause learning and an efficient bounding procedure. MaxCDCL is among the top 5 out of a total of 15 exact solvers that participated in the 2020 MaxSAT Evaluation, solving several instances that other solvers cannot solve. Furthermore, MaxCDCL solves the highest number of instances from different MaxSAT Evaluations when combined with the best existing solvers. | Chu-Min Li, Zhenxing Xu, Jordi Coll, Felip Manyà, Djamal Habet, Kun He | null | null | 2,022 | ijcai |
Deep Cooperation of CDCL and Local Search for SAT (Extended Abstract) | null | Modern SAT solvers are based on a paradigm named conflict driven clause learning (CDCL), while local search is an important alternative. Although there have been attempts combining these two methods, this work proposes deeper cooperation techniques. First, we relax the CDCL framework by extending promising branches to complete assignments and calling a local search solver to search for a model nearby. More importantly, the local search assignments and the conflict frequency of variables in local search are exploited in the phase selection and branching heuristics of CDCL. We use our techniques to improve three typical CDCL solvers (glucose, MapleLCMDistChronoBT and Kissat). Experiments on benchmarks from the Main tracks of SAT Competitions 2017-2020 and a real world benchmark of spectrum allocation show that the techniques bring significant improvements, particularly on satisfiable instances. A resulting solver won the Main Track SAT category in SAT Competition 2020 and also performs very well on the spectrum allocation benchmark. As far as we know, this is the first work that meets the standard of the challenge ``Demonstrate the successful combination of stochastic search and systematic search techniques, by the creation of a new algorithm that outperforms the best previous examples of both approaches.'' (AAAI 1997) on standard application benchmarks. | Shaowei Cai, Xindi Zhang | null | null | 2,022 | ijcai |
A norm optimisation approach to SDGs: tackling poverty by acting on discrimination | null | Policies that seek to mitigate poverty by acting on equal opportunity have been found to aggravate discrimination against the poor (aporophobia), since individuals are made responsible for not progressing in the social hierarchy. Only a minority of the poor benefit from meritocracy in this era of growing inequality, generating resentment among those who seek to escape their needy situations by trying to climb up the ladder. Through the formulation and development of an agent-based social simulation, this study aims to analyse the role of norms implementing equal opportunity and social solidarity principles as enhancers or mitigators of aporophobia, as well as the threshold of aporophobia that would facilitate the success of poverty-reduction policies. The ultimate goal of the social simulation is to extract insights that could help inform and guide a new generation of policy making for poverty reduction by acting on the discrimination against the poor, in line with the UN “Leave No One Behind” principle. An “aporophobia-meter” will be developed and guidelines will be drafted based on both the simulation results and a review of poverty reduction policies at regional levels. | Georgina Curto, Nieves Montes, Carles Sierra, Nardine Osman, Flavio Comim | null | null | 2,022 | ijcai |
Capturing Homomorphism-Closed Decidable Queries with Existential Rules (Extended Abstract) | null | Existential rules are a very popular ontology-mediated query language for which the chase represents a generic computational approach for query answering. It is straightforward that existential rule queries exhibiting chase termination are decidable and can only recognize properties that are preserved under homomorphisms.
This paper is an extended abstract of our eponymous publication at KR 2021 where we show the converse: every decidable query that is closed under homomorphism can be expressed by an existential rule set for which the standard chase universally terminates. Membership in this fragment is not decidable, but we show via a diagonalisation argument that this is unavoidable. | Camille Bourgaux, David Carral, Markus Krötzsch, Sebastian Rudolph, Michaël Thomazo | null | null | 2,022 | ijcai |
Complex Query Answering with Neural Link Predictors (Extended Abstract)* | null | Neural link predictors are useful for identifying missing edges in large scale Knowledge Graphs. However, it is still not clear how to use these models for answering more complex queries containing logical conjunctions (∧), disjunctions (∨), and existential quantifiers (∃). We propose a framework for efficiently answering complex queries on in- complete Knowledge Graphs. We translate each query into an end-to-end differentiable objective, where the truth value of each atom is computed by a pre-trained neural link predictor. We then analyse two solutions to the optimisation problem, including gradient-based and combinatorial search. In our experiments, the proposed approach produces more accurate results than state-of-the-art methods — black-box models trained on millions of generated queries — without the need for training on a large and diverse set of complex queries. Using orders of magnitude less training data, we obtain relative improvements ranging from 8% up to 40% in Hits@3 across multiple knowledge graphs. We find that it is possible to explain the outcome of our model in terms of the intermediate solutions identified for each of the complex query atoms. All our source code and datasets are available online (https://github.com/uclnlp/cqd). | Pasquale Minervini, Erik Arakelyan, Daniel Daza, Michael Cochez | null | null | 2,022 | ijcai |
Open Data Science to Fight COVID-19: Winning the 500k XPRIZE Pandemic Response Challenge (Extended Abstract) | null | We describe the deep learning-based COVID-19 cases predictor and the Pareto-optimal Non-Pharmaceutical Intervention (NPI) prescriptor developed by the winning team of the 500k XPRIZE Pandemic Response Challenge. The competition aimed at developing data-driven AI models to predict COVID-19 infection rates and to prescribe NPI Plans that governments, business leaders and organizations could implement to minimize harm when reopening their economies. In addition to the validation performed by XPRIZE with real data, our models were validated in a real-world scenario thanks to an ongoing collaboration with the Valencian Government in Spain. Our experience contributes to a necessary transition to more evidence-driven policy-making during a pandemic. | Miguel Angel Lozano, Òscar Garibo-i-Orts, Eloy Piñol, Miguel Rebollo, Kristina Polotskaya, Miguel Ángel García-March, J. Alberto Conejero, Francisco Escolano, Nuria Oliver | null | null | 2,022 | ijcai |
Allocating Opportunities in a Dynamic Model of Intergenerational Mobility (Extended Abstract) | null | Opportunities such as higher education can promote intergenerational mobility, leading individuals to achieve levels of socioeconomic status above that of their parents. In this work, which is an extended abstract of a longer paper in the proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, we develop a dynamic model for allocating such opportunities in a society that exhibits bottlenecks in mobility; the problem of optimal allocation reflects a trade-off between the benefits conferred by the opportunities in the current generation and the potential to elevate the socioeconomic status of recipients, shaping the composition of future generations in ways that can benefit further from the opportunities. We show how optimal allocations in our model arise as solutions to continuous optimization problems over multiple generations, and we find in general that these optimal solutions can favor recipients of low socioeconomic status over slightly higher-performing individuals of high socioeconomic status --- a form of socioeconomic affirmative action that the society in our model discovers in the pursuit of purely payoff-maximizing goals. We characterize how the structure of the model can lead to either temporary or persistent affirmative action, and we consider extensions of the model with more complex processes modulating the movement between different levels of socioeconomic status. | Hoda Heidari, Jon Kleinberg | null | null | 2,022 | ijcai |
Scalable Anytime Planning for Multi-Agent MDPs (Extended Abstract) | null | We present a scalable planning algorithm for multi-agent sequential decision problems that require dynamic collaboration.
Teams of agents need to coordinate decisions in many domains,
but naive approaches fail due to the exponential growth of the joint action space with the number of agents.
We circumvent this complexity through an anytime approach that allows us to trade computation for
approximation quality and also dynamically coordinate actions.
Our algorithm comprises three elements: online planning with Monte Carlo
Tree Search (MCTS), factorizing local agent interactions with coordination graphs, and
selecting optimal joint actions with the Max-Plus method.
On the benchmark SysAdmin domain with static coordination graphs, our approach achieves comparable performance with much lower computation cost than the MCTS baselines.
We also introduce a multi-drone delivery domain with dynamic, i.e., state-dependent coordination graphs, and demonstrate how our approach scales to large problems on this domain that are intractable for other MCTS methods. | Shushman Choudhury, Jayesh K. Gupta, Mykel J. Kochenderfer | null | null | 2,022 | ijcai |
Logic Rules Meet Deep Learning: A Novel Approach for Ship Type Classification (Extended Abstract) | null | The shipping industry is an important component of the global trade and economy. In order to ensure law compliance and safety, it needs to be monitored. In this paper, we present a novel ship type classification model that combines vessel transmitted data from the Automatic Identification System, with vessel imagery. The main components of our approach are the Faster R-CNN Deep Neural Network and a Neuro-Fuzzy system with IF-THEN rules. We evaluate our model using real world data and showcase the advantages of this combination while also compare it with other methods. Results show that our model can increase prediction scores by up to 15.4% when compared with the next best model we considered, while also maintaining a level of explainability as opposed to common black box approaches. | Manolis Pitsikalis, Thanh-Toan Do, Alexei Lisitsa, Shan Luo | null | null | 2,022 | ijcai |
Computing Programs for Generalized Planning as Heuristic Search (Extended Abstract) | null | Although heuristic search is one of the most successful approaches to classical planning, this planning paradigm does not apply straightforwardly to Generalized Planning (GP). This paper adapts the planning as heuristic search paradigm to the particularities of GP, and presents the first native heuristic search approach to GP. First, the paper defines a program-based solution space for GP that is independent of the number of planning instances in a GP problem, and the size of these instances. Second, the paper defines the BFGP algorithm for GP, that implements a best-first search in our program-based solution space, and that is guided by different evaluation and heuristic functions. | Javier Segovia-Aguas, Sergio Jiménez Celorrio, Anders Jonsson | null | null | 2,022 | ijcai |
Towards Facilitating Empathic Conversations in Online Mental Health Support: A Reinforcement Learning Approach (Extended Abstract) | null | Online peer-to-peer support platforms enable conversations between millions of people who seek and provide mental health support. If successful, web-based mental health conversations could improve access to treatment and reduce the global disease burden. Psychologists have repeatedly demonstrated that empathy, the ability to understand and feel the emotions and experiences of others, is a key component leading to positive outcomes in supportive conversations. However, recent studies have shown that highly empathic conversations are rare in online mental health platforms.
In this paper, we work towards improving empathy in online mental health support conversations. We introduce a new task of empathic rewriting which aims to transform low-empathy conversational posts to higher empathy. Learning such transformations is challenging and requires a deep understanding of empathy while maintaining conversation quality through text fluency and specificity to the conversational context. Here we propose Partner, a deep reinforcement learning (RL) agent that learns to make sentence-level edits to posts in order to increase the expressed level of empathy while maintaining conversation quality. Our RL agent leverages a policy network, based on a transformer language model adapted from GPT-2, which performs the dual task of generating candidate empathic sentences and adding those sentences at appropriate positions. Through a combination of automatic and human evaluation, we demonstrate that Partner successfully generates more empathic, specific, and diverse responses and outperforms NLP methods from related tasks such as style transfer and empathic dialogue generation. | Ashish Sharma, Inna W. Lin, Adam S. Miner, Dave C. Atkins, Tim Althoff | null | null | 2,022 | ijcai |
Homeomorphic-Invariance of EM: Non-Asymptotic Convergence in KL Divergence for Exponential Families via Mirror Descent (Extended Abstract) | null | Expectation maximization (EM) is the default algorithm for fitting probabilistic models with missing or latent variables, yet we lack a full understanding of its non-asymptotic convergence properties. Previous works show results along the lines of “EM converges at least as fast as gradient descent” by assuming the conditions for the convergence of gradient descent apply. This approach is not only loose, in that it does not capture that EM can make more progress than a gradient step, but the assumptions fail to hold for textbook examples of EM like Gaussian mixtures. In this work, we show that for the common setting of exponential family distributions, viewing EM as a mirror descent algorithm leads to convergence rates in Kullback-Leibler (KL) divergence and how the KL divergence is related to first-order stationarity via Bregman divergences. In contrast to previous works, the analysis is invariant to the choice of parametrization and holds with minimal assumptions. We also show applications of these ideas to local linear (and superlinear) convergence rates, generalized EM, and non-exponential family distributions. | Frederik Kunstner, Raunak Kumar, Mark Schmidt | null | null | 2,022 | ijcai |
Computationally Efficient Optimization of Plackett-Luce Ranking Models for Relevance and Fairness (Extended Abstract) | null | Computing the gradient of stochastic Plackett-Luce (PL) ranking models for relevance and fairness metrics can be infeasible because it requires iterating over all possible permutations of items.
In this paper, we introduce a novel algorithm: PL-Rank, that estimates the gradient of a PL ranking model through sampling. Unlike existing approaches, PL-Rank makes use of the specific structure of PL models and ranking metrics. Our experimental analysis shows that PL-Rank has a greater sample-efficiency and is computationally less costly than existing policy gradients, resulting in faster convergence at higher performance. | Harrie Oosterhuis | null | null | 2,022 | ijcai |
The Min-Max Complexity of Distributed Stochastic Convex Optimization with Intermittent Communication (Extended Abstract) | null | We resolve the min-max complexity of distributed stochastic convex optimization (up to a log factor) in the intermittent communication setting, where M machines work in parallel over the course of R rounds of communication to optimize the objective, and during each round of communication, each machine may sequentially compute K stochastic gradient estimates. We present a novel lower bound with a matching upper bound that establishes an optimal algorithm. | Blake Woodworth, Brian Bullins, Ohad Shamir, Nathan Srebro | null | null | 2,022 | ijcai |
ProtoAI: Model-Informed Prototyping for AI-Powered Interfaces (Extended Abstract) | null | When prototyping AI experiences (AIX), interface designers seek effective ways to support end-user tasks through AI capabilities. However, AI poses challenges to design due to its dynamic behavior in response to training data, end-user data, and feedback. Designers must consider AI's uncertainties and offer adaptations such as explainability, error recovery, and automation vs. human task control. Unfortunately, current prototyping tools assume a black-box view of AI, forcing designers to work with separate tools to explore machine learning models, understand model performance, and align interface choices with model behavior. This introduces friction to rapid and iterative prototyping. We propose Model-Informed Prototyping (MIP), a workflow for AIX design that combines model exploration with UI prototyping tasks. Our system, ProtoAI, allows designers to directly incorporate model outputs into interface designs, evaluate design choices across different inputs, and iteratively revise designs by analyzing model breakdowns. | Hariharan Subramonyam, Colleen Seifert, Eytan Adar | null | null | 2,022 | ijcai |
Detect, Understand, Act: A Neuro-Symbolic Hierarchical Reinforcement Learning Framework (Extended Abstract) | null | We introduce Detect, Understand, Act (DUA), a neuro-symbolic reinforcement learning framework. The Detect component is composed of a traditional computer vision object detector and tracker. The Act component houses a set of options, high-level actions enacted by pre-trained deep reinforcement learning (DRL) policies. The Understand component provides a novel answer set programming (ASP) paradigm for effectively learning symbolic meta-policies over options using inductive logic programming (ILP). We evaluate our framework on the Animal-AI (AAI) competition testbed, a set of physical cognitive reasoning problems. Given a set of pre-trained DRL policies, DUA requires only a few examples to learn a meta-policy that allows it to improve the state-of-the-art on multiple of the most challenging categories from the testbed. DUA constitutes the first holistic hybrid integration of computer vision, ILP and DRL applied to an AAI-like environment and sets the foundations for further use of ILP in complex DRL challenges. | Ludovico Mitchener, David Tuckey, Matthew Crosby, Alessandra Russo | null | null | 2,022 | ijcai |
Statistically-Guided Deep Network Transformation to Harness Heterogeneity in Space (Extended Abstract) | null | Spatial data are ubiquitous and have transformed decision-making in many critical domains, including public health, agriculture, transportation, etc. While recent advances in machine learning offer promising ways to harness massive spatial datasets (e.g., satellite imagery), spatial heterogeneity -- a fundamental property of spatial data -- poses a major challenge as data distributions or generative processes often vary over space. Recent studies targeting this difficult problem either require a known space-partitioning as the input, or can only support limited special cases (e.g., binary classification). Moreover, heterogeneity-pattern learned by these methods are locked to the locations of the training samples, and cannot be applied to new locations. We propose a statistically-guided framework to adaptively partition data in space during training using distribution-driven optimization and transform a deep learning model (of user's choice) into a heterogeneity-aware architecture. We also propose a spatial moderator to generalize learned patterns to new test regions. Experiment results on real-world datasets show that the framework can effectively capture footprints of heterogeneity and substantially improve prediction performances. | Yiqun Xie, Erhu He, Xiaowei Jia, Han Bao, Xun Zhou, Rahul Ghosh, Praveen Ravirathinam | null | null | 2,022 | ijcai |
Asymmetric Hybrids: Dialogues for Computational Concept Combination (Extended Abstract) | null | When considering two concepts in terms of extensional logic, their combination will often be trivial, returning an empty extension. Consider e.g. “a Fish Vehicle”, i.e., “a Vehicle which is also a Fish”. Still, people use sophisticated strategies to produce new, non-empty concepts. All these strategies involve the human ability to mend the conflicting attributes of the input concepts and to create new properties of the combination. We focus in particular on the case where a Head concept has superior ‘asymmetric’ control over steering the resulting combination (or hybridisation) with a Modifier concept. Specifically, we propose a dialogical model of the cognitive and logical mechanics of this asymmetric form of hybridisation. Its implementation is then evaluated using a combination of example ontologies. | Guendalina Righetti, Daniele Porello, Nicolas Troquard, Oliver Kutz, Maria Hedblom, Pietro Galliani | null | null | 2,022 | ijcai |
Black-box Audit of YouTube's Video Recommendation: Investigation of Misinformation Filter Bubble Dynamics (Extended Abstract) | null | In this paper, we describe a black-box sockpuppeting audit which we carried out to investigate the creation and bursting dynamics of misinformation filter bubbles on YouTube. Pre-programmed agents acting as YouTube users stimulated YouTube's recommender systems: they first watched a series of misinformation promoting videos (bubble creation) and then a series of misinformation debunking videos (bubble bursting). Meanwhile, agents logged videos recommended to them by YouTube. After manually annotating these recommendations, we were able to quantify the portion of misinformative videos among them. The results confirm the creation of filter bubbles (albeit not in all situations) and show that these bubbles can be bursted by watching credible content. Drawing a direct comparison with a previous study, we do not see improvements in overall quantities of misinformation recommended. | Matus Tomlein, Branislav Pecher, Jakub Simko, Ivan Srba, Robert Moro, Elena Stefancova, Michal Kompan, Andrea Hrckova, Juraj Podrouzek, Maria Bielikova | null | null | 2,022 | ijcai |
Unbiased Gradient Estimation in Unrolled Computation Graphs with Persistent Evolution Strategies (Extended Abstract) | null | Current approaches for optimizing parameters in unrolled computation graphs suffer from high variance gradients, bias, slow updates, or large memory usage. We introduce a method called Persistent Evolution Strategies (PES), which divides the computation graph into a series of truncated unrolls, and performs an evolution strategies-based update step after each unroll. PES eliminates bias from these truncations by accumulating correction terms over the entire sequence of unrolls. PES allows for rapid parameter updates, has low memory usage, is unbiased, and has reasonable variance. | Paul Vicol, Luke Metz, Jascha Sohl-Dickstein | null | null | 2,022 | ijcai |
Including Signed Languages in Natural Language Processing (Extended Abstract) | null | Signed languages are the primary means of communication for many deaf and hard of hearing individuals. Since signed languages exhibit all the fundamental linguistic properties of natural language, we believe that tools and theories of Natural Language Processing (NLP) are crucial towards its modeling. However, existing research in Sign Language Processing (SLP) seldom attempt to explore and leverage the linguistic organization of signed languages. This position paper calls on the NLP community to include signed languages as a research area with high social and scientific impact. We first discuss the linguistic properties of signed languages to consider during their modeling. Then, we review the limitations of current SLP models and identify the open challenges to extend NLP to signed languages. Finally, we urge (1) the adoption of an efficient tokenization method; (2) the development of linguistically-informed models; (3) the collection of real-world signed language data; (4) the inclusion of local signed language communities as an active and leading voice in research. | Kayo Yin, Malihe Alikhani | null | null | 2,022 | ijcai |
Rx-refill Graph Neural Network to Reduce Drug Overprescribing Risks (Extended Abstract) | null | Prescription (aka Rx) drugs can be easily overprescribed and lead to drug abuse or opioid overdose. Accordingly, a state-run prescription drug monitoring program (PDMP) in the United States has been developed to reduce overprescribing. However, PDMP has limited capability in detecting patients' potential overprescribing behaviors, impairing its effectiveness in preventing drug abuse and overdose in patients. In this paper, we propose a novel model RxNet, which builds 1) a dynamic heterogeneous graph to model Rx refills that are essentially prescribing and dispensing (P&D) relationships among various patients, 2) an RxLSTM network to explore the dynamic Rx-refill behavior and medical condition variation of patients, and 3) a dosing-adaptive network to extract and recalibrate dosing patterns and obtain the refined patient representations which are finally utilized for overprescribing detection. The extensive experimental results on a one-year state-wide PDMP data demonstrate that RxNet consistently outperforms state-of-the-art methods in predicting patients at high risk of opioid overdose and drug abuse. | Jianfei Zhang, Ai-Te Kuo, Jianan Zhao, Qianlong Wen, Erin Winstanley, Chuxu Zhang, Yanfang Ye | null | null | 2,022 | ijcai |
Fair Division of Indivisible Goods: A Survey | null | Allocating resources to individuals in a fair manner has been a topic of interest since the ancient times, with most of the early rigorous mathematical work on the problem focusing on infinitely divisible resources. Recently, there has been a surge of papers studying computational questions regarding various different notions of fairness for the indivisible case, like maximin share fairness (MMS) and envy-freeness up to any good (EFX). We survey the most important results in the discrete fair division literature, focusing on the case of additive valuation functions and paying particular attention to the progress made in the last 10 years. | Georgios Amanatidis, Georgios Birmpas, Aris Filos-Ratsikas, Alexandros A. Voudouris | null | null | 2,022 | ijcai |
A Survey on Word Meta-Embedding Learning | null | Meta-embedding (ME) learning is an emerging approach that attempts to learn more accurate word embeddings given existing (source) word embeddings as the sole input.
Due to their ability to incorporate semantics from multiple source embeddings in a compact manner with superior performance, ME learning has gained popularity among practitioners in NLP.
To the best of our knowledge, there exist no prior systematic survey on ME learning and this paper
attempts to fill this need.
We classify ME learning methods according to multiple factors such as whether they
(a) operate on static or contextualised embeddings, (b) trained in an unsupervised manner or (c) fine-tuned for a particular task/domain.
Moreover, we discuss the limitations of existing ME learning methods and highlight potential future research directions. | Danushka Bollegala, James O' Neill | null | null | 2,022 | ijcai |
Image-text Retrieval: A Survey on Recent Research and Development | null | In the past few years, cross-modal image-text retrieval (ITR) has experienced increased interest in the research community due to its excellent research value and broad real-world application. It is designed for the scenarios where the queries are from one modality and the retrieval galleries from another modality. This paper presents a comprehensive and up-to-date survey on the ITR approaches from four perspectives. By dissecting an ITR system into two processes: feature extraction and feature alignment, we summarize the recent advance of the ITR approaches from these two perspectives. On top of this, the efficiency-focused study on the ITR system is introduced as the third perspective. To keep pace with the times, we also provide a pioneering overview of the cross-modal pre-training ITR approaches as the fourth perspective. Finally, we outline the common benchmark datasets and evaluation metric for ITR, and conduct the accuracy comparison among the representative ITR approaches. Some critical yet less studied issues are discussed at the end of the paper. | Min Cao, Shiping Li, Juntao Li, Liqiang Nie, Min Zhang | null | null | 2,022 | ijcai |
Evidential Reasoning and Learning: a Survey | null | When collaborating with an artificial intelligence (AI) system, we need to assess when to trust its recommendations. Suppose we mistakenly trust it in regions where it is likely to err. In that case, catastrophic failures may occur, hence the need for Bayesian approaches for reasoning and learning to determine the confidence (or epistemic uncertainty) in the probabilities of the queried outcome. Pure Bayesian methods, however, suffer from high computational costs. To overcome them, we revert to efficient and effective approximations. In this paper, we focus on techniques that take the name of evidential reasoning and learning from the process of Bayesian update of given hypotheses based on additional evidence. This paper provides the reader with a gentle introduction to the area of investigation, the up-to-date research outcomes, and the open questions still left unanswered. | Federico Cerutti, Lance M. Kaplan, Murat Şensoy | null | null | 2,022 | ijcai |
Differential Privacy and Fairness in Decisions and Learning Tasks: A Survey | null | This paper surveys the recent work in the intersection of differential privacy (DP) and fairness. It focuses on surveying the work observing that DP systems may exacerbate bias and disparate impacts for different groups of individuals. The survey reviews the conditions under which privacy and fairness may be aligned or contrasting goals, analyzes how and why DP exacerbates bias and unfairness in decision problems and learning tasks, and reviews the available solutions to mitigate the fairness issues arising in DP systems. The survey provides a unified understanding of the main challenges and potential risks arising when deploying privacy-preserving machine learning or decisions making tasks under a fairness lens. | Ferdinando Fioretto, Cuong Tran, Pascal Van Hentenryck, Keyu Zhu | null | null | 2,022 | ijcai |
A Survey of Vision-Language Pre-Trained Models | null | As transformer evolves, pre-trained models have advanced at a breakneck pace in recent years. They have dominated the mainstream techniques in natural language processing (NLP) and computer vision (CV). How to adapt pre-training to the field of Vision-and-Language (V-L) learning and improve downstream task performance becomes a focus of multimodal learning. In this paper, we review the recent progress in Vision-Language Pre-Trained Models (VL-PTMs). As the core content, we first briefly introduce several ways to encode raw images and texts to single-modal embeddings before pre-training. Then, we dive into the mainstream architectures of VL-PTMs in modeling the interaction between text and image representations. We further present widely-used pre-training tasks, and then we introduce some common downstream tasks. We finally conclude this paper and present some promising research directions. Our survey aims to provide researchers with synthesis and pointer to related research. | Yifan Du, Zikang Liu, Junyi Li, Wayne Xin Zhao | null | null | 2,022 | ijcai |
Table Pre-training: A Survey on Model Architectures, Pre-training Objectives, and Downstream Tasks | null | Following the success of pre-training techniques in the natural language domain, a flurry of table pre-training frameworks have been proposed and have achieved new state-of-the-arts on various downstream tasks such as table question answering, table type recognition, column relation classification, table search, and formula prediction. Various model architectures have been explored to best capture the characteristics of (semi-)structured tables, especially specially-designed attention mechanisms. Moreover, to fully leverage the supervision signals in unlabeled tables, diverse pre-training objectives have been designed and evaluated, for example, denoising cell values, predicting numerical relationships, and learning a neural SQL executor. This survey aims to provide a comprehensive review of model designs, pre-training objectives, and downstream tasks for table pre-training, and we further share our thoughts on existing challenges and future opportunities. | Haoyu Dong, Zhoujun Cheng, Xinyi He, Mengyu Zhou, Anda Zhou, Fan Zhou, Ao Liu, Shi Han, Dongmei Zhang | null | null | 2,022 | ijcai |
Text Transformations in Contrastive Self-Supervised Learning: A Review | null | Contrastive self-supervised learning has become a prominent technique in representation learning. The main step in these methods is to contrast semantically similar and dissimilar pairs of samples. However, in the domain of Natural Language Processing (NLP), the augmentation methods used in creating similar pairs with regard to contrastive learning (CL) assumptions are challenging. This is because, even simply modifying a word in the input might change the semantic meaning of the sentence, and hence, would violate the distributional hypothesis. In this review paper, we formalize the contrastive learning framework, emphasize the considerations that need to be addressed in the data transformation step, and review the state-of-the-art methods and evaluations for contrastive representation learning in NLP. Finally, we describe some challenges and potential directions for learning better text representations using contrastive methods. | Amrita Bhattacharjee, Mansooreh Karami, Huan Liu | null | null | 2,022 | ijcai |
A Survey on Dialogue Summarization: Recent Advances and New Frontiers | null | Dialogue summarization aims to condense the original dialogue into a shorter version covering salient information, which is a crucial way to reduce dialogue data overload. Recently, the promising achievements in both dialogue systems and natural language generation techniques drastically lead this task to a new landscape, which results in significant research attentions. However, there still remains a lack of a comprehensive survey for this task. To this end, we take the first step and present a thorough review of this research field carefully and widely. In detail, we systematically organize the current works according to the characteristics of each domain, covering meeting, chat, email thread, customer service and medical dialogue. Additionally, we provide an overview of publicly available research datasets as well as organize two leaderboards under unified metrics. Furthermore, we discuss some future directions, including faithfulness, multi-modal, multi-domain and multi-lingual dialogue summarization, and give our thoughts respectively. We hope that this first survey of dialogue summarization can provide the community with a quick access and a general picture to this task and motivate future researches. | Xiachong Feng, Xiaocheng Feng, Bing Qin | null | null | 2,022 | ijcai |
A Survey on Machine Learning Approaches for Modelling Intuitive Physics | null | Research in cognitive science has provided extensive evidence of human cognitive ability in performing physical reasoning of objects from noisy perceptual inputs. Such a cognitive ability is commonly known as intuitive physics. With advancements in deep learning, there is an increasing interest in building intelligent systems that are capable of performing physical reasoning from a given scene for the purpose of building better AI systems. As a result, many contemporary approaches in modelling intuitive physics for machine cognition have been inspired by literature from cognitive science. Despite the wide range of work in physical reasoning for machine cognition, there is a scarcity of reviews that organize and group these deep learning approaches. Especially at the intersection of intuitive physics and artificial intelligence, there is a need to make sense of the diverse range of ideas and approaches. Therefore, this paper presents a comprehensive survey of recent advances and techniques in intuitive physics-inspired deep learning approaches for physical reasoning. The survey will first categorize existing deep learning approaches into three facets of physical reasoning before organizing them into three general technical approaches and propose six categorical tasks of the field. Finally, we highlight the challenges of the current field and present some future research directions. | Jiafei Duan, Arijit Dasgupta, Jason Fischer, Cheston Tan | null | null | 2,022 | ijcai |
Legal Judgment Prediction: A Survey of the State of the Art | null | Automatic legal judgment prediction (LJP) has recently received increasing attention in the natural language processing community in part because of its practical values as well as the associated research challenges. We present an overview of the major milestones made in LJP research covering multiple jurisdictions and multiple languages, and conclude with promising future research directions. | Yi Feng, Chuanyi Li, Vincent Ng | null | null | 2,022 | ijcai |
Neural Re-ranking in Multi-stage Recommender Systems: A Review | null | As the final stage of the multi-stage recommender system (MRS), re-ranking directly affects users’ experience and satisfaction by rearranging the input ranking lists, and thereby plays a critical role in MRS. With the advances in deep learning, neural re-ranking has become a trending topic and been widely adopted in industrial applications. This review aims at integrating re-ranking algorithms into a broader picture, and paving ways for more comprehensive solutions for future research. For this purpose, we first present a taxonomy of current methods on neural re-ranking. Then we give a description of these methods along with the historic development according to their objectives. The network structure, personalization, and complexity are also discussed and compared. Next, we provide a benchmark for the major neural re-ranking models and quantitatively analyze their re-ranking performance. Finally, the review concludes with a discussion on future prospects of this field. A list of papers discussed in this review, the benchmark datasets, our re-ranking library LibRerank, and detailed parameter settings are publicly available at https://github.com/LibRerank-Community/LibRerank. | Weiwen Liu, Yunjia Xi, Jiarui Qin, Fei Sun, Bo Chen, Weinan Zhang, Rui Zhang, Ruiming Tang | null | null | 2,022 | ijcai |
Deep Learning with Logical Constraints | null | In recent years, there has been an increasing interest in exploiting logically specified background knowledge in order to obtain neural models (i) with a better performance, (ii) able to learn from less data, and/or (iii) guaranteed to be compliant with the background knowledge itself, e.g., for safety-critical applications. In this survey, we retrace such works and categorize them based on (i) the logical language that they use to express the background knowledge and (ii) the goals that they achieve. | Eleonora Giunchiglia, Mihaela Catalina Stoian, Thomas Lukasiewicz | null | null | 2,022 | ijcai |
Who Says What to Whom: A Survey of Multi-Party Conversations | null | Multi-party conversations (MPCs) are a more practical and challenging scenario involving more than two interlocutors. This research topic has drawn significant attention from both academia and industry, and it is nowadays counted as one of the most promising research areas in the field of dialogue systems. In general, MPC algorithms aim at addressing the issues of Who says What to Whom, specifically, who speaks, say what, and address whom. The complicated interactions between interlocutors, between utterances, and between interlocutors and utterances develop many variant tasks of MPCs worth investigation. In this paper, we present a comprehensive survey of recent advances in text-based MPCs. In particular, we first summarize recent advances on the research of MPC context modeling including dialogue discourse parsing, dialogue flow modeling and self-supervised training for MPCs. Then we review the state-of-the-art models categorized by Who says What to Whom in MPCs. Finally, we highlight the challenges which are not yet well addressed in MPCs and present future research directions. | Jia-Chen Gu, Chongyang Tao, Zhen-Hua Ling | null | null | 2,022 | ijcai |
Goal-Conditioned Reinforcement Learning: Problems and Solutions | null | Goal-conditioned reinforcement learning (GCRL), related to a set of complex RL problems, trains an agent to achieve different goals under particular scenarios. Compared to the standard RL solutions that learn a policy solely depending on the states or observations, GCRL additionally requires the agent to make decisions according to different goals.
In this survey, we provide a comprehensive overview of the challenges and algorithms for GCRL. Firstly, we answer what the basic problems are studied in this field. Then, we explain how goals are represented and present how existing solutions are designed from different points of view. Finally, we make the conclusion and discuss potential future prospects that recent researches focus on. | Minghuan Liu, Menghui Zhu, Weinan Zhang | null | null | 2,022 | ijcai |
Survey on Efficient Training of Large Neural Networks | null | Modern Deep Neural Networks (DNNs) require significant memory to store weight, activations, and other intermediate tensors during training. Hence, many models don’t fit one GPU device or can be trained using only a small per-GPU batch size. This survey provides a systematic overview of the approaches that enable more efficient DNNs training. We analyze techniques that save memory and make good use of computation and communication resources on architectures with a single or several GPUs. We summarize the main categories of strategies and compare strategies within and across categories. Along with approaches proposed in the literature, we discuss available implementations. | Julia Gusak, Daria Cherniuk, Alena Shilova, Alexandr Katrutsa, Daniel Bershatsky, Xunyi Zhao, Lionel Eyraud-Dubois, Oleh Shliazhko, Denis Dimitrov, Ivan Oseledets, Olivier Beaumont | null | null | 2,022 | ijcai |
A Survey of Machine Narrative Reading Comprehension Assessments | null | As the body of research on machine narrative comprehension grows, there is a critical need for consideration of performance assessment strategies as well as the depth and scope of different benchmark tasks. Based on narrative theories, reading comprehension theories, as well as existing machine narrative reading comprehension tasks and datasets, we propose a typology that captures the main similarities and differences among assessment tasks; and discuss the implications of our typology for new task design and the challenges of narrative reading comprehension. | Yisi Sang, Xiangyang Mou, Jing Li, Jeffrey Stanton, Mo Yu | null | null | 2,022 | ijcai |
Deep Learning Meets Software Engineering: A Survey on Pre-Trained Models of Source Code | null | Recent years have seen the successful application of deep learning to software engineering (SE). In particular, the development and use of pre-trained models of source code has enabled state-of-the-art results to be achieved on a wide variety of SE tasks. This paper provides an overview of this rapidly advancing field of research and reflects on future research directions. | Changan Niu, Chuanyi Li, Bin Luo, Vincent Ng | null | null | 2,022 | ijcai |
Vision-and-Language Pretrained Models: A Survey | null | Pretrained models have produced great success in both Computer Vision (CV) and Natural Language Processing (NLP). This progress leads to learning joint representations of vision and language pretraining by feeding visual and linguistic contents into a multi-layer transformer, Visual-Language Pretrained Models (VLPMs). In this paper, we present an overview of the major advances achieved in VLPMs for producing joint representations of vision and language. As the preliminaries, we briefly describe the general task definition and genetic architecture of VLPMs. We first discuss the language and vision data encoding methods and then present the mainstream VLPM structure as the core content. We further summarise several essential pretraining and fine-tuning strategies. Finally, we highlight three future directions for both CV and NLP researchers to provide insightful guidance. | Siqu Long, Feiqi Cao, Soyeon Caren Han, Haiqin Yang | null | null | 2,022 | ijcai |
Survey on Graph Neural Network Acceleration: An Algorithmic Perspective | null | Graph neural networks (GNNs) have been a hot spot of recent research and are widely utilized in diverse applications. However, with the use of huger data and deeper models, an urgent demand is unsurprisingly made to accelerate GNNs for more efficient execution. In this paper, we provide a comprehensive survey on acceleration methods for GNNs from an algorithmic perspective. We first present a new taxonomy to classify existing acceleration methods into five categories. Based on the classification, we systematically discuss these methods and highlight their correlations. Next, we provide comparisons from aspects of the efficiency and characteristics of these methods. Finally, we suggest some promising prospects for future research. | Xin Liu, Mingyu Yan, Lei Deng, Guoqi Li, Xiaochun Ye, Dongrui Fan, Shirui Pan, Yuan Xie | null | null | 2,022 | ijcai |
Evaluation Methods for Representation Learning: A Survey | null | Representation learning enables us to automatically extract generic feature representations from a dataset to solve another machine learning task. Recently, extracted feature representations by a representation learning algorithm and a simple predictor have exhibited state-of-the-art performance on several machine learning tasks. Despite its remarkable progress, there exist various ways to evaluate representation learning algorithms depending on the application because of the flexibility of representation learning. To understand the current applications of representation learning, we review evaluation methods of representation learning algorithms. On the basis of our evaluation survey, we also discuss the future direction of representation learning. The extended version, https://arxiv.org/abs/2204.08226, gives more detailed discussions and a survey on theoretical analyses. | Kento Nozawa, Issei Sato | null | null | 2,022 | ijcai |
The Shapley Value in Machine Learning | null | Over the last few years, the Shapley value, a solution concept from cooperative game theory, has found numerous applications in machine learning. In this paper, we first discuss fundamental concepts of cooperative game theory and axiomatic properties of the Shapley value. Then we give an overview of the most important applications of the Shapley value in machine learning: feature selection, explainability, multi-agent reinforcement learning, ensemble pruning, and data valuation. We examine the most crucial limitations of the Shapley value and point out directions for future research. | Benedek Rozemberczki, Lauren Watson, Péter Bayer, Hao-Tsung Yang, Olivér Kiss, Sebastian Nilsson, Rik Sarkar | null | null | 2,022 | ijcai |
Predictive Coding: Towards a Future of Deep Learning beyond Backpropagation? | null | The backpropagation of error algorithm (BP) used to train deep neural networks has been fundamental to the successes of deep learning. However, it requires sequential backwards updates and non-local computations which make it challenging to parallelize at scale and is unlike how learning works in the brain. Neuroscience-inspired learning algorithms, however, such as \emph{predictive coding} which utilize local learning have the potential to overcome these limitations and advance beyond deep learning technologies in the future. While predictive coding originated in theoretical neuroscience as a model of information processing in the cortex, recent work has developed the idea into a general-purpose algorithm able to train neural networks using only local computations. In this survey, we review works that have contributed to this perspective and demonstrate the close connection between predictive coding and backpropagation in terms of generalization quality, as well as works that highlight the multiple advantages of using predictive coding models over backprop-trained neural networks. Specifically, we show the substantially greater flexibility of predictive coding networks against equivalent deep neural networks, which can function as classifiers, generators, and associative memories simultaneously, and can be defined on arbitrary graph topologies. Finally, we review direct benchmarks of predictive coding networks on machine learning classification tasks, as well as its close connections to control theory and applications in robotics. | Beren Millidge, Tommaso Salvatori, Yuhang Song, Rafal Bogacz, Thomas Lukasiewicz | null | null | 2,022 | ijcai |
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