query: Traffic Light Recognition for Complex Scene With Fusion Detections

positive: 3D Object Proposals for Accurate Object Class Detection In the Supplementary Material we first provide a description of the architecture of our object detection network. We then describe some details about our road estimation method. We also introduce the class-agnostic variant of our approach. We provide comprehensive results on object detection and orientation estimation, and recall statistics for both 2D and 3D box proposals. Finally, a visualization of success cases as well as failure modes are presented, followed by a detailed error analysis of our detector.

positive: Fast Feature Pyramids for Object Detection Multi-resolution image features may be approximated via extrapolation from nearby scales, rather than being computed explicitly. This fundamental insight allows us to design object detection algorithms that are as accurate, and considerably faster, than the state-of-the-art. The computational bottleneck of many modern detectors is the computation of features at every scale of a finely-sampled image pyramid. Our key insight is that one may compute finely sampled feature pyramids at a fraction of the cost, without sacrificing performance: for a broad family of features we find that features computed at octave-spaced scale intervals are sufficient to approximate features on a finely-sampled pyramid. Extrapolation is inexpensive as compared to direct feature computation. As a result, our approximation yields considerable speedups with negligible loss in detection accuracy. We modify three diverse visual recognition systems to use fast feature pyramids and show results on both pedestrian detection (measured on the Caltech, INRIA, TUD-Brussels and ETH data sets) and general object detection (measured on the PASCAL VOC). The approach is general and is widely applicable to vision algorithms requiring fine-grained multi-scale analysis. Our approximation is valid for images with broad spectra (most natural images) and fails for images with narrow band-pass spectra (e.g., periodic textures).

positive: Geodesic weighted Bayesian model for salient object detection In recent years, a variety of salient object detection methods under Bayesian framework have been proposed and many achieved state of the art. However, those ignore spatial relationships and thus background regions similar to the objects are also highlighted. In this paper, we propose a novel geodesic weighted Bayesian model to address this issue. We consider spatial relationships by attaching more importance to regions which are more likely to be parts of a salient object, thus suppressing background regions. First, we learn a combined similarity via multiple features to measure similarity of adjacent regions. Then, we apply the combined similarity as edge weight to construct an undirected weighted graph and compute geodesic distance. Last, we utilize the geodesic distance to weight the observation likelihood to infer a more precise saliency map. Experiments on several benchmark datasets demonstrate the effectiveness of our model.

positive: An Incremental Framework for Video-Based Traffic Sign Detection, Tracking, and Recognition Video-based traffic sign detection, tracking, and recognition is one of the important components for the intelligent transport systems. Extensive research has shown that pretty good performance can be obtained on public data sets by various state-of-the-art approaches, especially the deep learning methods. However, deep learning methods require extensive computing resources. In addition, these approaches mostly concentrate on single image detection and recognition task, which is not applicable in real-world applications. Different from previous research, we introduce a unified incremental computational framework for traffic sign detection, tracking, and recognition task using the mono-camera mounted on a moving vehicle under non-stationary environments. The main contributions of this paper are threefold: 1) to enhance detection performance by utilizing the contextual information, this paper innovatively utilizes the spatial distribution prior of the traffic signs; 2) to improve the tracking performance and localization accuracy under non-stationary environments, a new efficient incremental framework containing off-line detector, online detector, and motion model predictor together is designed for traffic sign detection and tracking simultaneously; and 3) to get a more stable classification output, a scale-based intra-frame fusion method is proposed. We evaluate our method on two public data sets and the performance has shown that the proposed system can obtain results comparable with the deep learning method with less computing resource in a near-real-time manner.

positive: Real time visual traffic lights recognition based on Spot Light Detection and adaptive traffic lights templates This paper introduces a new real-time traffic light recognition system for on-vehicle camera applications. This approach has been tested with good results in urban scenes. Thanks to the use of our generic “Adaptive Templates” it would be possible to recognize different kinds of traffic lights from various countries.

negative: Cascaded Attention based Unsupervised Information Distillation for Compressive Summarization When people recall and digest what they have read for writing summaries, the important content is more likely to attract their attention. Inspired by this observation, we propose a cascaded attention based unsupervised model to estimate the salience information from the text for compressive multi-document summarization. The attention weights are learned automatically by an unsupervised data reconstruction framework which can capture the sentence salience. By adding sparsity constraints on the number of output vectors, we can generate condensed information which can be treated as word salience. Fine-grained and coarse-grained sentence compression strategies are incorporated to produce compressive summaries. Experiments on some benchmark data sets show that our framework achieves better results than the state-of-the-art methods.

negative: Facial Performance Capture with Deep Neural Networks We present a deep learning technique for facial performance capture, i.e., the transfer of video footage into a motion sequence of a 3D mesh representing an actor’s face. Specifically, we build on a conventional capture pipeline based on computer vision and multiview video, and use its results to train a deep neural network to produce similar output from a monocular video sequence. Once trained, our network produces high-quality results for unseen inputs with greatly reduced effort compared to the conventional system. In practice, we have found that approximately 10 minutes worth of high-quality data is sufficient for training a network that can then automatically process as much footage from video to 3D as needed. This yields major savings in the development of modern narrativedriven video games involving digital doubles of actors and potentially hours of animated dialogue per character.

negative: 1 Sex , Syntax , and Semantics Many languages have a grammatical gender system whereby all nouns are assigned a gender (most commonly feminine, masculine, or neuter). Two studies examined whether (1) the assignment of genders to nouns is truly arbitrary (as has been claimed), and (2) whether the grammatical genders assigned to nouns have semantic consequences. In the first study, English speakers’ intuitions about the genders of animals (but not artifacts) were found to correlate with the grammatical genders assigned to the names of these objects in Spanish and German. These findings suggest that the assignment of genders to nouns is not entirely arbitrary but may to some extent reflect the perceived masculine or feminine properties of the nouns’ referents. Results of the second study suggested that people’s ideas about the genders of objects are strongly influenced by the grammatical genders assigned to these objects in their native language. Spanish and German speakers’ memory for object--name pairs (e.g., apple--Patricia) was better for pairs where the gender of the proper name was congruent with the grammatical gender of the object name (in their native language), than when the two genders were incongruent. This was true even though both groups performed the task in English. These results suggest that grammatical gender may not be as arbitrary or as purely grammatical as was previously

negative: Ayahuasca, dimethyltryptamine, and psychosis: a systematic review of human studies. Ayahuasca is a hallucinogen brew traditionally used for ritual and therapeutic purposes in Northwestern Amazon. It is rich in the tryptamine hallucinogens dimethyltryptamine (DMT), which acts as a serotonin 5-HT2A agonist. This mechanism of action is similar to other compounds such as lysergic acid diethylamide (LSD) and psilocybin. The controlled use of LSD and psilocybin in experimental settings is associated with a low incidence of psychotic episodes, and population studies corroborate these findings. Both the controlled use of DMT in experimental settings and the use of ayahuasca in experimental and ritual settings are not usually associated with psychotic episodes, but little is known regarding ayahuasca or DMT use outside these controlled contexts. Thus, we performed a systematic review of the published case reports describing psychotic episodes associated with ayahuasca and DMT intake. We found three case series and two case reports describing psychotic episodes associated with ayahuasca intake, and three case reports describing psychotic episodes associated with DMT. Several reports describe subjects with a personal and possibly a family history of psychosis (including schizophrenia, schizophreniform disorders, psychotic mania, psychotic depression), nonpsychotic mania, or concomitant use of other drugs. However, some cases also described psychotic episodes in subjects without these previous characteristics. Overall, the incidence of such episodes appears to be rare in both the ritual and the recreational/noncontrolled settings. Performance of a psychiatric screening before administration of these drugs, and other hallucinogens, in controlled settings seems to significantly reduce the possibility of adverse reactions with psychotic symptomatology. Individuals with a personal or family history of any psychotic illness or nonpsychotic mania should avoid hallucinogen intake.

negative: Identifying Potential CSCW Applications by Means of Activity Theory Concepts: A Case Example The paper presents some novel concepts and models derived from Activity Theory for to identify a potential CSCW application. It is suggested that the six elements of the structure of the activity concept might be useful for differentiating between areas of support, and that three levels of support are needed in order to cope with both routine and emergent features of cooperative work situations. Thus a 3x6 support type classification is formed and its usefulness studied by means of a real-world example. A work situation is analyzed, problems identified and possible areas of support defined. A temporary solution is produced and, by evaluating it, possible directions for the development of a “real” new CSCW application and the usefulness of the classification am discussed.

negative: Dynamic switching-based reliable flooding in low-duty-cycle wireless sensor networks Reliable flooding in wireless sensor networks (WSNs) is desirable for a broad range of applications and network operations. However, it is a challenging problem to ensure 100% flooding coverage efficiently considering the combined effects of low-duty-cycle operation and unreliable wireless transmission. In this work, we propose a novel dynamic switching-based reliable flooding (DSRF) framework, which is designed as an enhancement layer to provide efficient and reliable flooding over a variety of existing flooding tree structures in low-duty-cycle WSNs. Through comprehensive simulations, we demonstrate that DSRF can effectively improve both flooding energy efficiency and latency.

negative: Image Captioning with Object Detection and Localization Automatically generating a natural language description of an image is a task close to the heart of image understanding. In this paper, we present a multi-model neural network method closely related to the human visual system that automatically learns to describe the content of images. Our model consists of two sub-models: an object detection and localization model, which extract the information of objects and their spatial relationship in images respectively; Besides, a deep recurrent neural network (RNN) based on long short-term memory (LSTM) units with attention mechanism for sentences generation. Each word of the description will be automatically aligned to different objects of the input image when it is generated. This is similar to the attention mechanism of the human visual system. Experimental results on the COCO dataset showcase the merit of the proposed method, which outperform previous benchmark models.

negative: Building Occlusion Detection From Ghost Images This paper proposes a novel occlusion detection method for urban true orthophoto generation. In this new method, occlusion detection is performed using a ghost image; this method is therefore considerably different from the traditional Z-buffer method, in which occlusion detection is performed during the generation of a true orthophoto (to avoid ghost image occurrence). In the proposed method, a model is first established that describes the relationship between each ghost image and the boundary of the corresponding building occlusion, and then an algorithm is applied to identify the occluded areas in the ghost images using the building displacements. This theory has not previously been applied in true orthophoto generation. The experimental results demonstrate that the method proposed in this paper is capable of effectively avoiding pseudo-occlusion detection, with a success rate of 99.2%, and offers improved occlusion detection accuracy compared with the traditional Z-buffer detection method. The advantage of this method is that it avoids the shortcoming of performing occlusion detection and true orthophoto generation simultaneously, which results in false visibility and false occlusions; instead, the proposed method detects occlusions from ghost images and therefore provides simple and effective true orthophoto generation.

negative: Evaluation of supply chain structures through modularization and postponement This paper introduces a conceptual framework for evaluating dierent supply chain structures in the context of modularization and postponement. In our analysis, modularization is linked to inbound logistics as the combination of dierent components (or modules) allow for the assembly of the ®nal product. Postponement, however, corresponds with the outbound logistics since it is through the distribution function that customers' speci®c demand is satis®ed. Given this perspective, we introduce a taxonomy and develop a corresponding framework for the characterization of four supply chain structures, de®ned according to the combined levels of modularization and postponement: Rigid, Postponed, Modularized and Flexible. We also illustrate the applicability of the resulting framework through quantifying the total cost dierential for utilizing a particular supply chain structure. By quantifying the total cost for employing a particular supply chain structure we can numerically illustrate structural results that allow for an objective comparison among them. As such, our ®ndings substantiate the empirical evidence that vertical integration along the supply chain is not always desirable. A point of emphasis in this paper is the notion that ®rms may be better o making combined modularization (outsourcing) and postponement decisions as opposed to separate ones (as they currently do). This also suggests that modularization and postponement decisions need not be independent and that their joint consideration extends operational advantages worthy of contemplation. Ó 2000 Elsevier Science B.V. All rights reserved.

negative:
Database Description with SDM: A Semantic Database Model SDM is a high-level semantics-based database description and structuring formalism (database model) for databases. This database model is designed to capture more of the meaning of an application environment than is possible with contemporary database models. An SDM specification describes a database in terms of the kinds of entities that exist in the application environment, the classifications and groupings of those entities, and the structural interconnections among them. SDM provides a collection of high-level modeling primitives to capture the semantics of an application environment. By accommodating derived information in a database structural specification, SDM allows the same information to be viewed in several ways; this makes it possible to directly accommodate the variety of needs and processing requirements typically present in database applications. The design of the present SDM is based on our experience in using a preliminary version of it.
SDM is designed to enhance the effectiveness and usability of database systems. An SDM database description can serve as a formal specification and documentation tool for a database; it can provide a basis for supporting a variety of powerful user interface facilities, it can serve as a conceptual database model in the database design process; and, it can be used as the database model for a new kind of database management system.

negative: The Human Genetic History of East Asia: Weaving a Complex Tapestry East Asia encompasses a wide variety of environments, peoples, cultures and languages. Although this review focuses on East Asia, no geographic region can be considered in isolation in terms of human population history, and migrations to and from East Asia have had a major impact. Here, we review the following topics: the initial colonization of East Asia, the direction of migrations between southeast Asia and northern Asia, the genetic relationships of East Asian hunter-gatherers and the genetic impact of various social practices on East Asian populations. By necessity we focus on insights derived from mitochondrial DNA and/or Y-chromosome data; ongoing and future studies of genome-wide SNP or multi-locus re-sequencing data, combined with the use of simulation, model-based methods to infer demographic parameters, will undoubtedly provide additional insights into the population history of East Asia.

negative: Winter 2003 COMPETENCE , CULPABILITY , AND PUNISHMENT : IMPLICATIONS OF A TKINS FOR EXECUTING AND SENTENCING ADOLESCENTS The Supreme Court has explored the issues of culpability, proportionality, and deserved punishment most fully in the context of capital punishment. In death penalty decisions addressing developmental impairments and culpability, the Court has considered the cases of defendants with mental retardation and older adolescents, and has created an anomalous inconsistency by reaching opposite conclusions about the deserved punishment for each group of defendants. Recently, in Atkins v. Virginia, the Court relied on both empirical and normative justifications to categorically prohibit states from executing defendants with mental retardation. Atkins reasoned that mentally retarded offenders lacked the reasoning, judgment, and impulse control necessary to equate their culpability with that of other death-eligible criminal defendants. This Article contends that the same psychological and developmental * Centennial Professor of Law, University of Minnesota, B.A., University of Pennsylvania, 1966; J.D., University of Minnesota Law School, 1969; Ph.D. (Sociology), Harvard University, 1973. 1 would like to thank Steven Drizin, Richard Frase, Wayne Logan, Monte Norgaard, and Victor Streib for critical and helpful comments on an earlier draft of this article. The questions and comments of faculty and student participants at the University of Houston Law School Symposium, January, 2004, helped to sharpen the analysis. I received outstanding research assistance from Brittany Stephens, Class of 2005. 1 Feld: Competence, Culpability, and Punishment: Implications of Atkins f Published by Scholarly Commons at Hofstra Law, 2003

negative: WOMEN ’ S VIOLENCE TO MEN IN INTIMATE RELATIONSHIPS Working on a Puzzle Different notions among researchers about the nature of intimate partner violence have long been the subjects of popular and academic debate. Research findings are contradictory and point in two directions, with some revealing that women are as likely as men to perpetrate violence against an intimate partner (symmetry) and others showing that it is overwhelmingly men who perpetrate violence against women partners (asymmetry). The puzzle about who perpetrates intimate partner violence not only concerns researchers but also policy makers and community advocates who, in differing ways, have a stake in the answer to this question, since it shapes the focus of public concern, legislation, public policy and interventions for victims and offenders. The question of who are the most usual victims and perpetrators rests, to a large extent, on ‘what counts’ as violence. It is here that we begin to try to unravel the puzzle, by focusing on concept formation, definitions, forms of measurement, context, consequences and approaches to claim-making, in order better to understand how researchers have arrived at such apparently contradictory findings and claims. The question also turns on having more detailed knowledge about the nature, extent and consequences of women’s violence, in order to consider the veracity of these contradictory findings. To date, there has been very little in-depth research about women’s violence to male partners and it is difficult, if not impossible, to consider this debate without such knowledge. We present quantitative and qualitative findings from 190 interviews with 95 couples in which men and women reported separately upon their own violence and upon that of their partner. Men’s and women’s violence are compared. The findings suggest that intimate partner violence is primarily an asymmetrical problem of men’s violence to women, and women’s violence does not equate to men’s in terms of frequency, severity, consequences and the victim’s sense of safety and well-being. But why bother about the apparent contradictions in findings of research? For those making and implementing policies and expending public and private resources, the apparent contradiction about the very nature of this problem has real consequences for what might be done for those who are its victims and those who

negative: Deep joint demosaicking and denoising Demosaicking and denoising are the key first stages of the digital imaging pipeline but they are also a severely ill-posed problem that infers three color values per pixel from a single noisy measurement. Earlier methods rely on hand-crafted filters or priors and still exhibit disturbing visual artifacts in hard cases such as moiré or thin edges. We introduce a new data-driven approach for these challenges: we train a deep neural network on a large corpus of images instead of using hand-tuned filters. While deep learning has shown great success, its naive application using existing training datasets does not give satisfactory results for our problem because these datasets lack hard cases. To create a better training set, we present metrics to identify difficult patches and techniques for mining community photographs for such patches. Our experiments show that this network and training procedure outperform state-of-the-art both on noisy and noise-free data. Furthermore, our algorithm is an order of magnitude faster than the previous best performing techniques.

negative: Fingerprinting Information in JavaScript Implementations To date, many attempts have been made to fingerprint users on the web. These fingerprints allow browsing sessions to be linked together and possibly even tied to a user’s identity. They can be used constructively by sites to supplement traditional means of user authentication such as passwords; and they can be used destructively to counter attempts to stay anonymous online. In this paper, we identify two new avenues for browser fingerprinting. The new fingerprints arise from the browser’s JavaScript execution characteristics, making them difficult to simulate or mitigate in practice. The first uses the innate performance signature of each browser’s JavaScript engine, allowing the detection of browser version, operating system and microarchitecture, even when traditional forms of system identification (such as the user-agent header) are modified or hidden. The second subverts the whitelist mechanism of the popular NoScript Firefox extension, which selectively enables web pages’ scripting privileges to increase privacy by allowing a site to determine if particular domains exist in a user’s NoScript whitelist. We have experimentally verified the effectiveness of our system fingerprinting technique using a 1,015-person study on Amazon’s Mechanical Turk platform.

negative: Modeling trend progression through an extension of the Polya Urn Process Knowing how and when trends are formed is a frequently visited research goal. In our work, we focus on the progression of trends through (social) networks. We use a random graph (RG) model to mimic the progression of a trend through the network. The context of the trend is not included in our model. We show that every state of the RG model maps to a state of the Polya process. We find that the limit of the component size distribution of the RG model shows power-law behaviour. These results are also supported by simulations.

negative: A Benchmark for Geometric Facial Beauty Study

negative: WebQual: An Exploration of Web-Site Quality The issue of web-site quality is tackled from the perspective of the ‘voice of the customer’. Quality function deployment (QFD) is adopted as a framework for identifying web-site qualities demanded by users, which are gathered through a quality workshop. From the workshop an instrument for assessing web-site quality is developed (WebQual) and tested in the domain of UK business schools. The results of the WebQual survey are presented and analyzed, leading to the generation of a WebQual Index of web-site quality. Work under way to extend and refine the WebQual instrument includes electronic commerce evaluation, where web-site service quality is proposed as a key issue.

negative: Distributed Q-Learning for Interference Control in OFDMA-Based Femtocell Networks This paper proposes a self-organized power allocation technique to solve the interference problem caused by a femtocell network operating in the same channel as an orthogonal frequency division multiple access cellular network. We model the femto network as a multi-agent system where the different femto base stations are the agents in charge of managing the radio resources to be allocated to their femtousers. We propose a form of real-time multi-agent reinforcement learning, known as decentralized Q-learning, to manage the interference generated to macro-users. By directly interacting with the surrounding environment in a distributed fashion, the multi-agent system is able to learn an optimal policy to solve the interference problem. Simulation results show that the introduction of the femto network increases the system capacity without decreasing the capacity of the macro network.

negative: Guarding Against the Erosion of Competitive Advantage: A Knowledge Leakage Mitigation Model A critical objective of knowledge-intensive organizations is to prevent erosion of their competitive knowledge base through leakage. Our review of the literature highlights the need for a more refined conceptualization of perceived leakage risk. We propose a Knowledge Leakage Mitigation (KLM) model to explain the incongruity between perceived high-risk of leakage and lack of protective actions. We argue that an organization’s perceived risk of leakage increases if competitors can benefit from leakage incidents. Further, perceived leakage risk decreases if the organization is shielded from impact due to their diversity of knowledge assets and their ability to reconfigure knowledge resources to refresh their competitive knowledge base. We describe our approach to the design of a large-scale survey instrument that has been tested and refined in two stakeholder communities: 1) knowledge managers responsible for organizational strategy, and 2) Information security management consultants.

negative: Design and Analysis of Swapped Port Coupler and Its Application in a Miniaturized Butler Matrix This paper presents a novel size reduction technique for designing a Butler matrix using a new type of coupler. This coupler is half the length of a conventional coupler and has a swapped port characteristic wherein the locations of the isolation and coupled ports are switched. These features make the new coupler, called the "swapped port coupler," effective for reducing the size of the Butler matrix. The behavior of the swapped port coupler was mathematically analyzed, and its design equation was derived. Furthermore, the proposed coupler was employed to design a miniaturized Butler matrix. The area of this Butler matrix was 70 × 70 mm2, and its operating frequency was 1 GHz. When the area was normalized to the frequency, the area of the proposed Butler matrix was 10% of a conventional Butler matrix.

negative: Learning to Make Predictions on Graphs with Autoencoders We examine two fundamental tasks associated with graph representation learning: link prediction and semi-supervised node classification. We present a novel autoencoder architecture capable of learning a joint representation of both local graph structure and available node features for the multi-task learning of link prediction and node classification. Our autoencoder architecture is efficiently trained end-to-end in a single learning stage to simultaneously perform link prediction and node classification, whereas previous related methods require multiple training steps that are difficult to optimize. We provide a comprehensive empirical evaluation of our models on nine benchmark graph-structured datasets and demonstrate significant improvement over related methods for graph representation learning. Reference code and data are available at https://github.com/vuptran/graph-representation-learning.

negative: Culture, corruption, suicide, happiness and global social media use: a cross-cultural perspective This study was conducted to answer this simple question: ‘Can cultural values explain global social media use?’ Along with cultural dimensions introduced by past studies we also added several demographic, socio-economic and personality variables into this study that generated quite interesting findings. We found that there are low levels of suicide, more happiness and more corruption in societies that use social media heavily. We also observed that GDP per capita and median age are negatively related with social media use. Self-esteem stood out as important variable related to social media use intensity along with emotional expressiveness, openness and conscientiousness. Contrary to the common view, nation-level social capital achievement was negatively related with social media use and there was absolutely no relationship between high-context and low-context communication characteristics and local social media use. Some other findings also indicated that conservative and collectivistic countries use social media more often than do individualistic and developed countries. Schwartz’s cultural dimensions and the results of the GLOBE study accounted for a considerable amount of variation in country-level social media use where Hofstede and Trompenaars’ cultural dimensions were insignificant. Since most of the cultural values failed to explain the intensity of social media use, we also developed a cross-cultural online communication framework called cross-cultural self and others’ worth.

negative: Is beautiful really usable? Toward understanding the relation between usability, aesthetics, and affect in HCI This paper analyzes the relation between usability and aesthetics. In a laboratory study, 80 participants used one of four different versions of the same online shop, differing in interface-aesthetics (low vs. high) and interface-usability (low vs. high). Participants had to find specific items and rate the shop before and after usage on perceived aesthetics and perceived usability, which were assessed using four validated instruments. Results show that aesthetics does not affect perceived usability. In contrast, usability has an effect on post-use perceived aesthetics. Our findings show that the “what is beautiful is usable” notion, which assumes that aesthetics enhances the perception of usability can be reversed under certain conditions (here: strong usability manipulation combined with a medium to large aesthetics manipulation). Furthermore, our results indicate that the user’s affective experience with the usability of the shop might serve as a mediator variable within the aestheticsusability relation: The frustration of poor usability lowers ratings on perceived aesthetics. The significance of the results is discussed in context of the existing research on the relation between aesthetics and usability.

negative: Running head : SOCIAL NETWORKING SITE USE Psychological Predictors of Young Adults ' Use of Social Networking Sites Psychological predictors of young adults': use of social networking sites. Acknowledgements: The authors would like to thank Shari Walsh for assistance in the design of the study and Eric Livingston for assistance in data collection. Abstract Young people are increasingly using social networking sites (SNSs), like Myspace and Facebook, to engage with others. The use of SNSs can have both positive and negative effects on the individual; however, few research studies identify the types of people who frequent these Internet sites. This study sought to predict young adults' use of SNSs and addictive tendency towards the use of SNSs from their personality characteristics and levels of self-esteem. University students (N = 201), aged 17 to 24 years, reported their use of SNSs and addictive tendencies for SNSs use, and completed the NEO Five-Factor Personality Inventory 1 and the Coopersmith Self-Esteem Inventory. 2 Multiple regression analyses revealed that, as a group, the personality and self-esteem factors significantly predicted both level of SNS use and addictive tendency but did not explain a large amount of variance in either outcome measure. The findings indicated that extraverted and unconscientious individuals reported higher levels of both SNS use and addictive tendencies. Future research should attempt to identify which other psychosocial characteristics explain young people's level of use and propensity for addictive tendencies for these popular Internet sites. Social networking site use 3 The proliferation of social networking sites (SNSs) has created a phenomenon that engages millions of Internet users around the world, especially young people. Given the popularity of these sites and their importance in young people's lives to facilitate communication and relationships, it is important to understand the factors influencing SNS use, especially at higher levels, and to identify those who may be prone to developing addictive tendencies towards new communication technologies. 5 As with other communication technologies, 6,7 a useful starting point may be to examine the role of personality traits and self-esteem on young people's SNS use. Researchers have confirmed repeatedly that the five-factor model of personality adequately accounts for and explains personality by taking the approach that personality consists of five traits; openness to experience (pursuing and appreciating all types of experience), conscientiousness (control, regulation and direction of goals and impulses), extraversion (amount and intensity of interpersonal interactions), agreeableness (the type of interactions a person prefers to have with others), and neuroticism (degree of emotional adjustment and instability). 8 Self esteem …

negative: Self-Attentive Feature-Level Fusion for Multimodal Emotion Detection Multimodal emotion recognition is the task of detecting emotions present in user-generated multimedia content. Such resources contain complementary information in multiple modalities. A stiff challenge often faced is the complexity associated with feature-level fusion of these heterogeneous modes. In this paper, we propose a new feature-level fusion method based on self-attention mechanism. We also compare it with traditional fusion methods such as concatenation, outer-product, etc. Analyzed using textual and speech (audio) modalities, our results suggest that the proposed fusion method outperforms others in the context of utterance-level emotion recognition in videos.

negative: Traffic sign detection and recognition based on random forests In this paper we present a new traffic sign detection and recognition (TSDR) method, which is achieved in three main steps. The first step segments the image based on thresholding of HSI color space components. The second step detects traffic signs by processing the blobs extracted by the first step. The last one performs the recognition of the detected traffic signs. The main contributions of the paper are as follows. First, we propose, in the second step, to use invariant geometric moments to classify shapes instead of machine learning algorithms. Second, inspired by the existing features, new ones have been proposed for the recognition. The histogram of oriented gradients (HOG) features has been extended to the HSI raffic sign detection raffic sign recognition olor segmentation andom forests upport vector machines (SVMs) istogram of oriented gradients (HOG) color space and combined with the local self-similarity (LSS) features to get the descriptor we use in our algorithm. As a classifier, random forest and support vector machine (SVM) classifiers have been tested together with the new descriptor. The proposed method has been tested on both the German Traffic Sign Detection and Recognition Benchmark and the Swedish Traffic Signs Data sets. The results obtained are satisfactory when compared to the state-of-the-art methods. © 2016 Elsevier B.V. All rights reserved. 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 ocal self-similarity (LSS)

negative: Evolving boxes for fast vehicle detection We perform fast vehicle detection from traffic surveillance cameras. A novel deep learning framework, namely Evolving Boxes, is developed that proposes and refines the object boxes under different feature representations. Specifically, our framework is embedded with a light-weight proposal network to generate initial anchor boxes as well as to early discard unlikely regions; a fine-turning network produces detailed features for these candidate boxes. We show intriguingly that by applying different feature fusion techniques, the initial boxes can be refined for both localization and recognition. We evaluate our network on the recent DETRAC benchmark and obtain a significant improvement over the state-of-the-art Faster RCNN by 9.5% mAP. Further, our network achieves 9–13 FPS detection speed on a moderate commercial GPU.

negative: Evaluating State-of-the-Art Object Detector on Challenging Traffic Light Data Traffic light detection (TLD) is a vital part of both intelligent vehicles and driving assistance systems (DAS). General for most TLDs is that they are evaluated on small and private datasets making it hard to determine the exact performance of a given method. In this paper we apply the state-of-the-art, real-time object detection system You Only Look Once, (YOLO) on the public LISA Traffic Light dataset available through the VIVA-challenge, which contain a high number of annotated traffic lights, captured in varying light and weather conditions.,,,,,,The YOLO object detector achieves an AUC of impressively 90.49% for daysequence1, which is an improvement of 50.32% compared to the latest ACF entry in the VIVAchallenge. Using the exact same training configuration as the ACF detector, the YOLO detector reaches an AUC of 58.3%, which is in an increase of 18.13%.

negative: Automatic number plate recognition system using modified stroke width transform Automatic number plate recognition(ANPR) techniques occupy a significant role in intelligent traffic management systems. Most of the existing ANPR systems do not perform well under conditions like high vehicle speed, varying illuminations, changing back ground etc. In this paper we propose an ANPR technique which efficiently solves the above issues using the stroke width transform. In our method, first we pre-process the image to remove blurring artifacts. Then the significant edges in the image are detected and a new image is formed by grouping the connecting rays. This is followed by morphological processing which detects the letters in the image. Then an appropriate Optical Character Recognizer (OCR) recovers the final result. Experimental results show that the proposed method work effectively under varying light conditions and even in the presence of shadows.

negative: Road traffic sign detection and classification A vision-based vehicle guidance system for road vehicles can have three main roles: 1) road detection; 2) obstacle detection; and 3) sign recognition. The first two have been studied for many years and with many good results, but traffi c sign recognition is a less-studied field. Traffi c signs provide drivers with very valuable informatio n about the road, in order to make drivin g safer and easier. We think that traffi c signs must play the same role for autonomous vehicles. They are designed to be easily recognized by human driver s mainly because their color and shapes are very different from natural environments. The algorithm described in this paper takes advantage of these features. I t has two main parts. The first one, for the detection, uses color thresholding to segment the image and shape analysis to detect the signs. The second one, for the classification, uses a neural network. Some results from natural scenes are shown. On the other hand, the algorithm is valid to detect other kinds of marks that would tell the mobile robot to perform some task at that place.

negative: Vision-as-Inverse-Graphics: Obtaining a Rich 3D Explanation of a Scene from a Single Image We develop an inverse graphics approach to the problem of scene understanding, obtaining a rich representation that includes descriptions of the objects in the scene and their spatial layout, as well as global latent variables like the camera parameters and lighting. The framework's stages include object detection, the prediction of the camera and lighting variables, and prediction of object-specific variables (shape, appearance and pose). This acts like the encoder of an autoencoder, with graphics rendering as the decoder Importantly the scene representation is interpretable and is of variable dimension to match the detected number of objects plus the global variables. For the prediction of the camera latent variables we introduce a novel architecture termed Probabilistic HoughNets (PHNs), which provides a principled approach to combining information from multiple detections. We demonstrate the quality of the reconstructions obtained quantitatively on synthetic data, and qualitatively on real scenes.


query: Fundamental limits on adversarial robustness

positive: Explaining and Harnessing Adversarial Examples Several machine learning models, including neural networks, consistently misclassify adversarial examples—inputs formed by applying small but intentionally worst-case perturbations to examples from the dataset, such that the perturbed input results in the model outputting an incorrect answer with high confidence. Early attempts at explaining this phenomenon focused on nonlinearity and overfitting. We argue instead that the primary cause of neural networks’ vulnerability to adversarial perturbation is their linear nature. This explanation is supported by new quantitative results while giving the first explanation of the most intriguing fact about them: their generalization across architectures and training sets. Moreover, this view yields a simple and fast method of generating adversarial examples. Using this approach to provide examples for adversarial training, we reduce the test set error of a maxout network on the MNIST dataset.

positive: Towards Deep Neural Network Architectures Robust to Adversarial Examples Recent work has shown deep neural networks (DNNs) to be highly susceptible to well-designed, small perturbations at the input layer, or so-called adversarial examples. Taking images as an example, such distortions are often imperceptible, but can result in 100% mis-classification for a state of the art DNN. We study the structure of adversarial examples and explore network topology, pre-processing and training strategies to improve the robustness of DNNs. We perform various experiments to assess the removability of adversarial examples by corrupting with additional noise and pre-processing with denoising autoencoders (DAEs). We find that DAEs can remove substantial amounts of the adversarial noise. However, when stacking the DAE with the original DNN, the resulting network can again be attacked by new adversarial examples with even smaller distortion. As a solution, we propose Deep Contractive Network, a model with a new end-to-end training procedure that includes a smoothness penalty inspired by the contractive autoencoder (CAE). This increases the network robustness to adversarial examples, without a significant performance penalty.

positive: Deep neural networks are easily fooled: High confidence predictions for unrecognizable images Deep neural networks (DNNs) have recently been achieving state-of-the-art performance on a variety of pattern-recognition tasks, most notably visual classification problems. Given that DNNs are now able to classify objects in images with near-human-level performance, questions naturally arise as to what differences remain between computer and human vision. A recent study [30] revealed that changing an image (e.g. of a lion) in a way imperceptible to humans can cause a DNN to label the image as something else entirely (e.g. mislabeling a lion a library). Here we show a related result: it is easy to produce images that are completely unrecognizable to humans, but that state-of-the-art DNNs believe to be recognizable objects with 99.99% confidence (e.g. labeling with certainty that white noise static is a lion). Specifically, we take convolutional neural networks trained to perform well on either the ImageNet or MNIST datasets and then find images with evolutionary algorithms or gradient ascent that DNNs label with high confidence as belonging to each dataset class. It is possible to produce images totally unrecognizable to human eyes that DNNs believe with near certainty are familiar objects, which we call “fooling images” (more generally, fooling examples). Our results shed light on interesting differences between human vision and current DNNs, and raise questions about the generality of DNN computer vision.

positive: Analysis of classifiers’ robustness to adversarial perturbations The goal of this paper is to analyze the intriguing instability of classifiers to adversarial perturbations (Szegedy et al., in: International conference on learning representations (ICLR), 2014). We provide a theoretical framework for analyzing the robustness of classifiers to adversarial perturbations, and show fundamental upper bounds on the robustness of classifiers. Specifically, we establish a general upper bound on the robustness of classifiers to adversarial perturbations, and then illustrate the obtained upper bound on two practical classes of classifiers, namely the linear and quadratic classifiers. In both cases, our upper bound depends on a distinguishability measure that captures the notion of difficulty of the classification task. Our results for both classes imply that in tasks involving small distinguishability, no classifier in the considered set will be robust to adversarial perturbations, even if a good accuracy is achieved. Our theoretical framework moreover suggests that the phenomenon of adversarial instability is due to the low flexibility of classifiers, compared to the difficulty of the classification task (captured mathematically by the distinguishability measure). We further show the existence of a clear distinction between the robustness of a classifier to random noise and its robustness to adversarial perturbations. Specifically, the former is shown to be larger than the latter by a factor that is proportional to $$\sqrt{d}$$ d (with d being the signal dimension) for linear classifiers. This result gives a theoretical explanation for the discrepancy between the two robustness properties in high dimensional problems, which was empirically observed by Szegedy et al. in the context of neural networks. We finally show experimental results on controlled and real-world data that confirm the theoretical analysis and extend its spirit to more complex classification schemes.

positive: Intriguing properties of neural networks Deep neural networks are highly expressive models that have recently achieved state of the art performance on speech and visual recognition tasks. While their expressiveness is the reason they succeed, it also causes them to learn uninterpretable solutions that could have counter-intuitive properties. In this paper we report two such properties. First, we find that there is no distinction between individual high level units and random linear combinations of high level units, according to various methods of unit analysis. It suggests that it is the space, rather than the individual units, that contains the semantic information in the high layers of neural networks. Second, we find that deep neural networks learn input-output mappings that are fairly discontinuous to a significant extent. We can cause the network to misclassify an image by applying a certain hardly perceptible perturbation, which is found by maximizing the network’s prediction error. In addition, the specific nature of these perturbations is not a random artifact of learning: the same perturbation can cause a different network, that was trained on a different subset of the dataset, to misclassify the same input.

negative: The evolution of Crew Resource Management training in commercial aviation. In this study, we describe changes in the nature of Crew Resource Management (CRM) training in commercial aviation, including its shift from cockpit to crew resource management. Validation of the impact of CRM is discussed. Limitations of CRM, including lack of cross-cultural generality are considered. An overarching framework that stresses error management to increase acceptance of CRM concepts is presented. The error management approach defines behavioral strategies taught in CRM as error countermeasures that are employed to avoid error, to trap errors committed, and to mitigate the consequences of error.

negative:
Evaluating iterative optimization across 1000 datasets While iterative optimization has become a popular compiler optimization approach, it is based on a premise which has never been truly evaluated: that it is possible to learn the best compiler optimizations across data sets. Up to now, most iterative optimization studies find the best optimizations through repeated runs on the same data set. Only a handful of studies have attempted to exercise iterative optimization on a few tens of data sets.
 In this paper, we truly put iterative compilation to the test for the first time by evaluating its effectiveness across a large number of data sets. We therefore compose KDataSets, a data set suite with 1000 data sets for 32 programs, which we release to the public. We characterize the diversity of KDataSets, and subsequently use it to evaluate iterative optimization.We demonstrate that it is possible to derive a robust iterative optimization strategy across data sets: for all 32 programs, we find that there exists at least one combination of compiler optimizations that achieves 86% or more of the best possible speedup across all data sets using Intel's ICC (83% for GNU's GCC). This optimal combination is program-specific and yields speedups up to 1.71 on ICC and 2.23 on GCC over the highest optimization level (-fast and -O3, respectively). This finding makes the task of optimizing programs across data sets much easier than previously anticipated, and it paves the way for the practical and reliable usage of iterative optimization. Finally, we derive pre-shipping and post-shipping optimization strategies for software vendors.

negative: Understanding Types of Cyberbullying in an Anonymous Messaging Application The possibility of anonymity and lack of effective ways to identify inappropriate messages have resulted in a significant amount of online interaction data that attempt to harass, bully, or offend the recipient. In this work, we perform a preliminary linguistic study on messages exchanged using one such popular web/smartphone application—Sarahah, that allows friends to exchange messages anonymously. Since messages exchanged via Sarahah are private, we collect them when the recipient shares it on Twitter. We then perform an analysis of the different kinds of messages exchanged through this application. Our linguistic analysis reveals that a significant number of these messages (∼ 20%) include inappropriate, hurtful, or profane language intended to embarrass, offend, or bully the recipient. Our analysis helps in understanding the different ways in which anonymous message exchange platforms are used and the different types of bullying present in such exchanges.

negative: Transient response and dynamic power dissipation comparison of various Dickson charge pump configurations based on charge transfer switches Dickson charge pump is a switches-capacitor network providing a voltage gain and having open-circuit resistance dependent on the topology frequency and the size of storage capacitors. In this paper, the current status of research and development in the field of Dickson charge pump is extensively reviewed. Dickson charge pump is inductor-less DC to DC converter which uses a capacitor for its energy storage. The aim of this paper is to compare various techniques of Dickson charge pump currently explored in industry and academia. The parameters especially efficiency and voltage are compared. We have also discussed and evaluated the optimised criteria of capacitor sizing according to clock signals provided such that the charge pump performance could be properly optimised. We have simulated the transient response of DCP and provided the comprehensive study.

negative: From GPS traces to a routable road map This paper presents a method for automatically converting raw GPS traces from everyday vehicles into a routable road network. The method begins by smoothing raw GPS traces using a novel aggregation technique. This technique pulls together traces that belong on the same road in response to simulated potential energy wells created around each trace. After the traces are moved in response to the potential fields, they tend to coalesce into smooth paths. To help adjust the parameters of the constituent potential fields, we present a theoretical analysis of the behavior of our algorithm on a few different road configurations. With the resulting smooth traces, we apply a custom clustering algorithm to create a graph of nodes and edges representing the road network. We show how this network can be used to plan reasonable driving routes, much like consumer-oriented mapping Web sites. We demonstrate our algorithms using real GPS data collected on public roads, and we evaluate the effectiveness of our approach on public roads, and we evaluate the effectiveness of our approach by comparing the route planning results suggested by our generated graph to a commercial route planner.

negative: Modeling and control of a hexacopter with a rotating seesaw This paper presents a novel multicomputer type with a unique structure. This structure allows decoupling angular motion from translational motion along one of the body axes, and by that, five of the body degrees of freedom (DoF) can be controlled independently. The proposed aerial vehicle is a hexacopter with six propellers, such that four propellers are attached to the vehicle body and the other two attached to a seesaw. The seesaw is free to rotate around a single axis that is fixed in the body frame. Compared to a standard hexacopter, this unique design improves the maneuverability of the aerial vehicle, without the price of additional actuators. As opposed to tilting-rotor aerial-vehicle, in the proposed design all actuators generate lift. The paper describes the dynamical model of the novel vehicle and suggests a nonlinear controller to track desired trajectories along five of its DoF. The performances of the design are demonstrated numerically.

negative:
Peer victimization, psychosocial adjustment, and physical activity in overweight and at-risk-for-overweight youth. OBJECTIVE
To examine the relationship between peer victimization and child and parent reports of psychosocial adjustment and physical activity in a clinical sample of at-risk-for-overweight and overweight children and adolescents.

METHODS
The Schwartz Peer Victimization Scale, Children's Depression Inventory-Short Form, Multidimensional Anxiety Scale for Children, Social Physique Anxiety Scale, PACE+ Adolescent Physical Activity Measure, and Asher Loneliness Scale were administered to 92 children and adolescents (54 females) aged 8-18 years. The youth's parent/guardian completed the Child Behavior Checklist.

RESULTS
Peer victimization was positively related to child-reported depression, anxiety, social physique anxiety, and loneliness, and parent-reported internalizing and externalizing symptoms. Peer victimization was negatively related to physical activity. Depressive symptoms and loneliness mediated the relations between peer victimization and physical activity.

CONCLUSION
Recognition of the magnitude of the problem and the means of evaluating for peer victimization is important for clinicians who work with overweight youth. Assessing peer experiences may assist in understanding rates of physical activity and/or past nonadherence to clinician recommendations.

negative: Investigations on Knowledge Base Embedding for Relation Prediction and Extraction We report an evaluation of the effectiveness of the existing knowledge base embedding models for relation prediction and for relation extraction on a wide range of benchmarks. We also describe a new benchmark, which is much larger and complex than previous ones, which we introduce to help validate the effectiveness of both tasks. The results demonstrate that knowledge base embedding models are generally effective for relation prediction but unable to give improvements for the state-of-art neural relation extraction model with the existing strategies, while pointing limitations of existing methods.

negative: Agents on Stage: Advancing the State of the Art of AI Intelligent computer agents are both the original goal and the ultimate goal of artificial intelligence research. In striving toward that goal, our community has followed a practical research strategy of "divide-and-conquer," with different sub-communities attacking important component functions of intelligence, such as planning, search, knowledge representation, vision, and natural language. This strategy has been almost too successful, yielding both challenging theoretical problems that have come to dominate the inquiries of many researchers and a spate of practical techniques that have enabled other researchers to move into profitable commercial enterprises. While each of these pursuits is worthy in its own right, together they have had the unfortunate sideeffect of fragmenting the field and almost completely diverting it from efforts to build intelligent agents.

negative: Visual-Interactive Preprocessing of Time Series Data Time series data is an important data type in many different application scenarios. Consequently, there are a great variety of approaches for analyzing time series data. Within these approaches different strategies for cleaning, segmenting, representing, normalizing, comparing, and aggregating time series data can be found. When combining these operations, the time series analysis preprocessing workflow has many degrees of freedom. To define an appropriate preprocessing pipeline, the knowledge of experts coming from the application domain has to be included into the design process. Unfortunately, these experts often cannot estimate the effects of the chosen preprocessing algorithms and their parameterizations on the time series. We introduce a system for the visual-interactive exploitation of the preprocessing parameter space. In contrast to ‘black box’-driven approaches designed by computer scientists based on the requirements of domain experts, our system allows these experts to visual-interactively compose time series preprocessing pipelines by themselves. Visual support is provided to choose the right order and parameterization of the preprocessing steps. We demonstrate the usability of our approach with a case study from the digital library domain, in which time-oriented scientific research data has to be preprocessed to realize a visual search and analysis application.

negative: An Effective CU Size Decision Method for HEVC Encoders The emerging high efficiency video coding standard (HEVC) adopts the quadtree-structured coding unit (CU). Each CU allows recursive splitting into four equal sub-CUs. At each depth level (CU size), the test model of HEVC (HM) performs motion estimation (ME) with different sizes including 2N × 2N, 2N × N, N × 2N and N × N. ME process in HM is performed using all the possible depth levels and prediction modes to find the one with the least rate distortion (RD) cost using Lagrange multiplier. This achieves the highest coding efficiency but requires a very high computational complexity. In this paper, we propose a fast CU size decision algorithm for HM. Since the optimal depth level is highly content-dependent, it is not efficient to use all levels. We can determine CU depth range (including the minimum depth level and the maximum depth level) and skip some specific depth levels rarely used in the previous frame and neighboring CUs. Besides, the proposed algorithm also introduces early termination methods based on motion homogeneity checking, RD cost checking and SKIP mode checking to skip ME on unnecessary CU sizes. Experimental results demonstrate that the proposed algorithm can significantly reduce computational complexity while maintaining almost the same RD performance as the original HEVC encoder.

negative: Vectorization vs. compilation in query execution Compiling database queries into executable (sub-) programs provides substantial benefits comparing to traditional interpreted execution. Many of these benefits, such as reduced interpretation overhead, better instruction code locality, and providing opportunities to use SIMD instructions, have previously been provided by redesigning query processors to use a vectorized execution model. In this paper, we try to shed light on the question of how state-of-the-art compilation strategies relate to vectorized execution for analytical database workloads on modern CPUs. For this purpose, we carefully investigate the behavior of vectorized and compiled strategies inside the Ingres VectorWise database system in three use cases: Project, Select and Hash Join. One of the findings is that compilation should always be combined with block-wise query execution. Another contribution is identifying three cases where "loop-compilation" strategies are inferior to vectorized execution. As such, a careful merging of these two strategies is proposed for optimal performance: either by incorporating vectorized execution principles into compiled query plans or using query compilation to create building blocks for vectorized processing.

negative: "PASS" principles for predictable bone regeneration. Guided bone regeneration is a well-established technique used for augmentation of deficient alveolar ridges. Predictable regeneration requires both a high level of technical skill and a thorough understanding of underlying principles of wound healing. This article describes the 4 major biologic principles (i.e., PASS) necessary for predictable bone regeneration: primary wound closure to ensure undisturbed and uninterrupted wound healing, angiogenesis to provide necessary blood supply and undifferentiated mesenchymal cells, space maintenance/creation to facilitate adequate space for bone ingrowth, and stability of wound and implant to induce blood clot formation and uneventful healing events. In addition, a novel flap design and clinical cases using this principle are presented.

negative: Expression Profiling of Developing Zebrafish Retinal Cells. During retinal development, a variety of different types of neurons are produced. Understanding how each of these types of retinal nerve cells is generated is important from a developmental biology perspective. It is equally important if one is interested in how to regenerate cells after an injury or a disease. To gain more insight into how retinal neurons develop in the zebrafish, we performed single-cell mRNA profiling and in situ hybridizations (ISHs) on retinal sections and whole-mount zebrafish. Through the series of ISHs, designed and performed solely by undergraduate students in the laboratory, we were able to retrospectively identify our single-cell mRNA profiles as most likely coming from developing amacrine cells. Further analysis of these profiles will reveal genes that can be mutated using genome editing techniques. Together these studies increase our knowledge of the genes driving development of different cell types in the zebrafish retina.

negative: Context and behavioral processes in extinction. This article provides a selective review and integration of the behavioral literature on Pavlovian extinction. The first part reviews evidence that extinction does not destroy the original learning, but instead generates new learning that is especially context-dependent. The second part examines insights provided by research on several related behavioral phenomena (the interference paradigms, conditioned inhibition, and inhibition despite reinforcement). The final part examines four potential causes of extinction: the discrimination of a new reinforcement rate, generalization decrement, response inhibition, and violation of a reinforcer expectation. The data are consistent with behavioral models that emphasize the role of generalization decrement and expectation violation, but would be more so if those models were expanded to better accommodate the finding that extinction involves a context-modulated form of inhibitory learning.

negative: Forecasting seasonal time series with computational intelligence: On recent methods and the potential of their combinations Accurate time series forecasting is a key issue to support individual and organizational decision making. In this paper, we introduce novel methods for multi-step seasonal time series forecasting. All the presented methods stem from computational intelligence techniques: evolutionary artificial neural networks, support vector machines and genuine linguistic fuzzy rules. Performance of the suggested methods is experimentally justified on seasonal time series from distinct domains on three forecasting horizons. The most important contribution is the introduction of a new hybrid combination using linguistic fuzzy rules and the other computational intelligence methods. This hybrid combination presents competitive forecasts, when compared with the popular ARIMA method. Moreover, such hybrid model is more easy to interpret by decision-makers when modeling trended series. 2012 Elsevier Ltd. All rights reserved.

negative: A Wide-Range Model for Metal-Oxide Surge Arrester This paper presents an electric model of a metal-oxide surge arrester (MOSA). The proposed electric model accurately represents the MOSA in a wide range of frequencies and amplitudes. The model was developed and validated based on MOSA electrical behavior in each one of the three operating regions of the zinc-oxide (ZnO) surge arresters, and in a database composed of voltage and current waveforms measured from tests performed in 12 ZnO varistors having different physical dimensions and electrical characteristics—from five different manufacturers. These varistors were subjected to different voltage levels in the low current region, and multilevel amplitude of switching current impulses (30/60 <inline-formula><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula>s), lightning current impulses (8/20 <inline-formula><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula>s), high current impulses (4/10 <inline-formula><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula>s), and fast-front current impulses (1.5/26 <inline-formula><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula>s and 3/6 <inline-formula><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula>s) encompass the three regions of operation and a wide range of frequencies and amplitudes. The results provided by the MOSA wide-range (MWR) model were compared with those obtained in the laboratory. The MWR model has shown good agreement in terms of waveform, peak value, and absorbed energy for the evaluated cases.

negative: Perceptually based brush strokes for nonphotorealistic visualization An important problem in the area of computer graphics is the visualization of large, complex information spaces. Datasets of this type have grown rapidly in recent years, both in number and in size. Images of the data stored in these collections must support rapid and accurate exploration and analysis. This article presents a method for constructing visualizations that are both effective and aesthetic. Our approach uses techniques from master paintings and human perception to visualize a multidimensional dataset. Individual data elements are drawn with one or more brush strokes that vary their appearance to represent the element's attribute values. The result is a nonphotorealistic visualization of information stored in the dataset. Our research extends existing glyph-based and nonphotorealistic techniques by applying perceptual guidelines to build an effective representation of the underlying data. The nonphotorealistic properties the strokes employ are selected from studies of the history and theory of Impressionist art. We show that these properties are similar to visual features that are detected by the low-level human visual system. This correspondence allows us to manage the strokes to produce perceptually salient visualizations. Psychophysical experiments confirm a strong relationship between the expressive power of our nonphotorealistic properties and previous findings on the use of perceptual color and texture patterns for data display. Results from these studies are used to produce effective nonphotorealistic visualizations. We conclude by applying our techniques to a large, multidimensional weather dataset to demonstrate their viability in a practical, real-world setting.

negative: Heart-Brain Neurodynamics : The Making of Emotions As pervasive and vital as they are in human experience, emotions have long remained an enigma to science. This monograph explores recent scientific advances that clarify central controversies in the study of emotion, including the relationship between intellect and emotion, and the historical debate on the source of emotional experience. Particular attention is given to the intriguing body of research illuminating the critical role of ascending input from the body to the brain in the generation and perception of emotions. This discussion culminates in the presentation of a systems-oriented model of emotion in which the brain functions as a complex pattern-matching system, continually processing input from both the external and internal environments. From this perspective it is shown that the heart is a key component of the emotional system, thus providing a physiological basis for the long-acknowledged link between the heart and our emotional life.

negative: TEXT MINING : PROMISES AND CHALLENGES Text mining, also known as knowledge discovery from text, and document information mining, refers to the process of extracting interesting patterns from very large text corpus for the purposes of discovering knowledge. Text mining is an interdisciplinary field involving information retrieval, text understanding, information extraction, clustering, categorization, visualization, database technology, machine learning, and data mining. Regarded by many as the next wave of knowledge discovery, text mining has a very high commercial value. This talk presents a general framework for text mining, consisting of two stages: text refining that transforms unstructured text documents into an intermediate form; and knowledge distill ation that deduces patterns or knowledge from the intermediate form. We then survey the state-of-the-art text mining approaches, products, and applications by aligning them based on the text refining and knowledge distill ation functions as well as the intermediate form that they adopt. In conclusion, we highlight the upcoming challenges of text mining and the opportunities it offers.

negative: Soft-Switching Zeta–Flyback Converter With a Buck–Boost Type of Active Clamp This paper presents the system analysis, design consideration, and implementation of a soft-switching zeta-flyback converter to achieve zero-voltage switching (ZVS). In the proposed converter, the zeta and flyback topologies are adopted in the output side to achieve the following features: to share the power components in the transformer primary side, to achieve the partial magnetizing flux reset, and to share the output power. The buck-boost type of active clamp is connected in parallel with the primary side of the isolation transformer to recycle the energy stored in the leakage inductor of isolated transformer and to limit the peak voltage stress of switching devices due to the transformer leakage inductor when the main switch is turned off. The active clamp circuit can make the switching devices to turn on at ZVS. Experimental results taken from a laboratory prototype rated at 240 W, input voltage of 150 V, output voltage of 12 V, and switching frequency of 150 kHz are presented to demonstrate the converter performance. Based on the experimental results, the circuit efficiency is about 90.5% at rated output power, and the output voltage variation is about 1%.

negative: Incomplete Multisource Transfer Learning Transfer learning is generally exploited to adapt well-established source knowledge for learning tasks in weakly labeled or unlabeled target domain. Nowadays, it is common to see multiple sources available for knowledge transfer, each of which, however, may not include complete classes information of the target domain. Naively merging multiple sources together would lead to inferior results due to the large divergence among multiple sources. In this paper, we attempt to utilize incomplete multiple sources for effective knowledge transfer to facilitate the learning task in target domain. To this end, we propose an incomplete multisource transfer learning through two directional knowledge transfer, i.e., cross-domain transfer from each source to target, and cross-source transfer. In particular, in cross-domain direction, we deploy latent low-rank transfer learning guided by iterative structure learning to transfer knowledge from each single source to target domain. This practice reinforces to compensate for any missing data in each source by the complete target data. While in cross-source direction, unsupervised manifold regularizer and effective multisource alignment are explored to jointly compensate for missing data from one portion of source to another. In this way, both marginal and conditional distribution discrepancy in two directions would be mitigated. Experimental results on standard cross-domain benchmarks and synthetic data sets demonstrate the effectiveness of our proposed model in knowledge transfer from incomplete multiple sources.

negative: General Survey on Security Issues on Internet of Things Internet of Things is the integration of a variety of technologies. The Internet of Things incorporates transparently and impeccably large number of assorted end systems, providing open access to selected data for digital services. Internet of things is a promising research in commerce, industry, and education applications. The abundance of sensors and actuators motivates sensing and actuate devices in communication scenarios thus enabling sharing of information in Internet of Things. Advances in sensor data collection technology and Radio Frequency Identification technology has led large number of smart devices connected to the Internet, continuously transmitting data over time.In the context of security, due to different communication overloads and standards conventional security services are not applicable on Internet of Things as a result of which the technological loopholes leads to the generation of malicious data, devices are compromised and so on. Hence a flexible mechanism can deal with the security threats in the dynamic environment of Internet of Things and continuous researches and new ideas needs to be regulated periodically for various upcoming challenges. This paper basically tries to cover up the security issues and challenges of Internet of Things along with a

negative: Toward Characterizing the Productivity Benefits of Very Large Displays Larger display surfaces are becoming increasingly available due to multi-monitor capability built into many systems, in addition to the rapid decrease in their costs. However, little is known about the performance benefits of using these larger surfaces compared to traditional single-monitor displays. In addition, it is not clear that current software designs and interaction techniques have been properly tuned for these larger surfaces. A preliminary user study was carried out to provide some initial evidence about the benefits of large versus small display surfaces for complex, multi-application office work. Significant benefits were observed in the use of a prototype, larger display, in addition to significant positive user preference and satisfaction with its use over a small display. In addition, design guidelines for enhancing user interaction across large display surfaces were identified. User productivity could be significantly enhanced in future graphical user interface designs if developed with these findings in mind.

negative: Enhancing Knowledge Graph Completion By Embedding Correlations Despite their large sizes, modern Knowledge Graphs (KGs) are still highly incomplete. Statistical relational learning methods can detect missing links by "embedding" the nodes and relations into latent feature tensors. Unfortunately, these methods are unable to learn good embeddings if the nodes are not well-connected. Our proposal is to learn embeddings for correlations between subgraphs and add a post-prediction phase to counter the lack of training data. This technique, applied on top of methods like TransE or HolE, can significantly increase the predictions on realistic KGs.

negative: DeepDefense: Training Deep Neural Networks with Improved Robustness Despite the efficacy on a variety of computer vision tasks, deep neural networks (DNNs) are vulnerable to adversarial attacks, limiting their applications in securitycritical systems. Recent works have shown the possibility of generating imperceptibly perturbed image inputs (a.k.a., adversarial examples) to fool well-trained DNN classifiers into making arbitrary predictions. To address this problem, we propose a training recipe named “deep defense”. Our core idea is to integrate an adversarial perturbation-based regularizer into the classification objective, such that the obtained models learn to resist potential attacks, directly and precisely. The whole optimization problem is solved just like training a recursive network. Experimental results demonstrate that our method outperforms training with adversarial/Parseval regularizations by large margins on various datasets (including MNIST, CIFAR-10 and ImageNet) and different DNN architectures. Code and models for reproducing our results will be made publicly available.

negative: Stochastic Activation Pruning for Robust Adversarial Defense Neural networks have been found vulnerable to adversarial examples. Carefully chosen perturbations to real images, while imperceptible to humans, induce misclassification and threaten the reliability of deep learning systems in the wild. To guard against adversarial examples, we take inspiration from game theory. We cast the problem as a minimax zero-sum game between the adversary and the model. For such games, it is in general optimal for players to use a stochastic policy, also known as a mixed strategy. In light of this, we propose a mixed strategy for the model: stochastic activation pruning. SAP prunes a random subset of activations (preferentially pruning activations with smaller magnitude) and scales up the survivors to compensate. SAP can be applied to pre-trained neural networks—even adversarially trained models—without fine-tuning, providing robustness against adversarial examples. Experiments demonstrate that SAP confers robustness in the adversarial setting, increasing accuracy whilst preserving calibration.

negative: Adversarial Examples: Attacks on Machine Learning-based Malware Visualization Detection Methods As the threat of malicious software (malware) becomes urgently serious, automatic malware detection techniques have received increasing attention recently, where the machine learning (ML)-based visualization detection plays a significant role. However, this leads to a fundamental problem whether such detection methods can be robust enough against various potential attacks. Even though ML algorithms show superiority to conventional ones in malware detection in terms of high efficiency and accuracy, this paper demonstrates that such ML-based malware detection methods are vulnerable to adversarial examples (AE) attacks. We propose the first AE-based attack framework, named Adversarial Texture Malware Perturbation Attacks (ATMPA), based on the gradient descent or L-norm optimization method. By introducing tiny perturbations on the transformed dataset, ML-based malware detection methods completely fail. The experimental results on the MS BIG malware dataset show that a small interference can reduce the detection rate of convolutional neural network (CNN), support vector machine (SVM) and random forest(RF)-based malware detectors down to 0 and the attack transferability can achieve up to 88.7% and 74.1% on average in different ML-based detection methods.

negative: Adversarial support vector machine learning Many learning tasks such as spam filtering and credit card fraud detection face an active adversary that tries to avoid detection. For learning problems that deal with an active adversary, it is important to model the adversary's attack strategy and develop robust learning models to mitigate the attack. These are the two objectives of this paper. We consider two attack models: a free-range attack model that permits arbitrary data corruption and a restrained attack model that anticipates more realistic attacks that a reasonable adversary would devise under penalties. We then develop optimal SVM learning strategies against the two attack models. The learning algorithms minimize the hinge loss while assuming the adversary is modifying data to maximize the loss. Experiments are performed on both artificial and real data sets. We demonstrate that optimal solutions may be overly pessimistic when the actual attacks are much weaker than expected. More important, we demonstrate that it is possible to develop a much more resilient SVM learning model while making loose assumptions on the data corruption models. When derived under the restrained attack model, our optimal SVM learning strategy provides more robust overall performance under a wide range of attack parameters.

negative: Biologically inspired protection of deep networks from adversarial attacks Inspired by biophysical principles underlying nonlinear dendritic computation in neural circuits, we develop a scheme to train deep neural networks to make them robust to adversarial attacks. Our scheme generates highly nonlinear, saturated neural networks that achieve state of the art performance on gradient based adversarial examples on MNIST, despite never being exposed to adversarially chosen examples during training. Moreover, these networks exhibit unprecedented robustness to targeted, iterative schemes for generating adversarial examples, including second-order methods. We further identify principles governing how these networks achieve their robustness, drawing on methods from information geometry. We find these networks progressively create highly flat and compressed internal representations that are sensitive to very few input dimensions, while still solving the task. Moreover, they employ highly kurtotic weight distributions, also found in the brain, and we demonstrate how such kurtosis can protect even linear classifiers from adversarial attack.

negative: Adversarial Perturbations Against Deep Neural Networks for Malware Classification Deep neural networks, like many other machine learning models, have recently been shown to lack robustness against adversarially crafted inputs. These inputs are derived from regular inputs by minor yet carefully selected perturbations that deceive machine learning models into desired misclassifications. Existing work in this emerging field was largely specific to the domain of image classification, since the highentropy of images can be conveniently manipulated without changing the images’ overall visual appearance. Yet, it remains unclear how such attacks translate to more securitysensitive applications such as malware detection–which may pose significant challenges in sample generation and arguably grave consequences for failure. In this paper, we show how to construct highly-effective adversarial sample crafting attacks for neural networks used as malware classifiers. The application domain of malware classification introduces additional constraints in the adversarial sample crafting problem when compared to the computer vision domain: (i) continuous, differentiable input domains are replaced by discrete, often binary inputs; and (ii) the loose condition of leaving visual appearance unchanged is replaced by requiring equivalent functional behavior. We demonstrate the feasibility of these attacks on many different instances of malware classifiers that we trained using the DREBIN Android malware data set. We furthermore evaluate to which extent potential defensive mechanisms against adversarial crafting can be leveraged to the setting of malware classification. While feature reduction did not prove to have a positive impact, distillation and re-training on adversarially crafted samples show promising results.

negative: Adversarial Machine Learning at Scale Adversarial examples are malicious inputs designed to fool machine learning models. They often transfer from one model to another, allowing attackers to mount black box attacks without knowledge of the target model’s parameters. Adversarial training is the process of explicitly training a model on adversarial examples, in order to make it more robust to attack or to reduce its test error on clean inputs. So far, adversarial training has primarily been applied to small problems. In this research, we apply adversarial training to ImageNet (Russakovsky et al., 2014). Our contributions include: (1) recommendations for how to succesfully scale adversarial training to large models and datasets, (2) the observation that adversarial training confers robustness to single-step attack methods, (3) the finding that multi-step attack methods are somewhat less transferable than singlestep attack methods, so single-step attacks are the best for mounting black-box attacks, and (4) resolution of a “label leaking” effect that causes adversarially trained models to perform better on adversarial examples than on clean examples, because the adversarial example construction process uses the true label and the model can learn to exploit regularities in the construction process.