query: Not All Dialogues are Created Equal: Instance Weighting for Neural Conversational Models

positive: A Persona-Based Neural Conversation Model We present persona-based models for handling the issue of speaker consistency in neural response generation. A speaker model encodes personas in distributed embeddings that capture individual characteristics such as background information and speaking style. A dyadic speakeraddressee model captures properties of interactions between two interlocutors. Our models yield qualitative performance improvements in both perplexity and BLEU scores over baseline sequence-to-sequence models, with similar gains in speaker consistency as measured by human judges.

positive: Discriminative learning for differing training and test distributions We address classification problems for which the training instances are governed by a distribution that is allowed to differ arbitrarily from the test distribution---problems also referred to as classification under covariate shift. We derive a solution that is purely discriminative: neither training nor test distribution are modeled explicitly. We formulate the general problem of learning under covariate shift as an integrated optimization problem. We derive a kernel logistic regression classifier for differing training and test distributions.

positive: Unsupervised Modeling of Twitter Conversations We propose the first unsupervised approach to the problem of modeling dialogue acts in an open domain. Trained on a corpus of noisy Twitter conversations, our method discovers dialogue acts by clustering raw utterances. Because it accounts for the sequential behaviour of these acts, the learned model can provide insight into the shape of communication in a new medium. We address the challenge of evaluating the emergent model with a qualitative visualization and an intrinsic conversation ordering task. This work is inspired by a corpus of 1.3 million Twitter conversations, which will be made publicly available. This huge amount of data, available only because Twitter blurs the line between chatting and publishing, highlights the need to be able to adapt quickly to a new medium.

positive: Neural Responding Machine for Short-Text Conversation We propose Neural Responding Machine (NRM), a neural networ k-based response generator for Short-Text Conversation. NRM takes the general encoder-de coder framework: it formalizes the generation of response as a decoding process based on the lat ent representation of the input text, while both encoding and decoding are realized with recurren t n ural networks (RNN). The NRM is trained with a large amount of one-round conversation dat a collected from a microblogging service. Empirical study shows that NRM can generate gramma tically correct and content-wise appropriate responses to over 75% of the input text, outperf orming state-of-the-arts in the same setting, including retrieval-based and SMT-based models.

positive: OpenSubtitles2016: Extracting Large Parallel Corpora from Movie and TV Subtitles We present a new major release of the OpenSubtitles collection of parallel corpora. The release is compiled from a large database of movie and TV subtitles and includes a total of 1689 bitexts spanning 2.6 billion sentences across 60 languages. The release also incorporates a number of enhancements in the preprocessing and alignment of the subtitles, such as the automatic correction of OCR errors and the use of meta-data to estimate the quality of each subtitle and score subtitle pairs.

negative: Development of an OCV prediction mechanism for lithium-ion battery system This paper investigates the development of an open circuit voltage (OCV) prediction technique for lithium cells. The underlying principal of the technique described in this paper employs one simple equation to predict the equilibrated cell voltage after a small rest period. The technique was tested and verified by using results obtained from experiments conducted at the Centre for Automotive and Power System Engineering (CAPSE) battery laboratories at the University of South Wales. Lithium-ion battery system analysis was carried out with reference to the standard battery system models available in the literature.

negative: 'Just What the Doctor Ordered': A Revised UTAUT for EMR System Adoption and Use by Doctors Electronic medical record (EMR) systems can deliver many benefits to healthcare organizations and the patients they serve. However, one of the biggest stumbling blocks in garnering these benefits is the limited adoption and use by doctors. We employ the unified theory of acceptance and use of technology (UTAUT) as the theoretical foundation and adapt the theory to the context of EMR system adoption and use by doctors. Specifically, we suggest that age will be the only significant moderator, and gender, voluntariness and experience will not play significant moderating roles. We tested our model in a longitudinal study over a 7-month period in a hospital implementing a new EMR system. We collected 3 waves of survey data from 141 doctors and used system logs to measure use. While the original UTAUT only predicted about 20% of the variance in intention, the modified UTAUT predicted 44%. Both models were comparable in their prediction of use. In addition to contributing to healthcare IT and UTAUT research, we hope this work will serve as a foundation for future work that integrates UTAUT with other theoretical perspectives.

negative: Optical mouse sensor for detecting height variation and translation of a surface A new sensing system based on an optical mouse sensor is developed. The optical mouse sensor is traditionally used as a 2D motion sensor, and in this study it is creatively used for detecting the height variation and translation of the object surfaces at the same time. Based on the experimental characteristics of the sensing system, it can be seen that the sensing system is useful in many fields such as robot odometry. The sensing system is based on a low-cost photoelectric sensor, which is a commercial LED-based optical mouse sensor, and thus it is easily available and generalized. Specially, the outputs of the sensor are intuitively displayed on the computer screen and recorded on the hard disk, which greatly improves the convenience of the system. A periodically-square-shaped rubber surface and a periodically-arc-shaped rubber surface are studied in the experiments. Experiments show that the shape information of the object surfaces and the translation direction of the object surfaces can be obtained at the same time. Besides, in the experiments, it is found that the sensitivity of the optical mouse sensor in different translation direction is different.

negative: Human-level intelligence or animal-like abilities? What just happened in artificial intelligence and how it is being misunderstood.

negative: BCS: A Binary Cuckoo Search algorithm for feature selection Feature selection has been actively pursued in the last years, since to find the most discriminative set of features can enhance the recognition rates and also to make feature extraction faster. In this paper, the propose a new feature selection called Binary Cuckoo Search, which is based on the behavior of cuckoo birds. The experiments were carried out in the context of theft detection in power distribution systems in two datasets obtained from a Brazilian electrical power company, and have demonstrated the robustness of the proposed technique against with several others nature-inspired optimization techniques.

negative: A new miniaturized antenna for ISM 433 MHz frequency band A new miniaturized antenna for 433MHz frequency band is introduced for sensor networks. The antenna is a magnetic monopole loaded with a capacitor, which can significantly decrease the resonant frequency of the structure. It is designed to operate in the ISM 433MHz band on a small PCB (103mm⋆55mm) and has at least 3MHz bandwidth. Moreover, its radiation efficiency is suitable for the possible applications and its pattern is omnidirectionnal.

negative: Controllable Procedural Content Generation via Constrained Multi-Dimensional Markov Chain Sampling Statistical models, such as Markov chains, have recently started to be studied for the purpose of Procedural Content Generation (PCG). A major problem with this approach is controlling the sampling process in order to obtain output satisfying some desired constraints. In this paper we present three approaches to constraining the content generated using multi-dimensional Markov chains: (1) a generate and test approach that simply resamples the content until the desired constraints are satisfied, (2) an approach that finds and resamples parts of the generated content that violate the constraints, and (3) an incremental method that checks for constraint violations during sampling. We test our approaches by generating maps for two classic video games, Super Mario Bros. and Kid Icarus.

negative: On-Line Multi-View Video Summarization for Wireless Video Sensor Network Battery lifetime is critical for wireless video sensors. To enable battery-powered wireless video sensors, low-power design is required. In this paper, we consider applying multi-view summarization to wireless video sensors to remove redundant contents such that the compression and transmission power can be reduced. A low-complexity online multi-view video summarization scheme is proposed. Experiments show that the proposed summarization method successfully reduces the video content while keeping important events. A power analysis of the system also shows that a significant amount of energy can be saved.

negative: Chatbot for IT Security Training: Using Motivational Interviewing to Improve Security Behaviour We conduct a pre-study with 25 participants on Mechanical Turk to find out which security behavioural problems are most important for online users. These questions are based on motivational interviewing (MI), an evidence-based treatment methodology that enables to train people about different kinds of behavioural changes. Based on that the chatbot is developed using Artificial Intelligence Markup Language (AIML). The chatbot is trained to speak about three topics: passwords, privacy and secure browsing. These three topics were ’most-wanted’ by the users of the pre-study. With the chatbot three training sessions with people are conducted.

negative: Prometheus: User-Controlled P2P Social Data Management for Socially-Aware Applications Recent Internet applications, such as online social networks and user-generated content sharing, produce an unprecedented amount of social information, which is further augmented by location or collocation data collected from mobile phones. Unfortunately, this wealth of social information is fragmented across many different proprietary applications. Combined, it could provide a more accurate representation of the social world, and it could enable a whole new set of socially-aware applications. We introduce Prometheus, a peer-to-peer service that collects and manages social information from multiple sources and implements a set of social inference functions while enforcing user-defined access control policies. Prometheus is socially-aware: it allows users to select peers that manage their social information based on social trust and exploits naturallyformed social groups for improved performance. We tested our Prometheus prototype on PlanetLab and built a mobile social application to test the performance of its social inference functions under real-time constraints. We showed that the social-based mapping of users onto peers improves the service response time and high service availability is achieved with low overhead.

negative: Design pattern implementation in Java and aspectJ AspectJ implementations of the GoF design patterns show modularity improvements in 17 of 23 cases. These improvements are manifested in terms of better code locality, reusability, composability, and (un)pluggability.The degree of improvement in implementation modularity varies, with the greatest improvement coming when the pattern solution structure involves crosscutting of some form, including one object playing multiple roles, many objects playing one role, or an object playing roles in multiple pattern instances.

negative: A Semantic Framework for the Security Analysis of Ethereum smart contracts Smart contracts are programs running on cryptocurrency (e.g., Ethereum) blockchains, whose popularity stem from the possibility to perform financial transactions, such as payments and auctions, in a distributed environment without need for any trusted third party. Given their financial nature, bugs or vulnerabilities in these programs may lead to catastrophic consequences, as witnessed by recent attacks. Unfortunately, programming smart contracts is a delicate task that requires strong expertise: Ethereum smart contracts are written in Solidity, a dedicated language resembling JavaScript, and shipped over the blockchain in the EVM bytecode format. In order to rigorously verify the security of smart contracts, it is of paramount importance to formalize their semantics as well as the security properties of interest, in particular at the level of the bytecode being executed. In this paper, we present the first complete small-step semantics of EVM bytecode, which we formalize in the F* proof assistant, obtaining executable code that we successfully validate against the official Ethereum test suite. Furthermore, we formally define for the first time a number of central security properties for smart contracts, such as call integrity, atomicity, and independence from miner controlled parameters. This formalization relies on a combination of hyperand safety properties. Along this work, we identified various mistakes and imprecisions in existing semantics and verification tools for Ethereum smart contracts, thereby demonstrating once more the importance of rigorous semantic foundations for the design of security verification techniques.

negative: Axe UX: Exploring long-term user experience with iScale and AttrakDiff Positive user experience (UX) is an important goal in product design. Positive long-term UX is believed to improve customer loyalty, therefore being vital for continuous commercial success. Most UX research investigates momentary or short-term UX although the relationship between the user and the product evolves over time. There is a need to develop methods for measuring long-term UX and evaluate their feasibility in different product contexts. In this explorative study, 18 customers reported their experiences during the first three months of use of a non-interactive design tool, an axe. UX was evaluated with retrospective iScale tool and monthly repeated AttrakDiff questionnaire. iScale demonstrated the long-term trend of the attractiveness of the product well and provided information about the causes of the change in the experience. The AttrakDiff questionnaire was a good indicator of attractiveness during a longer period of time and is also well suited to remote studies.

negative: kAFL: Hardware-Assisted Feedback Fuzzing for OS Kernels Many kinds of memory safety vulnerabilities have been endangering software systems for decades. Amongst other approaches, fuzzing is a promising technique to unveil various software faults. Recently, feedback-guided fuzzing demonstrated its power, producing a steady stream of security-critical software bugs. Most fuzzing efforts—especially feedback fuzzing—are limited to user space components of an operating system (OS), although bugs in kernel components are more severe, because they allow an attacker to gain access to a system with full privileges. Unfortunately, kernel components are difficult to fuzz as feedback mechanisms (i.e., guided code coverage) cannot be easily applied. Additionally, non-determinism due to interrupts, kernel threads, statefulness, and similar mechanisms poses problems. Furthermore, if a process fuzzes its own kernel, a kernel crash highly impacts the performance of the fuzzer as the OS needs to reboot. In this paper, we approach the problem of coverageguided kernel fuzzing in an OS-independent and hardware-assisted way: We utilize a hypervisor and Intel’s Processor Trace (PT) technology. This allows us to remain independent of the target OS as we just require a small user space component that interacts with the targeted OS. As a result, our approach introduces almost no performance overhead, even in cases where the OS crashes, and performs up to 17,000 executions per second on an off-the-shelf laptop. We developed a framework called kernel-AFL (kAFL) to assess the security of Linux, macOS, and Windows kernel components. Among many crashes, we uncovered several flaws in the ext4 driver for Linux, the HFS and APFS file system of macOS, and the NTFS driver of Windows.

negative: High-Efficiency Slim Adapter With Low-Profile Transformer Structure This paper presents the implementation of a high-efficiency slim adapter using an resonant converter. A new structure of the slim-type transformer is proposed, which is composed of copper wire as the primary winding and printed-circuit-board winding on the outer layer as the secondary winding. The proposed structure is suitable for a slim and high-efficiency converter because it has advantages of easy utilization and wide conductive cross-sectional area. In addition, the voltage-doubler rectifier is applied to the secondary side due to its simple structure of secondary winding, and a filter is adopted to reduce the output filter size. The validity of this study is confirmed by the experimental results from an 85-W prototype.

negative: Thing broker: a twitter for things In the Web of Things, standard web technologies and protocols are used to represent and communicate with physical and virtual things. One challenge toward this vision is integrating things with different characteristics, protocols, interfaces and constraints while maintaining the simplicity and flexibility required for a variety of applications. In this paper we present the Thing Broker, a core platform for Web of Things that provides RESTFul interfaces to things using a Twitter-based set of abstractions and communication model. We present the key abstractions, a reference implementation and explain how a typical WoT application can be created using Thing Broker. We finish with a preliminary evaluation and draw some lessons from our experiences.

negative:
Research Paper: Automatic Detection of Acute Bacterial Pneumonia from Chest X-ray Reports OBJECTIVE
To evaluate the performance of a natural language processing system in extracting pneumonia-related concepts from chest x-ray reports.

METHODS

DESIGN
Four physicians, three lay persons, a natural language processing system, and two keyword searches (designated AAKS and KS) detected the presence or absence of three pneumonia-related concepts and inferred the presence or absence of acute bacterial pneumonia from 292 chest x-ray reports. Gold standard: Majority vote of three independent physicians. Reliability of the gold standard was measured.

OUTCOME MEASURES
Recall, precision, specificity, and agreement (using Finn's R: statistic) with respect to the gold standard. Differences between the physicians and the other subjects were tested using the McNemar test for each pneumonia concept and for the disease inference of acute bacterial pneumonia.

RESULTS
Reliability of the reference standard ranged from 0.86 to 0.96. Recall, precision, specificity, and agreement (Finn R:) for the inference on acute bacterial pneumonia were, respectively, 0.94, 0.87, 0.91, and 0.84 for physicians; 0.95, 0.78, 0.85, and 0.75 for natural language processing system; 0.46, 0.89, 0.95, and 0.54 for lay persons; 0.79, 0.63, 0.71, and 0.49 for AAKS; and 0.87, 0.70, 0.77, and 0.62 for KS. The McNemar pairwise comparisons showed differences between one physician and the natural language processing system for the infiltrate concept and between another physician and the natural language processing system for the inference on acute bacterial pneumonia. The comparisons also showed that most physicians were significantly different from the other subjects in all pneumonia concepts and the disease inference.

CONCLUSION
In extracting pneumonia related concepts from chest x-ray reports, the performance of the natural language processing system was similar to that of physicians and better than that of lay persons and keyword searches. The encoded pneumonia information has the potential to support several pneumonia-related applications used in our institution. The applications include a decision support system called the antibiotic assistant, a computerized clinical protocol for pneumonia, and a quality assurance application in the radiology department.

negative: Studying depression using imaging and machine learning methods Depression is a complex clinical entity that can pose challenges for clinicians regarding both accurate diagnosis and effective timely treatment. These challenges have prompted the development of multiple machine learning methods to help improve the management of this disease. These methods utilize anatomical and physiological data acquired from neuroimaging to create models that can identify depressed patients vs. non-depressed patients and predict treatment outcomes. This article (1) presents a background on depression, imaging, and machine learning methodologies; (2) reviews methodologies of past studies that have used imaging and machine learning to study depression; and (3) suggests directions for future depression-related studies.

negative: Detection of Surface Crack in Concrete Using Measurement Technique With Laser Displacement Sensor Detection of crack in concrete is an important issue as concrete is one of the principal material used in civil infrastructures. Cracks often occur first on the surface of concrete structure under load and provide an indication for further degradation. In this brief, an efficient noncontact method of the detection of such cracks in concrete using a measurement system with a laser displacement sensor (LDS) is presented. The proposed system consists of an LDS mounted on the scanner performing a raster scan. During the scanning process, the system gives the reading of displacement value from the sensor head to the laser spot on the target surface of the specimen. The proof of concept is given by testing with two different concrete specimens, namely, a concrete slab with a 0.7-mm-width through crack and a cylindrical concrete specimen with multiple cracks on the surface caused by loading effect. It is shown that a characteristic response of crack as a sharp distortion of the displacement reading occurs when the laser spot crosses the crack. The negligible standard deviation proves the repeatability and accuracy of the measurement and shows that this technique can be applied in a real-life scenario. A root-mean-square deviation in the displacement reading of the LDS is used as an index of crack. The effect of the shape of the specimen is investigated by detecting same-width cracks at different points along the circumference of a cylindrical specimen with different incident angles.

negative: Unified temporal and spatial calibration for multi-sensor systems In order to increase accuracy and robustness in state estimation for robotics, a growing number of applications rely on data from multiple complementary sensors. For the best performance in sensor fusion, these different sensors must be spatially and temporally registered with respect to each other. To this end, a number of approaches have been developed to estimate these system parameters in a two stage process, first estimating the time offset and subsequently solving for the spatial transformation between sensors. In this work, we present on a novel framework for jointly estimating the temporal offset between measurements of different sensors and their spatial displacements with respect to each other. The approach is enabled by continuous-time batch estimation and extends previous work by seamlessly incorporating time offsets within the rigorous theoretical framework of maximum likelihood estimation. Experimental results for a camera to inertial measurement unit (IMU) calibration prove the ability of this framework to accurately estimate time offsets up to a fraction of the smallest measurement period.

negative: Critical review on video game evaluation heuristics: social games perspective This paper presents the first step in creating design and evaluation heuristics for social games which emerge from the domain of social media. Initial high level heuristics for social games are offered by reviewing four existing video game heuristic models and analyzing two social games design frameworks.

negative: An empirical analysis of the distribution of unit test smells and their impact on software maintenance Unit testing represents a key activity in software development and maintenance. Test suites with high internal quality facilitate maintenance activities, such as code comprehension and regression testing. Several guidelines have been proposed to help developers write good test suites. Unfortunately, such rules are not always followed resulting in the presence of bad test code smells (or simply test smells). Test smells have been defined as poorly designed tests and their presence may negatively affect the maintainability of test suites and production code. Despite the many studies that address code smells in general, until now there has been no empirical evidence regarding test smells (i) distribution in software systems nor (ii) their impact on the maintainability of software systems. This paper fills this gap by presenting two empirical studies. The first study is an exploratory analysis of 18 software systems (two industrial and 16 open source) aimed at analyzing the distribution of test smells in source code. The second study, a controlled experiment involving twenty master students, is aimed at analyzing whether the presence of test smells affects the comprehension of source code during software maintenance. The results show that (i) test smells are widely spread throughout the software systems studied and (ii) most of the test smells have a strong negative impact on the comprehensibility of test suites and production code.

negative: Regenerative braking control method and optimal scheme for electric motorcycle This paper describes an optimal regenerative braking control scheme for a permanent magnet brushless DC motor of an electric motorcycle to achieve dual goals of the electric brake and the maximal power harvest in two cases of the road, downhill and flat, without any additional changes on the hardware. Based on regenerative braking of brushless DC motor in half bridge modulation mode, the relationship between average regenerative current on the DC bus and PWM duty cycle under different speeds was studied intensively from the three aspects of theoretical deduction, simulation based on Matlab/Simulink and platform experiments, and then a simple but effective scheme considering the actual application was proposed tentatively to achieve the dual goals. Since the braking kinetic energy is harvested in the form of the electric energy stored up in battery, the scheme could improve the driving range, safeness, noise preventability and cost effective of electric motorcycles.

negative: Design and optimisation of a voice coil motor (VCM) with a rotary actuator for an ultrasound scanner. IEEE Transactions on Industrial Electronics This paper proposes a new application for the rotary VCM. In developing a low cost ultrasound scanner for the developing world an oscillating transducer is required to sweep over the skin. The ultrasound scanner must operate from a USB power supply in remote locations. The application requires a 3.3N force on the coils of the motor to overcome the inertia of the skin. A proof of concept prototype motor with electronics has been designed, simulated and tested. The VCM optimisation is discussed in detail with the unique separation of the magnets being critical to reduce the axial bearing forces for this application.

negative: Design and Modeling of a Textile Pressure Sensor for Sitting Posture Classification A textile pressure sensor has been designed for measuring pressure distribution on the human body. Electrodes built with conductive textiles are arranged on both sides of a compressible spacer, forming a variable capacitor. The use of textiles makes unobtrusive, comfortable, lightweight, and washable sensors possible. This simplifies the goal to integrate such sensors into clothing in the future, to be simple, and fast to mount just as getting dressed. Hysteresis induced by the spacer of the sensor has been modeled with the Preisach model to reduce the measurement error from 24% to 5% on average and the maximal error from above 50% to below 10%. Standard textiles that are not specially optimized for low hysteresis can be used for designing the sensor due to the modeling. The model may also be used for other pressure or even strain sensors to reduce their hysteresis. The modeling enhances the accuracy of the textile sensor to those of commercial, nontextile pressure sensing mats. Furthermore, we used the sensor system for the classification of sitting postures in a chair. The data of nine subjects have been classified with Naive Bayes classifier, achieving an average recognition rate of 82%. We show that the textile sensor performs similarly to a commercial, nontextile pressure sensing mat for this application.

negative: Laughbot: Detecting Humor in Spoken Language with Language and Audio Cues We propose detecting and responding to humor in spoken dialogue by extracting language and audio cues and subsequently feeding these features into a combined recurrent neural network (RNN) and logistic regression model. In this paper, we parse Switchboard phone conversations to build a corpus of punchlines and unfunny lines where punchlines precede laughter tokens in Switchboard transcripts. We create a combined RNN and logistic regression model that uses both acoustic and language cues to predict whether a conversational agent should respond to an utterance with laughter. Our model achieves an F1-score of 63.2 and accuracy of 73.9. This model outperforms our logistic language model (F1-score 56.6) and RNN acoustic model (59.4) as well as the final RNN model of D. Bertero, 2016 (52.9). Using our final model, we create a “laughbot” that audibly responds to a user with laughter when their utterance is classified as a punchline. A conversational agent outfitted with a humorrecognition system such as the one we present in this paper would be valuable as these agents gain utility in everyday life. Keywords—Chatbots; spoken natural language processing; deep learning; machine learning

negative: SOCIAL DIALOGUE WITH EMBODIED CONVERSATIONAL AGENTS The functions of social dialogue between people in the context of performing a task is discussed, as well as approaches to modelling such dialogue in embodied conversational agents. A study of an agent’s use of social dialogue is presented, comparing embodied interactions with similar interactions conducted over the phone, assessing the impact these media have on a wide range of behavioural, task and subjective measures. Results indicate that subjects’ perceptions of the agent are sensitive to both interaction style (social vs. task-only dialogue) and

negative: PyDial: A Multi-domain Statistical Dialogue System Toolkit Statistical Spoken Dialogue Systems have been around for many years. However, access to these systems has always been difficult as there is still no publicly available end-to-end system implementation. To alleviate this, we present PyDial, an opensource end-to-end statistical spoken dialogue system toolkit which provides implementations of statistical approaches for all dialogue system modules. Moreover, it has been extended to provide multidomain conversational functionality. It offers easy configuration, easy extensibility, and domain-independent implementations of the respective dialogue system modules. The toolkit is available for download under the Apache 2.0 license.

negative: Weighted-to-Spherically-Uniform Quality Evaluation for Omnidirectional Video Omnidirectional video records a scene in all directions around one central position. It allows users to select viewing content freely in all directions. Assuming that viewing directions are uniformly distributed, the isotropic observation space can be regarded as a sphere. Omnidirectional video is commonly represented by different projection formats with one or multiple planes. To measure objective quality of omnidirectional video in observation space more accurately, a weighted-to-spherically-uniform quality evaluation method is proposed in this letter. The error of each pixel on projection planes is multiplied by a weight to ensure the equivalent spherical area in observation space, in which pixels with equal mapped spherical area have the same influence on distortion measurement. Our method makes the quality evaluation results more accurate and reliable since it avoids error propagation caused by the conversion from resampling representation space to observation space. As an example of such quality evaluation method, weighted-to-spherically-uniform peak signal-to-noise ratio is described and its performance is experimentally analyzed.

negative: Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks Factorization Machines (FMs) are a supervised learning approach that enhances the linear regression model by incorporating the second-order feature interactions. Despite effectiveness, FM can be hindered by its modelling of all feature interactions with the same weight, as not all feature interactions are equally useful and predictive. For example, the interactions with useless features may even introduce noises and adversely degrade the performance. In this work, we improve FM by discriminating the importance of different feature interactions. We propose a novel model named Attentional Factorization Machine (AFM), which learns the importance of each feature interaction from data via a neural attention network. Extensive experiments on two real-world datasets demonstrate the effectiveness of AFM. Empirically, it is shown on regression task AFM betters FM with a 8.6% relative improvement, and consistently outperforms the state-of-the-art deep learning methods Wide&Deep [Cheng et al., 2016] and DeepCross [Shan et al., 2016] with a much simpler structure and fewer model parameters. Our implementation of AFM is publicly available at: https://github. com/hexiangnan/attentional factorization machine

negative: A Modularization Method for Battery Equalizers Using Multiwinding Transformers This paper proposes a modularized global architecture using multi-winding transformers for battery cell balancing. The global balancing for a series-connected battery string is achieved based on forward conversion in each battery module and based on flyback conversion among modules. The demagnetization of the multiwinding transformers is also simultaneously achieved by the flyback conversion among modules without the need of additional demagnetizing circuits. Moreover, all MOSFET switches are driven by two complementary pulse width modulation signals without the requirement of cell voltage sensors, and energy can be automatically and simultaneously delivered from any high voltage cells to any low voltage cells. Compared with existing equalizers requiring additional balancing circuits for battery modules, the proposed modularized equalizer shares one circuit for the balancing among cells and modules. The balancing performance of the proposed equalizer is perfectly verified through experimental results, and the maximum balancing efficiency is up to 91.3%. In summary, the proposed modularized equalizer has the advantages of easier modularization, simpler control, higher efficiency, smaller size, and lower cost, ensuring the battery system higher reliability and easier implementation.

negative: Anger detection in call center dialogues We present a method to classify fixed-duration windows of speech as expressing anger or not, which does not require speech recognition, utterance segmentation, or separating the utterances of different speakers and can, thus, be easily applied to real-world recordings. We also introduce the task of ranking a set of spoken dialogues by decreasing percentage of anger duration, as a step towards helping call center supervisors and analysts identify conversations requiring further action. Our work is among the very few attempts to detect emotions in spontaneous human-human dialogues recorded in call centers, as opposed to acted studio recordings or human-machine dialogues. We show that despite the non-perfect performance (approx. 70% accuracy) of the window-level classifier, its decisions help produce a ranking of entire conversations by decreasing percentage of anger duration that is clearly better than a random ranking, which represents the case where supervisors and analysts randomly select conversations to inspect.


query: Hierarchical Character-Word Models for Language Identification

positive: Domain Adaptation of Recurrent Neural Networks for Natural Language Understanding The goal of this paper is to use multi-task learning to efficiently scale slot filling models for natural language understanding to handle multiple target tasks or domains. The key to scalability is reducing the amount of training data needed to learn a model for a new task. The proposed multi-task model delivers better performance with less data by leveraging patterns that it learns from the other tasks. The approach supports an open vocabulary, which allows the models to generalize to unseen words, which is particularly important when very little training data is used. A newly collected crowd-sourced data set, covering four different domains, is used to demonstrate the effectiveness of the domain adaptation and open vocabulary techniques.

positive: Adam: A Method for Stochastic Optimization We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. The hyper-parameters have intuitive interpretations and typically require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed. We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. Finally, we discuss AdaMax, a variant of Adam based on the infinity norm.

positive: Learning Character-level Representations for Part-of-Speech Tagging Distributed word representations have recently been proven to be an invaluable resource for NLP. These representations are normally learned using neural networks and capture syntactic and semantic information about words. Information about word morphology and shape is normally ignored when learning word representations. However, for tasks like part-of-speech tagging, intra-word information is extremely useful, specially when dealing with morphologically rich languages. In this paper, we propose a deep neural network that learns character-level representation of words and associate them with usual word representations to perform POS tagging. Using the proposed approach, while avoiding the use of any handcrafted feature, we produce stateof-the-art POS taggers for two languages: English, with 97.32% accuracy on the Penn Treebank WSJ corpus; and Portuguese, with 97.47% accuracy on the Mac-Morpho corpus, where the latter represents an error reduction of 12.2% on the best previous known result.

positive: Character-based Neural Machine Translation We introduce a neural machine translation model that views the input and output sentences as sequences of characters rather than words. Since word-level information provides a crucial source of bias, our input model composes representations of character sequences into representations of words (as determined by whitespace boundaries), and then these are translated using a joint attention/translation model. In the target language, the translation is modeled as a sequence of word vectors, but each word is generated one character at a time, conditional on the previous character generations in each word. As the representation and generation of words is performed at the character level, our model is capable of interpreting and generating unseen word forms. A secondary benefit of this approach is that it alleviates much of the challenges associated with preprocessing/tokenization of the source and target languages. We show that our model can achieve translation results that are on par with conventional word-based models.

positive: Better Word Representations with Recursive Neural Networks for Morphology Vector-space word representations have been very successful in recent years at improving performance across a variety of NLP tasks. However, common to most existing work, words are regarded as independent entities without any explicit relationship among morphologically related words being modeled. As a result, rare and complex words are often poorly estimated, and all unknown words are represented in a rather crude way using only one or a few vectors. This paper addresses this shortcoming by proposing a novel model that is capable of building representations for morphologically complex words from their morphemes. We combine recursive neural networks (RNNs), where each morpheme is a basic unit, with neural language models (NLMs) to consider contextual information in learning morphologicallyaware word representations. Our learned models outperform existing word representations by a good margin on word similarity tasks across many datasets, including a new dataset we introduce focused on rare words to complement existing ones in an interesting way.

negative: Adoption and Impact of IT Governance and Management Practices: A COBIT 5 Perspective This paper empirically investigates how adoption of IT governance and management processes, as identified in the IT governance framework COBIT 5, relates to the level of IT-related goals achievement, which in turn associates to the level of enterprise goals achievement. Simultaneously, this research project provides an international benchmark on how organizations are currently adopting the governance and management processes as identified in COBIT 5. The findings suggest that organizations are best in adopting the “IT factory” related management processes and that implementation scores drop in management and governance processes when more business and board involvement is required. Additionally, there are significant differences in perceived implementation maturity of COBIT 5 processes between SMEs and larger organizations. Also, the data offers empirical evidence that the COBIT 5 processes have a positive association with enterprise value creation. KeywORdS COBIT 5, Enterprise Goals, Enterprise Governance and Management of IT, IT Governance, SMEs

negative: Peephole: Predicting Network Performance Before Training The quest for performant networks has been a significant force that drives the advancements of deep learning in recent years. While rewarding, improving network design has never been an easy journey. The large design space combined with the tremendous cost required for network training poses a major obstacle to this endeavor. In this work, we propose a new approach to this problem, namely, predicting the performance of a network before training, based on its architecture. Specifically, we develop a unified way to encode individual layers into vectors and bring them together to form an integrated description via LSTM. Taking advantage of the recurrent network’s strong expressive power, this method can reliably predict the performances of various network architectures. Our empirical studies showed that it not only achieved accurate predictions but also produced consistent rankings across datasets – a key desideratum in performance prediction.

negative: Bangladeshi banknote recognition by neural network with axis symmetrical masks Automated banknote recognition system can be a very good utility in banking systems and other field of commerce. It can also aid visually impaired people. Although in Bangladesh, bill money recognition machines are not common but it is used in other countries. In this paper, for the first time, we have proposed a Neural Network based recognition scheme for Bangladeshi banknotes. The scheme can efficiently be implemented in cheap hardware which may be very useful in many places. The recognition system takes scanned images of banknotes which are scanned by low cost optoelectronic sensors and then fed into a Multilayer Perceptron, trained by Backpropagation algorithm, for recognition. Axis Symmetric Masks are used in preprocessing stage which reduces the network size and guarantees correct recognition even if the note is flipped. Experimental results are presented which show that this scheme can recognize currently available 8 notes (1, 2, 5, 10, 20, 50, 100 & 500 Taka) successfully with an average accuracy of 98.57%.

negative:
Cowden syndrome. BACKGROUND
Cowden syndrome is a rare genodermatosis charactarized by presence of multiple hamartomas. The aim of the study was to specify the clinical, therapeutic and prognostic aspects of Cowden syndrome.

CASES REPORT
Our study included 4 patients with Cowden syndrome, 2 males and 2 females between 14 and 46 years old. Clinical examination of the skin revealed facials papules (4 cases), acral keratosis (1 case), translucent keratotic papules (2 cases). Oral examination revealed papules (4 cases), papillomatosis (4 cases), gingival hypertrophy (4 cases) and scrotal tongue (2 cases). Investigations revealed thyroid lesions (2 cases), fibrocystic disease and lipoma of the breast in 1 case, "glycogenic acanthosis" (1 case), macrocephaly (2 cases), dysmorphic face (1 case) and lichen nitidus (1 case). Oral etretinate and acitretine were temporary efficient in 2 patients. Topical treatment with tretinoin lotion resulted in some improvement in cutaneous, but not mucosal lesions in one patient. No cancer was revealed.

CONCLUSION
The pathognomonic mucocutaneous lesions were found in all patients. However, no degenerative lesions have been revealed. A new association of Cowden syndrome with lichen nitidus was found. Treatment with oral retinoids was efficient on cutaneous lesions.

negative:
High resolution stationary digital breast tomosynthesis using distributed carbon nanotube x-ray source array. PURPOSE
The purpose of this study is to investigate the feasibility of increasing the system spatial resolution and scanning speed of Hologic Selenia Dimensions digital breast tomosynthesis (DBT) scanner by replacing the rotating mammography x-ray tube with a specially designed carbon nanotube (CNT) x-ray source array, which generates all the projection images needed for tomosynthesis reconstruction by electronically activating individual x-ray sources without any mechanical motion. The stationary digital breast tomosynthesis (s-DBT) design aims to (i) increase the system spatial resolution by eliminating image blurring due to x-ray tube motion and (ii) reduce the scanning time. Low spatial resolution and long scanning time are the two main technical limitations of current DBT technology.

METHODS
A CNT x-ray source array was designed and evaluated against a set of targeted system performance parameters. Simulations were performed to determine the maximum anode heat load at the desired focal spot size and to design the electron focusing optics. Field emission current from CNT cathode was measured for an extended period of time to determine the stable life time of CNT cathode for an expected clinical operation scenario. The source array was manufactured, tested, and integrated with a Selenia scanner. An electronic control unit was developed to interface the source array with the detection system and to scan and regulate x-ray beams. The performance of the s-DBT system was evaluated using physical phantoms.

RESULTS
The spatially distributed CNT x-ray source array comprised 31 individually addressable x-ray sources covering a 30 angular span with 1 pitch and an isotropic focal spot size of 0.6 mm at full width at half-maximum. Stable operation at 28 kV(peak) anode voltage and 38 mA tube current was demonstrated with extended lifetime and good source-to-source consistency. For the standard imaging protocol of 15 views over 14, 100 mAs dose, and 2 × 2 detector binning, the projection resolution along the scanning direction increased from 4.0 cycles/mm [at 10% modulation-transfer-function (MTF)] in DBT to 5.1 cycles/mm in s-DBT at magnification factor of 1.08. The improvement is more pronounced for faster scanning speeds, wider angular coverage, and smaller detector pixel sizes. The scanning speed depends on the detector, the number of views, and the imaging dose. With 240 ms detector readout time, the s-DBT system scanning time is 6.3 s for a 15-view, 100 mAs scan regardless of the angular coverage. The scanning speed can be reduced to less than 4 s when detectors become faster. Initial phantom studies showed good quality reconstructed images.

CONCLUSIONS
A prototype s-DBT scanner has been developed and evaluated by retrofitting the Selenia rotating gantry DBT scanner with a spatially distributed CNT x-ray source array. Preliminary results show that it improves system spatial resolution substantially by eliminating image blur due to x-ray focal spot motion. The scanner speed of s-DBT system is independent of angular coverage and can be increased with faster detector without image degration. The accelerated lifetime measurement demonstrated the long term stability of CNT x-ray source array with typical clinical operation lifetime over 3 years.

negative: From Vision to NLP : A Merge The study of artificial intelligence can be simplified into one goal: trying to mimic/enhance human senses. This paper attempts to combine computer vision and natural language processing to create a question answer system. This system takes a question and an image as input and outputs a response to the answer based on how the RCNN understands the question asked. The system correlates the question with the image by leveraging attention and memory mechanisms. Mentor: Arun Chaganty

negative: Fast high-quality non-blind deconvolution using sparse adaptive priors We present an efficient approach for high-quality non-blind deconvolution based on the use of sparse adaptive priors. Its regularization term enforces preservation of strong edges while removing noise. We model the image-prior deconvolution problem as a linear system, which is solved in the frequency domain. This clean formulation lends to a simple and efficient implementation. We demonstrate its effectiveness by performing an extensive comparison with existing non-blind deconvolution methods, and by using it to deblur photographs degraded by camera shake. Our experiments show that our solution is faster and its results tend to have higher peak signal-to-noise ratio than the state-of-the-art techniques. Thus, it provides an attractive alternative to perform high-quality non-blind deconvolution of large images, as well as to be used as the final step of blind-deconvolution algorithms.

negative: Towards Contactless, Low-Cost and Accurate 3D Fingerprint Identification Human identification using fingerprint impressions has been widely studied and employed for more than 2000 years. Despite new advancements in the 3D imaging technologies, widely accepted representation of 3D fingerprint features and matching methodology is yet to emerge. This paper investigates 3D representation of widely employed 2D minutiae features by recovering and incorporating (i) minutiae height z and (ii) its 3D orientation φ information and illustrates an effective matching strategy for matching popular minutiae features extended in 3D space. One of the obstacles of the emerging 3D fingerprint identification systems to replace the conventional 2D fingerprint system lies in their bulk and high cost, which is mainly contributed from the usage of structured lighting system or multiple cameras. This paper attempts to addresses such key limitations of the current 3D fingerprint technologies bydeveloping the single camera-based 3D fingerprint identification system. We develop a generalized 3D minutiae matching model and recover extended 3D fingerprint features from the reconstructed 3D fingerprints. 2D fingerprint images acquired for the 3D fingerprint reconstruction can themselves be employed for the performance improvement and have been illustrated in the work detailed in this paper. This paper also attempts to answer one of the most fundamental questions on the availability of inherent discriminableinformation from 3D fingerprints. The experimental results are presented on a database of 240 clients 3D fingerprints, which is made publicly available to further research efforts in this area, and illustrate the discriminant power of 3D minutiae representation andmatching to achieve performance improvement.

negative: PiCANet: Learning Pixel-wise Contextual Attention in ConvNets and Its Application in Saliency Detection 1Context plays an important role in many computer vision tasks. Previous models usually construct contextual information from the whole context region. However, not all context locations are helpful and some of them may be detrimental to the final task. To solve this problem, we propose a novel pixel-wise contextual attention network, i.e., the PiCANet, to learn to selectively attend to informative context locations for each pixel. Specifically, it can generate an attention map over the context region for each pixel, where each attention weight corresponds to the contextual relevance of each context location w.r.t. the specified pixel location. Thus, an attended contextual feature can be constructed by using the attention map to aggregate the contextual features. We formulate PiCANet in a global form and a local form to attend to global contexts and local contexts, respectively. Our designs for the two forms are both fully differentiable. Thus they can be embedded into any CNN architectures for various computer vision tasks in an end-to-end manner. We take saliency detection as an example application to demonstrate the effectiveness of the proposed PiCANets. Specifically, we embed global and local PiCANets into an encoder-decoder Convnet hierarchically. Thorough * This paper was previously submitted to CVPR 2017 and ICCV 2017. This is a slightly revised version based on our previous submission. analyses indicate that the global PiCANet helps to construct global contrast while the local PiCANets help to enhance the feature maps to be more homogenous, thus making saliency detection results more accurate and uniform. As a result, our proposed saliency model achieves state-of-the-art results on 4 benchmark datasets.

negative: Can virtual reality simulations be used as a research tool to study empathy, problems solving and perspective taking of educators?: theory, method and application Simulations in virtual environments are becoming an important research tool for educators. These simulations can be used in a variety of areas from the study of emotions and psychological disorders to more effective training. The current study uses animated narrative vignette simulation technology to observe a classroom situation depicting a low level aggressive peer-to-peer victim dyad. Participants were asked to respond to this situation as if they were the teacher, and these responses were then coded and analyzed. Consistent with other literature, the pre-service teachers expressed very little empathic concern, problem-solving or management of the situation with the victim. Future direction and educational implications are presented.

negative: Comparison of SVM and ANN for classification of eye events in EEG The eye events (eye blink, eyes close and eyes open) are usually considered as biological artifacts in the electroencephalographic (EEG) signal. One can control his or her eye blink by proper training and hence can be used as a control signal in Brain Computer Interface (BCI) applications. Support vector machines (SVM) in recent years proved to be the best classification tool. A comparison of SVM with the Artificial Neural Network (ANN) always provides fruitful results. A one-against-all SVM and a multilayer ANN is trained to detect the eye events. A comparison of both is made in this paper.

negative: Effects of Fitness Applications with SNS: How Do They Influence Physical Activity Fitness applications with social network services (SNS) have emerged for physical activity management. However, there is little understanding of the effects of these applications on users’ physical activity. Motivated thus, we develop a theoretical model based on social cognitive theory of self-regulation to explain the effects of goal setting, self-tracking, and SNS (through social comparison from browsing others’ tracking data and social support from sharing one’s own tracking data) on physical activity. The model was tested with objective data from 476 Runkeeper users. Our preliminary results show that goal setting, number of uses, the proportion of lower-performing friends, and number of likes positively influence users’ physical activities, while the proportion of higher-performing friends has a negative effect. Moreover, the effect of the proportion of higher-performing friends is moderated by the average difference between the friends and the individual. The initial contributions of the study and remaining research plan are described.

negative: Vehicle license plate detection and recognition using non-blind image de-blurring algorithm This paper proposes the method of vehicle license plate recognition, which is essential in the field of intelligent transportation system. The purpose of the study is to present a simple and effective vehicle license plate detection and recognition using non-bling image de-blurring algorithm. The sharpness of the edges in an image is restored by the prior information on images. The blue kernel is free of noise while using the non-blind image de-blurring algorithm. This non-blind image de-blurring (NBID) algorithm is involved in the process of removing the optimization difficulties with respect to unknown image and unknown blur. Estimation is carried out for the length of the motion kernel with Radon transform in Fourier domain. The proposed algorithm was tested on different vehicle images and achieved satisfactory results in license plate detection. The experimental results deal with the efficiency and robustness of the proposed algorithm in both synthesized and real images.

negative: Scale-Aware Fast R-CNN for Pedestrian Detection In this paper, we consider the problem of pedestrian detection in natural scenes. Intuitively, instances of pedestrians with different spatial scales may exhibit dramatically different features. Thus, large variance in instance scales, which results in undesirable large intracategory variance in features, may severely hurt the performance of modern object instance detection methods. We argue that this issue can be substantially alleviated by the divide-and-conquer philosophy. Taking pedestrian detection as an example, we illustrate how we can leverage this philosophy to develop a Scale-Aware Fast R-CNN (SAF R-CNN) framework. The model introduces multiple built-in subnetworks which detect pedestrians with scales from disjoint ranges. Outputs from all of the subnetworks are then adaptively combined to generate the final detection results that are shown to be robust to large variance in instance scales, via a gate function defined over the sizes of object proposals. Extensive evaluations on several challenging pedestrian detection datasets well demonstrate the effectiveness of the proposed SAF R-CNN. Particularly, our method achieves state-of-the-art performance on Caltech [P. Dollar, C. Wojek, B. Schiele, and P. Perona, “Pedestrian detection: An evaluation of the state of the art,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 34, no. 4, pp. 743–761, Apr. 2012], and obtains competitive results on INRIA [N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2005, pp. 886–893], ETH [A. Ess, B. Leibe, and L. V. Gool, “Depth and appearance for mobile scene analysis,” in Proc. Int. Conf. Comput. Vis., 2007, pp. 1–8], and KITTI [A. Geiger, P. Lenz, and R. Urtasun, “Are we ready for autonomous driving? The KITTI vision benchmark suite,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2012, pp. 3354–3361].

negative: Behavior-Based Telecommunication Churn Prediction with Neural Network Approach A behavior-based telecom customer churn prediction system is presented in this paper. Unlike conventional churn prediction methods, which use customer demographics, contractual data, customer service logs, call-details, complaint data, bill and payment as inputs and churn as target output, only customer service usage information is included in this system to predict customer churn using a clustering algorithm. It can solve the problems which traditional methods have to face, such as missing or non-reliable data and the correlation among inputs. This study provides a new way to solve traditional churn prediction problems.

negative: Affective Image Retrieval via Multi-Graph Learning Images can convey rich emotions to viewers. Recent research on image emotion analysis mainly focused on affective image classification, trying to find features that can classify emotions better. We concentrate on affective image retrieval and investigate the performance of different features on different kinds of images in a multi-graph learning framework. Firstly, we extract commonly used features of different levels for each image. Generic features and features derived from elements-of-art are extracted as low-level features. Attributes and interpretable principles-of-art based features are viewed as mid-level features, while semantic concepts described by adjective noun pairs and facial expressions are extracted as high-level features. Secondly, we construct single graph for each kind of feature to test the retrieval performance. Finally, we combine the multiple graphs together in a regularization framework to learn the optimized weights of each graph to efficiently explore the complementation of different features. Extensive experiments are conducted on five datasets and the results demonstrate the effectiveness of the proposed method.

negative: Everyday dwelling with WhatsApp In this paper, we present a study of WhatsApp, an instant messaging smartphone application. Through our interviews with participants, we develop anthopologist Tim Ingold's notion of dwelling, and discuss how use of WhatsApp is constitutive of a felt-life of being together with those close by. We focus on the relationship "doings" in WhatsApp and how this togetherness and intimacy are enacted through small, continuous traces of narrative, of tellings and tidbits, noticings and thoughts, shared images and lingering pauses; this is constitutive of dwelling. Further, we discuss how an intimate knowing of others in these relationships, through past encounters and knowledge of coming together in the future, pertain to the particular forms of relationship engagements manifest through the possibilities presented in WhatsApp. We suggest that this form of sociality is likely to be manifest in other smartphone IM-like applications.

negative: Enabling Real-Time Context-Aware Collaboration through 5G and Mobile Edge Computing Creating context-aware ad hoc collaborative systems remains to be one of the primary hurdles hampering the ubiquitous deployment of IT and communication services. Especially under mission-critical scenarios, these services must often adhere to strict timing deadlines. We believe empowering such realtime collaboration systems requires context-aware application platforms working in conjunction with ultra-low latency data transmissions. In this paper, we make a strong case that this could be accomplished by combining the novel communication architectures being proposed for 5G with the principles of Mobile Edge Computing (MEC). We show that combining 5G with MEC would enable inter- and intra-domain use cases that are otherwise not feasible.

negative: Modeling of magnetically biased graphene patch frequency selective surface (FSS) A free-standing magnetically biased graphene patch frequency selective surface (FSS) is modelled in this paper. Its transmission coefficients of co- and cross-polarizations can be obtained with an equivalent tensorial surface conductivity. Then, the rotation angle for normal incidence is explored with different values of biasing magnetic field B0. The maximum rotation angle provided by the magnetically biased graphene patch FSS is 43° at 4.7 THz with B0=2.5 T, which is much larger than the rotation angle provided by a graphene sheet. This is very promising for THz nano-devices based on the giant Faraday rotation of graphenes.

negative: Cooperative Localization for Autonomous Underwater Vehicles Self-localization of an underwater vehicle is particularly challenging due to the absence of Global Positioning System (GPS) reception or features at known positions that could otherwise have been used for position computation. Thus Autonomous Underwater Vehicle (AUV) applications typically require the pre-deployment of a set of beacons. This thesis examines the scenario in which the members of a group of AUVs exchange navigation information with one another so as to improve their individual position estimates. We describe how the underwater environment poses unique challenges to vehicle navigation not encountered in other environments in which robots operate and how cooperation can improve the performance of self-localization. As intra-vehicle communication is crucial to cooperation, we also address the constraints of the communication channel and the effect that these constraints have on the design of cooperation strategies. The classical approaches to underwater self-localization of a single vehicle, as well as more recently developed techniques are presented. We then examine how methods used for cooperating land-vehicles can be transferred to the underwater domain. An algorithm for distributed self-localization, which is designed to take the specific characteristics of the environment into account, is proposed. We also address how correlated position estimates of cooperating vehicles can lead to overconfidence in individual position estimates. Finally, key to any successful cooperative navigation strategy is the incorporation of the relative positioning between vehicles. The performance of localization algorithms with different geometries is analyzed and a distributed algorithm for the dynamic positioning of vehicles, which serve as dedicated navigation beacons for a fleet of AUVs, is proposed. Thesis Supervisor: John J. Leonard Title: Professor of Mechanical and Ocean Engineering Massachusetts Institute of Technology Acknowledgments This thesis would not have been possible without the help and support of many friends and colleagues who made the last five years at MIT an exceptionally fulfilling experience. I would like to thank my advisor John Leonard who strongly supported me ever since our first email exchange in 2003. He guided me all the way through the application process, research, thesis and finding a post doc position. His broad range of research interests enabled me to find a thesis subject that exactly matched my interest. And I very much appreciated him allowing me to take a significant amount of time to pursue other projects as well as travel. I would also like to thank my other committee members: Henrik Schmidt for his continued support and great company during several research cruises, and Hanu Singh and Arjuna Balasuriya for their helpful suggestions during my thesis writing. Many thanks to David Battle who helped me through my first steps with Autonomous Underwater Vehicles and was a great mate to have around. Many of the experiments presented in this thesis would not have been possible without the support of Andrew Patrikalakis. The results owe a lot to his countless hours of coding assistance and his efforts to ensure that the kayaks would be ready when needed. They were also made possible by Joe Curcio, the builder of the kayaks, and the support of Jacques Leedekerken and Kevin Cockrell. The last years would not have been the same without the many great people I met at MIT. Most important was Matt Walter who convinced me in August 2003 that MIT is not only an interesting place, but that it can be very friendly as well. Throughout the years we shared many great personal and academic experiences. I wish him all the best, wherever life may take him. Patrycja unfortunately left our lab, but made up for it by taking me on a very memorable trip across the country. I wish Alec, Emma, Albert, Tom, Olivier and Aisha all the best for their remaining time and life beyond. Iuliu was always a welcome distraction in the United States and abroad and a great help with all hardware questions. I was glad to join Carrick, Marty and David on a number of exciting research and conference trips, as well as their advisor Daniela Rus. In the last months I was also very fortunate to meet a new group of people. First, I am very thankful to Maurice for carrying on what I started I cannot imagine somebody better suited for it and also Been, Georgios, Hordur and Rob. I would also like to thank the many people of the SEA 2007 cruise, particularly the B watch and Julian, Jamie, Heather, Chris, Toby and Jane. One of the most exciting things during my time at MIT was that I was not only able to pursue my thesis topic but also two other projects. First, the flood warning project introduced me to Elizabeth Basha. We shared many joyful moments as well as blood, sweat and tears in the Central American wilderness. I hope that the end of my PhD only marks the beginning of that partnership. Second, the harbor porpoise tag project led by Stacy DeRuiter was a great design challenge. It also provided an opportunity to reach into other areas of ocean sciences by contributing to marine biology research. Her dedication along with the support from Mark Johnson, Peter Tyack and Tom Hurst ensured the project’s success. The exciting results and the process leading up to them rewarded me with a better experience at MIT than I could have ever hoped for. I would like to thank John Leonard for letting me take this scenic route. The path that led me to MIT would not have been possible without the support from people in the early stages of my engineering career who I would like to thank here: Raimund Eich for patiently answering my first electrical engineering questions; my best friends Jan Horn, Daniel Steffensky, Alexander Zimmer and Ulf Radenz for helping me through my university time in Germany; and John Peatman, Ludger Becks and Uwe Zimmer for their academic guidance. Finally, I would like to thank my parents for their continued support and especially Melissa Pitotti for her encouragement not only to start the work at this institution but also to finish it when the time had come. This work was funded by Office of Naval Research grants N00014-97-1-0202, N00014-05-1-0255, N00014-02-C-0210, N00014-07-1-1102 and the ASAP MURI program led by Naomi Leonard of Princeton University. “One degree is not a large distance. On a compass it is scarcely the thickness of a fingernail. But in certain conditions, one degree can be a very large distance. Enough to unmake a man.” The Mysterious Geographic Explorations of Jasper Morello, c © 3D Films, Australia 2005 Meinen Eltern & meinem Bruder

negative: Blitz: A Principled Meta-Algorithm for Scaling Sparse Optimization By reducing optimization to a sequence of small subproblems, working set methods achieve fast convergence times for many challenging problems. Despite excellent performance, theoretical understanding of working sets is limited, and implementations often resort to heuristics to determine subproblem size, makeup, and stopping criteria. We propose BLITZ, a fast working set algorithm accompanied by useful guarantees. Making no assumptions on data, our theory relates subproblem size to progress toward convergence. This result motivates methods for optimizing algorithmic parameters and discarding irrelevant variables as iterations progress. Applied to `1-regularized learning, BLITZ convincingly outperforms existing solvers in sequential, limited-memory, and distributed settings. BLITZ is not specific to `1-regularized learning, making the algorithm relevant to many applications involving sparsity or constraints.

negative: Using Case Studies in Research Case study as a research strategy often emerges as an obvious option for students and other new researchers who are seeking to undertake a modest scale research project based on their workplace or the comparison of a limited number of organisations. The most challenging aspect of the application of case study research in this context is to lift the investigation from a descriptive account of ‘what happens’ to a piece of research that can lay claim to being a worthwhile, if modest addition to knowledge. This article draws heavily on established textbooks on case study research and related areas, such as Yin, 1994, Hamel et al., 1993, Eaton, 1992, Gomm, 2000, Perry, 1998, and Saunders et al., 2000 but seeks to distil key aspects of case study research in such a way as to encourage new researchers to grapple with and apply some of the key principles of this research approach. The article explains when case study research can be used, research design, data collection, and data analysis, and finally offers suggestions for drawing on the evidence in writing up a report or dissertation.

negative: Comparative Evaluation of Binary Features Performance evaluation of salient features has a long-standing tradition in computer vision. In this paper, we fill the gap of evaluation for the recent wave of binary feature descriptors, which aim to provide robustness while achieving high computational efficiency. We use established metrics to embed our assessment into the body of existing evaluations, allowing us to provide a novel taxonomy unifying both traditional and novel binary features. Moreover, we analyze the performance of different detector and descriptor pairings, which are often used in practice but have been infrequently analyzed. Additionally, we complement existing datasets with novel data testing for illumination change, pure camera rotation, pure scale change, and the variety present in photo-collections. Our performance analysis clearly demonstrates the power of the new class of features. To benefit the community, we also provide a website for the automatic testing of new description methods using our provided metrics and datasets (www.cs.unc.edu/feature-evaluation).

negative: Appraisal of homogeneous techniques in Distributed Data Mining: classifier approach In recent years, Distributed Data Mining (DDM) has evolved in large space aiming at minimizing computation cost and memory overhead in processing huge geographically distributed data. There are two approaches of DDM -- homogeneous and heterogeneous classifier approach. This paper presents implementation of four homogeneous classifier techniques for DDM with different Electronic Health Records (EHRs) homogeneous datasets namely diabetes, hepatitis, hypothyroid and further analyzing results based on metric evaluation.

negative: Effects of loneliness and differential usage of Facebook on college adjustment of first-year students The popularity of social network sites (SNSs) among college students has stimulated scholarship examining the relationship between SNS use and college adjustment. The present research furthers our understanding of SNS use by studying the relationship between loneliness, varied dimensions of Facebook use, and college adjustment among first-year students. We looked at three facets of college adjustment: social adjustment, academic motivation, and perceived academic performance. Compulsive use of Facebook had a stronger association with academic motivation than habitual use of Facebook, but neither were directly correlated with academic performance. Too much time spent on Facebook was weakly but directly associated with poorer perceived academic performance. Loneliness was a stronger indicator of college adjustment than any dimension of Facebook usage. 2014 Elsevier Ltd. All rights reserved.

negative: Adversarial Objectives for Text Generation Language models can be used to generate text by iteratively sampling words conditioned on previously sampled words. In this work, we explore adversarial objectives to obtain good text generations by training a recurrent language model to keep its hidden state statistics during sampling similar to what it has sees during maximum likelihood estimation (MLE) training. We analyze the convergence of these models and discuss the effect of the adversarial objective on word-level and character-level language models. We find that using an adversarial objective assists the MLE objective and results in faster convergence and lower validation perplexities for character-level language models. We also collect sentence quality ratings from human participants and show that character-level language models with the adversarial objective generates qualitatively better sentences than the standard character-level recurrent language model.

negative: Word Translation Without Parallel Data State-of-the-art methods for learning cross-lingual word embeddings have relied on bilingual dictionaries or parallel corpora. Recent studies showed that the need for parallel data supervision can be alleviated with character-level information. While these methods showed encouraging results, they are not on par with their supervised counterparts and are limited to pairs of languages sharing a common alphabet. In this work, we show that we can build a bilingual dictionary between two languages without using any parallel corpora, by aligning monolingual word embedding spaces in an unsupervised way. Without using any character information, our model even outperforms existing supervised methods on cross-lingual tasks for some language pairs. Our experiments demonstrate that our method works very well also for distant language pairs, like English-Russian or EnglishChinese. We finally describe experiments on the English-Esperanto low-resource language pair, on which there only exists a limited amount of parallel data, to show the potential impact of our method in fully unsupervised machine translation. Our code, embeddings and dictionaries are publicly available1.

negative: Multilingual OCR (MOCR): An Approach to Classify Words to Languages There are immense efforts to design a complete OCR for most of the world’s leading languages, however, multilingual documents either of handwritten or of printed form. As a united attempt, Unicode based OCRs were studied mostly with some positive outcomes, despite the fact that a large character set slows down the recognition significantly. In this paper, we come out with a method to classify words to a language as the word segmentation is complete. For the purpose, we identified the characteristics of writings of several languages and utilized projecting method combined with some other feature extraction methods. In addition, this paper intends a modified statistical approach to correct the skewness before processing a segmented document. The proposed procedure, evaluated for a collection of both handwritten and printed documents, came with excellent outcomes in assigning words to languages. General Terms Pattern Recognition, Document Processing, Optical Character Recognition.

negative: Word segmentation for the Myanmar language This study reports the development of a Myanmar word segmentation method using Unicode standard encoding. Word segmentation is an essential step prior to natural language processing in the Myanmar language, because a Myanmar text is a string of characters without explicit word boundary delimiters. The proposed method has two phases: syllable segmentation and syllable merging. A rule-based heuristic approach was adopted for syllable segmentation, and a dictionary-based statistical approach for syllable merging. Evaluation of test results showed that the method is very effective for the Myanmar language.

negative:
A Seed-Based Method for Generating Chinese Confusion Sets In natural language, people often misuse a word (called a “confused word”) in place of other words (called “confusing words”). In misspelling corrections, many approaches to finding and correcting misspelling errors are based on a simple notion called a “confusion set.” The confusion set of a confused word consists of confusing words. In this article, we propose a new method of building Chinese character confusion sets.
 Our method is composed of two major phases. In the first phase, we build a list of seed confusion sets for each Chinese character, which is based on measuring similarity in character pinyin or similarity in character shape. In this phase, all confusion sets are constructed manually, and the confusion sets are organized into a graph, called a “seed confusion graph” (SCG), in which vertices denote characters and edges are pairs of characters in the form (confused character, confusing character).
 In the second phase, we extend the SCG by acquiring more pairs of (confused character, confusing character) from a large Chinese corpus. For this, we use several word patterns (or patterns) to generate new confusion pairs and then verify the pairs before adding them into a SCG. Comprehensive experiments show that our method of extending confusion sets is effective. Also, we shall use the confusion sets in Chinese misspelling corrections to show the utility of our method.

negative: Morphological Segmentation with Window LSTM Neural Networks Morphological segmentation, which aims to break words into meaning-bearing morphemes, is an important task in natural language processing. Most previous work relies heavily on linguistic preprocessing. In this paper, we instead propose novel neural network architectures that learn the structure of input sequences directly from raw input words and are subsequently able to predict morphological boundaries. Our architectures rely on Long Short Term Memory (LSTM) units to accomplish this, but exploit windows of characters to capture more contextual information. Experiments on multiple languages confirm the effectiveness of our models on this task.

negative: CA-LSTM: Search Task Identification with Context Attention based LSTM Search task identification aims to understand a user's information needs to improve search quality for applications such as query suggestion, personalized search, and advertisement retrieval. To properly identify the search task within long query sessions, it is important to partition these sessions into segments before further processing. In this paper, we present the first search session segmentation model that uses a long short-term memory (LSTM) network with an attention mechanism. This model considers sequential context knowledge, including temporal information, word and character, and essentially learns which parts of the input query sequence are relevant to the current segmentation task and determines the influence of these parts. This segmentation technique is also combined with an efficient clustering method using a novel query relevance metric for end-to-end search task identification. Using real-world datasets, we demonstrate that our segmentation technique improves task identification accuracy of existing clustering-based algorithms.