Query: On the Role of Lexical and World Knowledge in RTE3

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The Berkeley FrameNet Project <=SEP=> FrameNet is a three-year NSF-supported project in corpus-based computational lexicography, now in its second year (NSF IRI-9618838, "Tools for Lexicon Building"). The project's key features are (a) a commitment to corpus evidence for semantic and syntactic generalizations, and (b) the representation of the valences of its target words (mostly nouns, adjectives, and verbs) in which the semantic portion makes use of frame semantics. The resulting database will contain (a) descriptions of the semantic frames underlying the meanings of the words described, and (b) the valence representation (semantic and syntactic) of several thousand words and phrases, each accompanied by (c) a representative collection of annotated corpus attestations, which jointly exemplify the observed linkings between "frame elements" and their syntactic realizations (e.g. grammatical function, phrase type, and other syntactic traits). This report will present the project's goals and workflow, and information about the computational tools that have been adapted or created in-house for this work. 1 I n t r o d u c t i o n The Berkeley FrameNet project 1 is producing frame-semantic descriptions of several thousand English lexical items and backing up these descriptions with semantically annotated attestations from contemporary English corpora 2. 1The project is based at the International Computer Science Institute (1947 Center Street, Berkeley, CA). A fuller bibliography may be found in (Lowe et ai., 1997) 2Our main corpus is the British National Corpus. We have access to it through the courtesy of Oxford University Press; the POS-tagged and lemmatized version we use was prepared by the Institut flit Maschinelle Sprachverarbeitung of the University of Stuttgart). The These descriptions are based on hand-tagged semantic annotations of example sentences extracted from large text corpora and systematic analysis of the semantic patterns they exemplify by lexicographers and linguists. The primary emphasis of the project therefore is the encoding, by humans, of semantic knowledge in machine-readable form. The intuition of the lexicographers is guided by and constrained by the results of corpus-based research using highperformance software tools. The semantic domains to be covered are" HEALTH CARE, CHANCE, PERCEPTION, COMMUNICATION, TRANSACTION, TIME, SPACE, BODY (parts and functions of the body), MOTION, LIFE STAGES, SOCIAL CONTEXT, EMOTION and COGNITION. 1.1 Scope of t h e P r o j e c t The results of the project are (a) a lexical resource, called the FrameNet database 3, and (b) associated software tools. The database has three major components (described in more detail below: • Lexicon containing entries which are composed of: (a) some conventional dictionary-type data, mainly for the sake of human readers; (b) FORMULAS which capture the morphosyntactic ways in which elements of the semantic frame can be realized within the phrases or sentences built up around the word; (c) links to semantically ANNOTATED EXAMEuropean collaborators whose participation has made this possible are Sue Atkins, Oxford University Press, and Ulrich Held, IMS-Stuttgart. SThe database will ultimately contain at least 5,000 lexical entries together with a parallel annotated corpus, these in formats suitable for integration into applications which use other lexical resources such as WordNet and COMLEX. The final design of the database will be selected in consultation with colleagues at Princeton (WordNet), ICSI, and IMS, and with other members of the NLP community.

met*: A Method for Discriminating Matonymy and Metaphor by Computer <=SEP=> The met* method distinguishes selected examples of metonymy from metaphor and from literalness and anomaly in short English sentences. In the met* method, literalness is distinguished because it satisfies contextual constraints that the nonliteral others all violate. Metonymy is discriminated from metaphor and anomaly in a way that [1] supports Lakoff and Johnson's (1980) view that in metonymy one entity stands for another whereas in metaphor one entity is viewed as another, [2] permits chains of metonymies (Reddy 1979), and [3] allows metonymies to co-occur with instances of either literalness, metaphor, or anomaly. Metaphor is distinguished from anomaly because the former contains a relevant analogy, unlike the latter. The met* method is part of Collative Semantics, a semantics for natural language processing, and has been implemented in a computer program called meta5. Some examples of meta5's analysis of metaphor and metonymy are given. The met* method is compared with approaches from artificial intelligence, linguistics, philosophy, and psychology.

Discovery of inference rules for question-answering <=SEP=> One of the main challenges in question-answering is the potential mismatch between the expressions in questions and the expressions in texts. While humans appear to use inference rules such as X writes Y implies X is the author of Y in answering questions, such rules are generally unavailable to question-answering systems due to the inherent difficulty in constructing them. In this paper, we present an unsupervised algorithm for discovering inference rules from text. Our algorithm is based on an extended version of Harris Distributional Hypothesis, which states that words that occurred in the same contexts tend to be similar. Instead of using this hypothesis on words, we apply it to paths in the dependency trees of a parsed corpus. Essentially, if two paths tend to link the same set of words, we hypothesize that their meanings are similar. We use examples to show that our system discovers many inference rules easily missed by humans.

WordNet : an electronic lexical database <=SEP=> WordNet is perhaps the most important and widely used lexical resource for natural language processing systems up to now. WordNet: An Electronic Lexical Database, edited by Christiane Fellbaum, discusses the design of WordNet from both theoretical and historical perspectives, provides an up-to-date description of the lexical database, and presents a set of applications of WordNet. The book contains a foreword by George Miller, an introduction by Christiane Fellbaum, seven chapters from the Cognitive Sciences Laboratory of Princeton University, where WordNet was produced, and nine chapters contributed by scientists from elsewhere. Miller's foreword offers a fascinating account of the history of WordNet. He discusses the presuppositions of such a lexical database, how the top-level noun categories were determined, and the sources of the words in WordNet. He also writes about the evolution of WordNet from its original incarnation as a dictionary browser to a broad-coverage lexicon, and the involvement of different people during its various stages of development over a decade. It makes very interesting reading for casual and serious users of WordNet and anyone who is grateful for the existence of WordNet. The book is organized in three parts. Part I is about WordNet itself and consists of four chapters: "Nouns in WordNet" by George Miller, "Modifiers in WordNet" by Katherine Miller, "A semantic network of English verbs" by Christiane Fellbaum, and "Design and implementation of the WordNet lexical database and search software" by Randee Tengi. These chapters are essentially updated versions of four papers from Miller (1990). Compared with the earlier papers, the chapters in this book focus more on the underlying assumptions and rationales behind the design decisions. The description of the information contained in WordNet, however, is not as detailed as in Miller (1990). The main new additions in these chapters include an explanation of sense grouping in George Miller's chapter, a section about adverbs in Katherine Miller's chapter, observations about autohyponymy (one sense of a word being a hyponym of another sense of the same word) and autoantonymy (one sense of a word being an antonym of another sense of the same word) in Fellbaum's chapter, and Tengi's description of the Grinder, a program that converts the files the lexicographers work with to searchable lexical databases. The three papers in Part II are characterized as "extensions, enhancements and

Efficient clustering of high-dimensional data sets with application to reference matching <=SEP=> Many important problems involve clustering large datasets. Although naive implementations of clustering are computationally expensive, there are established efficient techniques for clustering when the dataset has either (1) a limited number of clusters, (2) a low feature dimensionality, or (3) a small number of data points. However, there has been much less work on methods of efficiently clustering datasets that are large in all three ways at once—for example, having millions of data points that exist in many thousands of dimensions representing many thousands of clusters. We present a new technique for clustering these large, highdimensional datasets. The key idea involves using a cheap, approximate distance measure to efficiently divide the data into overlapping subsets we call canopies. Then clustering is performed by measuring exact distances only between points that occur in a common canopy. Using canopies, large clustering problems that were formerly impossible become practical. Under reasonable assumptions about the cheap distance metric, this reduction in computational cost comes without any loss in clustering accuracy. Canopies can be applied to many domains and used with a variety of clustering approaches, including Greedy Agglomerative Clustering, K-means and Expectation-Maximization. We present experimental results on grouping bibliographic citations from the reference sections of research papers. Here the canopy approach reduces computation time over a traditional clustering approach by more than an order of magnitude and decreases error in comparison to a previously used algorithm by 25%.

Pedestrian intention recognition using Latent-dynamic Conditional Random Fields <=SEP=> We present a novel approach for pedestrian intention recognition for advanced video-based driver assistance systems using a Latent-dynamic Conditional Random Field model. The model integrates pedestrian dynamics and situational awareness using observations from a stereo-video system for pedestrian detection and human head pose estimation. The model is able to capture both intrinsic and extrinsic class dynamics. Evaluation of our method is performed on a public available dataset addressing scenarios of lateral approaching pedestrians that might cross the road, turn into the road or stop at the curbside. During experiments, we demonstrate that the proposed approach leads to better stability and class separation compared to state-of-the-art pedestrian intention recognition approaches.

Explainable User Clustering in Short Text Streams <=SEP=> User clustering has been studied from different angles: behavior-based, to identify similar browsing or search patterns, and content-based, to identify shared interests. Once user clusters have been found, they can be used for recommendation and personalization. So far, content-based user clustering has mostly focused on static sets of relatively long documents. Given the dynamic nature of social media, there is a need to dynamically cluster users in the context of short text streams. User clustering in this setting is more challenging than in the case of long documents as it is difficult to capture the users' dynamic topic distributions in sparse data settings. To address this problem, we propose a dynamic user clustering topic model (or UCT for short). UCT adaptively tracks changes of each user's time-varying topic distribution based both on the short texts the user posts during a given time period and on the previously estimated distribution. To infer changes, we propose a Gibbs sampling algorithm where a set of word-pairs from each user is constructed for sampling. The clustering results are explainable and human-understandable, in contrast to many other clustering algorithms. For evaluation purposes, we work with a dataset consisting of users and tweets from each user. Experimental results demonstrate the effectiveness of our proposed clustering model compared to state-of-the-art baselines.

Clustering of Gaze During Dynamic Scene Viewing is Predicted by Motion <=SEP=> Where does one attend when viewing dynamic scenes? Research into the factors influencing gaze location during static scene viewing have reported that low-level visual features contribute very little to gaze location especially when opposed by high-level factors such as viewing task. However, the inclusion of transient features such as motion in dynamic scenes may result in a greater influence of visual features on gaze allocation and coordination of gaze across viewers. In the present study, we investigated the contribution of low- to mid-level visual features to gaze location during free-viewing of a large dataset of videos ranging in content and length. Signal detection analysis on visual features and Gaussian Mixture Models for clustering gaze was used to identify the contribution of visual features to gaze location. The results show that mid-level visual features including corners and orientations can distinguish between actual gaze locations and a randomly sampled baseline. However, temporal features such as flicker, motion, and their respective contrasts were the most predictive of gaze location. Additionally, moments in which all viewers’ gaze tightly clustered in the same location could be predicted by motion. Motion and mid-level visual features may influence gaze allocation in dynamic scenes, but it is currently unclear whether this influence is involuntary or due to correlations with higher order factors such as scene semantics.

Variable Selection via Penalized Neural Network : a Drop-Out-One Loss Approach <=SEP=> We propose a variable selection method for high dimensional regression models, which allows for complex, nonlinear, and high-order interactions among variables. The proposed method approximates this complex system using a penalized neural network and selects explanatory variables by measuring their utility in explaining the variance of the response variable. This measurement is based on a novel statistic called Drop-Out-One Loss. The proposed method also allows (overlapping) group variable selection. We prove that the proposed method can select relevant variables and exclude irrelevant variables with probability one as the sample size goes to infinity, which is referred to as the Oracle Property. Experimental results on simulated and real world datasets show the efficiency of our method in terms of variable selection and prediction accuracy.

Abstraction and reformulation in artificial intelligence. <=SEP=> This paper contributes in two ways to the aims of this special issue on abstraction. The first is to show that there are compelling reasons motivating the use of abstraction in the purely computational realm of artificial intelligence. The second is to contribute to the overall discussion of the nature of abstraction by providing examples of the abstraction processes currently used in artificial intelligence. Although each type of abstraction is specific to a somewhat narrow context, it is hoped that collectively they illustrate the richness and variety of abstraction in its fullest sense.

Visual Tracking with Fully Convolutional Networks <=SEP=> We propose a new approach for general object tracking with fully convolutional neural network. Instead of treating convolutional neural network (CNN) as a black-box feature extractor, we conduct in-depth study on the properties of CNN features offline pre-trained on massive image data and classification task on ImageNet. The discoveries motivate the design of our tracking system. It is found that convolutional layers in different levels characterize the target from different perspectives. A top layer encodes more semantic features and serves as a category detector, while a lower layer carries more discriminative information and can better separate the target from distracters with similar appearance. Both layers are jointly used with a switch mechanism during tracking. It is also found that for a tracking target, only a subset of neurons are relevant. A feature map selection method is developed to remove noisy and irrelevant feature maps, which can reduce computation redundancy and improve tracking accuracy. Extensive evaluation on the widely used tracking benchmark [36] shows that the proposed tacker outperforms the state-of-the-art significantly.

Hybrid Fuzz Testing: Discovering Software Bugs via Fuzzing and Symbolic Execution <=SEP=> Random mutational fuzz testing (fuzzing) and symbolic executions are program testing techniques that have been gaining popularity in the security research community. Fuzzing finds bugs in a target program by natively executing it with random inputs while monitoring the execution for abnormal behaviors such as crashes. While fuzzing may have a reputation of being able to explore deep into a program’s state space efficiently, naı̈ve fuzzers usually have limited code coverage for typical programs since unconstrained random inputs are unlikely to drive the execution down many different paths. In contrast, symbolic execution tests a program by treating the program’s input as symbols and interpreting the program over such symbolic inputs. Although in theory symbolic execution is guaranteed to be effective in achieving code coverage if we explore all possible paths, this generally requires exponential resource and is thus not practical for many real-world programs. This thesis presents our attempt to attain the best of both worlds by combining fuzzing with symbolic execution in a novel manner. Our technique, called hybrid fuzzing, first uses symbolic execution to discover frontier nodes that represent unique paths in the program. After collecting as many frontier nodes as possible under a user-specifiable resource constraint, it transits to fuzz the program with preconditioned random inputs, which are provably random inputs that respect the path predicate leading to each frontier node. Our current implementation supports programs with linear path predicates and can automatically generate preconditioned random inputs from a polytope model of the input space extracted from binaries. These preconditioned random inputs can then be used with any fuzzer. Experiments show that our implementation is efficient in both time and space, and the inputs generated by it are able to gain extra breadth and depth over previous approaches.

Industrial Management & Data Systems Transformational leadership and innovative work behavior <=SEP=> Purpose – The purpose of this paper is to explore the mediating role of psychological empowerment and the moderating role of self-construal (independent and interdependent) on the relationship between transformational leadership and employees’ innovative work behavior (IWB). Design/methodology/approach – A total of 639 followers and 87 leaders filled out questionnaires from cross-industry sample of five most innovative companies of China. Structural equation modeling was used to analyze the relations. Findings – Results revealed that psychological empowerment mediated the relationship between transformational leadership and IWB. The research established that transformational leadership positively influences IWB which includes idea generation as well as idea implementation. The results also showed that the relationship between transformational leadership and IWB was stronger among employees with a higher interdependent self-construal and a lower independent self-construal. Originality/value – This study adds to IWB literature by empirically testing the moderating role of self-construal and the mediating role of psychological empowerment on transformational leadership-IWB link.

Perception and Emotion How We Recognize Facial Expressions <=SEP=> Perception and emotion interact, as is borne out by studies of how people recognize emotion from facial expressions. Psychological and neurological research has elucidated the processes, and the brain structures, that participate in facial emotion recognition. Studies have shown that emotional reactions to viewing faces can be very rapid and that these reactions may, in turn, be used to judge the emotion shown in the face. Recent experiments have argued that people actively explore facial expressions in order to recognize the emotion, a mechanism that emphasizes the instrumental nature of social cognition. KEYWORDS—face perception; faces; emotion; amygdala An influential psychological model of face processing argued that early perception (construction of a geometric representation of the face based on its features) led to subsequently separate processing of the identity of the face and of the emotional expression of the face (Bruce & Young, 1986). That model has received considerable support from neuroscience studies suggesting that the separate processes are based on separable neuroanatomical systems: In neuroimaging studies, different parts of the brain are activated in response to emotional expressions or identity changes; and brain damage can result in impairments in recognizing identity but not in recognizing emotional expressions, or the reverse. SOME PROCESSING IS RAPID AND AUTOMATIC AND CAN OCCUR IN THE ABSENCE OF AWARENESS OF THE STIMULI Some responses in the brain to emotional facial expressions are so rapid (less than 100 ms) that they could not plausibly be based on conscious awareness of the stimulus, although the responses, in turn, may contribute to conscious awareness. Evidence comes from studies using event-related potentials, measures of the brain’s electrical activity recorded at the scalp (or, much more rarely, directly from the brain in surgical patients). In those experiments, the responses to many presentations of emotional stimuli are averaged across stimuli, and the changes in electrical potential can be accurately timed in relation to stimulus onset. Evidence has also come from studies in which viewers were shown facial expressions subliminally. Especially salient aspects of faces are most potent in driving nonconscious responses: For instance, subliminal presentation of only the whites of the eyes of fearful faces results in measurable brain activation (Whalen et al., 2004). One specific structure involved in such rapid and automatic neural responses is the amygdala, a structure in the medial temporal lobe that is known to be involved in many aspects of emotion processing. Findings such as these have been of interest to psychologists, as they provide a potential mechanism consistent with two-factor theories of human behavior. For instance, the theory that affect and cognitive judgment are separate processes, and that affect can precede cognitive judgment, receives some support from the neuroscience findings (Zajonc, 1980). The data also add detail to theories of visual consciousness. In one set of studies, emotional facial expressions were shown to neurological patients who, because of their brain damage, were unable to report seeing the stimuli. An individual with damage to the visual cortex had ‘‘blindsight’’ for emotional faces: He could discriminate the emotion shown in faces by guessing, even though he reported no visual experience of seeing the faces. A neuroimaging experiment in this same individual revealed that the amygdala was activated differentially by different emotional faces, despite the absence of conscious visual experience (Morris, deGelder, Weiskrantz, & Dolan, 2001). These and other studies have suggested a distinction between subcortical processing of emotional visual stimuli (e.g., involving the amygdala and brainstem nuclei such as the superior colliculus), which may be independent of conscious vision (Johnson, 2005), and cortical processing, which is usually accompanied by conscious experience. It is interesting to note that amphibians and reptiles have only subcortical visual processing, since they lack a neocortex. One broad interpretation of these observations is thus that the subcortical route for processing emotional stimuli is the more ancient one, and that in Address correspondence to Ralph Adolphs, HSS 228-77, Caltech, Pasadena, CA 91125; e-mail: radolphs@hss.caltech.edu. CURRENT DIRECTIONS IN PSYCHOLOGICAL SCIENCE 222 Volume 15—Number 5 Copyright r 2006 Association for Psychological Science mammals an additional, cortical route has evolved that probably allows more flexible behaviors based on learning and conscious deliberation. A final wrinkle has come from psychological studies of the relationship between attention and emotion. It has been known for some time that emotionally salient stimuli can capture attention, an interaction that makes much adaptive sense (Ohman, Flykt, & Esteves, 2001). But more recent studies have also shown the reverse: that volitionally allocating attention to stimuli can influence their emotional evaluation. Inhibiting attention to visual stimuli that are distractors in a search task, for example, results in devaluation of those stimuli when subjects are asked to rate them on affective dimensions (Raymond, Fenske, & Tavassoli, 2003). Emotions thus represent the value of stimuli—what people approach or avoid, cognitively or behaviorally, volitionally or automatically. FEAR AND DISGUST MAY BE PROCESSED SPECIALLY Several studies have found evidence that the amygdala, the subcortical structure discussed earlier, is disproportionately important for processing facial expressions of fear, although by no means exclusively so. The only other emotion for which a similar neuroanatomical specificity has been reported is disgust. A region of the cortex called the insula represents motor and sensory aspects of that emotional response (Calder, Lawrence, & Young, 2001). In lesion studies, damage to the amygdala is shown to result in impairment in the ability to recognize fear from facial expressions. Similarly, damage to the insula can result in impairment in the ability to recognize disgust from facial expressions. However, questions remain regarding exactly which emotion category or dimension is being encoded in these brain regions, since other emotions are often also variably impaired. Especially informative have been studies in a rare patient, SM, who has total damage to the amygdala on both sides of the brain. The subject can accurately judge age and gender from faces, and she has no difficulty recognizing familiar individuals from their faces. She also has little difficulty recognizing most facial expressions, with the notable exception of fear. When asked to judge the fearfulness of faces, SM is selectively and severely impaired (Adolphs, Tranel, Damasio, & Damasio, 1994). However, other patients with similar damage are impaired on a wider array of emotions; in functional neuroimaging studies, activation of the amygdala is seen in response to several emotions in addition to fear; and even SM’s highly specific impairment depends on the questions she is asked. For example, when asked to classify faces into categories of basic emotions, SM is rather selectively impaired on fear; but when asked to rate how arousing the emotion shown in the face is, she is impaired on all emotions of negative valence (Adolphs, Russell, & Tranel, 1999). These data suggest that, while the amygdala is critical for recognizing fear in faces, its role encompasses a broader, more abstract, and perhaps dimensional rather than categorical aspect of emotions, for which fear is but one instance—an issue I take up again below. MANY BRAIN STRUCTURES ARE INVOLVED Just as there is good evidence that the amygdala does more than solely detect fear, there are of course brain structures other than the amygdala that participate in perceiving emotion from faces. Brain-imaging studies, in particular, have found evidence for a large number of other brain structures that may come into play, depending on the emotion shown in the face and on the demands of the experimental task. One framework that summarizes these data runs as follows. Visual areas in the temporal cortex encode the face’s features and bind them into a global perceptual representation of the face. Subcortical visual areas (such as the superior colliculus, prominent in animals such as frogs) carry out coarser but faster processing of the face. Both routes, the cortical and the subcortical, feed visual information about the face into the amygdala. The amygdala then associates the visual representation of the face with an emotional response in the body, as well as with changes in the operation of other brain structures. For instance, it likely triggers autonomic responses (such as skin-conductance response, the sympathetic autonomic response of sweating on the palms of the hands) to the face, and it also modulates visual attention to the face. Tracing the path from initial perception of the face to recognizing the emotion it expresses is complicated by feedback loops and by multiple pathways that can be engaged. One class of proposals, as influential as they are controversial, argues that the emotional response elicited by the amygdala can in fact be used by the viewer to reconstruct knowledge about the emotion shown in the face. Roughly: if I experience a pang of fear within myself upon seeing a fearful face, I can use the knowledge of my own emotion to infer what the emotional state of the person whose face is shown in the stimulus might be. Broader theories in a similar vein do not restrict themselves to the amygdala or to fear, but more generally propose that we make inferences about other people’s emotional states by simulating within ourselves aspects of those states (Goldman & Sripada, 2005). Emotional contagion and imitation may be the earliest aspects of such a mechanism that can already be seen in infants

DeepQA Jeopardy! Gamification: A Machine-Learning Perspective <=SEP=> DeepQA is a large-scale natural language processing (NLP) question-and-answer system that responds across a breadth of structured and unstructured data, from hundreds of analytics that are combined with over 50 models, trained through machine learning. After the 2011 historic milestone of defeating the two best human players in the Jeopardy! game show, the technology behind IBM Watson, DeepQA, is undergoing gamification into real-world business problems. Gamifying a business domain for Watson is a composite of functional, content, and training adaptation for nongame play. During domain gamification for medical, financial, government, or any other business, each system change affects the machine-learning process. As opposed to the original Watson Jeopardy!, whose class distribution of positive-to-negative labels is 1:100, in adaptation the computed training instances, question-and-answer pairs transformed into true-false labels, result in a very low positive-to-negative ratio of 1:100 000. Such initial extreme class imbalance during domain gamification poses a big challenge for the Watson machine-learning pipelines. The combination of ingested corpus sets, question-and-answer pairs, configuration settings, and NLP algorithms contribute toward the challenging data state. We propose several data engineering techniques, such as answer key vetting and expansion, source ingestion, oversampling classes, and question set modifications to increase the computed true labels. In addition, algorithm engineering, such as an implementation of the Newton-Raphson logistic regression with a regularization term, relaxes the constraints of class imbalance during training adaptation. We conclude by empirically demonstrating that data and algorithm engineering are complementary and indispensable to overcome the challenges in this first Watson gamification for real-world business problems.

Temporally Grounding Natural Sentence in Video <=SEP=> We introduce an effective and efficient method that grounds (i.e., localizes) natural sentences in long, untrimmed video sequences. Specifically, a novel Temporal GroundNet (TGN)1 is proposed to temporally capture the evolving fine-grained frame-by-word interactions between video and sentence. TGN sequentially scores a set of temporal candidates ended at each frame based on the exploited frameby-word interactions, and finally grounds the segment corresponding to the sentence. Unlike traditional methods treating the overlapping segments separately in a sliding window fashion, TGN aggregates the historical information and generates the final grounding result in one single pass. We extensively evaluate our proposed TGN on three public datasets with significant improvements over the stateof-the-arts. We further show the consistent effectiveness and efficiency of TGN through an ablation study and a runtime test.

Deep Constrained Clustering - Algorithms and Advances <=SEP=> The area of constrained clustering has been extensively explored by researchers and used by practitioners. Constrained clustering formulations exist for popular algorithms such as k-means, mixture models, and spectral clustering but have several limitations. We explore a deep learning formulation of constrained clustering and in particular explore how it can extend the field of constrained clustering. We show that our formulation can not only handle standard together/apart constraints without the well documented negative effects reported but can also model instance level constraints (level-of-difficulty), cluster level constraints (balancing cluster size) and triplet constraints. The first two are new ways for domain experts to enforce guidance whilst the later importantly allows generating ordering constraints from continuous side-information.

MOTHERS WHO KILL THEIR CHILDREN A literature review <=SEP=> Maternal filicide, the murder of a child by its mother, is a complex phenomenon with various causes and characteristics. Research, by means of the development of several classification systems and in identifying particular risk factors, has been conducted with the aim of better prevention of this emotionally evocative crime. Various disciplines have offered a wide range of perspectives on why women kill their biological children. These are intended to yield a better understanding of the aetiology of this crime. This literature review delineates three dominant perspectives: psychiatric, psychological, and sociological. The main findings of each perspective are discussed. However, these three perspectives frequently operate in conjunction with each other in that both intrapsychic and interpersonal dynamics play a role in acts of maternal filicide. The most vulnerable women appear to be those who have had a severely deficient developmental history (trauma and/or grossly inadequate parenting), those who experience current difficult psychosocial circumstances, and those who have been diagnosed with a psychiatric illness. However, not all women who experience such problems kill their children. In this regard, individual differences have an important role to play and more carefully delineated future research is suggested. One of the most significant findings of this literature review is that there appears to be a paucity of systematic research on the South African phenomenon of parental child homicide.

Fast trajectory clustering using Hashing methods <=SEP=> There has been an explosion in the usage of trajectory data. Clustering is one of the simplest and most powerful approaches for knowledge discovery from trajectories. In order to produce meaningful clusters, well-defined metrics are required to capture the essence of similarity between trajectories. One such distance function is Dynamic Time Warping (DTW), which aligns two trajectories together in order to determine similarity. DTW has been widely accepted as a very good distance measure for trajectory data. However, trajectory clustering is very expensive due to the complexity of the similarity functions, for example, DTW has a high computational cost O(n2), where n is the average length of the trajectory, which makes the clustering process very expensive. In this paper, we propose the use of hashing techniques based on Distance-Based Hashing (DBH) and Locality Sensitive Hashing (LSH) to produce approximate clusters and speed up the clustering process.

Human-computer interaction for hybrid carving <=SEP=> In this paper we explore human-computer interaction for carving, building upon our previous work with the FreeD digital sculpting device. We contribute a new tool design (FreeD V2), with a novel set of interaction techniques for the fabrication of static models: personalized tool paths, manual overriding, and physical merging of virtual models. We also present techniques for fabricating dynamic models, which may be altered directly or parametrically during fabrication. We demonstrate a semi-autonomous operation and evaluate the performance of the tool. We end by discussing synergistic cooperation between human and machine to ensure accuracy while preserving the expressiveness of manual practice.

ExtRA: Extracting Prominent Review Aspects from Customer Feedback <=SEP=> Many existing systems for analyzing and summarizing customer reviews about products or service are based on a number of prominent review aspects. Conventionally, the prominent review aspects of a product type are determined manually. This costly approach cannot scale to large and cross-domain services such as Amazon.com, Taobao.com or Yelp.com where there are a large number of product types and new products emerge almost everyday. In this paper, we propose a novel framework, for extracting the most prominent aspects of a given product type from textual reviews. The proposed framework, ExtRA, extracts K most prominent aspect terms or phrases which do not overlap semantically automatically without supervision. Extensive experiments show that ExtRA is effective and achieves the state-of-the-art performance on a dataset consisting of different product types.

A general framework for benchmarking firewall optimization techniques <=SEP=> Firewalls are among the most pervasive network security mechanisms, deployed extensively from the borders of networks to end systems. The complexity of modern firewall policies has raised the computational requirements for firewall implementations, potentially limiting the throughput of networks. Administrators currently rely on ad hoc solutions to firewall optimization. To address this problem, a few automatic firewall optimization techniques have been proposed, but there has been no general approach to evaluate the optimality of these techniques. In this paper we present a general framework for rule-based firewall optimization. We give a precise formulation of firewall optimization as an integer programming problem and show that our framework produces optimal reordered rule sets that are semantically equivalent to the original rule set. Our framework considers the complex interactions among the rules in firewall configurations and relies on a novel partitioning of the packet space defined by the rules themselves. For validation, we employ this framework on real firewall rule sets for a quantitative evaluation of existing heuristic approaches. Our results indicate that the framework is general and faithfully captures performance benefits of firewall optimization heuristics.

Graph Data Management and Mining: A Survey of Algorithms and Applications <=SEP=> Graph mining and management has become a popular area of research in recent years because of its numerous applications in a wide variety of practical fields, including computational biology, software bug localization and computer networking. Different applications result in graphs of different sizes and complexities. Correspondingly, the applications have different requirements for the underlying mining algorithms. In this chapter, we will provide a survey of different kinds of graph mining and management algorithms. We will also discuss a number of applications, which are dependent upon graph representations. We will discuss how the different graph mining algorithms can be adapted for different applications. Finally, we will discuss important avenues of future research

Assessing the veracity of identity assertions via OSNs <=SEP=> Anonymity is one of the main virtues of the Internet, as it protects privacy and enables users to express opinions more freely. However, anonymity hinders the assessment of the veracity of assertions that online users make about their identity attributes, such as age or profession. We propose FaceTrust, a system that uses online social networks to provide lightweight identity credentials while preserving a user's anonymity. Face-Trust employs a “game with a purpose” design to elicit the opinions of the friends of a user about the user's self-claimed identity attributes, and uses attack-resistant trust inference to assign veracity scores to identity attribute assertions. FaceTrust provides credentials, which a user can use to corroborate his assertions. We evaluate our proposal using a live Facebook deployment and simulations on a crawled social graph. The results show that our veracity scores strongly correlate with the ground truth, even when a large fraction of the social network users is dishonest and employs the Sybil attack.

A mixed-signal X-band SiGe multi-function control MMIC for phased array radar applications <=SEP=> In this paper is reported the design, fabrication and test of a mixed-signal SiGe X-band multi-function control MMIC for phased array radar applications. Said MMIC, fabricated, with the ST-Microelectronics BiCMOS7RF SiGe technology, comprises a 5-bit phase shifter, 5-bit attenuator, SPDT switches, several gain amplifiers and a digital serial to parallel converter to reduce the number of MMIC I/O control lines. The gain amplifiers are implemented using SiGe HBTs, while phase shifter, attenuator and SPDT switches are based on CMOS transistors. The measurement results show a return loss better than 15 dB and a gain of 17 dB, equal in both RX and TX state. In RX mode the obtained noise figure is lower than 10 dB, while in TX mode the output P1dB is higher than 12 dBm. The achieved RF performance, the low power consumption and associated low cost, make this SiGe control-chip an attractive solution for high performance/low cost Tx/Rx components for phased array radar applications.

One-Shot Segmentation in Clutter <=SEP=> We tackle the problem of one-shot segmentation: finding and segmenting a previously unseen object in a cluttered scene based on a single instruction example. We propose a novel dataset, which we call cluttered Omniglot. Using a baseline architecture combining a Siamese embedding for detection with a U-net for segmentation we show that increasing levels of clutter make the task progressively harder. Using oracle models with access to various amounts of ground-truth information, we evaluate different aspects of the problem and show that in this kind of visual search task, detection and segmentation are two intertwined problems, the solution to each of which helps solving the other. We therefore introduce MaskNet, an improved model that attends to multiple candidate locations, generates segmentation proposals to mask out background clutter and selects among the segmented objects. Our findings suggest that such image recognition models based on an iterative refinement of object detection and foreground segmentation may provide a way to deal with highly cluttered scenes.

Modelling consumer credit risk via survival analysis <=SEP=> Credit risk models are used by financial companies to evaluate in advance the insolvency risk caused by credits that enter into default. Many models for credit risk have been developed over the past few decades. In this paper, we focus on those models that can be formulated in terms of the probability of default by using survival analysis techniques. With this objective three different mechanisms are proposed based on the key idea of writing the default probability in terms of the conditional distribution function of the time to default. The first method is based on a Cox’s regression model, the second approach uses generalized linear models under censoring and the third one is based on nonparametric kernel estimation, using the product-limit conditional distribution function estimator by Beran. The resulting nonparametric estimator of the default probability is proved to be consistent and asymptotically normal. An empirical study, based on modified real data, illustrates the three methods. MSC: 62P20, 91B30, 62G05, 62N01

Translating evidence-based decision making into practice: EBDM concepts and finding the evidence. <=SEP=> This is the first of 2 articles that focuses on strategies that can be used to integrate an evidence-based decision making [EBDM] approach into practice. The articles will focus on EBDM methodology and enhancing skills, including how to find valid evidence to answer clinical questions, critically appraise the evidence found and determine if it applies. In addition, online resources will be identified to supplement information presented in each article. The purpose of this article is to define evidence-based decision making and discuss skills necessary for practitioners to efficiently adopt EBDM. It will provide a guide for finding evidence to answer a clinical question using PubMed's specialized searching tools under Clinical Queries.

Revisiting Complex Moments for 2-D Shape Representation and Image Normalization <=SEP=> When comparing 2-D shapes, a key issue is their normalization. Translation and scale are easily taken care of by removing the mean and normalizing the energy. However, defining and computing the orientation of a 2-D shape is not so simple. In fact, although for elongated shapes the principal axis can be used to define one of two possible orientations, there is no such tool for general shapes. As we show in the paper, previous approaches fail to compute the orientation of even noiseless observations of simple shapes. We address this problem and show how to uniquely define the orientation of an arbitrary 2-D shape, in terms of what we call its Principal Moments. We start by showing that a small subset of these moments suffices to describe the underlying 2-D shape, i.e., that they form a compact representation, which is particularly relevant when dealing with large databases. Then, we propose a new method to efficiently compute the shape orientation: Principal Moment Analysis. Finally, we discuss how this method can further be applied to normalize gray-level images. Besides the theoretical proof of correctness, we describe experiments demonstrating robustness to noise and illustrating with real images.

IoT Security Framework for Smart Cyber Infrastructures <=SEP=> The Internet of Things (IoT) will connect not only computers and mobile devices, but it will also interconnect smart buildings, homes, and cities, as well as electrical grids, gas, and water networks, automobiles, airplanes, etc. IoT will lead to the development of a wide range of advanced information services that need to be processed in real-time and require data centers with large storage and computing power. The integration of IoT with Cloud and Fog Computing can bring not only the required computational power and storage capacity, but they enable IoT services to be pervasive, cost-effective, and can be accessed from anywhere using any device (mobile or stationary). However, IoT infrastructures and services will introduce grand security challenges due to the significant increase in the attack surface, complexity, heterogeneity and number of resources. In this paper, we present an IoT security framework for smart infrastructures such as Smart Homes (SH) and smart buildings (SB). We also present a general threat model that can be used to develop a security protection methodology for IoT services against cyber-attacks (known or unknown). Additionally, we show that Anomaly Behavior Analysis (ABA) Intrusion Detection System (ABA-IDS) can detect and classify a wide range of attacks against IoT sensors.