title: On understanding data abstraction, revisited

abstract: In 1985 Luca Cardelli and Peter Wegner, my advisor, published an ACM Computing Surveys paper called "On understanding types, data abstraction, and polymorphism". Their work kicked off a flood of research on semantics and type theory for object-oriented programming, which continues to this day. Despite 25 years of research, there is still widespread confusion about the two forms of data abstraction, abstract data types and objects. This essay attempts to explain the differences and also why the differences matter.


title: A Scalable Dual-Clock FIFO for Data Transfers Between Arbitrary and Haltable Clock Domains

abstract: A robust, scalable, and power efficient dual-clock first-input first-out (FIFO) architecture which is useful for transferring data between modules operating in different clock domains is presented. The architecture supports correct operation in applications where multiple clock cycles of latency exist between the data producer, FIFO, and the data consumer; and with arbitrary clock frequency changes, halting, and restarting in either or both clock domains. The architecture is demonstrated in both a 0.18- mum CMOS full-custom design and a 0.18-mum CMOS standard cell design used in a globally asynchronous locally synchronous array processor. It achieves 580-MHz operation and 10.3-mW power dissipation while performing simultaneous FIFO read and write operations at 1.8 V.


title: Fuzzy-Model-Based Piecewise ${\mathscr H}_{\infty }$ Static-Output-Feedback Controller Design for Networked Nonlinear Systems

abstract: This paper investigates the problem of robust H∞ output-feedback control for a class of nonlinear systems under unreliable communication links. The nonlinear plant is represented by a Takagi-Sugeno (T-S) uncertain fuzzy model, and the communication links between the plant and controller are assumed to be imperfect, i.e., data-packet dropouts occur intermittently, which is often the case in a network environment. Stochastic variables that satisfy the Bernoulli random-binary distribution are adopted to characterize the data-missing phenomenon, and the attention is focused on the design of a piecewise static-output-feedback (SOF) controller such that the closed-loop system is stochastically stable with a guaranteed H∞ performance. Based on a piecewise Lyapunov function combined with some novel convexifying techniques, the solutions to the problem are formulated in the form of linear matrix inequalities (LMIs). Finally, simulation examples are also provided to illustrate the effectiveness of the proposed approaches.


title: Fusing Local and Global Features for High-Resolution Scene Classification

abstract: In this paper, a fused global saliency-based multiscale multiresolution multistructure local binary pattern (salM 3LBP) feature and local codebookless model (CLM) feature is proposed for high-resolution image scene classification. First, two different but complementary types of descriptors (pixel intensities and differences) are developed to extract global features, characterizing the dominant spatial features in multiple scale, multiple resolution, and multiple structure manner. The micro/macrostructure information and rotation invariance are guaranteed in the global feature extraction process. For dense local feature extraction, CLM is utilized to model local enrichment scale invariant feature transform descriptor and dimension reduction is conducted via joint low-rank learning with support vector machine. Finally, a fused feature representation between salM3LBP and CLM as the scene descriptor to train a kernel-based extreme learning machine for scene classification is presented. The proposed approach is extensively evaluated on three challenging benchmark scene datasets (the 21-class land-use scene, 19-class satellite scene, and a newly available 30-class aerial scene), and the experimental results show that the proposed approach leads to superior classification performance compared with the state-of-the-art classification methods.


title: Interpreting TF-IDF term weights as making relevance decisions

abstract: A novel probabilistic retrieval model is presented. It forms a basis to interpret the TF-IDF term weights as making relevance decisions. It simulates the local relevance decision-making for every location of a document, and combines all of these “local” relevance decisions as the “document-wide” relevance decision for the document. The significance of interpreting TF-IDF in this way is the potential to: (1) establish a unifying perspective about information retrieval as relevance decision-making; and (2) develop advanced TF-IDF-related term weights for future elaborate retrieval models. Our novel retrieval model is simplified to a basic ranking formula that directly corresponds to the TF-IDF term weights. In general, we show that the term-frequency factor of the ranking formula can be rendered into different term-frequency factors of existing retrieval systems. In the basic ranking formula, the remaining quantity - log p(rvt ∈ d) is interpreted as the probability of randomly picking a nonrelevant usage (denoted by r) of term t. Mathematically, we show that this quantity can be approximated by the inverse document-frequency (IDF). Empirically, we show that this quantity is related to IDF, using four reference TREC ad hoc retrieval data collections.


title: Topic-Sensitive Influencer Mining in Interest-Based Social Media Networks via Hypergraph Learning

abstract: Social media is emerging as a new mainstream means of interacting around online media. Social influence mining in social networks is therefore of critical importance in real-world applications such as friend suggestion and photo recommendation. Social media is inherently multimodal, including rich types of user contributed content and social link information. Most of the existing research suffers from two limitations: 1) only utilizing the textual information, and/or 2) only analyzing the generic influence but ignoring the more important topic-level influence. To address these limitations, in this paper we develop a novel Topic-Sensitive Influencer Mining (TSIM) framework in interest-based social media networks. Specifically, we take Flickr as the study platform. People in Flickr interact with each other through images. TSIM aims to find topical influential users and images. The influence estimation is determined with a hypergraph learning approach. In the hypergraph, the vertices represent users and images, and the hyperedges are utilized to capture multi-type relations including visual-textual content relations among images, and social links between users and images. Algorithmwise, TSIM first learns the topic distribution by leveraging user-contributed images, and then infers the influence strength under different topics for each node in the hypergraph. Extensive experiments on a real-world dataset of more than 50 K images and 70 K comment/favorite links from Flickr have demonstrated the effectiveness of our proposed framework. In addition, we also report promising results of friend suggestion and photo recommendation via TSIM on the same dataset.


Simplicity and Representation Change in Grammar Induction


title: On the Interdisciplinary Foundations of Diversity

abstract: Designing new-generation diversity-aware tools that face up to the emerging complexity in knowledge is one of the biggest research challenges in recent years. In this paper, we provide key notions about opinion, bias and diversity, and propose an interdisciplinary approach when managing them. Our basic tenet is that diversity should be seen as an asset rather than as a problem to be avoided a priori.


title: Denoising Bodies to Titles: Retrieving Similar Questions with Recurrent Convolutional Models.

abstract: Question answering forums are rapidly growing in size with no automated ability to refer to and reuse existing answers. In this paper, we develop a methodology for finding semantically related questions. The task is difficult since 1) key pieces of information are often buried in extraneous detail in the question body and 2) available annotations are scarce and fragmented, driven by participants. We design a novel combination of recurrent and convolutional models (gated convolutions) to effectively map questions to their semantic representations. The models are pre-trained within an encoder-decoder framework (from body to title) on the basis of the entire raw corpus, and fine-tuned discriminatively from limited annotations. Our evaluation demonstrates that our model yields 10\% gain over a standard IR baseline, and 6\% over standard neural network architectures (including CNNs and LSTMs) trained analogously.


title: mLDM: a new hierarchical Bayesian statistical model for sparse microbioal association discovery

abstract: Interpretive analysis of metagenomic data depends on an understanding of the underlying associations among microbes from metagenomic samples. Although several statistical tools have been developed for metage-nomic association studies, they suffer from compositional bias or fail to take into account environmental factors that directly affect the composition of a given microbial community. In this paper, we propose metagenomic Lognormal-Dirichlet-Multinomial (mLDM), a hierarchical Bayesian model with sparsity constraints to bypass compositional bias and discover new associations among microbes and between microbes and environmental factors. The mLD-M model can 1) infer both conditionally dependent associations among microbes and direct associations between microbes and environmental factors; 2) consider both compositional bias and variance of metagenomic data; and 3) estimate absolute abundance for microbes. Thus, conditionally dependent association can capture direct relationship underlying microbial pairs and remove the indirect connections induced from other common factors. Empirical studies show the effectiveness of the mLDM model, using both synthetic data and the TARA Oceans eukaryotic data by comparing it with several state-of-the-art methodologies. Finally, mLDM is applied to western English Channel data and finds some interesting associations.


title: An Empirical Exploration of Curriculum Learning for Neural Machine Translation

abstract: Machine translation systems based on deep neural networks are expensive to train. Curriculum learning aims to address this issue by choosing the order in which samples are presented during training to help train better models faster. We adopt a probabilistic view of curriculum learning, which lets us flexibly evaluate the impact of curricula design, and perform an extensive exploration on a German-English translation task. Results show that it is possible to improve convergence time at no loss in translation quality. However, results are highly sensitive to the choice of sample difficulty criteria, curriculum schedule and other hyperparameters.


title: Exploring event correlation for failure prediction in coalitions of clusters

abstract: In large-scale networked computing systems, component failures become norms instead of exceptions. Failure prediction is a crucial technique for self-managing resource burdens. Failure events in coalition systems exhibit strong correlations in time and space domain. In this paper, we develop a spherical covariance model with an adjustable timescale parameter to quantify the temporal correlation and a stochastic model to describe spatial correlation. We further utilize the information of application allocation to discover more correlations among failure instances. We cluster failure events based on their correlations and predict their future occurrences. We implemented a failure prediction framework, called PREdictor of Failure Events Correlated Temporal-Spatially (hPREFECTs), which explores correlations among failures and forecasts the time-between-failure of future instances. We evaluate the performance of hPREFECTs in both offline prediction of failure by using the Los Alamos HPC traces and online prediction in an institute-wide clusters coalition environment. Experimental results show the system achieves more than 76% accuracy in offline prediction and more than 70% accuracy in online prediction during the time from May 2006 to April 2007.


title: Duty-related trauma exposure in 911 telecommunicators: Considering the risk for posttraumatic stress

abstract: Peritraumatic distress may increase the risk for posttraumatic stress disorder (PTSD) in police officers. Much less is known about emotional reactions and PTSD symptomatology in 911 telecommunicators. The current study assessed duty-related exposure to potentially traumatic calls, peritraumatic distress, and PTSD symptomatology in a cross-sectional, convenience sample of 171 telecommunicators. Results showed that telecommunicators reported high levels of peritraumatic distress and a moderate, positive relationship was found between peritraumatic distress and PTSD symptom severity (r = .34). The results suggest that 911 telecommunicators are exposed to duty-related trauma that may lead to the development of PTSD, and that direct, physical exposure to trauma may not be necessary to increase risk for PTSD in this population. Research has begun to examine the mental health impact of occupational exposure to potentially traumatic events in police officers, with rates of duty-related presumed posttraumatic stress disorder (PTSD) ranging from 7% to 19% (Marmar et al., 2006). These numbers are notably greater than the lifetime prevalence rate of 7.8%, and 12-month prevalence rate of 3.5%, observed in the general population in the United States (Kessler, Chiu, Demler, & Walters, 2005; Kessler, Sonnega, Bromet, Hughes, & Nelson, 1995). Yet, research on PTSD in 911 telecommunicators, who may experience significant dutyrelated trauma exposure, has remained largely absent. Telecommunicators rely on their interrogative skills to assess an incident, secure the emergency scene, and send appropriate help, all within minutes of answering a call. Crucial to success is the ability to remain calm and suppress emotional reactions. Yet little is known about the emotional reactions and mental health of telecommunicators. It is possible that physical distance from trauma (i.e., limited risk of physical injury) serves to buffer against posttrauma psychopathology; research has shown that threat to an individual’s physical integrity heightens risk for the development of PTSD symptoms (e.g., Carlier, Lamberts, & Gersons, 2000). Telecommunicators, however, have limited


title: Creativity and Artificial Intelligence: A Digital Art Perspective

abstract: This paper describes the application of artificial intelligence to the creation of digital art. AI is a computational paradigm that codifies intelligence into machines. There are generally three types of artificial intelligence and these are machine learning, evolutionary programming and soft computing. Machine learning is the statistical approach to building intelligent systems. Evolutionary programming is the use of natural evolutionary systems to design intelligent machines. Some of the evolutionary programming systems include genetic algorithm which is inspired by the principles of evolution and swarm optimization which is inspired by the swarming of birds, fish, ants etc. Soft computing includes techniques such as agent based modelling and fuzzy logic. Opportunities on the applications of these to digital art are explored.


title: Multi-label Class-imbalanced Action Recognition in Hockey Videos via 3D Convolutional Neural Networks

abstract: Automatic analysis of the video is one of most complex problems in the fields of computer vision and machine learning. A significant part of this research deals with (human) activity recognition (HAR) since humans, and the activities that they perform, generate most of the video semantics. Video-based HAR has applications in various domains, but one of the most important and challenging is HAR in sports videos. Some of the major issues include high inter- and intra-class variations, large class imbalance, the presence of both group actions and single player actions, and recognizing simultaneous actions, i.e., the multi-label learning problem. Keeping in mind these challenges and the recent success of CNNs in solving various computer vision problems, in this work, we implement a 3D CNN based multi-label deep HAR system for multi-label class-imbalanced action recognition in hockey videos. We test our system for two different scenarios: an ensemble of $k$ binary networks vs. a single $k$-output network, on a publicly available dataset. We also compare our results with the system that was originally designed for the chosen dataset. Experimental results show that the proposed approach performs better than the existing solution.


title: Comparison of sampler and VNA based Large Signal Measurement Systems (LSNA) under CW and pulsed operation

abstract: Here we present a comparison of the Large Signal Measurements provided by both a VNA based and a sampler based LSNA system. In this study, in order to achieve a robust and reliable relative phase calibration a dual frequency phase differential method was used. Significantly this work compares measurements performed under both CW and pulse mode operation. Very similar RF I-V waveforms are achieved in both cases, verifying that both architectures can provide comparable measurements not only in CW but also under pulsed operation. The test device was a GaN on silicon carbide HFET.


title: Traceability and visual analytics for the Internet-of-Things (IoT) architecture

abstract: There are several billion network-oriented devices in use today that are facilitated to inter-communicate; thereby forming a giant neural-like architecture known as the Internet-of-Things (IoT). The benefits of the IoT cut across all spectrums of our individual lives, corporate culture, and societal co-existence. This is because IoT devices support health tracking, security monitoring, consumer tracking, forecasting, and so on. However, the huge interconnectedness in IoT architectures complicates traceability and faulty data propagation is not easily detected since there are challenges with data origin authentication. Thus, this research proposes a provenance technique to deal with these issues. The technique is based on associative rules and lexical chaining methodologies, which enable traceability through the identification of propagation routes of data and object-to-object communications. Through visualization tools, the proposed methodologies also enabled us to determine linkability and unlinkability between IoT devices in a network which further leads to mechanisms to check correctness in sensor data propagation.


title: The hope construct, will, and ways: Their relations with self-efficacy, optimism, and general well-being

abstract: This investigation (N = 204) examined (a) the relations between the hope construct (Snyder, Harris et al., 1991; Snyder, Irving, & Anderson, 1991) and its two essential components, “will” and “ways,” and the related constructs of self-efficacy and optimism; and (b) the ability of hope, self-efficacy, and optimism to predict general well-being. Maximum-likelihood factor analysis recovered will, ways, self-efficacy, and optimism as generally distinct and independent entities. Results of multiple regression analyses predicting well-being indicated that (a) hope taken as a whole predicts unique variance independent of self-efficacy and optimism, (b) will predicts unique variance independent of self-efficacy, and (c) ways predicts unique variance independent of optimism. Overall, findings suggest that will, ways, self-efficacy, and optimism are related but not identical constructs. © 1999 John Wiley & Sons, Inc. J Clin Psychol 55: 539–551, 1999.


title: Achievement Motivation: Conceptions of Ability, Subjective Experience, Task Choice, and Performance

abstract: Achievement behavior is denned as behavior directed at developing or demonstrating high rather than low ability. It is shown that ability can be conceived in two ways. First, ability can be judged high or low with reference to the individual's own past performance or knowledge. In this context, gains in mastery indicate competence. Second, ability can be judged as capacity relative to that of others. In this context, a gain in mastery alone does not indicate high ability. To demonstrate high capacity, one must achieve more with equal effort or use less effort than do others for an equal performance. The conditions under which these different conceptions of ability function as individuals' goals and the nature of subjective experience in each case are specified. Different predictions of task choice and performance are derived and tested for each case. In this article, predictions of task choice, performance, and subjective experience in experimental achievement settings are derived, and relevant evidence is examined. An intentional view of behavior (Dennett, 1978) is adopted. In this, action is construed as a rational attempt to attain goals or incentives. In commonsense terms, individuals' actions serve to achieve purposes efficiently or economically. In the terms of games theory, action maximizes


title: Mapping QTLs for sucrose content, yield and quality in a sugar beet population fingerprinted by EST-related markers

abstract: Seventy five expressed sequence tags (ESTs) that are associated with functions in carbohydrate and nitrogen metabolism were genotyped in 108 plants of an F2 population of sugar beet (Beta vulgaris L.) segregating for sugar quality and yield parameters. Supplemented by known RFLP and AFLP markers, the resulting map spans 446 cM of the 758-Mbp genome of sugar beet. F3 test-cross plants were analysed for corrected sugar yield, beet yield, ion balance and the content of sugar, amino nitrogen, potassium and sodium in six locations. Twenty one significant quantitative trait loci (QTLs) were detected using the composite interval mapping approach. Expressed genes flanking the QTLs were identified in all cases. Correlations between QTLs and potential candidate genes are discussed.


title: Intrusion Detection Using Ensemble of Soft Computing Paradigms

abstract: Soft computing techniques are increasingly being used for problem solving. This paper addresses using ensemble approach of different soft computing techniques for intrusion detection. Due to increasing incidents of cyber attacks, building effective intrusion detection systems (IDSs) are essential for protecting information systems security, and yet it remains an elusive goal and a great challenge. Two classes of soft computing techniques are studied: Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs). We show that ensemble of ANN and SVM is superior to individual approaches for intrusion detection in terms of classification accuracy.


title: Abstract Distance Fields for Rapid Collision Detection in Physically Based Modeling

abstract: In this paper we address the problem of rapid distance computation between rigid objects and highly deformable objects, which is important in the context of physically based modeling of e.g hair or clothing. Our method is in particular useful when modeling deformable objects with particle systems—the most common approach to simulate such objects. We combine some already known techniques about distance fields into an algorithm for rapid collision detection. Only the rigid objects of an environment are represented by distance fields. In the context of proximity queries, which are essential for proper collision detection, this representation has two main advantages: First, any given boundary representation can be approximated quite easily, no high-degree polynomials or complicated approximation algorithms are needed. Second, the evaluation of distances and normals needed for collision response is extremely fast and independent of the complexity of the object. In the course of the paper we propose a simple, but fast algorithm for partial distance field computation. The sources are triangular meshes. Then, we present our approach for collision detection in detail. Examples from an interactive cloth animation system show the advantages of our approach in practice. We conclude that our method allows real-time animations of complex deformable objects in non-trivial environments on standard PC hardware.


title: An automatic bus route number recognition

abstract: This paper presents a bus route number recognition using image processing and machine learning techniques. The prototype is designed to extract bus route number from scene images and synthesize audio of bus number for people with low vision. The proposed system consists of two parts, text detection and digit recognition. Text detection based on MSERs is used to find and segment text area from input images. Digit recognizer based on Convolutional Neural Network is used to recognize digits on the detected text area. Finally, the recognized digit is converted to audio which can help the visually impaired people (low vision) to take the correct bus. The experimental results of the proposed method presented in precision, recall and F-measure in text detection and acquire 73.47% accuracy of a digit segment in self-collect Thai bus dataset.


title: Can predictive coding explain repetition suppression?

abstract: While in earlier work various local or bottom-up neural mechanisms were proposed to give rise to repetition suppression (RS), current theories suggest that top-down processes play a role in determining the repetition related reduction of the neural responses. In the current review we summarise those results, which support the role of these top-down processes, concentrating on the Bayesian models of predictive coding (PC). Such models assume that RS is related to the statistical probabilities of prior stimulus occurrences and to the future predictability of these stimuli. Here we review the current results that support or argue against this explanation. We point out that the heterogeneity of experimental manipulations that are thought to reflect predictive processes are likely to measure different processing steps, making their direct comparison difficult. In addition we emphasize the importance of identifying these sub-processes and clarifying their role in explaining RS. Finally, we propose a two-stage model for explaining the relationships of repetition and expectation phenomena in the human cortex.


Efficient DNN Neuron Pruning by Minimizing Layer-wise Nonlinear Reconstruction Error


title: Collaborative process deduction to help the crisis cell emerging ecosystem to coordinate the crisis response

abstract: One objective of the French-funded (ANR-2006-SECU-006) ISyCri Project (ISyCri stands for Interoperability of Systems in Crisis situation) is to provide the crisis cell in charge of the situation management with an information system (IS) able to support the interoperability of partners involved in this collaborative situation. Such a system is called Mediation Information System (MIS). This system must be in charge of (i) information exchange, (ii) services sharing and (iii) behavior orchestration. This paper presents the first step of the MIS engineering, the deduction of a collaborative process used to coordinate actors of the crisis cell. Especially, this paper give a formal definition of the deduction rules used to deduce the collaborative process.


title: What is the expectation maximization algorithm?

abstract: The expectation maximization algorithm arises in many computational biology applications that involve probabilistic models. What is it good for, and how does it work?


title: A cognitive architecture that combines internal simulation with a global workspace

abstract: This paper proposes a brain-inspired cognitive architecture that incorporates approximations to the concepts of consciousness, imagination, and emotion. To emulate the empirically established cognitive efficacy of conscious as opposed to non-conscious information processing in the mammalian brain, the architecture adopts a model of information flow from global workspace theory. Cognitive functions such as anticipation and planning are realised through internal simulation of interaction with the environment. Action selection, in both actual and internally simulated interaction with the environment, is mediated by affect. An implementation of the architecture is described which is based on weightless neurons and is used to control a simulated robot.


title: Building the Internet of Things Using RFID: The RFID Ecosystem Experience

abstract: At the University of Washington, the RFID ecosystem creates a microcosm for the Internet of Things. The authors developed a suite of Web-based, user-level tools and applications designed to empower users by facilitating their understanding, management, and control of personal RFID data and privacy settings. They deployed these applications in the RFID ecosystem and conducted a four-week user study to measure trends in adoption and utilization of the tools and applications as well as users' qualitative reactions.


title: LIPIcs Leibniz International Proceedings in Informatics Fighting Bit Rot with Types (Experience Report: Scala Collections)

abstract: We report on our experiences in redesigning Scala's collection libraries, focussing on the role that type systems play in keeping software architectures coherent over time. Type systems can make software architecture more explicit but, if they are too weak, can also cause code du- plication. We show that code duplication can be avoided using two of Scala's type constructions: higher-kinded types and implicit parameters and conversions.


title: Towards selecting robust hand gestures for automotive interfaces

abstract: Driver distraction is a serious threat to automotive safety. The visual-manual interfaces in cars are a source of distraction for drivers. Automotive touch-less hand gesture-based user interfaces can help to reduce driver distraction and enhance safety and comfort. The choice of hand gestures in automotive interfaces is central to their success and widespread adoption. In this work we evaluate the recognition accuracy of 25 different gestures for state-of-the-art computer vision-based gesture recognition algorithms and for human observers. We show that some gestures are consistently recognized more accurately than others by both vision-based algorithms and humans. We further identify similarities in the hand gesture recognition abilities of vision-based systems and humans. Lastly, by merging pairs of gestures with high miss-classification rates, we propose ten robust hand gestures for automotive interfaces, which are classified with high and equal accuracy by vision-based algorithms.


title: Cyber extremism in Web 2.0: An exploratory study of international Jihadist groups

abstract: As part of the NSF-funded Dark Web research project, this paper presents an exploratory study of cyber extremism on the Web 2.0 media: blogs, YouTube, and Second Life. We examine international Jihadist extremist groups that use each of these media. We observe that these new, interactive, multimedia-rich forms of communication provide effective means for extremists to promote their ideas, share resources, and communicate among each other. The development of automated collection and analysis tools for Web 2.0 can help policy makers, intelligence analysts, and researchers to better understand extremistspsila ideas and communication patterns, which may lead to strategies that can counter the threats posed by extremists in the second-generation Web.


Does the ENP respond to the EU's post‐enlargement challenges?


title: Postoperative trans women in sexual health clinics: managing common problems after vaginoplasty

abstract: UK figures estimate that in 1998 there were 3170 people over the age of 15 years assigned as male at birth who had presented with gender dysphoria. This figure is comparable to that found in the Netherlands where 2440 have presented;1 however, far fewer people actually undergo sex reassignment surgery. Recent statistics from the Netherlands indicate that about 1 in 12 000 natal males undergo sex-reassignment and about 1 in 34 000 natal females.2 Since April 2013, English gender identity services have been among the specialised services commissioned centrally by NHS England and this body is therefore responsible for commissioning transgender surgical services. ::: ::: The growth in the incidence of revealed gender dysphoria amongst both young and adult people has major implications for commissioners and providers of public services. The present annual requirement is 480 genital and gonadal male-to-female reassignment procedures. There are currently three units in the UK offering this surgery for National Health Service (NHS) patients. Prior to surgery trans women will have had extensive evaluation, including blood tests, advice on smoking, alcohol and obesity, and psychological/psychiatric evaluation. They usually begin to take female hormones after 3 months of transition, aiming to encourage development of breast buds and alter muscle and fat distribution. Some patients may elect at this stage to have breast surgery. Before genital surgery can be considered the patient must have demonstrated they have lived for 1 year full-time as a woman. Figure 1 shows a typical post-surgical result. ::: ::: ::: ::: Figure 1 ::: Photograph illustrating the typical results of male-to-female reassignment genital surgery. ::: ::: ::: ::: A trans person who has lived exclusively in their identified gender for at least 2 years (as required by the Gender Recognition Act 2004) can apply for a …


title: Hyperspectral Image Segmentation Using a New Bayesian Approach With Active Learning

abstract: This paper introduces a new supervised Bayesian approach to hyperspectral image segmentation with active learning, which consists of two main steps. First, we use a multinomial logistic regression (MLR) model to learn the class posterior probability distributions. This is done by using a recently introduced logistic regression via splitting and augmented Lagrangian algorithm. Second, we use the information acquired in the previous step to segment the hyperspectral image using a multilevel logistic prior that encodes the spatial information. In order to reduce the cost of acquiring large training sets, active learning is performed based on the MLR posterior probabilities. Another contribution of this paper is the introduction of a new active sampling approach, called modified breaking ties, which is able to provide an unbiased sampling. Furthermore, we have implemented our proposed method in an efficient way. For instance, in order to obtain the time-consuming maximum a posteriori segmentation, we use the α-expansion min-cut-based integer optimization algorithm. The state-of-the-art performance of the proposed approach is illustrated using both simulated and real hyperspectral data sets in a number of experimental comparisons with recently introduced hyperspectral image analysis methods.


CPSC 514 Term Project – Implementation of Photorealistic Scene Reconstruction by Voxel Coloring


title: Proving Properties of Programs by Structural Induction

abstract: This paper discusses the technique of structural induction for proving theorems about programs. This technique is closely related to recursion induction but makes use of the inductive definition of the data structures handled by the programs. It treats programs with recursion but without assignments or jumps. Some syntactic extensions to Landin's functional programming language ISWIM are suggested which make it easier to program the manipulation of data structures and to develop proofs about such programs. Two sample proofs are given to demonstrate the technique, one for a tree sorting algorithm and one for a simple compiler for expressions. (First received April 1968 and in revised form August 1968)


title: Application of LSVT BIG Intervention to Address Gait, Balance, Bed Mobility, and Dexterity in People With Parkinson Disease: A Case Series

abstract: Background and Purpose Lee Silverman Voice Treatment Big (LSVT BIG) is characterized by intensive exercising of high-amplitude movements to overcome bradykinesia and hypokinesia in patients with Parkinson disease (PD). The aim of the present case series was to explore possible beneficial effects of LSVT BIG training on gait, balance, bed mobility, and dexterity. ::: ::: Case Description Three patients with mild to moderate PD (all male; aged 52, 54, and 70 years; Hoehn & Yahr stages I–III) completed a 4-week LSVT BIG training program (16 individual 1-hour sessions) and an intensive home training program in accordance with the LSVT BIG protocol. Two certified LSVT BIG physical therapists delivered the LSVT BIG training sessions. ::: ::: Outcomes The outcome measures for gait and balance included the Functional Gait Assessment (FGA), Functional Reach Test (FRT), Timed “Up & Go” Test (TUG), Freezing of Gait Questionnaire (FOGQ), and motor score on part III of the Unified Parkinson's Disease Rating Scale (UPDRS III). Bed mobility was addressed using the Lindop Parkinson's Disease Mobility Assessment (LPA). The Nine-Hole Peg Test (9HPT) was used to measure dexterity. The 3 patients performed better on balance and gait assessments, as indicated by increased scores on the FRT and FGA (all 3 patients) and decreased scores on the TUG, FOGQ, and UPDRS III, of which scores of the FRT and UPDRS III achieved the minimal detectable change. Furthermore, the patients were quicker in tasks related to bed mobility (LPA). The patients' dexterity skills did not improve for their dominant (right) hand (9HPT). ::: ::: Discussion This case series suggests that the LSVT BIG may be beneficial for gait, balance, and bed mobility. Future work is needed to ascertain the effectiveness by means of randomized controlled trials.


title: Developing a predictive model of quality of experience for internet video

abstract: Improving users' quality of experience (QoE) is crucial for sustaining the advertisement and subscription based revenue models that enable the growth of Internet video. Despite the rich literature on video and QoE measurement, our understanding of Internet video QoE is limited because of the shift from traditional methods of measuring video quality (e.g., Peak Signal-to-Noise Ratio) and user experience (e.g., opinion scores). These have been replaced by new quality metrics (e.g., rate of buffering, bitrate) and new engagement centric measures of user experience (e.g., viewing time and number of visits). The goal of this paper is to develop a predictive model of Internet video QoE. To this end, we identify two key requirements for the QoE model: (1) it has to be tied in to observable user engagement and (2) it should be actionable to guide practical system design decisions. Achieving this goal is challenging because the quality metrics are interdependent, they have complex and counter-intuitive relationships to engagement measures, and there are many external factors that confound the relationship between quality and engagement (e.g., type of video, user connectivity). To address these challenges, we present a data-driven approach to model the metric interdependencies and their complex relationships to engagement, and propose a systematic framework to identify and account for the confounding factors. We show that a delivery infrastructure that uses our proposed model to choose CDN and bitrates can achieve more than 20\% improvement in overall user engagement compared to strawman approaches.


title: A Low-Noise Wideband Digital Phase-Locked Loop Based on a Coarse–Fine Time-to-Digital Converter With Subpicosecond Resolution

abstract: This paper presents the design of a digital PLL which uses a high-resolution time-to-digital converter (TDC) for wide loop bandwidth. The TDC uses a time amplification technique to reduce the quantization noise down to less than 1 ps root mean square (RMS). Additionally TDC input commutation reduces low-frequency spurs due to inaccurate TDC scaling factor in a counter-assisted digital PLL. The loop bandwidth is set to 400 kHz with a 25 MHz reference. The in-band phase noise contribution from the TDC is -116 dBc/Hz, the phase noise is -117 dBc/Hz at high band (1.8 GHz band) 400 kHz offset, and the RMS phase error is 0.3deg.


title: Plant genome editing made easy: targeted mutagenesis in model and crop plants using the CRISPR/Cas system

abstract: Targeted genome engineering (also known as genome editing) has emerged as an alternative to classical plant breeding and transgenic (GMO) methods to improve crop plants. Until recently, available tools for introducing site-specific double strand DNA breaks were restricted to zinc finger nucleases (ZFNs) and TAL effector nucleases (TALENs). However, these technologies have not been widely adopted by the plant research community due to complicated design and laborious assembly of specific DNA binding proteins for each target gene. Recently, an easier method has emerged based on the bacterial type II CRISPR (clustered regularly interspaced short palindromic repeats)/Cas (CRISPR-associated) immune system. The CRISPR/Cas system allows targeted cleavage of genomic DNA guided by a customizable small noncoding RNA, resulting in gene modifications by both non-homologous end joining (NHEJ) and homology-directed repair (HDR) mechanisms. In this review we summarize and discuss recent applications of the CRISPR/Cas technology in plants.


title: A hierarchical, objectives-based framework for the digital investigations process

abstract: Digital investigations, whether forensic in nature or not, require scientific rigor and are facilitated through the use of standard processes. Such processes can be complex in nature. A more comprehensive, generally accepted digital investigation process framework is therefore sought to enhance scientific rigor and facilitate education, application, and research. Previously proposed frameworks are predominantly single-tier, higher order process models that focus on the abstract, rather than the more concrete principles of the investigation. We contend that these frameworks, although useful in explaining overarching concepts, fail to support the inclusion of additional layers of detail needed by various framework users. We therefore propose a multi-tier, hierarchical framework to guide digital investigations. Our framework includes objectives-based phases and sub-phases that are applicable to various layers of abstraction, and to which additional layers of detail can easily be added as needed. Our framework also includes principles that are applicable in varied ways to all phases. The data analysis function intended to identify and recover digital evidence is used as an example of how the framework might be further populated and used. The framework is then applied using two different case scenarios. At its highest level, the proposed framework provides a simplified view and conceptual understanding of the overall process. At lower levels, the proposed framework provides the granularity needed to achieve practicality and specificity goals set by practitioners and researchers alike.


title: Entity-Centric Coreference Resolution with Model Stacking

abstract: Mention pair models that predict whether or not two mentions are coreferent have historically been very effective for coreference resolution, but do not make use of entity-level information. However, we show that the scores produced by such models can be aggregated to define powerful entity-level features between clusters of mentions. Using these features, we train an entity-centric coreference system that learns an effective policy for building up coreference chains incrementally. The mention pair scores are also used to prune the search space the system works in, allowing for efficient training with an exact loss function. We evaluate our system on the English portion of the 2012 CoNLL Shared Task dataset and show that it improves over the current state of the art.


title: A Generalized Iterated Shrinkage Algorithm for Non-convex Sparse Coding

abstract: In many sparse coding based image restoration and image classification problems, using non-convex Ip-norm minimization (0 ≤ p <; 1) can often obtain better results than the convex l1-norm minimization. A number of algorithms, e.g., iteratively reweighted least squares (IRLS), iteratively thresholding method (ITM-Ip), and look-up table (LUT), have been proposed for non-convex Ip-norm sparse coding, while some analytic solutions have been suggested for some specific values of p. In this paper, by extending the popular soft-thresholding operator, we propose a generalized iterated shrinkage algorithm (GISA) for Ip-norm non-convex sparse coding. Unlike the analytic solutions, the proposed GISA algorithm is easy to implement, and can be adopted for solving non-convex sparse coding problems with arbitrary p values. Compared with LUT, GISA is more general and does not need to compute and store the look-up tables. Compared with IRLS and ITM-Ip, GISA is theoretically more solid and can achieve more accurate solutions. Experiments on image restoration and sparse coding based face recognition are conducted to validate the performance of GISA.


title: Hierarchical Random Walk Inference in Knowledge Graphs

abstract: Relational inference is a crucial technique for knowledge base population. The central problem in the study of relational inference is to infer unknown relations between entities from the facts given in the knowledge bases. Two popular models have been put forth recently to solve this problem, which are the latent factor models and the random-walk models, respectively. However, each of them has their pros and cons, depending on their computational efficiency and inference accuracy. In this paper, we propose a hierarchical random-walk inference algorithm for relational learning in large scale graph-structured knowledge bases, which not only maintains the computational simplicity of the random-walk models, but also provides better inference accuracy than related works. The improvements come from two basic assumptions we proposed in this paper. Firstly, we assume that although a relation between two entities is syntactically directional, the information conveyed by this relation is equally shared between the connected entities, thus all of the relations are semantically bidirectional. Secondly, we assume that the topology structures of the relation-specific subgraphs in knowledge bases can be exploited to improve the performance of the random-walk based relational inference algorithms. The proposed algorithm and ideas are validated with numerical results on experimental data sampled from practical knowledge bases, and the results are compared to state-of-the-art approaches.


title: Neural Belief Tracker: Data-Driven Dialogue State Tracking

abstract: One of the core components of modern spoken dialogue systems is the belief tracker, which estimates the user's goal at every step of the dialogue. However, most current approaches have difficulty scaling to larger, more complex dialogue domains. This is due to their dependency on either: a) Spoken Language Understanding models that require large amounts of annotated training data; or b) hand-crafted lexicons for capturing some of the linguistic variation in users' language. We propose a novel Neural Belief Tracking (NBT) framework which overcomes these problems by building on recent advances in representation learning. NBT models reason over pre-trained word vectors, learning to compose them into distributed representations of user utterances and dialogue context. Our evaluation on two datasets shows that this approach surpasses past limitations, matching the performance of state-of-the-art models which rely on hand-crafted semantic lexicons and outperforming them when such lexicons are not provided.


title: Compact Broadband Gysel Power Divider With Arbitrary Power-Dividing Ratio Using Microstrip/Slotline Phase Inverter

abstract: A compact broadband Gysel power divider with arbitrary power-dividing ratio is proposed. This divider is derived from the conventional Gysel power divider by substituting an ideal phase inverter for the 180° phase shifter, which significantly improves the bandwidth and reduces the circumference to 1 λg evaluated at the center frequency. A wide range of power-dividing ratio (1-6.25) is supported by changing the impedance ratio of the transmission lines, while the dual-band impedance matching characteristic with frequency ratio (1-2.9) is obtained only by varying the impedances of the transmission lines (20-135 Ω). Closed-form equations are derived for design parameters. The ideal phase inverter is realized by two transitions: microstrip-to-slotline and slotline-to-microstrip transitions. By utilizing this phase inverter, two microstrip Gysel dividers with power-dividing ratios of 1:1 and 2:1 and operating at 1.5/2.5 and 1/2 GHz, respectively, are designed, fabricated, and measured. The measured results are in good agreement with the simulated ones. The first measured results show a bandwidth of 80% with better than 15-dB return loss and 29-dB isolation from 1.19 to 2.76 GHz. The second measured results show a bandwidth of 95.8% with better than 12-dB return loss and 24-dB isolation from 0.8 to 2.27 GHz. To the authors' knowledge, the most wideband Gysel power divider using microstrip technology is proposed for the first time.


title: Computational Models for Speech Production

abstract: Major speech production models from speech science literature and a number of popular statistical “generative” models of speech used in speech technology are surveyed. Strengths and weaknesses of these two styles of speech models are analyzed, pointing to the need to integrate the respective strengths while eliminating the respective weaknesses. As an example, a statistical task-dynamic model of speech production is described, motivated by the original deterministic version of the model and targeted for integrated-multilingual speech recognition applications. Methods for model parameter learning (training) and for likelihood computation (recognition) are described based on statistical optimization principles integrated in neural network and dynamic system theories.


title: Long-term use of a cannabis-based medicine in the treatment of spasticity and other symptoms in multiple sclerosis

abstract: The object of this study was to monitor the safety and efficacy of long-term use of an oromucosal cannabis-based medicine (CBM) in patients with multiple sclerosis (MS). A total of 137 MS patients with symptoms not controlled satisfactorily using standard drugs entered this open-label trial following a 10-week, placebo-controlled study. Patients were assessed every eight weeks using visual analogue scales and diary scores of main symptoms, and were followed for an average of 434 days (range: 21- 814). A total of 58 patients (42.3%) withdrew due to lack of efficacy (24); adverse events (17); withdrew consent (6); lost to follow-up (3); and other (8). Patients reported 292 unwanted effects, of which 251 (86%) were mild to moderate, including oral pain (28), dizziness (20), diarrhoea (17), nausea (15) and oromucosal disorder (12). Three patients had five ‘serious adverse events’ between them - two seizures, one fall, one aspiration pneumonia, one gastroenteritis. Four patients had first-ever seizures. The im...


title: Model-Driven ERP Implementation

abstract: Enterprise Resource Planning (ERP) implementations are very complex. To obtain a fair level of understanding of the system, it is then necessary to model the supported business processes. However, the problem is the accuracy of the mapping between this model and the actual technical implementation. A solution is to make use of the OMG’s Model-Driven Architecture (MDA) framework. In fact, this framework lets the developer model his system at a high abstraction level and allows the MDA tool to generate the implementation details. This paper presents our results in applying the MDA framework to ERP implementation based on a high level model of the business processes. Then, we show how our prototype is structured and implemented in the IBM/Rational XDE environment


title: A combined batteryless radio and WiFi indoor Positioning System

abstract: This paper presents a design of an indoor localization system that can be used in hospital nurse calling systems. The call button of patient is batteryless and wireless. The positioning system is developed for this batteryless button. When the switch is pressed, a wireless telegram is sent to reference nodes that act like Wireless Sensor Networks (WSN). This wireless message contains the module ID and the Received Signal Strength Indicator (RSSI) value. The reference nodes or beacons forward the information gathered from target node to a central receiver attached to a computer. A program is developed to read the incoming signals to the receiver module for processing. This program calculate the distance between target node (unknown location) and each reference node (known location), and then it determines the position of the call button which held by the patient. In parallel, nurses hold smart phones, and the position is determined via WiFi-Based Positioning System (WPS). Both systems are combined and linked to the nurse calling system. Therefore, when a patient hits the batteryless button, the system will compare the patient position with nurses' positions and notify the nearest nurse. The main goal of developing such system is to decrease the time taking for the nurse to provide healthcare for patients.


title: An intelligent tutoring system for deaf learners of written English

abstract: This paper describes progress toward a prototype implementation of a tool which aims to improve literacy in deaf high school and college students who are native (or near native) signers of American Sign Language (ASL). We envision a system that will take a piece of text written by a deaf student, analyze that text for grammatical errors, and engage that student in a tutorial dialogue, enabling the student to generate appropriate corrections to the text. A strong focus of this work is to develop a system which adapts this process to the knowledge level and learning strengths of the user and which has the flexibility to engage in multi-modal, multi-lingual tutorial instruction utilizing both English and the native language of the user.


title: Cognitive psychology meets psychometric theory: on the relation between process models for decision making and latent variable models for individual differences.

abstract: This article analyzes latent variable models from a cognitive psychology perspective. We start by discussing work by Tuerlinckx and De Boeck (2005), who proved that a diffusion model for 2-choice response processes entails a 2-parameter logistic item response theory (IRT) model for individual differences in the response data. Following this line of reasoning, we discuss the appropriateness of IRT for measuring abilities and bipolar traits, such as pro versus contra attitudes. Surprisingly, if a diffusion model underlies the response processes, IRT models are appropriate for bipolar traits but not for ability tests. A reconsideration of the concept of ability that is appropriate for such situations leads to a new item response model for accuracy and speed based on the idea that ability has a natural zero point. The model implies fundamentally new ways to think about guessing, response speed, and person fit in IRT. We discuss the relation between this model and existing models as well as implications for psychology and psychometrics.


title: Estimating locations and coverage areas of mobile network cells based on crowdsourced data

abstract: Mobile network operators usually keep the data about their mobile network topology very secret. However, having this data, many sophisticated and innovative applications could be realized. Today's smartphones provide the means to measure cell data with which the mobile network topology of different operators can be approximately derived by applying various position estimation algorithms. These approaches deliver different results in terms of distance to the real positions of base stations and positions calculated by Google. In this paper, we analyze and evaluate state-of-the-art approaches for estimating base station positions out of crowdsourced data and integrate the most suitable methode(s) into the OpenMobileNetwork, which is an open-source initiative for publishing mobile network data using the semantic principles of Linked Data. The results show that the grid-based approach provides the best estimates for base station positions when compared with their real locations.


title: Alignment of continuous video onto 3D point clouds

abstract: We propose a general framework for aligning continuous (oblique) video onto 3D sensor data. We align a point cloud computed from the video onto the point cloud directly obtained from a 3D sensor. This is in contrast to existing techniques where the 2D images are aligned to a 3D model derived from the 3D sensor data. Using point clouds enables the alignment for scenes full of objects that are difficult to model; for example, trees. To compute 3D point clouds from video, motion stereo is used along with a state-of-the-art algorithm for camera pose estimation. Our experiments with real data demonstrate the advantages of the proposed registration algorithm for texturing models in large-scale semiurban environments. The capability to align video before a 3D model is built from the 3D sensor data offers new practical opportunities for 3D modeling. We introduce a novel modeling-through-registration approach that fuses 3D information from both the 3D sensor and the video. Initial experiments with real data illustrate the potential of the proposed approach.


title: Automatic discovery of data-centric and artifact-centric processes

abstract: Process discovery is a technique that allows for automatically discovering a process model from recorded executions of a process as it happens in reality. This technique has successfully been applied for classical processes where one process execution is constituted by a single case with a unique case identifier. Data-centric and artifact-centric systems such as ERP systems violate this assumption. Here a process execution is driven by process data having various notions of interrelated identifiers that distinguish the various interrelated data objects of the process. Classical process mining techniques fail in this setting. This paper presents an automatic technique for discovering for each notion of data object in the process a separate process model that describes the evolution of this object, also known as artifact life-cycle model. Given a relational database that stores process execution information of a data-centric system, the technique extracts event information, case identifiers and their interrelations, discovers the central process data objects and their associated events, and decomposes the data source into multiple logs, each describing the cases of a separate data object. Then classical process discovery techniques can be applied to obtain a process model for each object. The technique is implemented and has been evaluated on the production ERP system of a large retailer.


title: ValenTo: Sentiment Analysis of Figurative Language Tweets with Irony and Sarcasm

abstract: This paper describes the system used by the ValenTo team in the Task 11, Sentiment Analysis of Figurative Language in Twitter, at SemEval 2015. Our system used a regression model and additional external resources to assign polarity values. A distinctive feature of our approach is that we used not only wordsentiment lexicons providing polarity annotations, but also novel resources for dealing with emotions and psycholinguistic information. These are important aspects to tackle in figurative language such as irony and sarcasm, which were represented in the dataset. The system also exploited novel and standard structural features of tweets. Considering the different kinds of figurative language in the dataset our submission obtained good results in recognizing sentiment polarity in both ironic and sarcastic tweets.


title: A case for toggle-aware compression for GPU systems

abstract: Data compression can be an effective method to achieve higher system performance and energy efficiency in modern data-intensive applications by exploiting redundancy and data similarity. Prior works have studied a variety of data compression techniques to improve both capacity (e.g., of caches and main memory) and bandwidth utilization (e.g., of the on-chip and off-chip interconnects). In this paper, we make a new observation about the energy-efficiency of communication when compression is applied. While compression reduces the amount of transferred data, it leads to a substantial increase in the number of bit toggles (i.e., communication channel switchings from 0 to 1 or from 1 to 0). The increased toggle count increases the dynamic energy consumed by on-chip and off-chip buses due to more frequent charging and discharging of the wires. Our results show that the total bit toggle count can increase from 20% to 2.2x when compression is applied for some compression algorithms, averaged across different application suites. We characterize and demonstrate this new problem across 242 GPU applications and six different compression algorithms. To mitigate the problem, we propose two new toggle-aware compression techniques: Energy Control and Metadata Consolidation. These techniques greatly reduce the bit toggle count impact of the data compression algorithms we examine, while keeping most of their bandwidth reduction benefits.


ANDROID BASED CANTEEN AUTOMATION USING WIFI


title: Performance evaluation of seven machine learning classification techniques for movie box office success prediction

abstract: Movie industry is a multi-billion-dollar industry and now there is a huge amount of data available on the internet related to movie industry. Researchers have developed different machine learning methods which can make good classification models. In this paper, various machine learning classification methods are implemented on our own movie dataset for multi class classification. The main goal of this paper is to conduct performance comparison among various machine learning methods. We choose seven machine learning techniques for this comparison such as Support Vector Machine (SVM), Logistic Regression, Multilayer Perceptron Neural Network, Gaussian Naive Bayes, Random Forest, AdaBoost and Stochastic Gradient Descent (SGD). All of these methods predict an approximate net profit value of a movie by analyzing historical data from different sources like IMDb, Rotten Tomatoes, Box Office Mojo and Meta Critic. For all these seven methods, the system predicts a movie box office profit based on some pre-released features and post-released features. This paper analyzes the performance assessment of all these seven machine learning techniques based on our own dataset which contains 755 movies. Among these seven algorithms, Multilayer perceptron Neural Network gives better result.


title: A Cross Sectional Study on Procrastination: Who Procrastinate More?

abstract: In the present research was to investigate the levels and the prevalence of academic procrastination on high school, undergraduate and graduate students. In this respect, Procrastination Assessment Scale-Student (PASS) was administered to a total of 448 students who were 149 (83 female; 66 male) high-school, 150 (80 female; 70 male) undergraduate and 148 (84 female; 64 male) graduate students. The average age was 15.5 years old (SD = .56) for High-school, 20.4 years old (SD = 1.71) for Undergraduate, and 25.5 years old (SD = 2.32) for Graduate students. Results showed a significant difference among the academic levels of the students. Specifically, undergraduate students claimed to procrastinate more than graduate and high school students. High school and undergraduate students claimed to be nearly always or always procrastinator on studying for exams, while graduate students procrastinate more on writing term papers.


title: Advances in Giant Magnetoresistance Biosensors With Magnetic Nanoparticle Tags: Review and Outlook

abstract: We present a review of giant magnetoresistance (GMR) spin valve sensors designed for detection of magnetic nanoparticles as biomolecular labels (nanotags) in magneto-nano biodetection technology. We discuss the intricacy of magneto-nano biosensor design and show that as few as approximately 14 monodisperse 16-nm superparamagnetic nanoparticles can be detected by submicron spin valve sensors at room temperature without resorting to lock-in (narrow band) detection. GMR biosensors and biochips have been successfully applied to the detection of biological events in the form of both protein and DNA assays with great speed, sensitivity, selectivity, and economy. The limit of molecular detection is well below 10 pM in concentration, and the protein or DNA assay time can be under two hours. The technology is highly scalable to deep multiplex detection of biomarkers in a complex disease, and amenable to integration of microfluidics and CMOS electronics for portable applications. On-chip CMOS circuitry makes a sensor density of 0.1-1 million sensors per square centimeter feasible and affordable. The theoretical and experimental results thus far suggest that magneto-nano biochip-based GMR sensor arrays and nanotags hold great promise in biomedicine, particularly for point-of-care molecular diagnostics of cancer, infectious diseases, radiation injury, cardiac diseases, and other diseases.


title: Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual Tracking

abstract: Discriminative Correlation Filters (DCF) have demonstrated excellent performance for visual object tracking. The key to their success is the ability to efficiently exploit available negative data b ...


title: Applying One-Versus-One SVMs to classify multi-label data with large labels using spark

abstract: Multi-Label classification aims to classify an example that can belong to many classes. Although One-versus-All (OVA) is the most common approach, our prior work has shown that the proposed One-versus-One (OVO) always gives higher prediction accuracy than OVA. However, OVO requires an extremely high computational cost when there are a large number of labels. In this paper, we apply our OVO SVMs on the proposed Spark framework along with a mechanism to split a job into a set of small jobs and then process them in parallel. The framework can induce OVO SVMs very fast, while maintaining the prediction accuracy even though there are a large number of classes. The experiment was conducted on six standard benchmarks. The result shows that our framework can really reduce computing time on Spark environment, while significantly outperforms OVA in terms of F1 on all data.


title: Improving the Dynamic Response of a Flying-Capacitor Three-Level Buck Converter

abstract: This study presents a modified flying capacitor three-level buck dc-dc converter with improved dynamic response. First, the limitations in the transient response improvement of the conventional and three-level buck converters are discussed. Then, the three-level buck converter is modified in a way that it would benefit from a faster dynamic during sudden changes in the load. Finally, a controller is proposed that detects load transients and responds appropriately. In order to verify the effectiveness of the modified topology and the proposed transient controller, a simulation model and a hardware prototype are developed. Analytical, simulation, and experimental results show a significant dynamic response improvement.


title: The Digital Marauder's Map: A New Threat to Location Privacy

abstract: "The Marauder's Map" is a magical map in J. K. Rowling's fantasy series, "Harry Potter and the Prisoner of Azkaban". It shows all moving objects within the boundary of the "Hogwarts School of Witchcraft and Wizardry". In this paper, we introduce a similar attack to location privacy in wireless networks. Our system, namely the digital Marauder's map, can reveal the locations of WiFi-enabled mobile devices within the coverage area of a single high-gain antenna. The digital Marauder's map is built solely with off-the-shelf wireless equipments, and features a mobile design that can be quickly deployed to a new location and instantly used without training. We present a comprehensive set of theoretical analysis and experimental results which demonstrate the coverage and localization accuracy of the digital Marauder's map.


title: Turning a denoiser into a super-resolver using plug and play priors

abstract: Denoising and Super-Resolution are two inverse problems that have been extensively studied. Over the years, these two tasks were treated as two distinct problems that deserve a different algorithmic solution. In this paper we wish to exploit the recently introduced Plug-and-Play Prior (PPP) approach [1] to connect between the two. Using the PPP, we turn leading denoisers into super-resolution solvers. As a case-study we demonstrate this on the NCSR algorithm, which has two variants: one for denoising and one for superresolution. We show that by using the NCSR denoiser, one can get equal or even better results when compared with the NCSR super-resolution.


title: “Nomophobia”: Impact of Cell Phone Use Interfering with Symptoms and Emotions of Individuals with Panic Disorder Compared with a Control Group

abstract: Panic disorder refers to the frequent and recurring acute attacks of anxiety. Objective: This study describes the routine use of mobiles phones (MPs) and investigates the appearance of possible emotional alterations or symptoms related to their use in patients with panic disorder (PD). Background: We compared patients with PD and agoraphobia being treated at the Panic and Respiration Laboratory of The Institute of Psychiatry, Federal University of Rio de Janeiro, Brazil, to a control group of healthy volunteers. Methods: An MP-use questionnaire was administered to a consecutive sample of 50 patients and 70 controls. Results: People with PD showed significant increases in anxiety, tachycardia, respiratory alterations, trembling, perspiration, panic, fear and depression related to the lack of an MP compared to the control group. Conclusions: Both groups exhibited dependence on and were comforted by having an MP; however, people with PD and agoraphobia showed significantly more emotional alterations as well as intense physical and psychological symptoms when they were apart from or unable to use an MP compared to healthy volunteers.


title: Autobiographical design in HCI research: designing and learning through use-it-yourself

abstract: Designing a system with yourself as a target user and evaluating the design through your own self-usage is commonly considered a questionable approach in HCI research. Perhaps for this reason, HCI research including extensive self-usage of a design is underdocumented. Yet such self-usage does happen and many researchers have found great value in the lessons learned from it. Our goal in this paper is to bring these hidden practices to light and offer guidelines for how HCI researchers can usefully engage in what we term 'autobiographical design'---design research drawing on extensive, genuine usage by those creating or building a system. Through interviews with HCI experts who have engaged in variations of autobiographical design, we draw out the possibilities and limitations of autobiographical design methods and lay out best practices for its use as an HCI research method.


title: Mutual Component Analysis for Heterogeneous Face Recognition

abstract: Heterogeneous face recognition, also known as cross-modality face recognition or intermodality face recognition, refers to matching two face images from alternative image modalities. Since face images from different image modalities of the same person are associated with the same face object, there should be mutual components that reflect those intrinsic face characteristics that are invariant to the image modalities. Motivated by this rationality, we propose a novel approach called Mutual Component Analysis (MCA) to infer the mutual components for robust heterogeneous face recognition. In the MCA approach, a generative model is first proposed to model the process of generating face images in different modalities, and then an Expectation Maximization (EM) algorithm is designed to iteratively learn the model parameters. The learned generative model is able to infer the mutual components (which we call the hidden factor, where hidden means the factor is unreachable and invisible, and can only be inferred from observations) that are associated with the person’s identity, thus enabling fast and effective matching for cross-modality face recognition. To enhance recognition performance, we propose an MCA-based multiclassifier framework using multiple local features. Experimental results show that our new approach significantly outperforms the state-of-the-art results on two typical application scenarios: sketch-to-photo and infrared-to-visible face recognition.


title: A User Study to Compare Four Uncertainty Visualization Methods for 1D and 2D Datasets

abstract: Many techniques have been proposed to show uncertainty in data visualizations. However, very little is known about their effectiveness in conveying meaningful information. In this paper, we present a user study that evaluates the perception of uncertainty amongst four of the most commonly used techniques for visualizing uncertainty in one-dimensional and two-dimensional data. The techniques evaluated are traditional errorbars, scaled size of glyphs, color-mapping on glyphs, and color-mapping of uncertainty on the data surface. The study uses generated data that was designed to represent the systematic and random uncertainty components. Twenty-seven users performed two types of search tasks and two types of counting tasks on 1D and 2D datasets. The search tasks involved finding data points that were least or most uncertain. The counting tasks involved counting data features or uncertainty features. A 4 times 4 full-factorial ANOVA indicated a significant interaction between the techniques used and the type of tasks assigned for both datasets indicating that differences in performance between the four techniques depended on the type of task performed. Several one-way ANOVAs were computed to explore the simple main effects. Bonferronni's correction was used to control for the family-wise error rate for alpha-inflation. Although we did not find a consistent order among the four techniques for all the tasks, there are several findings from the study that we think are useful for uncertainty visualization design. We found a significant difference in user performance between searching for locations of high and searching for locations of low uncertainty. Errorbars consistently underperformed throughout the experiment. Scaling the size of glyphs and color-mapping of the surface performed reasonably well. The efficiency of most of these techniques were highly dependent on the tasks performed. We believe that these findings can be used in future uncertainty visualization design. In addition, the framework developed in this user study presents a structured approach to evaluate uncertainty visualization techniques, as well as provides a basis for future research in uncertainty visualization.


title: Ultra-wideband electronically tunable pulse generators

abstract: We report on the development of new low-cost, compact ultra-wideband microstrip pulse generators capable of varying the pulse duration electronically. These electronically tunable pulse generators generate an initial step function using a step recovery diode, which is then converted into pulses of various durations by alternately switching on one of the switches, realized by PIN diode or MESFET, spatially located along a short-circuited transmission line. Representative pulse-duration variations from 300 to 800 ps have been demonstrated experimentally and theoretically. Good symmetry and low distortion have been achieved for the pulses. Measured results also confirm the simulations.


RDB Data Triplification vs. Ontologies: The Case of Municipal Registry Data


Introspection, Empathy, and Psychoanalysis An Examination of the Relationship between Mode of Observation and Theory


title: Design of A Microstrip Rectifier with High Conversion Efficiency for Low Levels of Power

abstract: A high-efficiency for low power level rectifier has been designed at 2.45 GHz for applications involving wireless power transmission. In order to reach the desired results, we have employed a Zero-Bias HSMS2820 Schottky diode as a rectifying device well known by its low threshold, connected by using the series rectifier topology with micro strip lines technology on an FR4 substrate with dielectric permittivity constant 4.4, thickness of 1.6mm and loss tangent of 0.025. A miscrostrip stepped-impedance Low-Pass Filter (LPF) was implemented at the output of the rectifier to block higher order harmonics generated by the diode and produce more stable DC power. The final rectifier structure presents satisfying results in terms of RF-DC conversion efficiency and output voltage for low levels of power.


title: SemEval-2015 Task 11: Sentiment Analysis of Figurative Language in Twitter

abstract: This report summarizes the objectives and evaluation of the SemEval 2015 task on the sentiment analysis of figurative language on Twitter (Task 11). This is the first sentiment analysis task wholly dedicated to analyzing figurative language on Twitter. Specifically, three broad classes of figurative language are considered: irony, sarcasm and metaphor. Gold standard sets of 8000 training tweets and 4000 test tweets were annotated using workers on the crowdsourcing platform CrowdFlower. Participating systems were required to provide a fine-grained sentiment score on an 11-point scale (-5 to +5, including 0 for neutral intent) for each tweet, and systems were evaluated against the gold standard using both a Cosinesimilarity and a Mean-Squared-Error measure.


title: A miniature lumped element LTCC bandpass filter with high stopband attenuation for GPS applications

abstract: We present a new fully embedded low-temperature co-fired ceramic (LTCC) bandpass filter with the highest reported stopband attenuation in its class. The filter occupies only 0.03 λ x 0.05 λ x 0.004 λ of conventional low permittivity LTCC substrate. An advantage of this filter is that it is a true fourth-order design utilizing four multilayer L-C resonators. For the first time, measured results are shown for this novel topology and indicate an insertion loss of 6.7 dB and a return loss of 17.2 dB at the center frequency of 1.64 GHz. The stopband attenuation is greater than 50 dB at frequencies beyond ±300MHz of the passband, making this filter suitable for GPS environments with harsh spectral interference. This is believed to be the highest performing filter, in terms of out of band attenuation, realized in LTCC.


title: A Spoke-Type IPM Machine With Novel Alternate Airspace Barriers and Reduction of Unipolar Leakage Flux by Step-Staggered Rotor

abstract: This paper proposes a spoke-type IPM machine with novel alternate airspace barriers, through which higher torque capability and better manufacturability can be obtained, without remarkable deterioration in mechanical strength. Further, the influence of end leakage flux in the novel IPM design is investigated by 3-dimensional (3D) finite element (FE) method, including the leakage flux characteristics and torque capability. Furthermore, a step-staggered rotor is proposed to reduce the unipolar leakage flux and magnetization of the end shaft. As an extension, unipolar leakage flux is further investigated in consequent-pole PM machines, as well as the validity of step-staggered method. Finally, prototypes are fabricated and tested to verify the foregoing analyses, including the magnetic field at the end shaft and the output capability.


title: Analysis of Node Velocity Effects in MANET Routing Protocols using Network Simulator (NS3)

abstract: Mobile Ad hoc Network) is an infrastructure less decentralized wireless network, which do not depend on centralized organization or switching points. MANET is a self-organizing and self-configuring network. In ad-hoc networks, routing protocols postulate communication between routers and prompt them to select routes between a source and a destination. Route choices are performed by the routing algorithms. In this paper, we used network simulator-3 to simulate comparative performance analysis of three MANET routing protocols. They are AODV (Ad-hoc On Demand Distance Vector Routing), DSDV (Destination Sequenced Distance Vector Routing) and OLSR (Optimized Link State Routing). We analyzed performance comparisons of these routing protocols using different performance metrics such as throughput, packet delivery ratio, end to end delay and packet loss. General terms MANET, AODV, DSDV, OLSR, RREQ, RREP, RERR DBF, MPR, TC, NS3, RWMM, PDR,EED,NRL, Throughput, Packet delivery ratio, End to end delay, Packet loss.


title: An efficient explanation of individual classifications using game theory

abstract: We present a general method for explaining individual predictions of classification models. The method is based on fundamental concepts from coalitional game theory and predictions are explained with contributions of individual feature values. We overcome the method's initial exponential time complexity with a sampling-based approximation. In the experimental part of the paper we use the developed method on models generated by several well-known machine learning algorithms on both synthetic and real-world data sets. The results demonstrate that the method is efficient and that the explanations are intuitive and useful.


title: Tensor Algebra and Multidimensional Harmonic Retrieval in Signal Processing for MIMO Radar

abstract: Detection and estimation problems in multiple-input multiple-output (MIMO) radar have recently drawn considerable interest in the signal processing community. Radar has long been a staple of signal processing, and MIMO radar presents challenges and opportunities in adapting classical radar imaging tools and developing new ones. Our aim in this article is to showcase the potential of tensor algebra and multidimensional harmonic retrieval (HR) in signal processing for MIMO radar. Tensor algebra and multidimensional HR are relatively mature topics, albeit still on the fringes of signal processing research. We show they are in fact central for target localization in a variety of pertinent MIMO radar scenarios. Tensor algebra naturally comes into play when the coherent processing interval comprises multiple pulses, or multiple transmit and receive subarrays are used (multistatic configuration). Multidimensional harmonic structure emerges for far-field uniform linear transmit/receive array configurations, also taking into account Doppler shift; and hybrid models arise in-between. This viewpoint opens the door for the application and further development of powerful algorithms and identifiability results for MIMO radar. Compared to the classical radar-imaging-based methods such as Capon or MUSIC, these algebraic techniques yield improved performance, especially for closely spaced targets, at modest complexity.


title: Assessing the effective use of mosquito nets in the prevention of malaria in some parts of Mezam division, Northwest Region Cameroon

abstract: In Cameroon, malaria continues to be endemic and the first major cause of morbidity and mortality among the most vulnerable groups—children under 5 years of age, pregnant women, people living with HIV/AIDS and the poor. The use of long-lasting insecticide-treated bed nets (LLINs) is one of the recommended measures to prevent malaria. The present study was aimed at accessing the acceptability and effective use of LLINs on the prevalence of malaria in PMI Nkwen, Bambui and its environs. Hospital-based diagnosis consisted of 476 blood samples that were screened using the rapid diagnostic kits to determine the prevalence of malaria among users and non-users of LLINs. A structured questionnaire was also administered to pregnant women and children less than 5 years of age (476 hospital-based and 350 from the community) which consisted of demographic information, availability, accessibility affordability, acceptability, effective use and problems encountered with the use of LLINs. Result obtained showed that out of the 476 hospital-based patients, 29 tested positive for malaria giving an overall prevalence of 6.09 %. Equally, results of the questionnaire showed that 743 (89.9 %) of the respondents owned LLINs with up to 649 (87.3 %) having been given to them free-of-charge, and that 578 (77.8 %) were using their LLINs to sleep, even though 18.2 % of the respondents used their LLINs for other purposes, such as fishing, nursing seeds and footfall nets. Malaria was minimal among users of LLINs than non-users and the results were significant at P ≤ 0.05. Also 71.9 % of the respondents said that their nets were in good condition while 52.2 % of them said the major problem with the usage of LLINs was heat and the feeling of suffocation. These results indicate that LLINs have significantly reduced the prevalence of malaria among the studied population, and so the government should not relent its efforts in the distribution of these nets especially to the vulnerable groups in order eliminate malaria and other mosquito-borne diseases. Utilization of LLINs needs to be encouraged to match ownership, while free distribution of ITNs to vulnerable groups needs to be continuous and consistent.


title: Sense of relatedness as a factor in children’s academic engagement and performance

abstract: Children’s sense of relatedness is vital to their academic motivation from 3rd to 6th grade. Children’s (n 641) reports of relatedness predicted changes in classroom engagement over the school year and contributed over and above the effects of perceived control. Regression and cumulative risk analyses revealed that relatedness to parents, teachers, and peers each uniquely contributed to students’ engagement, especially emotional engagement. Girls reported higher relatedness than boys, but relatedness to teachers was a more salient predictor of engagement for boys. Feelings of relatedness to teachers dropped from 5th to 6th grade, but the effects of relatedness on engagement were stronger for 6th graders. Discussion examines theoretical, empirical, and practical implications of relatedness as a key predictor of children’s academic motivation and performance. When explaining motivational dynamics in school, psychologists frequently point to differences in children’s underlying beliefs and capacities. Decades of research show that children’s self-perceptions, such as self-efficacy, goal orientations, or autonomy, are robust predictors of motivation and performance in school, both concurrently and over many years (for reviews, see Eccles, Wigfield, & Schiefele, 1998; Stipek, 2002). At the same time, however, researchers note the centrality of social factors in children’s motivation (Connell & Wellborn, 1991; Deci & Ryan, 1985; Eccles et al., 1998; Goldstein, 1999; Juvonen & Wentzel, 1996; Resnick et al., 1997; Weiner, 1990). Research from multiple traditions demonstrates the impact on children’s motivation and learning of relationships with parents (Steinberg, Darling, & Fletcher, 1995), teachers (Stipek, 2002), and peers (Hymel, Comfort, Schonert-Reichl, & McDougall, 1996). Recently, these two general lines of thinking, one about selfperceptions and one about interpersonal relationships, have converged in the study of the motivational consequences of children’s sense of self in relationships. Studied under a variety of labels, such as social cognitive views of motivation (Weiner, 1990), internal working models (Bretherton, 1985), relationship representations (Ryan, Stiller, & Lynch, 1994), classroom climate (Anderson, 1982), and perceived social support (Wentzel, 1999), the core notion is that a history of interactions with specific social partners leads children to construct generalized expectations about the nature of the self in relationships. Also referred to as a sense of relatedness (Connell, 1990), connectedness (Weiner, 1990), or belonging (Goodenow, 1993), these organized self-system processes include views about the self as lovable (or unworthy of love) and about the social world as trustworthy (or hostile). Children rely on these beliefs when predicting, interpreting, and responding to social exchanges, and these exchanges can in turn be used to confirm or revise children’s beliefs. A sense of relatedness may function as a motivational resource when children are faced with challenge or difficulties. In times of stress, children who experience trusted others as “backing them up” respond with more vigor, flexibility, and constructive actions. A sense of relatedness is the focus of the present study. Building on the growing body of work on the role of relationship representations, we attempted to explore the effects of a sense of relatedness, both generally and toward specific social partners, on children’s academic motivation and performance during middle childhood.


title: Managing Digital Forensic Knowledge An Applied Approach

abstract: The science of digital forensics is continually changing as technological advances are made and new digital devices are developed. This environment forces analysts to regularly extend their skills with training and frequent research to develop new and admissible techniques. Unfortunately, the same and similar methods are re-discovered by other analysts who are unaware of earlier peer efforts. The situation is aggravated by a nearly universal backlog in qualified digital forensics facilities. This leaves little time for communication between analysts even within a single agency. To address these issues and facilitate an increase in efficiency across all law enforcement agencies, we apply the lessons of knowledge management to digital forensics and extend them with special characteristics required by the law enforcement profession. The result is the development of the National Repository of Digital Forensic Intelligence. This system has been implemented in the largest accredited digital forensics lab in the world and is currently being extended to many other local, state, and federal agencies to increase effectiveness and efficiency among analysts.


A Survey on Mobile Device Threats, Vulnerabilities and their Defensive Mechanism


title: Automatic assessment of macular edema from color retinal images

abstract: Diabetic macular edema (DME) is an advanced symptom of diabetic retinopathy and can lead to irreversible vision loss. In this paper, a two-stage methodology for the detection and classification of DME severity from color fundus images is proposed. DME detection is carried out via a supervised learning approach using the normal fundus images. A feature extraction technique is introduced to capture the global characteristics of the fundus images and discriminate the normal from DME images. Disease severity is assessed using a rotational asymmetry metric by examining the symmetry of macular region. The performance of the proposed methodology and features are evaluated against several publicly available datasets. The detection performance has a sensitivity of 100% with specificity between 74% and 90%. Cases needing immediate referral are detected with a sensitivity of 100% and specificity of 97%. The severity classification accuracy is 81% for the moderate case and 100% for severe cases. These results establish the effectiveness of the proposed solution.


title: Algorithmic Prediction of Health-Care Costs

abstract: The rising cost of health care is one of the world's most important problems. Accordingly, predicting such costs with accuracy is a significant first step in addressing this problem. Since the 1980s, there has been research on the predictive modeling of medical costs based on (health insurance) claims data using heuristic rules and regression methods. These methods, however, have not been appropriately validated using populations that the methods have not seen. We utilize modern data-mining methods, specifically classification trees and clustering algorithms, along with claims data from over 800,000 insured individuals over three years, to provide rigorously validated predictions of health-care costs in the third year, based on medical and cost data from the first two years. We quantify the accuracy of our predictions using unseen (out-of-sample) data from over 200,000 members. The key findings are: (a) our data-mining methods provide accurate predictions of medical costs and represent a powerful tool for prediction of health-care costs, (b) the pattern of past cost data is a strong predictor of future costs, and (c) medical information only contributes to accurate prediction of medical costs of high-cost members.


title: StakeRare: Using Social Networks and Collaborative Filtering for Large-Scale Requirements Elicitation

abstract: Requirements elicitation is the software engineering activity in which stakeholder needs are understood. It involves identifying and prioritizing requirements-a process difficult to scale to large software projects with many stakeholders. This paper proposes StakeRare, a novel method that uses social networks and collaborative filtering to identify and prioritize requirements in large software projects. StakeRare identifies stakeholders and asks them to recommend other stakeholders and stakeholder roles, builds a social network with stakeholders as nodes and their recommendations as links, and prioritizes stakeholders using a variety of social network measures to determine their project influence. It then asks the stakeholders to rate an initial list of requirements, recommends other relevant requirements to them using collaborative filtering, and prioritizes their requirements using their ratings weighted by their project influence. StakeRare was evaluated by applying it to a software project for a 30,000-user system, and a substantial empirical study of requirements elicitation was conducted. Using the data collected from surveying and interviewing 87 stakeholders, the study demonstrated that StakeRare predicts stakeholder needs accurately and arrives at a more complete and accurately prioritized list of requirements compared to the existing method used in the project, taking only a fraction of the time.


title: A Tutorial on Deep Neural Networks for Intelligent Systems

abstract: Developing Intelligent Systems involves artificial intelligence approaches including artificial neural networks. Here, we present a tutorial of Deep Neural Networks (DNNs), and some insights about the origin of the term "deep"; references to deep learning are also given. Restricted Boltzmann Machines, which are the core of DNNs, are discussed in detail. An example of a simple two-layer network, performing unsupervised learning for unlabeled data, is shown. Deep Belief Networks (DBNs), which are used to build networks with more than two layers, are also described. Moreover, examples for supervised learning with DNNs performing simple prediction and classification tasks, are presented and explained. This tutorial includes two intelligent pattern recognition applications: hand- written digits (benchmark known as MNIST) and speech recognition.


title: Beyond Play: A New Approach to Games

abstract: Games have intruded into popular, academic, and policy-maker awareness to an unprecedented level, and this creates new opportunities for advancing our understanding of the relationship of games to society. The author offers a new approach to games that stresses them as characterized by process. Games, the author argues, are domains of contrived contingency, capable of generating emergent practices and interpretations, and are intimately connected with everyday life to a degree heretofore poorly understood. This approach is both consistent with a range of existing social theory and avoids many of the limitations that have characterized much games scholarship to date, in particular its tendency toward unsustainable formalism and exceptionalism. Rather than seeing gaming as a subset of play, and therefore as an activity that is inherently separable, safe, and pleasurable, the author offers a pragmatic rethinking of games as social artifacts in their own right that are always in the process of becoming. This ...


title: Understanding Cyber Attack Behaviors with Sentiment Information on Social Media

abstract: In today’s increasingly connected world, cyber attacks have become a serious threat with detrimental effects on individuals, businesses, and broader society. Truly mitigating the negative impacts of these attacks requires a deeper understanding of malicious cyber activities and the capability of predicting these attacks before they occur. However, detecting the occurrence of cyber attacks is non-trivial due to the anonymity of cyber attacks and the ambiguity or unavailability of network data collected within organizations. Thus, we need to explore more nuanced auxiliary information that can provide improved predictive power and insight into the behavioral factors involved in planning and executing a cyber attack. Evidence suggests that public discourse in online sources, such as social media, is strongly correlated with the occurrence of real-world behavior; we believe this same premise can provide predictive indicators of cyber attacks. For example, extreme negative sentiments towards an organization may indicate a higher probability that it will be the target of a cyber attack. In this paper, we propose to use sentiment in social media as a sensor to better understand, detect, and predict cyber attacks. We develop an effective unsupervised sentiment predictor model utilizing emotional signals, such as emoticons or punctuation, common in social media communications, and a method for using this model as part of a logistic regression predictor to correlate changes in sentiment to the probability of an attack. Experiments on real-world social media data around well-known hacktivist attacks demonstrate the efficacy of the proposed sentiment model for cyber attack understanding and prediction.