title: Clonal evolution in breast cancer revealed by single nucleus genome sequencing

abstract: Sequencing studies of breast tumour cohorts have identified many prevalent mutations, but provide limited insight into the genomic diversity within tumours. Here we developed a whole-genome and exome single cell sequencing approach called nuc-seq that uses G2/M nuclei to achieve 91% mean coverage breadth. We applied this method to sequence single normal and tumour nuclei from an oestrogen-receptor-positive (ER(+)) breast cancer and a triple-negative ductal carcinoma. In parallel, we performed single nuclei copy number profiling. Our data show that aneuploid rearrangements occurred early in tumour evolution and remained highly stable as the tumour masses clonally expanded. In contrast, point mutations evolved gradually, generating extensive clonal diversity. Using targeted single-molecule sequencing, many of the diverse mutations were shown to occur at low frequencies (<10%) in the tumour mass. Using mathematical modelling we found that the triple-negative tumour cells had an increased mutation rate (13.3×), whereas the ER(+) tumour cells did not. These findings have important implications for the diagnosis, therapeutic treatment and evolution of chemoresistance in breast cancer.


title: What do students want?: towards an instrument for students' evaluation of quality of learning analytics services

abstract: Quality assurance in any organization is important for ensuring that service users are satisfied with the service offered. For higher education institutes, the use of service quality measures allows for ideological gaps to be both identified and resolved. The learning analytic community, however, has rarely addressed the concept of service quality. A potential outcome of this is the provision of a learning analytics service that only meets the expectations of certain stakeholders (e.g., managers), whilst overlooking those who are most important (e.g., students). In order to resolve this issue, we outline a framework and our current progress towards developing a scale to assess student expectations and perceptions of learning analytics as a service.


title: Making Usability Recommendations Useful and Usable

abstract: This paper evaluates the quality of recommendations for improving a user interface resulting from a usability evaluation. The study compares usability comments written by different authors, but describing similar usability issues. The usability comments were provided by 17 professional teams who independently evaluated the usability of the website for the Hotel Pennsylvania in New York. The study finds that only 14 of the 84 studied comments (17%) addressing six usability problems contained recommendations that were both useful and usable. Fourteen recommendations were not useful at all. Sixteen recommendations were not usable at all. Quality problems include recommendations that are vague or not actionable, and ones that may not improve the overall usability of the application. The paper suggests characteristics for "useful and usable recommendations," that is, recommendations for solving usability problems that lead to changes that efficiently improve the usability of a product.


: Formal Verification of Smart Contracts


title: A Transportation $$L^p$$ L p Distance for Signal Analysis

abstract: Transport based distances, such as the Wasserstein distance and earth mover'sdistance, have been shown to be an effective tool in signal and image analysis. The success of transport based distances is in part due to their Lagrangian nature which allows it to capture the important variations in many signal classes. However these distances require the signal to be nonnegative and normalized. Furthermore, the signals are considered as measures and compared by redistributing (transporting) them, which does not directly take into account the signal intensity. Here we study a transport-based distance, called the TLp distance, that combines Lagrangian and intensity modelling and is directly applicable to general, non-positive and multi-channelled signals. The distance can be computed by existing numerical methods. We give an overview of the basic properties of this distance and applications to classification, with multi-channelled non-positive one-dimensional signals and two-dimensional images, and color transfer.


title: When intelligence meets data: game story generation by compositional creativity

abstract: Creative computing concerns itself with theoretical and practical issues in the interdisciplinary study of creativity and computing. This paper covers a variety of preliminary compositional considerations relevant to stories generation, including conceptualising a theme, generating a storyline, and developing story narratives. An interactive game story generation system with compositional creativity as the main technique is proposed in this study. Generally speaking, a story composition process includes two major tasks: story plotting and narrative generation. This paper proposes using compositional creativity to produce credible story characters and consistent stories while addressing the needs and preferences of game players. Stories are generated according to the blind selection of story characters, events and organisations use the story theme as a selective-retention criteria taking into account the temporal, spatial and logical relationships and constraints.


title: Self-Efficacy, Health Literacy, and Nutrition and Exercise Behaviors in a Low-Income, Hispanic Population

abstract: Public health goals have emphasized healthy nutrition and exercise behaviors, especially in underserved populations. According to social cognitive theory (SCT), self-efficacy and capability (e.g., health literacy) may interact to predict preventative behaviors. We surveyed 100 low-income, native Spanish-speakers living in the United States who were low in English proficiency and predominantly of Mexican heritage. Participants reported their nutritional and exercise self-efficacy, Spanish health literacy, and nutrition and physical activity behaviors. Consistent with SCT, the interaction of self-efficacy and health literacy significantly predicted fruit and vegetable consumption and weekly exercise, and marginally predicted avoidance of high fat foods. For all three interactions, higher health literacy levels strengthened the positive relationship between self-efficacy and health behaviors. The results offer support for the tenets of SCT and suggest—for low-income, Spanish-speaking adults—that a combination of behavioral confidence and literacy capability are necessary to enact appropriate health behaviors.


title: An induction generator based AC/DC hybrid electric power generation system for more electric aircraft

abstract: In more electric aircraft (MEA) system, both AC and DC electric power with multiple voltage levels are required for various aircraft loads. This paper presents an induction generator based AC/DC hybrid electric power generation system for MEA. In the proposed system, a high speed induction starter/generator and a low speed induction generator are installed on the high pressure (HP) and low pressure (LP) spools of the engine, respectively. In generating mode of operation, all of the constant voltage variable frequency AC power is generated by the HP generator while the DC power demand is shared by both HP and LP generators. A control scheme is developed to regulate the AC load voltage and coordinate DC power generation between the two generators. The proposed induction generator based AC/DC hybrid generation system presents reduced hardware installment compared to existing AC and DC primary generation systems.


title: EXUBERA®: Pharmaceutical Development of a Novel Product for Pulmonary Delivery of Insulin

abstract: Development of a product for pulmonary delivery of insulin presented significant technology challenges for this first-in-class pharmaceutical product. These included developing (a) a chemically stabilized protein, (b) a dry powder formulation exhibiting required aerosol physical characteristics, (c) low-dose powder filling and packaging technology, and (d) a mechanical device for powder dispersal and reliable dosing to the patient. The insulin drug is formulated using a novel excipient combination to create a powder with a high glass transition temperature (Tg). The high Tg minimizes insulin mobility (thus reactivity), enabling ambient storage conditions. The formulation composition results in minimal hygroscopicity, where customized packaging produced product ruggedness to humidity. The formulated insulin powder is manufactured by spray-drying. This technology was further engineered to produce the desired reproducible powder characteristics with tight control over particle size and moisture content. A so...


title: PSYCHOLOGICAL SCIENCE Research Article The Restraint Bias How the Illusion of Self-Restraint Promotes Impulsive Behavior

abstract: Four studies examined how impulse-control beliefs—beliefs regarding one's ability to regulate visceral impulses, such as hunger, drug craving, and sexual arousal—influence the self-control process. The findings provide evidence for a restraint bias: a tendency for people to overestimate their capacity for impulse control. This biased perception of restraint had important consequences for people's self-control strategies. Inflated impulse-control beliefs led people to overexpose themselves to temptation, thereby promoting impulsive behavior. In Study 4, for example, the impulse-control beliefs of recovering smokers predicted their exposure to situations in which they would be tempted to smoke. Recovering smokers with more inflated impulse-control beliefs exposed themselves to more temptation, which led to higher rates of relapse 4 months later. The restraint bias offers unique insight into how erroneous beliefs about self-restraint promote impulsive behavior.


title: Measures of Fit for Logistic Regression

abstract: One of the most common questions about logistic regression is “How do I know if my model fits the data?” There are many approaches to answering this question, but they generally fall into two categories: measures of predictive power (like R-square) and goodness of fit tests (like the Pearson chi-square). This presentation looks first at R-square measures, arguing that the optional R-squares reported by PROC LOGISTIC might not be optimal. Measures proposed by McFadden and Tjur appear to be more attractive. As for goodness of fit, the popular Hosmer and Lemeshow test is shown to have some serious problems. Several alternatives are considered.


title: Learning to associate: HybridBoosted multi-target tracker for crowded scene

abstract: We propose a learning-based hierarchical approach of multi-target tracking from a single camera by progressively associating detection responses into longer and longer track fragments (tracklets) and finally the desired target trajectories. To define tracklet affinity for association, most previous work relies on heuristically selected parametric models; while our approach is able to automatically select among various features and corresponding non-parametric models, and combine them to maximize the discriminative power on training data by virtue of a HybridBoost algorithm. A hybrid loss function is used in this algorithm because the association of tracklet is formulated as a joint problem of ranking and classification: the ranking part aims to rank correct tracklet associations higher than other alternatives; the classification part is responsible to reject wrong associations when no further association should be done. Experiments are carried out by tracking pedestrians in challenging datasets. We compare our approach with state-of-the-art algorithms to show its improvement in terms of tracking accuracy.


title: A Fast Similarity Join Algorithm Using Graphics Processing Units

abstract: A similarity join operation A BOWTIEepsiv B takes two sets of points A, B and a value epsiv isin Ropf, and outputs pairs of points p isin A,q isin B, such that the distance D(p, q) les epsiv. Similarity joins find use in a variety of fields, such as clustering, text mining, and multimedia databases. A novel similarity join algorithm called LSS is presented that executes on a graphics processing unit (GPU), exploiting its parallelism and high data throughput. As GPUs only allow simple data operations such as the sorting and searching of arrays, LSS uses these two operations to cast a similarity join operation as a GPU sort-and-search problem. It first creates, on the fly, a set of space-filling curves on one of its input datasets, using a parallel GPU sort routine. Next, LSS processes each point p of the other dataset in parallel. For each p, it searches an interval of one of the space-filling curves guaranteed to contain all the pairs in which p participates. Using extensive theoretical and experimental analysis, LSS is shown to offer a good balance between time and work efficiency. Experimental results demonstrate that LSS is suitable for similarity joins in large high-dimensional datasets, and that it performs well when compared against two existing prominent similarity join methods.


Madeleine Leininger and the Transcultural Theory of Nursing


title: An Hybrid Approach to Quality Evaluation across Big Data Value Chain

abstract: While the potential benefits of Big Data adoption are significant, and some initial successes have already been realized, there remain many research and technical challenges that must be addressed to fully realize this potential. The Big Data processing, storage and analytics, of course, are major challenges that are most easily recognized. However, there are additional challenges related for instance to Big Data collection, integration, and quality enforcement. This paper proposes a hybrid approach to Big Data quality evaluation across the Big Data value chain. It consists of assessing first the quality of Big Data itself, which involve processes such as cleansing, filtering and approximation. Then, assessing the quality of process handling this Big Data, which involve for example processing and analytics process. We conduct a set of experiments to evaluate Quality of Data prior and after its pre-processing, and the Quality of the pre-processing and processing on a large dataset. Quality metrics have been measured to access three Big Data quality dimensions: accuracy, completeness, and consistency. The results proved that combination of data-driven and process-driven quality evaluation lead to improved quality enforcement across the Big Data value chain. Hence, we recorded high prediction accuracy and low processing time after we evaluate 6 well-known classification algorithms as part of processing and analytics phase of Big Data value chain.


title: Security situation management - developing a concept of operations and threat prediction capability

abstract: This paper addresses a collaborative security situation management capability for air navigation. In particular, we formulate the development of a threat prediction capability as a situation management problem mapping the concepts of situation awareness and information fusion. Air transportation and air navigation is undergoing a fundamental transformation. This also requires novel approaches to system security and the management of security incidents across a network of actors. The Global ATM Security Management project addresses this problem space. The work reported in this paper, conceptualizes a security function that supports the management of security incidents on a local, national, and regional level supporting the collaborative effort of classical air traffic management stakeholders and security stakeholders. The security function is based on a network of distributed nodes and capabilities. One such a capability is the threat prediction model. This component is based on a representation of the (sub-) system context as a network of supporting assets, event detection sensors, and associated security controls. Based on the description of the (sub-)system context as a sequence of situations, the threat prediction capability addresses the identification of a security incident and its potential impact as an optimization problem. This paper reflects the work of the first year of the project. In particular, it demonstrates the general feasibility of the approach and the further modelling and preparatory work for further validation activities.


title: A broad-coverage natural language processing system

abstract: Natural language processing systems (NLP) that extract clinical information from textual reports were shown to be effective for limited domains and for particular applications. Because an NLP system typically requires substantial resources to develop, it is beneficial if it is designed to be easily extendible to multiple domains and applications. This paper describes multiple extensions of an NLP system called MedLEE, which was originally developed for the domain of radiological reports of the chest, but has subsequently been extended to mammography, discharge summaries, all of radiology, electrocardiography, echocardiography, and pathology.


title: Bayes' Bluff: Opponent Modelling in Poker

abstract: Poker is a challenging problem for artificial intelligence, with non-deterministic dynamics, partial observability, and the added difficulty of unknown adversaries. Modelling all of the uncertainties in this domain is not an easy task. In this paper we present a Bayesian probabilistic model for a broad class of poker games, separating the uncertainty in the game dynamics from the uncertainty of the opponent's strategy. We then describe approaches to two key subproblems: (i) inferring a posterior over opponent strategies given a prior distribution and observations of their play, and (ii) playing an appropriate response to that distribution. We demonstrate the overall approach on a reduced version of poker using Dirichlet priors and then on the full game of Texas hold'em using a more informed prior. We demonstrate methods for playing effective responses to the opponent, based on the posterior.


title: Exclusivity-Consistency Regularized Multi-view Subspace Clustering

abstract: Multi-view subspace clustering aims to partition a set of multi-source data into their underlying groups. To boost the performance of multi-view clustering, numerous subspace learning algorithms have been developed in recent years, but with rare exploitation of the representation complementarity between different views as well as the indicator consistency among the representations, let alone considering them simultaneously. In this paper, we propose a novel multi-view subspace clustering model that attempts to harness the complementary information between different representations by introducing a novel position-aware exclusivity term. Meanwhile, a consistency term is employed to make these complementary representations to further have a common indicator. We formulate the above concerns into a unified optimization framework. Experimental results on several benchmark datasets are conducted to reveal the effectiveness of our algorithm over other state-of-the-arts.


title: Anatomy and physiology of the pelvic floor.

abstract: Lower urinary tract function consists of a complex interplay of neural input consisting of both involuntary and voluntary controls interacting with specialized anatomical structures. Damage to anatomical support structures may lead to derangements in urethral and bladder function. In addition, disorders related to physiological and neurological function of the lower urinary tract can result in abnormalities in the storage and evacuation of urine. The ultimate goal in the evaluation and management of women with pelvic floor dysfunction is to correlate functional derangements with anatomical changes. The understanding of various pelvic support defects in the pelvic visceral supports also allows the physician to perform the correct surgery needed for successful outcomes.


HINDI SPEECH RECOGNITION SYSTEM USING HTK


title: Occlusion Handling via Random Subspace Classifiers for Human Detection

abstract: This paper describes a general method to address partial occlusions for human detection in still images. The random subspace method (RSM) is chosen for building a classifier ensemble robust against partial occlusions. The component classifiers are chosen on the basis of their individual and combined performance. The main contribution of this work lies in our approach's capability to improve the detection rate when partial occlusions are present without compromising the detection performance on non occluded data. In contrast to many recent approaches, we propose a method which does not require manual labeling of body parts, defining any semantic spatial components, or using additional data coming from motion or stereo. Moreover, the method can be easily extended to other object classes. The experiments are performed on three large datasets: the INRIA person dataset, the Daimler Multicue dataset, and a new challenging dataset, called PobleSec, in which a considerable number of targets are partially occluded. The different approaches are evaluated at the classification and detection levels for both partially occluded and non-occluded data. The experimental results show that our detector outperforms state-of-the-art approaches in the presence of partial occlusions, while offering performance and reliability similar to those of the holistic approach on non-occluded data. The datasets used in our experiments have been made publicly available for benchmarking purposes.


title: A particle swarm optimization algorithm for flexible job shop scheduling problem

abstract: The classical job shop scheduling problem (JSP) is the most popular machine scheduling model in practice and is well known as NP-hard. The formulation of the JSP is based on the assumption that for each part type or job there is only one process plan that prescribes the sequence of operations and the machine on which each operation has to be performed. Flexible job shop scheduling problem (FJSP) is an extension of the JSP, which allows an operation to be processed by any machine from a given set. Since FJSP requires an additional decision of machine allocation during scheduling, therefore it is much more complex problem than JSP. To solve such NP-hard problems, heuristic approaches are commonly preferred over the traditional mathematical techniques. This paper proposes a particle swarm optimization (PSO) based heuristic for solving the FJSP for minimum makespan time criterion. The performance of the proposed PSO is evaluated by comparing its results with the results obtained using ILOG Solver, a constraint-programming tool. The comparison of the results proves the effectiveness of the proposed PSO for solving FJSP instances.


title: The mf-index: A Citation-Based Multiple Factor Index to Evaluate and Compare the Output of Scientists

abstract: Comparing the output of scientists as objective as possible is an important factor for, e.g., the approval of research funds or the filling of open positions at universities. Numeric indices, which express the scientific output in the form of a concrete value, may not completely supersede an overall view of a researcher, but provide helpful indications for the assessment. This work introduces the most important citation-based indices, analyzes their advantages and disadvantages and provides an overview of the aspects considered by them. On this basis, we identify the criteria that an advanced index should fulfill, and develop a new index, the mf-index. The objective of the mf-index is to combine the benefits of the existing indices, while avoiding as far as possible their drawbacks and to consider additional aspects. Finally, an evaluation based on data of real publications and citations compares the mf-index with existing indices and verifies that its advantages in theory can also be determined in practice.


title: Hypertension and the brain: vulnerability of the prefrontal regions and executive functions.

abstract: Untreated hypertension negatively affects brain anatomy and cognitive functions, but the effects of medically treated hypertension are unclear. The authors compared 40 middle-age and older adults diagnosed with essential hypertension to demographically matched normotensive peers. Volumes of 7 brain regions and deep and periventricular white-matter hyperintensities (WMH) were measured on magnetic resonance imaging scans. Performance in 4 cognitive domains (perseveration, working memory, fluid reasoning, and vocabulary knowledge) was evaluated. Persons with hypertension had smaller prefrontal cortex and underlying white matter volumes and increased frontal WMH. No group differences were found in other examined brain regions. Among examined cognitive variables, hypertensive patients committed significantly more perseverative errors. Thus, even controlled hypertension may be associated with deficits in brain structure and cognition, warranting further study. Essential hypertension is a chronic, age-related condition associated with multiple changes in the vascular system (Marin & Rodriguez-Martinez, 1999), and in the vast majority of cases it has no singular identifiable pathological cause (Beevers, Lip, & O’Brien, 2001). The definition of hypertension varies. Although most authorities agree that sustained diastolic pressure greater than 90 mm of mercury (mm Hg) constitutes a cause for diagnosis, the


- 1-Tracing technology evolution pathways by combining patent citation analysis and tech mining


title: What Strikes the Strings of Your Heart?—Feature Mining for Music Emotion Analysis

abstract: Music can convey and evoke powerful emotions. This amazing ability has not only fascinated the general public but also attracted the researchers from different fields to discover the relationship between music and emotion. Psychologists have indicated that some specific characters of rhythm, harmony, and melody can evoke certain kinds of emotions. Those hypotheses are based on real life experience and proved by psychological paradigms on human beings. Aiming at the same target, this paper intends to design a systematic and quantitative framework, and answer three widely interested questions: 1) what are the intrinsic features embedded in music signal that essentially evoke human emotions; 2) to what extent these features influence human emotions; and 3) whether the findings from computational models are consistent with the existing research results from psychology. We formulate these tasks as a multi-label dimensionality reduction problem and propose an algorithm called multi-emotion similarity preserving embedding (ME-SPE). To adapt to the second-order music signals, we extend ME-SPE to its bilinear version. The proposed techniques show good performance in two standard music emotion datasets. Moreover, they demonstrate some interesting results for further research in this interdisciplinary topic.


title: Few-Shot Learning with Metric-Agnostic Conditional Embeddings

abstract: Learning high quality class representations from few examples is a key problem in metric-learning approaches to few-shot learning. To accomplish this, we introduce a novel architecture where class representations are conditioned for each few-shot trial based on a target image. We also deviate from traditional metric-learning approaches by training a network to perform comparisons between classes rather than relying on a static metric comparison. This allows the network to decide what aspects of each class are important for the comparison at hand. We find that this flexible architecture works well in practice, achieving state-of-the-art performance on the Caltech-UCSD birds fine-grained classification task.


title: Presto lift—a facelift that preserves the retaining ligaments and SMAS tethering

abstract: BACKGROUND ::: Producing youthful facial appearance by face-lifting often comes along with an undesired loss of patient's individual phenotype. This may result from insufficient preservation of retaining ligaments, the "guardians of facial identify," and from severance of the intersegmental connections of the superficial musculo-aponeurotic system (SMAS), which tether, structure, and compartmentalize facial soft tissue into defined, relevant anatomical zones. ::: ::: ::: METHODS ::: The technique reported here preserves most retaining ligaments. They serve to fix the facial soft tissue mass in loco. With the possible exception of the zygomatic-cutaneous ligament, they are only carefully distended. The SMAS intersegmental connections and the zygomatic SMAS border are preserved to retain effective points of facial tissue fixture. Aging-associated thinning and lengthening of the lower eyelid are reduced by midfacial-submalar preparation (Aston 1996). Subplatysmal preparation and disconnection of the cranial-platysmal border permits optimal modeling of neck structure. ::: ::: ::: RESULTS ::: The combination of preservation of retaining ligaments and SMAS tethering ("PRESTO facelift") introduced here as a novel face-lifting technique conserves the individual esthetics of the patient by approaching her/his individual phenotype from decades ago. In addition, undesired outcomes of facelift surgery and common risks of facelift surgery are circumvented. ::: ::: ::: CONCLUSIONS ::: The PRESTO facelift technique generates optimal esthetic results that conserve a patient's personal facial identity, besides restoring a more youthful appearance and being rapid and safe.


title: Measurement and Influence Factors of Technological Structure of Exports:Based on Provincial Panel Data

abstract: This paper employs index of revealed technological value-added to get the export sophistication of China after elimination of processing trade in every category of product based on the panel data of twenty-nine provinces in China. Then the econometric model is established and the methods of static panel and dynamic GMM are adopted successively to analyze the influence factors of export sophistication in order to overcome the endogenous problems. The research shows that the ratios of capital and labor, RD, trade openness and infrastructure have dramatically positive effect on export sophistication. The cross terms of human capital with RD and FDI respectively are remarkably positive. The natural resources endowment is negatively related to export sophistication, which indicates the cursing effect still exists in the export sophistication. However, the improvement of infrastructure is helpful to decrease the negative effect of natural resources on export sophistication.


title: False data injection attacks against state estimation in electric power grids

abstract: A power grid is a complex system connecting electric power generators to consumers through power transmission and distribution networks across a large geographical area. System monitoring is necessary to ensure the reliable operation of power grids, and state estimation is used in system monitoring to best estimate the power grid state through analysis of meter measurements and power system models. Various techniques have been developed to detect and identify bad measurements, including interacting bad measurements introduced by arbitrary, nonrandom causes. At first glance, it seems that these techniques can also defeat malicious measurements injected by attackers. In this article, we expose an unknown vulnerability of existing bad measurement detection algorithms by presenting and analyzing a new class of attacks, called false data injection attacks, against state estimation in electric power grids. Under the assumption that the attacker can access the current power system configuration information and manipulate the measurements of meters at physically protected locations such as substations, such attacks can introduce arbitrary errors into certain state variables without being detected by existing algorithms. Moreover, we look at two scenarios, where the attacker is either constrained to specific meters or limited in the resources required to compromise meters. We show that the attacker can systematically and efficiently construct attack vectors in both scenarios to change the results of state estimation in arbitrary ways. We also extend these attacks to generalized false data injection attacks, which can further increase the impact by exploiting measurement errors typically tolerated in state estimation. We demonstrate the success of these attacks through simulation using IEEE test systems, and also discuss the practicality of these attacks and the real-world constraints that limit their effectiveness.


title: Novice Programming Environments. Scratch & App Inventor: a first comparison

abstract: The teaching of programming has been acknowledged as a difficult task since its first introduction in schools. The learning results are unsatisfactory, raising questions about the effectiveness of the method used. As it seems, students usually consider programming as a boring and tiring procedure. The problem would be solved if we could make the introductory programming easy and entertaining for the students. This can be achieved by reducing the obstacles for the introduction of programming with the use of special programming environments for the beginners such as Scratch and the App Inventor both of which have been created by MIT. Our research focuses on the similarities and differences between the above mentioned programming environments in our effort to find which of the two is the most appropriate for schools.


title: Ranking and retrieval of image sequences from multiple paragraph queries

abstract: We propose a method to rank and retrieve image sequences from a natural language text query, consisting of multiple sentences or paragraphs. One of the method's key applications is to visualize visitors' text-only reviews on TRIPADVISOR or YELP, by automatically retrieving the most illustrative image sequences. While most previous work has dealt with the relations between a natural language sentence and an image or a video, our work extends to the relations between paragraphs and image sequences. Our approach leverages the vast user-generated resource of blog posts and photo streams on the Web. We use blog posts as text-image parallel training data that co-locate informative text with representative images that are carefully selected by users. We exploit large-scale photo streams to augment the image samples for retrieval. We design a latent structural SVM framework to learn the semantic relevance relations between text and image sequences. We present both quantitative and qualitative results on the newly created DISNEYLAND dataset.


title: Hurricane Disaster Assessments With Image-Driven Data Mining in High-Resolution Satellite Imagery

abstract: Detection, classification, and attribution of high-resolution satellite image features in nearshore areas in the aftermath of Hurricane Katrina in Gulfport, MS, are investigated for damage assessments and emergency response planning. A system-level approach based on image-driven data mining with sigma-tree structures is demonstrated and evaluated. Results show a capability to detect hurricane debris fields and storm-impacted nearshore features (such as wind-damaged buildings, sand deposits, standing water, etc.) and an ability to detect and classify nonimpacted features (such as buildings, vegetation, roadways, railways, etc.). The sigma-tree-based image information mining capability is demonstrated to be useful in disaster response planning by detecting blocked access routes and autonomously discovering candidate rescue/recovery staging areas


title: Development of a measure of workplace deviance

abstract: The purpose of this research was to develop broad, theoretically derived measure(s) of deviant behavio r in the workplace. Two scales were developed: a 12-ite m scale of organizational deviance (deviant behaviors directly harmful to the organization) and a 7-item scale of interpersonal deviance (deviant behaviors directly harmful to other individuals withi n the organization). These scales were found to have internal reliabilities o f .81 and .78, respectively. Confirmatory factor analysis verified that a 2-factor structure had acceptable fit. Preliminary evidence of construct validity is also provided. The implications of this instrument for future empirical research on workplace deviance are discussed. Workplace deviance is a pervasive and expensive problem for organizations. Fo r example, 75 % of employees have reportedly stolen from their employer at least once (McGurn, 1988), and it has been estimated tha t 33% to 75% of all employees have engaged in behaviors such as theft, fraud, vandalism, sabotage, and voluntary absenteeism (Harper , 1990) . In recent studies, almost 25% of an employee sample indicated knowledge of illicit drug use among coworkers during the past year (Lehman, Wolcom, & Simpson, 1990) , 42% of a surveyed sample of women reported experiencing sexual harassment at work (Webb, 1991), and 7% of a sample of employees reported being victims of physical threats (Northwestern Life Insurance Company, 1993). It is not surprising that the prevalence of workplace deviance poses a serious economic threat to organizations. The annual costs of workplace deviance have been estimated to be as high as $4.2 billion fo r workplace violence alone (Bensimon, 1994) , $4 0 to $120 billion for theft (Buss, 1993 ; Camara & Schneider, 1994) , and $6 to $200 billion for a wide range of delinquent organizational behavior (Murphy, 1993) . Despite the prevalence an d costs of workplace deviance, our current understanding of workplace deviance remains limited, and much empirical research has yet to be done. This empirical re search may be enhanced by the availability of a validated measure of workplace Deviance. The purpose of this study is to produce such a measure.


title: Analysis of Docker Security

abstract: Over the last few years, the use of virtualization technologies has increased dramatically. This makes the demand for efficient and secure virtualization solutions become more obvious. Container-based virtualization and hypervisor-based virtualization are two main types of virtualization technologies that have emerged to the market. Of these two classes, container-based virtualization is able to provide a more lightweight and efficient virtual environment, but not without security concerns. In this paper, we analyze the security level of Docker, a well-known representative of container-based approaches. The analysis considers two areas: (1) the internal security of Docker, and (2) how Docker interacts with the security features of the Linux kernel, such as SELinux and AppArmor, in order to harden the host system. Furthermore, the paper also discusses and identifies what could be done when using Docker to increase its level of security.


title: Microwave/millimeter-wave power HBTs with regrown extrinsic base layers

abstract: This paper reports the first power application of AlGaAs/GaAs HBTs with selective regrown extrinsic base layers. The ultra-high carbon doping of the regrown extrinsic base led to an extremely low base resistance, which resulted in f/sub max/ of 143 GHz. Reliability test under both thermal and current stresses demonstrated the stability of the regrown HBT structure. A three-dimensional thermal simulator was also developed to determine the suitable pattern layout for the multi-finger structure. A common base HBT with six emitter fingers (240 /spl mu/m/sup 2/) delivered RF power performances of 365 mW CW output power with 23% power added efficiency and 9.1 dB power gain at 25.2 GHz. >


title: Face Recognition Drone

abstract: Paper presents a brief idea about advancements in drones using Raspberry PI. A camera is attached with drone which helps PI to capture images and then PI can process it further to recognize person. The drone can be controlled from server room. The video can be seen live in the server room and simultaneously stored in the server. The main aim of the project is to recognize the face of person using drone camera and load all information about that person sitting in the server room. It is useful for biometric attendance, for military operations at remote areas and for surveillance purpose. Prototype is designed with high-tech specifications where it is made more stable and noise reduction feature is added. Depending upon the criteria more feature can be added.


title: Assessment and Standardization of Autonomous Vehicles

abstract: Autonomous vehicle technology presents a huge challenge to standardization and legal bodies. This is a new, rapidly emerging domain, which has created a complete industry within a mere decade. The objective assessment and quantification of self-driving algorithms and system for safety is now in the focus of numerous Standard Development Organizations, but as of today, only limited aspects of the safety domain are covered. This paper overviews the most recent trends and current international efforts to support the autonomous vehicle developers and manufacturers with an assessment and standard framework. The focus is especially put on the validation of higher level of autonomy self-driving capabilities.


title: A Linear Doubly Salient Permanent-Magnet Motor With Modular and Complementary Structure

abstract: A linear doubly salient permanent magnet (LDSPM) motor is particularly suitable for long stator applications due to its simple and low cost stator, which consists of only iron. This paper proposes a new LDSPM motor design with complementary and modular structure. The key of this structure is that the primary mover is composed of two modules whose positions are mutually four and one half of the stator pole pitch apart and there is a flux barrier between them. Hence, the back electromotive force (EMF) waveform and cogging force of the two modules have 180 electrical degree differences. This design results in the total cogging force being significantly reduced and the back-EMF of each phase becoming symmetrical because the even harmonics are canceled. For fair comparison, an existing linear LDSPM motor is designed based on the same electromagnetic parameters and compared by the means of finite element analysis (FEA). The results reveal that the proposed LDSPM motor can offer symmetrical back-EMF waveforms, smaller cogging force, lower force ripple, and higher magnet utilization factor than the existing one.


title: When Specialists and Generalists Work Together: Overcoming Domain Dependence in Sentiment Tagging

abstract: This study presents a novel approach to the problem of system portability across different domains: a sentiment annotation system that integrates a corpus-based classifier trained on a small set of annotated in-domain data and a lexicon-based system trained on WordNet. The paper explores the challenges of system portability across domains and text genres (movie reviews, news, blogs, and product reviews), highlights the factors affecting system performance on out-of-domain and smallset in-domain data, and presents a new system consisting of the ensemble of two classifiers with precision-based vote weighting, that provides significant gains in accuracy and recall over the corpus-based classifier and the lexicon-based system taken individually.


title: Modeling disease progression via fused sparse group lasso

abstract: Alzheimer's Disease (AD) is the most common neurodegenerative disorder associated with aging. Understanding how the disease progresses and identifying related pathological biomarkers for the progression is of primary importance in Alzheimer's disease research. In this paper, we develop novel multi-task learning techniques to predict the disease progression measured by cognitive scores and select biomarkers predictive of the progression. In multi-task learning, the prediction of cognitive scores at each time point is considered as a task, and multiple prediction tasks at different time points are performed simultaneously to capture the temporal smoothness of the prediction models across different time points. Specifically, we propose a novel convex fused sparse group Lasso (cFSGL) formulation that allows the simultaneous selection of a common set of biomarkers for multiple time points and specific sets of biomarkers for different time points using the sparse group Lasso penalty and in the meantime incorporates the temporal smoothness using the fused Lasso penalty. The proposed formulation is challenging to solve due to the use of several non-smooth penalties. We show that the proximal operator associated with the proposed formulation exhibits a certain decomposition property and can be computed efficiently; thus cFSGL can be solved efficiently using the accelerated gradient method. To further improve the model, we propose two non-convex formulations to reduce the shrinkage bias inherent in the convex formulation. We employ the difference of convex programming technique to solve the non-convex formulations. Our extensive experiments using data from the Alzheimer's Disease Neuroimaging Initiative demonstrate the effectiveness of the proposed progression models in comparison with existing methods for disease progression. We also perform longitudinal stability selection to identify and analyze the temporal patterns of biomarkers in disease progression.


title: Visual discomfort with stereo displays: effects of viewing distance and direction of vergence-accommodation conflict

abstract: Prolonged use of conventional stereo displays causes viewer discomfort and fatigue because of the vergence-accommodation conflict. We used a novel volumetric display to examine how viewing distance and the sign of the vergence-accommodation conflict affect discomfort and fatigue. In the first experiment, we presented a fixed conflict at short, medium, and long viewing distances. We compared subjects' symptoms in that condition and one in which there was no conflict. We observed more discomfort and fatigue with a given vergence-accommodation conflict at the longer distances. The second experiment compared symptoms when the conflict had one sign compared to when it had the opposite sign at short, medium, and long distances. We observed greater symptoms with uncrossed disparities at long distances and with crossed disparities at short distances. These findings help define comfortable viewing conditions for stereo displays.


title: Influence of Exercise on Patients with Guillain-Barré Syndrome: A Systematic Review

abstract: Purpose: To evaluate the effects of exercise interventions on improving physical outcomes in patients with Guillain-Barré syndrome (GBS). Methods: The PubMed database was searched for articles published up to and including February 2015. Randomized controlled trials (RCTs), case reports, and quasi-experimental and single-subject designs published in English-language, peer-reviewed journals that assessed the impact of physical exercise on patients with GBS were included; study quality was assessed using Sackett's rules of evidence. Data are presented qualitatively and quantitatively using numerical values and percentages. Results: Seven articles were included in the systematic review. One RCT showed that high-intensity relative to lower intensity exercise significantly reduced disability in patients with GBS, as measured with the FIM (p<0.005, r=0.71). Overall, various types of exercise programmes improve physical outcomes such as functional mobility, cardiopulmonary function, isokinetic muscle strength, and work rate and reduce fatigue in patients with GBS. Conclusion: Because of insufficient high-quality literature, making confident conclusions about the effects of exercise interventions on physical outcomes in patients with GBS is not possible. Future research should consider using higher quality study designs to confirm the results outlined in this article.


title: Freshmen adaptation to university life: Depressive symptoms, stress, and coping

abstract: Attending a university for the first time can be a stressful experience for many new college students. This study examines the relationships among femininity and masculinity, depressive symptomatology, levels of stress, and the types of coping strategies used by college freshmen. Results of this study suggest that these variables were related uniquely for first-year college students. Masculinity and femininity significantly predicted problem-focused coping, and femininity significantly predicted emotion-focused coping. Further, the levels of family and college stress reported by college students, as well as their endorsement of avoidant coping, significantly predicted their levels of depressive symptoms. Overall, the results of this study suggest that understanding the relationships among the gender role, the levels of depressive symptomatology, and the levels of stress exhibited by college freshmen may be important in facilitating their transition and adjustment to university life. © 2006 Wiley Periodicals, Inc. J Clin Psychol 62: 1231–1244, 2006.


title: Classical Conditioning of Preferences for Stimuli

abstract: Several authors have recently discussed the implications of classical conditioning for consumer behavior (e.g., McSweeney and Bierley 1984; Nord and Peter 1980). However, little empirical evidence actually shows that classical conditioning can alter behaviors that are of interest to consumer research. The present experiment provides some initial evidence that it can. In this experiment, preference ratings for stimuli that predicted pleasant music were significantly greater than preference ratings for stimuli that predicted the absence of music. These preferences also generalized to other stimuli that resembled the ones actually used.


Statistical En-route Detection and Filtering of Injected False Data in Sensor Networks


title: Securing smart maintenance services: Hardware-security and TLS for MQTT

abstract: Increasing the efficiency of production and manufacturing processes is a key goal of initiatives like Industry 4.0. Within the context of the European research project ARROWHEAD, we enable and secure smart maintenance services. An overall goal is to proactively predict and optimize the Maintenance, Repair and Operations (MRO) processes carried out by a device maintainer, for industrial devices deployed at the customer. Therefore it is necessary to centrally acquire maintenance relevant equipment status data from remotely located devices over the Internet. Consequently, security and privacy issues arise from connecting devices to the Internet, and sending data from customer sites to the maintainer's back-end. In this paper we consider an exemplary automotive use case with an AVL Particle Counter (APC) as device. The APC transmits its status information by means of a fingerprint via the publish-subscribe protocol Message Queue Telemetry Transport (MQTT) to an MQTT Information Broker in the remotely located AVL back-end. In a threat analysis we focus on the MQTT routing information asset and identify two elementary security goals in regard to client authentication. Consequently we propose a system architecture incorporating a hardware security controller that processes the Transport Layer Security (TLS) client authentication step. We validate the feasibility of the concept by means of a prototype implementation. Experimental results indicate that no significant performance impact is imposed by the hardware security element. The security evaluation confirms the advanced security of our system, which we believe lays the foundation for security and privacy in future smart service infrastructures.


title: A dual-polarized slotted-waveguide antenna based on gap waveguide technology

abstract: This paper describes a wide-band dual-polarized slotted-waveguide antenna working at V-band (57–66 GHz) based on Gap Waveguide concept. The antenna has three layers. The first one (feeding-layer 1) is practically identical to the second one (feeding-layer 2). This optimizes the design and facilitates manufacturing. The corporate-feeding networks on the two layers are rotated 90 degrees to each other to get the two orthogonal polarizations. Radiating elements are square apertures located on the top of the antenna. Simulated results show both impedance bandwidth and radiation pattern bandwidth greater than 15% for both polarizations.


title: User-level sentiment analysis incorporating social networks

abstract: We show that information about social relationships can be used to improve user-level sentiment analysis. The main motivation behind our approach is that users that are somehow "connected" may be more likely to hold similar opinions; therefore, relationship information can complement what we can extract about a user's viewpoints from their utterances. Employing Twitter as a source for our experimental data, and working within a semi-supervised framework, we propose models that are induced either from the Twitter follower/followee network or from the network in Twitter formed by users referring to each other using "@" mentions. Our transductive learning results reveal that incorporating social-network information can indeed lead to statistically significant sentiment-classification improvements over the performance of an approach based on Support Vector Machines having access only to textual features.


A Review-LOOP Dependence Analysis for Parallelizing Compiler


title: Design and optimization of a nonplanar multidipole array using genetic algorithms for mobile communications

abstract: This paper presents the application of a genetic algorithm (GA) to the design and optimization of a nonplanar dipole array in light of its application at a cell-site antenna for mobile communication. Cell-site antennas demand widely different radiation patterns depending upon the geographical location, terrain, and interference conditions of the cell site. The theoretical analysis for the radiation characteristics of the array is developed first, and subsequently, the GA is applied for obtaining the optimized parameters of the array to obtain the desired pattern as needed in different application areas of the cell site. © 2002 John Wiley & Sons, Inc. Microwave Opt Technol Lett 32: 301–304, 2002.


title: Crowd-based city logistics

abstract: Cities are drivers of economic development, providing infrastructure to support countless activities and services. Today, the world's 750 biggest cities account for more than 57% of the global gross domestic product (GDP) and this number is expected to increase to 61% by 2030. More than half of the world's population lives in cities, or urban areas, and this share will continue to grow. Rapid urban growth has posed both challenges and opportunities for city planners, not least when it comes to the design of transportation and logistics systems for freight. But urbanization also fosters innovation and sharing, which have led to new models for organizing movement of goods within the city. In this chapter, we highlight one of these new models: crowd logistics. We define the characterizing features of crowd logistics, review applications of crowd-based services within urban environments, and discuss research opportunities in the area of crowd logistics.


title: Multiple-input multiple-output (MIMO) radar: performance issues

abstract: The application of multiple-input multiple-output (MIMO) techniques to multistage radar offers a number of advantages, including improved resolution and sensitivity. Depending upon the radar's mode of operation, the array design and the environment, these advantages may or may not be significant. In this paper, a simple analytic model for ground moving target indicator (GMTI) radar detection is presented and its predictions are compared with simulation. Conventional single-input multiple output (SIMO) and MIMO GMTI radars are compared in terms of minimum detectable velocity, area-search rates and array sidelobes. Optimal waveform cross-correlation matrices are identified for MIMO radars, given different performance criteria. Finally, bounds are developed for MIMO arrays with uniformly sampled virtual apertures.


title: High Power Factor AC–DC LED Driver With Film Capacitors

abstract: In this paper, a new method is proposed to eliminate electrolytic capacitors in a two-stage ac-dc light-emitting diode (LED) driver. DC-biased sinusoidal or square-wave LED driving-current can help to reduce the power imbalance between ac input and dc output. In doing so, film capacitors can be adopted to improve LED driver's lifetime. The relationship between the peak-to-average ratio of the pulsating current in LEDs and the storage capacitance according to given storage capacitance is derived. Using the proposed “zero-low-level square-wave driving current” scheme, the storage capacitance in the LED driver can be reduced to 52.7% comparing with that in the driver using constant dc driving current. The input power factor is almost unity, which complies with lighting equipment standards such as IEC-1000-3-2 for Class C equipments. The voltage across the storage capacitors is analyzed and verified during the whole pulse width modulation dimming range. For the ease of dimming and implementation, a 50 W LED driver with zero-low-level square-wave driving current is built and the experimental results are presented to verify the proposed methods.


Structured Prediction in Time Series Data.


title: A Machine Learning Approach to Software Requirements Prioritization

abstract: Deciding which, among a set of requirements, are to be considered first and in which order is a strategic process in software development. This task is commonly referred to as requirements prioritization. This paper describes a requirements prioritization method called Case-Based Ranking (CBRank), which combines project's stakeholders preferences with requirements ordering approximations computed through machine learning techniques, bringing promising advantages. First, the human effort to input preference information can be reduced, while preserving the accuracy of the final ranking estimates. Second, domain knowledge encoded as partial order relations defined over the requirement attributes can be exploited, thus supporting an adaptive elicitation process. The techniques CBRank rests on and the associated prioritization process are detailed. Empirical evaluations of properties of CBRank are performed on simulated data and compared with a state-of-the-art prioritization method, providing evidence of the method ability to support the management of the tradeoff between elicitation effort and ranking accuracy and to exploit domain knowledge. A case study on a real software project complements these experimental measurements. Finally, a positioning of CBRank with respect to state-of-the-art requirements prioritization methods is proposed, together with a discussion of benefits and limits of the method.


title: Robust and Agile 3D Biped Walking With Steering Capability Using a Footstep Predictive Approach

abstract: In this paper, we formulate a novel hierarchical controller for walking of torque controlled humanoid robots. Our method uses a whole body optimization approach which generates joint torques, given Cartesian accelerations of different points on the robot. Over such variable translation, we can plan our desired foot trajectories in Cartesian space between starting and ending positions of the foot on the ground. On top level, we use the simplified Linear Inverted Pendulum Model to predict the future motion of the robot. With LIPM, we derive a formulation where the whole system is described by the state of center of mass and footstep locations serve as discrete inputs to this linear system. We then use model predictive control to plan optimal future footsteps which resemble a reference plan, given desired sagittal and steering velocities determined by the high-end user. Using simulations on a kid-size torque controlled humanoid robot, the method tolerates various disturbances such as external pushes, sensor noises, model errors and delayed communication in the control loop. It can perform robust walking over slopes and uneven terrains blindly and turn rapidly at the same time. Our generic dynamics model-based method does not depend on any off-line optimization, being suitable for typical torque controlled humanoid robots.


title: Robust Speaker-Adaptive HMM-Based Text-to-Speech Synthesis

abstract: This paper describes a speaker-adaptive HMM-based speech synthesis system. The new system, called ldquoHTS-2007,rdquo employs speaker adaptation (CSMAPLR+MAP), feature-space adaptive training, mixed-gender modeling, and full-covariance modeling using CSMAPLR transforms, in addition to several other techniques that have proved effective in our previous systems. Subjective evaluation results show that the new system generates significantly better quality synthetic speech than speaker-dependent approaches with realistic amounts of speech data, and that it bears comparison with speaker-dependent approaches even when large amounts of speech data are available. In addition, a comparison study with several speech synthesis techniques shows the new system is very robust: It is able to build voices from less-than-ideal speech data and synthesize good-quality speech even for out-of-domain sentences.


title: Multimodal Sentiment Analysis of Social Media

abstract: This paper describes the approach we take to the analysis of social media, combining opinion mining from text and multimedia (images, videos, etc), and centred on entity and event recognition. We examine a particular use case, which is to help archivists select material for inclusion in an archive of social media for preserving community memories, moving towards structured preservation around semantic categories. The textual approach we take is rule-based and builds on a number of sub-components, taking into account issues inherent in social media such as noisy ungrammatical text, use of swear words, sarcasm etc. The analysis of multimedia content complements this work in order to help resolve ambiguity and to provide further contextual information. We provide two main innovations in this work: first, the novel combination of text and multimedia opinion mining tools; and second, the adaptation of NLP tools for opinion mining specific to the problems of social media.


title: Using simulation-based scheduling to maximize demand fulfillment in a semiconductor assembly facility

abstract: We describe how a large number of products are scheduled to run in parallel on a pool of wire-bond machines to meet weekly demand. We seek to maximize demand fulfillment subject to system constraints. The schedule is generated by a simulation engine and used to control the machines at execution time and also to plan for the start of material. By using online data for equipment status and WIP availability, the schedule adapts to "unforeseen" changes on the shop floor after a simulation run. The frequently updated schedule redirects the line towards maximum demand fulfillment based on the latest status of the line.


Considering the Source of Information in the Evaluation of Maltreatment Experiences


title: SVM Based Speaker Verification using a GMM Supervector Kernel and NAP Variability Compensation

abstract: Gaussian mixture models with universal backgrounds (UBMs) have become the standard method for speaker recognition. Typically, a speaker model is constructed by MAP adaptation of the means of the UBM. A GMM supervector is constructed by stacking the means of the adapted mixture components. A recent discovery is that latent factor analysis of this GMM supervector is an effective method for variability compensation. We consider this GMM supervector in the context of support vector machines. We construct a support vector machine kernel using the GMM supervector. We show similarities based on this kernel between the method of SVM nuisance attribute projection (NAP) and the recent results in latent factor analysis. Experiments on a NIST SRE 2005 corpus demonstrate the effectiveness of the new technique.


title: Object detection in cluttered infrared images

abstract: Implementation of the Johnson criteria for infrared images is the probabilities of a discrimination technique. The inputs to the model are the size of the target, the range to it, and the temperature difference against the background. The temperature difference is calculated without taking the background structure into consideration, but it may have a strong influence on the visibility of the target. We investigated whether a perceptually based temperature difference should be used as input. Four different models are discussed: 1. a probability of discrimination model largely based on the Johnson criteria for infrared images, 2. a peak signal-to-noise ratio model, 3. a signal-to-clutter ratio model, and 4. two versions of an image discrimination model based on how human vision analyzes spatial information. The models differ as to how much they try to simulate human perception. To test the models, a psychophysical experiment was carried out with ten test persons, measuring contrast threshold detection in five different infrared backgrounds using a method based on a two-alternative forced-choice methodology. Predictions of thresholds in contrast energy were calculated for the different models and compared to the empirical values. Thresholds depend on the background, and these can be predicted well by the image discrimination models, and better than the other models. Future extensions are discussed.


title: Visualizing and Understanding Curriculum Learning for Long Short-Term Memory Networks

abstract: Curriculum Learning emphasizes the order of training instances in a computational learning setup. The core hypothesis is that simpler instances should be learned early as building blocks to learn more complex ones. Despite its usefulness, it is still unknown how exactly the internal representation of models are affected by curriculum learning. In this paper, we study the effect of curriculum learning on Long Short-Term Memory (LSTM) networks, which have shown strong competency in many Natural Language Processing (NLP) problems. Our experiments on sentiment analysis task and a synthetic task similar to sequence prediction tasks in NLP show that curriculum learning has a positive effect on the LSTM's internal states by biasing the model towards building constructive representations i.e. the internal representation at the previous timesteps are used as building blocks for the final prediction. We also find that smaller models significantly improves when they are trained with curriculum learning. Lastly, we show that curriculum learning helps more when the amount of training data is limited.


title: Propulsion and steering of a floating mini-robot based on Marangoni flow actuation

abstract: This paper discusses the propulsion and steering of a floating mini-robot based on thermal Marangoni flow. The normal component of the surface tension force keeps the robot floating on the liquid surface, while the localized heating of liquid surface results in tangential Marangoni flows which propel the robot. Lift force modeling shows that six legs of 1 cm long and 300 µm in diameter generate a lift force of 7.56 mN. Simulations suggest that a propulsion force of 0.638 mN per leg can be generated for a heater temperature of 60°C. Preliminary experiments using a 2×1.5cm2 H-shaped structure demonstrate that a velocity of 4.4 mm/sec and a rotational velocity of 0.2 rad/sec can be achieved.


title: Improving emotional prosody detection in the attending ear by cathodal tDCS suppression of the competing channel

abstract: Currently it is assumed that cathodal stimulation (in transcranial direct current stimulation, tDCS) degrades the neural firing rate, and thus it is believed to degrade cognitive performance. Here we challenge this assumption by predicting that under high competition the cathodal stimulation might act as a noise filter, leading to an improved performance. We presented auditory targets with different emotional valence using a dichotic listening paradigm. We found that cathodal, but not anodal stimulation of the right IFG generated better prosody comprehension. Cathodal stimulation in competitive situations, such as the dichotic listening paradigm, can act like a noise filter, and may in fact enhance cognitive performance. This study contributes to understanding the way the IFG is engaged with prosody functions, and explains the cathodal effects of tDCS. This might lead to the development of more efficient brain stimulation protocols.


title: Microstrip Antenna Array With a Beam Focused in the Near-Field Zone for Application in Noncontact Microwave Industrial Inspection

abstract: This paper presents a novel antenna having a beam focused to a small spot in the near-field zone which is intended for use in microwave industrial inspection. The array is implemented using broadband U-slotted microstrip patch-antenna elements and an integrated microstrip feeding network. Analogy with a dielectric lens is used to generate appropriate feeding coefficients for the elements of the array. The concept is demonstrated with two practical implementations having 300- and 126-mm focal distances. The measured results are presented and shown to be in good agreement with the predictions, validating the development concept.


title: Network Sketching: Exploiting Binary Structure in Deep CNNs

abstract: Convolutional neural networks (CNNs) with deep architectures have substantially advanced the state-of-the-art in computer vision tasks. However, deep networks are typically resource-intensive and thus difficult to be deployed on mobile devices. Recently, CNNs with binary weights have shown compelling efficiency to the community, whereas the accuracy of such models is usually unsatisfactory in practice. In this paper, we introduce network sketching as a novel technique of pursuing binary-weight CNNs, targeting at more faithful inference and better trade-off for practical applications. Our basic idea is to exploit binary structure directly in pre-trained filter banks and produce binary-weight models via tensor expansion. The whole process can be treated as a coarse-to-fine model approximation, akin to the pencil drawing steps of outlining and shading. To further speedup the generated models, namely the sketches, we also propose an associative implementation of binary tensor convolutions. Experimental results demonstrate that a proper sketch of AlexNet (or ResNet) outperforms the existing binary-weight models by large margins on the ImageNet large scale classification task, while the committed memory for network parameters only exceeds a little.


title: SceneNN: A Scene Meshes Dataset with aNNotations

abstract: Several RGB-D datasets have been publicized over the past few years for facilitating research in computer vision and robotics. However, the lack of comprehensive and fine-grained annotation in these RGB-D datasets has posed challenges to their widespread usage. In this paper, we introduce SceneNN, an RGB-D scene dataset consisting of 100 scenes. All scenes are reconstructed into triangle meshes and have per-vertex and per-pixel annotation. We further enriched the dataset with fine-grained information such as axis-aligned bounding boxes, oriented bounding boxes, and object poses. We used the dataset as a benchmark to evaluate the state-of-the-art methods on relevant research problems such as intrinsic decomposition and shape completion. Our dataset and annotation tools are available at http://www.scenenn.net.


Toward Non-Intuition-Based Machine Ethics


title: Social entrepreneurship: the need, relevance, facets and constraints

abstract: Social entrepreneurship is gradually becoming a crucial element in the worldwide discussion on volunteerism and civic commitment. It interleaves the passion of a common cause with industrial ethics and is notable and different from the present other types of entrepreneurship models due to its quest for mission associated influence. The previous few years have noticed a striking and surprising progress in the field of social entrepreneurship and has amplified attention ranging throughout all the diverse sectors. The critical difference between social and traditional entrepreneurship can be seen in the founding mission of the venture and the market impressions. Social entrepreneurs emphasize on ways to relieve or eradicate societal pressures and produce progressive externalities or public properties. This study focuses mainly on the meaning of social entrepreneurship to different genres and where does it stand in respect to other forms of entrepreneurship in today’s times.


title: Biomedical devices and systems security

abstract: Medical devices have been changing in revolutionary ways in recent years. One is in their form-factor. Increasing miniaturization of medical devices has made them wearable, light-weight, and ubiquitous; they are available for continuous care and not restricted to clinical settings. Further, devices are increasingly becoming connected to external entities through both wired and wireless channels. These two developments have tremendous potential to make healthcare accessible to everyone and reduce costs. However, they also provide increased opportunity for technology savvy criminals to exploit them for fun and profit. Consequently, it is essential to consider medical device security issues. In this paper, we focused on the challenges involved in securing networked medical devices. We provide an overview of a generic networked medical device system model, a comprehensive attack and adversary model, and describe some of the challenges present in building security solutions to manage the attacks. Finally, we provide an overview of two areas of research that we believe will be crucial for making medical device system security solutions more viable in the long run: forensic data logging, and building security assurance cases.


title: Virtual Reality-Enhanced Extinction of Phobias and Post-Traumatic Stress

abstract: Virtual reality (VR) refers to an advanced technological communication interface in which the user is actively participating in a computer-generated 3-dimensional virtual world that includes computer sensory input devices used to simulate real-world interactive experiences. VR has been used within psychiatric treatment for anxiety disorders, particularly specific phobias and post-traumatic stress disorder, given several advantages that VR provides for use within treatment for these disorders. Exposure therapy for anxiety disorder is grounded in fear-conditioning models, in which extinction learning involves the process through which conditioned fear responses decrease or are inhibited. The present review will provide an overview of extinction training and anxiety disorder treatment, advantages for using VR within extinction training, a review of the literature regarding the effectiveness of VR within exposure therapy for specific phobias and post-traumatic stress disorder, and limitations and future directions of the extant empirical literature.


What literature review is not: diversity, boundaries and recommendations


title: Improved vision-based lane tracker performance using vehicle localization

abstract: In this paper, we present improved lane tracking using vehicle localization. Lane markers are detected using a bank of steerable filters, and lanes are tracked using Kalman filtering. On-road vehicle detection has been achieved using an active learning approach, and vehicles are tracked using a Condensation particle filter. While most state-of-the art lane tracking systems are not capable of performing in high-density traffic scenes, the proposed framework exploits robust vehicle tracking to allow for improved lane tracking in high density traffic. Experimental results demonstrate that lane tracking performance, robustness, and temporal response are significantly improved in the proposed framework, while also tracking vehicles, with minimal additional hardware requirements.


title: Face sketch synthesis and recognition

abstract: We propose a novel face photo retrieval system using sketch drawings. By transforming a photo image into a sketch, we reduce the difference between photo and sketch significantly, thus allow effective matching between the two. To improve the synthesis performance, we separate shape and texture information in a face photo, and conduct transformation on them respectively. Finally a Bayesian classifier is used to recognize the probing sketch from the synthesized pseudo-sketches. Experiments on a data set containing 606 people clearly demonstrate the efficacy of the algorithm.


Ontology Development Tools for Ontology-Based Knowledge Management


title: Secure, Efficient and Fine-Grained Data Access Control Mechanism for P2P Storage Cloud

abstract: By combining cloud computing and Peer-to-Peer computing, a P2P storage cloud can be formed to offer highly available storage services, lowering the economic cost by exploiting the storage space of participating users. However, since cloud severs and users are usually outside the trusted domain of data owners, P2P storage cloud brings forth new challenges for data security and access control when data owners store sensitive data for sharing in the trusted domain. Moreover, there are no mechanisms for access control in P2P storage cloud. To address this issue, we design a ciphertext-policy attribute-based encryption (ABE) scheme and a proxy re-encryption scheme. Based on them, we further propose a secure, efficient and fine-grained data Access Control mechanism for P2P storage Cloud named ACPC. We enforce access policies based on user attributes, and integrate P2P reputation system in ACPC. ACPC enables data owners to delegate most of the laborious user revocation tasks to cloud servers and reputable system peers. Our security analysis demonstrates that ACPC is provably secure. The performance evaluation shows that ACPC is highly efficient under practical settings, and it significantly reduces the computation overheads brought to data owners and cloud servers during user revocation, compared with other state-of-the-art revocable ABE schemes.


title: Bayesian Robust Principal Component Analysis

abstract: A hierarchical Bayesian model is considered for decomposing a matrix into low-rank and sparse components, assuming the observed matrix is a superposition of the two. The matrix is assumed noisy, with unknown and possibly non-stationary noise statistics. The Bayesian framework infers an approximate representation for the noise statistics while simultaneously inferring the low-rank and sparse-outlier contributions; the model is robust to a broad range of noise levels, without having to change model hyperparameter settings. In addition, the Bayesian framework allows exploitation of additional structure in the matrix. For example, in video applications each row (or column) corresponds to a video frame, and we introduce a Markov dependency between consecutive rows in the matrix (corresponding to consecutive frames in the video). The properties of this Markov process are also inferred based on the observed matrix, while simultaneously denoising and recovering the low-rank and sparse components. We compare the Bayesian model to a state-of-the-art optimization-based implementation of robust PCA; considering several examples, we demonstrate competitive performance of the proposed model.


title: Automatic detection of bike-riders without helmet using surveillance videos in real-time

abstract: In this paper, we propose an approach for automatic detection of bike-riders without helmet using surveillance videos in real time. The proposed approach first detects bike riders from surveillance video using background subtraction and object segmentation. Then it determines whether bike-rider is using a helmet or not using visual features and binary classifier. Also, we present a consolidation approach for violation reporting which helps in improving reliability of the proposed approach. In order to evaluate our approach, we have provided a performance comparison of three widely used feature representations namely histogram of oriented gradients (HOG), scale-invariant feature transform (SIFT), and local binary patterns (LBP) for classification. The experimental results show detection accuracy of 93.80% on the real world surveillance data. It has also been shown that proposed approach is computationally less expensive and performs in real-time with a processing time of 11.58 ms per frame.


title: SIGKDD demo: sensors and software to allow computational entomology, an emerging application of data mining

abstract: The history of humankind is intimately connected to insects. Insect borne diseases kill a million people and destroy tens of billions of dollars worth of crops annually. However, at the same time, beneficial insects pollinate the majority of crop species, and it has been estimated that approximately one third of all food consumed by humans is directly pollinated by bees alone. Given the importance of insects in human affairs, it is somewhat surprising that computer science has not had a larger impact in entomology. We believe that recent advances in sensor technology are beginning change this, and a new field of Computational Entomology will emerge. We will demonstrate an inexpensive sensor that allows us to capture data from flying insects, and the software that allows us to analyze the data. Moreover, we will distribute both the sensors and software for free, to parties willing to take part in a crowdsourcing project on insect classification.


title: Exploiting smart-phone USB connectivity for fun and profit

abstract: The Universal Serial Bus (USB) connection has become the de-facto standard for both charging and data transfers for smart phone devices including Google's Android and Apple's iPhone. To further enhance their functionality, smart phones are equipped with programmable USB hardware and open source operating systems that empower them to alter the default behavior of the end-to-end USB communications. Unfortunately, these new capabilities coupled with the inherent trust that users place on the USB physical connectivity and the lack of any protection mechanisms render USB a insecure link, prone to exploitation. To demonstrate this new avenue of exploitation, we introduce novel attack strategies that exploit the functional capabilities of the USB physical link. In addition, we detail how a sophisticated adversary who has under his control one of the connected devices can subvert the other. This includes attacks where a compromised smart phone poses as a Human Interface Device (HID) and sends keystrokes in order to control the victim host. Moreover, we explain how to boot a smart phone device into USB host mode and take over another phone using a specially crafted cable. Finally, we point out the underlying reasons behind USB exploits and propose potential defense mechanisms that would limit or even prevent such USB borne attacks.


title: Explicit factor models for explainable recommendation based on phrase-level sentiment analysis

abstract: Collaborative Filtering(CF)-based recommendation algorithms, such as Latent Factor Models (LFM), work well in terms of prediction accuracy. However, the latent features make it difficulty to explain the recommendation results to the users. Fortunately, with the continuous growth of online user reviews, the information available for training a recommender system is no longer limited to just numerical star ratings or user/item features. By extracting explicit user opinions about various aspects of a product from the reviews, it is possible to learn more details about what aspects a user cares, which further sheds light on the possibility to make explainable recommendations. In this work, we propose the Explicit Factor Model (EFM) to generate explainable recommendations, meanwhile keep a high prediction accuracy. We first extract explicit product features (i.e. aspects) and user opinions by phrase-level sentiment analysis on user reviews, then generate both recommendations and disrecommendations according to the specific product features to the user's interests and the hidden features learned. Besides, intuitional feature-level explanations about why an item is or is not recommended are generated from the model. Offline experimental results on several real-world datasets demonstrate the advantages of our framework over competitive baseline algorithms on both rating prediction and top-K recommendation tasks. Online experiments show that the detailed explanations make the recommendations and disrecommendations more influential on user's purchasing behavior.


title: The Elements of Fashion Style

abstract: The outfits people wear contain latent fashion concepts capturing styles, seasons, events, and environments. Fashion theorists have proposed that these concepts are shaped by design elements such as color, material, and silhouette. A dress may be "bohemian" because of its pattern, material, trim, or some combination of them: it is not always clear how low-level elements translate to high-level styles. In this paper, we use polylingual topic modeling to learn latent fashion concepts jointly in two languages capturing these elements and styles. Using this latent topic formation we can translate between these two languages through topic space, exposing the elements of fashion style. We train the polylingual topic model (PLTM) on a set of more than half a million outfits collected from Polyvore, a popular fashion-based social net- work. We present novel, data-driven fashion applications that allow users to express their needs in natural language just as they would to a real stylist and produce tailored item recommendations for these style needs.


title: Cyborg Intelligence: Recent Progress and Future Directions

abstract: The combination of biological and artificial intelligence is a promising methodology to construct a novel intelligent modality, proposed as cyborg intelligence. The hierarchical conceptual framework is based on the interaction and combination of comparable components of biological cognitive units and computing intelligent units. The authors extend the previous conceptual framework and focus on sensorimotor circuits to explore the representation and integration of sensation. They then present a cognitive computing model for brain-computer integration and design efficient machine-learning algorithms for neural signal decoding. They also propose biological reconstruction methods for sensorimotor circuits to not only restore but enhance functionalities with AI. They develop a series of demonstrating systems to validate the conceptual framework of cyborg intelligence and possibly herald bright prospects and high values in diversified aspects of theoretical research, engineering techniques, and clinical applications. Finally, the authors summarize the latest research trends and challenges, which they believe will further boost new scientific frontiers in cyborg intelligence.


Do Transitioned Athletes Compete at an Advantage or Disadvantage as compared with Physically Born Men and Women : A review of the Scientific Literature


title: Avoiding moving obstacles with stochastic hybrid dynamics using PEARL: PrEference Appraisal Reinforcement Learning

abstract: Manual derivation of optimal robot motions for task completion is difficult, especially when a robot is required to balance its actions between opposing preferences. One solution has been proposed to automatically learn near optimal motions with Reinforcement Learning (RL). This has been successful for several tasks including swing-free UAV flight, table tennis, and autonomous driving. However, high-dimensional problems remain a challenge. We address this dimensionality constraint with PrEference Appraisal Reinforcement Learning (PEARL), which solves tasks with opposing preferences for acceleration controlled robots. PEARL projects the high-dimensional continuous robot state space to a low dimensional preference feature space resulting in efficient and adaptable planning. We demonstrate that on a dynamic obstacle avoidance robotic task, a single learning on a much simpler problem performs real-time decision-making for significantly larger, high-dimensional problems working in unbounded continuous states and actions. We trained the agent with 4 static obstacles, while the trained agent avoids up to 900 moving obstacles with complex hybrid stochastic obstacle dynamics in a highly constrained space using only limited information about the environment. We compare these tasks to traditional, often manually tuned solutions for these high-dimensional problems.


title: Using Filter Banks in Convolutional Neural Networks for Texture Classification

abstract: We adapt the CNN architecture to texture analysis.We introduce an energy layer to discard the overall shape information and focus on texture features.We evaluate the domain transferability and the depth of networks that are from scratch or pretrained.Our network is simpler than a classic CNN and obtains better classification results on texture datasets.We combine our texture CNN to a classic CNN (overall shape analysis) and further improve the results. Deep learning has established many new state of the art solutions in the last decade in areas such as object, scene and speech recognition. In particular Convolutional Neural Network (CNN) is a category of deep learning which obtains excellent results in object detection and recognition tasks. Its architecture is indeed well suited to object analysis by learning and classifying complex (deep) features that represent parts of an object or the object itself. However, some of its features are very similar to texture analysis methods. CNN layers can be thought of as filter banks of complexity increasing with the depth. Filter banks are powerful tools to extract texture features and have been widely used in texture analysis. In this paper we develop a simple network architecture named Texture CNN (T-CNN) which explores this observation. It is built on the idea that the overall shape information extracted by the fully connected layers of a classic CNN is of minor importance in texture analysis. Therefore, we pool an energy measure from the last convolution layer which we connect to a fully connected layer. We show that our approach can improve the performance of a network while greatly reducing the memory usage and computation.


title: Addressable wireless actuation for multijoint folding robots and devices

abstract: “Printing” robots and other complex devices through a process of origami-like folding is an emerging and promising manufacturing method due to the inherent simplicity and low cost of folding-based assembly. Folding is used in this class of device to create both complex static structures and flexure-based compliant mechanisms. Dependency on batteries to power these folds with no external wires is a hurdle to giving small-scale folding robots and devices functionality. We demonstrate a battery-free wireless folding method for dynamic multijoint structures, achieving addressable folding motions—both individual and collective folding—using only basic passive electronic components on the device. The method is based on electromagnetic power transmission and resonance selectivity for actuation of resistive shape memory alloy actuators without the need for physical connection or line of sight. We demonstrate the utility of this approach using two folded devices at different sizes using different circuit approaches.


Human Factors : The Journal of the Human Factors and Ergonomics Society


title: A Hybrid Approach to NAND-Flash-Based Solid-State Disks

abstract: Replacing power-hungry disks with NAND-flash-based solid-state disks (SSDs) is a recently emerging trend in flash-memory applications. One important SSD design issue is achieving a good balance between cost, performance, and lifetime. This study introduces a hybrid approach to large SSDs that combines MLC NAND flash and SLC NAND flash. Each of these flash architectures has its own drawbacks and benefits, and this study proposes that the two can complement each other. However, there are technical challenges pertaining to data placement, data migration, and wear leveling in heterogeneous NAND flash. The experimental results of our study show that combining 256 MB SLC flash with 20 GB MLC flash produces a hybrid SSD. This hybrid SSD is 1.8 times faster than a purely MLC-flash-based SSD in terms of average response time and improves energy consumption by 46 percent. The proposed hybrid SSD costs only four percent more than a purely MLC-flash-based SSD. The extra cost of a hybrid SSD is very limited and rewarding.


title: A Long Short-Term Memory Recurrent Neural Network Framework for Network Traffic Matrix Prediction

abstract: Network Traffic Matrix (TM) prediction is defined as the problem of estimating future network traffic from the previous and achieved network traffic data. It is widely used in network planning, resource management and network security. Long Short-Term Memory (LSTM) is a specific recurrent neural network (RNN) architecture that is well-suited to learn from experience to classify, process and predict time series with time lags of unknown size. LSTMs have been shown to model temporal sequences and their long-range dependencies more accurately than conventional RNNs. In this paper, we propose a LSTM RNN framework for predicting short and long term Traffic Matrix (TM) in large networks. By validating our framework on real-world data from GEANT network, we show that our LSTM models converge quickly and give state of the art TM prediction performance for relatively small sized models.


title: A New Method of Current Switching for Linear Wide-Range Measurement Systems

abstract: This new and general method here called overflow current switching allows a fast, continuous, and smooth transition between scales in wide-range current measurement systems, like electrometers. This is achieved, using a hydraulic analogy, by diverting only the overflow current, such that no slow element is forced to change its state during the switching. As a result, this approach practically eliminates the long dead time in low-current (picoamperes) switching. Similar to a logarithmic scale, a composition of n adjacent linear scales, like a segmented ruler, measures the current. The use of a linear wide-range system based on this technique assures fast and continuous measurement in the entire range, without blind regions during transitions and still holding suitable accuracy for many applications. A full mathematical development of the method is given. Several computer realistic simulations demonstrated the viability of the technique.


title: A data model and data structures for moving objects databases

abstract: We consider spatio-temporal databases supporting spatial objects with continuously changing position and extent, termed moving objects databases. We formally define a data model for such databases that includes complex evolving spatial structures such as line networks or multi-component regions with holes. The data model is given as a collection of data types and operations which can be plugged as attribute types into any DBMS data model (e.g. relational, or object-oriented) to obtain a complete model and query language. A particular novel concept is the sliced representation which represents a temporal development as a set of units, where unit types for spatial and other data types represent certain “simple” functions of time. We also show how the model can be mapped into concrete physical data structures in a DBMS environment.


title: Statistical mediation analysis with a multicategorical independent variable

abstract: Virtually all discussions and applications of statistical mediation analysis have been based on the condition that the independent variable is dichotomous or continuous, even though investigators frequently are interested in testing mediation hypotheses involving a multicategorical independent variable (such as two or more experimental conditions relative to a control group). We provide a tutorial illustrating an approach to estimation of and inference about direct, indirect, and total effects in statistical mediation analysis with a multicategorical independent variable. The approach is mathematically equivalent to analysis of (co)variance and reproduces the observed and adjusted group means while also generating effects having simple interpretations. Supplementary material available online includes extensions to this approach and Mplus, SPSS, and SAS code that implements it.


title: Characterization of silicone rubber based soft pneumatic actuators

abstract: Conventional pneumatic actuators have been a popular choice due to their decent force/torque output. Nowadays, new generation of pneumatic actuator made out of highly compliant elastomers, which we call soft pneumatic actuators (SPA), are drawing increasing attention due to their ease of fabrication, high customizability and innately softness. However, there is no effective method presented to characterize and understand these actuators, such as to measure the force and torque output, range of motion and the speed of actuation. In this work, we present two types of SPAs: bending and rotary actuators. In addition, we have developed two measurement setups to characterize actuators of different geometries. The measured force/torque outputs of different actuators are presented and analyzed. Step responses to certain pressure input are presented and discussed. A simple model is presented to provide physical insight to the observed behavior of the soft actuators. This work provides the basis for designing customized SPAs with application-specific requirements.