title: Practical Compressed Sensing: Modern data acquisition and signal processing

abstract: Since 2004, the field of compressed sensing has grown quickly and seen tremendous interest because it provides a theoretically sound and computationally tractable method to stably recover signals by sampling at the information rate. This thesis presents in detail the design of one of the world's first compressed sensing hardware devices, the random modulation pre-integrator (RMPI). The RMPI is an analog-to-digital converter (ADC) that bypasses a current limitation in ADC technology and achieves an unprecedented 8 effective number of bits over a bandwidth of 2.5 GHz. Subtle but important design considerations are discussed, and state-of-the-art reconstruction techniques are presented. Inspired by the need for a fast method to solve reconstruction problems for the RMPI, we develop two efficient large-scale optimization methods, NESTA and TFOCS, that are applicable to a wide range of other problems, such as image denoising and deblurring, MRI reconstruction, and matrix completion (including the famous Netflix problem). While many algorithms solve unconstrained l1 problems, NESTA and TFOCS can solve the constrained form of l1 minimization, and allow weighted norms. In addition to l1 minimization problems such as the LASSO, both NESTA and TFOCS solve total-variation minimization problem. TFOCS also solves the Dantzig selector and most variants of the nuclear norm minimization problem. A common theme in both NESTA and TFOCS is the use of smoothing techniques, which make the problem tractable, and the use of optimal first-order methods that have an accelerated convergence rate yet have the same cost per iteration as gradient descent. The conic dual methodology is introduced in TFOCS and proves to be extremely flexible, covering such generic problems as linear programming, quadratic programming, and semi-definite programming. A novel continuation scheme is presented, and it is shown that the Dantzig selector benefits from an exact-penalty property. Both NESTA and TFOCS are released as software packages available freely for academic use.


title: Towards Deeper Understanding of Variational Autoencoding Models

abstract: We propose a new family of optimization criteria for variational auto-encoding models, generalizing the standard evidence lower bound. We provide conditions under which they recover the data distribution and learn latent features, and formally show that common issues such as blurry samples and uninformative latent features arise when these conditions are not met. Based on these new insights, we propose a new sequential VAE model that can generate sharp samples on the LSUN image dataset based on pixel-wise reconstruction loss, and propose an optimization criterion that encourages unsupervised learning of informative latent features.


title: Health and safety in the catering industry

abstract: and disease among 315 catering students, at a large school of catering in the west of Ireland, over a 10 month academic year. A high incidence of accidents that caused injury was found. Cuts and lacerations, arising from accidents with knives, were the most common injuries seen, followed by burns and scalds from handling hot liquids. A significant level of work-related dermatitis was also recorded. Trainee chefs were identified as an occupational group with a high risk of occupational injury and disease. This group would benefit from vaccination against tetanus. Work-related injuries and disease generated a significant workload for the student health unit, which in itself is a good reason for catering establishments to make use of occupational health services. Health and safety procedures need to be audited and constantly reviewed at a local level. Further research is necessary to more accurately determine the incidence, nature and leading causes of work-related injury and disease in the catering industry in Ireland. This would help in the planning and implementation of an effective health and safety management system.


title: A Library of Analog Operators Based on the Hodgkin-Huxley Formalism for the Design of Tunable, Real-Time, Silicon Neurons

abstract: In this paper, we present a library of analog operators used for the analog real-time computation of the Hodgkin-Huxley formalism. These operators make it possible to design a silicon (Si) neuron that is dynamically tunable, and that reproduces different kinds of neurons. We used an original method in neuromorphic engineering to characterize this Si neuron. In electrophysiology, this method is well known as the “voltage-clamp” technique. We also compare the features of an application-specific integrated circuit built with this library with results obtained from software simulations. We then present the complex behavior of neural membrane voltages and the potential applications of this Si neuron.


title: VISUALIZING SOCIAL NETWORKS

abstract: With today‘s ubiquity and popularity of social network applications, the ability to analyze and understand large networks in an efficient manner becomes critically important. However, as networks become larger and more complex, reasoning about social dynamics via simple statistics is not a feasible option. To overcome these limitations, we can rely on visual metaphors. Visualization nowadays is no longer a passive process that produces images from a set of numbers. Recent years have witnessed a convergence of social network analytics and visualization, coupled with interaction, that is changing the way analysts understand and characterize social networks. In this chapter, we discuss the main goal of visualization and how different metaphors are aimed towards elucidating different aspects of social networks, such as structure and semantics. We also describe a number of methods where analytics and visualization are interwoven towards providing a better comprehension of social structure and dynamics.


title: A Comparative Study of Word Embeddings for Reading Comprehension

abstract: The focus of past machine learning research for Reading Comprehension tasks has been primarily on the design of novel deep learning architectures. Here we show that seemingly minor choices made on (1) the use of pre-trained word embeddings, and (2) the representation of out-of-vocabulary tokens at test time, can turn out to have a larger impact than architectural choices on the final performance. We systematically explore several options for these choices, and provide recommendations to researchers working in this area.


title: A Testbed to Evaluate the FIWARE-Based IoT Platform in the Domain of Precision Agriculture

abstract: Wireless sensor networks (WSNs) represent one of the most promising technologies for precision farming. Over the next few years, a significant increase in the use of such systems on commercial farms is expected. WSNs present a number of problems, regarding scalability, interoperability, communications, connectivity with databases and data processing. Different Internet of Things middleware is appearing to overcome these challenges. This paper checks whether one of these middleware, FIWARE, is suitable for the development of agricultural applications. To the authors' knowledge, there are no works that show how to use FIWARE in precision agriculture and study its appropriateness, its scalability and its efficiency for this kind of applications. To do this, a testbed has been designed and implemented to simulate different deployments and load conditions. The testbed is a typical FIWARE application, complete, yet simple and comprehensible enough to show the main features and components of FIWARE, as well as the complexity of using this technology. Although the testbed has been deployed in a laboratory environment, its design is based on the analysis of an Internet of Things use case scenario in the domain of precision agriculture.


title: Security of IoT systems: design challenges and opportunities

abstract: Computer-aided design (CAD), in its quest to facilitate new design revolutions, is again on the brink of changing its scope. Following both historical and recent technological and application trends, one can identify several emerging research and development directions in which CAD approaches and techniques may have major impacts. Among them, due to the potential to fundamentally alter everyday life as well as how science and engineering systems are designed and operated, the Internet of Things (IoT) stands out. IoT also poses an extraordinary system replete with conceptual and technical challenges. For instance, greatly reduced quantitative bounds on acceptable area and energy metrics require qualitative breakthroughs in design and optimization techniques. Most likely the most demanding of requirements for the widespread realization of many IoT visions is security. IoT security has an exceptionally wide scope in at least four dimensions. In terms of security scope it includes rarely addressed tasks such as trusted sensing, computation, communication, privacy, and digital forgetting. It also asks for new and better techniques for the protection of hardware, software, and data that considers the possibility of physical access to IoT devices. Sensors and actuators are common components of IoT devices and pose several unique security challenges including the integrity of physical signals and actuating events. Finally, during processing of collected data, one can envision many semantic attacks. Our strategic objective is to provide an impetus for the development of IoT CAD security techniques. We start by presenting a brief survey of IoT challenges and opportunities with an emphasis on security issues. Next, we discuss the potential of hardware-based IoT security approaches. Finally, we conclude with several case studies that advocate the use of stable PUFs and digital PPUFs for several IoT security protocols.


title: A respecification and extension of DeLone and McLean model of IS success in the citizen-centric e-governance

abstract: Citizen-centric e-governance is an emerging approach in the case of designing and implementing the e-governance systems with the specific focus on the effectiveness. Based on this perspective, the citizens' needs and values on e-governance have to be put first to make these systems effective. This study is an attempt to explore and propose the needs and values of citizens on the e-governance systems based on the determinants of information system (IS) success in the DeLone and McLean 2003 (D&M 2003) model of IS success. After conducting a comprehensive literature review, this study proposed new determinants of e-governance success and the citizen-centric e-governance success model. This model can be considered as the respecified and extended model of D&M 2003 model of IS success in the specific context of the citizen-centric e-governance systems.


title: An Empirical Evaluation of Current Convolutional Architectures’ Ability to Manage Nuisance Location and Scale Variability

abstract: We conduct an empirical study to test the ability of Convolutional Neural Networks (CNNs) to reduce the effects of nuisance transformations of the input data, such as location, scale and aspect ratio. We isolate factors by adopting a common convolutional architecture either deployed globally on the image to compute class posterior distributions, or restricted locally to compute class conditional distributions given location, scale and aspect ratios of bounding boxes determined by proposal heuristics. In theory, averaging the latter should yield inferior performance compared to proper marginalization. Yet empirical evidence suggests the converse, leading us to conclude that - at the current level of complexity of convolutional architectures and scale of the data sets used to train them - CNNs are not very effective at marginalizing nuisance variability. We also quantify the effects of context on the overall classification task and its impact on the performance of CNNs, and propose improved sampling techniques for heuristic proposal schemes that improve end-to-end performance to state-of-the-art levels. We test our hypothesis on a classification task using the ImageNet Challenge benchmark and on a wide-baseline matching task using the Oxford and Fischer's datasets.


title: New Economic Anthropology Paradigm: Interphase Operational Development Theory with Chinese Characteristics

abstract: KEYWORDS Development Study. Interphase Operational Principle. Minority Areas. Policy Proposals. Theoretically Rethinking ABSTRACT The theories and methods of economic anthropology have made great contributions in the development studies in China, especially to the development studies for the minority areas during the course of China's economic development. When one seriously studies Professor Yang's theory of interphase operations one can easily identify that the interphase operational development model is unique in terms of designing the best development policies in the Western China, where the minorities are concentrated. This paper probes and summarizes the path model of localizing economic anthropology with Chinese characteristics by examining Yang's interphase operating principle model from different perspectives.


title: A study on broadband printed bell-shaped monopole antenna with short stub

abstract: In this paper, a study on a miniaturized broadband printed monopole antenna is described. The design integrates a printed bell-shaped monopole antenna with short stub. The antenna improve the impedance matching in the lower frequency band by attaching the short stub. The designed antenna operates impedance bandwidth of 3.1∼10.6 GHz. The radiation patterns of omni-directional are obtained over the frequency range. This antenna acquired broadband characteristic covering the frequency band of UWB system by simulation.


title: Multiple-mode floating-point multiply-add fused unit for trading accuracy with power consumption

abstract: With the wide use of floating-point (FP) multiply and accumulate operations in multimedia and digital signal processing applications, many modern processors adopt FP multiply-add fused unit (MAF) to achieve high performance, improve accuracy and reduce power consumption. However, FP arithmetic units usually occupy the major portion of a processor's area and power dissipation. In this paper, we will propose a multiple-mode FP multiply-add fused unit which utilizes the iterative multiplication and truncated addition techniques to support seven operating modes with various errors for low power applications. It can execute either one multiply-accumulate operation with three modes, one multiplication operation with two modes or one addition operation with two modes. When compared to the traditional IEEE754 single-precision FP MAF, the proposed unit has 4.5% less area and 23% longer delay to achieve multiple modes which can sacrifice a little ( 33%) power consumption.


title: Unsupervised Named-Entity Recognition: Generating Gazetteers and Resolving Ambiguity

abstract: In this paper, we propose a named-entity recognition (NER) system that addresses two major limitations frequently discussed in the field. First, the system requires no human intervention such as manually labeling training data or creating gazetteers. Second, the system can handle more than the three classical named-entity types (person, location, and organization). We describe the system's architecture and compare its performance with a supervised system. We experimentally evaluate the system on a standard corpus, with the three classical named-entity types, and also on a new corpus, with a new named-entity type (car brands).


title: DTN Protocols for Vehicular Networks: An Application Oriented Overview

abstract: This survey provides an in-depth analysis of the different proposals for Vehicular Delay Tolerant Networks (VDTNs). We introduce the DTN architecture and classify VDTN proposals according to the type of knowledge needed to route messages. This classification also includes some Delay Tolerant Network (DTN) protocols originally designed for Opportunistic Networks to illustrate the evolution from Opportunistic DTN protocols to VDTN protocols. We also identify a set of common mechanisms that can be applied to almost all the VDTN protocols, heavily influencing their performance. Finally, we present some applications where VDTNs can be applicable and evaluate the suitability of the different proposals for each specific application. Moreover, this survey is not only limited to describing the different protocols but also focuses on the reproducibility and repeatability of experiments. With this in mind, we also review the evaluation methods used by VDTN researchers. We identify a lack of realism in most of the simulation models used by the VDTN research community, providing certain guidelines to address this issue.


title: Mate choice decisions: the role of facial beauty

abstract: For most people, facial beauty appears to play a prominent role in choosing a mate. Evidence from research on facial attractiveness indicates that physical beauty is a sexually selected trait mediated, in part, by pubertal facial hormone markers that signal important biological information about the displayer. Such signals would be ineffective if they did not elicit appropriate cognitive and/or emotional responses in members of the opposite sex. In this article, I argue that the effectiveness of these hormonal displays varies with perceivers' brains, which have been organized by the degree of steroid hormone exposure in the uterus, and activated by varying levels of circulating steroids following puberty. I further propose that the methodology used for examining mate choice decisions has general applicability for determining how cognitive and emotional evaluations enter into decision processes.


Experiments with Training Corpora for Statistical Text-to-speech Systems.


title: Can LSTM Learn to Capture Agreement? The Case of Basque

abstract: Sequential neural networks models are powerful tools in a variety of Natural Language Processing (NLP) tasks. The sequential nature of these models raises the questions: to what extent can these models implicitly learn hierarchical structures typical to human language, and what kind of grammatical phenomena can they acquire? ::: We focus on the task of agreement prediction in Basque, as a case study for a task that requires implicit understanding of sentence structure and the acquisition of a complex but consistent morphological system. Analyzing experimental results from two syntactic prediction tasks -- verb number prediction and suffix recovery -- we find that sequential models perform worse on agreement prediction in Basque than one might expect on the basis of a previous agreement prediction work in English. Tentative findings based on diagnostic classifiers suggest the network makes use of local heuristics as a proxy for the hierarchical structure of the sentence. We propose the Basque agreement prediction task as challenging benchmark for models that attempt to learn regularities in human language.


title: An Energy-Efficient and Reconfigurable Sensor IC for Bio-Impedance Spectroscopy and ECG Recording

abstract: This paper presents a low power reconfigurable IC for wearable health devices that perform bio-impedance (BioZ) spectroscopy, respiration, and ECG signals measurement. The IC consists of a broadband excitation current source (I-DAC) and a power efficient readout circuit (RO). Both are highly scalable to mitigate the tradeoff among noise, dynamic range, speed, power, and safe current limits. On circuit level, the I-DAC supports two broadband excitation modes based on maximum length sequence (MLS), a flexible selection of frequency points, bandwidth, and current magnitude. The RO employs a low noise instrumentation amplifier and a reconfigurable 8-to-12 bit ADC for flexible sensitivity, dynamic range and speed with an automatic scaling of the power consumption. On system level, calibration of the entire circuit chain, oversampling and MLS averaging further improve the bandwidth, accuracy and sensitivity of BioZ spectroscopy beyond the intrinsic circuit performance. As a result, the BioZ sensor IC achieves 2 $\text{m}\Omega $ sensitivity within 3.3 $\text{m}\Omega $ – $100~\Omega $ range and $0.6~\Omega $ sensitivity within $3.3~\Omega $ –100 $\text{k}\Omega $ range. The BioZ spectroscopy spans up to 125 kHz while consuming $155~\mu \text{W}$ . Furthermore, this IC can measure respiration and ECG simultaneously at a lower sampling rate of 20 kS/s while consuming $31~\mu \text{W}$ . Compared to prior art, to the best of our knowledge, this IC is the only work that can measure BioZ and ECG simultaneously with a single readout channel while consuming the lowest power and offering the highest flexibility in performance, application range, and power consumption.


title: Audio-visual integration in schizophrenia

abstract: Integration of information provided simultaneously by audition and vision was studied in a group of 18 schizophrenic patients. They were compared to a control group, consisting of 12 normal adults of comparable age and education. By administering two tasks, each focusing on one aspect of audio-visual integration, the study could differentiate between a spatial integration deficit and a speech-based integration deficit. Experiment 1 studied audio-visual interactions in the spatial localisation of sounds. Experiment 2 investigated integration of auditory and visual speech. The schizophrenic group performed as the control group on the sound localisation task, but in the audio-visual speech task, there was an impairment in lipreading as well as a smaller impact of lipreading on auditory speech information. Combined with findings about functional and neuro-anatomical specificity of intersensory integration, the data suggest that there is an integration deficit in the schizophrenic group that is related to the processing of phonetic information.


title: AN EXAMINATION OF THE RELATIONSHIP AMONG ACADEMIC STRESS, COPING, MOTIVATION, AND PERFORMANCE IN COLLEGE

abstract: Empirical evidence suggests that a domain-specific coping style may play an important role in the way students manage stressful academic events and perform at college. The purpose of this research was to examine the extent to which college students' academic coping style and motivation mediate their academic stress and performance. A structural equation analysis showed that the relationship between college students' academic stress and course grade was influenced by problem-focused coping and motivation but not emotion-focused coping. As expected, greater academic stress covaried with lower course grades; however, students who engaged in problem-focused coping were more likely to be motivated and perform better than students who engaged in emotion-focused coping. Strategies for promoting more effective coping in college students are discussed.


title: A Rare Case of Gestational Gigantomastia with Hypercalcemia: The Challenges of Management and Follow up

abstract: BACKGROUND ::: Gigantomastia is a breast disorder marked by exaggerated rapid growth of the breasts, generally bilaterally. Since this disorder is very rare and has been reported only in sparse case reports its etiology has yet to be fully established. Treatment is aimed at improving the clinical and psychological symptoms and reducing the treatment side effects; however, the best therapeutic option varies from case to case. ::: ::: ::: CASE PRESENTATION ::: The present report described a case of gestational gigantomastia in a 30-year-old woman, gravida 2, parity 1, 17 week pregnant admitted to Pars Hospital, Tehran, Iran, on May 2014. The patient was admitted to hospital at week 17 of pregnancy, although her breasts initially had begun to enlarge from the first trimester. The patient developed hypercalcemia in her 32nd week of pregnancy. The present report followed this patient from diagnosis until the completion of treatment. ::: ::: ::: CONCLUSION ::: Although gestational gigantomastia is a rare condition, its timely prognosis and careful examination of some conditions like hyperprolactinemia and hypercalcemia is essential in successful management of this condition.


title: Progress on a cognitive-motivational-relational theory of emotion.

abstract: The 2 main tasks of this article are 1st, to examine what a theory of emotion must do and basic issues that it must address. These include definitional issues, whether or not physiological activity should be a defining attribute, categorical versus dimensional strategies, the reconciliation of biological universals with sociocultural sources of variability, and a classification of the emotions. The 2nd main task is to apply an analysis of appraisal patterns and the core relational themes that they produce to a number of commonly identified emotions. Anger, anxiety, sadness, and pride (to include 1 positive emotion) are used as illustrations. The purpose is to show the capability of a cognitive-motivational-relational theory to explain and predict the emotions. The role of coping in emotion is also discussed, and the article ends with a response to criticisms of a phenomenological, folk-theory outlook.


Memory Efficient Sanitization of a Deduplicated Storage System


Relationship between Joint Position Sense, Force Sense, and Muscle Strength and the Impact of Gymnastic Training on Proprioception


title: Domain-Specific Languages

abstract: Domain-Specific Languages are used in software engineering in order to enhance quality, flexibility, and timely delivery of software systems, by taking advantage of specific properties of a particular application domain. This survey covers terminology, risks and benefits, examples, design methodologies, and implementation techniques of domain-specific languages as used for the construction and maintenance of software systems. Moreover, it covers an annotated selection of 75 key publications in the area of domain-specific languages.


title: Multimodel inference in ecology and evolution: challenges and solutions

abstract: Information theoretic approaches and model averaging are increasing in popularity, but this approach can be difficult to apply to the realistic, complex models that typify many ecological and evolutionary analyses. This is especially true for those researchers without a formal background in information theory. Here, we highlight a number of practical obstacles to model averaging complex models. Although not meant to be an exhaustive review, we identify several important issues with tentative solutions where they exist (e.g. dealing with collinearity amongst predictors; how to compute model-averaged parameters) and highlight areas for future research where solutions are not clear (e.g. when to use random intercepts or slopes; which information criteria to use when random factors are involved). We also provide a worked example of a mixed model analysis of inbreeding depression in a wild population. By providing an overview of these issues, we hope that this approach will become more accessible to those investigating any process where multiple variables impact an evolutionary or ecological response.


title: Generating Query Facets Using Knowledge Bases

abstract: A query facet is a significant list of information nuggets that explains an underlying aspect of a query. Existing algorithms mine facets of a query by extracting frequent lists contained in top search results. The coverage of facets and facet items mined by these kind of methods might be limited, because only a small number of search results are used. In order to solve this problem, we propose mining query facets by using knowledge bases which contain high-quality structured data. Specifically, we first generate facets based on the properties of the entities which are contained in Freebase and correspond to the query. Second, we mine initial query facets from search results, then expanding them by finding similar entities from Freebase. Experimental results show that our proposed method can significantly improve the coverage of facet items over the state-of-the-art algorithms.


CLASSIFICATION BASED ON ASSOCIATION-RULE MINING TECHNIQUES: A GENERAL SURVEY AND EMPIRICAL COMPARATIVE EVALUATION


title: Promoting integrative bargaining: mental contrasting with implementation intentions

abstract: Purpose – The present experiment aimed to test the impact of a self‐regulatory strategy of goal pursuit – called mental contrasting with implementation intentions (MCII) – on an integrative bargaining task.Design/methodology/approach – Participants were randomly assigned to dyads and negotiated over the sale of a car. Before negotiating, participants were prompted to engage in MCII, or one or the other of its two component strategies: to contrast mentally achieving success in the integrative bargaining task with the reality standing in the way of this success (MC), to form implementation intentions on how to bargain (i.e. if‐then plans) (II), or both to contrast mentally and form implementation intentions (MCII).Findings – The strategy of mental contrasting with implementation intentions led dyads to reach the largest joint agreements, compared to dyads that only used mental contrasting or if‐then plans. Moreover, participants who mentally contrasted formed more cooperative implementation intentions than ...


title: Accurate Optical Flow via Direct Cost Volume Processing

abstract: We present an optical flow estimation approach that operates on the full four-dimensional cost volume. This direct approach shares the structural benefits of leading stereo matching pipelines, which are known to yield high accuracy. To this day, such approaches have been considered impractical due to the size of the cost volume. We show that the full four-dimensional cost volume can be constructed in a fraction of a second due to its regularity. We then exploit this regularity further by adapting semi-global matching to the four-dimensional setting. This yields a pipeline that achieves significantly higher accuracy than state-of-the-art optical flow methods while being faster than most. Our approach outperforms all published general-purpose optical flow methods on both Sintel and KITTI 2015 benchmarks.


Automatic Extraction of Domain Name Generation Algorithms from Current Malware


title: Autodigestion of lexA and phage lambda repressors.

abstract: Proteolytic cleavage of lexA repressor is an early step in derepression of the SOS regulatory system of Escherichia coli. In vivo and in vitro data have indicated a role for recA protein in this specific proteolytic reaction. I show here that, under certain conditions, specific in vitro cleavage of highly-purified lexA protein can take place in the absence of recA protein. This autodigestion reaction cleaved the same alanine-glycine bond as did the recA-dependent cleavage reaction. Several lines of evidence argued that it was not due to a contaminating protease activity. Autodigestion was stimulated by alkaline pH. It occurred in the presence of EDTA but was stimulated several fold by the presence of Ca2+, Co2+, or Mg2+. The reaction appeared to be first-order, and its rate was independent of protein concentration over a wide range, strongly suggesting that it is intramolecular. Purified phage lambda repressor also broke down under similar conditions to yield products like those resulting from recA protein action. Phage lambda repressor broke down at a far slower rate than did lexA, as previously observed in the recA-catalyzed in vitro reaction and in vivo. This correlation between the two types of cleavage also extended to the reactions with mutant repressor proteins; taken together with the site specificity, it suggests that autodigestion and recA-dependent cleavage follow, at least in part, a similar reaction pathway. These findings indicate that specific cleavage of lexA protein can be catalyzed by the protein itself and suggest that recA protein plays an indirect stimulatory role, perhaps as an allosteric effector, in the recA-dependent reaction, rather than acting directly as a protease. The protease active site and the recA-recognition site lie in the central or COOH-terminal portion of the lexA protein, since a tryptic fragment containing these portions of lexA protein could take part in both reactions.


title: An EM Algorithm for Localizing Multiple Sound Sources in Reverberant Environments

abstract: We present a method for localizing and separating sound sources in stereo recordings that is robust to reverberation and does not make any assumptions about the source statistics. The method consists of a probabilistic model of binaural multi-source recordings and an expectation maximization algorithm for finding the maximum likelihood parameters of that model. These parameters include distributions over delays and assignments of time-frequency regions to sources. We evaluate this method against two comparable algorithms on simulations of simultaneous speech from two or three sources. Our method outperforms the others in anechoic conditions and performs as well as the better of the two in the presence of reverberation.


title: Deep Learning for Distant Speech Recognition

abstract: Deep learning is an emerging technology that is considered one of the most promising directions for reaching higher levels of artificial intelligence. Among the other achievements, building computers that understand speech represents a crucial leap towards intelligent machines. Despite the great efforts of the past decades, however, a natural and robust human-machine speech interaction still appears to be out of reach, especially when users interact with a distant microphone in noisy and reverberant environments. The latter disturbances severely hamper the intelligibility of a speech signal, making Distant Speech Recognition (DSR) one of the major open challenges in the field. This thesis addresses the latter scenario and proposes some novel techniques, architectures, and algorithms to improve the robustness of distant-talking acoustic models. We first elaborate on methodologies for realistic data contamination, with a particular emphasis on DNN training with simulated data. We then investigate on approaches for better exploiting speech contexts, proposing some original methodologies for both feed-forward and recurrent neural networks. Lastly, inspired by the idea that cooperation across different DNNs could be the key for counteracting the harmful effects of noise and reverberation, we propose a novel deep learning paradigm called network of deep neural networks. The analysis of the original concepts were based on extensive experimental validations conducted on both real and simulated data, considering different corpora, microphone configurations, environments, noisy conditions, and ASR tasks.


title: Compact Circularly Polarized Rectenna With Unbalanced Circular Slots

abstract: A novel rectifying antenna (rectenna) using a compact circularly polarized (CP) patch antenna with RF-to-DC power conversion part at 2.45 GHz is introduced, in which the unbalanced slots structure is adopted for size reduction and 2nd harmonic rejection. To contribute a rectenna for RF power conversion, the back side of the CP antenna is the doubler rectifier circuit with 3rd order harmonic rejection radial stub for efficiency optimization and harmonic power re-radiation elimination. The adopted CP antenna built on low cost FR-4 substrate has measured bandwidth of 137 MHz (10 dB return loss) as well as 30 MHz CP bandwidth (3 dB axial ratio). By up to 3rd order harmonic rejection, the RF-to-DC conversion efficiency would reach 53% and 75% with 1 K Omega resistor load under ANSI/IEEE uncontrolled and controlled RF human exposure limit respectively.


title: Structuring for high reliability : HR practices and mindful processes in reliability-seeking organizations

abstract: Summary This paper theoretically and empirically connects the literature on high-reliability organizations (HROs) to a broader set of organizations, which we call reliability-seeking organizations. Unlike HROs, which operate high-hazard technologies, reliability-seeking organizations operate in high-hazard environments. Reliability-seeking organizations are tightly coupled to their unpredictable and complex environments in such a manner that although the human mortality rate is low, the risk of small failures amplifying into organizational mortality is high. To cope with these environments, reliability-seeking organizations organize to remain open and flexible to emerging information and achieve the reliability demanded by their environments— intensity of innovation. These organizations utilize skilled temporary employees, positive employee relations, and an emphasis on training to innovate, and, in turn, generate greater financial performance. We test these hypotheses using a sample of 184 initial public offering (IPO) software firms that conducted their IPO between 1993 and 1996 and our results are consistent with our theorizing. Firms that utilized these human resource practices innovated more frequently and firms with more innovations had higher stock prices over time. Our findings combine to suggest a theoretical model of structural antecedents of a different type of reliability—intensity of innovation. Copyright # 2003 John Wiley & Sons, Ltd.


title: Genome scan for meat quality traits in Nelore beef cattle

abstract: Meat quality traits are economically important because they affect consumers' acceptance, which, in turn, influences the demand for beef. However, selection to improve meat quality is limited by the small numbers of animals on which meat tenderness can be evaluated due to the cost of performing shear force analysis and the resultant damage to the carcass. Genome wide-association studies for Warner-Bratzler shear force measured at different times of meat aging, backfat thickness, ribeye muscle area, scanning parameters [lightness, redness (a*), and yellowness] to ascertain color characteristics of meat and fat, water-holding capacity, cooking loss (CL), and muscle pH were conducted using genotype data from the Illumina BovineHD BeadChip array to identify quantitative trait loci (QTL) in all phenotyped Nelore cattle. Phenotype count for these animals ranged from 430 to 536 across traits. Meat quality traits in Nelore are controlled by numerous QTL of small effect, except for a small number of large-effect Q...


title: Fuzzy nonlinear predictive control of Stewart platform

abstract: In this paper a fuzzy robust nonlinear model predictive controller based on state estimation for Stewart platform is designed. The control law is based on prediction model, which is carried out via Uncertainties fuzzy estimator. The optimal control is computed directly from the minimization of receding horizon cost function. There is no need to an online optimization. A fuzzy estimator is designed to deal with system uncertainties such as unknown external disturbances, unmodeled quantities and parametric uncertainties. The global stability closed loop system is proved analytically via Lyapunov stability theory. The performance of the proposed method is compared with PID controller.


title: Timeline index: a unified data structure for processing queries on temporal data in SAP HANA

abstract: Managing temporal data is becoming increasingly important for many applications. Several database systems already support the time dimension, but provide only few temporal operators, which also often exhibit poor performance characteristics. On the academic side, a large number of algorithms and data structures have been proposed, but they often address a subset of these temporal operators only. In this paper, we develop the Timeline Index as a novel, unified data structure that efficiently supports temporal operators such as temporal aggregation, time travel, and temporal joins. As the Timeline Index is independent of the physical order of the data, it provides flexibility in physical design; e.g., it supports any kind of compression scheme, which is crucial for main memory column stores. Our experiments show that the Timeline Index has predictable performance and beats state-of-the-art approaches significantly, sometimes by orders of magnitude.


title: The Well-being of Australian Adolescents and Young Adults with Self-reported Long-term Health Conditions, Impairments or Disabilities: 2001 and 2006

abstract: As noted in the preamble to the 2007 UN Convention on the Rights of Persons with Disabilities, 'persons with disabilities continue to face barriers in their participation as equal members of society and violations of their human rights in all parts of the world'. In this paper we present nationally representative data on the nature and level of disadvantage faced by young Australians with a long term health condition, impairment or disability in 2001 and 2006. The results suggest that: (1) when compared with their non disabled peers, young Australians with a long term health condition, impairment or disability face pervasive social and material hardship and have lower subjective well being; (2) no progress was made between 2001 and 2006 in equalising the opportunities of young Australians with a long term health condition, impairment or disability; (3) lower subjective well being is not inherently associated with disability, but is contingent on the experience of social exclusion and material hardship.


title: Multi-level Residual Networks from Dynamical Systems View

abstract: Deep residual networks (ResNets) and their variants are widely used in many computer vision applications and natural language processing tasks. However, the theoretical principles for designing and training ResNets are still not fully understood. Recently, several points of view have emerged to try to interpret ResNet theoretically, such as unraveled view, unrolled iterative estimation and dynamical systems view. In this paper, we adopt the dynamical systems point of view, and analyze the lesioning properties of ResNet both theoretically and experimentally. Based on these analyses, we additionally propose a novel method for accelerating ResNet training. We apply the proposed method to train ResNets and Wide ResNets for three image classification benchmarks, reducing training time by more than 40\% with superior or on-par accuracy.


title: Biharmonic Distance

abstract: Measuring distances between pairs of points on a 3D surface is a fundamental problem in computer graphics and geometric processing. For most applications, the important properties of a distance are that it is a metric, smooth, locally isotropic, globally “shape-aware,” isometry-invariant, insensitive to noise and small topology changes, parameter-free, and practical to compute on a discrete mesh. However, the basic methods currently popular in computer graphics (e.g., geodesic and diffusion distances) do not have these basic properties. In this article, we propose a new distance measure based on the biharmonic differential operator that has all the desired properties. This new surface distance is related to the diffusion and commute-time distances, but applies different (inverse squared) weighting to the eigenvalues of the Laplace-Beltrami operator, which provides a nice trade-off between nearly geodesic distances for small distances and global shape-awareness for large distances. The article provides theoretical and empirical analysis for a large number of meshes.


title: Understanding Students’ Use of Code-Switching in a Learning by Teaching Technology

abstract: Personalized learning systems have shown significant learning gains when used in formal classroom teaching. Systems that use pedagogical agents for teaching have become popular, but typically their design does not account for multilingual classrooms. We investigated one such system in classrooms in the Philippines to see if and how students used code-switching when providing explanations of algebra problem solving. We found significant amounts of code-switching and explored cognitive and social factors such as explanation quality and affective valence that serve as evidence for code-switching motivations and effects. These results uncover complex social and cognitive interactions that occur during learning interactions with a virtual peer, and call for more affordances to support multilingual students.


title: Block-Sparse RPCA for Salient Motion Detection

abstract: Recent evaluation [2], [13] of representative background subtraction techniques demonstrated that there are still considerable challenges facing these methods. Challenges in realistic environment include illumination change causing complex intensity variation, background motions (trees, waves, etc.) whose magnitude can be greater than those of the foreground, poor image quality under low light, camouflage, etc. Existing methods often handle only part of these challenges; we address all these challenges in a unified framework which makes little specific assumption of the background. We regard the observed image sequence as being made up of the sum of a low-rank background matrix and a sparse outlier matrix and solve the decomposition using the Robust Principal Component Analysis method. Our contribution lies in dynamically estimating the support of the foreground regions via a motion saliency estimation step, so as to impose spatial coherence on these regions. Unlike smoothness constraint such as MRF, our method is able to obtain crisply defined foreground regions, and in general, handles large dynamic background motion much better. Furthermore, we also introduce an image alignment step to handle camera jitter. Extensive experiments on benchmark and additional challenging data sets demonstrate that our method works effectively on a wide range of complex scenarios, resulting in best performance that significantly outperforms many state-of-the-art approaches.


title: Obstacle detection on around view monitoring system

abstract: This paper proposes a method that realizes moving object detection (MOD) and static obstacle detection (SOD) in real time utilizing the fisheye cameras of the around viewing system (AVM). The topview of the AVM is used to calculate the vehicles movement between two frames using homograph estimation. Image features are detected and tracked evenly using cell detection technique. Then the features are projected onto the unit sphere of the fisheye camera model and the epipolar constraint is used to discriminate moving features from static ones. Moving features are clustered into different objects according to their position and orientation. 3D position of the static feature is calculated using triangular principle and potential obstacle is defined as objects above the ground and near the vehicle. The experiment shows the proposed method is robust and accurate for MOD and SOD.


title: Bullying, cyberbullying, and mental health in young people

abstract: OBJECTIVE ::: To investigate the factors associated with exposure to in-real-life (IRL) bullying, cyberbullying, and both IRL and cyberbullying and to explore the relationship between these types of bullying and mental health among 13-16-year-old Swedish boys and girls. ::: ::: ::: METHODS ::: Data was derived from a cross-sectional web-based study of 13-16-year-old students in northern Sweden (n=1214, response rate 81.9%). ::: ::: ::: RESULTS ::: The combination of IRL- and cyberbullying was the most common type of bullying. A non-supportive school environment and poor body image were related to exposure to bullying for both genders but the relationship was more distinct in girls. All types of bullying were associated with depressive symptoms in both boys and girls and all forms of bullying increased the likelihood of psychosomatic problems in girls. ::: ::: ::: CONCLUSIONS ::: Cyberbullying can be seen as an extension of IRL bullying. A combination of IRL- and cyberbullying seems to be particularly negative for mental health. Interventions should focus on improved school environment and body image as well as anti-violence programmes. Gender aspects of bullying need to be acknowledged.


title: The promise of sustainable happiness

abstract: From ancient history to recent times, philosophers, writers, self-help gurus, and now scientists have taken up the challenge of how to foster greater happiness. This chapter discusses why some people are happier than others, focusing on the distinctive ways that happy and unhappy individuals construe themselves and others, respond to social comparisons, make decisions, and self-reflect. We suggest that, despite several barriers to increased well-being, less happy people can strive successfully to be happier by learning a variety of effortful strategies and practicing them with determination and commitment. The sustainable happiness model (Lyubomirsky, Sheldon, & Schkade, 2005) provides a theoretical framework for experimental intervention research on how to increase and maintain happiness. According to this model, three factors contribute to an individual’s chronic happiness level: (a) the set point, (b) life circumstances, and (c) intentional activities, or effortful acts that are naturally variable and episodic. Such activities, which include committing acts of kindness, expressing gratitude or optimism, and savoring joyful life events, represent the most promising route to sustaining enhanced happiness. We describe a half-dozen randomized controlled interventions testing the efficacy of each of these activities in raising and maintaining well-being, as well as the mediators and moderators underlying their effects. Future researchers must endeavor not only to learn which particular practices make people happier, but how and why they do so.


title: mm-Wave SAR demonstrator as a test bed for advanced solutions in microwave imaging

abstract: The capability of imaging and surveying ground areas with airborne and spaceborne sensors has a very high priority in many applications, both in the civilian and military sectors. One of the most essential uses of this capability is in disaster monitoring, where up-to-date, reliable images and data are vital for the undertaking and coordinating of rescue actions. Sensors in space are very accurate but typically have a long revisit time, and thus their use for continuous monitoring is limited. Manned aircrafts equipped with optical, infrared, and synthetic aperture radar (SAR) are costly, and their operation in poor weather conditions (like heavy storms) are limited due to concerns over pilot safety. Unmanned aerial vehicles (UAV) are ideal candidates for the safe and cheap surveillance and imaging of such areas. They can provide continuous monitoring at very low cost, be deployed very quickly, and pose no risk of the loss of life in the case of a platform malfunction caused by heavy weather conditions, technical problems, or human error. The disadvantage of small, unmanned platforms is that they only offer limited space for payloads and have a low electrical power supply. This lays down the conditions for the design of an airborne sensor for small- and medium-sized UAVs. The cheapest solution for UAV surveillance is the use of visual light cameras. Optical cameras are cheap, light, and require a low supply of power, but their usage is limited to the daytime and good weather conditions. The presence of heavy rain, clouds, fog, or smoke can significantly reduce their imaging distance, sometimes down to just a few meters. Far better results can be obtained using much longer electromagnetic waves. The use of far infrared (e.g. in the 10 μm region) can provide thermal information and can be used for the detection of people in both day and night conditions, but it does not provide satisfactory images of land and infrastructures. The use of millimeter and centimeter microwaves with active illumination, combined with SAR technology, can provide high resolution images in all weather conditions, day and night, at a distance of several kilometers.


title: BoostMap: A method for efficient approximate similarity rankings

abstract: This paper introduces BoostMap, a method that can significantly reduce retrieval time in image and video database systems that employ computationally expensive distance measures, metric or non-metric. Database and query objects are embedded into a Euclidean space, in which similarities can be rapidly measured using a weighted Manhattan distance. Embedding construction is formulated as a machine learning task, where AdaBoost is used to combine many simple, ID embeddings into a multidimensional embedding that preserves a significant amount of the proximity structure in the original space. Performance is evaluated in a hand pose estimation system, and a dynamic gesture recognition system, where the proposed method is used to retrieve approximate nearest neighbors under expensive image and video similarity measures: In both systems, in quantitative experiments, BoostMap significantly increases efficiency, with minimal losses in accuracy. Moreover, the experiments indicate that BoostMap compares favorably with existing embedding methods that have been employed in computer vision and database applications, i.e., FastMap and Bourgain embeddings.


title: Construct-related validity of the Toglia Category Assessment and the Deductive Reasoning test with children who are typically developing.

abstract: OBJECTIVE: Both classification and deductive reasoning are essential cognitive components underlying any learning process and, therefore, are important to assess in children with developmental or learning disabilities. The aim of this study was to establish construct-related validity of the Toglia Category Assessment (TCA) and the Deductive Reasoning test, which were originally developed to evaluate adults with brain injury. This study represents the first step in validating and adapting the two tests for children who are typically developing. METHOD: The study population consisted of 235 children without disabilities in 6 different age groups (from 5-11 years of age). Both the TCA and the Deductive Reasoning test were administered to all participants. RESULTS: The results of the study indicate significant differences in the average performance of children in several age groups on both the TCA and the Deductive Reasoning test, but they do not show differences among all the groups. Children could not predict their ability in either test, but most were able to estimate their ability after actual performance of the TCA. A significant correlation was found only for the 5-year-olds and 7-year-olds between the Deductive Reasoning test final score and the children's estimation of their performance. CONCLUSION: The findings of this study support the suitability of the TCA and the Deductive Reasoning test for use with children who are typically developing. We recommend that both tools be studied further to amplify their validity.


title: Convolutional recurrent neural networks: Learning spatial dependencies for image representation

abstract: In existing convolutional neural networks (CNNs), both convolution and pooling are locally performed for image regions separately, no contextual dependencies between different image regions have been taken into consideration. Such dependencies represent useful spatial structure information in images. Whereas recurrent neural networks (RNNs) are designed for learning contextual dependencies among sequential data by using the recurrent (feedback) connections. In this work, we propose the convolutional recurrent neural network (C-RNN), which learns the spatial dependencies between image regions to enhance the discriminative power of image representation. The C-RNN is trained in an end-to-end manner from raw pixel images. CNN layers are firstly processed to generate middle level features. RNN layer is then learned to encode spatial dependencies. The C-RNN can learn better image representation, especially for images with obvious spatial contextual dependencies. Our method achieves competitive performance on ILSVRC 2012, SUN 397, and MIT indoor.


title: Counting blessings in early adolescents: An experimental study of gratitude and subjective well-being !

abstract: Abstract The development and manifestation of gratitude in youth is unclear. We examined the effects of a grateful outlook on subjective well-being and other outcomes of positive psychological functioning in 221 early adolescents. Eleven classes were randomly assigned to either a gratitude, hassles, or control condition. Results indicated that counting blessings was associated with enhanced self-reported gratitude, optimism, life satisfaction, and decreased negative affect. Feeling grateful in response to aid mediated the relationship between experimental condition and general gratitude at the 3-week follow-up. The most significant finding was the robust relationship between gratitude and satisfaction with school experience at both the immediate post-test and 3-week follow-up. Counting blessings seems to be an effective intervention for well-being enhancement in early adolescents.


title: Identification of protein coding regions by database similarity search

abstract: Sequence similarity between a translated nucleotide sequence and a known biological protein can provide strong evidence for the presence of a homologous coding region, even between distantly related genes. The computer program BLASTX performed conceptual translation of a nucleotide query sequence followed by a protein database search in one programmatic step. We characterized the sensitivity of BLASTX recognition to the presence of substitution, insertion and deletion errors in the query sequence and to sequence divergence. Reading frames were reliably identified in the presence of 1% query errors, a rate that is typical for primary sequence data. BLASTX is appropriate for use in moderate and large scale sequencing projects at the earliest opportunity, when the data are most prone to containing errors.


title: Comlex Syntax: Building A Computational Lexicon

abstract: We describe the design of Comlex Syntax, a computational lexicon providing detailed syntactic information for approximately 38,000 English headwords. We consider the types of errors which arise in creating such a lexicon, and how such errors can be measured and controlled.


title: CARLOC: Precise Positioning of Automobiles

abstract: Precise positioning of an automobile to within lane-level precision can enable better navigation and context-awareness. However, GPS by itself cannot provide such precision in obstructed urban environments. In this paper, we present a system called CARLOC for lane-level positioning of automobiles. CARLOC uses three key ideas in concert to improve positioning accuracy: it uses digital maps to match the vehicle to known road segments; it uses vehicular sensors to obtain odometry and bearing information; and it uses crowd-sourced location of estimates of roadway landmarks that can be detected by sensors available in modern vehicles. CARLOC unifies these ideas in a probabilistic position estimation framework, widely used in robotics, called the sequential Monte Carlo method. Through extensive experiments on a real vehicle, we show that CARLOC achieves sub-meter positioning accuracy in an obstructed urban setting, an order-of-magnitude improvement over a high-end GPS device.


CREDIT CARD FRAUD DETECTION BASED ON BEHAVIOR MINING


title: Predicting software reliability with neural network ensembles

abstract: Software reliability is an important factor for quantitatively characterizing software quality and estimating the duration of software testing period. Traditional parametric software reliability growth models (SRGMs) such as nonhomogeneous Poisson process (NHPP) models have been successfully utilized in practical software reliability engineering. However, no single such parametric model can obtain accurate prediction for all cases. In addition to the parametric models, non-parametric models like neural network have shown to be effective alternative techniques for software reliability prediction. In this paper, we propose a non-parametric software reliability prediction system based on neural network ensembles. The effects of system architecture on the performance are investigated. The comparative studies between the proposed system with the single neural network based system and three parametric NHPP models are carried out. The experimental results demonstrate that the system predictability can be significantly improved by combing multiple neural networks.


title: Security and privacy in mobile cloud computing

abstract: With the development of cloud computing and mobility, mobile cloud computing has emerged and become a focus of research. By the means of on-demand self-service and extendibility, it can offer the infrastructure, platform, and software services in a cloud to mobile users through the mobile network. Security and privacy are the key issues for mobile cloud computing applications, and still face some enormous challenges. In order to facilitate this emerging domain, we firstly in brief review the advantages and system model of mobile cloud computing, and then pay attention to the security and privacy in the mobile cloud computing. By deeply analyzing the security and privacy issues from three aspects: mobile terminal, mobile network and cloud, we give the current security and privacy approaches.


title: Recognition using visual phrases

abstract: In this paper we introduce visual phrases, complex visual composites like “a person riding a horse”. Visual phrases often display significantly reduced visual complexity compared to their component objects, because the appearance of those objects can change profoundly when they participate in relations. We introduce a dataset suitable for phrasal recognition that uses familiar PASCAL object categories, and demonstrate significant experimental gains resulting from exploiting visual phrases. We show that a visual phrase detector significantly outperforms a baseline which detects component objects and reasons about relations, even though visual phrase training sets tend to be smaller than those for objects. We argue that any multi-class detection system must decode detector outputs to produce final results; this is usually done with non-maximum suppression. We describe a novel decoding procedure that can account accurately for local context without solving difficult inference problems. We show this decoding procedure outperforms the state of the art. Finally, we show that decoding a combination of phrasal and object detectors produces real improvements in detector results.


title: Fooling OCR Systems with Adversarial Text Images

abstract: We demonstrate that state-of-the-art optical character recognition (OCR) based on deep learning is vulnerable to adversarial images. Minor modifications to images of printed text, which do not change the meaning of the text to a human reader, cause the OCR system to "recognize" a different text where certain words chosen by the adversary are replaced by their semantic opposites. This completely changes the meaning of the output produced by the OCR system and by the NLP applications that use OCR for preprocessing their inputs.


title: Bayesian Map Learning in Dynamic Environments

abstract: We consider the problem of learning a grid-based map using a robot with noisy sensors and actuators. We compare two approaches: online EM, where the map is treated as a fixed parameter, and Bayesian inference, where the map is a (matrix-valued) random variable. We show that even on a very simple example, online EM can get stuck in local minima, which causes the robot to get "lost" and the resulting map to be useless. By contrast, the Bayesian approach, by maintaining multiple hypotheses, is much more robust. We then introduce a method for approximating the Bayesian solution, called Rao-Blackwellised particle filtering. We show that this approximation, when coupled with an active learning strategy, is fast but accurate.


title: Fair energy-efficient sensing task allocation in participatory sensing with smartphones

abstract: With the proliferation of smartphones, participatory sensing using smartphones provides unprecedented opportunities for collecting enormous sensing data. There are two crucial requirements in participatory sensing, fair task allocation and energy efficiency, which are particularly challenging given high combinatorial complexity, tradeoff between energy efficiency and fairness, and dynamic and unpredictable task arrivals. In this paper, we present a novel fair energy-efficient allocation framework whose objective is characterized by min-max aggregate sensing time. We rigorously prove that optimizing the min-max aggregate sensing time is NP hard even when the tasks are assumed as a priori. We consider two allocation models: offline allocation and online allocation. For the offline allocation model, we design an efficient approximation algorithm with the approximation ratio of 2 - 1/m, where m is the number of member smartphones in the system. For the online allocation model, we propose a greedy online algorithm which achieves a competitive ratio of at most m. The results demonstrate that the approximation algorithm reduces over 81% total sensing time, the greedy online algorithm reduces more than 73% total sensing time, and both algorithms achieve over 3x better min-max fairness.


title: Securing elastic applications on mobile devices for cloud computing

abstract: Cloud computing provides elastic computing infrastructure and resources which enable resource-on-demand and pay-as-you-go utility computing models. We believe that new applications can leverage these models to achieve new features that are not available for legacy applications. In our project we aim to build elastic applications which augment resource-constrained platforms, such as mobile phones, with elastic computing resources from clouds. An elastic application consists of one or more weblets, each of which can be launched on a device or cloud, and can be migrated between them according to dynamic changes of the computing environment or user preferences on the device. This paper overviews the general concept of this new application model, analyzes its unique security requirements, and presents our design considerations to build secure elastic applications. As first steps we propose a solution for authentication and secure session management between weblets running device side and those on the cloud. We then propose secure migration and how to authorize cloud weblets to access sensitive user data such as via external web services. We believe some principles in our solution can be applied in other cloud computing scenarios such as application integration between private and public clouds in an enterprise environment.


title: Flat-Start Single-Stage Discriminatively Trained HMM-Based Models for ASR

abstract: In recent years, end-to-end approaches to automatic speech recognition have received considerable attention as they are much faster in terms of preparing resources. However, conventional multistage approaches, which rely on a pipeline of training hidden Markov models (HMM)-GMM models and tree-building steps still give the state-of-the-art results on most databases. In this study, we investigate flat-start one-stage training of neural networks using lattice-free maximum mutual information (LF-MMI) objective function with HMM for large vocabulary continuous speech recognition. We thoroughly look into different issues that arise in such a setup and propose a standalone system, which achieves word error rates (WER) comparable with that of the state-of-the-art multi-stage systems while being much faster to prepare. We propose to use full biphones to enable flat-start context-dependent (CD) modeling and show through experiments that our CD modeling approach can be almost as effective as regular tree-based CD modeling. We show that our flat-start LF-MMI setup together with this tree-free CD modeling technique achieves 10 to 25 % relative WER reduction compared to other end-to-end methods on well-known databases. The improvements are larger for smaller databases.


title: Vulnerabilities of cyber-physical systems to stale data - Determining the optimal time to launch attacks

abstract: Abstract This paper presents a new vulnerability assessment model based on timing attacks. In particular, it examines the problem where an adversary has access to a certain sensor reading or a controller output signal in real time, but can only cause denial of service (DoS). Jamming the communications to a device can cause the system to work with stale data that, in turn, could interfere with the control algorithm to the extent of driving the system to an undesirable state. If the DoS attack is not timed properly, the use of stale data by the controller or actuator would have a limited impact on the process. However, if the attacker is able to launch the DoS attack at the correct time, the use of stale data can drive the system to an unsafe state. This paper uses the Tennessee Eastman challenge process to investigate the problem of an attacker who has to identify (in real time) the optimal moment to launch a DoS attack. The results suggest that, by attacking sensor and controller signals, the attacker can manipulate the process at will, but the success of the attack depends considerably on the specific stale values due to the dynamic nature of the process. The choice of time to begin an attack is forward-looking, requiring the attacker to consider each current opportunity against the possibility of a better opportunity in the future; this lends itself to the theory of optimal stopping problems. In particular, this paper studies the applicability of the Best Choice Problem (also known as the Secretary Problem), quickest change detection and statistical process outliers. The analysis can be used to identify specific sensor measurements that need to be protected and the time-to-response necessary to enable process operators and asset owners to define appropriate attack response strategies.


title: DeltaIoT: A Self-Adaptive Internet of Things Exemplar

abstract: Internet of Things (IoT) consists of networked tiny embedded computers (motes) that are capable of monitoring and controlling the physical world. Examples range from building security monitoring to smart factories. A central problem of IoT is minimising the energy consumption of the motes, while guaranteeing high packet delivery performance, regardless of uncertainties such as sudden changes in traffic load and communication interference. Traditionally, to deal with uncertainties the network settings are either hand-tuned or over-provisioned, resulting in continuous network maintenance or inefficiencies. Enhancing the IoT network with self-adaptation can automate these tasks. This paper presents DeltaIoT, an exemplar that enables researchers to evaluate and compare new methods, techniques and tools for self-adaptation in IoT. DeltaIoT is the first exemplar for research on self-adaptation that provides both a simulator for offline experimentation and a physical setup that can be accessed remotely for real-world experimentation.


title: Feature-based tracking on a multi-omnidirectional camera dataset

abstract: Omnidirectional cameras have a lot of potential for surveillance and ambient intelligence applications, since they provide increased coverage with fewer cameras. We introduce the new BOMNI dataset, collected with two omnidirectional cameras simultaneously. The dataset contains single subject and multi-subject interaction scenarios, as well as actions relevant for ambient assisted living, such as falling down. We describe evaluation protocols on this dataset, and provide benchmarking baseline results for two tracking systems based on bounding box and interest point matching after foreground-background segmentation, respectively.


title: Informative Change Detection by Unmixing for Hyperspectral Images

abstract: Applying spectral unmixing on a series of multitemporal hyperspectral images for change detection has the potential to reveal important subpixel-level information, such as the abundance variation of each underlying material in a given location or the change in the distribution of materials throughout the scene, with time or resulting from significant events such as a natural disaster. However, change detection by spectral unmixing for hyperspectral images has not been extensively studied up to now, and most studies have been limited to specific cases and data sets. This is caused by the scarcity of real multitemporal hyperspectral data and the inherent difficulties in applying unmixing to multitemporal hyperspectral data in a coherent way. In this letter, we investigate change detection for hyperspectral images by spectral unmixing and systematically present the advantages that can be gained by using such an approach, supported by experimental studies conducted on carefully prepared synthetic data sets and also with real data sets.


title: Improving Span-based Question Answering Systems with Coarsely Labeled Data

abstract: We study approaches to improve fine-grained short answer Question Answering models by integrating coarse-grained data annotated for paragraph-level relevance and show that coarsely annotated data can bring significant performance gains. Experiments demonstrate that the standard multi-task learning approach of sharing representations is not the most effective way to leverage coarse-grained annotations. Instead, we can explicitly model the latent fine-grained short answer variables and optimize the marginal log-likelihood directly or use a newly proposed \emph{posterior distillation} learning objective. Since these latent-variable methods have explicit access to the relationship between the fine and coarse tasks, they result in significantly larger improvements from coarse supervision.


Storj A Peer-to-Peer Cloud Storage Network


title: Scalable testing of file system checkers

abstract: File system checkers (like e2fsck) are critical, complex, and hard to develop, and developers today rely on hand-written tests to exercise this intricate code. Test suites for file system checkers take a lot of effort to develop and require careful reasoning to cover a sufficiently comprehensive set of inputs and recovery mechanisms. We present a tool and methodology for testing file system checkers that reduces the need for a specification of the recovery process and the development of a test suite. Our methodology splits the correctness of the checker into two objectives: consistency and completeness of recovery. For each objective, we leverage either the file system checker code itself or a comparison among the outputs of multiple checkers to extract an implicit specification of correct behavior. Our methodology is embodied in a testing tool called SWIFT, which uses a mix of symbolic and concrete execution; it introduces two new techniques: a specific concretization strategy and a corruption model that leverages test suites of file system checkers. We used SWIFT to test the file system checkers of ext2, ext3, ext4, ReiserFS, and Minix; we found bugs in all checkers, including cases leading to data loss. Additionally, we automatically generated test suites achieving code coverage on par with manually constructed test suites shipped with the checkers.


title: Relevance of Emoticons in Computer-Mediated Communication Contexts: An Overview

abstract: With the constant growth in Information and Communication Technology (ICT) in the last 50 years or so, electronic communication has become part of the present day system of living. Equally, smileys or emoticons were innovated in 1982, and today the genre has attained a substantial patronage in various aspects of computer-mediated communication (CMC). Ever since written forms of electronic communication lack the face-to-face (F2F) situation attributes, emoticons are seen as socio-emotional suppliers to the CMC. This article reviews scholarly research in that field in order to compile variety of investigations on the application of emoticons in some facets of CMC, i.e. Facebook, Instant Messaging (IM), and Short Messaging Service (SMS). Key findings of the review show that emoticons do not just serve as paralanguage elements rather they are compared to word morphemes with distinctive significative functions. In other words, they are morpheme-like units and could be derivational, inflectional, or abbreviations but not unbound. The findings also indicate that emoticons could be conventionalized as well as being paralinguistic elements, therefore, they should be approached as contributory to conversation itself not mere compensatory to language.


title: Medication adherence monitoring using modern technology

abstract: Medication adherence is a major problem in the healthcare sector, because failure to adhere to taking medication has major impact on the individual's health. Technologies such as sensor networks and RFID are being used more frequently by the wireless-enabled health applications, which make them dominant fields in the era of connected health. In this paper, we tackle some of the recent applications for medication adherence monitoring that rely on technologies such as wireless sensor networks, RFID, and others and study their advantages and disadvantages from a technical point of view. Nonetheless, as each of these technologies comes with limitations, we then provide our vision towards a non-invasive system that encompasses several sensors for the aim of performing medication adherence monitoring collaboratively, with low enough energy to make the system feasible.


title: Proactive elasticity and energy awareness in data stream processing

abstract: We design a predictive methodology for elastic data stream processing.We exploit Model Predictive Control to design the predictive controller.We regulate the number of used cores and the CPU frequency.The approach targets multicore-based shared-memory systems.The approach allows the design of strategies that achieve good SASO trade-offs. Data stream processing applications have a long running nature (24hr/7d) with workload conditions that may exhibit wide variations at run-time. Elasticity is the term coined to describe the capability of applications to change dynamically their resource usage in response to workload fluctuations. This paper focuses on strategies for elastic data stream processing targeting multicore systems. The key idea is to exploit Model Predictive Control, a control-theoretic method that takes into account the system behavior over a future time horizon in order to decide the best reconfiguration to execute. We design a set of energy-aware proactive strategies, optimized for throughput and latency QoS requirements, which regulate the number of used cores and the CPU frequency through the Dynamic Voltage and Frequency Scaling (DVFS) support offered by modern multicore CPUs. We evaluate our strategies in a high-frequency trading application fed by synthetic and real-world workload traces. We introduce specific properties to effectively compare different elastic approaches, and the results show that our strategies are able to achieve the best outcome.


title: BOLD neurovascular coupling does not change significantly with normal aging

abstract: Studies of cognitive function that compare the blood oxygenation level dependent (BOLD) signal across age groups often require the assumption that neurovascular coupling does not change with age. Tests of this assumption have produced mixed results regarding the strength of the coupling and its relative time course. Using deconvolution, we found that age does not have a significant effect on the time course of the hemodynamic impulse response function or on the slope of the BOLD versus stimulus duration relationship. These results suggest that in cognitive studies of healthy aging, group differences in BOLD activation are likely due to age-related changes in cognitive-neural interactions and information processing rather than to impairments in neurovascular coupling. Hum Brain Mapp 38:3538-3551, 2017. © 2017 Wiley Periodicals, Inc.


title: Shape matching: similarity measures and algorithms

abstract: Shape matching is an important ingredient in shape retrieval, recognition and classification, alignment and registration, and approximation and simplification. This paper treats various aspects that are needed to solve shape matching problems: choosing the precise problem, selecting the properties of the similarity measure that are needed for the problem, choosing the specific similarity measure, and constructing the algorithm to compute the similarity. The focus is on methods that lie close to the field of computational geometry.


title: Patch-Based Image Inpainting via Two-Stage Low Rank Approximation

abstract: To recover the corrupted pixels, traditional inpainting methods based on low-rank priors generally need to solve a convex optimization problem by an iterative singular value shrinkage algorithm. In this paper, we propose a simple method for image inpainting using low rank approximation, which avoids the time-consuming iterative shrinkage. Specifically, if similar patches of a corrupted image are identified and reshaped as vectors, then a patch matrix can be constructed by collecting these similar patch-vectors. Due to its columns being highly linearly correlated, this patch matrix is low-rank. Instead of using an iterative singular value shrinkage scheme, the proposed method utilizes low rank approximation with truncated singular values to derive a closed-form estimate for each patch matrix. Depending upon an observation that there exists a distinct gap in the singular spectrum of patch matrix, the rank of each patch matrix is empirically determined by a heuristic procedure. Inspired by the inpainting algorithms with component decomposition, a two-stage low rank approximation (TSLRA) scheme is designed to recover image structures and refine texture details of corrupted images. Experimental results on various inpainting tasks demonstrate that the proposed method is comparable and even superior to some state-of-the-art inpainting algorithms.


title: Detection of design pattern using Graph Isomorphism and Normalized Cross Correlation

abstract: Present day software engineering concept gives emphasis on the use of design patterns for developing software which are recurring in nature. Detection of design pattern is one of the emerging field of Software Reverse Engineering. In this work, an attempt has been made to present an approach for design pattern detection with the help of Graph Isomorphism and Normalized Cross Correlation techniques. In this study, system and design pattern are presented in the form of graphs. The Graph Isomorphism technique finds the pattern subgraph in the system graph and Normalized Cross Correlation provides a way to formulate the percentage existence of the design pattern in the system. An Eclipse Plug-in i.e., ObjectAid is used to extract Unified Modeling Language (UML) class diagrams as well as eXtensible Markup Language (XML) files from the software system and design patterns. An algorithm is proposed to identify relevant information from the XML files. Then Graph Isomorphism and Normalized Cross Correlation techniques are used to find the pattern subgraph and its percentage existence in the system. This approach has been applied on four open source software tools for the evaluation of five design patterns, such as Composite, Facade, Flyweight, State, and Template Method.


title: Compact Ultrawideband Circularly Polarized Weakly Coupled Patch Array Antenna

abstract: A circularly polarized (CP) patch array antenna is proposed for ultrawideband operation by introducing weak-coupling mechanism into feeding network, feeding unit, and radiation structure, respectively. As an example, a wideband $2 \times 2$ patch array antenna consists of a weakly coupled-line four-feed network, a coupled feeding unit, and four weakly coupled sequentially rotated symmetric hexagonal patches is designed for verification. Measured results show that the proposed antenna exhibits the 10-dB impedance bandwidth of 98% (2–5.92 GHz), 3-dB axial ratio bandwidth of 96.3% (2.15–6 GHz), and 3-dB gain variation bandwidth of 82.5% (2.65–5.95 GHz) with the peak gain of 9.5 dBic at 5.15 GHz and the maximum efficiency of 88%. By the weak-coupling method, the design achieves not only the largest CP bandwidth and the symmetric radiation patterns without any beam squint over the whole band, but also the smaller volume of ( $1.13 \times 1.13 \times 0.15$ ) $\lambda _{0}^{3}$ ( $\lambda _{0}$ is the wavelength at 4 GHz) compared with the reported patch array antennas. Therefore, the proposed CP patch array antenna is very promising for modern wireless applications such as WiFi, global navigation satellite system, and radio frequency identification systems.


title: D-MFVI: Distributed Mean Field Variational Inference using Bregman ADMM

abstract: Bayesian models provide a framework for probabilistic modelling of complex datasets. However, many of such models are computationally demanding especially in the presence of large datasets. On the other hand, in sensor network applications, statistical (Bayesian) parameter estimation usually needs distributed algorithms, in which both data and computation are distributed across the nodes of the network. In this paper we propose a general framework for distributed Bayesian learning using Bregman Alternating Direction Method of Multipliers (B-ADMM). We demonstrate the utility of our framework, with Mean Field Variational Bayes (MFVB) as the primitive for distributed Matrix Factorization (MF) and distributed affine structure from motion (SfM).


title: Efficient visual object tracking with online nearest neighbor classifier

abstract: A tracking-by-detection framework is proposed that combines nearest-neighbor classification of bags of features, efficient subwindow search, and a novel feature selection and pruning method to achieve stability and plasticity in tracking targets of changing appearance. Experiments show that near-frame-rate performance is achieved (sans feature detection), and that the state of the art is improved in terms of handling occlusions, clutter, changes of scale, and of appearance. A theoretical analysis shows why nearest neighbor works better than more sophisticated classifiers in the context of tracking.


title: Neuron Activity Related to Short-Term Memory

abstract: Nerve cells in the monkey9s prefrontal cortex and nucleus medialis dorsalis of the thalamus show changes of firing frequency associated with the performance of a delayed response test. Most cells increase firing during the cue presentation period or at the beginning of the ensuing delay; spike discharge highler than that in intertrial periods is present in some cells throughout the delay. These changes are interpreted as suggestive evidence of a role of frontothalamic circuits in the attentive process involved in short-term memory


title: A Grammar-Based Structural CNN Decoder for Code Generation

abstract: Code generation maps a program description to executable source code in a programming language. Existing approaches mainly rely on a recurrent neural network (RNN) as the decoder. However, we find that a program contains significantly more tokens than a natural language sentence, and thus it may be inappropriate for RNN to capture such a long sequence. In this paper, we propose a grammar-based structural convolutional neural network (CNN) for code generation. Our model generates a program by predicting the grammar rules of the programming language; we design several CNN modules, including the tree-based convolution and pre-order convolution, whose information is further aggregated by dedicated attentive pooling layers. Experimental results on the HearthStone benchmark dataset show that our CNN code generator significantly outperforms the previous state-of-the-art method by 5 percentage points; additional experiments on several semantic parsing tasks demonstrate the robustness of our model. We also conduct in-depth ablation test to better understand each component of our model.


title: CyberGate: a design framework and system for text analysis of computer-mediated communication

abstract: Content analysis of computer-mediated communication (CMC) is important for evaluating the effectiveness of electronic communication in various organizational settings. CMC text analysis relies on systems capable of providing suitable navigation and knowledge discovery functionalities. However, existing CMC systems focus on structural features, with little support for features derived from message text. This deficiency is attributable to the informational richness and representational complexities associated with CMC text. In order to address this shortcoming, we propose a design framework for CMC text analysis systems. Grounded in systemic functional linguistic theory, the proposed framework advocates the development of systems capable of representing the rich array of information types inherent in CMC text. It also provides guidelines regarding the choice of features, feature selection, and visualization techniques that CMC text analysis systems should employ. The CyberGate system was developed as an instantiation of the design framework. CyberGate incorporates a rich feature set and complementary feature selection and visualization methods, including the writeprints and ink blots techniques. An application example was used to illustrate the system's ability to discern important patterns in CMC text. Furthermore, results from numerous experiments conducted in comparison with benchmark methods confirmed the viability of CyberGate's features and techniques. The results revealed that the CyberGate system and its underlying design framework can dramatically improve CMC text analysis capabilities over those provided by existing systems.


title: Topical ALA-photodynamic therapy for the treatment of acne vulgaris.

abstract: Topical aminolevulinic acid is converted into a potent photosensitizer, protoporphyrin, in human hair follicles and sebaceous glands. Photodynamic therapy with topical aminolevulinic acid was tested for the treatment of acne vulgaris, in an open-label prospective human study. Each of 22 subjects with acne on the back was treated in four sites with aminolevulinic acid plus red light, aminolevulinic acid alone, light alone, and untreated control. Half of the subjects were treated once; half were treated four times. Twenty percent topical aminolevulinic acid was applied with 3 h occlusion, and 150 J per cm2 broad-band light (550-700 nm) was given. Sebum excretion rate and auto-fluorescence from follicular bacteria were measured before, and 2, 3, 10, and 20 wk after, treatment. Histologic changes and protoporphyrin synthesis in pilosebaceous units were observed from skin biopsies. Aminolevulinic acid plus red light caused a transient acne-like folliculitis. Sebum excretion was eliminated for several weeks, and decreased for 20 wk after photodynamic therapy; multiple treatments caused greater suppression of sebum. Bacterial porphyrin fluorescence was also suppressed by photodynamic therapy. On histology, sebaceous glands showed acute damage and were smaller 20 wk after photodynamic therapy. There was clinical and statistically significant clearance of inflammatory acne by aminolevulinic acid plus red light, for at least 20 wk after multiple treatments and 10 wk after a single treatment. Transient hyperpigmentation, superficial exfoliation, and crusting were observed, which cleared without scarring. Topical aminolevulinic acid plus red light is an effective treatment of acne vulgaris, associated with significant side-effects. Aminolevulinic acid plus red light causes phototoxicity to sebaceous follicles, prolonged suppression of sebaceous gland function, and apparent decrease in follicular bacteria after photodynamic therapy. Potentially, aminolevulinic acid plus red light may be useful for some patients with acne.


title: Small-Angle X-ray Scattering of Polymers

abstract: An image coding method including: binarizing last position information to generate (i) a binary signal which includes a first signal having a length smaller than or equal to a predetermined maximum length and does not include a second signal or (ii) a binary signal which includes the first signal having the predetermined maximum length and the second signal; first coding for arithmetically coding each of binary symbols included in the first signal using a context switched among a plurality of contexts according to a bit position of the binary symbol; and second coding for arithmetically coding the second signal using a fixed probability when the binary signal includes the second signal, wherein in the first coding, a binary symbol at a last bit position of the first signal is arithmetically coded using a context exclusive to the last bit position, when the first signal has the predetermined maximum length.


Understanding the Limitations of Existing Energy-Efficient Design Approaches for Deep Neural Networks


title: Distributed Optimization Using the Primal-Dual Method of Multipliers

abstract: In this paper, we propose the primal-dual method of multipliers (PDMM) for distributed optimization over a graph. In particular, we optimize a sum of convex functions defined over a graph, where every edge in the graph carries a linear equality constraint. In designing the new algorithm, an augmented primal-dual Lagrangian function is constructed which smoothly captures the graph topology. It is shown that a saddle point of the constructed function provides an optimal solution of the original problem. Further under both the synchronous and asynchronous updating schemes, PDMM has the convergence rate of $O(1/K)$ (where $K$ denotes the iteration index) for general closed, proper, and convex functions. Other properties of PDMM such as convergence speeds versus different parameter-settings and resilience to transmission failure are also investigated through the experiments of distributed averaging.


title: Targeting the endocannabinoid system: future therapeutic strategies.

abstract: The endocannabinoid system (ECS) is involved in many physiological regulation pathways in the human body, which makes this system the target of many drugs and therapies. In this review, we highlight the latest studies regarding the role of the ECS and the drugs that target it, with a particular focus on the basis for the discovery of new cannabinoid-based drugs. In addition, we propose some key steps, such as the creation of a cannabinoid-receptor interaction matrix (CRIM) and the use of metabolomics, toward the development of improved and more specific drugs for each relevant disease.


title: Knowing what to think by knowing who you are: Self-categorization and the nature of norm formation, conformity and group polarization*

abstract: Contrasted theoretical approaches to social influence that stress interpersonal dependence or group membership, using 3 influence paradigms: (1) M. Sherif's (1936) autokinetic effect paradigm (AEP) (2) S. Asch's (1952) conformity effect paradigm (CEP) and (3) a group polarization paradigm (GPP). Three experiments were conducted with a total of 194 undergraduates and 116 16–17 yr olds. In the AEP, the impact of confederates on the formation of a norm decreased as their membership of a different category was made more salient. In the CEP, surveillance exerted normative pressure if done by an in-group but not an out-group. In the GPP, when categorical differences between 2 subgroups of a discussion were made salient, convergence of opinion between the subgroups was inhibited.


BGA and Advanced Package Wire to Wire Bonding for Backside Emission Microscopy


title: An Iterative Reinforcement Approach for Fine-Grained Opinion Mining

abstract: With the in-depth study of sentiment analysis research, finer-grained opinion mining, which aims to detect opinions on different review features as opposed to the whole review level, has been receiving more and more attention in the sentiment analysis research community recently. Most of existing approaches rely mainly on the template extraction to identify the explicit relatedness between product feature and opinion terms, which is insufficient to detect the implicit review features and mine the hidden sentiment association in reviews, which satisfies (1) the review features are not appear explicit in the review sentences; (2) it can be deduced by the opinion words in its context. From an information theoretic point of view, this paper proposed an iterative reinforcement framework based on the improved information bottleneck algorithm to address such problem. More specifically, the approach clusters product features and opinion words simultaneously and iteratively by fusing both their semantic information and co-occurrence information. The experimental results demonstrate that our approach outperforms the template extraction based approaches.


title: Theories of Decision-Making in Economics and Behavioural Science

abstract: Recent years have seen important new explorations along the boundaries between economics and psychology. For the economist, the immediate question about these developments is whether they include new advances in psychology that can fruitfully be applied to economics. But the psychologist will also raise the converse question—whether there are developments in economic theory and observation that have implications for the central core of psychology. If economics is able to find verifiable and verified generalisations about human economic behaviour, then these generalisations must have a place in the more general theories of human behaviour to which psychology and sociology aspire. Influence will run both ways.2


Curve skeletonization of surface-like objects in 3D images guided by voxel classification