title: TB infection prevention and control experiences of South African nurses - a phenomenological study

abstract: BackgroundThe tuberculosis (TB) epidemic in South Africa is characterised by one of the highest levels of TB/HIV co-infection and growing multidrug-resistant TB worldwide. Hospitals play a central role in the management of TB. We investigated nurses' experiences of factors influencing TB infection prevention and control (IPC) practices to identify risks associated with potential nosocomial transmission.MethodsThe qualitative study employed a phenomenological approach, using semi-structured interviews with a quota sample of 20 nurses in a large tertiary academic hospital in Cape Town, South Africa. The data was subjected to thematic analysis.ResultsNurses expressed concerns about the possible risk of TB transmission to both patients and staff. Factors influencing TB-IPC, and increasing the potential risk of nosocomial transmission, emerged in interconnected overarching themes. Influences related to the healthcare system included suboptimal IPC provision such as the lack of isolation facilities and personal protective equipment, and the lack of a TB-IPC policy. Further influences included inadequate TB training for staff and patients, communication barriers owing to cultural and linguistic differences between staff and patients, the excessive workload of nurses, and a sense of duty of care. Influences related to wider contextual conditions included TB concerns and stigma, and the role of traditional healers. Influences related to patient behaviour included late uptake of hospital care owing to poverty and the use of traditional medicine, and poor adherence to IPC measures by patients, family members and carers.ConclusionsSeveral interconnected influences related to the healthcare system, wider contextual conditions and patient behavior could increase the potential risk of nosocomial TB transmission at hospital level. There is an urgent need for the implementation and evaluation of a comprehensive contextually appropriate TB IPC policy with the setting and auditing of standards for IPC provision and practice, adequate TB training for both staff and patients, and the establishment of a cross-cultural communication strategy, including rapid access to interpreters.


title: Expanding Pediatric Psychology Beyond Hospital Walls to Meet the Health Care Needs of Ethnic Minority Children

abstract: Objective: To discuss the need for an ecological model and new approaches for meeting the psychological and physical health care needs of minority children. Method: I support approaches that are informal, empowerment oriented, and culturally sensitive and that address illness prevention and health promotion. Discussion: Assessment, intervention, and research challenges involved in implementing these approaches are identified, and some strategies for overcoming these challenges are discussed. Conclusions: Pediatric psychology can be expanded to embrace the advocated model and intervention approaches to better meet the health care needs of minority children.


title: The vehicle routing problem with time windows and temporal dependencies

abstract: In this article, we formulate the vehicle routing problem with time windows and temporal dependencies. The problem is an extension of the well studied vehicle routing problem with time windows. In addition to the usual constraints, a scheduled time of one visit may restrain the scheduling options of other visits. Special cases of temporal dependencies are synchronization and precedence constraints. Two compact formulations of the problem are introduced and the Dantzig–Wolfe decompositions of these formulations are presented to allow for a column generation-based solution approach. Temporal dependencies are modeled by generalized precedence constraints. Four different master problem formulations are proposed and it is shown that the formulations can be ranked according to the tightness with which they describe the solution space. A tailored time window branching is used to enforce feasibility on the relaxed master problems. Finally, a computational study is performed to quantitatively reveal strengths and weaknesses of the proposed formulations. It is concluded that, depending on the problem at hand, the best performance is achieved either by relaxing the generalized precedence constraints in the master problem, or by using a time-indexed model, where generalized precedence constraints are added as cuts when they become severely violated. © 2011 Wiley Periodicals, Inc. NETWORKS, Vol. 58(4), 273–289 2011 © 2011 Wiley Periodicals, Inc.


title: On current trends in security and privacy of cloud computing

abstract: One of the major components of the Cloud Computing is “Security and Privacy”. These concerns about security and privacy directly address the trustworthy and reliability levels of a system. The researches and studies about security and privacy on cloud computing are continuing. The aim of this paper is to analyze the privacy and security requirements and highlight new tools and open research topics for cloud computing systems.


title: A Practical Message Falsification Attack on WPA

abstract: In 2008, Beck and Tews have proposed a practical attack on WPA. Their attack (called the Beck-Tews attack) can recover plaintext from an encrypted short packet, and can falsify it. The execution time of the Beck-Tews attack is about 12-15 minutes. However, the attack has the limitation, namely, the targets are only WPA implementations those support IEEE802.11e QoS features. In this paper, we propose a practical message falsification attack on any WPA implementation. In order to ease targets of limitation of wireless LAN products, we apply the Beck-Tews attack to the man-in-the-middle attack. In the man-inthe-middle attack, the user’s communication is intercepted by an attacker until the attack ends. It means that the users may detect our attack when the execution time of the attack is large. Therefore, we give methods for reducing the execution time of the attack. As a result, the execution time of our attack becomes about one minute in the best case.


title: A magnetic climbing robot for steel bridge inspection

abstract: Corrosion can cause section loss or cracks in the steel members which is one of the most important causes of deterioration of steel bridges. For some critical components of a steel bridge, it is fatal and could even cause the collapse of the whole bridge. Nowadays the most common approach to steel bridge inspection is visual inspection by inspectors with inspection trucks. This paper mainly presents a climbing robot with magnetic wheels which can move on the surface of steel bridge. Experiment results shows that the climbing robot can move on the steel bridge freely without disrupting traffic to reduce the risks to the inspectors.


title: BIG & QUIC: Sparse Inverse Covariance Estimation for a Million Variables

abstract: The l1-regularized Gaussian maximum likelihood estimator (MLE) has been shown to have strong statistical guarantees in recovering a sparse inverse covariance matrix even under high-dimensional settings. However, it requires solving a difficult non-smooth log-determinant program with number of parameters scaling quadratically with the number of Gaussian variables. State-of-the-art methods thus do not scale to problems with more than 20,000 variables. In this paper, we develop an algorithm BIGQUIC, which can solve 1 million dimensional l1-regularized Gaussian MLE problems (which would thus have 1000 billion parameters) using a single machine, with bounded memory. In order to do so, we carefully exploit the underlying structure of the problem. Our innovations include a novel block-coordinate descent method with the blocks chosen via a clustering scheme to minimize repeated computations; and allowing for inexact computation of specific components. In spite of these modifications, we are able to theoretically analyze our procedure and show that BIGQUIC can achieve super-linear or even quadratic convergence rates.


title: Factors involved in the etiology of temporomandibular disorders - a literature review

abstract: Background and aim. This review aims at presenting a current view on the most frequent factors involved in the mechanisms causing temporomandibular disorders (TMD). Method. We conducted a critical review of the literature for the period January 2000 to December 2014 to identify factors related to TMD development and persistence. Results. The etiology of TMD is multidimensional: biomechanical, neuromuscular, bio-psychosocial and biological factors may contribute to the disorder. Occlusal overloading and parafunctions (bruxism) are frequently involved as biomechanical factors; increased levels of estrogen hormones are considered biological factors affecting the temporo-mandibular-joint. Among bio-psychosocial factors, stress, anxiety or depression, were frequently encountered. Conclusions. The etiopathogenesis of this condition is poorly understood, therefore TMDs are difficult to diagnose and manage. Early and correct identification of the possible etiologic factors will enable the appropriate treatment scheme application in order to reduce or eliminate TMDs debilitating signs and symptoms.


title: Wireless Sensor Network-Based Greenhouse Environment Monitoring and Automatic Control System for Dew Condensation Prevention

abstract: Dew condensation on the leaf surface of greenhouse crops can promote diseases caused by fungus and bacteria, affecting the growth of the crops. In this paper, we present a WSN (Wireless Sensor Network)-based automatic monitoring system to prevent dew condensation in a greenhouse environment. The system is composed of sensor nodes for collecting data, base nodes for processing collected data, relay nodes for driving devices for adjusting the environment inside greenhouse and an environment server for data storage and processing. Using the Barenbrug formula for calculating the dew point on the leaves, this system is realized to prevent dew condensation phenomena on the crop’s surface acting as an important element for prevention of diseases infections. We also constructed a physical model resembling the typical greenhouse in order to verify the performance of our system with regard to dew condensation control.


title: A Framework of Fog Computing: Architecture, Challenges, and Optimization

abstract: Fog computing (FC) is an emerging distributed computing platform aimed at bringing computation close to its data sources, which can reduce the latency and cost of delivering data to a remote cloud. This feature and related advantages are desirable for many Internet-of-Things applications, especially latency sensitive and mission intensive services. With comparisons to other computing technologies, the definition and architecture of FC are presented in this paper. The framework of resource allocation for latency reduction combined with reliability, fault tolerance, privacy, and underlying optimization problems are also discussed. We then investigate an application scenario and conduct resource optimization by formulating the optimization problem and solving it via a genetic algorithm. The resulting analysis generates some important insights on the scalability of the FC systems.


title: A Corpus Study Of Evaluative And Speculative Language

abstract: This paper presents a corpus study of evaluative and speculative language. Knowledge of such language would be useful in many applications, such as text categorization and summarization. Analyses of annotator agreement and of characteristics of subjective language are performed. This study yields knowledge needed to design effective machine learning systems for identifying subjective language.


title: Median frequency of the myoelectric signal

abstract: SummaryA study was performed to investigate the changes that occur in the median frequency of the myoelectric signal during local ischemia or reduction of intramuscular temperature produced by surface cooling. Data was obtained from experiments which involved the first dorsal interosseous muscle of 10 female and 16 male subjects. These subjects were asked to perform isometric constant-force abduction contractions of the index finger at 20% and 80% of maximal voluntary contraction level. The initial median frequency (IMF) of the myoelectric signal during the first 0.5 s of contraction was calculated. Results showed a significant reduction of the IMF in contractions performed under ischemic conditions; upon release, the IMF recovered quickly. At 80% maximal voluntary level of contraction, a greater decrease of the IMF was recorded. Similar results were demonstrated during reduction of intramuscular temperature with gradual recovery of the IMF after cooling. These results demonstrate that the median frequency of the myoelectric signal displays behavior similar to that reported for conduction velocity and this is consistent with the notion that accumulation of metabolic byproducts in muscle tissue causes a decrease in the conduction velocity of the muscle fibers.


Annotate-Sample-Average (ASA): A New Distant Supervision Approach for Twitter Sentiment Analysis


title: Falling Things: A Synthetic Dataset for 3D Object Detection and Pose Estimation

abstract: We present a new dataset, called Falling Things (FAT), for advancing the state-of-the-art in object detection and 3D pose estimation in the context of robotics. By synthetically combining object models and backgrounds of complex composition and high graphical quality, we are able to generate photorealistic images with accurate 3D pose annotations for all objects in all images. Our dataset contains 60k annotated photos of 21 household objects taken from the YCB dataset. For each image, we provide the 3D poses, per-pixel class segmentation, and 2D/3D bounding box coordinates for all objects. To facilitate testing different input modalities, we provide mono and stereo RGB images, along with registered dense depth images. We describe in detail the generation process and statistical analysis of the data.


title: Fuzzy Logic, Soft Computing, and Applications ⋆

abstract: We survey on the theoretical and practical developments of the theory of fuzzy logic and soft computing. Specifically, we briefly review the history and main milestones of fuzzy logic (in the wide sense), the more recent development of soft computing, and finalise by presenting a panoramic view of applications: from the most abstract to the most practical ones.


title: Development and Clinical Evaluation of an mHealth Application for Stress Management

abstract: A large number of individuals experience mental health disorders, with cognitive behavioral therapy (CBT) emerging as a standard practice for reduction in psychiatric symptoms including stress, anger, anxiety and depression. However, CBT is associated with significant patient dropout, and lacks the means to provide objective data regarding a patient’s experience and symptoms between sessions. Emerging wearables and mobile health (mHealth) applications represent an approach that may provide objective data to the patient and provider between CBT sessions. Here we describe the development of a classifier of real-time physiological stress in a healthy population (n=35), and apply it in a controlled clinical evaluation for armed forces veterans undergoing CBT for stress and anger management (n=16). Using cardiovascular and electrodermal inputs from a wearable device, the classifier was able to detect physiological stress in a non-clinical sample with an accuracy greater than 90%. In a small clinical sample, patients who used the classifier and an associated mHealth application were less likely to discontinue therapy (p=0.016, d=1.34) and significantly improved on measures of stress (p=0.032, d=1.61), anxiety (p=0.050, d=1.26), and anger (p=0.046, d=1.41) compared to controls undergoing CBT alone. Given the large number of individuals that experience mental health disorders, and the unmet need for treatment, especially in developing nations, such mHealth approaches have the potential to provide or augment treatment at low cost in the absence of in-person care.


title: Neural Versus Symbolic Rap Battle Bots

abstract: We contrast two opposing approaches to building bots that autonomously learn to rap battle: a symbolic probabilistic approachbasedoninductionofstochastictransductiongrammars, versus a neural network approach based on backpropagation through unconventional transduction recursive autoassociative memory (TRAAM) models. Rap battling is modeled as a quasi-translation problem, in which an appropriate output response must be improvised given any input challenge line of lyrics. Both approaches attempt to tackle the difficult problem of compositionality: for any challenge line, constructing a good response requires making salient associations while satisfying contextual preferences at many different, overlapping levels of granularity between the challenge and response lines. The contextual preferences include fluency, partial metrical or syntactic parallelism, and rhyming at various points across the lines. During both the learning and improvisation stages, the symbolic approach attempts to explicitly enumerate as many hypotheses as possible, whereas the neural approach attempts to evolve vector representations that better implicitly generalize over soft regions or neighborhoods of hypotheses. The brute force symbolic approach is more precise, but quickly generates combinatorial numbers of hypotheses when searching for generalizations. The distributed vector based neural approach can more easily confuse hypotheses, but maintains a constant level of complexity while retaining its implicit generalization bias. We contrast both the theoretical formulation and experimental outputs of the two approaches.


MaZda User ' s Manual MaZda User ' s Manual


title: Evidence for constructive, self-regulatory, and collaborative processes in problem-based learning.

abstract: The goal of this study was to increase our understanding of the learning-oriented verbal interactions taking place between students during the problem-based learning (PBL) cycle. The verbal interactions of one PBL group of five students throughout an entire PBL cycle were recorded in this data-intensive case study. The verbatim transcript consisting of more than 1,000 utterances was analyzed to investigate whether and how PBL stimulates students towards constructive, self-directed and collaborative learning. Our results demonstrate the occurrence of all above-mentioned learning activities, with 53.3% of episodes being collaborative, 27.2% self-directed and 15.7% constructive.


title: Image statistics and the perception of surface qualities

abstract: The world is full of surfaces, and by looking at them we can judge their material qualities. Properties such as colour or glossiness can help us decide whether a pancake is cooked, or a patch of pavement is icy. Most studies of surface appearance have emphasized textureless matte surfaces, but real-world surfaces, which may have gloss and complex mesostructure, are now receiving increased attention. Their appearance results from a complex interplay of illumination, reflectance and surface geometry, which are difficult to tease apart given an image. If there were simple image statistics that were diagnostic of surface properties it would be sensible to use them. Here we show that the skewness of the luminance histogram and the skewness of sub-band filter outputs are correlated with surface gloss and inversely correlated with surface albedo (diffuse reflectance). We find evidence that human observers use skewness, or a similar measure of histogram asymmetry, in making judgements about surfaces. When the image of a surface has positively skewed statistics, it tends to appear darker and glossier than a similar surface with lower skewness, and this is true whether the skewness is inherent to the original image or is introduced by digital manipulation. We also find a visual after-effect based on skewness: adaptation to patterns with skewed statistics can alter the apparent lightness and glossiness of surfaces that are subsequently viewed. We suggest that there are neural mechanisms sensitive to skewed statistics, and that their outputs can be used in estimating surface properties.


title: Slow cooling and highly efficient extraction of hot carriers in colloidal perovskite nanocrystals

abstract: Hot-carrier solar cells can overcome the Shockley-Queisser limit by harvesting excess energy from hot carriers. Inorganic semiconductor nanocrystals are considered prime candidates. However, hot-carrier harvesting is compromised by competitive relaxation pathways (for example, intraband Auger process and defects) that overwhelm their phonon bottlenecks. Here we show colloidal halide perovskite nanocrystals transcend these limitations and exhibit around two orders slower hot-carrier cooling times and around four times larger hot-carrier temperatures than their bulk-film counterparts. Under low pump excitation, hot-carrier cooling mediated by a phonon bottleneck is surprisingly slower in smaller nanocrystals (contrasting with conventional nanocrystals). At high pump fluence, Auger heating dominates hot-carrier cooling, which is slower in larger nanocrystals (hitherto unobserved in conventional nanocrystals). Importantly, we demonstrate efficient room temperature hot-electrons extraction (up to ∼83%) by an energy-selective electron acceptor layer within 1 ps from surface-treated perovskite NCs thin films. These insights enable fresh approaches for extremely thin absorber and concentrator-type hot-carrier solar cells. Harvesting excess energy from above-band gap photons could lead to solar cells which exceed conventional efficiency limits. Liet al., study hot carrier cooling in hybrid perovskite materials with reduced dimensionality using transient absorption spectroscopy and demonstrate efficient hot-electron extraction in such systems.


title: Band Selection in Multispectral Images by Minimization of Dependent Information

abstract: In this paper, a band selection technique for hyperspectral image data is proposed. Supervised feature extraction techniques allow a reduction of the dimensionality to extract relevant features through a labeled training set. This implies an analysis of the existing class distributions, which usually means, in the case of hyperspectral imaging, a large number of samples, making the labeling process difficult. A possible alternative could be the use of information measures, which are the basis of the proposed method. The present approach basically behaves as an unsupervised feature selection criterion, to obtain the relevant spectral bands from a set of sample images. The relations of information content between spectral bands are analyzed, leading to the proposed technique based on the minimization of the dependent information between spectral bands, while trying to maximize the conditional entropies of the selected bands


title: A Highly-Efficient Memory-Compression Scheme for GPU-Accelerated Intrusion Detection Systems

abstract: Pattern Matching is a computationally intensive task used in many research fields and real world applications. Due to the ever-growing volume of data to be processed, and increasing link speeds, the number of patterns to be matched has risen significantly. In this paper we explore the parallel capabilities of modern General Purpose Graphics Processing Units (GPGPU) applications for high speed pattern matching. A highly compressed failure-less Aho-Corasick algorithm is presented for Intrusion Detection Systems on off-the-shelf hardware. This approach maximises the bandwidth for data transfers between the host and the Graphics Processing Unit (GPU). Experiments are performed on multiple alphabet sizes, demonstrating the capabilities of the library to be used in different research fields, while sustaining an adequate throughput for intrusion detection systems or DNA sequencing. The work also explores the performance impact of adequate prefix matching for alphabet sizes and varying pattern numbers achieving speeds up to 8Gbps and low memory consumption for intrusion detection systems.


title: A 2.45-GHz high-efficiency loop-shaped PIFA rectenna for portable devices and wireless sensors

abstract: An efficient compact rectenna based on a novel asymmetric loop-shaped planar inverted-F antenna (PIFA) is proposed for portable devices and wireless sensors. The antenna element, or the loop-shaped PIFA, has a compact uniplanar structure and exhibits higher radiation efficiency. The rectifier used is a half-bridge boost converter, which can double the output dc voltage. In between the rectifier and loop-shaped PIFA is a matching circuit with harmonic suppression capability, further enhancing the conversion efficiency. For the 2.45-GHz prototype design, the peak conversion efficiency of 61.4% is achieved as the incident power density is 7.4 mW/m2 and the load resistance is 510 Ω.


title: Machine Learning-Based Sentimental Analysis for Twitter Accounts

abstract: Growth in the area of opinion mining and sentiment analysis has been rapid and aims to explore the opinions or text present on different platforms of social media through machine-learning techniques with sentiment, subjectivity analysis or polarity calculations. Despite the use of various machine-learning techniques and tools for sentiment analysis during elections, there is a dire need for a state-of-the-art approach. To deal with these challenges, the contribution of this paper includes the adoption of a hybrid approach that involves a sentiment analyzer that includes machine learning. Moreover, this paper also provides a comparison of techniques of sentiment analysis in the analysis of political views by applying supervised machine-learning algorithms such as Naive Bayes and support vector machines (SVM).


title: Browsers Fingerprinting Motives, Methods, and Countermeasures

abstract: With the continuous and aggressive competition in advertising businesses, uncontrollably desires have emerged to identify and classify consumers. It is proven that companies must have a clear definition of its target market. Based on this we have seen different ways to identify, analyze, and track consumers, either voluntarily or without their consent. Browser fingerprinting techniques have evolved from being privacy-friendly to privacy intrusive to serve these demands. This also has pushed privacy concerned people to save no effort to advance countermeasures. In this paper we introduce different browser fingerprinting techniques and their relevant technologies in the wild. It additionally elaborates widely known countermeasures.


title: An approach of cardiac disease prediction by analyzing ECG signal

abstract: Electrocardiogram (ECG) gives useful information about morphological and functional details of heart which is used to predict various cardiac diseases. In this paper a method of detecting cardiac diseases using support vector machine (SVM) is proposed. In this proposed method diseases are modeled using the time domain features of ECG signal which are extracted using BIOPAC AcqKnowledge software. Raw ECG signal contains these useful features which can be used to detect cardiac arrhythmia. The various ECG parameters like heart rate, QRS complex, PR interval, ST segment elevation, ST interval of ECG signal are used for analysis. Based on these parameters of ECG signal, different heart disease like atrial fibrillation, sinus tachycardia, myocardial infarction and apnea are detected. The individual accuracy of tachycardia arrhythmia, MI arrhythmia, atrial fibrillation arrhythmia and apnea proposed by SVM are 83.3%, 86.4%, 88% and 85.7% respectively.


title: Properties of Sparse Distributed Representations and their Application to Hierarchical Temporal Memory

abstract: Empirical evidence demonstrates that every region of the neocortex represents information using sparse activity patterns. This paper examines Sparse Distributed Representations (SDRs), the primary information representation strategy in Hierarchical Temporal Memory (HTM) systems and the neocortex. We derive a number of properties that are core to scaling, robustness, and generalization. We use the theory to provide practical guidelines and illustrate the power of SDRs as the basis of HTM. Our goal is to help create a unified mathematical and practical framework for SDRs as it relates to cortical function.


title: Multiband Planar Vivaldi Antenna for Mobile Communication and Industrial Applications

abstract: The proposed antenna topology is an interesting radiating element, characterized by broadband or multiband capabilities. The exponential and soft/tapered design of the edge transitions and feeding makes it a challenging item to design and tune, leading though to impressive results. The antenna is build on Rogers RO3010 material. The bands in which the antenna works are GPS and Galileo (1.57 GHz), UMTS (1.8–2.17 GHz) and ISM 2.4 GHz (Bluetooth WiFi). The purpose of such an antenna is to be embedded in an Assisted GPS (A-GPS) reference station. Such a device serves as a fix GPS reference distributing the positioning information to mobile device users and delivering at the same time services via GSM network standards or via Wi-Fi / Bluetooth connections.


title: CiteSight: supporting contextual citation recommendation using differential search

abstract: A person often uses a single search engine for very different tasks. For example, an author editing a manuscript may use the same academic search engine to find the latest work on a particular topic or to find the correct citation for a familiar article. The author's tolerance for latency and accuracy may vary according to task. However, search engines typically employ a consistent approach for processing all queries. In this paper we explore how a range of search needs and expectations can be supported within a single search system using differential search. We introduce CiteSight, a system that provides personalized citation recommendations to author groups that vary based on task. CiteSight presents cached recommendations instantaneously for online tasks (e.g., active paper writing), and refines these recommendations in the background for offline tasks (e.g., future literature review). We develop an active cache-warming process to enhance the system as the author works, and context-coupling, a technique for augment sparse citation networks. By evaluating the quality of the recommendations and collecting user feedback, we show that differential search can provide a high level of accuracy for different tasks on different time scales. We believe that differential search can be used in many situations where the user's tolerance for latency and desired response vary dramatically based on use.


Mathematical Models of Flow Shop and Job Shop Scheduling Problems


title: 3D voxel construction based on epipolar geometry

abstract: In this paper, we propose an approach for construction of a projective 3D voxel space based on epipolar geometry obtained with weak calibration. This concept of voxel space defines the epipolar lines following to the coordinate axis. In the field of computer vision, it is common to reconstruct 3D geometry data based on camera calibration data and disparities of matching points in each image. In the case that the goal of the system is generating the images from another point of view, complete 3D geometry reconstruction is not presently required. However, detecting the consistent matching points in several pairs of images without complete 3D geometry information is difficult. This paper describes the concept of projective 3D voxel, which makes it possible to handle 3D geometric data without complete 3D geometry information.


title: Low Complexity Message Passing Algorithm for SCMA System

abstract: Sparse code multiple access (SCMA) is a novel non-orthogonal air-interface technology in which several users share a same frequency resource simultaneously. The SCMA decoder is based on a message passing algorithm (MPA) for the sparsity of codeword. In the original MPA, all the users update a message until the maximum number of iterations is reached, resulting in high computational complexity. This letter puts forward a threshold-based MPA, where a belief threshold is applied to control the algorithm process. Simulation results show that the proposed scheme obtains a low computational complexity with only a slight performance degradation when the threshold is set appropriately.


title: Community detection and stochastic block models: recent developments

abstract: The stochastic block model (SBM) is a random graph model with planted clusters. It is widely employed as a canonical model to study clustering and community detection, and provides generally a fertile ground to study the statistical and computational tradeoffs that arise in network and data sciences. This note surveys the recent developments that establish the fundamental limits for community detection in the SBM, both with respect to information-theoretic and computational thresholds, and for various recovery requirements such as exact, partial and weak recovery (a.k.a., detection). The main results discussed are the phase transitions for exact recovery at the Chernoff-Hellinger threshold, the phase transition for weak recovery at the Kesten-Stigum threshold, the optimal distortion-SNR tradeoff for partial recovery, the learning of the SBM parameters and the gap between information-theoretic and computational thresholds. The note also covers some of the algorithms developed in the quest of achieving the limits, in particular two-round algorithms via graph-splitting, semi-definite programming, linearized belief propagation, classical and nonbacktracking spectral methods. A few open problems are also discussed.


title: Texture synthesis and the controlled generation of natural stimuli using convolutional neural networks

abstract: It is a long standing question how biological systems transform visual inputs to robustly infer high-level visual information. Research in the last decades has established that much of the underlying computations take place in a hierarchical fashion along the ventral visual pathway. However, the exact processing stages along this hierarchy are difficult to characterise. Here we present a method to generate stimuli that will allow a principled description of the processing stages along the ventral stream. We introduce a new parametric texture model based on the powerful feature spaces of convolutional neural networks optimised for object recognition. We show that constraining a spatial summary statistic over feature maps suffices to synthesise high-quality natural textures. Moreover we establish that our texture representations continuously disentangle high-level visual information and demonstrate that the hierarchical parameterisation of the texture model naturally enables us to generate novel types of stimuli for systematically probing mid-level vision.


title: The $\chi$-Divergence for Approximate Inference

abstract: Variational inference enables Bayesian analysis for complex probabilistic models with massive data sets. It posits a family of approximating distributions and finds the member closest to the posterior. While successful, variational inference methods can run into pathologies; for example, they typically underestimate posterior uncertainty. In this paper we propose CHIVI, a complementary algorithm to traditional variational inference. CHIVI is a black box algorithm that minimizes the $\chi$-divergence from the posterior to the family of approximating distributions and provides an upper bound of the model evidence. We studied CHIVI in several scenarios. On Bayesian probit regression and Gaussian process classification it yielded better classification error rates than expectation propagation (EP) and classical variational inference (VI). When modeling basketball data with a Cox process, it gave better estimates of posterior uncertainty. Finally, we show how to use the CHIVI upper bound and classical VI lower bound to sandwich estimate the model evidence.


title: Ergative Case and the Transitive Subject: A View from Nez Perce

abstract: Ergative case, the special case of transitive subjects, raises questions not only for the theory of case but also for theories of subjecthood and transitivity. This paper analyzes the case system of Nez Perce, a “three-way ergative” language, with an eye towards a formalization of the category of transitive subject. I show that it is object agreement that is determinative of transitivity, and hence of ergative case, in Nez Perce. I further show that the transitivity condition on ergative case must be coupled with a criterion of subjecthood that makes reference to participation in subject agreement, not just to origin in a high argument-structural position. These two results suggest a formalization of the transitive subject as that argument uniquely accessing both high and low agreement information, the former through its (agreement-derived) connection with T and the latter through its origin in the specifier of a head associated with object agreement (v). In view of these findings, I argue that ergative case morphology should be analyzed not as the expression of a syntactic primitive but as the morphological spell-out of subject agreement and object agreement on a nominal.


Proofs of Useful Work.


title: Multi-modal deep feature learning for RGB-D object detection

abstract: We present an approach for RGB-D object detection, which can exploit both modality-correlated and modality-specific relationships between RGB and depth images.The shared weights strategy and a parameter-free-correlation layer are introduced to extract the modality-correlated representations.The proposed approach can simultaneously generate RGB-D region proposals and perform region-wise RGB-D object recognition. We present a novel multi-modal deep feature learning architecture for RGB-D object detection. The current paradigm for object detection typically consists of two stages: objectness estimation and region-wise object recognition. Most existing RGB-D object detection approaches treat the two stages separately by extracting RGB and depth features individually, thus ignore the correlated relationship between these two modalities. In contrast, our proposed method is designed to take full advantages of both depth and color cues by exploiting both modality-correlated and modality-specific features and jointly performing RGB-D objectness estimation and region-wise object recognition. Specifically, shared weights strategy and a parameter-free correlation layer are exploited to carry out RGB-D-correlated objectness estimation and region-wise recognition in conjunction with RGB-specific and depth-specific procedures. The parameters of these three networks are simultaneously optimized via end-to-end multi-task learning. The multi-modal RGB-D objectness estimation results and RGB-D object recognition results are both boosted by late-fusion ensemble. To validate the effectiveness of the proposed approach, we conduct extensive experiments on two challenging RGB-D benchmark datasets, NYU Depth v2 and SUN RGB-D. The experimental results show that by introducing the modality-correlated feature representation, the proposed multi-modal RGB-D object detection approach is substantially superior to the state-of-the-art competitors. Moreover, compared to the expensive deep architecture (VGG16) that the state-of-the-art methods preferred, our approach, which is built upon more lightweight deep architecture (AlexNet), performs slightly better.


title: Learning Joint Gaussian Representations for Movies, Actors, and Literary Characters.

abstract: Understanding of narrative content has become an increasingly popular topic. However, narrative semantics pose difficult challenges as the effects of multiple narrative facets, such as the text, events, character types, and genres, are tightly intertwined. We present a joint representation learning framework for embedding actors, literary characters, movies, genres, and descriptive keywords as Gaussian distributions and translation vectors on the Gaussian means. The Gaussian variance naturally corresponds to actors' versatility, a central concept in acting. Our estimate of actors' versatility agree with domain experts' rankings 65.95% of the time. This is, to our knowledge, the first computational technique for estimating this semantic concept. Additionally, the model substantially outperforms a TransE baseline in prediction of actor casting choices.


title: Rhabdomyolysis and Its Treatments

abstract: Rhabdomyolysis is a life threataning diesase .Most common complications of rhabdomyolysis disseminated intravascular coagulation, and compartment syndrome. In this article mainly we discuss about Common and important causes and treatments.


Supplementary material for the paper : Discriminative Correlation Filter with Channel and Spatial Reliability DCF-CSR


Single-cell ChIP-seq reveals cell subpopulations defined by chromatin state


title: A simple approach to Bayesian network computations

abstract: The general problem of computing poste rior probabilities in Bayesian networks is NP hard Cooper However e cient algorithms are often possible for particular applications by exploiting problem struc tures It is well understood that the key to the materialization of such a possibil ity is to make use of conditional indepen dence and work with factorizations of joint probabilities rather than joint probabilities themselves Di erent exact approaches can be characterized in terms of their choices of factorizations We propose a new approach which adopts a straightforward way for fac torizing joint probabilities In comparison with the clique tree propagation approach our approach is very simple It allows the pruning of irrelevant variables it accommo dates changes to the knowledge base more easily it is easier to implement More importantly it can be adapted to utilize both intercausal independence and condi tional independence in one uniform frame work On the other hand clique tree prop agation is better in terms of facilitating pre computations


title: Personalized Courses Recommendation Functionality for Flex-eL

abstract: Consider substantial scope of courses with theirs complex relationships, it is not a trivial task for each elearner to find coherent and sound courses to enroll in elearning environment where face to face consultation is not the norm. In this paper, we extend the scope of Flex-eL services by providing functionality which delivers smart and automate personalized courses recommendation that are coherent and sound for each elearner. The outcome is a set of ranked recommendation goals for each individual based on his/her completed courses.


title: The NASD Securities Observation, News Analysis & Regulation System (SONAR)

abstract: The Securities Observation, News Analysis, and Regulation (SONAR) system was developed by NASD to monitor the Nasdaq, Over the Counter (OTC), and Nasdaq-Liffe (futures) stock markets for potential insider trading and fraud through misrepresentation. SONAR has been in operational use at NASD since December 2001, processing approximately 10,000 news wires stories and SEC filings, evaluating price/volume models for 25,000 securities, and generating 50-60 alerts (or “breaks”) per day for review by several groups of regulatory analysts and investigators. In addition, SONAR has greatly expanded surveillance coverage to new areas of the market and increased accuracy significantly over an earlier break detection system. SONAR makes use of several AI and statistical techniques, including NLP text mining, statistical regression, rule-based inference, uncertainty, and fuzzy matching. Sonar combines these enabling technologies in a system designed to deliver a steady stream of high-quality breaks to the analysts for further investigation. Additional components including visualization, text search agents, and flexible displays add to the system’s utility. Finally, SONAR is designed as a knowledge-based system. Domain knowledge is maintained by knowledge engineers working closely with the regulatory analysts. In this way, SONAR is adaptable to new market conditions, data sources, and regulatory concerns.


Human Computer Interaction : Pros and Cons


title: Goal programming approaches to obtain the priority vectors from the intuitionistic fuzzy preference relations

abstract: The priority method on the intuitionistic fuzzy preference relation (IFPR) is proposed. In order to avoid the operational difficulty in dealing with the intuitionistic sets, the equivalent interval matrices of the IFPR are introduced. Based on the multiplicative consistent definition of the fuzzy interval preference relation (FIPR), the goal programming models for deriving the priority vector of the IFPR have been put forward by analyzing the relation between the IFNPR and the IFPR. This goal programming method is generalized to the case of group decision making with the weight information defined by each DM. Two numerical examples are provided to illustrate the application of the proposed models.


title: Analysis of SEAndroid Policies: Combining MAC and DAC in Android

abstract: Android has become a dominant computing platform, and its popularity has coincided with a surge of malware. The incorporation of Security-Enhanced Linux in Android (SEAndroid) is an important security enhancement to the platform. While SEAndroid adds the benefits of mandatory protection that SELinux brought to desktops and servers, the protection is only as good as the policy. Existing Android devices contain a wide variety of SEAndroid policies, depending on both the version of Android as well as the device manufacturer. In this paper, we present an approach to analyze SEAndroid policies in conjunction with the underlying Linux/Unix Discretionary Access Control policies. We apply our approach to four different versions of Android Open Source Project (AOSP) as well as devices from seven different manufacturers, and find several forms of unintentional privilege assignments.


title: Internet of Things and Big Data Analytics for Smart and Connected Communities

abstract: This paper promotes the concept of smart and connected communities SCC, which is evolving from the concept of smart cities. SCC are envisioned to address synergistically the needs of remembering the past (preservation and revitalization), the needs of living in the present (livability), and the needs of planning for the future (attainability). Therefore, the vision of SCC is to improve livability, preservation, revitalization, and attainability of a community. The goal of building SCC for a community is to live in the present, plan for the future, and remember the past. We argue that Internet of Things (IoT) has the potential to provide a ubiquitous network of connected devices and smart sensors for SCC, and big data analytics has the potential to enable the move from IoT to real-time control desired for SCC. We highlight mobile crowdsensing and cyber-physical cloud computing as two most important IoT technologies in promoting SCC. As a case study, we present TreSight, which integrates IoT and big data analytics for smart tourism and sustainable cultural heritage in the city of Trento, Italy.


title: Deep Fully-Connected Networks for Video Compressive Sensing

abstract: In this work we present a deep learning framework for video compressive sensing. The proposed formulation enables recovery of video frames in a few seconds at significantly improved reconstruction quality compared to previous approaches. Our investigation starts by learning a linear mapping between video sequences and corresponding measured frames which turns out to provide promising results. We then extend the linear formulation to deep fully-connected networks and explore the performance gains using deeper architectures. Our analysis is always driven by the applicability of the proposed framework on existing compressive video architectures. Extensive simulations on several video sequences document the superiority of our approach both quantitatively and qualitatively. Finally, our analysis offers insights into understanding how dataset sizes and number of layers affect reconstruction performance while raising a few points for future investigation. ::: Code is available at Github: this https URL


title: Low-Reverse Recovery Charge Superjunction MOSFET With a p-Type Schottky Body Diode

abstract: A novel low-reverse recovery charge superjunction MOSFET (SJ-MOSFET) with a p-type Schottky body diode is proposed in this letter. The device has a p-type Schottky contact on the p-pillar at the drain side. Electrons are prevented from injecting into the drain side by the p-type Schottky contact, and the total carrier concentration is greatly reduced. Compared with the conventional device, the proposed SJ-MOSFET has a lower reverse recovery charge and a larger soft factor. Simulated results show that the reverse recovery charge is reduced by 81.3% and 76.0% at 300 K and 400 K, respectively, with a metal work function of 4.5 eV. The optimized metal work function range is 4.3–4.6 eV.


title: Applying Machine Learning to Reduce the Adaptation Space in Self-Adaptive Systems : an exploratory work

abstract: Self-adaptive systems are capable of autonomously adjusting their behavior at runtime to accomplish particular adaptation goals. The most common way to realize self-adaption is using a feedback loo ...


Effective Interaction-aware Trajectory Prediction using Temporal Convolutional Neural Networks


title: Cytochrome P4502E1 (CYP2E1) genetic polymorphisms in a Lebanese population: frequency distribution and association with morbid diseases.

abstract: BACKGROUND ::: The drug-metabolizing enzyme CYP2E1 is of great interest in environmental medicine because of its involvement in the bioactivation of multiple procarcinogens. ::: ::: ::: AIM ::: This study is aimed at determining the frequency of genetic polymorphisms of the three restriction fragment length polymorphisms: PstI and RsaI (CYP2E1*5B) and DraI (CYP2E1*6) in 216 cancer-free Lebanese individuals, as well as assessing potential association with morbid diseases in this specific population. ::: ::: ::: RESULTS ::: The frequency of C-T allele of CYP2E*5B was 0.7% and that of A CYP2E1*6 was 6.3%. All those who carried the CYP2E1*5B allele also carried the CYP2E1*6 allele. There was a significant decrease in coronary artery disease incidence in patients carrying a CYP2E1*6 genetic polymorphism (39.7% of the noncarriers vs. 13.6% of the carriers had coronary artery disease; p = 0.019); similar results were found with the haplotype analysis (p = 0.03) but not with CYP2E1*5B alone. ::: ::: ::: CONCLUSION ::: This is the first study on the genetic polymorphism of CYP2E1 in a Lebanese population. These data will be useful for future assessment of the role of CYP2E1 polymorphisms in the cancer population in Lebanon. It is recommended that careful population selection be performed in designing case-control studies that evaluate the association between CYP2E1 and cancer incidence. The most important factors to be controlled for are sex, body mass index, environmental exposure, lifestyle habits, and possibly, history of coronary artery disease.


Identifying Quora question pairs having the same intent


title: Real-Time Detection and Recognition of Road Traffic Signs

abstract: This paper proposes a novel system for the automatic detection and recognition of traffic signs. The proposed system detects candidate regions as maximally stable extremal regions (MSERs), which offers robustness to variations in lighting conditions. Recognition is based on a cascade of support vector machine (SVM) classifiers that were trained using histogram of oriented gradient (HOG) features. The training data are generated from synthetic template images that are freely available from an online database; thus, real footage road signs are not required as training data. The proposed system is accurate at high vehicle speeds, operates under a range of weather conditions, runs at an average speed of 20 frames per second, and recognizes all classes of ideogram-based (nontext) traffic symbols from an online road sign database. Comprehensive comparative results to illustrate the performance of the system are presented.


title: Frugal Streaming for Estimating Quantiles

abstract: Modern applications require processing streams of data for estimating statistical quantities such as quantiles with small amount of memory. In many such applications, in fact, one needs to compute such statistical quantities for each of a large number of groups (e.g.,network traffic grouped by source IP address), which additionally restricts the amount of memory available for the stream for any particular group. We address this challenge and introduce frugal streaming, that is algorithms that work with tiny – typically, sub-streaming – amount of memory per group.


title: Learning Relational Representations by Analogy using Hierarchical Siamese Networks

abstract: We address relation extraction as an analogy problem by proposing a novel approach to learn representations of relations expressed by their textual mentions. In our assumption, if two pairs of entities belong to the same relation, then those two pairs are analogous. Following this idea, we collect a large set of analogous pairs by matching triples in knowledge bases with web-scale corpora through distant supervision. We leverage this dataset to train a hierarchical siamese network in order to learn entity-entity embeddings which encode relational information through the different linguistic paraphrasing expressing the same relation. We evaluate our model in a one-shot learning task by showing a promising generalization capability in order to classify unseen relation types, which makes this approach suitable to perform automatic knowledge base population with minimal supervision. Moreover, the model can be used to generate pre-trained embeddings which provide a valuable signal when integrated into an existing neural-based model by outperforming the state-of-the-art methods on a downstream relation extraction task.


title: Understanding and evaluating blind deconvolution algorithms

abstract: Blind deconvolution is the recovery of a sharp version of a blurred image when the blur kernel is unknown. Recent algorithms have afforded dramatic progress, yet many aspects of the problem remain challenging and hard to understand. The goal of this paper is to analyze and evaluate recent blind deconvolution algorithms both theoretically and experimentally. We explain the previously reported failure of the naive MAP approach by demonstrating that it mostly favors no-blur explanations. On the other hand we show that since the kernel size is often smaller than the image size a MAP estimation of the kernel alone can be well constrained and accurately recover the true blur. The plethora of recent deconvolution techniques makes an experimental evaluation on ground-truth data important. We have collected blur data with ground truth and compared recent algorithms under equal settings. Additionally, our data demonstrates that the shift-invariant blur assumption made by most algorithms is often violated.


title: Estimating 3-D rigid body transformations: a comparison of four major algorithms

abstract: A common need in machine vision is to compute the 3-D rigid body transformation that aligns two sets of points for which correspondence is known. A comparative analysis is presented here of four popular and efficient algorithms, each of which computes the translational and rotational components of the transform in closed form, as the solution to a least squares formulation of the problem. They differ in terms of the transformation representation used and the mathematical derivation of the solution, using respectively singular value decomposition or eigensystem computation based on the standard \([ \vec{R}, \vec{T} ]\) representation, and the eigensystem analysis of matrices derived from unit and dual quaternion forms of the transform. This comparison presents both qualitative and quantitative results of several experiments designed to determine (1) the accuracy and robustness of each algorithm in the presence of different levels of noise, (2) the stability with respect to degenerate data sets, and (3) relative computation time of each approach under different conditions. The results indicate that under “ideal” data conditions (no noise) certain distinctions in accuracy and stability can be seen. But for “typical, real-world” noise levels, there is no difference in the robustness of the final solutions (contrary to certain previously published results). Efficiency, in terms of execution time, is found to be highly dependent on the computer system setup.


title: Complex ratio masking for joint enhancement of magnitude and phase

abstract: The phase response of noisy speech has largely been ignored, but recent research shows the importance of phase for perceptual speech quality. A few phase enhancement approaches have been developed. These systems, however, require a separate algorithm for enhancing the magnitude response. In this paper, we present a novel framework for performing monaural speech separation in the complex domain. We show that much structure is exhibited in the real and imaginary components of the short-time Fourier transform, making the complex domain appropriate for supervised estimation. Consequently, we define the complex ideal ratio mask (cIRM) that jointly enhances the magnitude and phase of noisy speech. We then employ a single deep neural network to estimate both the real and imaginary components of the cIRM. The evaluation results show that complex ratio masking yields high quality speech enhancement, and outperforms related methods that operate in the magnitude domain or separately enhance magnitude and phase.


title: Sparse Convolutional Neural Networks

abstract: Deep neural networks have achieved remarkable performance in both image classification and object detection problems, at the cost of a large number of parameters and computational complexity. In this work, we show how to reduce the redundancy in these parameters using a sparse decomposition. Maximum sparsity is obtained by exploiting both inter-channel and intra-channel redundancy, with a fine-tuning step that minimize the recognition loss caused by maximizing sparsity. This procedure zeros out more than 90% of parameters, with a drop of accuracy that is less than 1% on the ILSVRC2012 dataset. We also propose an efficient sparse matrix multiplication algorithm on CPU for Sparse Convolutional Neural Networks (SCNN) models. Our CPU implementation demonstrates much higher efficiency than the off-the-shelf sparse matrix libraries, with a significant speedup realized over the original dense network. In addition, we apply the SCNN model to the object detection problem, in conjunction with a cascade model and sparse fully connected layers, to achieve significant speedups.


title: Anxiety in high functioning children with autism

abstract: High-functioning children with autism were compared with two control groups on measures of anxiety and social worries. Comparison control groups consisted of children with specific language impairment (SLI) and normally developing children. Each group consisted of 15 children between the ages of 8 and 12 years and were matched for age and gender. Children with autism were found to be most anxious on both measures. High anxiety subscale scores for the autism group were separation anxiety and obsessive-compulsive disorder. These findings are discussed within the context of theories of autism and anxiety in the general population of children. Suggestions for future research are made.


title: Finding a team of experts in social networks

abstract: Given a task T, a pool of individuals X with different skills, and a social network G that captures the compatibility among these individuals, we study the problem of finding X, a subset of X, to perform the task. We call this the T EAM F ORMATION problem. We require that members of X' not only meet the skill requirements of the task, but can also work effectively together as a team. We measure effectiveness using the communication cost incurred by the subgraph in G that only involves X'. We study two variants of the problem for two different communication-cost functions, and show that both variants are NP-hard. We explore their connections with existing combinatorial problems and give novel algorithms for their solution. To the best of our knowledge, this is the first work to consider the T EAM F ORMATION problem in the presence of a social network of individuals. Experiments on the DBLP dataset show that our framework works well in practice and gives useful and intuitive results.


title: Human hand modelling: kinematics, dynamics, applications

abstract: An overview of mathematical modelling of the human hand is given. We consider hand models from a specific background: rather than studying hands for surgical or similar goals, we target at providing a set of tools with which human grasping and manipulation capabilities can be studied, and hand functionality can be described. We do this by investigating the human hand at various levels: (1) at the level of kinematics, focussing on the movement of the bones of the hand, not taking corresponding forces into account; (2) at the musculotendon structure, i.e. by looking at the part of the hand generating the forces and thus inducing the motion; and (3) at the combination of the two, resulting in hand dynamics as well as the underlying neurocontrol. Our purpose is to not only provide the reader with an overview of current human hand modelling approaches but also to fill the gaps with recent results and data, thus allowing for an encompassing picture.


title: Moving vehicle detection and tracking in unstructured environments

abstract: The detection and tracking of moving vehicles is a necessity for collision-free navigation. In natural unstructured environments, motion-based detection is challenging due to low signal to noise ratio. This paper describes our approach for a 14 km/h fast autonomous outdoor robot that is equipped with a Velodyne HDL-64E S2 for environment perception. We extend existing work that has proven reliable in urban environments. To overcome the unavailability of road network information for background separation, we introduce a foreground model that incorporates geometric as well as temporal cues. Local shape estimates successfully guide vehicle localization. Extensive evaluation shows that the system works reliably and efficiently in various outdoor scenarios without any prior knowledge about the road network. Experiments with our own sensor as well as on publicly available data from the DARPA Urban Challenge revealed more than 96% correctly identified vehicles.


title: Design and experimental validation of HyTAQ, a Hybrid Terrestrial and Aerial Quadrotor

abstract: This paper details the design, modeling, and experimental validation of a novel mobile robot capable of both aerial and terrestrial locomotion. Flight is achieved through a quadrotor configuration; four actuators provide the required thrust. Adding a rolling cage to the quadrotor makes terrestrial locomotion possible using the same actuator set and control system. Thus, neither the mass nor the system complexity is increased by inclusion of separate actuators for terrestrial and aerial locomotion. An analysis of the system's energy consumption demonstrates that during terrestrial locomotion, the robot only needs to overcome rolling resistance and consumes much less energy compared to the aerial mode. This solves one of the most vexing problems of quadrotors and rotorcraft in general - their short operation time. Experimental results show that the hybrid robot can travel a distance four times greater and operate almost six times longer than an aerial only system. It also solves one of the most challenging problems in terrestrial robot design - obstacle avoidance. When an obstacle is encountered, the system simply flies over it.


title: Internet Gaming Addiction: A Systematic Review of Empirical Research

abstract: The activity of play has been ever present in human history and the Internet has emerged as a playground increasingly populated by gamers. Research suggests that a minority of Internet game players experience symptoms traditionally associated with substance-related addictions, including mood modification, tolerance and salience. Because the current scientific knowledge of Internet gaming addiction is copious in scope and appears relatively complex, this literature review attempts to reduce this confusion by providing an innovative framework by which all the studies to date can be categorized. A total of 58 empirical studies were included in this literature review. Using the current empirical knowledge, it is argued that Internet gaming addiction follows a continuum, with antecedents in etiology and risk factors, through to the development of a “full-blown” addiction, followed by ramifications in terms of negative consequences and potential treatment. The results are evaluated in light of the emergent discrepancies in findings, and the consequent implications for future research.


title: Reproducing standard SCME channel models for massive MIMO base station radiated testing

abstract: Massive MIMO is a multi-user technology, where radio base stations (BSs) are equipped with a large number of antennas to simultaneously serve many terminals in the same time-frequency resource. Performance evaluation of such large-scale antenna systems in the design and development stage is challenging. In this paper, we propose to evaluate massive MIMO BSs with a sectorized multi-probe anechoic chamber (MPAC) setup. A sectorized MPAC setup with 16 probe antennas distributed uniformly within [−60°, 60°] in azimuth domain is utilized to reproduce target channel models. A 8 × 8 and a 16 × 16 uniform planar array at 3.5 GHz are selected as the BS under evaluation, respectively. Radio channel emulation accuracies in terms of power-angular spectrum, spatial correlation and beamforming pattern are investigated for the proposed MPAC setup and desired channel models.


title: Towards a cognitive theory of information retrieval

abstract: Abstract A framework for constructing a cognitive model of users' information searching behaviour is described. The motivation for the framework is to create explanatory and predictive theories of information searching to improve the design of information retrieval (IR) systems. The framework proposes a taxonomy of components for process models of the information seeking task, information need types and knowledge sources necessary to support the task. The framework is developed into a preliminary version of a cognitive theory of information searching by the addition of strategies and correspondence rules which predict user behaviour in different task stages according to information need types, facilities provided by the IR system and knowledge held by the user. The theory is evaluated by using claims analysis based on empirical observations of users information retrieval and by a walkthrough of an IR session to investigate how well the theory can account for empirical evidence. Results show that the theory can indicate the expert strategies which should be followed in different task contexts but predictions of actual user behaviour are less accurate. The future possibilities for employing the theoretical model as a tutorial advisor for information retrieval and as an evaluation method for IR systems are reviewed. The role and potential of cognitive theories of user task-action in Information Retrieval and Human Computer Interaction are discussed.


title: Power System Transient Stability Assessment Based on Big Data and the Core Vector Machine

abstract: In this paper, an online power system transient stability assessment (TSA) problem is mapped as a two-class classification problem and a novel data mining algorithm the core vector machine (CVM) is proposed to solve the problem based on phasor measurement units (PMUs) big data. First of all, an offline training, online application framework is proposed, which contained four sub-steps, namely features selection, offline training, online application, and assessment evaluation. First, 24 features are selected to present the system status. Then in the offline training procedure, the PMU big data is generated by time domain simulation, and a CVM model is trained and tested. In the online application procedure, an interface between PMU data center and feature calculation program is set up to collect real time specific PMU big data and the CVM trained is applied to the TSA problem. Last but not least, the evaluation indices are calculated. Compared with other support vector machines, the proposed CVM based assessment algorithm has the higher precision, meanwhile, it has the least time consumption and space complexity. As long as online PMU big data are received, TSA can be done simultaneously. Case studies on the IEEE New England 39-bus system, and real systems in China and the U.S., exhibit the speed and effectiveness of the proposed algorithm.


A social influence based trust model for recommender systems


title: Radiance caching for efficient global illumination computation

abstract: In this paper we present a ray tracing based method for accelerated global illumination computation in scenes with low-frequency glossy BRDFs. The method is based on sparse sampling, caching, and interpolating radiance on glossy surfaces. In particular we extend the irradiance caching scheme of [WRC88] to cache and interpolate directional incoming radiance instead of irradiance. The incoming radiance at a point is represented by a vector of coefficients with respect to a spherical or hemispherical basis. The surfaces suitable for interpolation are selected automatically according to the glossiness of their BRDF. We also propose a novel method for computing translational radiance gradient at a point.


title: Socket Augmentation: Rationale and Technique

abstract: The consequences of exodontia include alveolar bone resorption and ultimately atrophy to basal bone of the edentulous site/ridges. Ridge resorption proceeds quickly after tooth extraction and significantly reduces the possibility of placing implants without grafting procedures. The aims of this article are to describe the rationale behind alveolar ridge augmentation procedures aimed at preserving or minimizing the edentulous ridge volume loss. Because the goal of these approaches is to preserve bone, exodontia should be performed to preserve as much of the alveolar process as possible. After severance of the supra- and subcrestal fibrous attachment using scalpels and periotomes, elevation of the tooth frequently allows extraction with minimal socket wall damage. Extraction sockets should not be acutely infected and be completely free of any soft tissue fragments before any grafting or augmentation is attempted. Socket bleeding that mixes with the grafting material seems essential for success of this procedure. Various types of bone grafting materials have been suggested for this purpose, and some have shown promising results. Coverage of the grafted extraction site with wound dressing materials, coronal flap advancement, or even barrier membranes may enhance wound stability and an undisturbed healing process. Future controlled clinical trials are necessary to determine the ideal regimen for socket augmentation.


Effects of Code Obfuscation on Android App Similarity Analysis


title: From value chain to value constellation: designing interactive strategy.

abstract: In today's fast-changing competitive environment, strategy is no longer a matter of positioning a fixed set of activities along that old industrial model, the value chain. Successful companies increasingly do not just add value, they reinvent it. The key strategic task is to reconfigure roles and relationships among a constellation of actors - suppliers, partners, customers - in order to mobilize the creation of value by new combinations of players. What is so different about this new logic of value? It breaks down the distinction between products and services and combines them into activity-based "offerings" from which customers can create value for themselves. But as potential offerings grow more complex, so do the relationships necessary to create them. As a result, a company's strategic task becomes the ongoing reconfiguration and integration of its competencies and customers. The authors provide three illustrations of these new rules of strategy. IKEA has blossomed into the world's largest retailer of home furnishings by redefining the relationships and organizational practices of the furniture business. Danish pharmacies and their national association have used the opportunity of health care reform to reconfigure their relationships with customers, doctors, hospitals, drug manufacturers, and with Danish and international health organizations to enlarge their role, competencies, and profits. French public-service concessionaires have mastered the art of conducting a creative dialogue between their customers - local governments in France and around the world - and a perpetually expanding set of infrastructure competencies.


title: Terrorism, Security, and the Threat of Counterterrorism

abstract: In America, Britain and Australia the threat of terrorism has been used to justify radical new legislation that gives police and intelligence agencies unprecedented powers to detain and question people believed to have information connected to terrorism. In this paper I explore the nature of the threat of non-state terrorism—threat to national security and the well-being of citizens. I argue that terrorism does not pose a threat sufficient to justify the kinds of counterterrorism legislation currently being enacted. Furthermore many of the current counterterrorism practices pose a greater threat to individual physical security and well-being than non-state terrorism. We should fear counterterrorism more than we fear terrorism.


title: Flexible Capacitive Tactile Sensor Array With Truncated Pyramids as Dielectric Layer for Three-Axis Force Measurement

abstract: This paper presents a flexible capacitive tactile sensor array embedded with a truncated polydimethylsiloxane pyramid array as a dielectric layer. The proposed sensor array has been fabricated with $4\times 4$ sensor units. The measurement ranges of forces in the $x$ -axis, $y$ -axis, and $z$ -axis are 0–0.5, 0–0.5, and 0–4 N, respectively. In the range of 0–0.5 N, the sensitivities of the sensor unit are 58.3%/N, 57.4%/N, and 67.2%/N in the $x$ -axis, $y$ -axis, and $z$ -axis, respectively. In the range of 0.5–4 N, the sensitivity in the $z$ -axis is 7.7%/N. Three-axis force measurement has been conducted for all the sensor units. The average errors between the applied and calculated forces are 11.8% ± 6.4%. The sensor array has been mounted on a prosthetic hand. A paper cup and a cube are grasped by the prosthetic hand and the three-axis contact force is measured in real time by the sensor array. Results show that the sensor can capture the three-axis contact force image both in light and tight grasping. The proposed capacitive tactile sensor array can be utilized in robotics and prosthetic hand applications. [2014-0350]


title: Improving Malware Detection Accuracy by Extracting Icon Information

abstract: Detecting PE malware files is now commonly approached using statistical and machine learning models. While these models commonly use features extracted from the structure of PE files, we propose that icons from these files can also help better predict malware. We propose an innovative machine learning approach to extract information from icons. Our proposed approach consists of two steps: 1) extracting icon features using summary statics, histogram of gradients (HOG), and a convolutional autoencoder, 2) clustering icons based on the extracted icon features. Using publicly available data and by using machine learning experiments, we show our proposed icon clusters significantly boost the efficacy of malware prediction models. In particular, our experiments show an average accuracy increase of 10% when icon clusters are used in the prediction model.


title: Efficient anytime algorithm for large-scale QoS-aware web service composition

abstract: The QoS-aware web service composition WSC problem aims at the fully automatic construction of a composite web service with the optimal accumulated QoS value. It is, however, intractable to solve the QoS-aware WSC problem for large scale instances since the problem corresponds to a global optimisation problem. That is, in the real world, traditional algorithms can require significant amount of time to finally find the optimal solution, and such an unexpected long delay is unfavourable to users. In this paper, we propose a novel anytime algorithm using dynamic beam widths for the QoS-aware WSC problem. Our algorithm generates early sub-optimal solutions and keeps improving the quality of the solution along with the execution time, up to the optimal solution if a client allows enough time. We empirically validate that our algorithm can identify composite web services with high quality much earlier than an optimal algorithm and the beam stack search.


title: Potential Use of Bacillus coagulans in the Food Industry

abstract: Probiotic microorganisms are generally considered to beneficially affect host health when used in adequate amounts. Although generally used in dairy products, they are also widely used in various commercial food products such as fermented meats, cereals, baby foods, fruit juices, and ice creams. Among lactic acid bacteria, Lactobacillus and Bifidobacterium are the most commonly used bacteria in probiotic foods, but they are not resistant to heat treatment. Probiotic food diversity is expected to be greater with the use of probiotics, which are resistant to heat treatment and gastrointestinal system conditions. Bacillus coagulans (B. coagulans) has recently attracted the attention of researchers and food manufacturers, as it exhibits characteristics of both the Bacillus and Lactobacillus genera. B. coagulans is a spore-forming bacterium which is resistant to high temperatures with its probiotic activity. In addition, a large number of studies have been carried out on the low-cost microbial production of industrially valuable products such as lactic acid and various enzymes of B. coagulans which have been used in food production. In this review, the importance of B. coagulans in food industry is discussed. Moreover, some studies on B. coagulans products and the use of B. coagulans as a probiotic in food products are summarized.


title: Fine-Grained Opinion Mining from Mobile App Reviews with Word Embedding Features

abstract: Existing approaches for opinion mining mainly focus on reviews from Amazon, domain-specific review websites or social media. Little efforts have been spent on fine-grained analysis of opinions in review texts from mobile smart phone applications. In this paper, we propose an aspect and subjective phrase extraction model for German reviews from the Google Play store. We analyze the impact of different features, including domain-specific word embeddings. Our best model configuration shows a performance of 0.63 \(F_1\) for aspects and 0.62 \(F_1\) for subjective phrases. Further, we perform cross-domain experiments: A model trained on Amazon reviews and tested on app reviews achieves lower performance (drop by 27% points for aspects and 15% points for subjective phrases). The results indicate that there are strong differences in the way personal opinions on product aspects are expressed in the particular domains.


title: Land cover mapping at very high resolution with rotation equivariant CNNs: towards small yet accurate models

abstract: In remote sensing images, the absolute orientation of objects is arbitrary. Depending on an object's orientation and on a sensor's flight path, objects of the same semantic class can be observed in different orientations in the same image. Equivariance to rotation, in this context understood as responding with a rotated semantic label map when subject to a rotation of the input image, is therefore a very desirable feature, in particular for high capacity models, such as Convolutional Neural Networks (CNNs). If rotation equivariance is encoded in the network, the model is confronted with a simpler task and does not need to learn specific (and redundant) weights to address rotated versions of the same object class. In this work we propose a CNN architecture called Rotation Equivariant Vector Field Network (RotEqNet) to encode rotation equivariance in the network itself. By using rotating convolutions as building blocks and passing only the the values corresponding to the maximally activating orientation throughout the network in the form of orientation encoding vector fields, RotEqNet treats rotated versions of the same object with the same filter bank and therefore achieves state-of-the-art performances even when using very small architectures trained from scratch. We test RotEqNet in two challenging sub-decimeter resolution semantic labeling problems, and show that we can perform better than a standard CNN while requiring one order of magnitude less parameters.


title: Attention-GAN for Object Transfiguration in Wild Images

abstract: This paper studies the object transfiguration problem in wild images. The generative network in classical GANs for object transfiguration often undertakes a dual responsibility: to detect the objects of interests and to convert the object from source domain to target domain. In contrast, we decompose the generative network into two separat networks, each of which is only dedicated to one particular sub-task. The attention network predicts spatial attention maps of images, and the transformation network focuses on translating objects. Attention maps produced by attention network are encouraged to be sparse, so that major attention can be paid to objects of interests. No matter before or after object transfiguration, attention maps should remain constant. In addition, learning attention network can receive more instructions, given the available segmentation annotations of images. Experimental results demonstrate the necessity of investigating attention in object transfiguration, and that the proposed algorithm can learn accurate attention to improve quality of generated images.


title: Automatic question-answering using a deep similarity neural network

abstract: Automatic question-answering is a classical problem in natural language processing, which aims at designing systems that can automatically answer a question, in the same way as human does. In this work, we propose a deep learning based model for automatic question-answering. First the questions and answers are embedded using neural probabilistic modeling. Then a deep similarity neural network is trained to find the similarity score of a pair of answer and question. Then for each question, the best answer is found as the one with the highest similarity score. We first train this model on a large-scale public question-answering database, and then fine-tune it to transfer to the customer-care chat data. We have also tested our framework on a public question-answering database and achieved very good performance.


Plagiarism Detection in Arabic Documents : Approaches , Architecture and Systems


title: Respite Care, Marital Quality, and Stress in Parents of Children with Autism Spectrum Disorders

abstract: Parents of children with autism spectrum disorders (ASD) are at risk for having higher stress and lower marital quality than other parents. Survey data regarding respite care, marital quality, and daily hassles and uplifts were obtained from 101 mother-father dyads who were together raising at least one child with ASD (total # of children = 118). Number of hours of respite care was positively related to improved marital quality for both husbands and wives, such that a 1-h increase in weekly respite care was associated with a one-half standard deviation increase in marital quality. This relationship was significantly mediated by perceived daily stresses and uplifts in both husbands and wives. More respite care was associated with increased uplifts and reduced stress; increased uplifts were associated with improved marital quality; and more stress was associated with reduced marital quality. The number of children in the family was associated with greater stress, and reduced relational quality and daily uplifts. Results suggest policymakers and practitioners should develop supports for providing respite for families raising children with ASD.


title: An Ensemble Deep Learning Based Approach for Red Lesion Detection in Fundus Images

abstract: Diabetic retinopathy is one of the leading causes of preventable blindness in the world. Its earliest sign are red lesions, a general term that groups both microaneurysms and hemorrhages. In daily clinical practice, these lesions are manually detected by physicians using fundus photographs. However, this task is tedious and time consuming, and requires an intensive effort due to the small size of the lesions and their lack of contrast. Computer-assisted diagnosis of DR based on red lesion detection is being actively explored due to its improvement effects both in clinicians consistency and accuracy. Several methods for detecting red lesions have been proposed in the literature, most of them based on characterizing lesion candidates using hand crafted features, and classifying them into true or false positive detections. Deep learning based approaches, by contrast, are scarce in this domain due to the high expense of annotating the lesions manually. In this paper we propose a novel method for red lesion detection based on combining both deep learned and domain knowledge. Features learned by a CNN are augmented by incorporating hand crafted features. Such ensemble vector of descriptors is used afterwards to identify true lesion candidates using a Random Forest classifier. We empirically observed that combining both sources of information significantly improve results with respect to using each approach separately. Furthermore, our method reported the highest performance on a per-lesion basis on DIARETDB1 and e-ophtha, and for screening and need for referral on MESSIDOR compared to a second human expert. Results highlight the fact that integrating manually engineered approaches with deep learned features is relevant to improve results when the networks are trained from lesion-level annotated data. An open source implementation of our system is publicly available online.


title: Fuzzy Integral and Cuckoo Search Based Classifier Fusion for Human Action Recognition

abstract: The human activity recognition is an important issue for sports analysis and health monitoring. The early recognition of human actions is used in areas such as detection of criminal ac ...


title: Design and simulation of a 1 to 14 GHz broadband electromagnetic compatibility DRGH antenna

abstract: This work presents the design and simulation of a 1-14 GHz double ridged guide horn antenna (DRGH) with coaxial input feed section. This antenna, due to the large frequency bands required by standards, is suitable to be used in electromagnetic compatibility (EMC) testing. The horn-antenna model analyzed dates back to the early 1970s when J. L. Kerr suggested the use of a feed horn launcher whose dimensions where found experimentally. Although this type of horn has become the preferred test antenna of EMC testing for the 1-18 GHz range, which has been widely used for over four decades, no explanation of the effect of the launcher dimensions and the ridges shape into the flared section on the antenna parameters was found in the open literature. To investigate this phenomenon in detail, the entire horn has been modeled, including the coaxial feed using a time domain method. The simulations indicate that deficiencies in the radiation pattern start to appear at frequencies above 6 GHz and it starts to split at frequencies above 12 GHz. In spite those problems, the designed antenna can work well up to 14 GHz.


title: Implicit Distortion and Fertility Models for Attention-based Encoder-Decoder NMT Model

abstract: Neural machine translation has shown very promising results lately. Most NMT models follow the encoder-decoder framework. To make encoder-decoder models more flexible, attention mechanism was introduced to machine translation and also other tasks like speech recognition and image captioning. We observe that the quality of translation by attention-based encoder-decoder can be significantly damaged when the alignment is incorrect. We attribute these problems to the lack of distortion and fertility models. Aiming to resolve these problems, we propose new variations of attention-based encoder-decoder and compare them with other models on machine translation. Our proposed method achieved an improvement of 2 BLEU points over the original attention-based encoder-decoder.


title: Accurate odometry and error modelling for a mobile robot

abstract: This paper presents the key steps involved in the design, calibration and error modelling of a low cost odometry system capable of achieving high accuracy dead-reckoning. A consistent error model for estimating position and orientation errors has been developed. Previous work on propagating odometry error covariance relies on incrementally updating the covariance matrix in small time steps. The approach taken here sums the noise theoretically over the entire path length to produce simple closed form expressions, allowing efficient covariance matrix updating after the completion of path segments. Systematic errors due to wheel radius and wheel base measurement were first calibrated with UMBmark test. Experimental results show that, despite its low cost, our system's performance, with regard to dead-reckoning accuracy, is comparable to some of the best reported odometry vehicle.


title: Compact Dual-Band Gysel Power Divider Based on Composite Right- and Left-Handed Transmission Lines

abstract: A novel compact dual-band Gysel power divider with controllable frequency ratio using fully integrated composite right/left-handed (CRLH) transmission lines (TL) is presented in this letter. The $\Pi$ -shaped CRLH TLs are utilized to realize the dual-band response. Theoretical analysis indicates that the frequency ratio can be controlled by changing the element values and the number of the constructing cells of the CRLH TLs. In addition, the proposed structure is compact. For demonstration, a dual-band Gysel power divider operating at 2.4 and 3.5 GHz, respectively, is implemented. Measured results show that good impedance matching, isolation, and power transmission can be satisfied at the dual passband simultaneously.


Improving flight performance of the flapping wing MAV DelFly II


title: Registration and Analysis of Vascular Images

abstract: We have developed a method for rigidly aligning images of tubes. This paper presents an evaluation of the consistency of that method for three-dimensional images of human vasculature. Vascular images may contain alignment ambiguities, poorly corresponding vascular networks, and non-rigid deformations, yet the Monte Carlo experiments presented in this paper show that our method registers vascular images with sub-voxel consistency in a matter of seconds. Furthermore, we show that the method's insensitivity to non-rigid deformations enables the localization, quantification, and visualization of those deformations. ::: ::: Our method aligns a source image with a target image by registering a model of the tubes in the source image directly with the target image. Time can be spent to extract an accurate model of the tubes in the source image. Multiple target images can then be registered with that model without additional extractions. ::: ::: Our registration method builds upon the principles of our tubular object segmentation work that combines dynamic-scale central ridge traversal with radius estimation. In particular, our registration method's consistency stems from incorporating multi-scale ridge and radius measures into the model-image match metric. Additionally, the method's speed is due in part to the use of coarse-to-fine optimization strategies that are enabled by measures made during model extraction and by the parameters inherent to the model-image match metric.


title: Evolutionary and epidemiological analyses based on spike genes of porcine epidemic diarrhea virus circulating in Thailand in 2008–2015

abstract: Porcine epidemic diarrhea (PED) has been endemic causing sporadic outbreaks in Thailand since 2007. In 2014-2015, several herds had experienced severe PED outbreaks and the reason of the re-current outbreaks was unknown. Whether or not the introduction of exotic strains or continual evolution of existing PEDV, genetic analyses would provide a more understanding in its evolutionary pattern. In the study, 117 complete spike gene sequences of Thai PED virus (PEDV) collected from 2008 to 2015 were clustered along with 95 references of PEDV spike sequences, and analyzed with the US sequences dataset (n=99). The phylogenetic analysis demonstrated that Thai PEDV spike sequences were genetically diverse and had been influenced by multiple introduction of exotic strains. Although Thai PEDV have been evolved into 6 subgroups (TH1-6), Subgroup TH1 strains with the unique 9 nucleotides (CAA GGG AAT) insertion between 688th-689th position of spike (changing amino acid from N to TREY) insertion has become the dominant subgroup since 2014. Thai PEDV spike gene have higher evolutionary rate compare to that of the US sequences. One contributing factor would be the intra-recombination between subgroups. Thailand endemic strain should be assigned into new subclade of G2 (Thai pandemic variant).


title: Reward representations and reward-related learning in the human brain: insights from neuroimaging

abstract: This review outlines recent findings from human neuroimaging concerning the role of a highly interconnected network of brain areas including orbital and medial prefrontal cortex, amygdala, striatum and dopaminergic mid-brain in reward processing. Distinct reward-related functions can be attributed to different components of this network. Orbitofrontal cortex is involved in coding stimulus reward value and in concert with the amygdala and ventral striatum is implicated in representing predicted future reward. Such representations can be used to guide action selection for reward, a process that depends, at least in part, on orbital and medial prefrontal cortex as well as dorsal striatum.


IEC 61850 GOOSE Message over WAN


title: HANDSET ANTENNA DESIGN: PRACTICE AND THEORY

abstract: In this paper, an attempt is made to present a theory for the design of handset antennas, which results from the long experience that the authors have in the field of handset antenna design. The proposed theory is based on the well-known skin effect and constructs the antenna using a thin wire model that represent the backbone of the final antenna. The analytical solution for the thin wire model is first obtained, and the main properties (such as the return loss and the radiation properties) of the antenna can then be studied using this analytical solution. Once the antenna backbone is constructed, other elements, such as stubs, patches, etc., can be added to optimize the match at the desired frequency bands. A number of numerical and analytical examples are provided throughout the paper to validate the theory. Different antenna types, such as wire antennas and planar antennas, are tested and designed using the thin wire model. The correspondence between the analytical results and those from the numerical simulations using full-wave solvers agree very well in all examples. The authors also present in this paper a novel design of three small antennas for handset applications, which are based on the simple wire monopole, but in a three-dimensional form. The proposed three-dimensional monopole antennas have multi-band and broadband properties that cover most frequency bands being used for the handset device. The antennas feature remarkable properties while occupying a significantly small space, which makes them strong candidates for handset applications and for the future multi-antenna applications too.


title: E-Voting and Biometric Systems.

abstract: As e-Voting gains more importance while practicable solutions are being implemented, more questions arise concerning alternative possibilities for a secure and feasible authentication. The specific peculiarities of secure authentication to a system are various and for a sensitive area like e-Voting also challenging. In this paper we evaluate biometric systems in order to prove their capabilities for e-Voting systems.