title: Comparing the effects of website quality on customer initial purchase and continued purchase at e-commerce websites

abstract: To succeed in the highly competitive e-commerce environment, it is vital to understand the impact of website quality in enhancing customer conversion and retention. Although numerous contingent website attributes have been identified in the extant website quality studies, there is no unified framework to classify these attributes and no comparison done between customer conversion and retention according to the different website quality attributes and their varying impact. This study adopts the model of Information Systems (IS) success by DeLone and McLean to provide a parsimonious and unified view of website quality, and compares the impact of website quality on intention of initial purchase with that on intention of continued purchase. With the proposed framework, we seek to understand how a company can increase customer conversion and/or retention. Our findings demonstrate the strength of our framework in explaining the impact of website quality on intention to purchase on the Web, and that website quality constructs exert different impact on intention of initial purchase and intention of continued purchase. The results suggest that an online company should focus on system quality to increase customer conversion, and on service quality for customer retention.


The Digital Health Companion : Personalized Health Support on Smartwatches via Recognition of Activity-and Vital-Data


An automated approach for abstracting execution logs to execution events


title: FastRoute: a scalable load-aware anycast routing architecture for modern CDNs

abstract: Performance of online applications directly impacts user satisfaction. A major component of the user-perceived performance of the application is the time spent in transit between the user's device and the application existing in data centers. Content Delivery Networks (CDNs) are typically used to improve user-perceived application performance through a combination of caching and intelligent routing via proxies. In this paper, we describe FastRoute, a highly scalable and operational anycast-based system that has significantly improved the performance of numerous popular online services. While anycast is a common technique in modern CDNs for providing high-performance proximity routing, it sacrifices control over the load arriving at any individual proxy. We demonstrate that by collocating DNS and proxy services in each FastRoute node location, we can create a high-performance, completely distributed system for routing users to a nearby proxy while still enabling the graceful avoidance of overload an any individual proxy.


title: Controlled Experiments for Word Embeddings

abstract: An experimental approach to studying the properties of word embeddings is proposed. Controlled experiments, achieved through modifications of the training corpus, permit the demonstration of direct relations between word properties and word vector direction and length. The approach is demonstrated using the word2vec CBOW model with experiments that independently vary word frequency and word co-occurrence noise. The experiments reveal that word vector length depends more or less linearly on both word frequency and the level of noise in the co-occurrence distribution of the word. The coefficients of linearity depend upon the word. The special point in feature space, defined by the (artificial) word with pure noise in its c o-occurrence distribution, is found to be small but non-zero.


title: User interactions in social networks and their implications

abstract: Social networks are popular platforms for interaction, communication and collaboration between friends. Researchers have recently proposed an emerging class of applications that leverage relationships from social networks to improve security and performance in applications such as email, web browsing and overlay routing. While these applications often cite social network connectivity statistics to support their designs, researchers in psychology and sociology have repeatedly cast doubt on the practice of inferring meaningful relationships from social network connections alone. This leads to the question: Are social links valid indicators of real user interaction? If not, then how can we quantify these factors to form a more accurate model for evaluating socially-enhanced applications? In this paper, we address this question through a detailed study of user interactions in the Facebook social network. We propose the use of interaction graphs to impart meaning to online social links by quantifying user interactions. We analyze interaction graphs derived from Facebook user traces and show that they exhibit significantly lower levels of the "small-world" properties shown in their social graph counterparts. This means that these graphs have fewer "supernodes" with extremely high degree, and overall network diameter increases significantly as a result. To quantify the impact of our observations, we use both types of graphs to validate two well-known social-based applications (RE and SybilGuard). The results reveal new insights into both systems, and confirm our hypothesis that studies of social applications should use real indicators of user interactions in lieu of social graphs.


title: Noise reduction of diffusion tensor images by sparse representation and dictionary learning

abstract: Background ::: The low quality of diffusion tensor image (DTI) could affect the accuracy of oncology diagnosis.


Haptics: Perception, Devices, Control, and Applications


title: A Systematic Study of Online Class Imbalance Learning With Concept Drift

abstract: As an emerging research topic, online class imbalance learning often combines the challenges of both class imbalance and concept drift. It deals with data streams having very skewed class distributions, where concept drift may occur. It has recently received increased research attention; however, very little work addresses the combined problem where both class imbalance and concept drift coexist. As the first systematic study of handling concept drift in class-imbalanced data streams, this paper first provides a comprehensive review of current research progress in this field, including current research focuses and open challenges. Then, an in-depth experimental study is performed, with the goal of understanding how to best overcome concept drift in online learning with class imbalance.


title: AptRank: an adaptive PageRank model for protein function prediction on bi‐relational graphs

abstract: Diffusion-based network models are widely used for protein function prediction using protein network data and have been shown to outperform neighborhood- and module-based methods. Recent studies have shown that integrating the hierarchical structure of the Gene Ontology (GO) data dramatically improves prediction accuracy. However, previous methods usually either used the GO hierarchy to refine the prediction results of multiple classifiers, or flattened the hierarchy into a function-function similarity kernel. No study has taken the GO hierarchy into account together with the protein network as a two-layer network model. ::: We first construct a Bi-relational graph (Birg) model comprised of both protein-protein association and function-function hierarchical networks. We then propose two diffusion-based methods, BirgRank and AptRank, both of which use PageRank to diffuse information on this two-layer graph model. BirgRank is an application of traditional PageRank with fixed decay parameters. In contrast, AptRank uses an adaptive mechanism to improve the performance of BirgRank. We evaluate both methods in predicting protein function on yeast, fly, and human datasets, and compare with four previous methods: GeneMANIA, TMC, ProteinRank and clusDCA. We design three validation strategies: missing function prediction, de novo function prediction, and guided function prediction to comprehensively evaluate all six methods. We find that both BirgRank and AptRank outperform the others, especially in missing function prediction when using only 10% of the data for training. ::: AptRank combines protein-protein associations and the GO function-function hierarchy into a two-layer network model without flattening the hierarchy into a similarity kernel. Introducing an adaptive mechanism to the traditional, fixed-parameter model of PageRank greatly improves the accuracy of protein function prediction.


title: Rise of Deep Learning for Genomic, Proteomic, and Metabolomic Data Integration in Precision Medicine

abstract: Machine learning (ML) is being ubiquitously incorporated into everyday products such as Internet search, email spam filters, product recommendations, image classification, and speech recognition. New approaches for highly integrated manufacturing and automation such as the Industry 4.0 and the Internet of things are also converging with ML methodologies. Many approaches incorporate complex artificial neural network architectures and are collectively referred to as deep learning (DL) applications. These methods have been shown capable of representing and learning predictable relationships in many diverse forms of data and hold promise for transforming the future of omics research and applications in precision medicine. Omics and electronic health record data pose considerable challenges for DL. This is due to many factors such as low signal to noise, analytical variance, and complex data integration requirements. However, DL models have already been shown capable of both improving the ease of data encoding and predictive model performance over alternative approaches. It may not be surprising that concepts encountered in DL share similarities with those observed in biological message relay systems such as gene, protein, and metabolite networks. This expert review examines the challenges and opportunities for DL at a systems and biological scale for a precision medicine readership.


title: Lexicosyntactic Inference in Neural Models

abstract: We investigate neural models' ability to capture lexicosyntactic inferences: inferences triggered by the interaction of lexical and syntactic information. We take the task of event factuality prediction as a case study and build a factuality judgment dataset for all English clause-embedding verbs in various syntactic contexts. We use this dataset, which we make publicly available, to probe the behavior of current state-of-the-art neural systems, showing that these systems make certain systematic errors that are clearly visible through the lens of factuality prediction.


title: Fp-Scanner: The Privacy Implications of Browser Fingerprint Inconsistencies

abstract: By exploiting the diversity of device and browser configurations, browser fingerprinting established itself as a viable technique to enable stateless user tracking in production. Companies and academic communities have responded with a wide range of countermeasures. However , the way these countermeasures are evaluated does not properly assess their impact on user privacy, in particular regarding the quantity of information they may indirectly leak by revealing their presence. In this paper, we investigate the current state of the art of browser fingerprinting countermeasures to study the inconsistencies they may introduce in altered fingerprints , and how this may impact user privacy. To do so, we introduce FP-SCANNER as a new test suite that explores browser fingerprint inconsistencies to detect potential alterations, and we show that we are capable of detecting countermeasures from the inconsistencies they introduce. Beyond spotting altered browser fingerprints, we demonstrate that FP-SCANNER can also reveal the original value of altered fingerprint attributes, such as the browser or the operating system. We believe that this result can be exploited by fingerprinters to more accurately target browsers with countermeasures.


title: Movement intention decoding based on deep learning for multiuser myoelectric interfaces

abstract: Recently, the development of practical myoelectric interfaces has resulted in the emergence of wearable rehabilitation robots such as arm prosthetics. In this paper, we propose a novel method of movement intention decoding based on the deep feature learning using electromyogram of human biosignals. In daily life, the inter-user variability cause decreases in performance by modulating target EMG patterns across different users. Therefore, we propose a user-adaptive decoding method for robust movement intention decoding in the inter-user variability, employing the convolutional neural network for the deep feature learning, trained by different users. In our experimental results, the proposed method predicted hand movement intention more accurately than a competing method.


title: On the distribution of source code file sizes

abstract: Source code size is an estimator of software effort. Size is also often used to calibrate models and equations to estimate the cost of software. The distribution of source code file sizes has been shown in the literature to be a lognormal distribution. In this paper, we measure the size of a large collection of software (the Debian GNU/Linux distribution version 5.0.2), and we find that the statistical distribution of its source code file sizes follows a double Pareto distribution. This means that large files are to be found more often than predicted by the lognormal distribution, therefore the previously proposed models underestimate the cost of software.


title: Fairness Comparison of Uplink NOMA and OMA

abstract: In this paper, we compare the resource allocation fairness of uplink communications between non-orthogonal multiple access (NOMA) schemes and orthogonal multiple access (OMA) schemes. Through characterizing the contribution of the individual user data rate to the system sum rate, we analyze the fundamental reasons that NOMA offers a more fair resource allocation than that of OMA in asymmetric channels. Furthermore, a fairness indicator metric based on Jain's index is proposed to measure the asymmetry of multiuser channels. More importantly, the proposed metric provides a selection criterion for choosing between NOMA and OMA for fair resource allocation. Based on this discussion, we propose a hybrid NOMA-OMA scheme to further enhance the users fairness. Simulation results confirm the accuracy of the proposed metric and demonstrate the fairness enhancement of the proposed hybrid NOMA-OMA scheme compared to the conventional OMA and NOMA schemes.


title: Does low self-esteem predict depression and anxiety? A meta-analysis of longitudinal studies.

abstract: Low self-esteem and depression are strongly related, but there is not yet consistent evidence on the nature of the relation. Whereas the vulnerability model states that low self-esteem contributes to depression, the scar model states that depression erodes self-esteem. Furthermore, it is unknown whether the models are specific for depression or whether they are also valid for anxiety. We evaluated the vulnerability and scar models of low self-esteem and depression, and low self-esteem and anxiety, by meta-analyzing the available longitudinal data (covering 77 studies on depression and 18 studies on anxiety). The mean age of the samples ranged from childhood to old age. In the analyses, we used a random-effects model and examined prospective effects between the variables, controlling for prior levels of the predicted variables. For depression, the findings supported the vulnerability model: The effect of self-esteem on depression (β = -.16) was significantly stronger than the effect of depression on self-esteem (β = -.08). In contrast, the effects between low self-esteem and anxiety were relatively balanced: Self-esteem predicted anxiety with β = -.10, and anxiety predicted self-esteem with β = -.08. Moderator analyses were conducted for the effect of low self-esteem on depression; these suggested that the effect is not significantly influenced by gender, age, measures of self-esteem and depression, or time lag between assessments. If future research supports the hypothesized causality of the vulnerability effect of low self-esteem on depression, interventions aimed at increasing self-esteem might be useful in reducing the risk of depression.


AnOverview on Image Forensics


title: Video Compression - From Concepts to the H.264/AVC Standard

abstract: Over the last one and a half decades, digital video compression technologies have become an integral part of the way we create, communicate, and consume visual information. In this paper, techniques for video compression are reviewed, starting from basic concepts. The rate-distortion performance of modern video compression schemes is the result of an interaction between motion representation techniques, intra-picture prediction techniques, waveform coding of differences, and waveform coding of various refreshed regions. The paper starts with an explanation of the basic concepts of video codec design and then explains how these various features have been integrated into international standards, up to and including the most recent such standard, known as H.264/AVC.


title: Watch me playing, i am a professional: a first study on video game live streaming

abstract: "Electronic-sport" (E-Sport) is now established as a new entertainment genre. More and more players enjoy streaming their games, which attract even more viewers. In fact, in a recent social study, casual players were found to prefer watching professional gamers rather than playing the game themselves. Within this context, advertising provides a significant source of revenue to the professional players, the casters (displaying other people's games) and the game streaming platforms. For this paper, we crawled, during more than 100 days, the most popular among such specialized platforms: Twitch.tv. Thanks to these gigabytes of data, we propose a first characterization of a new Web community, and we show, among other results, that the number of viewers of a streaming session evolves in a predictable way, that audience peaks of a game are explainable and that a Condorcet method can be used to sensibly rank the streamers by popularity. Last but not least, we hope that this paper will bring to light the study of E-Sport and its growing community. They indeed deserve the attention of industrial partners (for the large amount of money involved) and researchers (for interesting problems in social network dynamics, personalized recommendation, sentiment analysis, etc.).


title: Factors Affecting Isfahanian Mobile Banking Adoption Based on the Technology Acceptance Model

abstract: With the convergence of banking services and mobile technologies, users are able to conduct banking services at any place and at any time through mobile banking (Gu, Lee and Suh, 2009). The term "Mobile Banking" refers to application of cell phone as a channel to provide and deliver the bank services which include both traditional services such as cash transferring and new services such as online or electronic payments. Mobile Banking services got a growing trend ahead so that economic experts predict that by 2013, 300 billion transactions worth over 860 billion dollars will be done through mobile banking (Rogers, 1983). The current exploratory study is an attempt to investigate the factors that influence Isfahanian’ intention to adopt mobile banking by extending the renowned framework of Technology Acceptance Model (TAM) by additionally examining the effects of compatibility, trust and perceived risk on behavioral intention. A self-administrated questionnaire had been developed and distributed in Isfahan city. Out of 400 questionnaires that have been distributed, 315 are returned (78.0%). Of these, five(5) responses had to be discarded due to invalid or incomplete data entries. Thus the sample comprising of a total of 310 respondents was used for analysis. The data was analyzed by AMOS software. In the survey, factors that may affect Isfahanian mobile phone users' to adopt mobile banking services were examined. Factors such as perceived usefulness (PU), perceived ease of use (PEOU) and compatibility were found positively related with the intention to adopt mobile banking services. Compatibility not only had a strong direct effect but was also identified as an important antecedent for perceived ease of use and perceived usefulness. However, trust was the only factor found insignificant. As expected, perceived risk (PR) was negatively associated with the mobile banking adoption. The research findings provide several important implications for banks, service developers, and software engineer with better strategic insights to design and implement mobile banking services to yield higher consumer acceptance towards mobile banking in Isfahan city.


title: Improving Sparsity Problem in Group Recommendation

abstract: Group recommendation systems can be very challenging when the datasets are sparse and there are not many available ratings for items. In this paper, by enhancing basic memorybased techniques we resolve the data sparsity problem for users in the group. The results have shown that by conducting our techniques for the users in the group we have a higher group satisfaction and lower group dissatisfaction.


title: An anatomically comprehensive atlas of the adult human brain transcriptome

abstract: Neuroanatomically precise, genome-wide maps of transcript distributions are critical resources to complement genomic sequence data and to correlate functional and genetic brain architecture. Here we describe the generation and analysis of a transcriptional atlas of the adult human brain, comprising extensive histological analysis and comprehensive microarray profiling of ~900 neuroanatomically precise subdivisions in two individuals. Transcriptional regulation varies enormously by anatomical location, with different regions and their constituent cell types displaying robust molecular signatures that are highly conserved between individuals. Analysis of differential gene expression and gene co-expression relationships demonstrates that brain-wide variation strongly reflects the distributions of major cell classes such as neurons, oligodendrocytes, astrocytes and microglia. Local neighbourhood relationships between fine anatomical subdivisions are associated with discrete neuronal subtypes and genes involved with synaptic transmission. The neocortex displays a relatively homogeneous transcriptional pattern, but with distinct features associated selectively with primary sensorimotor cortices and with enriched frontal lobe expression. Notably, the spatial topography of the neocortex is strongly reflected in its molecular topography—the closer two cortical regions, the more similar their transcriptomes. This freely accessible online data resource forms a high-resolution transcriptional baseline for neurogenetic studies of normal and abnormal human brain function.


title: The power of habits: Unhealthy snacking behaviour is primarily predicted by habit strength.

abstract: Objective. Although increasing evidence shows the importance of habits in explaining health behaviour, many studies still rely solely on predictors that emphasize the role of conscious intentions. The present study was designed to test the importance of habit strength in explaining unhealthy snacking behaviour in a large representative community sample (N = 1,103). To test our hypothesis that habits are crucial when explaining unhealthy snacking behaviour, their role was compared to the ‘Power of Food’, a related construct that addresses sensitivity to food cues in the environment. Moreover, the relation between Power of Food and unhealthy snacking habits was assessed. Design and Methods. A prospective design was used to determine the impact of habits in relation to intention, Power of Food and a number of demographic variables. One month after filling out the questionnaire, including measures of habit strength and Power of Food, participants reported their unhealthy snacking behaviour by means of a 7-day snack diary. Results. Results showed that habit strength was the most important predictor, outperforming all other variables in explaining unhealthy snack intake. Conclusions. The findings demonstrate that snacking habits provide a unique contribution in explaining unhealthy snacking behaviour, stressing the importance of addressing habit strength in further research and interventions concerning unhealthy snacking behaviour.


title: Gradient clock synchronization in wireless sensor networks

abstract: Accurately synchronized clocks are crucial for many applications in sensor networks. Existing time synchronization algorithms provide on average good synchronization between arbitrary nodes, however, as we show in this paper, close-by nodes in a network may be synchronized poorly. We propose the Gradient Time Synchronization Protocol (GTSP) which is designed to provide accurately synchronized clocks between neighbors. GTSP works in a completely decentralized fashion: Every node periodically broadcasts its time information. Synchronization messages received from direct neighbors are used to calibrate the logical clock. The algorithm requires neither a tree topology nor a reference node, which makes it robust against link and node failures. The protocol is implemented on the Mica2 platform using TinyOS. We present an evaluation of GTSP on a 20-node testbed setup and simulations on larger network topologies.


title: Analysis of BLDC motor with zigzag and rhombic winding

abstract: The paper presents a detailed analysis and comparison of two types of non-axial windings used for slotless permanent magnet (PM) motors: zigzag and rhombic ones. The motor mathematical model is verified by 3D FEM software. Finally, the motor prototype is presented.


Active Social Media Management: The Case of Health


The Epipolar Geometry Toolbox (EGT) for MATLAB


title: A Slim RFID Tag Antenna Design for Metallic Object Applications

abstract: A slim radio frequency identification (RFID) tag antenna design for metallic objects application is proposed in this letter. It is designed based on a high-impedance surface (HIS) unit cell structure directly rather than adopting a large HIS ground plane. The antenna structure consists of metallic rectangular patches electrically connected through vias to the ground plane to form an RFID tag antenna that is suitable for mounting on metallic objects. The experimental tests show that the maximum read range of RFID tag placed on a metallic object is about 3.1 m and the overall size is 65 times 20 times 1.5 mm3. It is thinner than inverted-F antenna (IFA), planar inverted-F antenna (PIFA), or patch-type antennas for metallic objects. Simulation and measurement results of the proposed RFID tag antenna are also presented in this letter.


title: Brain-EE: Brain Enjoyment Evaluation using Commercial EEG Headband

abstract: Previous studies that involve measuring EEG, or electroencephalograms, have mainly been experimentally-driven projects; for instance, EEG has long been used in research to help identify and elucidate our understanding of many neuroscientific, cognitive, and clinical issues (e.g., sleep, seizures, memory). However, advances in technology have made EEG more accessible to the population. This opens up lines for EEG to provide more information about brain activity in everyday life, rather than in a laboratory setting. To take advantage of the technological advances that have allowed for this, we introduce the Brain-EE system, a method for evaluating user engaged enjoyment that uses a commercially available EEG tool (Muse). During testing, fifteen participants engaged in two tasks (playing two different video games via tablet), and their EEG data were recorded. The Brain-EE system supported much of the previous literature on enjoyment; increases in frontal theta activity strongly and reliably predicted which game each individual participant preferred. We hope to develop the Brain-EE system further in order to contribute to a wide variety of applications (e.g., usability testing, clinical or experimental applications, evaluation methods, etc.).


title: MADAM: Effective and Efficient Behavior-based Android Malware Detection and Prevention

abstract: Android users are constantly threatened by an increasing number of malicious applications (apps), generically called malware. Malware constitutes a serious threat to user privacy, money, device and file integrity. In this paper we note that, by studying their actions, we can classify malware into a small number of behavioral classes, each of which performs a limited set of misbehaviors that characterize them. These misbehaviors can be defined by monitoring features belonging to different Android levels. In this paper we present MADAM, a novel host-based malware detection system for Android devices which simultaneously analyzes and correlates features at four levels: kernel, application, user and package, to detect and stop malicious behaviors. MADAM has been specifically designed to take into account those behaviors that are characteristics of almost every real malware which can be found in the wild. MADAM detects and effectively blocks more than 96 percent of malicious apps, which come from three large datasets with about 2,800 apps, by exploiting the cooperation of two parallel classifiers and a behavioral signature-based detector. Extensive experiments, which also includes the analysis of a testbed of 9,804 genuine apps, have been conducted to show the low false alarm rate, the negligible performance overhead and limited battery consumption.


title: Design and optimization of a compact, large amplification XY flexure-mechanism

abstract: This article presents the design methodology, optimization, and computational verification of a compact, high-gain planar XY flexure mechanism. The presented mechanism consists of a decoupled XY mechanism, and a modified Scott-Russel mechanism, which maintain linear XY motion, and amplify the input piezoelectric actuator (PEA) displacement respectively. Each mechanism offers the potential for use in isolation. When operated together, the combined mechanism is capable of nanometer scale precision and millimeter scale range in a vacuum-compatible design. The presented mechanism could therefore be used in micro-assembly operations including those performed in electron microscopes. The design methodology and mechanism models are presented, with the selected design computationally optimized and analyzed. By using a broad design space, and minimizing assumptions, the optimized mechanism produces workspace of approximately 930 × 940 μm2 from 15 μm of input displacement.


title: A Survey of Resource Management Toward 5G Radio Access Networks

abstract: The next generation 5G radio access network (RAN) system is believed to be a true world wide wireless web (WWWW). This is because such system will seamlessly and ubiquitously connect everything, and support at least 1000-fold traffic volumes, 100 billion connected wireless devices, and diversified use cases as well as quality of service (QoS) requirements (e.g., reliability, latency, data rates, coverage, security, and privacy) of multimedia applications by 2020. Recently, a number of research challenges including the explosive growth in mobile traffic volumes, unprecedented connected devices, and diversified use cases have been identified for the 5G RAN systems. In addition, specific technologies such as multitier communication, massive MIMOs, mmWave backhauling, extreme densifications of nodes (UDNs), full-duplex communications (FDCs), and energy harvesting techniques have emerged in the literature to resolve some of these challenges of the 5G RAN systems. However, the research activities defining specific technical advancements for 5G RAN systems are yet to continue in the next half decade before specifications for standardization and commercialization are concluded. Motivated by the limited number of existing surveys of such technical advancements in a broader perspective (i.e., interference, spectrum-efficient, and energy-efficient management schemes), this paper seeks to take stock of state-of-the-art (SOTA) on such technical developments. Our attention focuses on relevant radio interference and resource management (RIRM) schemes that have been proposed in the last five years. Our contribution lies in the analysis, synthesis, and summarized alignments of the conventional RIRM schemes toward overcoming the identified challenges for the $5G$ RAN systems. Finally, the review highlights a number of open research issues deduced from recently proposed RIRM schemes.


title: Nonlinear Electrical Impedance Tomography Reconstruction Using Artificial Neural Networks and Particle Swarm Optimization

abstract: Electrical impedance tomography (EIT) is an imaging technology that offers the advantages of being noninvasive, and it does not generate ionizing radiation. The main difficulty in applying EIT is to solve an ill-posed nonlinear inverse problem. Given a set of electrical voltages measured at the surface of a volume conductor, the goal is to identify the materials that are present in the domain by determining their electrical conductivities. However, since EIT is a nonlinear problem, various algorithms proposed in the literature can only approximate real conductivity distributions. Nonlinear algorithms, especially artificial neural networks (ANNs), have been proposed to solve this inverse problem, but these algorithms are usually limited by slow convergence issues during the training phase. In this paper, the particle swarm optimization (PSO) method is used to train an ANN to solve the EIT problem. It has been found that, compared with the back-propagation algorithm, PSO is capable of generating both faster and higher convergence. This paper also shows that the proposed method is capable of dealing with noisy data and the imperfections in the finite-element discretization, an important source of errors in EIT imaging.


title: CrystalBall: Statically analyzing runtime behavior via deep sequence learning

abstract: Understanding dynamic program behavior is critical in many stages of the software development lifecycle, for purposes as diverse as optimization, debugging, testing, and security. This paper focuses on the problem of predicting dynamic program behavior statically. We introduce a novel technique to statically identify hot paths that leverages emerging deep learning techniques to take advantage of their ability to learn subtle, complex relationships between sequences of inputs. This approach maps well to the problem of identifying the behavior of sequences of basic blocks in program execution. Our technique is also designed to operate on the compiler's intermediate representation (IR), as opposed to the approaches taken by prior techniques that have focused primarily on source code, giving our approach language-independence. We describe the pitfalls of conventional metrics used for hot path prediction such as accuracy, and motivate the use of Area Under the Receiver Operating Characteristic curve (AUROC). Through a thorough evaluation of our technique on complex applications that include the SPEC CPU2006 benchmarks, we show that our approach achieves an AUROC of 0.85.


title: Inverse kinematics of a 3 DOF parallel manipulator: A conformal geometric algebra approach

abstract: In this paper we propose a method to solve the inverse kinematic of a 3 DOF parallel manipulator, best know as Stewart platform. Using the entities of the rotors and motors it is possible define an algorithm to find the joint values of each actuator that allow to reach the desired position and orientation of the manipulator, describing only one geometric solution for one arm of the platform and applying a rigig transformation to this solution in order to define the solution for the rest of the arms of the robot. Simulations and real time implementation of the algorithm were done to prove the effectiveness of the proposal.


title: A Survey on Cloud Computing Scheduling Algorithms

abstract: Cloud computing is a distributed computing modelwhich enables developers to automatically deploy applicationsduring task allocation and storage distribution. Cloud computingintends to share a pool of virtualized computer resources andequipment's of computation, storage and information. In cloudcomputing environment, task scheduling is a key problem whichneeds to consider various factors restricting the user tasks and isresponsible for selecting the most suitable resources of cloudcomputing for user task. Task scheduling algorithm is an NPcompleteproblem which places an important role in cloudcomputing. This article surveys the literature on job schedulingalgorithms as they apply to cloud computing environment. Itsummarizes keys of performance of each algorithm in different loadstates from conducted simulation.


Sentence Boundary Detection for Social Media Text.


title: TeamWorkStation: towards a seamless shared workspace

abstract: This paper introduces TeamWorkStation (TWS), a new desktop real-time shared workspace characterized by reduced cognitive seams. TWS integrates two existing kinds of individual workspaces, computers and desktops, to create a virtual shared workspace. The key ideas are the overlay of individual workspace images in a virtual shared workspace and the creation of a shared drawing surface. Because each co-worker can continue to use his/her favorite application programs or manual tools in the virtual shared workspace, the cognitive discontinuity (seam) between the individual and shared workspaces is greatly reduced, and users can shuttle smoothly between these two workspaces. This paper discusses where the seams exist in the current CSCW environment to clarify the issue of shared workspace design. The new technique of fusing individual workspaces is introduced. The application of TWS to the remote teaching of calligraphy is presented to show its potential. The prototype system is described and compared with other comparable approaches.


title: Stud krill herd algorithm

abstract: Recently, Gandomi and Alavi proposed a meta-heuristic optimization algorithm, called Krill Herd (KH), for global optimization [Gandomi AH, Alavi AH. Krill Herd: A New Bio-Inspired Optimization Algorithm. Communications in Nonlinear Science and Numerical Simulation, 17(12), 4831-4845, 2012.]. This paper represents an optimization method to global optimization using a novel variant of KH. This method is called the Stud Krill Herd (SKH). Similar to genetic reproduction mechanisms added to KH method, an updated genetic reproduction schemes, called stud selection and crossover (SSC) operator, is introduced into the KH during the krill updating process dealing with numerical optimization problems. The introduced SSC operator is originated from original Stud genetic algorithm. In SSC operator, the best krill, the Stud, provides its optimal information for all the other individuals in the population using general genetic operators instead of stochastic selection. This approach appears to be well capable of solving various functions. Several problems are used to test the SKH method. In addition, the influence of the different crossover types on convergence and performance is carefully studied. Experimental results indicate an instructive addition to the portfolio of swarm intelligence techniques.


title: Bisimilar linear systems

abstract: The notion of bisimulation in theoretical computer science is one of the main complexity reduction methods for the analysis and synthesis of labeled transition systems. Bisimulations are special quotients of the state space that preserve many important properties expressible in temporal logics, and, in particular, reachability. In this paper, the framework of bisimilar transition systems is applied to various transition systems that are generated by linear control systems. Given a discrete-time or continuous-time linear system, and a finite observation map, we characterize linear quotient maps that result in quotient transition systems that are bisimilar to the original system. Interestingly, the characterizations for discrete-time systems are more restrictive than for continuous-time systems, due to the existence of an atomic time step. We show that computing the coarsest bisimulation, which results in maximum complexity reduction, corresponds to computing the maximal controlled or reachability invariant subspace inside the kernel of the observations map. These results establish strong connections between complexity reduction concepts in control theory and computer science.


title: 3D Reconstruction in the presence of glasses by acoustic and stereo fusion

abstract: We present a practical and inexpensive method to reconstruct 3D scenes that include piece-wise planar transparent objects. Our work is motivated by the need for automatically generating 3D models of interior scenes, in which glass structures are common. These large structures are often invisible to cameras or even our human visual system. Existing 3D reconstruction methods for transparent objects are usually not applicable in such a room-size reconstruction setting. Our approach augments a regular depth camera (e.g., the Microsoft Kinect camera) with a single ultrasonic sensor, which is able to measure distance to any objects, including transparent surfaces. We present a novel sensor fusion algorithm that first segments the depth map into different categories such as opaque/transparent/infinity (e.g., too far to measure) and then updates the depth map based on the segmentation outcome. Our current hardware setup can generate only one additional point measurement per frame, yet our fusion algorithm is able to generate satisfactory reconstruction results based on our probabilistic model. We highlight the performance in many challenging indoor benchmarks.


title: A transfer learning and progressive stacking approach to reducing deep model sizes with an application to speech enhancement

abstract: Leveraging upon transfer learning, we distill the knowledge in a conventional wide and deep neural network (DNN) into a narrower yet deeper model with fewer parameters and comparable system performance for speech enhancement. We present three transfer-learning solutions to accomplish our goal. First, the knowledge embedded in the form of the output values of a high-performance DNN is used to guide the training of a smaller DNN model in sequential transfer learning. In the second multi-task transfer learning solution, the smaller DNN is trained to learn the output value of the larger DNN, and the speech enhancement task in parallel. Finally, a progressive stacking transfer learning is accomplished through multi-task learning, and DNN stacking. Our experimental evidences demonstrate 5 times parameter reduction while maintaining similar enhancement performance with the proposed framework.


title: Streamflow Programming Model for Data Streaming in Scientific Workflows

abstract: Geo-sciences involve large-scale parallel models, high resolution real time data from highly asynchronous and heterogeneous sensor networks and instruments, and complex analysis and visualization tools. Scientific workflows are an accepted approach to executing sequences of tasks on scientists’ behalf during scientific investigation. Many geo-science workflows have the need to interact with sensors that produce large continuous streams of data, but programming models provided by scientific workflows are not equipped to handle continuous data streams. This paper proposes a framework that utilizes scientific workflow infrastructure and the benefits of complex event processing to compensate for the impedance mismatch between scientific workflows and continuous data streams. Further we propose and formalize new workflow semantics that would allow the users to not only incorporate stream in scientific workflow, but also make use of the functionalities provided by the complex event processing systems effective within the scientific workflows.


title: Dynamic reconfiguration of human brain networks during learning

abstract: Human learning is a complex phenomenon requiring flexibility to adapt existing brain function and precision in selecting new neurophysiological activities to drive desired behavior. These two attributes—flexibility and selection—must operate over multiple temporal scales as performance of a skill changes from being slow and challenging to being fast and automatic. Such selective adaptability is naturally provided by modular structure, which plays a critical role in evolution, development, and optimal network function. Using functional connectivity measurements of brain activity acquired from initial training through mastery of a simple motor skill, we investigate the role of modularity in human learning by identifying dynamic changes of modular organization spanning multiple temporal scales. Our results indicate that flexibility, which we measure by the allegiance of nodes to modules, in one experimental session predicts the relative amount of learning in a future session. We also develop a general statistical framework for the identification of modular architectures in evolving systems, which is broadly applicable to disciplines where network adaptability is crucial to the understanding of system performance.


title: Automatic verification of the TLS handshake protocol

abstract: E-commerce is based on transactions between client and server agents. These transactions require a protocol that provides privacy and reliability between these two agents. A widely used protocol on e-commerce is Transport Layer Security (TLS). In this paper we present a way to use Formal Methods to ensure the e-commerce properties of this protocol. Specifically we use a known tool for Model Checking (UPPAAL) to describe and analyze the behaviour of the protocol (by means of timed automata). Thus, with this tool we can make an automatic verification of TLS.


title: Exponentially Long Orbits in Hopfield Neural Networks

abstract: We show that Hopfield neural networks with synchronous dynamics and asymmetric weights admit stable orbits that form sequences of maximal length. For [Formula: see text] units, these sequences have length [Formula: see text]; that is, they cover the full state space. We present a mathematical proof that maximal-length orbits exist for all [Formula: see text], and we provide a method to construct both the sequence and the weight matrix that allow its production. The orbit is relatively robust to dynamical noise, and perturbations of the optimal weights reveal other periodic orbits that are not maximal but typically still very long. We discuss how the resulting dynamics on slow time-scales can be used to generate desired output sequences.


title: 1 Neuro-Fuzzy DC Motor Speed Control Using Particle Swarm Optimization

abstract: This paper presents an application of Adaptive Neuro-Fuzzy Inference System (ANFIS) control for DC motor speed optimized with swarm collective intelligence. First, the controller is designed according to Fuzzy rules such that the systems are fundamentally robust. Secondly, an adaptive Neuro-Fuzzy controller of the DC motor speed is then designed and simulated; the ANFIS has the advantage of expert knowledge of the Fuzzy inference system and the learning capability of neural networks. Finally, the ANFIS is optimized by Swarm Intelligence. Digital simulation results demonstrate that the deigned ANFIS-Swarm speed controller realize a good dynamic behavior of the DC motor, a perfect speed tracking with no overshoot, give better performance and high robustness than those obtained by the ANFIS alone.


title: Implementation of a Cognitive Radio Front-End Using Rotatable Controlled Reconfigurable Antennas

abstract: This communication presents a new antenna system designed for cognitive radio applications. The antenna structure consists of a UWB antenna and a frequency reconfigurable antenna system. The UWB antenna scans the channel to discover “white space” frequency bands while tuning the reconfigurable section to communicate within these bands. The frequency agility is achieved via a rotational motion of the antenna patch. The rotation is controlled by a stepper motor mounted on the back of the antenna structure. The motor's rotational motion is controlled by LABVIEW on a computer connected to the motor through its parallel port. The computer's parallel port is connected to a NPN Darlington array that is used to drive the stepper motor. The antenna has been simulated with the driving motor being taken into consideration. A good agreement is found between the simulated and the measured antenna radiation properties.


title: Decoding of EEG Signals Using Deep Long Short-Term Memory Network in Face Recognition Task

abstract: The paper proposes a novel approach to classify the human memory response involved in the face recognition task by the utilization of event related potentials. Electroencephalographic signals are acquired when a subject engages himself/herself in familiar or unfamiliar face recognition tasks. The signals are analyzed through source Iocalization using eLORETA and artifact removal by ICA from a set of channels corresponding to those selected sources, with an ultimate aim to classify the EEG responses of familiar and unfamiliar faces. The EEG responses of the two different classes (familiar and unfamiliar face recognition)are distinguished by analyzing the Event Related Potential signals that reveal the existence of large N250 and P600 signals during familiar face recognition.The paper introduces a novel LSTM classifier network which is designed to classify the ERP signals to fulfill the prime objective of this work. The first layer of the novel LSTM network evaluates the spatial and local temporal correlations between the obtained samples of local EEG time-windows. The second layer of this network models the temporal correlations between the time-windows. An attention mechanism has been introduced in each layer of the proposed model to compute the contribution of each EEG time-window in face recognition task. Performance analysis reveals that the proposed LSTM classifier with attention mechanism outperforms the efficiency of the conventional LSTM and other classifiers with a significantly large margin. Moreover, source Iocalization using eLORETA shows the involvement of inferior temporal and frontal lobes during familiar face recognition and pre-frontal lobe during unfamiliar face recognition. Thus, the present research outcome can be used in criminal investigation, where meticulous differentiation of familiar and unfamiliar face detection by criminals can be performed from their acquired brain responses.


title: A Survey of Named Entity Recognition and Classification

abstract: This survey covers fifteen years of research in the Named Entity Recognition and Classification (NERC) field, from 1991 to 2006. We report observations about languages, named entity types, domains and textual genres studied in the literature. From the start, NERC systems have been developed using hand-made rules, but now machine learning techniques are widely used. These techniques are surveyed along with other critical aspects of NERC such as features and evaluation methods. Features are word-level, dictionary-level and corpus-level representations of words in a document. Evaluation techniques, ranging from intuitive exact match to very complex matching techniques with adjustable cost of errors, are an indisputable key to progress.


title: Human enabled green IoT in 5G networks

abstract: Internet of Things (IoT) plays a major role in connecting the physical world with the cyber world through new services and seamless interconnection between heterogeneous devices. Such heterogeneous devices tend to generate a massive volume of Big Data. However, exploiting green schemes for IoT is still a challenge since IoT attains a large scale and becomes more multifaceted, the current trends of analyzing Big Data are not directly applicable to it. Similarly, achieving green IoT through the use of 5G also poses new challenges when it comes to transferring huge volume of data in an efficient way. To address the challenges above, this paper presents a scheme for human- enabled green IoT in 5G network. Green IoT is achieved by grouping mobile nodes in a cluster. Also, a mobility management model is designed that helps in triggering efficient handover and selecting optimal networks based on multi-criteria decision modeling. Afterward, we design a network architecture that integrates green IoT with 5G network. Moreover, the 5G network architecture is supported by proposed protocol stack, which maps Internet Protocol (IP), Medium Access Protocol (MAC), and Location identifiers (LOC). The proposed scheme is also implemented using C programming language to validate mobility model in 5G, regarding cost, energy, and Quality of Service.


title: GraphMat: High performance graph analytics made productive

abstract: Given the growing importance of large-scale graph analytics, there is a need to improve the performance of graph analysis frameworks without compromising on productivity. GraphMat is our solution to bridge this gap between a user-friendly graph analytics framework and native, hand-optimized code. GraphMat functions by taking vertex programs and mapping them to high performance sparse matrix operations in the backend. We thus get the productivity benefits of a vertex programming framework without sacrificing performance. GraphMat is a single-node multicore graph framework written in C++ which has enabled us to write a diverse set of graph algorithms with the same effort compared to other vertex programming frameworks. GraphMat performs 1.1-7X faster than high performance frameworks such as GraphLab, CombBLAS and Galois. GraphMat also matches the performance of MapGraph, a GPU-based graph framework, despite running on a CPU platform with significantly lower compute and bandwidth resources. It achieves better multicore scalability (13-15X on 24 cores) than other frameworks and is 1.2X off native, hand-optimized code on a variety of graph algorithms. Since GraphMat performance depends mainly on a few scalable and well-understood sparse matrix operations, GraphMat can naturally benefit from the trend of increasing parallelism in future hardware.


title: Distributed Systems of Microservices Using Docker and Serfnode

abstract: We review container technology and the challenge of service discovery in micro service architectures and introduce Serf node, a fully decentralized open source solution to the service discovery problem, based on the Serf project. Serf node is a non-intrusive Docker image that composes one or more arbitrary Docker containers. The new images can be deployed into a cluster of Serf nodes, where it advertises itself and provides service discovery mechanisms, monitoring, and self-healing. The resulting cluster is a homogeneous and complete graph, with no master node. We survey existing solutions to the service discovery problem and compare them to Serf node. As an example of the extensibility of Serf node, we show the construction of a file system synchronization solution between Docker containers using Git.


title: Zoom-Net: Mining Deep Feature Interactions for Visual Relationship Recognition

abstract: Recognizing visual relationships \(\langle \)subject-predicate-object\(\rangle \) among any pair of localized objects is pivotal for image understanding. Previous studies have shown remarkable progress in exploiting linguistic priors or external textual information to improve the performance. In this work, we investigate an orthogonal perspective based on feature interactions. We show that by encouraging deep message propagation and interactions between local object features and global predicate features, one can achieve compelling performance in recognizing complex relationships without using any linguistic priors. To this end, we present two new pooling cells to encourage feature interactions: (i) Contrastive ROI Pooling Cell, which has a unique deROI pooling that inversely pools local object features to the corresponding area of global predicate features. (ii) Pyramid ROI Pooling Cell, which broadcasts global predicate features to reinforce local object features. The two cells constitute a Spatiality-Context-Appearance Module (SCA-M), which can be further stacked consecutively to form our final Zoom-Net. We further shed light on how one could resolve ambiguous and noisy object and predicate annotations by Intra-Hierarchical trees (IH-tree). Extensive experiments conducted on Visual Genome dataset demonstrate the effectiveness of our feature-oriented approach compared to state-of-the-art methods (Acc@1 \(11.42\%\) from \(8.16\%\)) that depend on explicit modeling of linguistic interactions. We further show that SCA-M can be incorporated seamlessly into existing approaches to improve the performance by a large margin.


title: Neural Bases of Social Anxiety Disorder: Emotional Reactivity and Cognitive Regulation During Social and Physical Threat

abstract: Context Social anxiety disorder is thought to involve emotional hyperreactivity, cognitive distortions, and ineffective emotion regulation. While the neural bases of emotional reactivity to social stimuli have been described, the neural bases of emotional reactivity and cognitive regulation during social and physical threat, and their relationship to social anxiety symptom severity, have yet to be investigated. Objective To investigate behavioral and neural correlates of emotional reactivity and cognitive regulation in patients and controls during processing of social and physical threat stimuli. Design Participants were trained to implement cognitive-linguistic regulation of emotional reactivity induced by social (harsh facial expressions) and physical (violent scenes) threat while undergoing functional magnetic resonance imaging and providing behavioral ratings of negative emotion experience. Setting Academic psychology department. Participants Fifteen adults with social anxiety disorder and 17 demographically matched healthy controls. Main Outcome Measures Blood oxygen level–dependent signal and negative emotion ratings. Results Behaviorally, patients reported greater negative emotion than controls during social and physical threat but showed equivalent reduction in negative emotion following cognitive regulation. Neurally, viewing social threat resulted in greater emotion-related neural responses in patients than controls, with social anxiety symptom severity related to activity in a network of emotion- and attention-processing regions in patients only. Viewing physical threat produced no between-group differences. Regulation during social threat resulted in greater cognitive and attention regulation–related brain activation in controls compared with patients. Regulation during physical threat produced greater cognitive control–related response (ie, right dorsolateral prefrontal cortex) in patients compared with controls. Conclusions Compared with controls, patients demonstrated exaggerated negative emotion reactivity and reduced cognitive regulation–related neural activation, specifically for social threat stimuli. These findings help to elucidate potential neural mechanisms of emotion regulation that might serve as biomarkers for interventions for social anxiety disorder.


title: Reinforcement Learning Signals in the Human Striatum Distinguish Learners from Nonlearners during Reward- Based Decision Making

abstract: The computational framework of reinforcement learning has been used to forward our understanding of the neural mechanisms underlying reward learning and decision-making behavior. It is known that humans vary widely in their performance in decision-making tasks. Here, we used a simple four-armed bandit task in which subjects are almost evenly split into two groups on the basis of their performance: those who do learn to favor choice of the optimal action and those who do not. Using models of reinforcement learning we sought to determine the neural basis of these intrinsic differences in performance by scanning both groups with functional magnetic resonance imaging. We scanned 29 subjects while they performed the reward-based decision-making task. Our results suggest that these two groups differ markedly in the degree to which reinforcement learning signals in the striatum are engaged during task performance. While the learners showed robust prediction error signals in both the ventral and dorsal striatum during learning, the nonlearner group showed a marked absence of such signals. Moreover, the magnitude of prediction error signals in a region of dorsal striatum correlated significantly with a measure of behavioral performance across all subjects. These findings support a crucial role of prediction error signals, likely originating from dopaminergic midbrain neurons, in enabling learning of action selection preferences on the basis of obtained rewards. Thus, spontaneously observed individual differences in decision making performance demonstrate the suggested dependence of this type of learning on the functional integrity of the dopaminergic striatal system in humans.


title: Students’ Misconceptions and Other Difficulties in Introductory Programming: A Literature Review

abstract: Efforts to improve computer science education are underway, and teachers of computer science are challenged in introductory programming courses to help learners develop their understanding of programming and computer science. Identifying and addressing students’ misconceptions is a key part of a computer science teacher's competence. However, relevant research on this topic is not as fully developed in the computer science education field as it is in mathematics and science education. In this article, we first review relevant literature on general definitions of misconceptions and studies about students’ misconceptions and other difficulties in introductory programming. Next, we investigate the factors that contribute to the difficulties. Finally, strategies and tools to address difficulties including misconceptions are discussed. Based on the review of literature, we found that students exhibit various misconceptions and other difficulties in syntactic knowledge, conceptual knowledge, and strategic knowledge. These difficulties experienced by students are related to many factors including unfamiliarity of syntax, natural language, math knowledge, inaccurate mental models, lack of strategies, programming environments, and teachers’ knowledge and instruction. However, many sources of students’ difficulties have connections with students’ prior knowledge. To better understand and address students’ misconceptions and other difficulties, various instructional approaches and tools have been developed. Nevertheless, the dissemination of these approaches and tools has been limited. Thus, first, we suggest enhancing the dissemination of existing tools and approaches and investigating their long-term effects. Second, we recommend that computing education research move beyond documenting misconceptions to address the development of students’ (mis)conceptions by integrating conceptual change theories. Third, we believe that developing and enhancing instructors’ pedagogical content knowledge (PCK), including their knowledge of students’ misconceptions and ability to apply effective instructional approaches and tools to address students’ difficulties, is vital to the success of teaching introductory programming.


title: Hierarchical monte-carlo planning

abstract: Monte-Carlo Tree Search, especially UCT and its POMDP version POMCP, have demonstrated excellent performance on many problems. However, to efficiently scale to large domains one should also exploit hierarchical structure if present. In such hierarchical domains, finding rewarded states typically requires to search deeply; covering enough such informative states very far from the root becomes computationally expensive in flat non-hierarchical search approaches. We propose novel, scalable MCTS methods which integrate a task hierarchy into the MCTS framework, specifically leading to hierarchical versions of both, UCT and POMCP. The new method does not need to estimate probabilistic models of each subtask, it instead computes subtask policies purely sample-based. We evaluate the hierarchical MCTS methods on various settings such as a hierarchical MDP, a Bayesian model-based hierarchical RL problem, and a large hierarchical POMDP.


title: Iterative reconstruction techniques for computed tomography Part 1: Technical principles

abstract: OBJECTIVES ::: To explain the technical principles of and differences between commercially available iterative reconstruction (IR) algorithms for computed tomography (CT) in non-mathematical terms for radiologists and clinicians. ::: ::: ::: METHODS ::: Technical details of the different proprietary IR techniques were distilled from available scientific articles and manufacturers' white papers and were verified by the manufacturers. Clinical results were obtained from a literature search spanning January 2006 to January 2012, including only original research papers concerning IR for CT. ::: ::: ::: RESULTS ::: IR for CT iteratively reduces noise and artefacts in either image space or raw data, or both. Reported dose reductions ranged from 23 % to 76 % compared to locally used default filtered back-projection (FBP) settings, with similar noise, artefacts, subjective, and objective image quality. ::: ::: ::: CONCLUSION ::: IR has the potential to allow reducing the radiation dose while preserving image quality. Disadvantages of IR include blotchy image appearance and longer computational time. Future studies need to address differences between IR algorithms for clinical low-dose CT. ::: ::: ::: KEY POINTS ::: • Iterative reconstruction technology for CT is presented in non-mathematical terms. • IR reduces noise and artefacts compared to filtered back-projection. • IR can improve image quality in routine-dose CT and lower the radiation dose. • IR's disadvantages include longer computation and blotchy appearance of some images.


title: Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam

abstract: Uncertainty computation in deep learning is essential to design robust and reliable systems. Variational inference (VI) is a promising approach for such computation, but requires more effort to implement and execute compared to maximum-likelihood methods. In this paper, we propose new natural-gradient algorithms to reduce such efforts for Gaussian mean-field VI. Our algorithms can be implemented within the Adam optimizer by perturbing the network weights during gradient evaluations, and uncertainty estimates can be cheaply obtained by using the vector that adapts the learning rate. This requires lower memory, computation, and implementation effort than existing VI methods, while obtaining uncertainty estimates of comparable quality. Our empirical results confirm this and further suggest that the weight-perturbation in our algorithm could be useful for exploration in reinforcement learning and stochastic optimization.


title: Connecting Intelligent Things in Smart Hospitals Using NB-IoT

abstract: The widespread use of Internet of Things (IoT), especially smart wearables, will play an important role in improving the quality of medical care, bringing convenience for patients and improving the management level of hospitals. However, due to the limitation of communication protocols, there exists non unified architecture that can connect all intelligent things in smart hospitals, which is made possible by the emergence of the Narrowband IoT (NB-IoT). In light of this, we propose an architecture to connect intelligent things in smart hospitals based on NB-IoT, and introduce edge computing to deal with the requirement of latency in medical process. As a case study, we develop an infusion monitoring system to monitor the real-time drop rate and the volume of remaining drug during the intravenous infusion. Finally, we discuss the challenges and future directions for building a smart hospital by connecting intelligent things.


title: BA-Net: Dense Bundle Adjustment Network

abstract: This paper introduces a network architecture to solve the structure-from-motion (SfM) problem via feature-metric bundle adjustment (BA), which explicitly enforces multi-view geometry constraints in the form of feature-metric error. The whole pipeline is differentiable so that the network can learn suitable features that make the BA problem more tractable. Furthermore, this work introduces a novel depth parameterization to recover dense per-pixel depth. The network first generates several basis depth maps according to the input image and optimizes the final depth as a linear combination of these basis depth maps via feature-metric BA. The basis depth maps generator is also learned via end-to-end training. The whole system nicely combines domain knowledge (i.e. hard-coded multi-view geometry constraints) and deep learning (i.e. feature learning and basis depth maps learning) to address the challenging dense SfM problem. Experiments on large scale real data prove the success of the proposed method.


title: Increased density of intraepithelial mast cells in patients with exercise-induced bronchoconstriction regulated through epithelially derived thymic stromal lymphopoietin and IL-33

abstract: Background Exercise-induced bronchoconstriction (EIB) is a prototypical feature of indirect airway hyperresponsiveness. Mast cells are implicated in EIB, but the characteristics, regulation, and function of mast cells in patients with EIB are poorly understood. Objectives We sought to examine mast cell infiltration of the airway epithelium in patients with EIB and the regulation of mast cell phenotype and function by epithelially derived cytokines. Methods Endobronchial biopsy specimens, epithelial brushings, and induced sputum were obtained from asthmatic patients with and without EIB and healthy control subjects. Mast cell proteases were quantified by using quantitative PCR, and mast cell density was quantified by using design-based stereology. Airway epithelial responses to wounding and osmotic stress were assessed in primary airway epithelial cells and ex vivo murine lung tissue. Mast cell granule development and function were examined in cord blood–derived mast cells. Results Tryptase and carboxypeptidase A3 expression in epithelial brushings and epithelial mast cell density were selectively increased in the asthma group with EIB. An in vitro scratch wound initiated the release of thymic stromal lymphopoietin, which was greater in epithelial cells derived from asthmatic patients. Osmotic stress induced the release of IL-33 from explanted murine lungs, which was increased in allergen-treated mice. Thymic stromal lymphopoietin combined with IL-33 increased tryptase and carboxypeptidase A3 immunostaining in mast cell precursors and selectively increased cysteinyl leukotriene formation by mast cells in a manner that was independent of in vitro sensitization. Conclusions Mast cell infiltration of the epithelium is a critical determinant of indirect airway hyperresponsiveness, and the airway epithelium might serve as an important regulator of the development and function of this mast cell population.


title: Zero-Shot Object Detection: Learning to Simultaneously Recognize and Localize Novel Concepts

abstract: Current Zero-Shot Learning (ZSL) approaches are restricted to recognition of a single dominant unseen object category in a test image. We hypothesize that this setting is ill-suited for real-world applications where unseen objects appear only as a part of a complex scene, warranting both the `recognition' and `localization' of an unseen category. To address this limitation, we introduce a new \emph{`Zero-Shot Detection'} (ZSD) problem setting, which aims at simultaneously recognizing and locating object instances belonging to novel categories without any training examples. We also propose a new experimental protocol for ZSD based on the highly challenging ILSVRC dataset, adhering to practical issues, e.g., the rarity of unseen objects. To the best of our knowledge, this is the first end-to-end deep network for ZSD that jointly models the interplay between visual and semantic domain information. To overcome the noise in the automatically derived semantic descriptions, we utilize the concept of meta-classes to design an original loss function that achieves synergy between max-margin class separation and semantic space clustering. Furthermore, we present a baseline approach extended from recognition to detection setting. Our extensive experiments show significant performance boost over the baseline on the imperative yet difficult ZSD problem.


title: Hough transform and thermo-vision for monitoring pantograph-catenary system

abstract: AbstractThe pantograph-overhead contact wire system is investigated by using an infrared camera. As the pantograph has a vertical motion because of the non-uniform elasticity of the catenary, in order to detect the temperature along the strip from a sequence of infrared images, a segment-tracking algorithm, based on the Hough transformation, has been employed. An analysis of the stored images could help maintenance operations revealing, for example, overheating of the pantograph strip, bursts of arcing, or an irregular positioning of the contact line. Obtained results are relevant for monitoring the status of the quality transmission of the current and for a predictive maintenance of the pantograph and of the catenary system. Examples of analysis from experimental data are reported in the paper.


title: Word Affect Intensities

abstract: Words often convey affect -- emotions, feelings, and attitudes. Lexicons of word-affect association have applications in automatic emotion analysis and natural language generation. However, existing lexicons indicate only coarse categories of affect association. Here, for the first time, we create an affect intensity lexicon with real-valued scores of association. We use a technique called best-worst scaling that improves annotation consistency and obtains reliable fine-grained scores. The lexicon includes terms common from both general English and terms specific to social media communications. It has close to 6,000 entries for four basic emotions. We will be adding entries for other affect dimensions shortly.


title: Generalized acquired cutis laxa type 1: a case report and brief review of literature.

abstract: Cutis laxa, clinically characterized by loose and pendulous skin related to loss of elastic tissue, is a rare heterogeneous condition. It is classified into congenital and acquired types. We report a case of generalized acquired cutis laxa type 1 in a young man following pruritic urticarial plaques. We have done a brief review of literature.


title: SelQA: A New Benchmark for Selection-Based Question Answering

abstract: This paper presents a new selection-based question answering dataset, SelQA. The dataset consists of questions generated through crowdsourcing and sentence length answers that are drawn from the ten most prevalent topics in the English Wikipedia. We introduce a corpus annotation scheme that enhances the generation of large, diverse, and challenging datasets by explicitly aiming to reduce word co-occurrences between the question and answers. Our annotation scheme is composed of a series of crowdsourcing tasks with a view to more effectively utilize crowdsourcing in the creation of question answering datasets in various domains. Several systems are compared on the tasks of answer sentence selection and answer triggering, providing strong baseline results for future work to improve upon.


title: 0.35μm, 30V fully isolated and low-Ron nLDMOS for DC-DC applications

abstract: In this paper, we present a new approach to integrate in a 0.35 μm BCD technology, low Ron LDMOS power transistors with highly competitive Specific Resistance figure of merit (Rsp, defined as Ron*Area). The LDMOS are fully isolated in order to support applications which may bias the source/drain electrodes below the substrate potential, which is critical for devices used in high-current, high-frequency switching applications. The new devices are suitable for high-efficiency DC-DC converter products with operating voltage of up to 30V, such as mobile PMICs. For maximum performance, two different extended-drain LDMOS structures have been developed to cover the entire operating voltage range: for 16V and below, a planar-gate structure is used and for 20V and above, a non-planar “offset-LOCOS” gate is used for 20V and above.


title: Using haptics in computer interfaces for blind people

abstract: This paper has its roots in Certec's efforts to find a way to use haptic technology (i.e. touch based interfaces) to provide new computer interaction techniques for visually impaired people and those with physical disabilities.The paper presents a set of recommendations that we have formulated during our research and development work. These rules of thumb are grouped under the headlines Navigation, Finding objects, Understanding objects, Haptic widgets and Physical interaction.


title: Aligning point cloud views using persistent feature histograms

abstract: In this paper we investigate the usage of persistent point feature histograms for the problem of aligning point cloud data views into a consistent global model. Given a collection of noisy point clouds, our algorithm estimates a set of robust 16D features which describe the geometry of each point locally. By analyzing the persistence of the features at different scales, we extract an optimal set which best characterizes a given point cloud. The resulted persistent features are used in an initial alignment algorithm to estimate a rigid transformation that approximately registers the input datasets. The algorithm provides good starting points for iterative registration algorithms such as ICP (Iterative Closest Point), by transforming the datasets to its convergence basin. We show that our approach is invariant to pose and sampling density, and can cope well with noisy data coming from both indoor and outdoor laser scans.


Depth Estimation Using Monocular Camera


title: Predicting statistics of asynchronous SGD parameters for a large-scale distributed deep learning system on GPU supercomputers

abstract: Many studies have shown that Deep Convolutional Neural Networks (DCNNs) exhibit great accuracies given large training datasets in image recognition tasks. Optimization technique known as asynchronous mini-batch Stochastic Gradient Descent (SGD) is widely used for deep learning because it gives fast training speed and good recognition accuracies, while it may increases generalization error if training parameters are in inappropriate ranges. We propose a performance model of a distributed DCNN training system called SPRINT that uses asynchronous GPU processing based on mini-batch SGD. The model considers the probability distribution of mini-batch size and gradient staleness that are the core parameters of asynchronous SGD training. Our performance model takes DCNN architecture and machine specifications as input parameters, and predicts time to sweep entire dataset, mini-batch size and staleness with 5%, 9% and 19% error in average respectively on several supercomputers with up to thousands of GPUs. Experimental results on two different supercomputers show that our model can steadily choose the fastest machine configuration that nearly meets a target mini-batch size.


title: Modeling the underwater acoustic channel in ns2

abstract: Underwater acoustic networks have the potential to support a large variety of applications, such as mining equipment and environmental monitoring. Although underwater acoustics has been studied for decades, underwater networking and protocol design is just beginning as a research field. One critical tool used for the design and testing of new protocols is a network simulator. For simulators to be useful tools, accurate models of both the channel and the modem need to be implemented. In this paper we present the design and implementation of our interface and channel model for underwater acoustic networks in the ns2 network simulator. We show that the models accurately predict the channel conditions and interface costs by comparing them to previously published numerical predictions of channel state. Finally, we present a case study of a protocol designed and simulated using our model. Our simulation code is open source and available for general use.


title: Developing Mobile BIM/2D Barcode-Based Automated Facility Management System

abstract: Facility management (FM) has become an important topic in research on the operation and maintenance phase. Managing the work of FM effectively is extremely difficult owing to the variety of environments. One of the difficulties is the performance of two-dimensional (2D) graphics when depicting facilities. Building information modeling (BIM) uses precise geometry and relevant data to support the facilities depicted in three-dimensional (3D) object-oriented computer-aided design (CAD). This paper proposes a new and practical methodology with application to FM that uses an integrated 2D barcode and the BIM approach. Using 2D barcode and BIM technologies, this study proposes a mobile automated BIM-based facility management (BIMFM) system for FM staff in the operation and maintenance phase. The mobile automated BIMFM system is then applied in a selected case study of a commercial building project in Taiwan to verify the proposed methodology and demonstrate its effectiveness in FM practice. The combined results demonstrate that a BIMFM-like system can be an effective mobile automated FM tool. The advantage of the mobile automated BIMFM system lies not only in improving FM work efficiency for the FM staff but also in facilitating FM updates and transfers in the BIM environment.


title: Billions and billions of constraints: Whitebox fuzz testing in production

abstract: We report experiences with constraint-based whitebox fuzz testing in production across hundreds of large Windows applications and over 500 machine years of computation from 2007 to 2013. Whitebox fuzzing leverages symbolic execution on binary traces and constraint solving to construct new inputs to a program. These inputs execute previously uncovered paths or trigger security vulnerabilities. Whitebox fuzzing has found one-third of all file fuzzing bugs during the development of Windows 7, saving millions of dollars in potential security vulnerabilities. The technique is in use today across multiple products at Microsoft. We describe key challenges with running whitebox fuzzing in production. We give principles for addressing these challenges and describe two new systems built from these principles: SAGAN, which collects data from every fuzzing run for further analysis, and JobCenter, which controls deployment of our whitebox fuzzing infrastructure across commodity virtual machines. Since June 2010, SAGAN has logged over 3.4 billion constraints solved, millions of symbolic executions, and tens of millions of test cases generated. Our work represents the largest scale deployment of whitebox fuzzing to date, including the largest usage ever for a Satisfiability Modulo Theories (SMT) solver. We present specific data analyses that improved our production use of whitebox fuzzing. Finally we report data on the performance of constraint solving and dynamic test generation that points toward future research problems.


title: An efficient 3D face recognition approach based on the fusion of novel local low-level features

abstract: We present a novel 3D face recognition approach based on low-level geometric features that are collected from the eyes-forehead and the nose regions. These regions are relatively less influenced by the deformations that are caused by facial expressions. The extracted features revealed to be efficient and robust in the presence of facial expressions. A region-based histogram descriptor computed from these features is used to uniquely represent a 3D face. A Support Vector Machine (SVM) is then trained as a classifier based on the proposed histogram descriptors to recognize any test face. In order to combine the contributions of the two facial regions (eyes-forehead and nose), both feature-level and score-level fusion schemes have been tested and compared. The proposed approach has been tested on FRGC v2.0 and BU-3DFE datasets through a number of experiments and a high recognition performance was achieved. Based on the results of ''neutral vs. non-neutral'' experiment of FRGC v2.0 and ''low-intensity vs. high-intensity'' experiment of BU-3DFE, the feature-level fusion scheme achieved verification rates of 97.6% and 98.2% at 0.1% False Acceptance Rate (FAR) and identification rates of 95.6% and 97.7% on the two datasets respectively. The experimental results also have shown that the feature-level fusion scheme outperformed the score-level fusion one.


title: An Improved Topology of SEPIC Converter With Reduced Output Voltage Ripple

abstract: An improved version of a single-ended primary inductor converter (SEPIC) is presented. The converter consists of a conventional SEPIC converter plus an additional high-frequency transformer and diode to maintain a freewheeling mode of the DC inductor currents during the switch on state. The voltage conversion ratio characteristics and semiconductor device voltage and current stresses are characterized. The main advantages of this converter are the continuous output current, smaller output voltage ripple, and lower semiconductors current stress compared with the conventional SEPIC converter. The design and simulation of the concept is verified by an experiment with a 48-V input and 12-V/3.75-A output converter.


title: Automated segmentation of multiple sclerosis lesion subtypes with multichannel MRI

abstract: PURPOSE ::: To automatically segment multiple sclerosis (MS) lesions into three subtypes (i.e., enhancing lesions, T1 "black holes", T2 hyperintense lesions). ::: ::: ::: MATERIALS AND METHODS ::: Proton density-, T2- and contrast-enhanced T1-weighted brain images of 12 MR scans were pre-processed through intracranial cavity (IC) extraction, inhomogeneity correction and intensity normalization. Intensity-based statistical k-nearest neighbor (k-NN) classification was combined with template-driven segmentation and partial volume artifact correction (TDS+) for segmentation of MS lesions subtypes and brain tissue compartments. Operator-supervised tissue sampling and parameter calibration were performed on 2 randomly selected scans and were applied automatically to the remaining 10 scans. Results from this three-channel TDS+ (3ch-TDS+) were compared to those from a previously validated two-channel TDS+ (2ch-TDS+) method. The results of both the 3ch-TDS+ and 2ch-TDS+ were also compared to manual segmentation performed by experts. ::: ::: ::: RESULTS ::: Intra-class correlation coefficients (ICC) of 3ch-TDS+ for all three subtypes of lesions were higher (ICC between 0.95 and 0.96) than that of 2ch-TDS+ for T2 lesions (ICC = 0.82). The 3ch-TDS+ also identified the three lesion subtypes with high specificity (98.7-99.9%) and accuracy (98.5-99.9%). Sensitivity of 3ch-TDS+ for T2 lesions was 16% higher than with 2ch-TDS+. Enhancing lesions were segmented with the best sensitivity (81.9%). "Black holes" were segmented with the least sensitivity (62.3%). ::: ::: ::: CONCLUSION ::: 3ch-TDS+ is a promising method for automated segmentation of MS lesion subtypes.


title: The Deterministic Dendritic Cell Algorithm

abstract: The Dendritic Cell Algorithm is an immune-inspired algorithm originally based on the function of natural dendritic cells. The original instantiation of the algorithm is a highly stochastic algorithm. While the performance of the algorithm is good when applied to large real-time datasets, it is difficult to analyse due to the number of random-based elements. In this paper a deterministic version of the algorithm is proposed, implemented and tested using a port scan dataset to provide a controllable system. This version consists of a controllable amount of parameters, which are experimented with in this paper. In addition the effects are examined of the use of time windows and variation on the number of cells, both which are shown to influence the algorithm. Finally a novel metric for the assessment of the algorithms output is introduced and proves to be a more sensitive metric than the metric used with the original Dendritic Cell Algorithm.


title: Mining the Minds of Customers from Online Chat Logs

abstract: This study investigates factors that may determine satisfaction in customer service operations. We utilized more than 170,000 online chat sessions between customers and agents to identify characteristics of chat sessions that incurred dissatisfying experience. Quantitative data analysis suggests that sentiments or moods conveyed in online conversation are the most predictive factor of perceived satisfaction. Conversely, other session related meta data (such as that length, time of day, and response time) has a weaker correlation with user satisfaction. Knowing in advance what can predict satisfaction allows customer service staffs to identify potential weaknesses and improve the quality of service for better customer experience.


title: Information extraction: Evaluating named entity recognition from classical Malay documents

abstract: Natural Language Processing (NLP) is an important field of research in Computer Science. NLP is the process of analyzing texts based on a set of theories and technologies, and recent studies focused more on Information Extraction (IE). In Information Extraction, there are few steps or commonly known as task to be followed, which are named entity recognition, relation detection and classification, temporal and event processing, and template filling. Recent researches in Malay languages mainly focused on newspaper articles and since this research experiment is experimenting on classical documents, there is a need to identify the best way to extract noun from existing methods. This paper proposes to conduct a research about extracting nouns from Malay classical documents. The result shows that experiment using the Noun Extraction using Morphological Rules (Verb, Adjective and Noun Affixes) that has 77.61% chances of identifying a noun to contribute to the existing Malay noun list. As there is not any existing completed Malay noun list or dictionary that can be used as a guide, the results extracted still need to be judged by the language experts.


abstract: CogSci2000 Scaling and Testing for Non-Euclidean Spaces Jesse Spencer-Smith ( jbspence@indiana.edu ) Department of Psychology; Indiana University; 1101 E. 10th St. Bloomington, IN 47405-7007 USA Most current models or theories that posit the existence of geometric or spatial representations (e.g., Valentine, 1991), or that rely on representations generated by multi- dimensional scaling such as the Generalized Context Model (Nosofsky, 1984), assume that the spaces in which the rep- resentations are embedded are Euclidean, or are endowed with a Minkowski power metric. Although the need for investigation of more general spaces was noted as early as 1964 (Shepard, 1964), and has been argued more recently (Townsend and Thomas, 1993), non-Euclidean spaces have been largely overlooked. A natural generalization of Euclidean space are the Riemannian spaces. The sphere (with distances measured on the surface) is an example of a Riemannian space. In the current investigation, qualitative and quantitative tests to uncover properties of perceptual spaces are developed and tested. Locally, Riemannian spaces are well-approximated by Euclidean spaces. Accordingly, for sufficiently-restricted stimulus sets, non-Euclidean properties of the spaces (such as curvature) may not become evident. If points on a sphere are sufficiently close, the distances between those points can be approximated by assuming the points lie on a plane. While cities in the continental U.S. can be approxi- mated as lying on a plane, regular discrepancies are appar- ent. Approximating cities in the western hemisphere as lying in a plane results in far greater errors, while an ap- proximation of the cities around the globe in a similar manner would misrepresents fundamental properties of the space. Between antipodal points on a sphere, for example, there exist not one shortest path, but infinitely many. A metric multidimensional scaling tool which assumes constant-curvature Riemannian spaces (such as the sphere, pseudosphere, or plane) based on work by Lindman and Caelli (1972; see also Indow, 1982) is implemented in Mat- lab and is applied to new and existing data. Qualitative tests for curvature are developed and demonstrated, and applied to new and existing data. Critiques of Geometrical Models Some of the most insightful and widely-cited critiques of geometric models of similarity (e.g. Gati and Tversky, 1982; Beals, Krantz and Tversky, 1968) address spaces which are endowed with Minkowski power metrics, and do not apply to spaces endowed with Riemannian metrics. The specific tests which fail with certain Riemannian spaces are detailed. Acknowledgments James T. Townsend, Rudy Professor of Psychology, Indiana University provided invaluable advice during the develop- ment of this work. The work has also benefited greatly from conversations with Bruce Solomon of the Department of Mathematics, Indiana University. References Beals, R., Krantz, D. H. & Tversky, A. (1968). Foundations of multidimensional scaling. Psychological Review, 75, Gati, I. & Tversky, A. (1982). Representations of qualita- tive and quantitative dimensions. Journal of Experimen- tal Psychology: Human Perception and Performance, 8, Indow, T. (1982). An approach to geometry of visual space with no a priori mapping functions: Multidimensional mapping according to Riemannian metrics. Journal of Mathematical Psychology, 26, (3), 204-236. Lindman, H. & Caelli, T. (1972). Constant curvature Rie- mannian scaling. Journal of Mathematical Psychology, Nosofsky, R. (1984). Choice, similarity, and the context theory of classification. Journal of Experimental Psy- chology: Learning, Memory, and Cognition, 10, (1), 104- Shepard, R. (1964). Attention and the metric structure of the stimulus space. Journal of Mathematical Psychology, Townsend, J. T. & Thomas, R. (1993). On the need for a general quantitative theory of pattern similarity. In S. C. Masin (Ed), Foundations of perceptual theory. Advances in psychology, Vol. 99. Amsterdam, Netherlands: North- Holland/Elsevier Science Publishers Valentine, T. (1991). A unified account of the effects of distinctiveness, inversion, and race in face recognition. Quarterly Journal of Experimental Psychology: Human Experimental Psychology, 43A,(2), 161-204.


title: ENCORE: External Neural Constraints Regularized Distant Supervision for Relation Extraction

abstract: Distant Supervision is a widely used approach for training relation extraction models. It generates noisy training samples by heuristically labeling a corpus using an existing knowledge base. Previous noise reduction methods for distant supervision fail to utilize information such as data credibility and sample confidence. In this paper, we proposed a novel neural framework, named ENCORE (External Neural COnstraints REgularized distant supervision), which allows an integration of other information for standard DS through regularizations under multiple external neural networks. In ENCORE, a teacher-student co-training mechanism is used to iterative distilling information from external neural networks to an existing relation extraction model. The experiment results demonstrated that without increasing any data or reshaping its original structure, ENCORE enhanced a CNN based relation extraction model for over 12%. The enhanced model also outperforms the state-of-the-art relation extraction method on the same dataset.


title: Computing similarity transformations from only image correspondences

abstract: We propose a novel solution for computing the relative pose between two generalized cameras that includes reconciling the internal scale of the generalized cameras. This approach can be used to compute a similarity transformation between two coordinate systems, making it useful for loop closure in visual odometry and registering multiple structure from motion reconstructions together. In contrast to alternative similarity transformation methods, our approach uses 2D-2D image correspondences thus is not subject to the depth uncertainty that often arises with 3D points. We utilize a known vertical direction (which may be easily obtained from IMU data or vertical vanishing point detection) of the generalized cameras to solve the generalized relative pose and scale problem as an efficient Quadratic Eigenvalue Problem. To our knowledge, this is the first method for computing similarity transformations that does not require any 3D information. Our experiments on synthetic and real data demonstrate that this leads to improved performance compared to methods that use 3D-3D or 2D-3D correspondences, especially as the depth of the scene increases.


title: Next-generation sequencing technologies for environmental DNA research

abstract: Since 2005, advances in next-generation sequencing technologies have revolutionized biological science. The analysis of environmental DNA through the use of specific gene markers such as species-specific DNA barcodes has been a key application of next-generation sequencing technologies in ecological and environmental research. Access to parallel, massive amounts of sequencing data, as well as subsequent improvements in read length and throughput of different sequencing platforms, is leading to a better representation of sample diversity at a reasonable cost. New technologies are being developed rapidly and have the potential to dramatically accelerate ecological and environmental research. The fast pace of development and improvements in next-generation sequencing technologies can reflect on broader and more robust applications in environmental DNA research. Here, we review the advantages and limitations of current next-generation sequencing technologies in regard to their application for environmental DNA analysis.