title: Determinants of Customer Purchase Intention in Electronic Service

abstract: Compared to traditional service, the characteristics of the customer behavior in electronic service are personalized demand, convenient consumed circumstance and perceptual consumer behavior. Therefore, customer behavior is an important factor to facilitate online electronic service. The purpose of this study is to explore the key success factors affecting customer purchase intention of electronic service through the behavioral perspectives of customers. Based on the theory of technology acceptance model (TAM) and self service technology (SST), the study proposes a theoretical model for the empirical examination of the customer intention for purchasing electronic services. A comprehensive survey of online customers having e-shopping experiences is undertaken. Then this model is tested by means of the statistical analysis method of structure equation model (SEM). The empirical results indicated that perceived usefulness and perceived assurance have a significant impact on purchase in e-service. Discussion and implication are presented in the end.


title: MoCap-guided Data Augmentation for 3D Pose Estimation in the Wild

abstract: This paper addresses the problem of 3D human pose estimation in the wild. A significant challenge is the lack of training data, i.e., 2D images of humans annotated with 3D poses. Such data is necessary to train state-of-the-art CNN architectures. Here, we propose a solution to generate a large set of photorealistic synthetic images of humans with 3D pose annotations. We introduce an image-based synthesis engine that artificially augments a dataset of real images with 2D human pose annotations using 3D Motion Capture (MoCap) data. Given a candidate 3D pose our algorithm selects for each joint an image whose 2D pose locally matches the projected 3D pose. The selected images are then combined to generate a new synthetic image by stitching local image patches in a kinematically constrained manner. The resulting images are used to train an end-to-end CNN for full-body 3D pose estimation. We cluster the training data into a large number of pose classes and tackle pose estimation as a K-way classification problem. Such an approach is viable only with large training sets such as ours. Our method outperforms the state of the art in terms of 3D pose estimation in controlled environments (Human3.6M) and shows promising results for in-the-wild images (LSP). This demonstrates that CNNs trained on artificial images generalize well to real images.


title: The internal calcar septum (femoral thigh spur) in computed tomography and conventional radiography

abstract: Objective. The femoral ”thigh spur”, a cortical septum in the region of the lesser trochanter of the human femur, was first described and named by the German anatomist Merkel in 1874, but it was never examined in detail. To evaluate the frequency and the shape of this structure, a combined anatomical and radiological study was performed using saw-cuts from specimens, high-resolution CT and conventional radiography.


Distinguishing Opinion from News


title: CRM and customer‐centric knowledge management: an empirical research

abstract: Current competitive challenges induced by globalization and advances in information technology have forced companies to focus on managing customer relationships, and in particular customer satisfaction, in order to efficiently maximize revenues. This paper reports exploratory research based on a mail survey addressed to the largest 1,000 Greek organizations. The objectives of the research were: to investigate the extent of the usage of customer‐ and market‐related knowledge management (KM) instruments and customer relationship management (CRM) systems by Greek organizations and their relationship with demographic and organizational variables; to investigate whether enterprises systematically carry out customer satisfaction and complaining behavior research; and to examine the impact of the type of the information system used and managers’ attitudes towards customer KM practices. In addition, a conceptual model of CRM development stages is proposed. The findings of the survey show that about half of the organizations of the sample do not adopt any CRM philosophy. The remaining organizations employ instruments to conduct customer satisfaction and other customer‐related research. However, according to the proposed model, they are positioned in the first, the preliminary CRM development stage. The findings also suggest that managers hold positive attitudes towards CRM and that there is no significant relationship between the type of the transactional information system used and the extent to which customer satisfaction research is performed by the organizations. The paper concludes by discussing the survey findings and proposing future research.


title: DSIP: A Scalable Inference Accelerator for Convolutional Neural Networks

abstract: This paper presents a scalable inference accelerator called a deep-learning specific instruction-set processor (DSIP) to support various convolutional neural networks (CNNs). For CNNs requiring a large amount of computations and memory accesses, a programmable inference system called master–slave instruction set architecture (ISA) is newly proposed to achieve high flexibility, processing speed, and energy efficiency. The master is responsible for sending and receiving feature maps in order to deal with neural networks in a scalable way, and the slave performs CNN operations, such as multiply accumulate, max pooling, and activation functions, on the features received from the master. The master–slave ISA maximizes computation speed by overlapping the off-chip data transmission and the CNN operations, and reduces power consumption by performing the convolution incrementally to reuse input and partial-sum data as maximally as possible. An inference system can be configured by connecting multiple DSIPs in a form of either 1-D or 2-D chain structure in order to enhance computation speed further. To evaluate the proposed accelerator, a prototype chip is implemented and evaluated for AlexNet. Compared to the state-of-the-art accelerator, the DSIP-based system enhances the energy efficiency by 2.17 $\times $ .


title: An Improved Weighted-Feature Clustering Algorithm for K-anonymity

abstract: Chiu proposed a clustering algorithm adjusting the numeric feature weights automatically for k-anonymity implementation and this approach gave a better clustering quality over the traditional generalization and suppression methods. In this paper, we propose an improved weighted-feature clustering algorithm which takes the weight of categorical attributes and the thesis of optimal k-partition into consideration. To show the effectiveness of our method, we do some information loss experiments to compare it with greedy k-member clustering algorithm.


title: Data cube computational model with Hadoop MapReduce

abstract: XML has become a widely used and well structured data format for digital document handling and message transmission. To find useful knowledge in XML data, data warehouse and OLAP applications aimed at providing supports for decision making should be developed. Apache Hadoop is an open source cloud computing framework that provides a distributed file system for large scale data processing. In this paper, we discuss an XML data cube model which offers us the complete views to observe XML data, and present a basic algorithm to implement its building process on Hadoop. To improve the efficiency, an optimized algorithm more suitable for this kind of XML data is also proposed. The experimental results given in the paper prove the effectiveness of our optimization strategies.


title: Choosing among alternative service delivery modes: an investigation of customer trial of self-service technologies

abstract: Abstract Electronic commerce is an increasingly popular business model with a wide range of tools available to firms. An application that is becoming more common is the use of self-service technologies (SSTs), such as telephone banking, automated hotel checkout, and online investment trading, whereby customers produce services for themselves without assistance from firm employees. Widespread introduction of SSTs is apparent across industries, yet relatively little is known about why customers decide to try SSTs and why some SSTs are more widely accepted than others. In this research, the authors explore key factors that influence the initial SST trial decision, specifically focusing on actual behavior in situations in which the consumer has a choice among delivery modes. The authors show that the consumer readiness variables of role clarity, motivation, and ability are key mediators between established adoption constructs (innovation characteristics and individual differences) and the likelihood of trial.


title: One button machine for automating feature engineering in relational databases

abstract: Feature engineering is one of the most important and time consuming tasks in predictive analytics projects. It involves understanding domain knowledge and data exploration to discover relevant hand-crafted features from raw data. In this paper, we introduce a system called One Button Machine, or OneBM for short, which automates feature discovery in relational databases. OneBM automatically performs a key activity of data scientists, namely, joining of database tables and applying advanced data transformations to extract useful features from data. We validated OneBM in Kaggle competitions in which OneBM achieved performance as good as top 16% to 24% data scientists in three Kaggle competitions. More importantly, OneBM outperformed the state-of-the-art system in a Kaggle competition in terms of prediction accuracy and ranking on Kaggle leaderboard. The results show that OneBM can be useful for both data scientists and non-experts. It helps data scientists reduce data exploration time allowing them to try and error many ideas in short time. On the other hand, it enables non-experts, who are not familiar with data science, to quickly extract value from their data with a little effort, time and cost.


title: PLM (Product Lifecycle Management) Model for Supply Chain Optimization

abstract: Product Lifecycle Management (PLM) is an integrated business approach to the collaborative creation, management and dissemination of engineering information throughout the extended enterprise.


title: M.: Query processing and optimization for regular path expressions

abstract: Regular path expression is one of the core components of XML query languages, and several approaches to evaluating regular path expressions have been proposed. In this paper, a new path expression evaluation approach, extent join, is proposed to compute both parent-children ('/') and ancestor-descendent ('//') connectors between path steps. Furthermore, two path expression optimization rules, pathshortening and path-complementing, are proposed. The former reduces the number of joins by shortening the path while the latter optimizes the execution of a path by using an equivalent complementary path expression to compute the original path. Experimental results show that the algorithms proposed in this paper are much more efficient than conventional ones.


title: WEBSITE FEATURES THAT GAVE RISE TO SOCIAL COMMERCE: A HISTORICAL ANALYSIS

abstract: Social commerce is a form of commerce mediated by social media and social networking services (SNS). As a multifaceted phenomenon, social commerce can be studied from different angles and analyzed through the lens of various disciplines. This article examines website technical features to depict the transformation of e-commerce into social commerce. We first develop a conceptual framework to capture three emphases of e-commerce: transactional, relational and social. Then, we use the framework to conduct an historical analysis of the actual website screen captures for five top e-commerce companies since their websites were established. We were able to identify and classify a total of 174 emerging technical features. Our results show that: (1) all three emphases were expressed in the websites and have been re-shaping their business and marketing strategies over the years; (2) there was a clear blooming of social features in 2007; and (3) there has been a significant effort to strengthen customer and merchant ties through relational features. Our findings signal that there still is room for further exploration of the social emphasis.


title: DeepCut: Joint Subset Partition and Labeling for Multi Person Pose Estimation

abstract: This paper considers the task of articulated human pose estimation of multiple people in real world images. We propose an approach that jointly solves the tasks of detection and pose estimation: it infers the number of persons in a scene, identifies occluded body parts, and disambiguates body parts between people in close proximity of each other. This joint formulation is in contrast to previous strategies, that address the problem by first detecting people and subsequently estimating their body pose. We propose a partitioning and labeling formulation of a set of body-part hypotheses generated with CNN-based part detectors. Our formulation, an instance of an integer linear program, implicitly performs non-maximum suppression on the set of part candidates and groups them to form configurations of body parts respecting geometric and appearance constraints. Experiments on four different datasets demonstrate state-of-the-art results for both single person and multi person pose estimation1.


Satisfaction conditions in anticipatory mechanisms


title: Islanding and Power Quality Disturbance Detection in Grid-Connected Hybrid Power System Using Wavelet and $S$-Transform

abstract: In this paper, comparative study between wavelet transform (WT) and S-transform (ST) based on extracted features for detection of islanding and power quality (PQ) disturbances in hybrid distributed generation (DG) system is presented. The hybrid system consists of DG resources like photovoltaic, fuel cell, and wind energy systems connected to grid. The negative sequence component of the voltage signal is used in islanding detection of these resources from the grid. Voltage signal extracted directly at the point of common coupling is considered for detection of PQ disturbances. Further, the effect of variation of grid impedances on islanding and PQ disturbances and effect of islanding on the coherency between the energy resources is also presented in this paper. The study for different scenarios of DG system is presented in the form of time-frequency analysis. The energy content and standard deviation of ST contour and WT signal is also reported in order to validate the graphical results. The results demonstrate the advantages of S -transform over WT in detection of islanding and different disturbances under noise-free as well as noisy scenarios.


title: A Vehicle Recognition Algorithm Based on Deep Transfer Learning with a Multiple Feature Subspace Distribution

abstract: Vehicle detection is a key component of environmental sensing systems for Intelligent Vehicles (IVs). The traditional shallow model and offline learning-based vehicle detection method are not able to satisfy the real-world challenges of environmental complexity and scene dynamics. Focusing on these problems, this work proposes a vehicle detection algorithm based on a multiple feature subspace distribution deep model with online transfer learning. Based on the multiple feature subspace distribution hypothesis, a deep model is established in which multiple Restricted Boltzmann Machines (RBMs) construct the lower layers and a Deep Belief Network (DBN) composes the superstructure. For this deep model, an unsupervised feature extraction method is applied, which is based on sparse constraints. Then, a transfer learning method with online sample generation is proposed based on the deep model. Finally, the entire classifier is retrained online with supervised learning. The experiment is actuated using the KITTI road image datasets. The performance of the proposed method is compared with many state-of-the-art methods and it is demonstrated that the proposed deep transfer learning-based algorithm outperformed existing state-of-the-art methods.


title: Real-time pothole detection on TMS320C6678 DSP

abstract: In this paper, a previously proposed pothole detection algorithm is improved in order to increase its performance in terms of speed and detection rate and to evaluate the performance. The algorithm was implemented for the TMS320C6678 SoC Digital Signal Processor (DSP). Potholes can be detected by comparing the disparity values of the scene to that of the disparity of the road surface if known. In a practical situation, the disparity of the road surface is not known but can be estimated. In this paper, the disparity of the road surface is efficiently estimated by modeling the road surface. Connected Component Labeling for size and severity estimation of individual potholes is also used. The main improvement of the existing algorithm follows the introduction of sampling when estimating model parameters. The sampling algorithm uses prior knowledge of the points' distribution and thus reduces the randomness when selecting data points. To achieve a high frame rate, the system was not only improved at the algorithm level but also taking advantage of the architecture of the SoC, using the various compiler optimization switches and selecting the appropriate memory arrangement. Finally, to take advantage of the multicore SoC, OpenMP APIs for code parallelization was used for the implementation. This has shown an improvement of around 3.4 times when using 5 cores. It is also shown that using more than 5 cores can result in performance degradation. Experimental results show that the system achieves real-time high performance of 145fps on images with 1050×600 resolution with 100% recall and 98% accuracy for pothole detection.


title: Artificial Intelligence as an Enabler for Cognitive Self-Organizing Future Networks

abstract: The explosive increase in number of smart devices hosting sophisticated applications is rapidly affecting the landscape of information communication technology industry. Mobile subscriptions, expected to reach 8.9 billion by 2022, would drastically increase the demand of extra capacity with aggregate throughput anticipated to be enhanced by a factor of 1000. In an already crowded radio spectrum, it becomes increasingly difficult to meet ever growing application demands of wireless bandwidth. It has been shown that the allocated spectrum is seldom utilized by the primary users and hence contains spectrum holes that may be exploited by the unlicensed users for their communication. As we enter the Internet Of Things (IoT) era in which appliances of common use will become smart digital devices with rigid performance requirements (such as low latency, energy efficiency, etc.), current networks face the vexing problem of how to create sufficient capacity for such applications. The fifth generation of cellular networks (5G) envisioned to address these challenges are thus required to incorporate cognition and intelligence to resolve the aforementioned issues.


title: Recent advances on SVM based fault diagnosis and process monitoring in complicated industrial processes

abstract: With the advancement of industrial systems, fault monitoring and diagnosis methods based on the data-driven attract much attention in recent years. This kind of methods are widely used in engineering projects, especially in those big and complicated machines, whose conditions are difficult to obtain from straight view. They can provide the administrator with effective fault information in initial phase and therefore reduce the loss caused by faults. This paper reviews the research and development of fault diagnosis and monitoring approach based on support vector machine (SVM). While many other methods, such as expert system and artificial neural network, have been used in fault monitoring and diagnosis, SVM shows its advantage in generalization performance and in case of small sample. Therefore, it should attract more attention.


title: Brain points: a growth mindset incentive structure boosts persistence in an educational game

abstract: There is great interest in leveraging video games to improve student engagement and motivation. However, educational games are not uniformly effective, and little is known about how in-game rewards affect children's learning-related behavior. In this work, we argue that educational games can be improved by fundamentally changing their incentive structures to promote the growth mindset, or the belief that intelligence is malleable. We present "brain points," a system that encourages the development of growth mindset behaviors by directly incentivizing effort, use of strategy, and incremental progress. Through a study of 15,000 children, we show that the "brain points" system encourages more low-performing students to persist in the educational game Refraction when compared to a control, and increases overall time played, strategy use, and perseverance after challenge. We believe that this growth mindset incentive structure has great potential in many educational environments.


title: Image-based correction of continuous and discontinuous non-planar axial distortion in serial section microscopy.

abstract: MOTIVATION ::: Serial section microscopy is an established method for detailed anatomy reconstruction of biological specimen. During the last decade, high resolution electron microscopy (EM) of serial sections has become the de-facto standard for reconstruction of neural connectivity at ever increasing scales (EM connectomics). In serial section microscopy, the axial dimension of the volume is sampled by physically removing thin sections from the embedded specimen and subsequently imaging either the block-face or the section series. This process has limited precision leading to inhomogeneous non-planar sampling of the axial dimension of the volume which, in turn, results in distorted image volumes. This includes that section series may be collected and imaged in unknown order. ::: ::: ::: RESULTS ::: We developed methods to identify and correct these distortions through image-based signal analysis without any additional physical apparatus or measurements. We demonstrate the efficacy of our methods in proof of principle experiments and application to real world problems. ::: ::: ::: AVAILABILITY AND IMPLEMENTATION ::: We made our work available as libraries for the ImageJ distribution Fiji and for deployment in a high performance parallel computing environment. Our sources are open and available at http://github.com/saalfeldlab/section-sort, http://github.com/saalfeldlab/z-spacing and http://github.com/saalfeldlab/z-spacing-spark CONTACT: : saalfelds@janelia.hhmi.orgSupplementary information: Supplementary data are available at Bioinformatics online.


Applying the ISO 9126 model to the evaluation of an elearning


title: Evaluation of a Haptic Mixed Reality System for Interactions with a Virtual Control Panel

abstract: We present a haptic feedback technique that combines feedback from a portable force-feedback glove with feedback from direct contact with rigid passive objects. This approach is a haptic analogue of visual mixed reality, since it can be used to haptically combine real and virtual elements in a single display. We discuss device limitations that motivated this combined approach and summarize technological challenges encountered. We present three experiments to evaluate the approach for interactions with buttons and sliders on a virtual control panel. In our first experiment, this approach resulted in better task performance and better subjective ratings than the use of only a force-feedback glove. In our second experiment, visual feedback was degraded and the combined approach resulted in better performance than the glove-only approach and in better ratings of slider interactions than both glove-only and passive-only approaches. A third experiment allowed subjective comparison of approaches and provided additional evidence that the combined approach provides the best experience.


THE EFFECT OF CUSTOMER TRUST ON CUSTOMER LOYALTY AND REPURCHASE INTENTION : THE MODERATING INFLUENCE OF PERCEIVED CSR


title: Bounding memory interference delay in COTS-based multi-core systems

abstract: In commercial-off-the-shelf (COTS) multi-core systems, a task running on one core can be delayed by other tasks running simultaneously on other cores due to interference in the shared DRAM main memory. Such memory interference delay can be large and highly variable, thereby posing a significant challenge for the design of predictable real-time systems. In this paper, we present techniques to provide a tight upper bound on the worst-case memory interference in a COTS-based multi-core system. We explicitly model the major resources in the DRAM system, including banks, buses and the memory controller. By considering their timing characteristics, we analyze the worst-case memory interference delay imposed on a task by other tasks running in parallel. To the best of our knowledge, this is the first work bounding the request re-ordering effect of COTS memory controllers. Our work also enables the quantification of the extent by which memory interference can be reduced by partitioning DRAM banks. We evaluate our approach on a commodity multi-core platform running Linux/RK. Experimental results show that our approach provides an upper bound very close to our measured worst-case interference.


title: Mesencephalic mechanisms for integration of female reproductive behavior in the rat

abstract: Placement of bilateral electrolytic lesions in the mesencephalic central gray (CG) of estrogen-primed ovariectomized female rats produced an immediate decline in performance of the lordosis reflex. Lesions that destroyed the dorsal half of the CG and the adjacent subtectal region were effective. The decrease in individual animals in terms of the lordosis reflex score ranged from 20 to 100% of the prelesion performance. Such lesions abolished the facilitation of lordosis by electrical stimulation of the ventromedial nucleus of the hypothalamus. Similar abrupt losses of lordosis followed bilateral lesions of either a) the area between CG and the cuneiform nucleus of the mesencephalic reticular formation (NCf); or b) the ventrolateral quadrant of the NCf. The difference between these two lesions was that the effect of the latter could be overridden by electrical stimulation of the CG, whereas that of the former could not. We conclude that the CG is an important supraspinal component of the circuit for lordosis behavior, constituting a link between ascending somatosensory and descending motor systems for lordosis. It probably facilitates lordosis when activated by behaviorally relevant peripheral somatosensory and/or ventromedial hypothalamic inputs.


title: A Unified Local and Global Model for Discourse Coherence

abstract: We present a model for discourse coherence which combines the local entitybased approach of (Barzilay and Lapata, 2005) and the HMM-based content model of (Barzilay and Lee, 2004). Unlike the mixture model of (Soricut and Marcu, 2006), we learn local and global features jointly, providing a better theoretical explanation of how they are useful. As the local component of our model we adapt (Barzilay and Lapata, 2005) by relaxing independence assumptions so that it is effective when estimated generatively. Our model performs the ordering task competitively with (Soricut and Marcu, 2006), and significantly better than either of the models it is based on.


title: Design of current limiting circuit in low dropout linear voltage regulator

abstract: A current limiting circuit applied in low dropout linear voltage regulator is designed and simulated. It can limit the maximum output current and the output short current of the regulator within 320mA and 30mA, respectively, and well realize the purpose of overcurrent protection. Prototype of the circuit can be fabricated using the 0.5/spl mu/m CMOS process.


Pure Jump Lévy Processes for Asset Price Modelling


title: ISC: An Iterative Social Based Classifier for Adult Account Detection on Twitter

abstract: The widespread of adult content on online social networks (e.g., Twitter) is becoming an emerging yet critical problem. An automatic method to identify accounts spreading sexually explicit content (i.e., adult account) is of significant values in protecting children and improving user experiences. Traditional adult content detection techniques are ill-suited for detecting adult accounts on Twitter due to the diversity and dynamics in Twitter content. In this paper, we formulate the adult account detection as a graph based classification problem and demonstrate our detection method on Twitter by using social links between Twitter accounts and entities in tweets. As adult Twitter accounts are mostly connected with normal accounts and post many normal entities, which makes the graph full of noisy links, existing graph based classification techniques cannot work well on such a graph. To address this problem, we propose an iterative social based classifier (ISC), a novel graph based classification technique resistant to the noisy links. Evaluations using large-scale real-world Twitter data show that, by labeling a small number of popular Twitter accounts, ISC can achieve satisfactory performance in adult account detection, significantly outperforming existing techniques.


title: Efficient energy-optimal routing for electric vehicles

abstract: Traditionally routing has focused on finding shortest paths in networks with positive, static edge costs representing the distance between two nodes. Energy-optimal routing for electric vehicles creates novel algorithmic challenges, as simply understanding edge costs as energy values and applying standard algorithms does not work. First, edge costs can be negative due to recuperation, excluding Dijkstra-like algorithms. Second, edge costs may depend on parameters such as vehicle weight only known at query time, ruling out existing preprocessing techniques. Third, considering battery capacity limitations implies that the cost of a path is no longer just the sum of its edge costs. This paper shows how these challenges can be met within the framework of A* search. We show how the specific domain gives rise to a consistent heuristic function yielding an O(n2) routing algorithm. Moreover, we show how battery constraints can be treated by dynamically adapting edge costs and hence can be handled in the same way as parameters given at query time, without increasing run-time complexity. Experimental results with real road networks and vehicle data demonstrate the advantages of our solution.


title: Hand Talk-Implementation of a Gesture Recognizing Glove

abstract: We present our prototype for a gesture recognizing glove (data glove). We use low cost packaging material (velostat) for making piezoresistive sensors. These flex sensors detect a bend in fingers and we map this data to a character set by implementing a Minimum Mean Square Error machine learning algorithm. The recognized character is transmitted via Bluetooth, to an Android phone, which performs a text to speech conversion. Our motivation for Hand Talk is to compare hand configurations with sign language charts and generate artificial speech which articulates the gestured words. This technology also has further applications as a 3D mouse, virtual keyboard, control for precision control of robotic arms.


Unsupervised Text Classification for Natural Language Interactive Narratives


title: Factors affecting pass-along email intentions (PAEIs): Integrating the social capital and social cognition theories

abstract: While eWOM advertising has recently emerged as an effective marketing strategy among marketing practitioners, comparatively few studies have been conducted to examine the eWOM from the perspective of pass-along emails. Based on social capital theory and social cognitive theory, this paper develops a model involving social enablers and personal cognition factors to explore the eWOM behavior and its efficacy. Data collected from 347 email users have lent credit to the model proposed. Tested by LISREL 8.70, the results indicate that the factors such as message involvement, social interaction tie, affection outcome expectations and message passing self-efficacy exert significant influences on pass-along email intentions (PAEIs). The study result may well be useful to marketing practitioners who are considering email marketing, especially to those who are in the process of selecting key email users and/or designing product advertisements to heighten the eWOM effect.


title: Evolutionary View Planning for Optimized UAV Terrain Modeling in a Simulated Environment

abstract: This work demonstrates the use of genetic algorithms in optimized view planning for 3D reconstruction applications using small unmanned aerial vehicles (UAVs). The quality of UAV site models is currently highly dependent on manual pilot operations or grid-based automation solutions. When applied to 3D structures, these approaches can result in gaps in the total coverage or inconsistency in final model resolution. Genetic algorithms can effectively explore the search space to locate image positions that produce high quality models in terms of coverage and accuracy. A fitness function is defined, and optimization parameters are selected through semi-exhaustive search. A novel simulation environment for evaluating view plans is demonstrated using terrain generation software. The view planning algorithm is tested in two separate simulation cases: a water drainage structure and a reservoir levee, as representative samples of infrastructure monitoring. The optimized flight plan is compared against three alternate flight plans in each case. The optimized view plan is found to yield terrain models with up to 43% greater accuracy than a standard grid flight pattern, while maintaining comparable coverage and completeness.


title: Design Of A Lexical Database For Sanskrit

abstract: We present the architectural design rationale of a Sanskrit computational linguistics platform, where the lexical database has a central role. We explain the structuring requirements issued from the interlinking of grammatical tools through its hypertext rendition.


title: A planar 3DOF robotic exoskeleton for rehabilitation and assessment

abstract: A new robotic exoskeleton for the upper-limb has been designed and constructed. Its primary purpose is to act as a proof-of-concept prototype for a more sophisticated rehabilitation and assessment device that is currently in development. Simultaneously, it is intended to extend the capabilities of an existing planar exoskeleton device. The robot operates in the horizontal plane and provides independent control of a user's shoulder, elbow and wrist joints using a cable-driven actuation system. The novel component of the design is a curved track and carriage which allows the mechanism that drives the shoulder joint to be located away from the user, underneath their arm. This paper describes the design of the robot, and provides an initial indication of its performance.


title: Energy disaggregation meets heating control

abstract: Heating control is of particular importance, since heating accounts for the biggest amount of total residential energy consumption. Smart heating strategies allow to reduce such energy consumption by automatically turning off the heating when the occupants are sleeping or away from home. The present context or occupancy state of a household can be deduced from the appliances that are currently in use. In this study we investigate energy disaggregation techniques to infer appliance states from an aggregated energy signal measured by a smart meter. Since most household devices have predictable energy consumption, we propose to use the changes in aggregated energy consumption as features for the appliance/occupancy state classification task. We evaluate our approach on real-life energy consumption data from several households, compare the classification accuracy of various machine learning techniques, and explain how to use the inferred appliance states to optimize heating schedules.


title: Vertically Stacked Cantilever n-Type Poly-Si Junctionless Nanowire Transistor and Its Series Resistance Limit

abstract: We had successfully suspended the vertically stacked cantilever (VSC) nanowire by two approaches: 1) inserting a SiN layer as reinforcement to sustain the gate-stack thermal budget and 2) adopting high- ${k}$ metal gate low-temperature process and realizing gate-all-around structure, which shows better subthreshold characteristics. Feasibility of improving current level within the same footprint and without degrading subthreshold performance is demonstrated. Series resistance limit is pointed out as a bottle neck for current increment with respect to layers of channels. Further investigation of reducing the series resistance of VSC nanowire is needed for any future circuit integration.


title: Image and depth from a conventional camera with a coded aperture

abstract: A conventional camera captures blurred versions of scene information away from the plane of focus. Camera systems have been proposed that allow for recording all-focus images, or for extracting depth, but to record both simultaneously has required more extensive hardware and reduced spatial resolution. We propose a simple modification to a conventional camera that allows for the simultaneous recovery of both (a) high resolution image information and (b) depth information adequate for semi-automatic extraction of a layered depth representation of the image. ::: Our modification is to insert a patterned occluder within the aperture of the camera lens, creating a coded aperture. We introduce a criterion for depth discriminability which we use to design the preferred aperture pattern. Using a statistical model of images, we can recover both depth information and an all-focus image from single photographs taken with the modified camera. A layered depth map is then extracted, requiring user-drawn strokes to clarify layer assignments in some cases. The resulting sharp image and layered depth map can be combined for various photographic applications, including automatic scene segmentation, post-exposure refocusing, or re-rendering of the scene from an alternate viewpoint.


Legal Documents Clustering using Latent Dirichlet Allocation


title: The organization of intrinsic computation: complexity-entropy diagrams and the diversity of natural information processing.

abstract: Intrinsic computation refers to how dynamical systems store, structure, and transform historical and spatial information. By graphing a measure of structural complexity against a measure of randomness, complexity-entropy diagrams display the different kinds of intrinsic computation across an entire class of systems. Here, we use complexity-entropy diagrams to analyze intrinsic computation in a broad array of deterministic nonlinear and linear stochastic processes, including maps of the interval, cellular automata, and Ising spin systems in one and two dimensions, Markov chains, and probabilistic minimal finite-state machines. Since complexity-entropy diagrams are a function only of observed configurations, they can be used to compare systems without reference to system coordinates or parameters. It has been known for some time that in special cases complexity-entropy diagrams reveal that high degrees of information processing are associated with phase transitions in the underlying process space, the so-called "edge of chaos." Generally, though, complexity-entropy diagrams differ substantially in character, demonstrating a genuine diversity of distinct kinds of intrinsic computation.


title: Design and Implementation of a Smart IoT Gateway

abstract: With rapid development of Internet of things (IoT), there exists an ever-growing demand for ubiquitous connectivity to integrate multiple heterogeneous networks, such as Zigbee Ad hoc network, wireless LAN, cable network, etc. Some typical applications including environmental monitoring, intelligent transportation, medical care, smart home, industry control, safety defense, etc. require a smart gateway to provide high data-rate, end-to-end connectivity utilizing the higher bandwidth of multi-hop networks among those heterogeneous networks. This paper proposes a novel configurable smart IoT gateway which has three important benefits. Firstly, the gateway has plug gable architecture, whose modules with different communication protocols can be customized and plugged in according to different networks. Secondly, it has unified external interfaces which are fit for flexible software development. Finally, it has flexible protocol to translate different sensor data into a uniform format. Compared with similar research, the gateway has better scalability, flexibility and lower cost.


title: An exploration of proximity measures in information retrieval

abstract: In most existing retrieval models, documents are scored primarily based on various kinds of term statistics such as within-document frequencies, inverse document frequencies, and document lengths. Intuitively, the proximity of matched query terms in a document can also be exploited to promote scores of documents in which the matched query terms are close to each other. Such a proximity heuristic, however, has been largely under-explored in the literature; it is unclear how we can model proximity and incorporate a proximity measure into an existing retrieval model. In this paper,we systematically explore the query term proximity heuristic. Specifically, we propose and study the effectiveness of five different proximity measures, each modeling proximity from a different perspective. We then design two heuristic constraints and use them to guide us in incorporating the proposed proximity measures into an existing retrieval model. Experiments on five standard TREC test collections show that one of the proposed proximity measures is indeed highly correlated with document relevance, and by incorporating it into the KL-divergence language model and the Okapi BM25 model, we can significantly improve retrieval performance.


Improving short text classification using unlabeled background knowledge to assess document similarity


title: Development and control of a wearable robot for rehabilitation of elbow and shoulder joint movements

abstract: We have been developing an exoskeleton robot (ExoRob) for assisting daily upper limb movements (i.e., shoulder, elbow and wrist). In this paper we have focused on the development of a 2DOF ExoRob to rehabilitate elbow joint flexion/extension and shoulder joint internal/external rotation, as a step toward the development of a complete (i.e., 3DOF) shoulder motion assisted exoskeleton robot. The proposed ExoRob is designed to be worn on the lateral side of the upper arm in order to provide naturalistic movements at the level of elbow (flexion/extension) and shoulder joint internal/external rotation. This paper also focuses on the modeling and control of the proposed ExoRob. A kinematic model of ExoRob has been developed based on modified Denavit-Hartenberg notations. In dynamic simulations of the proposed ExoRob, a novel nonlinear sliding mode control technique with exponential reaching law and computed torque control technique is employed, where trajectory tracking that corresponds to typical rehab (passive) exercises has been carried out to evaluate the effectiveness of the developed model and controller. Simulated results show that the controller is able to drive the ExoRob efficiently to track the desired trajectories, which in this case consisted in passive arm movements. Such movements are used in rehabilitation and could be performed very efficiently with the developed ExoRob and the controller. Experiments were carried out to validate the simulated results as well as to evaluate the performance of the controller.


title: Global and local evaluation of link prediction tasks with neural embeddings

abstract: We focus our attention on the link prediction problem for knowledge graphs, which is treated herein as a binary classification task on neural embeddings of the entities. By comparing, combining and extending different methodologies for link prediction on graph-based data coming from different domains, we formalize a unified methodology for the quality evaluation benchmark of neural embeddings for knowledge graphs. This benchmark is then used to empirically investigate the potential of training neural embeddings globally for the entire graph, as opposed to the usual way of training embeddings locally for a specific relation. This new way of testing the quality of the embeddings evaluates the performance of binary classifiers for scalable link prediction with limited data. Our evaluation pipeline is made open source, and with this we aim to draw more attention of the community towards an important issue of transparency and reproducibility of the neural embeddings evaluations.


title: Scalable Column Concept Determination for Web Tables Using Large Knowledge Bases

abstract: Tabular data on the Web has become a rich source of structured data that is useful for ordinary users to explore. Due to its potential, tables on the Web have recently attracted a number of studies with the goals of understanding the semantics of those Web tables and providing effective search and exploration mechanisms over them. An important part of table understanding and search is column concept determination, i.e., identifying the most appropriate concepts associated with the columns of the tables. The problem becomes especially challenging with the availability of increasingly rich knowledge bases that contain hundreds of millions of entities. ::: ::: In this paper, we focus on an important instantiation of the column concept determination problem, namely, the concepts of a column are determined by fuzzy matching its cell values to the entities within a large knowledge base. We provide an efficient and scalable MapReduce-based solution that is scalable to both the number of tables and the size of the knowledge base and propose two novel techniques: knowledge concept aggregation and knowledge entity partition. We prove that both the problem of finding the optimal aggregation strategy and that of finding the optimal partition strategy are NP-hard, and propose efficient heuristic techniques by leveraging the hierarchy of the knowledge base. Experimental results on real-world datasets show that our method achieves high annotation quality and performance, and scales well.


title: A Market Making Quotation Strategy Based on Dual Deep Learning Agents for Option Pricing and Bid-Ask Spread Estimation

abstract: Traditional professional traders and institutional investors utilized complex statistical models to price various derivative contracts and make trading decisions in the option and future markets. In recent years, with the rapid growth of algorithmic trading and program trading, the advanced information and communication technology has become an indispensable element for high-frequency traders, especially for the market makers. In addition, artificial intelligence and deep learning also plays an important role in novel financial technology (FinTech) research field. In this paper, we proposed a market making quotation strategy based on deep learning structure and practical finance domain knowledge. The proposed dual agents will simultaneously model the option prices and bid-ask spreads. The experiments demonstrate that our system can precisely estimate the value of options than famous financial engineering models. It also can be extended to develop proper market making quotation strategies to trade the options of Taiwan Stock Exchange Capitalization Weighted Stock Index(TAIEX).


title: Accident Forecasting in CCTV Traffic Camera Videos.

abstract: This paper presents a novel dataset for traffic accidents analysis.Our goal is to resolve the lack of public data for research about automatic spatio-temporal annotations for traffic safety in the roads. Our Car Accident Detection and Prediction(CADP) dataset consists of 1,416 video segments collected from YouTube, with 205 video segments having full spatio-temporal annotations. To the best of our knowledge, our dataset is largest in terms of number of traffic accidents, compared to related datasets. Through the analysis of the proposed dataset, we observed a significant degradation of object detection in pedestrian category in our dataset, due to the object sizes and complexity of the scenes. To this end, we propose to integrate contextual information into conventional Faster R-CNN using Context Mining(CM) and Augmented Context Mining(ACM) to complement the accuracy for small pedestrian detection. Our experiments indicate a considerable improvement in object detection accuracy: +8.51 % for CM and +6.20 % for ACM. For person(pedestrian) category, we observed significant improvements:+46.45 % for CM and 45.22 % for ACM, compared to Faster R-CNN. Finally, we demonstrate the performance of accident forecasting in our dataset using Faster R-CNN and an Accident LSTM architecture. We achieved an average of 1.359 seconds in terms of Time-To-Accident measure with an Average Precision of 47.36 %. Our Webpage for the paper is this https URL


title: (A)Social reputation: Exploring the relationship between online video game involvement and social competence

abstract: Abstract Affordable and accessible Internet has changed the way video games are played, allowing individuals to connect worldwide in shared gaming spaces. On the surface participation within these environments may seem socially beneficial, as these highly social, playful spaces allow players to connect, interact with, and learn from others. However, there is a growing concern that increased participation within online gaming environments comes with a wide variety of social consequences, contributing to numerous losses in ‘offline’ sociability. While considerable research has examined these claims, consistent relationships between social competence and online video game involvement have yet to be established. The current work ( N = 515) aimed to examine the extent to which online video game involvement may support, or undermine, the development and maintenance of traditional social skills. The results indicate that there are unique relationships between social skills and video game involvement within online gaming populations. However, among online video game players, these links were not as negative or broad as hypothesized. The results of this assessment dispute the anecdotal attribution of a global level of social ineptitude amongst online video game players and provide insight into the potential effects of online video game play on skill development and maintenance.


title: A Comparative Study of Crossover Operators for Genetic Algorithms to Solve the Job Shop Scheduling Problem

abstract: Genetic algorithms (GA) are wide class of global optimization methods. Many genetic algorithms have been applied to solve combinatorial optimization problems. One of the problems in using genetic algorithms is the choice of crossover operator. The aim of this paper is to show the influence of genetic crossover operators on the performance of a genetic algorithm. The GA is applied to the job shop scheduling problem (JSSP). To achieve this aim an experimental study of a set of crossover operators is presented. The experimental study is based on a decision support system (DSS). To compare the abilities of different crossover operators, the DSS was designed giving all the operators the same opportunities. The genetic crossover operators are tested on a set of standard instances taken from the literature. The makespan is the measure used to evaluate the genetic crossover operators. The main conclusion is that there is a crossover operator having the best average performance on a specific set of solved instances.


title: A resource-based view of competitive advantage at the Port of Singapore

abstract: The purpose of this paper is to discuss the resources, including operations and information technology that have contributed to the competitive position of the Port of Singapore. We present a detailed discussion of the Port and its resources, and analyze the case using the resource-based view of strategy. A firm with a competitive advantage excels in time, quality, or cost, or a combination of such over its competitors. We argue that a combination of resources including supportive government policies, ample investment, and well thought out operations and information technology along with location and a natural deep harbor to help create a sustainable advantage for the Port. We find that Singapore compensated for some of its natural disadvantages like small land area by successfully applying information technology in critical areas to increase the island's capacity to handle shipping.


title: FSP: towards flexible synchronous parallel framework for expectation-maximization based algorithms on cloud

abstract: Myriad of parameter estimation algorithms can be performed by an Expectation-Maximization (EM) approach. Traditional synchronous frameworks can parallelize these EM algorithms on the cloud to accelerate computation while guaranteeing the convergence. However, expensive synchronization costs pose great challenges for efficiency. Asynchronous solutions have been recently designed to bypass high-cost synchronous barriers but at expense of potentially losing convergence guarantee. This paper first proposes a flexible synchronous parallel framework (FSP) that provides the capability of synchronous EM algorithms implementations, as well as significantly reduces the barrier cost. Under FSP, every distributed worker can immediately suspend local computation when necessary, to quickly synchronize with each other. That maximizes the time fast workers spend doing useful work, instead of waiting for slow, straggling workers. We then formally prove the algorithm convergence. Further, we analyze how to automatically identify a proper barrier interval to strike a nice balance between reduced synchronization costs and the convergence speed. Empirical results demonstrate that on a broad spectrum of real-world and synthetic datasets, FSP achieves as much as 3x speedup over the up-to-date synchronous solution.


title: Integrating User and Agent Models: A Deep Task-Oriented Dialogue System

abstract: Task-oriented dialogue systems can efficiently serve a large number of customers and relieve people from tedious works. However, existing task-oriented dialogue systems depend on handcrafted actions and states or extra semantic labels, which sometimes degrades user experience despite the intensive human intervention. Moreover, current user simulators have limited expressive ability so that deep reinforcement Seq2Seq models have to rely on selfplay and only work in some special cases. To address those problems, we propose a uSer and Agent Model IntegrAtion (SAMIA) framework inspired by an observation that the roles of the user and agent models are asymmetric. Firstly, this SAMIA framework model the user model as a Seq2Seq learning problem instead of ranking or designing rules. Then the built user model is used as a leverage to train the agent model by deep reinforcement learning. In the test phase, the output of the agent model is filtered by the user model to enhance the stability and robustness. Experiments on a real-world coffee ordering dataset verify the effectiveness of the proposed SAMIA framework.


title: Application of the Health Belief Model (HBM) in HIV Prevention: A Literature Review

abstract: As part of a study that explored the factors associated with consistent condom use among senior secondary school female learners in Mbonge subdivision of rural Cameroon, the Health Belief Model (HBM) was used as the framework. Literature was reviewed to ascertain how the entire HBM has been defined and what recommendations have been made as to how to apply the HBM as a framework in studies regarding HIV/AIDS prevention. To achieve this, a systemic review of literature was undertaken. Electronic databases, academic journals and books from various sources were accessed. Several key search terms relating to the HBM and HIV/AIDS prevention were used. Only references deemed useful for bibliographies of relevant texts and journal articles were included. The inclusion criteria were articles that provided information about HIV/AIDS prevention and the HBM constructs. Six constructs of the HBM (perceived susceptibility to HIV/AIDS, perceived severity of HIV/AIDS, perceived benefit of condom use, perceived barriers to condom use, cues to action for condom use and condom use self-efficacy), and modifying factors were identified and applied as the framework for the study. The HBM was identified as the most commonly used theory in health education, health promotion and disease prevention, and thus provided the framework for the study. The underlying concept of the HBM is that behaviour is determined by personal beliefs or perceptions about a disease and the strategies available to decrease its occurrence.


title: Risk factors for pressure ulcer development in critically Ill patients: A conceptual model to guide research

abstract: This paper presents a proposed conceptual model to guide research on pressure ulcer risk in critically ill patients, who are at high risk for pressure ulcer development. However, no conceptual model exists that guides risk assessment in this population. Results from a review of prospective studies were evaluated for design quality and level of statistical reporting. Multivariate findings from studies having high or medium design quality by the National Institute of Health and Clinical Excellence standards were conceptually grouped. The conceptual groupings were integrated into Braden and Bergstrom's (Braden and Bergstrom [1987] Rehabilitation Nursing, 12, 8-12, 16) conceptual model, retaining their original constructs and augmenting their concept of intrinsic factors for tissue tolerance. The model could enhance consistency in research on pressure ulcer risk factors.


title: Five-factor model of personality and transformational leadership.

abstract: This study linked traits from the 5-factor model of personality (the Big 5) to transformational leadership behavior. Neuroticism, Extraversion, Openness to Experience, and Agreeableness were hypothesized to predict transformational leadership. Results based on 14 samples of leaders from over 200 organizations revealed that Extraversion and Agreeableness positively predicted transformational leadership; Openness to Experience was positively correlated with transformational leadership, but its effect disappeared once the influence of the other traits was controlled. Neuroticism and Conscientiousness were unrelated to transformational leadership. Results further indicated that specific facets of the Big 5 traits predicted transformational leadership less well than the general constructs. Finally, transformational leadership behavior predicted a number of outcomes reflecting leader effectiveness, controlling for the effect of transactional leadership. Given the centrality of leadership to the success or failure of organizations and even societies, there are few more important questions than, "What makes a leader great?" Attempts to answer this question can be traced to the earliest discussions of the concept of leadership. It is only in this century, however (particularly in this half century), that leadership has become an area of serious academic investigation. In the last 20 years, considerable progress has been made in addressing leader effectiveness according to one theoretical perspective, transformational leadership theory (also known as charismatic leadership). The concept of transformational leadership dates to Burns' (1978) Pulitzer-Prize-winning book on leadership. At about the same time, House (1977) and Bass (1985) developed their own theories of leadership that were compatible with and—in Bass' case—inspired by Burns' writing. Although numerous other leadership theories continue to attract the attention of organizational researchers, it is safe to say that transformational leadership theory has garnered most of the attention in recent leadership research.1 Burns (1978) distinguished transformational leaders from transactional leaders. In contrast to transformational leaders, who obtain support by inspiring followers to identify with a vision that reaches beyond their own immediate self-interests, transactional leaders obtain cooperation by establishing exchanges with followers and then monitoring the exchange relationship. Although Burns considered transformational and transactional leadership to be polar opposites, Bass' (1985) theory postulated that leaders could be both transformational and transactional (or neither). According to Bass' (1985) theory, there are four dimensions of transformational leadership. These dimensions initially were derived from interviews in which individuals were asked to describe


title: Online news recommender based on stacked auto-encoder

abstract: Because of the popularity of Internet and mobile Internet, people are facing serious information overloading problems nowadays. Recommendation engine is very useful to help people to reach the Internet news they want through the network. Collaborative filtering (CF), such as item-based CF, is the most popular branch in recommendation domain. But the data's high-dimension as well as data sparsity are always the main problems. A novel CF method is introduced in this article, which uses stacked auto-encoder with denoising, an unsupervised deep learning method, to extract the useful low-dimension features from the original sparse user-item matrices. Together with proper similarity computing algorithms, the method provided in this article is proved to be more precise than the methods based on SVD or item-based CF.


title: Efficient Multi-core AUTOSAR-Platform Based on an Input/Output Gateway Core

abstract: The AUTOSAR standard provides support for multicore systems since version 4. However, this AUTOSAR multicore version focuses on inter-core communication with a shared memory approach. In contrast, the paradigm of message-based network-on-chips provides multiples advantages for real-time embedded systems such as automotive electronics including better temporal predictability, fault containment and energy efficiency. In this paper we propose an efficient multicore architecture for AUTOSAR based on time-triggered network-on-chips and dedicated input/output cores. Additionally, a health monitoring service is integrated into the AUTOSAR ECU architecture in order to provide recovery actions in case of failures of the automotive application or the hardware of a specific core in the multiprocessor. The results demonstrate how the operating system overhead decreases considerably when using the defined input/output cores that serve as hardware accelerators for the AUTOSAR software. Also, the reliability of the system is improved significantly due to the implemented health monitoring service.


title: Real time vital sign transmission using IEEE 802.15.7 VLC PHY-I transceiver

abstract: Visible Light Communication (VLC) Technology is the short range optical wireless communication technology using the light source such as LEDs for data transmission. The major advantage of VLC is that it causes no interference to RF-based devices and no bad effect to human body. Thus, this makes it possible to be used in intrinsically safe environment such as in a hospital. Moreover, it doesn't have the restriction likes a radio wave, VLC using a license free frequency bands. IEEE 802.15.7 VLC standard is one of the VLC standard. This standard consists of PHY I, II and III operating modes. In this paper, we first focus on analyzing PHY I with OOK modulation, which is targeted towards applications requiring data rate range from 11.67 kb/s to 100 kb/s with 200 kHz optical clock rate. Moreover, we propose the improvement of the real time vital sign transmission based on IEEE 802.15.7 VLC standard using PHY I operating modes for sending the vital signs data, which are temperature and heart rate. Compared with our previous system, there are improvements in many directions. The obvious improvement is the communication speed. Moreover, the transmission range is able to be increased by adding the VLC repeater.


SPEED CONTROL OF DC MOTOR USING PID CONTROLLER


title: Learning to cluster in order to transfer across domains and tasks

abstract: This paper introduces a novel method to perform transfer learning across domains and tasks, formulating it as a problem of learning to cluster. The key insight is that, in addition to features, we can transfer similarity information and this is sufficient to learn a similarity function and clustering network to perform both domain adaptation and cross-task transfer learning. We begin by reducing categorical information to pairwise constraints, which only considers whether two instances belong to the same class or not. This similarity is category-agnostic and can be learned from data in the source domain using a similarity network. We then present two novel approaches for performing transfer learning using this similarity function. First, for unsupervised domain adaptation, we design a new loss function to regularize classification with a constrained clustering loss, hence learning a clustering network with the transferred similarity metric generating the training inputs. Second, for cross-task learning (i.e., unsupervised clustering with unseen categories), we propose a framework to reconstruct and estimate the number of semantic clusters, again using the clustering network. Since the similarity network is noisy, the key is to use a robust clustering algorithm, and we show that our formulation is more robust than the alternative constrained and unconstrained clustering approaches. Using this method, we first show state of the art results for the challenging cross-task problem, applied on Omniglot and ImageNet. Our results show that we can reconstruct semantic clusters with high accuracy. We then evaluate the performance of cross-domain transfer using images from the Office-31 and SVHN-MNIST tasks and present top accuracy on both datasets. Our approach doesn't explicitly deal with domain discrepancy. If we combine with a domain adaptation loss, it shows further improvement.


title: Efficient linear solution of exterior orientation

abstract: This paper concerns an efficient algorithm for the solution of the exterior orientation problem. Orthogonal decompositions are used to first isolate the unknown depths of feature points in the camera reference frame, allowing the problem to be reduced to an absolute orientation with scale problem, which is solved using the singular value decomposition (SVD). The key feature of this approach is the low computational cost compared to existing approaches.


Don't Count on ASR to Transcribe for You: Breaking Bias with Two Crowds.


title: Geometric mean metric learning for partial label data

abstract: Abstract Partial label learning (PLL) is a new weakly supervised learning framework that addresses the classification problems, where the true label of each training sample is concealed in a set of candidate labels. To learn from such weakly supervised training data, the key is to disambiguate the ambiguous labeling information. Because it is difficult to address by only focusing on the manipulation in the label space, manifold structure among training data in the feature space has gradually been exploited simultaneously to facilitate the disambiguation process by researchers in recent years. However, the manifold structure is commonly analyzed under an assumption that the samples close to each other in the feature space will share identical labels in the label space, which may be not correct in many real-world problems. In this paper, geometric mean metric learning approach is employed to learn a distance metric for PLL problems such that can maintain the aforementioned assumption correct in as many situations as possible. It is significantly more challenging than the conventional setup of distance metric learning because it is difficult to precisely identify whether a pair of training samples belong to the same class. We propose an alternative approach in which each training sample and its neighbor with shared candidate label are taken as a similarity pair, and each training sample and its neighbor without shared candidate label are taken as a dissimilarity pair. Considering that two samples with shared candidate label do not necessarily come from the same class, a weight is placed on each similarity pair. The experimental results on twenty four controlled UCI data sets and six real-world PLL problems show the proposed distance metric learning approach can be used as a front end of both the PLL algorithms exploiting the manifold structure among training data and other existing distance-based PLL algorithms to significantly improve their performance.


title: An adaptive approach for UAV-based pesticide spraying in dynamic environments

abstract: It is proposed a system that optimizes the intensity of course correction policies of UAVs employed for pesticide spraying.A new Genetic Algorithm is proposed to find appropriate course correction intensities on dynamic weather conditions.A modular system that enables UAVs to adapt their behavior to the weather conditions.New spraying methods that are more accurate than existing solutions in the scientific literature. Agricultural production has become a key factor for the stability of the world economy. The use of pesticides provides a more favorable environment for the crops in agricultural production. However, the uncontrolled and inappropriate use of pesticides affect the environment by polluting preserved areas and damaging ecosystems. In the precision agriculture literature, several authors have proposed solutions based on Unmanned Aerial Vehicles (UAVs) and Wireless Sensor Networks (WSNs) for developing spraying processes that are safer and more precise than the use of manned agricultural aircraft. However, the static configuration usually adopted in these proposals makes them inefficient in environments with changing weather conditions (e.g. sudden changes of wind speed and direction). To overcome this deficiency, this paper proposes a computer-based system that is able to autonomously adapt the UAV control rules, while keeping precise pesticide deposition on the target fields. Different versions of the proposal, with autonomously route adaptation metaheuristics based on Genetic Algorithms, Particle Swarm Optimization, Simulated Annealing and Hill-Climbing for optimizing the intensity of route changes are evaluated in this study. Additionally, this study evaluates the use of a ground control station and an embedded hardware to run the route adaptation metaheuristics. Experimental results show that the proposed computer-based system approach with autonomous route change metaheuristics provides more precise changes in the UAVs flight route, with more accurate deposition of the pesticide and less environmental damage.


title: OLAM: A wearable, non-contact sensor for continuous heart-rate and activity monitoring

abstract: A wearable, multi-modal sensor is presented that can non-invasively monitor a patient's activity level and heart function concurrently for more than a week. The 4in2 sensor incorporates both a non-contact heartrate sensor and a 5-axis inertial measurement unit (IMU), allowing simultaneous heart, respiration, and movement monitoring without requiring physical contact with the skin [1]. Hence, this Oregon State University Life and Activity Monitor (OLAM) provides the unique opportunity to combine motion data with heart-rate information, enabling assessment of actual physical activity beyond conventional movement sensors. OLAM also provides a unique platform for non-contact sensing, enabling the filtering of movement artifacts generated by the non-contact capacitive interface, using the IMU data as a movement noise channel. Intended to be used in clinical trials for weeks at a time with no physician intervention, the OLAM allows continuous non-invasive monitoring of patients, providing the opportunity for long-term observation into a patient's physical activity and subtle longitudinal changes.


title: Sequence-based prediction of protein protein interaction using a deep-learning algorithm

abstract: Protein-protein interactions (PPIs) are critical for many biological processes. It is therefore important to develop accurate high-throughput methods for identifying PPI to better understand protein function, disease occurrence, and therapy design. Though various computational methods for predicting PPI have been developed, their robustness for prediction with external datasets is unknown. Deep-learning algorithms have achieved successful results in diverse areas, but their effectiveness for PPI prediction has not been tested. We used a stacked autoencoder, a type of deep-learning algorithm, to study the sequence-based PPI prediction. The best model achieved an average accuracy of 97.19% with 10-fold cross-validation. The prediction accuracies for various external datasets ranged from 87.99% to 99.21%, which are superior to those achieved with previous methods. To our knowledge, this research is the first to apply a deep-learning algorithm to sequence-based PPI prediction, and the results demonstrate its potential in this field.


Topic Modeling for the Social Sciences


title: A Distributed Parameter Electromechanical Model for Cantilevered Piezoelectric Energy

abstract: Cantilevered beams with piezoceramic layers have been frequently used as piezoelectric vibration energy harvesters in the past five years. The literature includes several single degree-of-freedom models, a few approximate distributed parameter models and even some incorrect approaches for predicting the electromechanical behavior of these harvesters. In this paper, we present the exact analytical solution of a cantilevered piezoelectric energy harvester with Euler–Bernoulli beam assumptions. The excitation of the harvester is assumed to be due to its base motion in the form of translation in the transverse direction with small rotation, and it is not restricted to be harmonic in time. The resulting expressions for the coupled mechanical response and the electrical outputs are then reduced for the particular case of harmonic behavior in time and closed-form exact expressions are obtained. Simple expressions for the coupled mechanical response, voltage, current, and power outputs are also presented for excitations around the modal frequencies. Finally, the model proposed is used in a parametric case study for a unimorph harvester, and important characteristics of the coupled distributed parameter system, such as short circuit and open circuit behaviors, are investigated in detail. Modal electromechanical coupling and dependence of the electrical outputs on the locations of the electrodes are also discussed with examples.


title: AUTOSAR unit testing approach based on virtual prototyping for software components

abstract: As software on current automobiles has become very critical, large and complex, enhancing reliability of AUTOSAR software components by testing is very important. However, the unit testing of AUTOSAR SW component is difficult since they are tightly connected with sensor and actuator hardware. In this paper, we provide a state based VPs replace those hardware and VP based unit testing approach for AUTOSAR software components. Also to make VP easier, we suggest VP composing tool. By configuring the VP environment, various scenarios can be easily tested.


title: Visual Event Summarization on Social Media using Topic Modelling and Graph-based Ranking Algorithms

abstract: Due to the increasing popularity of microblogging platforms, the amount of messages (posts) related to public events, especially posts encompassing multimedia content, is steadily increasing. The inclusion of images can convey much more information about the event, compared to their text, which is typically very short (e.g., tweets). Although such messages can be quite informative regarding different aspects of the event, there is a lot of spam and redundancy making it challenging to extract pertinent insights. In this work, we describe a summarization framework that, given a set of social media messages about an event, aims to select a subset of images derived from them, that, at the same time, maximizes the relevance of the selected images and minimizes their redundancy. To this end, we propose a topic modeling technique to capture the relevance of messages to event topics and a graph-based algorithm to produce a diverse ranking of the selected high-relevance images. A user-centered evaluation on a large Twitter dataset around several real-world events demonstrates that the proposed method considerably outperforms a number of state-of-the-art summarization algorithms in terms of result relevance, while at the same time it is also highly competitive in terms of diversity. Namely, we get an improvement of 25% in terms of precision compared to the second best result, and 7% in terms of diversity.


title: Generative adversarial networks for brain lesion detection

abstract: Manual segmentation of brain lesions from Magnetic Resonance Images (MRI) is cumbersome and introduces errors due to inter-rater variability. This paper introduces a semi-supervised technique for detection of brain lesion from MRI using Generative Adversarial Networks (GANs). GANs comprises of a Generator network and a Discriminator network which are trained simultaneously with the objective of one bettering the other. The networks were trained using non lesion patches (n=13,000) from 4 different MR sequences. The network was trained on BraTS dataset and patches were extracted from regions excluding tumor region. The Generator network generates data by modeling the underlying probability distribution of the training data, (PData). The Discriminator learns the posterior probability P (Label Data) by classifying training data and generated data as “Real” or “Fake” respectively. The Generator upon learning the joint distribution, produces images/patches such that the performance of the Discriminator on them are random, i.e. P (Label Data = GeneratedData) = 0.5. During testing, the Discriminator assigns posterior probability values close to 0.5 for patches from non lesion regions, while patches centered on lesion arise from a different distribution (PLesion) and hence are assigned lower posterior probability value by the Discriminator. On the test set (n=14), the proposed technique achieves whole tumor dice score of 0.69, sensitivity of 91% and specificity of 59%. Additionally the generator network was capable of generating non lesion patches from various MR sequences.


title: Novel Bow-tie–AMC Combination for 5.8-GHz RFID Tags Usable With Metallic Objects

abstract: A novel combination of coplanar waveguide (CPW)-fed double bow-tie slot antenna and artificial magnetic conductor (AMC), which meets the requirements of a 5.8-GHz SHF RFID tag antenna usable on metallic objects, is presented. The manufactured prototype is characterized in terms of return loss, gain, and radiation pattern measurements in an anechoic chamber, both alone and on a metallic plate.


title: MBO: marriage in honey bees optimization-a Haplometrosis polygynous swarming approach

abstract: Honey-bees are one of the most well studied social insects. They exhibit many features that distinguish their use as models for intelligent behavior. These features include division of labor, communication on the individual and group level, and cooperative behavior. In this paper, we present a unified model for the marriage in honey-bees within an optimization context. The model simulates the evolution of honey-bees starting with a solitary colony (single queen without a family) to the emergence of an eusocial colony (one or more queens with a family). From optimization point of view, the model is a committee machine approach where we evolve solutions using a committee of heuristics. The model is applied to a fifty propositional satisfiability problems (SAT) with 50 variables and 215 constraints to guarantee that the problems are centered on the phase transition of 3-SAT. Our aim in this paper is to analyze the behavior of the algorithm using biological concepts (number of queens, spermatheca size, and number of broods) rather than trying to improve the performance of the algorithm while losing the underlying biological essence. Notwithstanding, the algorithm outperformed WalkSAT, one of the state-of-the-art algorithms for SAT.


title: The influence of drug-like concepts on decision-making in medicinal chemistry

abstract: The application of guidelines linked to the concept of drug-likeness, such as the 'rule of five', has gained wide acceptance as an approach to reduce attrition in drug discovery and development. However, despite this acceptance, analysis of recent trends reveals that the physical properties of molecules that are currently being synthesized in leading drug discovery companies differ significantly from those of recently discovered oral drugs and compounds in clinical development. The consequences of the marked increase in lipophilicity--the most important drug-like physical property--include a greater likelihood of lack of selectivity and attrition in drug development. Tackling the threat of compound-related toxicological attrition needs to move to the mainstream of medicinal chemistry decision-making.


title: Big Data Semantics

abstract: Big Data technology has discarded traditional data modeling approaches as no longer applicable to distributed data processing. It is, however, largely recognized that Big Data impose novel challenges in data and infrastructure management. Indeed, multiple components and procedures must be coordinated to ensure a high level of data quality and accessibility for the application layers, e.g., data analytics and reporting. In this paper, the third of its kind co-authored by members of IFIP WG 2.6 on Data Semantics, we propose a review of the literature addressing these topics and discuss relevant challenges for future research. Based on our literature review, we argue that methods, principles, and perspectives developed by the Data Semantics community can significantly contribute to address Big Data challenges.


title: Crowd Monitoring Using Image Processing

abstract: The understanding of crowd behaviour in semi-confined spaces is an important part of the design of new pedestrian facilities, for major layout modifications to existing areas and for the daily management of sites-subject to crowd traffic. Conventional manual measurement techniques are not suitable for comprehensive data collection of patterns of site occupation and movement. Real-time monitoring is tedious and tiring, but safety-critical. This paper presents some image processing techniques being developed at King's College London which, using existing closed-circuit television systems, can support both data collection and on-line monitoring of crowds. The application of these methods could lead to a better understanding of crowd behaviour, improved design of the built environment and increased pedestrian safety.


title: Image Representation Using 2D Gabor Wavelets

abstract: This paper extends to two dimensions the frame criterion developed by Daubechies for one-dimensional wavelets, and it computes the frame bounds for the particular case of 2D Gabor wavelets. Completeness criteria for 2D Gabor image representations are important because of their increasing role in many computer vision applications and also in modeling biological vision, since recent neurophysiological evidence from the visual cortex of mammalian brains suggests that the filter response profiles of the main class of linearly-responding cortical neurons (called simple cells) are best modeled as a family of self-similar 2D Gabor wavelets. We therefore derive the conditions under which a set of continuous 2D Gabor wavelets will provide a complete representation of any image, and we also find self-similar wavelet parametrization which allow stable reconstruction by summation as though the wavelets formed an orthonormal basis. Approximating a "tight frame" generates redundancy which allows low-resolution neural responses to represent high-resolution images.


title: Metadata Embeddings for User and Item Cold-start Recommendations

abstract: I present a hybrid matrix factorisation model representing users and items as linear combinations of their content features' latent factors. The model outperforms both collaborative and content-based models in cold-start or sparse interaction data scenarios (using both user and item metadata), and performs at least as well as a pure collaborative matrix factorisation model where interaction data is abundant. Additionally, feature embeddings produced by the model encode semantic information in a way reminiscent of word embedding approaches, making them useful for a range of related tasks such as tag recommendations.


title: Insights into the Social Behavior of Surface and Cave-Dwelling Fish (Poecilia mexicana) in Light and Darkness through the Use of a Biomimetic Robot

abstract: Biomimetic Robots (BRs) are becoming more common in behavioral research and, if they are accepted as conspecifics, allow for new forms of experimental manipulations of social interactions. Nevertheless, it is often not clear which cues emanating from a BR are actually used as communicative signals and how species or populations with different sensory make-ups react to specific types of BRs. We herein present results from experiments using two populations of livebearing fishes that differ in their sensory capabilities. In the South of Mexico, surface-dwelling mollies (Poecilia mexicana) successfully invaded caves and adapted to dark conditions. While almost without pigment, these cave mollies possess smaller but still functional eyes. Although previous studies found cave mollies to show reduced shoaling preferences with conspecifics in light compared to surface mollies, it is assumed that they possess specialized adaptations to maintain some kind of sociality also in their dark habitats. By testing surface and cave-dwelling mollies with RoboFish, a BR made for use in laboratory experiments with guppies and sticklebacks, we asked to what extent visual and non-visual cues play a role in their social behavior. Both cave and surface-dwelling mollies followed the biomimetic robot as well as a live companion when tested in light. However, when tested in darkness, only surface-dwelling fish were attracted by a live conspecific while cave fish were not. Neither cave nor surface-dwelling mollies were attracted to RoboFish in darkness. This is the first study to use biomimetic robots for the investigation of social behavior in mollies and to compare responses to BRs both in light and darkness. As our RoboFish is accepted as conspecific by both used populations of the Atlantic molly only under light conditions but not in darkness, we argue that our replica is providing mostly visual cues.


title: Deep Learning with a Rethinking Structure for Multi-label Classification

abstract: Multi-label classification (MLC) is an important learning problem that expects the learning algorithm to take the hidden correlation of the labels into account. Extracting the hidden correlation is generally a challenging task. In this work, we propose a novel deep learning framework to better extract the hidden correlation with the help of the memory structure within recurrent neural networks. The memory stores the temporary guesses on the labels and effectively allows the framework to rethink about the goodness and correlation of the guesses before making the final prediction. Furthermore, the rethinking process makes it easy to adapt to different evaluation criterion to match real-world application needs. Experimental results across many real-world data sets justify that the rethinking process indeed improves MLC performance across different evaluation criteria and leads to superior performance over state-of-the-art MLC algorithms.


title: Monaural Speech Enhancement Using Deep Neural Networks by Maximizing a Short-Time Objective Intelligibility Measure

abstract: In this paper we propose a Deep Neural Network (DNN) based Speech Enhancement (SE) system that is designed to maximize an approximation of the Short-Time Objective Intelligibility (STOI) measure. We formalize an approximate-STOI cost function and derive analytical expressions for the gradients required for DNN training and show that these gradients have desirable properties when used together with gradient based optimization techniques. We show through simulation experiments that the proposed SE system achieves large improvements in estimated speech intelligibility, when tested on matched and unmatched natural noise types, at multiple signal-to-noise ratios. Furthermore, we show that the SE system, when trained using an approximate-STOI cost function performs on par with a system trained with a mean square error cost applied to short-time temporal envelopes. Finally, we show that the proposed SE system performs on par with a traditional DNN based Short-Time Spectral Amplitude (STSA) SE system in terms of estimated speech intelligibility. These results are important because they suggest that traditional DNN based STSA SE systems might be optimal in terms of estimated speech intelligibility.


Biometrics: Face Recognition in Thermal Infrared


title: Ultrafast and memory-efficient alignment of short DNA sequences to the human genome

abstract: Bowtie is an ultrafast, memory-efficient alignment program for aligning short DNA sequence reads to large genomes. For the human genome, Burrows-Wheeler indexing allows Bowtie to align more than 25 million reads per CPU hour with a memory footprint of approximately 1.3 gigabytes. Bowtie extends previous Burrows-Wheeler techniques with a novel quality-aware backtracking algorithm that permits mismatches. Multiple processor cores can be used simultaneously to achieve even greater alignment speeds. Bowtie is open source http://bowtie.cbcb.umd.edu.


title: Incorporating Relation Paths in Neural Relation Extraction

abstract: Distantly supervised relation extraction has been widely used to find novel relational facts from plain text. To predict the relation between a pair of two target entities, existing methods solely rely on those direct sentences containing both entities. In fact, there are also many sentences containing only one of the target entities, which provide rich and useful information for relation extraction. To address this issue, we build inference chains between two target entities via intermediate entities, and propose a path-based neural relation extraction model to encode the relational semantics from both direct sentences and inference chains. Experimental results on real-world datasets show that, our model can make full use of those sentences containing only one target entity, and achieves significant and consistent improvements on relation extraction as compared with baselines.


title: Silicon carbide power MOSFETs: Breakthrough performance from 900 V up to 15 kV

abstract: Since Cree, Inc.'s 2 ::: nd ::: generation 4H-SiC MOSFETs were commercially released with a specific on-resistance (R ::: ON, SP ::: ) of 5 mΩ·cm ::: 2 ::: for a 1200 V-rating in early 2013, we have further optimized the device design and fabrication processes as well as greatly expanded the voltage ratings from 900 V up to 15 kV for a much wider range of high-power, high-frequency, and high-voltage energy-conversion and transmission applications. Using these next-generation SiC MOSFETs, we have now achieved new breakthrough performance for voltage ratings from 900 V up to 15 kV with a R ::: ON, SP ::: as low as 2.3 mΩ·cm ::: 2 ::: for a breakdown voltage (BV) of 1230 V and 900 V-rating, 2.7 mΩ·cm ::: 2 ::: for a BV of 1620 V and 1200 V-rating, 3.38 mΩ·cm ::: 2 ::: for a BV of 1830 V and 1700 V-rating, 10.6 mΩ·cm ::: 2 ::: for a BV of 4160 V and 3300 V-rating, 123 mΩ·cm ::: 2 ::: for a BV of 12 kV and 10 kV-rating, and 208 mΩ·cm ::: 2 ::: for a BV of 15.5 kV and 15 kV-rating. In addition, due to the lack of current tailing during the bipolar device switching turn-off, the SiC MOSFETs reported in this work exhibit incredibly high frequency switching performance over their silicon counter parts.


title: DeepSetNet: Predicting Sets with Deep Neural Networks

abstract: This paper addresses the task of set prediction using deep learning. This is important because the output of many computer vision tasks, including image tagging and object detection, are naturally expressed as sets of entities rather than vectors. As opposed to a vector, the size of a set is not fixed in advance, and it is invariant to the ordering of entities within it. We define a likelihood for a set distribution and learn its parameters using a deep neural network. We also derive a loss for predicting a discrete distribution corresponding to set cardinality. Set prediction is demonstrated on the problem of multi-class image classification. Moreover, we show that the proposed cardinality loss can also trivially be applied to the tasks of object counting and pedestrian detection. Our approach outperforms existing methods in all three cases on standard datasets.


title: A new approach to DNS security (DNSSEC)

abstract: The Domain Name System (DNS) is a distributed database that allows convenient storing and retrieving of resource records. DNS has been extended to provide security services (DNSSEC) mainly through public-key cryptography. We propose a new approach to DNSSEC that may result in a significantly more efficient protocol. We introduce a new strategy to build chains of trust from root servers to authoritative servers. The techniques we employ are based on symmetric-key cryptography.


title: Maestro: An EMG-driven assistive hand exoskeleton for spinal cord injury patients

abstract: In this paper, we present an electromyography (EMG)-driven assistive hand exoskeleton for spinal-cord-injury (SCI) patients. We developed an active assistive orthosis, called Maestro, which is light, comfortable, compliant, and capable of providing various hand poses. The EMG signals are obtained from a subject's forearm, post-processed, and classified for operating Maestro. The performance of Maestro is evaluated by a standardized hand function test, called the Sollerman hand function test. The experimental results show that Maestro improved the hand function of the SCI patients.


title: Analysis of jitter due to power-supply noise in phase-locked loops

abstract: Phase-locked loops (PLL) in RF and mixed signal VLSI circuits experience supply noise which translates to a timing jitter. In this paper an analysis of the timing jitter due to the noise on the power supply rails is presented. Stochastic models of the power supply noise in VLSI circuits for different values of on-chip decoupling capacitances are presented first. This is followed by calculation of the phase noise of the voltage-controlled oscillator (VCO) in terms of the statistical properties of supply noise. Finally the timing jitter of the PLL is predicted in response to the VCO phase noise. A PLL circuit has been designed in 0.35 /spl mu/m CMOS process, and our mathematical model was applied to determine the timing jitter. Experimental results prove the accuracy of the predicted model.


title: Can i foresee the success of my meetup group?

abstract: 1Success of Meetup groups is of utmost importance for the members who organize them. Given a wide variety of such groups, a single metric may not be indicative of success for different groups; rather, success measure should be specific to the interest of a group. In this paper, accounting for the group diversity, we systematically define Meetup group success metrics and use them to generate labels for our machine learnt models. We crawl the Meetup dataset for three US cities namely New York, Chicago and San Francisco over a period of 8 months. The data study reveals the key players (such as core members, new members etc.) behind the success of the Meetup groups. This study leverages semantic, syntactic, temporal and location based features to discriminate between successful and unsuccessful groups. Finally, we present a model to predict success of the Meetup groups with high accuracy (0.81 with AUC = 0.86). Our approach generalizes well across groups, categories and cities. Additionally, the model performs reasonably well for new groups with little history (cold start problem), exhibiting high accuracy for the cross city validation.


title: On m-polar fuzzy graph structures

abstract: Sometimes information in a network model is based on multi-agent, multi-attribute, multi-object, multi-polar information or uncertainty rather than a single bit. An m-polar fuzzy model is useful for such network models which gives more and more precision, flexibility, and comparability to the system as compared to the classical, fuzzy and bipolar fuzzy models. In this research article, we introduce the notion of m-polar fuzzy graph structure and present various operations, including Cartesian product, strong product, cross product, lexicographic product, composition, union and join of m-polar fuzzy graph structures. We illustrate these operations by several examples. We also investigate some of their related properties.


title: A Novel 1.03 ppm/°C Wide-Temperature-Range Curvature-Compensated Bandgap Voltage Reference

abstract: This paper proposes a novel bandgap voltage reference with an accurate curvature compensation technique for completely compensating the thermal nonlinearity of the emitter-base voltage. With VIS 0.15 standard CMOS process, the proposed bandgap voltage reference circuit achieves temperature coefficient of 1.03 ppm/°C over the temperature range from −55°C to 155°C with the supply voltage of 1.8V. In addition, the circuit achieves line regulation performance of 0.02%/V with the supply voltage from 1.8V to 3V and the power supply rejection (PSR) levels are 83dB, 77dB, 58dB at ultra-low frequency (<200Hz), 1kHz and 10kHz, respectively.