title: Near-Duplicate Video Retrieval with Deep Metric Learning

abstract: This work addresses the problem of Near-Duplicate Video Retrieval (NDVR). We propose an effective video-level NDVR scheme based on deep metric learning that leverages Convolutional Neural Network (CNN) features from intermediate layers to generate discriminative global video representations in tandem with a Deep Metric Learning (DML) framework with two fusion variations, trained to approximate an embedding function for accurate distance calculation between two near-duplicate videos. In contrast to most state-of-the-art methods, which exploit information deriving from the same source of data for both development and evaluation (which usually results to dataset-specific solutions), the proposed model is fed during training with sampled triplets generated from an independent dataset and is thoroughly tested on the widely used CC_WEB_VIDEO dataset, using two popular deep CNN architectures (AlexNet, GoogleNet). We demonstrate that the proposed approach achieves outstanding performance against the state-of-the-art, either with or without access to the evaluation dataset.


title: A smart low-consumption IoT framework for location tracking and its real application

abstract: Location-based services are increasingly popular. Smart devices embedded with GPS receiver are widely used in vehicle tracking, law enforcement and person monitoring. To provide real-time data collecting and analyzing, a backend server is necessary. A mobile app can work as a user client for accessing location data and remote control. In this paper, we describe the design and implementation of a system providing real-time positioning and tracking service. Our system consists of three parts: tracking device, backend server and mobile app. Our tracking device integrates GPS module, GSM/GPRS module and TCP/IP stack, capable of generating location data, establishing TCP connection and receiving SMS. Location data are transmitted over TCP connection from device to our backend server. The server is responsible for collecting and storing data. The server also provides an HTTP-based RESTful API for mobile app and web service. A mobile app is built for displaying data and controlling device remotely. Data accessing is via calling REST API and remote control is sent to device by SMS. Our device is compact in volume, accurate in positioning, and long in battery life. Our device has been integrated in a walking stick as a real-world product.


title: Visualizing geospatial information uncertainty: What we know and what we need to know. Cartography and Geographic

abstract: Developing reliable methods for representing and managing information uncertainty remains a persistent and relevant challenge to GIScience. Information uncertainty is an intricate idea, and recent examinations of this concept have generated many perspectives on its representation and visualization, with perspectives emerging from a wide range of disciplines and application contexts. In this paper, we review and assess progress toward visual tools and methods to help analysts manage and understand information uncertainty. Specifically, we report on efforts to conceptualize uncertainty, decision making with uncertainty, frameworks for representing uncertainty, visual representation and user control of displays of information uncertainty, and evaluative efforts to assess the use and usability of visual displays of uncertainty. We conclude by identifying seven key research challenges in visualizing information uncertainty, particularly as it applies to decision making and analysis.


title: Automatic Text Classification: A Technical Review

abstract: Automatic Text Classification is a semi-supervised machine learning task that automatically assigns a given document to a set of pre-defined categories based on its textual content and extracted features. Automatic Text Classification has important applications in content management, contextual search, opinion mining, product review analysis, spam filtering and text sentiment mining. This paper explains the generic strategy for automatic text classification and surveys existing solutions to major issues such as dealing with unstructured text, handling large number of attributes and selecting a machine learning technique appropriate to the text-classification application.


title: High-Efficiency Charge Pumps for Low-Power On-Chip Applications

abstract: This paper proposes charge pumps with improved power efficiency suitable for low-power on-chip applications. Undesired charge transfer, which has a direction opposite to that of the intended current flow, presents a significant source of power loss in charge pumps. The proposed charge pump circuit utilizes charge transfer switches with a complementary branch scheme to significantly reduce undesired charge transfer, thereby improving power efficiency and increasing output voltage effectively. An optimized gate control strategy is applied to further decrease the power loss caused by undesired charge transfer. Simulations of 8-stage charge pumps in a $0.13~\mu \text{m}$ standard CMOS technology show that for an input supply voltage of 1.2 V, the proposed charge pump circuit reaches a power efficiency of 58.72% with an output voltage of 7.45 V, when delivering 5-mA load current, and is able to maintain a power efficiency of around 50% and an output voltage of over 5 V as the load current increases to 10 mA. Compared with the other charge pump circuits, the simulation results demonstrate better performance of proposed charge pump circuits in terms of both voltage pumping gain and power efficiency.


title: 3D convolutional neural networks by modal fusion

abstract: We propose multi-view and volumetric convolutional neural networks (ConvNets) for 3D shape recognition, which combines surface normal and height fields to capture local geometry and physical size of an object. This strategy helps distinguishing between objects with similar geometries but different sizes. This is especially useful for enhancing volumetric ConvNets and classifying 3D scans with insufficient surface details. Experimental results on CAD and real-world scan datasets showed that our technique outperforms previous approaches.


E-government Implementation in Kenya, an evaluation of Factors hindering or promoting e-government successful implementation


title: Training Adaptation and Heart Rate Variability in Elite Endurance Athletes: Opening the Door to Effective Monitoring

abstract: The measurement of heart rate variability (HRV) is often considered a convenient non-invasive assessment tool for monitoring individual adaptation to training. Decreases and increases in vagal-derived indices of HRV have been suggested to indicate negative and positive adaptations, respectively, to endurance training regimens. However, much of the research in this area has involved recreational and well-trained athletes, with the small number of studies conducted in elite athletes revealing equivocal outcomes. For example, in elite athletes, studies have revealed both increases and decreases in HRV to be associated with negative adaptation. Additionally, signs of positive adaptation, such as increases in cardiorespiratory fitness, have been observed with atypical concomitant decreases in HRV. As such, practical ways by which HRV can be used to monitor training status in elites are yet to be established. This article addresses the current literature that has assessed changes in HRV in response to training loads and the likely positive and negative adaptations shown. We reveal limitations with respect to how the measurement of HRV has been interpreted to assess positive and negative adaptation to endurance training regimens and subsequent physical performance. We offer solutions to some of the methodological issues associated with using HRV as a day-to-day monitoring tool. These include the use of appropriate averaging techniques, and the use of specific HRV indices to overcome the issue of HRV saturation in elite athletes (i.e., reductions in HRV despite decreases in resting heart rate). Finally, we provide examples in Olympic and World Champion athletes showing how these indices can be practically applied to assess training status and readiness to perform in the period leading up to a pinnacle event. The paper reveals how longitudinal HRV monitoring in elites is required to understand their unique individual HRV fingerprint. For the first time, we demonstrate how increases and decreases in HRV relate to changes in fitness and freshness, respectively, in elite athletes.


title: Cyber-physical systems for water sustainability: challenges and opportunities

abstract: Water plays a vital role in the proper functioning of the Earth’s ecosystems, and practically all human activities, such as agriculture, manufacturing, transportation, and energy production. The proliferation of industrial and agricultural activities in modern society, however, poses threats to water resources in the form of chemical, biological, and thermal pollution. On the other hand, tremendous advancements in science and technology offer valuable tools to address water sustainability challenges. Key technologies, including sensing technology, wireless communications and networking, hydrodynamic modeling, data analysis, and control, enable intelligently wireless networked water cyber-physical systems (CPS) with embedded sensors, processors, and actuators that can sense and interact with the water environment. This article provides an overview of water CPS for sustainability from four critical aspects: sensing and instrumentation; communications and networking; computing; and control. The article also explores opportunities and design challenges of relevant techniques.


title: Regulatory Flexibility An Individual Differences Perspective on Coping and Emotion Regulation

abstract: People respond to stressful events in different ways, depending on the event and on the regulatory strategies they choose. Coping and emotion regulation theorists have proposed dynamic models in which these two factors, the person and the situation, interact over time to inform adaptation. In practice, however, researchers have tended to assume that particular regulatory strategies are consistently beneficial or maladaptive. We label this assumption the fallacy of uniform efficacy and contrast it with findings from a number of related literatures that have suggested the emergence of a broader but as yet poorly defined construct that we refer to as regulatory flexibility. In this review, we articulate this broader construct and define both its features and limitations. Specifically, we propose a heuristic individual differences framework and review research on three sequential components of flexibility for which propensities and abilities vary: sensitivity to context, availability of a diverse repertoire o...


title: Informational privacy, data mining, and the Internet

abstract: Privacy concerns involving data mining are examined in terms of four questions: (1) What exactly is data mining? (2) How does data mining raise concerns for personal privacy? (3) How do privacy concerns raised by data mining differ from those concerns introduced by ‘traditional’ information-retrieval techniques in computer databases? (4) How do privacy concerns raised by mining personal data from the Internet differ from those concerns introduced by mining such data from ‘data warehouses?’ It is argued that the practice of using data-mining techniques, whether on the Internet or in data warehouses, to gain information about persons raises privacy concerns that (a) go beyond concerns introduced in traditional information-retrieval techniques in computer databases and (b) are not covered by present data-protection guidelines and privacy laws.


title: Building social media theory from case studies: A new frontier for IS research

abstract: This paper concentrates on a major concern in the IS field – theory building – and couples it with a major development in our field, social media-related environments, to consider how we might build theory, using digital texts, within the case study methodology. The growing popularity and constant innovations of social media platforms and applications have transformed ways of interacting, working, creating value and innovating. There is a need -to theorize these new environments, and the intriguing social and technical dynamics they make possible. We elaborate upon how building theory from case studies should be adapted to the opportunities and challenges of social media environments. We delve into key challenges of the research process: case study design, data analysis, and engaging in multi methods. Doing so, we identify some key considerations that can help IS researchers navigate the still new and not yet fully understood characteristics of these environments for theory building purposes.


title: The coefficients of potential of two-dimensional regions with rotational antisymmetry

abstract: If a two-dimensional compound region with rotational antisymmetry is sixfold, eightfold, or twelvefold, the matrix of coefficients of potential can be derived from the consideration of previous letters [1], [2]. This matrix leads us to the exact resistance values of various connections of electrodes.


title: On the infinite width limit of neural networks with a standard parameterization

abstract: There are currently two parameterizations used to derive fixed kernels corresponding to infinite width neural networks, the NTK (Neural Tangent Kernel) parameterization and the naive standard parameterization. However, the extrapolation of both of these parameterizations to infinite width is problematic. The standard parameterization leads to a divergent neural tangent kernel while the NTK parameterization fails to capture crucial aspects of finite width networks such as: the dependence of training dynamics on relative layer widths, the relative training dynamics of weights and biases, and a nonstandard learning rate scale. Here we propose an improved extrapolation of the standard parameterization that preserves all of these properties as width is taken to infinity and yields a well-defined neural tangent kernel. We show experimentally that the resulting kernels typically achieve similar accuracy to those resulting from an NTK parameterization, but with better correspondence to the parameterization of typical finite width networks. Additionally, with careful tuning of width parameters, the improved standard parameterization kernels can outperform those stemming from an NTK parameterization. We release code implementing this improved standard parameterization as part of the Neural Tangents library at this https URL.


title: Graph Matching with Adaptive and Branching Path Following

abstract: Graph matching aims at establishing correspondences between graph elements, and is widely used in many computer vision tasks. Among recently proposed graph matching algorithms, those utilizing the path following strategy have attracted special research attentions due to their exhibition of state-of-the-art performances. However, the paths computed in these algorithms often contain singular points, which could hurt the matching performance if not dealt properly. To deal with this issue, we propose a novel path following strategy, named branching path following (BPF), to improve graph matching accuracy. In particular, we first propose a singular point detector by solving a KKT system, and then design a branch switching method to seek for better paths at singular points. Moreover, to reduce the computational burden of the BPF strategy, an adaptive path estimation (APE) strategy is integrated into BPF to accelerate the convergence of searching along each path. A new graph matching algorithm named ABPF-G is developed by applying APE and BPF to a recently proposed path following algorithm named GNCCP (Liu & Qiao 2014). Experimental results reveal how our approach consistently outperforms state-of-the-art algorithms for graph matching on five public benchmark datasets.


Improving Japanese-to-English Neural Machine Translation by Paraphrasing the Target Language


title: Personalized Research Paper Recommendation using Deep Learning

abstract: With the increasing number of scientific publications, research paper recommendation has become increasingly important for scientists. Most researchers rely on keyword-based search or following citations in other papers, in order to find relevant research articles. And usually they spend a lot of time without getting satisfactory results. This study aims to propose a personalized research paper recommendation system, that facilitate this task by recommending papers based on users' explicit and implicit feedback. The users will be allowed to explicitly specify the papers of interest. In addition, user activities (e.g., viewing abstracts or full-texts) will be analyzed in order to enhance users' profiles. Most of the current research paper recommendation and information retrieval systems use the classical bag-of-words methods, which don't consider the context of the words and the semantic similarity between the articles. This study will use Recurrent Neural Networks (RNNs) to discover continuous and latent semantic features of the papers, in order to improve the recommendation quality. The proposed approach utilizes PubMed so far, since it is frequently used by physicians and scientists, but it can easily incorporate other datasets in the future.


title: Game, set and match? Substantive issues and future directions in performance analysis.

abstract: This article discusses the main substantive issues surrounding performance analysis and considers future directions in this recently formed sub-discipline of sport science. It is argued that it is insufficient to bring together sport biomechanics and notational analysis on the basis that they share a number of commonalities, such as they both aim to enhance performance, they both make extensive use of information and communications technology, and both are concerned with producing valid and reliable data. Rather, it is suggested that the common factor linking sport biomechanics and notational analysis is that they can both be used to measure and describe the same phenomenon (i.e. emergent pattern formation) at different scales of analysis (e.g. intra-limb, inter-limb and torso, and inter-personal). Key concepts from dynamical system theory, such as self-organization and constraints, can then be used to explain stability, variability and transitions among coordinative states. By adopting a constraints-based approach, performance analysis could be effectively opened up to sport scientists from other sub-disciplines of sport science, such as sport physiology and psychology, rather than solely being the preserve of sport biomechanists and notational analysts. To conclude, consideration is given to how a more unified approach, based on the tenets of dynamical systems theory, could impact on the future of performance analysis.


title: Design methodology for the multi-beam phased array of antennas with relatively arbitrary coverage sector

abstract: This paper presents the design methodology of multi-beam phased array of antennas that may provide high gain and narrow beam radiation patterns within a desired coverage range. The advantages of high energy efficiency in the coverage area provide the flexibility of applications in various area such as wireless power transmission and mobile communications. The relatively flexible mechanism to determine the number of beams, beamwidth and beam directions makes the proposed method very useful in the practical implementation of multi-beam antennas. The methodology is summarized with an example to demonstrate the feasibility.


title: Verification of Rogowski current transducer's ability to measure fast switching transients

abstract: Rogowski current transducers combine a high bandwidth, an easy to use thin flexible coil, and low insertion impedance making them an ideal device for measuring pulsed currents in power electronic applications. Practical verification of a Rogowski transducer's ability to measure current transients due to the fastest MOSFET and IGBT switching requires a calibrated test facility capable of generating a pulse with a rise time of the order of a few 10's ns. A flexible 8-module system has been built which gives a 2000A peak current with a rise time of 40ns. The modular approach enables verification for a range of transducer coil sizes and ratings.


title: Observations on the biology of Cinara ponderosae (Williams) (Homoptera: Aphididae) in the westside forests of the Sierra Nevada

abstract: Observations on the biology and population dynamics of Cinara ponderosae Williams) in the westside Sierra Nevada forests of California are presented. ::: The species is believed to be anholocyclic, because sexuales, eggs, and fundatrices ::: were absent. In the spring following winter survival on ponderosa pine (Pinus ponderosa Lawson) in the foothills, populations migrated up the west slope of the Sierra Nevada. ::: The local population in Blodgett Forest, Eldorado County, began in May with the number ::: of aphids and colonies expanding throughout June and collapsing by the end of July. ::: Detailed study of instar distribution, based on a sampling every four days, showed ::: a gradual increase in older instars until population collapse. First generation 4th ::: instars and apt era e moved throughout the tree and began new colonies. ::: Predators, primary and secondary parasites, and ant associates were noted and discussed.


title: CFUN: Combining Faster R-CNN and U-net Network for Efficient Whole Heart Segmentation

abstract: In this paper, we propose a novel heart segmentation pipeline Combining Faster R-CNN and U-net Network (CFUN). Due to Faster R-CNN's precise localization ability and U-net's powerful segmentation ability, CFUN needs only one-step detection and segmentation inference to get the whole heart segmentation result, obtaining good results with significantly reduced computational cost. Besides, CFUN adopts a new loss function based on edge information named 3D Edge-loss as an auxiliary loss to accelerate the convergence of training and improve the segmentation results. Extensive experiments on the public dataset show that CFUN exhibits competitive segmentation performance in a sharply reduced inference time. Our source code and the model are publicly available at https://github.com/Wuziyi616/CFUN.


title: Paraphrase identification in short texts using grammar patterns

abstract: We can determine whether two texts are paraphrases of each other by finding out the extent to which the texts are similar. The typical lexical matching technique works by matching the sequence of tokens between the texts to recognize paraphrases, and fails when different words are used to convey the same meaning. We can improve this simple method by combining lexical with syntactic or semantic representations of the input texts. The present work makes use of syntactical information in the texts and computes the similarity between them using word similarity measures based on WordNet and lexical databases. The texts are converted into a unified semantic structural model through which the semantic similarity of the texts is obtained. An approach is presented to assess the semantic similarity and the results of applying this approach is evaluated using the Microsoft Research Paraphrase (MSRP) Corpus.


Direct URCA processes involving hyperons and cooling of neutron stars


title: Deep Learning in Automotive Software

abstract: Deep-learning-based systems are becoming pervasive in automotive software. So, in the automotive software engineering community, the awareness of the need to integrate deep-learning-based development with traditional development approaches is growing, at the technical, methodological, and cultural levels. In particular, data-intensive deep neural network (DNN) training, using ad hoc training data, is pivotal in the development of software for vehicle functions that rely on deep learning. Researchers have devised a development lifecycle for deep-learning-based development and are participating in an initiative, based on Automotive SPICE (Software Process Improvement and Capability Determination), that's promoting the effective adoption of DNN in automotive software. This article is part of a theme issue on Automotive Software.


title: Improved Gait recognition based on specialized deep convolutional neural networks

abstract: Gait recognition is a biometric technique that is used in order to determine the identity of humans based on the style and the manner of their walk. Yet, Gait recognition performance is often degraded by some covariate factors such as a viewing angle variation, clothing and carrying condition changes, and a low-image resolution. In general, current tactics to object recognition highly depend on the use of machine learning techniques. Therefore, a deep convolutional neural network (CNN) is one of the most advanced machine learning techniques that has the ability to approximate complex non-linear functions from high-dimensional input data in a hierarchical process. In this paper, we develop a specialized deep CNN architecture, which consists of multilayers of convolutional and subsampling layers. The proposed technique is less sensitive to several cases of the common variations and occlusions that affect and degrade gait recognition performance. We avoided the use of the typical subspace learning methods, along with its shortcomings, that are widely used in gait recognition. When applied the proposed deep CNN model to CASIA-B large gait database, the experimental results show that the deep CNN model developed in this paper outperforms the other state of art gait recognition techniques in several cases.


title: Design of spoof surface plasmon polariton based terahertz delay lines

abstract: We present the analysis and design of fixed physical length, spoof Surface Plasmon Polariton based waveguides with adjustable delay at terahertz frequencies. The adjustable delay is obtained using Corrugated Planar Goubau Lines (CPGL) by changing its corrugation depth without changing the total physical length of the waveguide. Our simulation results show that electrical lengths of 237.9°, 220.6°, and 310.6° can be achieved by physical lengths of 250 μm and 200 μm at 0.25, 0.275, and 0.3 THz, respectively, for demonstration purposes. These simulations results are also consistent with our analytical calculations using the physical parameter and material properties. When we combine pairs of same length delay lines as if they are two branches of a terahertz phase shifter, we achieved an error rate of relative phase shift estimation better than 5.8%. To the best of our knowledge, this is the first-time demonstration of adjustable spoof Surface Plasmon Polariton based CPGL delay lines. The idea can be used for obtaining tunable delay lines with fixed lengths and phase shifters for the terahertz band circuitry.


title: Mesofluidic actuation for articulated finger and hand prosthetics

abstract: The loss of fingers and hands severely limits career and lifestyle options for the amputee. Unfortunately, while there have been strides made in advancements of upper arm and leg prosthetics, the state of the art in prosthetic hands is lagging far behind. Options are generally limited to claw like devices that provide limited gripping capacity. The overall objective of this paper is to demonstrate a path towards a low-cost prosthetic hand with multiple articulated fingers and a thumb that rivals the human hand in terms of weight, size, dexterity, range of motion, force carrying capacity and speed. We begin with a description of the functional requirements for a human hand. When comparing requirements with actuation technologies, the fluid power approach has the potential to realize a prosthetic hand that rivals a human hand in size, strength and dexterity. We introduce a new actuation technology, mesofluidics, that focuses on miniaturization of fluid power to the meso-scale (mm to cm). As a novel demonstration of the potential for this technology, we describe a proof-of-principle mesofluidic finger that has intrinsic actuation and control (actuators and control valves within the volume of the finger). This finger weighs 63 grams, is sized to the 50th percentile male finger, has a total of 25 mechanical parts and is capable of providing 10 kg (22 lbs) of pinch force.


Wireless Sensor Network and RFID for Smart Parking System


title: H. P. Luhn A Statistical Approach to Mechanized Encoding and Searching of Literary Information*

abstract: Written communication of ideas is carried out on the basis of statistical probability in that a writer chooses that level of subject specificity and that combination of words which he feels will convey the most meaning. Since this process varies among individuals and since similar ideas are therefore relayed at different levels of specificity and by means of different words, the problem of literature searching by machines still presents major difficulties. A statistical approach to this problem will be outlined and the various steps of a system based on this approach will be described. Steps include the statistical analysis of a collection of documents in a field of interest, the establishment of a set of "notions" and the vocabulary by which they are expressed, the compilation of a thesaurus-type dictionary and index, the automatic encoding of documents by machine with the aid of such a dictionary, the encoding of topological notations (such as branched structures), the recording of the coded information, the establishment of a searching pattern for finding pertinent information, and the programming of appropriate machines to carry out a search.


title: Defining an Optimal Cut-Point Value in ROC Analysis: An Alternative Approach

abstract: ROC curve analysis is often applied to measure the diagnostic accuracy of a biomarker. The analysis results in two gains: diagnostic accuracy of the biomarker and the optimal cut-point value. There are many methods proposed in the literature to obtain the optimal cut-point value. In this study, a new approach, alternative to these methods, is proposed. The proposed approach is based on the value of the area under the ROC curve. This method defines the optimal cut-point value as the value whose sensitivity and specificity are the closest to the value of the area under the ROC curve and the absolute value of the difference between the sensitivity and specificity values is minimum. This approach is very practical. In this study, the results of the proposed method are compared with those of the standard approaches, by using simulated data with different distribution and homogeneity conditions as well as a real data. According to the simulation results, the use of the proposed method is advised for finding the true cut-point.


title: K-strings algorithm, a new approach based on Kmeans

abstract: K-means is a popular clustering algorithm which is widely used in anomaly-based intrusion detection. It tries to classify a given data set into k (a predefined number) categories. However, to apply to a high dimensional dataset, we believe that the calculated distance of the multitude of different attributes along with diverse ranges, that depend on a sole center point are not fair and limit the chance to generate good quality clusters. Aiming to cluster a high dimensional dataset more effective, we propose K-string clustering algorithm in this paper. In which, we obtain a set of center points, that play the role as the backbone of a cluster, instead of a unique centroid to generate clusters. To evaluate we use fitness function, and absolutely the output clusters shown a competition results when comparing to K-means algorithm and Genetic Algorithm [6].


title: Discovering and understanding word level user intent in Web search queries

abstract: Identifying and interpreting user intent are fundamental to semantic search. In this paper, we investigate the association of intent with individual words of a search query. We propose that words in queries can be classified as either content or intent, where content words represent the central topic of the query, while users add intent words to make their requirements more explicit. We argue that intelligent processing of intent words can be vital to improving the result quality, and in this work we focus on intent word discovery and understanding. Our approach towards intent word detection is motivated by the hypotheses that query intent words satisfy certain distributional properties in large query logs similar to function words in natural language corpora. Following this idea, we first prove the effectiveness of our corpus distributional features, namely, word co-occurrence counts and entropies, towards function word detection for five natural languages. Next, we show that reliable detection of intent words in queries is possible using these same features computed from query logs. To make the distinction between content and intent words more tangible, we additionally provide operational definitions of content and intent words as those words that should match, and those that need not match, respectively, in the text of relevant documents. In addition to a standard evaluation against human annotations, we also provide an alternative validation of our ideas using clickthrough data. Concordance of the two orthogonal evaluation approaches provide further support to our original hypothesis of the existence of two distinct word classes in search queries. Finally, we provide a taxonomy of intent words derived through rigorous manual analysis of large query logs.


title: Trends in Model-based GUI Testing

abstract: This chapter gives an overview of the recent advances in GUI testing. Considering the increasing popularity and fast software development cycles (e.g., desktop and mobile applications), GUI testing gains more importance as it allows us to verify the behavior of a system from the user’s perspective. Thus, it can quickly uncover relevant bugs, which a user could face. ::: ::: Traditional capture-replay GUI testing approaches do not meet the demands of developers anymore. Therefore, there is an increasing research activity in model-based GUI testing, where the user interaction behavior is simulated using a graph-based model. In the following, we outline different graphical notations to describe feasible user interactions, and methods to generate and execute test cases from these models. We discuss the benefits and limitations of the state-of-the-art in GUI testing research and give a brief outlook about new trends and possibilities to improve the GUI testing automation.


title: Clustering algorithm for internet of vehicles (IoV) based on dragonfly optimizer (CAVDO)

abstract: Internet of vehicles (IoV) is a branch of the internet of things (IoT) which is used for communication among vehicles. As vehicular nodes are considered always in motion, hence it causes the frequent changes in the topology. These changes cause major issues in IoV like scalability, dynamic topology changes, and shortest path for routing. Clustering is among one of the solutions for such type of issues. In this paper, the stability of IoV topology in a dynamic environment is focused. The proposed metaheuristic dragonfly-based clustering algorithm CAVDO is used for cluster-based packet route optimization to make stable topology, and mobility aware dynamic transmission range algorithm (MA-DTR) is used with CAVDO for transmission range adaptation on the basis of traffic density. The proposed CAVDO with MA-DTR is compared with the progressive baseline techniques ant colony optimization (ACO) and comprehensive learning particle swarm optimization (CLPSO). Numerous experiments were performed keeping in view the transmission dynamics for stable topology. CAVDO performed better in many cases providing minimum number of clusters according to current channel condition. Considerable important parameters involved in clustering process are: number of un-clustered nodes as a re-clustering criterion, clustering time, re-clustering delay, dynamic transmission range, direction, and speed. According to these parameters, results indicate that CAVDO outperformed ACO-based clustering and CLPSO in various network settings. Additionally, to improve the network availability and to incorporate the functionalities of next-generation network infrastructure, 5G-enabled architecture is also utilized.


title: A seeded fuzzy C-means based approach to automatic cup-to-disc ratio measurement

abstract: Glaucoma is an eye disease that causes irreversible vision loss. Retinography is done manually by the ophthalmologist and is the cheapest, least invasive and most effective way to diagnose glaucoma. The ratio between the diameter of the outer part of the Optic Disc (OD) and the cup (internal part) called CDR (cup-to-disc ratio) is an important indicator of glaucoma presence in patients. This paper proposes a semiautomatic approach that includes the segmentation of OD and cup regions. The proposed approach consists of four stages. The first stage consists of preprocessing the retinal image, in order to remove blood vessels and a possible influence in the segmentation stage. In the second stage we apply the Seeded Fuzzy C-means algorithm to segment the preprocessed image in order to indentify cup and OD. The third step involves the application of a post-processing so that non-segmented regions are filled. Finally, the last step calculates the value of the CDR associated with the retinal image. To verify the applicability of the proposed approach, we carried out tests in two public image databases: DRISHTI-GS and RIM-ONE r3. The results obtained illustrate the feasibility of applying the approach in order to effectively assist ophthalmologists in the segmentation of cup and OD, as well as the calculation of the CDR.


title: An autonomous and efficient controller-based routing scheme for networking Named-Data mobility

abstract: The number of routes to store in Named-Data Networking (NDN) requires a huge amount of control messages to exchange because of the Named-Data and the non-aggregated prefixes. Multiple copies of content in different locations and content mobility worsen the scalability challenge. We propose a Controller-based Routing Scheme, named CRoS that runs on top of NDN and, thus, preserves all NDN features using the same interest and data packets. We define specific names and procedures for routers and controller efficient communication over NDN. CRoS adds router actions and avoids control message overhead by coding signaling information on content names. Our scheme enables data mobility and avoids the replications of routing information from controller to routers because they request the routes on-demand. Our proposal also requires low router memory size because it stores only the routes for simultaneously consumed prefixes. Furthermore, the scheme automates router provisioning and efficiently installs a new route, on all routers, in a path with a single Route Request to controller. We provide a protocol proposal description using the Specification and Description Language and we validate the protocol, proving that CRoS behavior is free of dead or live locks. The simulation results show that the efficiency of the proposed scheme is robust when the consumer-interest rate increases with additional throughput of more links. The scheme efficiency is close to optimum when routers operate with enough memory.


title: A robust FISTA-like algorithm

abstract: The Fast Iterative Shrinkage-Thresholding Algorithm (FISTA) is regarded as the state-of-the-art among a number of proximal gradient-based methods used for addressing large-scale optimization problems with simple but non-differentiable objective functions. However, the efficiency of FISTA in a wide range of applications is hampered by a simple drawback in the line search scheme. The local estimate of the Lipschitz constant, the inverse of which gives the step size, can only increase while the algorithm is running. As a result, FISTA can slow down significantly if the initial estimate of the Lipschitz constant is excessively large or if the local Lipschitz constant decreases in the vicinity of the optimal point. We propose a new FISTA-like method endowed with a robust step size search procedure and demonstrate its effectiveness by means of a rigorous theoretical convergence analysis and simulations.


title: The Value of a New Filler Material in Corrective and Cosmetic Surgery: DermaLive and DermaDeep

abstract: DermaLive® is a long-term wrinkle reduction product including two types of components: pure hyaluronic acid, produced in cell culture, and an acrylic hydrogel. The product was first marketed in France and the rest of Europe in 1998. We have over three years of experience with this product. Several studies have been conducted simultaneously, mainly in Germany and in France. At present, DermaLive® is used in the long-term correction of natural or acquired skin depressions (caused by aging, trauma) or the creation of volume (lips, sunken cheekbones). It provides a worthwhile alternative to good-quality bioresorbable materials (pure hyaluronic acid or collagen)—materials that are so resorbable, in fact, that most patients are ultimately dissatisfied with the results—and to other so-called permanent materials that patients do not tolerate well. Filling involving the use of autologous fat transplantations, which yields satisfactory results, is not appropriate for ambulatory injection and, therefore, will not be examined in detail. The tolerance experienced with DermaLive three years on is, at present, considered highly satisfactory. The wrinkle reduction effect obtained after the first injection is long-lasting with 60% resorption of the initial material. Two or three injections, with an interval of at least three months between each, may be required to bring about the reduction of some wrinkles or the correction of post-scar depressions. Side effects occurring long after the injection are rare (1.2 for 1,000). They appear mainly as palpable nodules occurring about six months after injection. They are treated by intralesional injection of corticoids. DermaLive and DermaDeep are both filler implants with a long-lasting effect designed for corrective and aesthetic surgery. Instruction of use and intervals between injection sessions must be respected. After DermaLive or DermaDeep injection, injections of pure hyaluronic acid (Juvederm® or Restylane®) for treatment of fine lines and superficial defects yield safe and excellent aesthetic results.


title: Direct Torque Control of an IPM-Synchronous Motor Drive at Very Low Speed Using a Sliding-Mode Stator Flux Observer

abstract: Direct torque control of interior permanent-magnet synchronous motors is known to deliver fast torque and flux dynamic responses. However, the poor flux estimation at very low speeds has been its largest drawback. A multitude of flux observers have been proposed for flux estimation, but most of them fail to fare in the low-speed region. This paper proposes a sliding-mode stator flux observer for improved flux estimation at very low speeds. Unlike conventional flux observers, this observer does not require any speed adaptation mechanism and is immune to speed estimation error. A novel stator resistance estimator is incorporated into the sensorless drive to compensate the effects of stator resistance variation. Global asymptotic stability of both the flux observer and stator resistance estimator is guaranteed by the Lyapunov stability analysis. DC-offset effects are mitigated by introducing a small integral component in the observer gains. Simulation and experimental results at very low speeds, including 0 and 2 r/min, without signal injection confirm the effectiveness of the proposed method.


title: On anonymity in an electronic society: A survey of anonymous communication systems

abstract: The past two decades have seen a growing interest in methods for anonymous communication on the Internet, both from the academic community and the general public. Several system designs have been proposed in the literature, of which a number have been implemented and are used by diverse groups, such as journalists, human rights workers, the military, and ordinary citizens, to protect their identities on the Internet. In this work, we survey the previous research done to design, develop, and deploy systems for enabling private and anonymous communication on the Internet. We identify and describe the major concepts and technologies in the field, including mixes and mix networks, onion routing, and Dining Cryptographers networks. We will also review powerful traffic analysis attacks that have motivated improvements and variations on many of these anonymity protocols made since their introduction. Finally, we will summarize some of the major open problems in anonymous communication research and discuss possible directions for future work in the field.


title: Handwriting recognition in historical documents using very large vocabularies

abstract: Language models are used in automatic transcription system to resolve ambiguities. This is done by limiting the vocabulary of words that can be recognized as well as estimating the n-gram probability of the words in the given text. In the context of historical documents, a non-unified spelling and the limited amount of written text pose a substantial problem for the selection of the recognizable vocabulary as well as the computation of the word probabilities. In this paper we propose for the transcription of historical Spanish text to keep the corpus for the n-gram limited to a sample of the target text, but expand the vocabulary with words gathered from external resources. We analyze the performance of such a transcription system with different sizes of external vocabularies and demonstrate the applicability and the significant increase in recognition accuracy of using up to 300 thousand external words.


Maps and Compasses in the Brain


title: Identification of a Threshold Value for the DEMATEL Method: Using the Maximum Mean De-Entropy Algorithm

abstract: To deal with complex problems, structuring them through graphical representations and analyzing causal influences can aid in illuminating complex issues, systems, or concepts. The DEMATEL method is a methodology which can be used for researching and solving complicated and intertwined problem groups. The end product of the DEMATEL process is a visual representation—the impact-relations map—by which respondents organize their own actions in the world. The applicability of the DEMATEL method is widespread, ranging from analyzing world problematique decision making to industrial planning. The most important property of the DEMATEL method used in the multi-criteria decision making (MCDM) field is to construct interrelations between criteria. In order to obtain a suitable impact-relations map, an appropriate threshold value is needed to obtain adequate information for further analysis and decision-making. In this paper, we propose a method based on the entropy approach, the maximum mean de-entropy algorithm, to achieve this purpose. Using real cases to find the interrelationships between the criteria for evaluating effects in E-learning programs as an examples, we will compare the results obtained from the respondents and from our method, and discuss that the different impact-relations maps from these two methods.


Disparate voxel based morphometry (VBM) results between SPM and FSL softwares in ALS patients with frontotemporal dementia: which VBM results to consider?


title: Evaluating real-life performance of the state-of-the-art in facial expression recognition using a novel YouTube-based datasets

abstract: Facial expression recognition (FER) is one of the most active areas of research in computer science, due to its importance in a large number of application domains. Over the years, a great number of FER systems have been implemented, each surpassing the other in terms of classification accuracy. However, one major weakness found in the previous studies is that they have all used standard datasets for their evaluations and comparisons. Though this serves well given the needs of a fair comparison with existing systems, it is argued that this does not go in hand with the fact that these systems are built with a hope of eventually being used in the real-world. It is because these datasets assume a predefined camera setup, consist of mostly posed expressions collected in a controlled setting, using fixed background and static ambient settings, and having low variations in the face size and camera angles, which is not the case in a dynamic real-world. The contributions of this work are two-fold: firstly, using numerous online resources and also our own setup, we have collected a rich FER dataset keeping in mind the above mentioned problems. Secondly, we have chosen eleven state-of-the-art FER systems, implemented them and performed a rigorous evaluation of these systems using our dataset. The results confirm our hypothesis that even the most accurate existing FER systems are not ready to face the challenges of a dynamic real-world. We hope that our dataset would become a benchmark to assess the real-life performance of future FER systems.


title: Document Hierarchies from Text and Links

abstract: Hierarchical taxonomies provide a multi-level view of large document collections, allowing users to rapidly drill down to fine-grained distinctions in topics of interest. We show that automatically induced taxonomies can be made more robust by combining text with relational links. The underlying mechanism is a Bayesian generative model in which a latent hierarchical structure explains the observed data --- thus, finding hierarchical groups of documents with similar word distributions and dense network connections. As a nonparametric Bayesian model, our approach does not require pre-specification of the branching factor at each non-terminal, but finds the appropriate level of detail directly from the data. Unlike many prior latent space models of network structure, the complexity of our approach does not grow quadratically in the number of documents, enabling application to networks with more than ten thousand nodes. Experimental results on hypertext and citation network corpora demonstrate the advantages of our hierarchical, multimodal approach.


title: Iris on the Move: Acquisition of Images for Iris Recognition in Less Constrained Environments

abstract: Iris recognition is one of the most powerful techniques for biometric identification ever developed. Commercial systems based on the algorithms developed by John Daugman have been available since 1995 and have been used in a variety of practical applications. However, all currently available systems impose substantial constraints on subject position and motion during the recognition process. These constraints are largely driven by the image acquisition process, rather than the particular pattern-matching algorithm used for the recognition process. In this paper we present results of our efforts to substantially reduce constraints on position and motion by means of a new image acquisition system based on high-resolution cameras, video synchronized strobed illumination, and specularity based image segmentation. We discuss the design tradeoffs we made in developing the system and the performance we have been able to achieve when the image acquisition system is combined with a standard iris recognition algorithm. The Iris on the Move (IOM) system is the first system to enable capture of iris images of sufficient quality for iris recognition while the subject is moving at a normal walking pace through a minimally confining portal


title: Wheel Slip Control via Second-Order Sliding-Mode Generation

abstract: During skid braking and spin acceleration, the driving force exerted by the tires is reduced considerably, and the vehicle cannot speed up or brake as desired. It may become very difficult to control the vehicle under these conditions. To solve this problem, a second-order sliding-mode traction controller is presented in this paper. The controller design is coupled with the design of a suitable sliding-mode observer to estimate the tire-road adhesion coefficient. The traction control is achieved by maintaining the wheel slip at a desired value. In particular, by controlling the wheel slip at the optimal value, the proposed traction control enables antiskid braking and antispin acceleration, thus improving safety in difficult weather conditions, as well as stability during high-performance driving. The choice of second-order sliding-mode control methodology is motivated by its robustness feature with respect to parameter uncertainties and disturbances, which are typical of the automotive context. Moreover, the proposed second-order sliding-mode controller, in contrast to conventional sliding-mode controllers, generates continuous control actions, thus being particularly suitable for application to automotive systems.


Peer Assessment in MOOCs Using Preference Learning via Matrix Factorization


title: Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learning

abstract: In recent years, neural networks have enjoyed a renaissance as function approximators in reinforcement learning. Two decades after Tesauro's TD-Gammon achieved near top-level human performance in backgammon, the deep reinforcement learning algorithm DQN achieved human-level performance in many Atari 2600 games. The purpose of this study is twofold. First, we propose two activation functions for neural network function approximation in reinforcement learning: the sigmoid-weighted linear unit (SiLU) and its derivative function (dSiLU). The activation of the SiLU is computed by the sigmoid function multiplied by its input. Second, we suggest that the more traditional approach of using on-policy learning with eligibility traces, instead of experience replay, and softmax action selection with simple annealing can be competitive with DQN, without the need for a separate target network. We validate our proposed approach by, first, achieving new state-of-the-art results in both stochastic SZ-Tetris and Tetris with a small 10$\times$10 board, using TD($\lambda$) learning and shallow dSiLU network agents, and, then, by outperforming DQN in the Atari 2600 domain by using a deep Sarsa($\lambda$) agent with SiLU and dSiLU hidden units.


title: Privately Evaluating Decision Trees and Random Forests

abstract: Decision trees and random forests are com- mon classifiers with widespread use. In this paper, we develop two protocols for privately evaluating decision trees and random forests. We operate in the standard two-party setting where the server holds a model (ei- ther a tree or a forest), and the client holds an input (a feature vector). At the conclusion of the protocol, the client learns only the model's output on its input and a few generic parameters concerning the model; the server learns nothing. The first protocol we develop provides security against semi-honest adversaries. We then give an extension of the semi-honest protocol that is robust against malicious adversaries. We implement both pro- tocols and show that both variants are able to process trees with several hundred decision nodes in just a few seconds and a modest amount of bandwidth. Compared to previous semi-honest protocols for private decision tree evaluation, we demonstrate a tenfold improvement in computation and bandwidth.


title: Treatment of unstable thoracolumbar junction burst fractures with short- or long-segment posterior fixation in magerl type a fractures

abstract: The treatment of thoracolumbar fractures remains controversial. A review of the literature showed that short-segment posterior fixation (SSPF) alone led to a high incidence of implant failure and correction loss. The aim of this retrospective study was to compare the outcomes of the SS- and long-segment posterior fixation (LSPF) in unstable thoracolumbar junction burst fractures (T12-L2) in Magerl Type A fractures. The patients were divided into two groups according to the number of instrumented levels. Group I included 32 patients treated by SSPF (four screws: one level above and below the fracture), and Group II included 31 patients treated by LSPF (eight screws: two levels above and below the fracture). Clinical outcomes and radiological parameters (sagittal index, SI; and canal compromise, CC) were compared according to demographic features, localizations, load-sharing classification (LSC) and Magerl subgroups, statistically. The fractures with more than 10 degrees correction loss at sagittal plane were analyzed in each group. The groups were similar with regard to age, gender, LSC, SI, and CC preoperatively. The mean follow-ups were similar for both groups, 36 and 33 months, respectively. In Group II, the correction values of SI, and CC were more significant than in Group I. More than 10 degrees correction loss occurred in six of the 32 fractures in Group I and in two of the 31 patients in Group II. SSPF was found inadequate in patients with high load sharing scores. Although radiological outcomes (SI and CC remodeling) were better in Group II for all fracture types and localizations, the clinical outcomes (according to Denis functional scores) were similar except Magerl type A33 fractures. We recommend that, especially in patients, who need more mobility, with LSC point 7 or less with Magerl Type A31 and A32 fractures (LSC point 6 or less in Magerl Type A3.3) without neurological deficit, SSPF achieves adequate fixation, without implant failure and correction loss. In Magerl Type A33 fractures with LSC point 7 or more (LSC points 8-9 in Magerl Type A31 and A32) without severe neurologic deficit, LSPF is more beneficial.


title: SDR-based radar-detectors embedded on tablet devices

abstract: Software-Defined Radio (SDR) technology has already cleared up passive radar applications. Nevertheless, until now, no work has pointed how this flexible radio could fully and directly exploit pulsed radar signals. This paper aims at introducing this field of study presenting not only an SDR-based radar-detector but also how it could be conceived on a low power consumption device as a tablet, which would make convenient a passive network to identify and localize aircraft as a redundancy to the conventional air traffic control in adverse situations. After a brief approach of the main features of the equipment, as well as of the developed processing script, indoor experiments took place. Their results demonstrate that the processing of pulsed radar signal allows emitters to be identified when a local database is confronted. All this commitment has contributed to a greater proposal of an Electronic Intelligence (ELINT) or Electronic Support Measures (ESM) system embedded on a tablet, presenting characteristics of portability and furtiveness. This study is suggested for the areas of Software-Defined Radio, Electronic Warfare, Electromagnetic Devices and Radar Signal Processing.


title: Optimum operation of asymmetrical-cells-based linear Doherty power Amplifiers-uneven power drive and power matching

abstract: We developed a Doherty amplifier with uneven input drive and optimized individual matching for the carrier and peaking cells. In the proposed amplifier, higher input power is delivered to the peaking cell rather than the carrier cell for optimized linear power operation, especially for appropriate load modulation. Both cells are matched differently to further optimize the performance. We analyzed the efficiency of the proposed amplifier as a function of the input drive ratio for the two cells. To interpret the linearity related to the load modulation and harmonic cancellation mechanisms, we simulated the third-order intermodulation amplitude and phase of each cell of the proposed amplifier. For verification, we implemented the asymmetric power amplifier with uneven drive and optimized power matching using Motorola's MRF281SR1 LDMOSFET with a 4-W peak envelope power. For a 2.14-GHz forward-link wireless code-division multiple-access signal, the measured drain efficiency of the amplifier is 40%, and the measured average output power is 33 dBm at an adjacent channel leakage ratio (ACLR) of -35 dBc, while those of the comparable class-AB amplifier are 21% and 30.6 dBm at the same ACLR level, respectively.


title: Cooling, Heating, Generating Power, and Recovering Waste Heat with Thermoelectric Systems

abstract: Thermoelectric materials are solid-state energy converters whose combination of thermal, electrical, and semiconducting properties allows them to be used to convert waste heat into electricity or electrical power directly into cooling and heating. These materials can be competitive with fluid-based systems, such as two-phase air-conditioning compressors or heat pumps, or used in smaller-scale applications such as in automobile seats, night-vision systems, and electrical-enclosure cooling. More widespread use of thermoelectrics requires not only improving the intrinsic energy-conversion efficiency of the materials but also implementing recent advancements in system architecture. These principles are illustrated with several proven and potential applications of thermoelectrics.


title: Cyber-Physical Attack-Resilient Wide-Area Monitoring, Protection, and Control for the Power Grid

abstract: Cybersecurity and resiliency of wide-area monitoring, protection, and control (WAMPAC) applications is critically important to ensure secure, reliable, and economical operation of the bulk power system. WAMPAC relies heavily on the security of measurements and control commands transmitted over wide-area communication networks for real-time operational, protection, and control functions. The current “N–1” security criterion for grid operation is inadequate to address malicious cyber events; therefore, it is important to fundamentally redesign WAMPAC and to enhance energy management system applications to make them attack resilient. In this paper, we present three key contributions to enhance the cybersecurity and resiliency of WAMPAC. First, we describe an end-to-end attack-resilient cyber–physical security framework for WAMPAC applications encompassing the entire security life cycle including risk assessment, attack prevention, attack detection, attack mitigation, and attack resilience. Second, we describe a defense-in-depth architecture that incorporates attack resilience at both the infrastructure layer and the application layer by leveraging domain-specific security approaches at the WAMPAC application layer in addition to traditional cybersecurity measures at the information technology infrastructure layer. Third, we discuss several attack-resilient algorithms for WAMPAC that leverage measurement design and cyber–physical system model-based anomaly detection and mitigation along with illustrative case studies. We believe that the research issues and solutions identified in this paper will open up several avenues for research in this area. In particular, the proposed framework, architectural concepts, and attack-resilient algorithms would serve as essential building blocks to transform the “fault-resilient” grid of today into an “attack-resilient” grid of the future.


title: ID-based Encryption Scheme Secure against

abstract: ID-based encryption allows for a sender to encrypt a message to an identity without access to a public key certificate. Based on the bilinear pairing, Boneh and Franklin proposed the first practical IDbased encryption scheme and used the padding technique of FujisakiOkamto to extend it to be a chosen ciphertext secure version. In this letter, we would like to use another padding technique to propose a new ID-based encryption scheme secure against chosen ciphertext attacks. The security of our scheme is based on the Gap bilinear Diffie-Hellman assumption in the random oracle model.


title: PQ-U control method of grid-connected PV inverter under weak grid

abstract: Under the condition of low short circuit capacity and high grid impedance of weak grid, the grid voltage is easily fluctuated due to the load change, affecting the power quality of the grid. In view of this problem, firstly, this paper analyzes the principle of the grid-connected voltage change caused by the load change. Secondly, an improved voltage-power control method (PQ-U) for three-phase grid-connected PV inverter is proposed. This method changes the output reactive power of PV inverter to maintain the stability of the bus voltage when the inductive or capacitive load changes significantly. Besides, the advantages of PQ-U control method relative to traditional PQ control method are compared and analyzed by Matlab software. The simulation results show that PQ-U control method can maintain the stability of bus voltage in inductive or capacitive load significant change and play a supporting role for weak grid voltage. Finally, experiments are done to verify the validity of the PQ-U control method.


title: A 1.8-GHz 2-Watt Fully Integrated CMOS Push-Pull Parallel-Combined Power Amplifier Design

abstract: This paper newly presents a push-pull parallel-combined CMOS power amplifier (PA) and its analysis of operation. The proposed class-E CMOS PA incorporates the push-pull parallel-combined power devices with the 1:1:2 (two single-turn primary windings and a two-turn secondary winding) step-up on-chip transformer. The PA is fully integrated in a standard 0.18-mum CMOS technology without any external balun or matching networks. The operation of the PA with a multi-turn on-chip transformer is substantially analyzed in order to optimize the device size and its structure. Experimental data demonstrates the output power of 2-watt and the power-added efficiency (PAE) of more than 30% with a 3.3-V of power supply at 1.8 GHz. This is the new demonstration of the compact fully integrated CMOS PA with 2-watt of output power with very stable operation at 1.8 GHz range.


title: Adaptive block truncation filter for MVC depth image enhancement

abstract: In Multiview Video plus Depth (MVD) format, virtual views are generated from decoded texture videos with decoded depth images through Depth Image based Rendering (DIBR). 3DV-ATM is a reference model for H.264/AVC based Multiview Video Coding (MVC) and aims at achieving high coding efficiency for 3D video in MVD format. Depth images are first downsampled then coded by 3DV-ATM. However, sharp object boundary characteristic of depth images does not well match with the transform coding of 3DV-ATM. Depth boundaries are often blurred with ringing artifacts in the decoded depth images that result in noticeable artifacts in synthesized views. This paper presents a low complexity adaptive block truncation filter to recover the sharp object boundaries of depth images using adaptive block repositioning and expansion for increasing the depth values refinement accuracy. This new approach is very efficient and can avoid false depth boundary refinement when block boundaries lie around the depth edge regions and ensure sufficient information within the processing block for depth layers classification. Experimental results show that sharp depth edges can be recovered using the proposed filter and boundary artifacts in the synthesized views can be removed. The proposed method can provide improvement up to 3.25dB in the depth map enhancement and bitrate reduction of 3.06% in the synthesized views. Index Terms— Multiview Video plus Depth (MVD), Depth Image based Rendering (DIBR), 3DV-ATM, Multiview Video Coding (MVC), depth image compression, depth image filter.


title: Multimedia, hypermedia, and hypertext: Motivation considered and reconsidered

abstract: Computer-based instruction (CBI) is becoming increasingly popular in the classroom, particularly because the latest technological advancements allow for visually rich and interactive environments. While the inherent nature of CBIs is often thought to engage learners, research examining the role of motivation in learning with these environments has resulted in mixed findings. These findings are further complicated by unique design characteristics of distinct CBIs. This literature review synthesizes research that has examined the role of theoretically-grounded constructs of motivation in the context of three popular CBIs, multimedia, hypermedia, and hypertext. Specifically, this literature review considered empirical studies that examined the effect of these CBIs on motivation, in addition to the effect of motivation on learning outcomes and the learning process within the context of these environments. The literature review concludes with a theoretical consideration of previous research and a discussion of a framework for future directions.


title: Classification in biological networks with hypergraphlet kernels

abstract: Biological and cellular systems are often modeled as graphs in which vertices represent objects of interest (genes, proteins, drugs) and edges represent relational ties among these objects (binds-to, interacts-with, regulates). This approach has been highly successful owing to the theory, methodology and software that support analysis and learning on graphs. Graphs, however, often suffer from information loss when modeling physical systems due to their inability to accurately represent multiobject relationships. Hypergraphs, a generalization of graphs, provide a framework to mitigate information loss and unify disparate graph-based methodologies. In this paper, we present a hypergraph-based approach for modeling physical systems and formulate vertex classification, edge classification and link prediction problems on (hyper)graphs as instances of vertex classification on (extended, dual) hypergraphs in a semi-supervised setting. We introduce a novel kernel method on vertex- and edge-labeled (colored) hypergraphs for analysis and learning. The method is based on exact and inexact (via hypergraph edit distances) enumeration of small simple hypergraphs, referred to as hypergraphlets, rooted at a vertex of interest. We extensively evaluate this method and show its potential use in a positive-unlabeled setting to estimate the number of missing and false positive links in protein-protein interaction networks.


title: WHEN ADOPTION BRINGS ADDICTION: A USE- DIFFUSION MODEL FOR SOCIAL INFORMATION SYSTEMS

abstract: Recently there has been a dramatic proliferation on the usage of social networking websites, blogs and other information systems that have a social orientation. However, there is limited research on what drives individual to use such information systems and moreover what are the outcomes of their usage. This paper develops and empirically tests a model of user acceptance of social information systems. Taking into account the particular characteristics of these systems, the proposed model has several features that extend technology acceptance literature. Firstly, instead of the behavioral intention as the key dependent variable, the rate and the variety of use are used in order to model technology acceptance based on the use-diffusion theory. Secondly, exploiting a relational perspective of user-technology interaction the concept of user stickiness with an information system is investigated as usage outcome. Finally, the concept of addiction tendency is proposed to be another outcome modeling the excessive and intensive use of social information systems. In order to empirically test the proposed model a survey with the participation of 456 facebook users is conducted. Results, analyzed with structural equation modeling, confirm study’s hypotheses and show that social information systems could be a new field for theorizing about human behavior and technology.


title: Anomaly discovery and resolution in web access control policies

abstract: The advent of emerging technologies such as Web services, service-oriented architecture, and cloud computing has enabled us to perform business services more efficiently and effectively. However, we still suffer from unintended security leakages by unauthorized actions in business services while providing more convenient services to Internet users through such a cutting-edge technological growth. Furthermore, designing and managing Web access control policies are often error-prone due to the lack of effective analysis mechanisms and tools. In this paper, we represent an innovative policy anomaly analysis approach for Web access control policies. We focus on XACML (eXtensible Access Control Markup Language) policy since XACML has become the de facto standard for specifying and enforcing access control policies for various Web-based applications and services. We introduce a policy-based segmentation technique to accurately identify policy anomalies and derive effective anomaly resolutions. We also discuss a proof-of-concept implementation of our method called XAnalyzer and demonstrate how efficiently our approach can discover and resolve policy anomalies.


title: The Impact of Organizational Culture on Information Technology Practices and Performance

abstract: Researchers and practitioners acknowledge that organizational culture plays a vital role in information technology (IT) implementation and influences organizational performance. However, there has been little research in this area. We aim to address this gap in the information systems (IS) literature. Using Schein’s conceptualization of organizational culture, we analyze culture on three levels: underlying assumptions, espoused beliefs and values, and artifacts. Underlying assumptions refer to the unconscious and taken-for-granted beliefs, perceptions, thoughts and feelings that members of the organization share. Espoused beliefs and values are the espoused justifications including strategies, goals, and philosophies. Artifacts are the visible organizational structures and processes. We propose a research model that relates the culture of innovation and espoused values to IT practices and performance. Using a survey method, we plan to test the relationships among these constructs. We believe this study presents a significant step toward a better understanding of embedded values and assumptions that influence IT practices and performance.


title: 84 Review TRENDS in Cognitive Sciences Vol.7 No.2 February 2003 Grounding conceptual knowledge in modality-specific systems

abstract: The human conceptual system contains knowledge that supports all cognitive activities, including perception, memory, language and thought. According to most current theories, states in modality-specific systems for perception, action and emotion do not represent knowledge ‐ rather, redescriptions of these states in amodal representational languages do. Increasingly, however, researchers report that re-enactments of states in modality-specific systems underlie conceptual processing. In behavioral experiments, perceptual and motor variables consistently produce effects in conceptual tasks. In brain imaging experiments, conceptual processing consistently activates modality-specific brain areas. Theoretical research shows how modality-specific re-enactments could produce basic conceptual functions, such as the type‐token distinction, categorical inference, productivity, propositions and abstract concepts. Together these empirical results and theoretical analyses implicate modality-specific systems in the representation and use of conceptual knowledge. The human conceptual system contains people’s knowledge of the world. In most theories, the basic unit of knowledge is the concept. This construct is highly contentious, however (see Box 1). Following psychological


title: Classification and tracking of dynamic objects with multiple sensors for autonomous driving in urban environments

abstract: Future driver assistance systems are likely to use a multisensor approach with heterogeneous sensors for tracking dynamic objects around the vehicle. The quality and type of data available for a data fusion algorithm depends heavily on the sensors detecting an object. This article presents a general framework which allows the use sensor specific advantages while abstracting the specific details of a sensor. Different tracking models are used depending on the current set of sensors detecting the object. A sensor independent algorithm for classifying objects regarding their current and past movement state is presented. The described architecture and algorithms have been successfully implemented in Tartan racingpsilas autonomous vehicle for the urban grand challenge. Results are presented and discussed.


title: Vulnerability to climate change in the Arctic:

abstract: Abstract This paper develops a vulnerability-based approach to characterize the human implications of climate change in Arctic Bay, Canada. It focuses on community vulnerabilities associated with resource harvesting and the processes through which people adapt to them in the context of livelihood assets, constraints, and outside influences. Inuit in Arctic Bay have demonstrated significant adaptability in the face of changing climate-related exposures. This adaptability is facilitated by traditional Inuit knowledge, strong social networks, flexibility in seasonal hunting cycles, some modern technologies, and economic support. Changing Inuit livelihoods, however, have undermined certain aspects of adaptive capacity, and have resulted in emerging vulnerabilities in certain sections of the community.


title: Economic Disadvantage and Young Children’s Emotional and Behavioral Problems: Mechanisms of Risk

abstract: This study aimed to establish potential mechanisms through which economic disadvantage contributes to the development of young children’s internalizing and externalizing problems. Prospective data from fetal life to age 3 years were collected in a total of 2,169 families participating in the Generation R Study. The observed physical home environment, the provision of learning materials in the home, maternal depressive symptoms, parenting stress, and harsh disciplining practices were all analyzed as potential mediators of the association between economic disadvantage and children’s internalizing and externalizing problem scores. Findings from structural equation modeling showed that for both internalizing and externalizing problems, the mechanisms underlying the effect of economic disadvantage included maternal depressive symptoms, along with parenting stress and harsh disciplining. For internalizing but not for externalizing problem scores, the lack of provision of learning materials in the home was an additional mechanism explaining the effect of economic disadvantage. The current results suggest that interventions that focus solely on raising income levels may not adequately address problems in the family processes that emerge as a result of economic disadvantage. Policies to improve the mental health of mothers with young children but also their home environments are needed to change the economic gradient in child behavior.


title: New algorithms for skewing correction and slant removal on word-level [OCR]

abstract: The word preprocessing is a crucial stage of OCR systems. Here we present two algorithms appropriate for this stage. The first one corrects the skewing of words and the second removes the slant from handwritten words. Both algorithms make use of the Wigner-Ville distribution and the projection profile technique. The algorithms have been tested on words taken from more than 200 writers and the results obtained can be considered very satisfactory, since the overall accuracy of our OCR system is notably improved.


title: Reconciling movement and exercise with pain neuroscience education: A case for consistent education

abstract: ABSTRACTThis article will introduce a conceptual framework of kinesthetic education that is consistent with and reinforces pain neuroscience education. This article will also provide some specific guidance for integrating pain neuroscience education with exercise and movement in a more congruent manner. Our belief is that this will enhance the effectiveness of specific movement approaches such as graded exposure techniques. Over the past decade, a new paradigm of pain education has been explored in an effort to improve patient outcomes. Using advances in pain neuroscience, patients are educated in the biological and physiological processes involved in their pain experience. Growing evidence supports the ability of pain neuroscience education (PNE) to positively impact a person’s pain ratings, disability, pain catastrophization, and movement limitations. What is often overlooked, however, is the consistency between the messages of PNE and those of other therapeutic interventions, including movement therapi...


Random Early Detection Gateways for Congestion Avoidance


title: An Eight-Way Power Combiner Based on a Transition Between Rectangular Waveguide and Multiple Microstrip Lines

abstract: This paper presents a broadband and low-loss power combiner based on a transition between eight microstrip lines and a rectangular waveguide. A tray-type structure is utilized in this design and all the trays are stacked up together along the narrow wall of the rectangular waveguide to take advantage of its uniform electric field distribution. The combiner has a wide bandwidth of 5.8-8.2 GHz, which is only limited by the operating frequency range of the WR-137 rectangular waveguide. The measured output return loss is better than 22 dB over the whole frequency range. The measured insertion loss of a back-to-back configuration is less than 0.78 dB, which is equivalent to a combining efficiency of 91.4% for a single combiner. The measured isolations between input ports are better than 12 dB. With eight microstrip input ports where three-terminal solid-state power amplifier devices are integrated and with a rectangular waveguide output port, the proposed structure is very suitable for high power-combining applications.


title: Automatic data-driven real-time segmentation and recognition of surgical workflow

abstract: Purpose ::: With the intention of extending the perception and action of surgical staff inside the operating room, the medical community has expressed a growing interest towards context-aware systems. Requiring an accurate identification of the surgical workflow, such systems make use of data from a diverse set of available sensors. In this paper, we propose a fully data-driven and real-time method for segmentation and recognition of surgical phases using a combination of video data and instrument usage signals, exploiting no prior knowledge. We also introduce new validation metrics for assessment of workflow detection.


title: Densest Subgraph in Dynamic Graph Streams

abstract: In this paper, we consider the problem of approximating the densest subgraph in the dynamic graph stream model. In this model of computation, the input graph is defined by an arbitrary sequence of edge insertions and deletions and the goal is to analyze properties of the resulting graph given memory that is sub-linear in the size of the stream. We present a single-pass algorithm that returns a $(1+\epsilon)$ approximation of the maximum density with high probability; the algorithm uses $O(\epsilon^{-2} n \polylog n)$ space, processes each stream update in $\polylog (n)$ time, and uses $\poly(n)$ post-processing time where $n$ is the number of nodes. The space used by our algorithm matches the lower bound of Bahmani et al.~(PVLDB 2012) up to a poly-logarithmic factor for constant $\epsilon$. The best existing results for this problem were established recently by Bhattacharya et al.~(STOC 2015). They presented a $(2+\epsilon)$ approximation algorithm using similar space and another algorithm that both processed each update and maintained a $(4+\epsilon)$ approximation of the current maximum density in $\polylog (n)$ time per-update.


title: Recent Advances in Active Noise Control Inside Automobile Cabins: Toward quieter cars

abstract: Minimization of interior cabin noise has been a key topic of research in the automobile industry for the last two decades. This problem was initially approached via passive noise cancelation methods, where physical treatments such as structural damping and acoustic absorption were used. However, with vehicle manufacturers striving for more economical and lightweight designs, the resulting car interiors invariably became noisier due to the increased structural vibrations. These noise fields are generally dominanted by low frequencies (i.e., 0-500 Hz) [1], [2], hence, the conventional passive noise cancelation approaches are less effective. In an attempt to resolve the aforementioned problem, active noise control (ANC) methods were developed where secondary sources were proposed to attenuate the noise inside the cabin. With modern in-car entertainment systems providing four to six built-in loudspeakers, the addition of an ANC system comes at a relatively low additional cost.


title: Research on leg-wheel hybrid stair-climbing robot, Zero Carrier

abstract: It is greatly demanded that to develop a kind of stably stair climbing mobile vehicle to assist the physically handicapped in moving outdoors. In this paper, we first propose a novel leg-wheel hybrid stair-climbing vehicle, "Zero Carrier", which consists of eight unified prismatic-joint legs, four of which attached active wheels and other four attached passive casters. Zero Carrier can be designed lightweight, compact, powerful, together with its significant stability on stair climbing motion, since its mechanism is mostly concentrated in its eight simplified legs. We discuss the leg mechanism and control method of the first trial model, Zero Carrier I, and verify its performance based on the experiments of stair climbing and moving over obstacles performed by Zero Carrier I


title: Design of an Intelligent Management System for Agricultural Greenhouses Based on the Internet of Things

abstract: China is a large agricultural country with the largest population in the world. This creates a high demand for food, which is prompting the study of high quality and high-yielding crops. China's current agricultural production is sufficient to feed the nation; however, compared with developed countries agricultural farming is still lagging behind, mainly due to the fact that the system of growing agricultural crops is not based on maximizing output, the latter would include scientific sowing, irrigation and fertilization. In the past few years many seasonal fruits have been offered for sale in markets, but these crops are grown in traditional backward agricultural greenhouses and large scale changes are needed to modernize production. The reform of small-scale greenhouse agricultural production is relatively easy and could be implemented. The concept of the Agricultural Internet of Things utilizes networking technology in agricultural production, the hardware part of this agricultural IoT include temperature, humidity and light sensors and processors with a large data processing capability; these hardware devices are connected by short-distance wireless communication technology, such as Bluetooth, WIFI or Zigbee. In fact, Zigbee technology, because of its convenient networking and low power consumption, is widely used in the agricultural internet. The sensor network is combined with well-established web technology, in the form of a wireless sensor network, to remotely control and monitor data from the sensors.In this paper a smart system of greenhouse management based on the Internet of Things is proposed using sensor networks and web-based technologies. The system consists of sensor networks and asoftware control system. The sensor network consists of the master control center and various sensors using Zigbee protocols. The hardware control center communicates with a middleware system via serial network interface converters. The middleware communicates with a hardware network using an underlying interface and it also communicates with a web system using an upper interface. The top web system provides users with an interface to view and manage the hardware facilities ; administrators can thus view the status of agricultural greenhouses and issue commands to the sensors through this system in order to remotely manage the temperature, humidity and irrigation in the greenhouses. The main topics covered in this paper are:1. To research the current development of new technologies applicable to agriculture and summarizes the strong points concerning the application of the Agricultural Internet of Things both at home and abroad. Also proposed are some new methods of agricultural greenhouse management.2. An analysis of system requirements, the users’ expectations of the system and the response to needs analysis, and the overall design of the system to determine it’s architecture.3. Using software engineering to ensure that functional modules of the system, as far as possible, meet the requirements of high cohesion and low coupling between modules, also detailed design and implementation of each module is considered.


title: A survey of indoor positioning systems for wireless personal networks

abstract: Recently, indoor positioning systems (IPSs) have been designed to provide location information of persons and devices. The position information enables location-based protocols for user applications. Personal networks (PNs) are designed to meet the users' needs and interconnect users' devices equipped with different communications technologies in various places to form one network. Location-aware services need to be developed in PNs to offer flexible and adaptive personal services and improve the quality of lives. This paper gives a comprehensive survey of numerous IPSs, which include both commercial products and research-oriented solutions. Evaluation criteria are proposed for assessing these systems, namely security and privacy, cost, performance, robustness, complexity, user preferences, commercial availability, and limitations.We compare the existing IPSs and outline the trade-offs among these systems from the viewpoint of a user in a PN.


title: Data Mining and Its Applications for Knowledge Management: A Literature Review from 2007 to 2012

abstract: Data mining is one of the most important steps of the knowledge discovery in databases process and is considered as significant subfield in knowledge management. Research in data mining continues growing in business and in learning organization over coming decades. This review paper explores the applications of data mining techniques which have been developed to support knowledge management process. The journal articles indexed in ScienceDirect Database from 2007 to 2012 are analyzed and classified. The discussion on the findings is divided into 4 topics: (i) knowledge resource; (ii) knowledge types and/or knowledge datasets; (iii) data mining tasks; and (iv) data mining techniques and applications used in knowledge management. The article first briefly describes the definition of data mining and data mining functionality. Then the knowledge management rationale and major knowledge management tools integrated in knowledge management cycle are described. Finally, the applications of data mining techniques in the process of knowledge management are summarized and discussed.


title: Stimuli-responsive nanocarriers for drug delivery

abstract: Nanoscale materials that deliver drugs in response to specific stimuli offer enhanced control of the drugs' release profile and distribution. This Review provides a comprehensive discussion of progress during the past five years in the design of nanoscale systems that can respond to exogenous stimuli such as temperature or variations in light or magnetic-field intensities, or to endogenous stimuli such as redox gradients or changes in pH or enzyme concentration.


Improving Mispronunciation Detection for Non-Native Learners with Multisource Information and LSTM-Based Deep Models


title: SEISMIC: A Self-Exciting Point Process Model for Predicting Tweet Popularity

abstract: Social networking websites allow users to create and share content. Big information cascades of post resharing can form as users of these sites reshare others' posts with their friends and followers. One of the central challenges in understanding such cascading behaviors is in forecasting information outbreaks, where a single post becomes widely popular by being reshared by many users. In this paper, we focus on predicting the final number of reshares of a given post. We build on the theory of self-exciting point processes to develop a statistical model that allows us to make accurate predictions. Our model requires no training or expensive feature engineering. It results in a simple and efficiently computable formula that allows us to answer questions, in real-time, such as: Given a post's resharing history so far, what is our current estimate of its final number of reshares? Is the post resharing cascade past the initial stage of explosive growth? And, which posts will be the most reshared in the future? We validate our model using one month of complete Twitter data and demonstrate a strong improvement in predictive accuracy over existing approaches. Our model gives only 15% relative error in predicting final size of an average information cascade after observing it for just one hour.


title: Psychology and neurobiology of simple decisions

abstract: Patterns of neural firing linked to eye movement decisions show that behavioral decisions are predicted by the differential firing rates of cells coding selected and nonselected stimulus alternatives. These results can be interpreted using models developed in mathematical psychology to model behavioral decisions. Current models assume that decisions are made by accumulating noisy stimulus information until sufficient information for a response is obtained. Here, the models, and the techniques used to test them against response-time distribution and accuracy data, are described. Such models provide a quantitative link between the time-course of behavioral decisions and the growth of stimulus information in neural firing data.


title: Exposure to Mozart music reduces cognitive impairment in pilocarpine-induced status epilepticus rats

abstract: Patients with temporal lobe epilepsy (TLE) often display cognitive deficits. However, current epilepsy therapeutic interventions mainly aim at how to reduce the frequency and degree of epileptic seizures. Recovery of cognitive impairment is not attended enough, resulting in the lack of effective approaches in this respect. In the pilocarpine-induced temporal lobe epilepsy rat model, memory impairment has been classically reported. Here we evaluated spatial cognition changes at different epileptogenesis stages in rats of this model and explored the effects of long-term Mozart music exposure on the recovery of cognitive ability. Our results showed that pilocarpine rats suffered persisting cognitive impairment during epileptogenesis. Interestingly, we found that Mozart music exposure can significantly enhance cognitive ability in epileptic rats, and music intervention may be more effective for improving cognitive function during the early stages after Status epilepticus. These findings strongly suggest that Mozart music may help to promote the recovery of cognitive damage due to seizure activities, which provides a novel intervention strategy to diminish cognitive deficits in TLE patients.


title: Improving memory in old age through implicit self-stereotyping.

abstract: This research demonstrates that subliminally activated stereotypes can alter judgments about oneself and can change cognitive performance. In the first study, an intervention that activated positive stereotypes of aging without the participants' awareness tended to improve memory performance, memory self-efficacy, and views of aging in old individuals; in contrast, an intervention that activated negative stereotypes of aging tended to worsen memory performance, memory self-efficacy, and views of aging in old participants. A second study demonstrated that for the strong effects to emerge from the shifting stereotypes, the stereotypes must be important to one's self-image: Young individuals randomly assigned to the same conditions as the old participants in the first study did not exhibit any of the significant interactions that emerged among the old participants. This research highlights the potential for memory improvement in old individuals when the negative stereotypes of aging that dominate the American culture are shifted to more positive stereotypes.


title: Extracting news content with visual unit of web pages

abstract: The Document Object Model (DOM) provides a tree structure called DOM tree for representing with objects in HTML. Many researchers have considered using leaf nodes of DOM tree as basic objects in extracting information from web pages. However, web pages are more of information blocks which each have a consistent visual format rather than individual DOM tree nodes. And those information blocks do not necessarily have a direct map to DOM tree nodes. In this paper, we propose a visual oriented extraction method that extracts news content by visual unit (vu, for short). Visual units are identified by a top-down approach based on visual features and text features. After that, page content is extracted according to domain characteristic. In experiments, the proposed approach achieves 94.86% accuracy over 700 news web pages from 7 different news sites. The result demonstrates that our method represents a promising approach for news content extraction with visual units and domain characteristic.