title: Generation of electrical power using gymnasium bicycle

abstract: Now a days the basic need of human life is power and it can manipulate major developments any country. Bangladesh is a developing country but limited resources for generation of electricity is one of the major reason behind that. In Bangladesh most of the energy is generated using conventional energy sources and they are diminishing day by day. On the other hand conventional energy sources are responsible for pollution and global warming. That's why we need non-conventional energy source to develop power generating which are eco-friendly, clean and sustainable. This is why our research introduces a system to produce renewable energy from human wastage energy. Our proposed mechanism harvest electricity using the pressure energy from the movement of human through gymnasium bicycle. It is an Electro-Mechanical system. This proposed system shows the conversation of kinetic energy to electrical energy through alternator and tap it using battery. This paper also presents the efficiency of our system. We hope that our energy converting system (mechanical to electrical) is contain low losses to produce electricity. Actually we are trying to presents such a mechanism through which the electricity problem of Bangladesh will be reduced significantly.


title: A case study of open source software development: the Apache server

abstract: According to its proponents, open source style software development has the capacity to compete successfully, and perhaps in many cases displace, traditional commercial development methods. In order to begin investigating such claims, we examine the development process of a major open source application, the Apache web server. By using email archives of source code change history and problem reports we quantify aspects of developer participation, core team size, code ownership, productivity, defect density, and problem resolution interval for this OSS project. This analysis reveals a unique process, which performs well on important measures. We conclude that hybrid forms of development that borrow the most effective techniques from both the OSS and commercial worlds may lead to high performance software processes.


title: On the quality assessment of enhanced images: A database, analysis, and strategies for augmenting existing methods

abstract: Most methods of image quality assessment (QA) have been designed for QA of degraded images. This paper presents the results of a study designed to investigate whether existing QA methods can be adapted to succeed on enhanced images. We developed a database containing digitally enhanced images and associated subjective quality ratings. Next, we analyzed the efficacy of select QA methods and their reverse-mode versions in predicting the ratings. Given the fact that an enhanced image makes the original image appear degraded, we tested both normal and reverse-mode versions, where the latter were implemented by specifying the enhanced image as the reference and the original image as the “degraded” image. Our results demonstrate that this reverse-mode approach improves QA of enhanced images. We present a strategy for further improving the QA methods by using measures of contrast, sharpness, and color saturation.


title: Patch-Based Image Warping for Content-Aware Retargeting

abstract: Image retargeting is the process of adapting images to fit displays with various aspect ratios and sizes. Most studies on image retargeting focus on shape preservation, but they do not fully consider the preservation of structure lines, which are sensitive to human visual system. In this paper, a patch-based retargeting scheme with an extended significance measurement is introduced to preserve shapes of both visually salient objects and structure lines while minimizing visual distortions. In the proposed scheme, a similarity transformation constraint is used to force visually salient content to undergo as-rigid-as-possible deformation, while an optimization process is performed to smoothly propagate distortions. These processes enable our approach to yield pleasing content-aware warping and retargeting. Experimental results and a user study show that our results are better than those generated by state-of-the-art approaches.


title: Control and Operation of a DC Microgrid With Variable Generation and Energy Storage

abstract: Control and operation of a dc microgrid, which can be operated at grid connected or island modes, are investigated in this paper. The dc microgrid consists of a wind turbine, a battery energy storage system, dc loads, and a grid-connected converter system. When the system is grid connected, active power is balanced through the grid supply during normal operation to ensure a constant dc voltage. Automatic power balancing during a grid ac fault is achieved by coordinating the battery energy storage system and the grid converter. To ensure that the system can operate under island conditions, a coordinated strategy for the battery system, wind turbine, and load management, including load shedding, are proposed. PSCAD/EMTDC simulations are presented to demonstrate the robust operation performance and to validate the proposed control system during various operating conditions, such as variations of wind power generation and load, grid ac faults, and islanding.


title: Spatio-Temporal Matching for Human Pose Estimation in Video

abstract: Detection and tracking humans in videos have been long-standing problems in computer vision. Most successful approaches (e.g., deformable parts models) heavily rely on discriminative models to build appearance detectors for body joints and generative models to constrain possible body configurations (e.g., trees). While these $2$ D models have been successfully applied to images (and with less success to videos), a major challenge is to generalize these models to cope with camera views. In order to achieve view-invariance, these $2$ D models typically require a large amount of training data across views that is difficult to gather and time-consuming to label. Unlike existing $2$ D models, this paper formulates the problem of human detection in videos as spatio-temporal matching (STM) between a $3$ D motion capture model and trajectories in videos. Our algorithm estimates the camera view and selects a subset of tracked trajectories that matches the motion of the $3$ D model. The STM is efficiently solved with linear programming, and it is robust to tracking mismatches, occlusions and outliers. To the best of our knowledge this is the first paper that solves the correspondence between video and $3$ D motion capture data for human pose detection. Experiments on the CMU motion capture, Human3.6M, Berkeley MHAD and CMU MAD databases illustrate the benefits of our method over state-of-the-art approaches.


title: Bebras - a Sustainable Community Building Model for the Concept Based Learning of Informatics and Computational Thinking

abstract: As an international informatics contest, or challenge, Bebras has started the second decade of its existence. The contest attracts more and more countries every year, recently there have been over 40 participating countries. From a single contest-focused annual event Bebras developed to a multifunctional challenge and an activities-based educational community building model. This paper aims to introduce the Bebras model using ten years of observations in implementing the contest in different countries. The model is essentially based on democratic and inclusive education values. Systematic literature review of research papers concerning Bebras activities has made an integral background for this model. The model is represented both at international and national levels and consists of several components where the development of Bebras tasks has taken a very significant role. Reasoning on innovated learning informatics and strengthening computational thinking by utilising carefully selected informatics concepts is discussed as well.


title: Evaluating Robustness of Neural Networks with Mixed Integer Programming

abstract: Neural networks have demonstrated considerable ::: success on a wide variety of real-world problems. However, neural ::: networks can be fooled by adversarial examples – slightly ::: perturbed inputs that are misclassified with high confidence. ::: Verification of networks enables us to gauge their vulnerability to ::: such adversarial examples. We formulate verification of ::: piecewise-linear neural networks as a mixed integer program. Our ::: verifier finds minimum adversarial distortions two to three orders ::: of magnitude more quickly than the state-of-the-art. We achieve ::: this via tight formulations for non-linearities, as well as a novel ::: presolve algorithm that makes full use of all information ::: available. The computational speedup enables us to verify ::: properties on convolutional networks with an order of magnitude ::: more ReLUs than had been previously verified by any complete ::: verifier, and we determine for the first time the exact adversarial ::: accuracy of an MNIST classifier to perturbations with bounded ::: l[infinity] norm e = 0:1. On this network, we find an adversarial ::: example for 4.38% of samples, and a certificate of robustness for ::: the remainder. Across a variety of robust training procedures, we ::: are able to certify more samples than the state-of-the-art and find ::: more adversarial examples than a strong first-order attack for ::: every network.


14 BODILY EXPRESSION FOR AUTOMATIC AFFECT RECOGNITION


title: Tunable combline filter with continuous control of center frequency and bandwidth

abstract: A new combline filter structure with a continuous tunability for both the center frequency and bandwidth is presented in this paper. The passband-width tunability is achieved by placing variable coupling reducers between the filter resonators. The coupling reducers, operating as bandwidth control subnetworks, are designed as detuned resonators made up of a line segment ending in a variable capacitor. The proposed filter structure is experimentally validated with the design, construction in suspended stripline technology, and characterization of a low-cost filter prototype for terrestrial digital video broadcasting receivers operating in the UHF band (470-862 MHz). Other relevant factors, such as the intermodulation distortion produced by the varactors used to control the bandwidth electronically or the power-handling performance of the constructed filter, are also discussed. The reconfigurable filter module described in this paper is very suitable for the design of flexible multifunction receiver subsystems simultaneously supporting signals with a different bandwidth.


title: Current Issues in the Diagnosis of Attention Deficit Hyperactivity Disorder, Oppositional Defiant Disorder, and Conduct Disorder

abstract: This review evaluates the diagnostic criteria for three of the most common disorders for which children and adolescents are referred for mental health treatment: attention deficit hyperactivity disorder (ADHD), oppositional defiant disorder (ODD), and conduct disorder (CD). Although research supports the validity and clinical utility of these disorders, several issues are highlighted that could enhance the current diagnostic criteria. For ADHD, defining the core features of the disorder and its fit with other disorders, enhancing the validity of the criteria through the lifespan, considering alternative ways to form subtypes of the disorder, and modifying the age-of-onset criterion are discussed relative to the current diagnostic criteria. For ODD, eliminating the exclusionary criteria of CD, recognizing important symptom domains within the disorder, and using the cross-situational pervasiveness of the disorder as an index of severity are highlighted as important issues for improving classification. Finally, for CD, enhancing the current subtypes related to age of onset and integrating callous-unemotional traits into the diagnostic criteria are identified as key issues for improving classification.


A study of primary sources and Special Education.


title: MaxProp: Routing for Vehicle-Based Disruption-Tolerant Networks

abstract: Disruption-tolerant networks (DTNs) attempt to route network messages via intermittently connected nodes. Routing in such environments is difficult because peers have little information about the state of the partitioned network and transfer opportunities between peers are of limited duration. In this paper, we propose MaxProp, a protocol for effective routing of DTN messages. MaxProp is based on prioritizing both the schedule of packets transmitted to other peers and the schedule of packets to be dropped. These priorities are based on the path likelihoods to peers according to historical data and also on several complementary mechanisms, including acknowledgments, a head-start for new packets, and lists of previous intermediaries. Our evaluations show that MaxProp performs better than protocols that have access to an oracle that knows the schedule of meetings between peers. Our evaluations are based on 60 days of traces from a real DTN network we have deployed on 30 buses. Our network, called UMassDieselNet, serves a large geographic area between five colleges. We also evaluate MaxProp on simulated topologies and show it performs well in a wide variety of DTN environments.


title: Shield Synthesis: Runtime Enforcement for Reactive Systems

abstract: Scalability issues may prevent users from verifying critical properties of a complex hardware design. In this situation, we propose to synthesize a"safety shield"that is attached to the design to enforce the properties at run time. Shield synthesis can succeed where model checking and reactive synthesis fail, because it only considers a small set of critical properties, as opposed to the complex design, or the complete specification in the case of reactive synthesis. The shield continuously monitors the input/output of the design and corrects its erroneous output only if necessary, and as little as possible, so other non-critical properties are likely to be retained. Although runtime enforcement has been studied in other domains such as action systems, reactive systems pose unique challenges where the shield must act without delay. We thus present the first shield synthesis solution for reactive hardware systems and report our experimental results. This is an extended version of [5], featuring an additional appendix.


Smart Wheelchairs -- State of the Art in an Emerging Market


Event Detection and Semantic Storytelling: Generating a Travelogue from a large Collection of Personal Letters.


title: Auto-recognizing DBMS Workload Based on C5.0 Algorithm

abstract: The type of the workload is one of the key factors on database management system (DBMS) tuning. Different types of workload (OLTP, Online Transaction Processing and OLAP, Online Analytical Processing) mean different resource allocation strategies. In this paper, we present an approach to automatically identify a DBMS workload as either OLTP or OLAP. We use C5.0 algorithm to construct a set of classifiers based on the characteristics that differentiate OLTP and OLAP and then use the classifier to identify the workload type. The experiments show that the classifiers can be able to accurately identify the OLTP and OLAP workloads.


title: Social software and participatory learning: Pedagogical choices with technology affordances in the Web 2.0 era

abstract: School of EducationCharles Sturt UniversityThe two-way Web has arrived, accompanied by a raft of affordances that expand how weteach, communicate, learn and create knowledge. New trends are emerging in the wayinformation is distributed and consumed. Emerging “Web 2.0” services such as blogs,wikis and social bookmarking applications, as well as social networking sites like


title: SerialRank: Spectral Ranking using Seriation

abstract: We describe a seriation algorithm for ranking a set of n items given pairwise comparisons between these items. Intuitively, the algorithm assigns similar rankings to items that compare similarly with all others. It does so by constructing a similarity matrix from pairwise comparisons, using seriation methods to reorder this matrix and construct a ranking. We first show that this spectral seriation algorithm recovers the true ranking when all pairwise comparisons are observed and consistent with a total order. We then show that ranking reconstruction is still exact even when some pairwise comparisons are corrupted or missing, and that seriation based spectral ranking is more robust to noise than other scoring methods. An additional benefit of the seriation formulation is that it allows us to solve semi-supervised ranking problems. Experiments on both synthetic and real datasets demonstrate that seriation based spectral ranking achieves competitive and in some cases superior performance compared to classical ranking methods.


title: Secure Proximity Detection for NFC Devices Based on Ambient Sensor Data

abstract: In certain applications, it is important for a remote server to securely determine whether or not two mobile devices are in close physical proximity. In particular, in the context of an NFC transaction, the bank server can validate the transaction if both the NFC phone and reader are precisely at the same location thereby preventing a form of a devastating relay attack against such systems.


title: Multisensor Fusion and Integration: A Review on Approaches and Its Applications in Mechatronics

abstract: The objective of this paper is to review the theories and approaches of multisensor fusion and integration (MFI) with its application in mechatronics. MFI helps the system perceiving changes of the environment and monitoring the system itself. Since each individual sensor has its own inherent defects and limitations, MFI merges the redundant information acquired by multiple sensors synergistically to provide a more accurate perception and make an optimal decision in further. The wide application spectrum of MFI in mechatronics includes the industrial automation, the development of intelligent robots, military applications, biomedical applications, etc. In this paper, the architecture and algorithms of MFI are reviewed, and some implementation examples in industrial automation and robotic applications are presented. Furthermore, sensor fusion methods at different levels, namely, estimation methods, classification methods and inference methods, the most frequently used algorithms in previous researches with their advantages and limitations are summarized. Applications of MFI in robotics and mechatronics are discussed. Future perspectives of MFI deployment are included in the concluding remarks.


title: Reflectance Capture Using Univariate Sampling of BRDFs

abstract: We propose the use of a light-weight setup consisting of a collocated camera and light source – commonly found on mobile devices – to reconstruct surface normals and spatially-varying BRDFs of near-planar material samples. A collocated setup provides only a 1-D “univariate” sampling of a 3-D isotropic BRDF. We show that a univariate sampling is sufficient to estimate parameters of commonly used analytical BRDF models. Subsequently, we use a dictionary-based reflectance prior to derive a robust technique for per-pixel normal and BRDF estimation. We demonstrate real-world shape and capture, and its application to material editing and classification, using real data acquired using a mobile phone.


Light scattering calculations for the nephelometer experiment on the 1981


title: A UWB transmit-only based scheme for multi-tag support in a millimeter accuracy localization system

abstract: Ultra Wideband (UWB) wireless positioning systems have many advantages for tracking and locating items in indoor environments. Surgical navigation and industrial process control are potential applications for high accuracy UWB localization systems with millimeter accuracy. Several experimental systems have achieved very high accuracy, but have generally neglected significant system features like multi-tag access. This paper outlines a UWB localization system, which addresses both multi-tag performance and localization accuracy using UWB transmit-only digital communication and time difference of arrival (TDOA). The scheme takes advantage of a digital sampling circuit that can be used for both sub-sampling and data reception. Preliminary 1-D experimental results using this system resulted in a total system update rate of 889 Hz and localization accuracy of 3.25mm.


title: Highly scalable algorithms for rectilinear and octilinear Steiner trees

abstract: The rectilinear Steiner minimum tree (RSMT) problem, which asks for a minimum-length interconnection of a given set of terminals in the rectilinear plane, is one of the fundamental problems in electronic design automation. Recently there has been renewed interest in this problem due to the need for highly scalable algorithms able to handle nets with tens of thousands of terminals. In this paper we give a practical O(n log/sup 2/ n) heuristic for computing near-optimal rectilinear Steiner trees based on a hatched version of the greedy triple contraction algorithm of Zelikovsky (Algorithmica vol. 9, pp. 463-470, 1993). Experiments conducted on both random and industry test cases show that our heuristic matches or exceeds the quality of best known RSMT heuristics, e.g. on random instances with more than 100 terminals our heuristic improves over the rectilinear minimum spanning tree by an average of 11%. Moreover, our heuristic has very good runtime scaling, e.g. it can route a 34k-terminals net extracted from a real design in less than 25 seconds compared to over 86 minutes needed by the O(n/sup 2/) edge-based heuristic of Borah, Owens, and Irwin (Discrete Appl. Math. vol. 90, pp. 51-67, 1999). Since our heuristic is graph-based, it can be easily modified to handle practical considerations such as routing obstacles, preferred directions, via costs, and octilinear routing - indeed, experimental results show only a small factor increase in runtime when switching from rectilinear to octilinear routing.


title: A rule-based servicescape design support system from the design patterns of theme parks

abstract: Servicescape design of leisure spaces such as theme parks influence the level of visitor satisfaction and the re-visitation rate. However, only a few large theme park operators have a separate theme of specialists with heuristic knowledge for servicescaping. In response, small companies turn to the specialists paying high fees and royalties usually leading to outdated servicescape designs. In order to help small companies to design servicescape that provide satisfying experience to the visitors, a systematical understanding of the multi-layered and complex service environment of theme parks is necessary. The purpose of this research is to integrate the concept of precedent-based design into a rule-based system by identifying and organizing the design patterns of servicescape into reusable knowledge, propose a servicescape design support system, and confirm whether if the system helps to increase designers credence regarding their design in terms of its market success. To accomplish these research aims, six steps were carried out: (1) selection of globally accepted facilities for being successful, (2) analysis of precedents for the discovery of repeating servicescape design patterns, (3) knowledge acquisition and organization for each design patterns, (4) formulation of servicescape rules using the acquired and organized knowledge, (5) integration of the rules into a general design process of a facility as a system, and (6) application of the system in practice. The resulting system was presented to an expert in the field of theme park designs to confirm whether the design process is applicable and how it will perform in practice. From this interview, we received positive feedbacks and well as feedbacks for improvements in the aspects of the systems applicability in practice, its functionality, and usability. After confirming the applicability of the overall process of the system, we conducted an experiment to experts in practice from various design and planning fields and asked to go through two sets of scenarios in which the first is to design a given site without the system and the second is to design the same site with the system. After each scenario, the subjects were asked to fill out a revised After-Scenario Questionnaire (ASQ). From this experiment, the results indicate that the subjects felt an increase in their credence toward their designs in terms of its market success while the satisfaction with the easiness and the time it took decreased. If our system is revised in the aspects of usability and functionality, our system which uses precedent design patterns could be used to help small company designers to produce servicescape concept designs with more design credence.


Multi-Task Learning and Weighted Cross-Entropy for DNN-Based Keyword Spotting


title: A soft computing-based approach for integrated training and rule extraction from artificial neural networks: DIFACONN-miner

abstract: Artificial neural network (ANN) is one of the most widely used techniques in classification data mining. Although ANNs can achieve very high classification accuracies, their explanation capability is very limited. Therefore one of the main challenges in using ANNs in data mining applications is to extract explicit knowledge from them. Based on this motivation, a novel approach is proposed in this paper for generating classification rules from feed forward type ANNs. Although there are several approaches in the literature for classification rule extraction from ANNs, the present approach is fundamentally different from them. In the previous studies, ANN training and rule extraction is generally performed independently in a sequential (hierarchical) manner. However, in the present study, training and rule extraction phases are integrated within a multiple objective evaluation framework for generating accurate classification rules directly. The proposed approach makes use of differential evolution algorithm for training and touring ant colony optimization algorithm for rule extracting. The proposed algorithm is named as DIFACONN-miner. Experimental study on the benchmark data sets and comparisons with some other classical and state-of-the art rule extraction algorithms has shown that the proposed approach has a big potential to discover more accurate and concise classification rules.


title: A hybrid RF and vibration energy harvester for wearable devices

abstract: This paper describes a hybrid radio frequency (RF) and piezoelectric thin film polyvinylidene fluoride (PVDF) vibration energy harvester for wearable devices. By exploiting the impedance characteristics of parasitic capacitances and discrete inductors, the proposed harvester not only scavenges 15 Hz vibration energy but also works as a 915 MHz flexible silver-ink RF dipole antenna. In addition, an interface circuit including a 6-stage Dickson RF-to-DC converter and a diode bridge rectifier to convert the RF and vibration outputs of the hybrid harvester into DC signals to power resistive loads is evaluated. A maximum DC output power of 20.9 μ”, when using the RF to DC converter and −8 dBm input RF power, is achieved at 36 % of the open-circuit output voltage while the DC power harvested from 3 g vibration excitation reaches a maximum of 2.8 μW at 51% of open-circuit voltage. Experimental results show that the tested hybrid harvesting system simultaneously generates 7.3 μW DC power, when the distance from the harvester to a 3 W EIRP 915 MHz transmitter is 5.5 m, and 1.8 μW DC power from a 1.8 g vibration acceleration peak.


title: WebVRGIS: WebGIS Based Interactive Online 3D Virtual Community

abstract: In this paper we present a WebVRGIS based Interactive On line 3D Virtual Community which is achieved based on WebGIS technology and web VR technology. It is Multi-Dimensional(MD) web geographic information system (WebGIS) based 3D interactive on line virtual community which is a virtual real-time 3D communication systems and web systems development platform. It is capable of running on a variety of browsers. In this work, four key issues are studied: (1) Multi-source MD geographical data fusion of the WebGIS, (2) scene combination with 3D avatar, (3) massive data network dispatch, and (4) multi-user avatar real-time interactive. Our system is divided into three modules: data preprocessing, background management and front end user interaction. The core of the front interaction module is packaged in the MD map expression engine 3GWebMapper and the free plug-in network 3D rendering engine WebFlashVR. We have evaluated the robustness of our system on three campus of Ocean University of China(OUC) as a testing base. The results shows high efficiency, easy to use and robustness of our system.


Tagging YouTube - A classification of tagging practice on YouTube


The Motherhood Wage Penalty in America


title: Regularization of neural networks using dropconnect

abstract: We introduce DropConnect, a generalization of Dropout (Hinton et al., 2012), for regularizing large fully-connected layers within neural networks. When training with Dropout, a randomly selected subset of activations are set to zero within each layer. DropConnect instead sets a randomly selected subset of weights within the network to zero. Each unit thus receives input from a random subset of units in the previous layer. We derive a bound on the generalization performance of both Dropout and DropConnect. We then evaluate DropConnect on a range of datasets, comparing to Dropout, and show state-of-the-art results on several image recognition benchmarks by aggregating multiple DropConnect-trained models.


Chapter pages in book: (1- 22) Climate Change Adaptations in Historical Perspective


title: Life "modes" in social media

abstract: Current social media products such as Facebook and Twitter have not sufficiently addressed how to help users organize people and content streams across different areas of their lives. We conducted a qualitative design research study to explore how we might best leverage natural models of social organization to improve experiences of social media. We found that participants organize their social worlds based on life 'modes', i.e., family, work and social. They strategically use communication technologies to manage intimacy levels within these modes, and levels of permeability through the boundaries between these modes. Mobile communication in particular enabled participants to aggregate and share content dynamically across life modes. While exploring problems with managing their social media streams, people showed a strong need for focused sharing - the ability to share content only with appropriate audiences within certain areas of life.


title: Detecting Multipliers of Jihadism on Twitter

abstract: Detecting terrorist related content on social media is a problem for law enforcement agency due to the large amount of information that is available. This work is aiming at detecting tweeps that are involved in media mujahideen - the supporters of jihadist groups who disseminate propaganda content online. To do this we use a machine learning approach where we make use of two sets of features: data dependent features and data independent features. The data dependent features are features that are heavily influenced by the specific dataset while the data independent features are independent of the dataset and can be used on other datasets with similar result. By using this approach we hope that our method can be used as a baseline to classify violent extremist content from different kind of sources since data dependent features from various domains can be added. In our experiments we have used the AdaBoost classifier. The results shows that our approach works very well for classifying English tweeps and English tweets but the approach does not perform as well on Arabic data.


title: Relationship between the retinacula cutis and sagging facial skin

abstract: BACKGROUND ::: Sagging skin is one of the most concerning esthetic issues for elderly individuals. Although reduced skin elasticity has been reported as the cause of sagging skin, a loss of skin elasticity alone is insufficient to explain sagging facial skin. This study investigated the mechanisms underlying sagging skin, with a focus on the subcutaneous network of collagenous fibers known as the retinacula cutis (RC). ::: ::: ::: METHODS ::: To evaluate the structure of the RC noninvasively, tomographic images of the face were obtained using magnetic resonance imaging (MRI). The RC was identified by comparing MRI results with histological specimens of human skin. A descriptive scale was used to evaluate the degree of sagging, and a device equipped with a 6-mm-diameter probe was used to measure the elasticity of deeper skin layers and evaluate the physical properties of the skin. ::: ::: ::: RESULTS ::: The density of RC in subcutaneous tissue correlated negatively with sagging scores and positively with elasticity. ::: ::: ::: CONCLUSION ::: These results imply that a sparse RC structure contributes to a reduction in the elasticity of subcutaneous tissue, resulting in a greater degree of sagging facial skin. These findings are expected to contribute to the understanding of the mechanisms underlying sagging skin.


MODELING THE PHYSICAL CHARACTERISTICS OF NAND FLASH MEMORY


title: Enabling FPGAs in Hyperscale Data Centers

abstract: FPGAs (Field Programmable Gate Arrays) are making their way into data centers (DCs) and are used to offload and accelerate specific services, but they are not yet available to cloud users. This puts the cloud deployment of compute-intensive workloads at a disadvantage compared with on-site infrastructure installations, where the performance and energy efficiency of FPGAs are increasingly being exploited for application-specific accelerators and heterogeneous computing. The cloud is housed in DCs, and DCs are based on ever shrinking servers. Today, we observe the emergence of hyper scale data centers, which are based on densely packaged servers. The shrinking form factor brings the potential to deploy FPGAs on a large scale in such DCs. These FPGAs must be deployed as independent DC resources, and they must be accessible to the cloud users. Therefore, we propose to change the traditional paradigm of the CPU-FPGA interface by decoupling the FPGA from the CPU and connecting the FPGA as a standalone resource to the DC network. This allows cloud vendors to offer an FPGA to users in a similar way as a standard server. As existing infrastructure-as-a-service (IaaS) mechanisms are not suitable, we propose a new Open Stack (open source cloud computing software) service to integrate FPGAs in the cloud. This proposal is complemented by a framework that enables cloud users to combine multiple FPGAs into a programmable fabric. The proposed architecture and framework address the scalability problem that makes it difficult to provision large numbers of FPGAs. Together, they offer a novel solution for processing large and heterogeneous data sets in the cloud.


title: Early initiation of breastfeeding: a systematic literature review of factors and barriers in South Asia

abstract: Early or timely initiation of breastfeeding is crucial in preventing newborn deaths and influences childhood nutrition however remains low in South Asia and the factors and barriers warrant greater consideration for improved action. This review synthesises the evidence on factors and barriers to initiation of breastfeeding within 1 h of birth in South Asia encompassing Afghanistan, Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan and Sri Lanka. Studies published between 1990 and 2013 were systematically reviewed through identification in Academic Search Complete, CINAHL, Global Health, MEDLINE and Scopus databases. Twenty-five studies meeting inclusion criteria were included for review. Structured thematic analysis based on leading frameworks was undertaken to understand factors and barriers. Factors at geographical, socioeconomic, individual, and health-specific levels, such as residence, education, occupation, income, mother’s age and newborn’s gender, and ill health of mother and newborn at delivery, affect early or timely breastfeeding initiation in South Asia. Reported barriers impact through influence on acceptability by traditional feeding practices, priests’ advice, prelacteal feeding and discarding colostrum, mother-in-law’s opinion; availability and accessibility through lack of information, low access to media and health services, and misperception, support and milk insufficiency, involvement of mothers in decision making. Whilst some barriers manifest similarly across the region some factors are context-specific thus tailored interventions are imperative. Initiatives halting factors and directed towards contextual barriers are required for greater impact on newborn survival and improved nutrition in the South Asia region.


title: Novel approaches to crawling important pages early

abstract: Web crawlers are essential to many Web applications, such as Web search engines, Web archives, and Web directories, which maintain Web pages in their local repositories. In this paper, we study the problem of crawl scheduling that biases crawl ordering toward important pages. We propose a set of crawling algorithms for effective and efficient crawl ordering by prioritizing important pages with the well-known PageRank as the importance metric. In order to score URLs, the proposed algorithms utilize various features, including partial link structure, inter-host links, page titles, and topic relevance. We conduct a large-scale experiment using publicly available data sets to examine the effect of each feature on crawl ordering and evaluate the performance of many algorithms. The experimental results verify the efficacy of our schemes. In particular, compared with the representative RankMass crawler, the FPR-title-host algorithm reduces computational overhead by a factor as great as three in running time while improving effectiveness by 5 % in cumulative PageRank.


Dropout as a Bayesian Approximation : Insights and Applications


title: A Hybrid Sensor System for Indoor Air Quality Monitoring

abstract: Indoor air quality is important. It influences human productivity and health. Personal pollution exposure can be measured using stationary or mobile sensor networks, but each of these approaches has drawbacks. Stationary sensor network accuracy suffers because it is difficult to place a sensor in every location people might visit. In mobile sensor networks, accuracy and drift resistance are generally sacrificed for the sake of mobility and economy. We propose a hybrid sensor network architecture, which contains both stationary sensors (for accurate readings and calibration) and mobile sensors (for coverage). Our technique uses indoor pollutant concentration prediction models to determine the structure of the hybrid sensor network. In this work, we have (1) developed a predictive model for pollutant concentration that minimizes prediction error; (2) developed algorithms for hybrid sensor network construction; and (3) deployed a sensor network to gather data on the airflow in a building, which are later used to evaluate the prediction model and hybrid sensor network synthesis algorithm. Our modeling technique reduces sensor network error by 40.4% on average relative to a technique that does not explicitly consider the inaccuracies of individual sensors. Our hybrid sensor network synthesis technique improves personal exposure measurement accuracy by 35.8% on average compared with a stationary sensor network architecture.


title: Collabode: collaborative coding in the browser

abstract: Collaborating programmers should use a development environment designed specifically for collaboration, not the same one designed for solo programmers with a few collaborative processes and tools tacked on. This paper describes Collabode, a web-based Java integrated development environment built to support close, synchronous collaboration between programmers. We discuss three collaboration models in which participants take on distinct roles: micro-outsourcing to combine small contributions from many assistants; test-driven pair programming for effective pairwise development; and a mobile instructor connected to the work of many students. In particular, we report very promising preliminary results using Collabode to support micro-outsourcing.


title: MMR: An algorithm for clustering categorical data using Rough Set Theory

abstract: A variety of cluster analysis techniques exist to group objects having similar characteristics. However, the implementation of many of these techniques is challenging due to the fact that much of the data contained in today's databases is categorical in nature. While there have been recent advances in algorithms for clustering categorical data, some are unable to handle uncertainty in the clustering process while others have stability issues. This research proposes a new algorithm for clustering categorical data, termed Min-Min-Roughness (MMR), based on Rough Set Theory (RST), which has the ability to handle the uncertainty in the clustering process.


title: CQI table design to support 256 QAM in small cell environment

abstract: The mobile data traffic is expected to grow beyond 1000 times by 2020 compared with it in 2010. In order to support 1000 times of capacity increase, improving spectrum efficiency is one of the important approaches. Meanwhile, in Long Term Evolution (LTE)-Advanced, small cell and hotspot are important scenarios for future network deployment to increase the capacity from the network density domain. Under such environment, the probability of high Signal to Interference plus Noise Ratio (SINR) region becomes larger which brings the possibility of introducing higher order modulation, i.e., 256 Quadrature Amplitude Modulation(QAM) to improve the spectrum efficiency. Channel quality indicator (CQI) table design is a key issue to support 256 QAM. In this paper, we investigate the feasibility of 256 QAM by SINR geometry and propose two methods on CQI table design to support the 256 QAM transmission. Simulation results show proposed methods can improve average user equipment (UE) throughput and cell center UE throughput with almost no loss on cell edge UE throughput.


title: Adversarial Learning with Local Coordinate Coding

abstract: Generative adversarial networks (GANs) aim to generate realistic data from some prior distribution (e.g., Gaussian noises). However, such prior distribution is often independent of real data and thus may lose semantic information (e.g., geometric structure or content in images) of data. In practice, the semantic information might be represented by some latent distribution learned from data, which, however, is hard to be used for sampling in GANs. In this paper, rather than sampling from the pre-defined prior distribution, we propose a Local Coordinate Coding (LCC) based sampling method to improve GANs. We derive a generalization bound for LCC based GANs and prove that a small dimensional input is sufficient to achieve good generalization. Extensive experiments on various real-world datasets demonstrate the effectiveness of the proposed method.


title: Spatial big-data challenges intersecting mobility and cloud computing

abstract: Increasingly, location-aware datasets are of a size, variety, and update rate that exceeds the capability of spatial computing technologies. This paper addresses the emerging challenges posed by such datasets, which we call Spatial Big Data (SBD). SBD examples include trajectories of cellphones and GPS devices, vehicle engine measurements, temporally detailed road maps, etc. SBD has the potential to transform society via next-generation routing services such as eco-routing. However, the envisaged SBD-based next-generation routing services pose several significant challenges for current routing techniques. SBD magnifies the impact of partial information and ambiguity of traditional routing queries specified by a start location and an end location. In addition, SBD challenges the assumption that a single algorithm utilizing a specific dataset is appropriate for all situations. The tremendous diversity of SBD sources substantially increases the diversity of solution methods. Newer algorithms may emerge as new SBD becomes available, creating the need for a flexible architecture to rapidly integrate new datasets and associated algorithms.


title: Heart rate variability in athletes.

abstract: This review examines the influence on heart rate variability (HRV) indices in athletes from training status, different types of exercise training, sex and ageing, presented from both cross-sectional and longitudinal studies. The predictability of HRV in over-training, athletic condition and athletic performance is also included. Finally, some recommendations concerning the application of HRV methods in athletes are made.The cardiovascular system is mostly controlled by autonomic regulation through the activity of sympathetic and parasympathetic pathways of the autonomic nervous system. Analysis of HRV permits insight in this control mechanism. It can easily be determined from ECG recordings, resulting in time series (RR-intervals) that are usually analysed in time and frequency domains. As a first approach, it can be assumed that power in different frequency bands corresponds to activity of sympathetic (0.04-0.15 Hz) and parasympathetic (0.15-0.4 Hz) nerves. However, other mechanisms (and feedback loops) are also at work, especially in the low frequency band. During dynamic exercise, it is generally assumed that heart rate increases due to both a parasympathetic withdrawal and an augmented sympathetic activity. However, because some authors disagree with the former statement and the fact that during exercise there is also a technical problem related to the non-stationary signals, a critical look at interpretation of results is needed. It is strongly suggested that, when presenting reports on HRV studies related to exercise physiology in general or concerned with athletes, a detailed description should be provided on analysis methods, as well as concerning population, and training schedule, intensity and duration. Most studies concern relatively small numbers of study participants, diminishing the power of statistics. Therefore, multicentre studies would be preferable. In order to further develop this fascinating research field, we advocate prospective, randomised, controlled, long-term studies using validated measurement methods. Finally, there is a strong need for basic research on the nature of the control and regulating mechanism exerted by the autonomic nervous system on cardiovascular function in athletes, preferably with a multidisciplinary approach between cardiologists, exercise physiologists, pulmonary physiologists, coaches and biomedical engineers.


title: Using Paxos to Build a Scalable, Consistent, and Highly Available Datastore

abstract: Spinnaker is an experimental datastore that is designed to run on a large cluster of commodity servers in a single datacenter. It features key-based range partitioning, 3-way replication, and a transactional get-put API with the option to choose either strong or timeline consistency on reads. This paper describes Spinnaker's Paxos-based replication protocol. The use of Paxos ensures that a data partition in Spinnaker will be available for reads and writes as long a majority of its replicas are alive. Unlike traditional master-slave replication, this is true regardless of the failure sequence that occurs. We show that Paxos replication can be competitive with alternatives that provide weaker consistency guarantees. Compared to an eventually consistent datastore, we show that Spinnaker can be as fast or even faster on reads and only 5% to 10% slower on writes.


title: Neuro-fuzzy speed control of traveling-wave type ultrasonic motor drive using frequency and phase modulation.

abstract: This paper presents a Fuzzy Neural Network (FNN) control system for a traveling-wave ultrasonic motor (TWUSM) driven by a dual mode modulation non-resonant driving circuit. First, the motor configuration and the proposed driving circuit of a TWUSM are introduced. To drive a TWUSM effectively, a novel driving circuit, that simultaneously employs both the driving frequency and phase modulation control scheme, is proposed to provide two-phase balance voltage for a TWUSM. Since the dynamic characteristics and motor parameters of the TWUSM are highly nonlinear and time-varying, a FNN control system is therefore investigated to achieve high-precision speed control. The proposed FNN control system incorporates neuro-fuzzy control and the driving frequency and phase modulation to solve the problem of nonlinearities and variations. The proposed control system is digitally implemented by a low-cost digital signal processor based microcontroller, hence reducing the system hardware size and cost. The effectiveness of the proposed driving circuit and control system is verified with hardware experiments under the occurrence of uncertainties. In addition, the advantages of the proposed control scheme are indicated in comparison with a conventional proportional-integral control system.


title: Predicting Future Clinical Changes of MCI Patients Using Longitudinal and Multimodal Biomarkers

abstract: Accurate prediction of clinical changes of mild cognitive impairment (MCI) patients, including both qualitative change (i.e., conversion to Alzheimer's disease (AD)) and quantitative change (i.e., cognitive scores) at future time points, is important for early diagnosis of AD and for monitoring the disease progression. In this paper, we propose to predict future clinical changes of MCI patients by using both baseline and longitudinal multimodality data. To do this, we first develop a longitudinal feature selection method to jointly select brain regions across multiple time points for each modality. Specifically, for each time point, we train a sparse linear regression model by using the imaging data and the corresponding clinical scores, with an extra 'group regularization' to group the weights corresponding to the same brain region across multiple time points together and to allow for selection of brain regions based on the strength of multiple time points jointly. Then, to further reflect the longitudinal changes on the selected brain regions, we extract a set of longitudinal features from the original baseline and longitudinal data. Finally, we combine all features on the selected brain regions, from different modalities, for prediction by using our previously proposed multi-kernel SVM. We validate our method on 88 ADNI MCI subjects, with both MRI and FDG-PET data and the corresponding clinical scores (i.e., MMSE and ADAS-Cog) at 5 different time points. We first predict the clinical scores (MMSE and ADAS-Cog) at 24-month by using the multimodality data at previous time points, and then predict the conversion of MCI to AD by using the multimodality data at time points which are at least 6-month ahead of the conversion. The results on both sets of experiments show that our proposed method can achieve better performance in predicting future clinical changes of MCI patients than the conventional methods.


title: A Novel Bayesian Similarity Measure for Recommender Systems

abstract: Collaborative filtering, a widely-used user-centric recommendation technique, predicts an item's rating by aggregating its ratings from similar users. User similarity is usually calculated by cosine similarity or Pearson correlation coefficient. However, both of them consider only the direction of rating vectors, and suffer from a range of drawbacks. To solve these issues, we propose a novel Bayesian similarity measure based on the Dirichlet distribution, taking into consideration both the direction and length of rating vectors. Further, our principled method reduces correlation due to chance. Experimental results on six real-world data sets show that our method achieves superior accuracy.


title: Morphological and molecular identification of Habronema spp. (Nematoda: Habronematidae) from donkeys in Xinjiang, China, and notes on the taxonomical status of Habronema majus (Creplin, 1849) and H. microstoma (Schneider, 1866)

abstract: Habronematid nematodes were collected from the stomachs of donkeys, Equus asinus L., in the Tarim Basin, Xinjiang, China. After examination by light and scanning electron microscopy, Habronema muscae (Carter, 1861) and H. majus (Creplin, 1849) were identified. The morphology of our specimens representing H. muscae (Carter, 1861) agreed well with previous redescriptions in the shape of the lateral lips, origin of the lateral alae, ratio of left and right spicules, and number and arrangement of caudal papillae. However, H. majus (Creplin, 1849) differs from H. microstoma (Schneider, 1866) in the arrangement of the caudal papillae in the male. Moreover, molecular analysis also showed interspecific differences of 26.2-28.2% in ITS2 and 8.6-8.9% in cox1 between H. majus and H. microstoma, a divergence much higher than the known intraspecific variation of Habronema spp. (6.6-8.7% in ITS2; 0.2-2.2% in cox1). The results indicate that both H. microstoma (Schneider, 1866) and H. majus (Creplin, 1849) are valid species.


title: Thunderstrike: EFI firmware bootkits for Apple MacBooks

abstract: There are several flaws in Apple's MacBook firmware security that allows untrusted modifications to be written to the SPI Flash boot ROM of these laptops. This capability represents a new class of persistent firmware rootkits, or 'bootkits', for the popular Apple MacBook product line. Stealthy bootkits can conceal themselves from detection and prevent software attempts to remove them. Malicious modifications to the boot ROM are able to survive re-installation of the operating system and even hard-drive replacement. Additionally, the malware can install a copy of itself onto other Thunderbolt devices' Option ROMs as a means to spread virally across air-gap security perimeters. Apple has fixed some of these flaws as part of CVE 2014-4498, but there is no easy solution to this class of vulnerability, since the MacBook lacks trusted hardware to perform cryptographic validation of the firmware at boot time.


title: Fingerprint Policy Optimisation for Robust Reinforcement Learning

abstract: Policy gradient methods have been successfully applied to a variety of reinforcement learning tasks. However, while learning in a simulator, these methods do not utilise the opportunity to improve learning by adjusting certain environment variables: unobservable state features that are randomly determined by the environment in a physical setting, but that are controllable in a simulator. This can lead to slow learning or convergence to highly suboptimal policies if the environment variable has a large impact on the transition dynamics. In this paper, we present fingerprint policy optimisation (FPO) which finds a policy that is optimal in expectation across the distribution of environment variables. The central idea is to use Bayesian optimisation (BO) to actively select the distribution of the environment variable that maximises the improvement generated by each iteration of the policy gradient method. To make this BO practical, we contribute two easy-to-compute low-dimensional fingerprints of the current policy. We apply FPO to a number of continuous control tasks of varying difficulty and show that FPO can efficiently learn policies that are robust to significant rare events, which are unlikely to be observable under random sampling but are key to learning good policies.


title: In-Session Personalization for Talent Search

abstract: Previous efforts in recommendation of candidates for talent search followed the general pattern of receiving an initial search criteria and generating a set of candidates utilizing a pre-trained model. Traditionally, the generated recommendations are final, that is, the list of potential candidates is not modified unless the user explicitly changes his/her search criteria. In this paper, we are proposing a candidate recommendation model which takes into account the immediate feedback of the user, and updates the candidate recommendations at each step. This setting also allows for very uninformative initial search queries, since we pinpoint the user's intent due to the feedback during the search session. To achieve our goal, we employ an intent clustering method based on topic modeling which separates the candidate space into meaningful, possibly overlapping, subsets (which we call intent clusters) for each position. On top of the candidate segments, we apply a multi-armed bandit approach to choose which intent cluster is more appropriate for the current session. We also present an online learning scheme which updates the intent clusters within the session, due to user feedback, to achieve further personalization. Our offline experiments as well as the results from the online deployment of our solution demonstrate the benefits of our proposed methodology.


title: High-Frequency Stock Trend Forecast Using LSTM Model

abstract: The prediction of price trend in stock market is a challenging task due to the inherent complexity and dynamics in price movement. Many machine learning algorithms, such as Support Vector Machine, Artificial Neural Network, and Hidden Markov Model, have been applied to it and achieved positive results. Long Short-Term Memory (LSTM), as a variant of RNN, can obtain hidden dependencies in data and has shown a significant performance in processing time series data. In this paper, we apply LSTM networks to predict the price movement of a short-term and test it by an experiment on some stocks randomly selected from CSI 300 constituent stocks. The experiment shows that the precision, recall rate and critical error of LSTM are all better than that of the random prediction. It indicates that LSTM can be used in the trend prediction of stock price. We also notice that many improvements need to be done in future.


title: Gut microbiota and IBD: causation or correlation?

abstract: Changes in the composition and metabolic function of the gut microbiota have been linked to IBD, but a direct causal association has yet to be established in humans. This Review discusses the evidence supporting dysbiosis in the gut microbiota in Crohn's disease and ulcerative colitis, exploring evidence from animal models and the translation to human disease.


title: Label Noise Reduction in Entity Typing by Heterogeneous Partial-Label Embedding

abstract: Current systems of fine-grained entity typing use distant supervision in conjunction with existing knowledge bases to assign categories (type labels) to entity mentions. However, the type labels so obtained from knowledge bases are often noisy (i.e., incorrect for the entity mention's local context). We define a new task, Label Noise Reduction in Entity Typing (LNR), to be the automatic identification of correct type labels (type-paths) for training examples, given the set of candidate type labels obtained by distant supervision with a given type hierarchy. The unknown type labels for individual entity mentions and the semantic similarity between entity types pose unique challenges for solving the LNR task. We propose a general framework, called PLE, to jointly embed entity mentions, text features and entity types into the same low-dimensional space where, in that space, objects whose types are semantically close have similar representations. Then we estimate the type-path for each training example in a top-down manner using the learned embeddings. We formulate a global objective for learning the embeddings from text corpora and knowledge bases, which adopts a novel margin-based loss that is robust to noisy labels and faithfully models type correlation derived from knowledge bases. Our experiments on three public typing datasets demonstrate the effectiveness and robustness of PLE, with an average of 25% improvement in accuracy compared to next best method.


title: Review of neural network applications in medical imaging and signal processing

abstract: The current applications of neural networks to in vivo medical imaging and signal processing are reviewed. As is evident from the literature neural networks have already been used for a wide variety of tasks within medicine. As this trend is expected to continue this review contains a description of recent studies to provide an appreciation of the problems associated with implementing neural networks for medical imaging and signal processing.


title: Performance-driven processor allocation

abstract: This work is focused on processor allocation in shared-memory multiprocessor systems, where no knowledge of the application is available when applications are submitted. We perform the processor allocation taking into account the characteristics of the application measured at run-time. We want to demonstrate the importance of an accurate performance analysis and the criteria used to distribute the processors. With this aim, we present the SelfAnalyzer, an approach to dynamically analyzing the performance of applications (speedup, efficiency and execution time), and the Performance-Driven Processor Allocation (PDPA), a new scheduling policy that distributes processors considering both the global conditions of the system and the particular characteristics of running applications. This work also defends the importance of the interaction between the medium-term and the long-term scheduler to control the multiprogramming level in the case of the clairvoyant scheduling pol-icies1. We have implemented our proposal in an SGI Origin2000 with 64 processors and we have compared its performance with that of some scheduling policies proposed so far and with the native IRIX scheduling policy. Results show that the combination of the SelfAnalyzer+PDPA with the medium/long-term scheduling interaction outperforms the rest of the scheduling policies evaluated. The evaluation shows that in workloads where a simple equipartition performs well, the PDPA also performs well, and in extreme workloads where all the applications have a bad performance, our proposal can achieve a speedup of 3.9 with respect to an equipartition and 11.8 with respect to the native IRIX scheduling policy.


On best Approximation of Continuous Functions.


title: Enabling Technologies for Green Internet of Things

abstract: Recent technological advances have led to an increase in the carbon footprint. Energy efficiency in the Internet of Things (IoT) has been attracting a lot of attention from researchers and designers over the last couple of years, paving the way for an emerging area called green IoT. There are various aspects (such as key enablers, communications, services, and applications) of IoT, where efficient utilization of energy is needed to enable a green IoT environment. We explore and discuss how the various enabling technologies (such as the Internet, smart objects, sensors, etc.) can be efficiently deployed to achieve a green IoT. Furthermore, we also review various IoT applications, projects and standardization efforts that are currently under way. Finally, we identify some of the emerging challenges that need to be addressed in the future to enable a green IoT.


title: The Effects of Social Capital on Firm Substantive and Symbolic Performance: In the Context of E-Business

abstract: This study examines the effects of social capital in the context of e-business and investigates how each of the three dimensions of social capital structural, relational and cognitive differentially influences a firm's substantive and symbolic performance. The study explores how structural capital and cognitive capital indirectly affect firm performance through relational capital. The research model is generally supported by data collected from a survey of 205 firms in China. The results suggest that structural and relational capital positively influence substantive and symbolic performance, respectively. However, cognitive capital does not have significant effects on substantive performance, though it positively affects symbolic performance. Also, the study found that structural capital and relational capital have stronger effects on substantive performance than symbolic performance. In contrast, cognitive capital has stronger effects on symbolic performance than substantive performance. Further, both structural capital and cognitive capital positively affect relational capital.


title: SysML-based and Prolog-supported FMEA

abstract: Failure Mode and Effects Analysis (FMEA) is a well-known technique for evaluating the effects of potential failure modes of components of a system. It is a crucial reliability and safety engineering activity for critical systems requiring systematic inductive reasoning from postulated component failures. We present an approach based on SysML and Prolog to support the tasks of an FMEA analyst. SysML block diagrams of the system under analysis are annotated with valid and error states of components and of their input flows, as well as with the logical conditions that may determine erroneous outputs. From the annotated model, a Prolog knowledge base is automatically built, transparently to the analyst. This can then be queried, e.g., to obtain the flows' and blocks' states that lead to system failures, or to trace the propagation of faults. The approach is suited for integration in modern model-driven system design processes. We describe a proof-of-concept implementation based on the Papyrus modeling tool under Eclipse, and show a demo example.


title: Wireless Network Intelligence at the Edge

abstract: Fueled by the availability of more data and computing power, recent breakthroughs in cloud-based machine learning (ML) have transformed every aspect of our lives from face recognition and medical diagnosis to natural language processing. However, classical ML exerts severe demands in terms of energy, memory, and computing resources, limiting their adoption for resource-constrained edge devices. The new breed of intelligent devices and high-stake applications (drones, augmented/virtual reality, autonomous systems, and so on) requires a novel paradigm change calling for distributed, low-latency and reliable ML at the wireless network edge (referred to as edge ML). In edge ML, training data are unevenly distributed over a large number of edge nodes, which have access to a tiny fraction of the data. Moreover, training and inference are carried out collectively over wireless links, where edge devices communicate and exchange their learned models (not their private data). In a first of its kind, this article explores the key building blocks of edge ML, different neural network architectural splits and their inherent tradeoffs, as well as theoretical and technical enablers stemming from a wide range of mathematical disciplines. Finally, several case studies pertaining to various high-stake applications are presented to demonstrate the effectiveness of edge ML in unlocking the full potential of 5G and beyond.


title: Parametrization of General Catmull-Clark Subdivision Surfaces and its Applications

abstract: AbstractA new parametrization technique and its applications for general Catmull-Clark subdivision surfaces are presented. The new technique extends J. Stam’s work by redefining all the eigen basis functions in the parametric representation for general Catmull-Clark subdivision surfaces and giving each of them an explicit form. The entire eigenstructure of the subdivision matrix and its inverse are computed exactly and explicitly with no need to precompute anything. Therefore, the new representation can be used not only for evaluation purpose, but for analysis purpose as well. The new approach is based on an Ω-partition of the parameter space and a detoured subdivision path. This results in a block diagonal matrix with constant size diagonal blocks (7×7) for the corresponding subdivision process. Consequently, eigen decomposition of the matrix is always possible and is simpler and more efficient. Furthermore, since the number of eigen basis functions required in the new approach is only one half of the pr...


title: A Self-Attention Network for Hierarchical Data Structures with an Application to Claims Management

abstract: Insurance companies must manage millions of claims per year. While most of these claims are non-fraudulent, fraud detection is core for insurance companies. The ultimate goal is a predictive model to single out the fraudulent claims and pay out the non-fraudulent ones immediately. Modern machine learning methods are well suited for this kind of problem. Health care claims often have a data structure that is hierarchical and of variable length. We propose one model based on piecewise feed forward neural networks (deep learning) and another model based on self-attention neural networks for the task of claim management. We show that the proposed methods outperform bag-of-words based models, hand designed features, and models based on convolutional neural networks, on a data set of two million health care claims. The proposed self-attention method performs the best.


title: Learning to Describe Differences Between Pairs of Similar Images

abstract: In this paper, we introduce the task of automatically generating text to describe the differences between two similar images. We collect a new dataset by crowd-sourcing difference descriptions for pairs of image frames extracted from video-surveillance footage. Annotators were asked to succinctly describe all the differences in a short paragraph. As a result, our novel dataset provides an opportunity to explore models that align language and vision, and capture visual salience. The dataset may also be a useful benchmark for coherent multi-sentence generation. We perform a firstpass visual analysis that exposes clusters of differing pixels as a proxy for object-level differences. We propose a model that captures visual salience by using a latent variable to align clusters of differing pixels with output sentences. We find that, for both single-sentence generation and as well as multi-sentence generation, the proposed model outperforms the models that use attention alone.


title: Bottom-Up Abstractive Summarization

abstract: Neural network-based methods for abstractive summarization produce outputs that are more fluent than other techniques, but which can be poor at content selection. This work proposes a simple technique for addressing this issue: use a data-efficient content selector to over-determine phrases in a source document that should be part of the summary. We use this selector as a bottom-up attention step to constrain the model to likely phrases. We show that this approach improves the ability to compress text, while still generating fluent summaries. This two-step process is both simpler and higher performing than other end-to-end content selection models, leading to significant improvements on ROUGE for both the CNN-DM and NYT corpus. Furthermore, the content selector can be trained with as little as 1,000 sentences, making it easy to transfer a trained summarizer to a new domain.


title: Joint Learning of Body and Part Representation for Person Re-Identification

abstract: Person re-identification (ReID), aiming to identify people among multiple camera views, has attracted an increasing attention due to the potential of application in surveillance security. Large variations in subjects’ postures, view angles, and illuminating conditions as well as non-ideal human detection significantly increase the difficulty of person ReID. Learning a robust metric for measuring the similarity between different person images is another under-addressed problem. In this paper, following the recent success of part-based models, in order to generate a discriminative and robust feature representation, we first propose to learn global and weighted local body-part features from pedestrian images. Then, in the training phase, angular loss and part-level classification loss are employed jointly as a similarity measure to train the network, which significantly improves the robustness of the resultant network against feature variance. Experimental results on several benchmark data sets demonstrate that our method outperforms the state-of-the-art methods.


title: Towards A Welsh Semantic Annotation System

abstract: Automatic semantic annotation of natural language data is an important task in Natural Language Processing, and a variety of semantic taggers have been developed for this task, particularly for English. However, for many languages, particularly for low-resource languages, such tools are yet to be developed. In this paper, we report on the development of an automatic Welsh semantic annotation tool (named CySemTagger) in the CorCenCC Project, which will facilitate semantic-level analysis of Welsh language data on a large scale. Based on Lancaster’s USAS semantic tagger framework, this tool tags words in Welsh texts with semantic tags from a semantic classification scheme, and is designed to be compatible with multiple Welsh POS taggers and POS tagsets by mapping different tagsets into a core shared POS tagset that is used internally by CySemTagger. Our initial evaluation shows that the tagger can cover up to 91.78% of words in Welsh text. This tagger is under continuous development, and will provide a critical tool for Welsh language corpus and information processing at semantic level.


title: Stabilization loop of a two axes gimbal system using self-tuning PID type fuzzy controller.

abstract: The application of inertial stabilization system is to stabilize the sensor's line of sight toward a target by isolating the sensor from the disturbances induced by the operating environment. The aim of this paper is to present two axes gimbal system. The gimbals torque relationships are derived using Lagrange equation considering the base angular motion and dynamic mass unbalance. The stabilization loops are constructed with cross coupling unit utilizing proposed fuzzy PID type controller. The overall control system is simulated and validated using MATLAB. Then, the performance of proposed controller is evaluated comparing with conventional PI controller in terms of transient response analysis and quantitative study of error analysis. The simulation results obtained in different conditions prove the efficiency of the proposed fuzzy controller which offers a better response than the classical one, and improves further the transient and steady-state performance.


title: A New Three Input DC/DC Converter for Hybrid PV/FC/Battery Applications

abstract: In this paper, a new three-port dc/dc converter is presented for hybrid photovoltaic (PV)/fuel cell (FC)/battery applications. The proposed structure comprises a conventional buck-boost and a boost converter. Four power switches and four diodes are employed in the proposed converter. The voltage gain of the presented converter is more than the conventional boost converter. This advantage and having two unidirectional and a bidirectional inputs make the structure a suitable power electronic interface for hybrid generation applications. In addition, there are no limitations in switching the modulation. Therefore, tracking the maximum power of the PV source, setting the FC power, controlling the battery power, and calibrating the output voltage can be equipped by controlling duty ratios of the switches. The input power sources can provide power to the load and either charge or discharge the battery individually or simultaneously. The steady-state analyses of the presented converter are discussed thoroughly in this paper. Finally, in order to validate the feasibility of the presented converter, experimental results are provided.


title: Mobilizing Information: Local News and the Formation of a Viable Political Community

abstract: This article offers a rhetorical understanding of the practices and influences of news media on democratic citizenship during an environmental conflict. I compared two newspapers' ability to foster and suppress the formation and activation potential of citizens to participate in the decision-making process of a solid waste facility siting. One newspaper used language that fostered the formation of community by overcoming apathy and encouraging residents to act collectively. In contrast, the other newspaper's coverage suppressed the formation of community by reinforcing the belief that residents were powerless against the entrenched economic and political power base. This research also establishes a rationale for why it is important to the discipline to expand the definition of mobilizing information in the news media.


title: Model and solution for capacitated facility location problem

abstract: This paper presents a new model for capacitated facility location problem, where serve radius and economic benefit are considered. In the new model, the objective is to maximize the total return investment. However, the objective for multiple knapsack problem is to maximize the total profit. So the capacitated facility location problem can be translated into multiple knapsack problem. Because this problem is difficult to solve, we propose a hybrid harmony search algorithm, which incorporates harmony search algorithm with greedy algorithm. Numerical results from computational experiments are presented and analyzed.


title: A 4th-Order Particle-in-Cell Method with Phase-Space Remapping for the Vlasov-Poisson Equation

abstract: Numerical solutions to the Vlasov-Poisson system of equations have important applications to both plasma physics and cosmology. In this paper, we present a new Particle-in-Cell (PIC) method for solving this system that is 4th-order accurate in both space and time. Our method is a high-order extension of one presented previously [B. Wang, G. Miller, and P. Colella, SIAM J. Sci. Comput., 33 (2011), pp. 3509--3537]. It treats all of the stages of the standard PIC update - charge deposition, force interpolation, the field solve, and the particle push - with 4th-order accuracy, and includes a 6th-order accurate phase-space remapping step for controlling particle noise. We demonstrate the convergence of our method on a series of one- and two- dimensional electrostatic plasma test problems, comparing its accuracy to that of a 2nd-order method. As expected, the 4th-order method can achieve comparable accuracy to the 2nd-order method with many fewer resolution elements.


title: Collab-ChiQat: A Collaborative Remaking of a Computer Science Intelligent Tutoring System

abstract: This paper focuses on the motivation, design, and initial prototype implementation of Collab-ChiQat. Collab-ChiQat is a collaborative reconceptualization of an existing intelligent tutoring system for Computer Science Education originally intended for one-to-one student-system tutoring. Collab-ChiQat allows students to work as pair programmers as they solve coding problems for linked lists, a foundational and difficult to grasp CS concept. The work is unique in it's comparison of how system structuring of collaboration affects both learning and actual collaboration. In one condition, students are left to themselves with no system feedback regarding their collaborative behavior. While in a second condition, the collaboration is semi-structured, meaning students received a visualization of their participation and other metrics.


title: The influence of personality on HE students' confidence in their academic abilities

abstract: Abstract Students’ confidence in their academic abilities, measured with the Individual Learning Profile (ILP) scale, was examined in relation to their personality traits and grades. To validate the ILP, in Study 1, factor analysis of data from 3003 students extracted six factors (Reading and Writing, Hard IT, Numeracy, Time Management, Speaking, and Easy IT) with good internal reliability. Subsequently, in Study 2, 130 students completed the refined ILP, and scales measuring the Big Five, Perfectionism, Anxiety, and Self-Esteem. Between 10% and 31% of the variance in four ILP factors, but not IT skills, could be predicted by personality traits, but Self-Esteem and Anxiety were not influential. Higher conscientiousness and openness positively predicted higher confidence in reading and writing, while agreeableness and three aspects of perfectionism predicted confidence in numeracy skills. Being introverted and female were predictive of lower confidence in speaking, as were low conscientiousness and the perfectionistic desire to be organised. Conscientiousness, Extraversion, and the perfectionistic desire to be organised were strong predictors of confidence in time-management skills, which in turn predicted first-year GPA. The reliability of the ILP was examined over the course of a one-year interval.


abstract: Multistep collocation methods for Volterra integro-differential equations are derived and analyzed. They increase the order of convergence of classical one-step collocation methods, at the same computational cost. The numerical stability analysis is carried out and classes of A"0-stable methods are provided. Numerical experiments confirm theoretical expectations and make comparisons with the one-step collocation methods.


title: A semantic occlusion model for human pose estimation from a single depth image

abstract: Human pose estimation from depth data has made significant progress in recent years and commercial sensors estimate human poses in real-time. However, state-of-the-art methods fail in many situations when the humans are partially occluded by objects. In this work, we introduce a semantic occlusion model that is incorporated into a regression forest approach for human pose estimation from depth data. The approach exploits the context information of occluding objects like a table to predict the locations of occluded joints. In our experiments on synthetic and real data, we show that our occlusion model increases the joint estimation accuracy and outperforms the commercial Kinect 2 SDK for occluded joints.


title: An open-source algorithm to detect onset of arterial blood pressure pulses

abstract: In this paper, we present an effective algorithm for detecting the onset of arterial blood pressure (ABP) pulses. The algorithm employs a windowed and weighted slope sum function (SSF) to extract ABP waveform features. Adaptive thresholding and search strategies are applied to the SSF signal to detect ABP pulses and to determine their onsets. Two evaluation procedures were employed. First, pulse detection accuracy was evaluated by comparing the algorithm's pulse detections with reference ECG annotations using the MIT-BIH Polysomnographic Database. The algorithm detected 99.31% of the 368,364 beats annotated in the ECG. Second, the accuracy of pulse onset determination was established using a newly created, manually-edited reference ABP signal database. For 96.41% of the 39,848 beats in the reference database, the difference between the manually-edited and algorithm-determined ABP pulse onset was less than or equal to 20 ms. The C source code of the algorithm has been contributed to PhysioToolkit and is freely available from the PhysioNet website (http://www.physionet.org).


title: Psychological interventions for distress in cancer patients: a review of reviews

abstract: We review a decade of review articles concerning psychosocial interventions for cancer patients. We find a distinct progression in the tone of interpretations of the literature, as better quality studies accumulate and the sophistication of reviews improves. The current literature does not make a compelling case for the value of these interventions for the typical cancer patient. The bulk of the literature reviews in this field take a narrative rather than a systematic approach, and serious compromises in standards are necessary to muster an adequate set of studies for review. The more rigorous the review, the less likely it is to conclude there is evidence that psychological interventions are effective.


title: Meta-analysis of Cohen’s kappa

abstract: Cohen’s κ is the most important and most widely accepted measure of inter-rater reliability when the outcome of interest is measured on a nominal scale. The estimates of Cohen’s κ usually vary from one study to another due to differences in study settings, test properties, rater characteristics and subject characteristics. This study proposes a formal statistical framework for meta-analysis of Cohen’s κ to describe the typical inter-rater reliability estimate across multiple studies, to quantify between-study variation and to evaluate the contribution of moderators to heterogeneity. To demonstrate the application of the proposed statistical framework, a meta-analysis of Cohen’s κ is conducted for pressure ulcer classification systems. Implications and directions for future research are discussed.


title: Low-power, folded cascode near rail-to-rail OTA for moderate frequency signal processing

abstract: This paper presents a gate-driven, folded-cascode operational transconductance amplifier (FCOTA), operating in strong inversion region. The input core utilizes two complementary PMOS and NMOS input pairs to ensure rail-to-rail input common mode range (ICMR). The cascoded load structure at output port performs the job of current summer and provides enhanced gain due to high output impedance for FCOTA. The proposed OTA has been employed to implement universal biquadratic low-pass, high-pass, band-pass and notch transfer functions, simultaneously available at different nodes of the filter. The proposed FCOTA used dual supply voltage of ± 0.9V and dissipates around 93.6 μW power and provides 80.24 dB open loop gain and gain bandwidth (GBW) of 6.03 MHz. The Cadence VIRTUOSO environment using UMC 0.18 μm CMOS process technology has been used to simulate the proposed circuit.


title: An isolated multiport DC-DC converter for simultaneous power management of multiple renewable energy sources

abstract: This paper proposes a new isolated multiport DC-DC converter for simultaneous power management of multiple sources, which can be of different types and capacities. The proposed DC-DC converter only uses one controllable switch in each port to which a source is connected. Therefore, it has the advantages of simple topology and minimum number of power switches. A general topology of the proposed converter is firstly introduced. Its principle and operation are then analyzed. The proposed converter is applied for maximum power point tracking (MPPT) control of a wind/solar hybrid system consisting of one wind turbine generator (WTG) and two photovoltaic (PV) panels. Simulation studies are carried out in MATLAB Simulink to validate the proposed converter and the MPPT control of the wind/solar hybrid system by using real wind speed and solar radiation data obtained from the National Renewable Energy Laboratory (NREL). Experimental results are then provided to further validate the effectiveness of using the proposed converter to achieve MPPT simultaneously for WTG and both PV panels.


title: A fast and effective ellipse detector for embedded vision applications

abstract: Several papers addressed ellipse detection as a first step for several computer vision applications, but most of the proposed solutions are too slow to be applied in real time on large images or with limited hardware resources. This paper presents a novel algorithm for fast and effective ellipse detection and demonstrates its superior speed performance on large and challenging datasets. The proposed algorithm relies on an innovative selection strategy of arcs which are candidate to form ellipses and on the use of Hough transform to estimate parameters in a decomposed space. The final aim of this solution is to represent a building block for new generation of smart-phone applications which need fast and accurate ellipse detection also with limited computational resources.


title: Rationale for Internet-Mediated Communities

abstract: This article discusses the theoretical foundations of Internet-mediated communities (IMCs), i.e., groups of people who share interests and make use at some time of the same Internet tools (for instance, a web site—the special concern of this study) in order to exchange information about the shared interests. From November 1999 to February 2001 a rationale was developed and validated for such communities and their web sites, due to the lack of theory unification in the field. The authors carried out five case studies of widely known IMCs, seven action researches with established groups of people who were given customized web sites to mediate their interaction, and an exploratory research supported by open-ended interviews with those groups. Additional empirical research is now needed to apply the theoretical grounds here developed to varying IMC contexts and to different communication technologies.


title: New oral solid dosage form for furosemide oral administration.

abstract: Furosemide (FURO) is a drug labeled in class IV of the Biopharmaceutics Classification System (BCS) as it is both poor soluble and poor permeable. The aim of this work was to improve FURO biopharmaceutical properties by its formulation in a new solid oral dosage form. It consists in the realization of the composite MgAl-HTlc-FURO, obtained by FURO intercalation into the inorganic matrix hydrotalcite (MgAl-HTlc), and its successive formulation in tablets intended to be swallowed whole and to disintegrate rapidly in the stomach. These formulations were prepared by direct compression of a simple powder mixture constituted by MgAl-HTlc-FURO, a super disintegrant (Explotab, PolyplasdoneXL, PolyplasdoneXL-10, PolyplasdoneINF 10 or L-HPCLH-21) and a filler. The prepared formulations were submitted to disintegration time tests, and only those displaying the lowest disintegration time in gastric medium were submitted to in vitro release studies. Drug dissolution profiles from MgAl-HTlc-FURO tablets were compared with those containing crystalline FURO alone or physically mixed to MgAl-HTlc instead of MgAl-HTlc-FURO. The results revealed that tablets containing MgAl-HTlc-FURO give the best dissolution profile and that L-HPCLH-21 is able to promote the highest drug release in gastric medium, resulting in the most suitable super disintegrant in comparison with the other tested.


title: Fast light-map computation with virtual polygon lights

abstract: We propose a new method for the fast computation of light maps using a many-light global-illumination solution. A complete scene can be light mapped on the order of seconds to minutes, allowing fast and consistent previews for editing or even generation at loading time. In our method, virtual point lights are clustered into a set of virtual polygon lights, which represent a compact description of the illumination in the scene. The actual light-map generation is performed directly on the GPU. Our approach degrades gracefully, avoiding objectionable artifacts even for very short computation times.


title: Automatically tracking and analyzing the behavior of live insect colonies

abstract: We introduce the study of {\it live} social insect colonies as a relevant and exciting domain for the development and application of multi-agent systems modeling tools. Social insects provide a rich source of {\it traceable} social behavior for testing multi-agent tracking, prediction and modeling algorithms. An additional benefit of this research is the potential for contributions to experimental biology --- the principled techniques developed for analyzing artificial multi-agent systems can be applied to advance the state of knowledge of insect behavior. We contribute a novel machine vision system that addresses the challenge of tracking hundreds of small animals simultaneously. Fast color-based tracking is combined with movement-based tracking to locate ants in a real-time video stream. We also introduce new methods for analyzing the spatial activity of ant colonies. The system was validated in experiments with laboratory colonies of {\it Camponotus festinatus} and several example analyses of the colonies' spatial behavior are provided.


title: Bimodal Distribution and Co-Bursting in Review Spam Detection

abstract: Online reviews play a crucial role in helping consumers evaluate and compare products and services. This critical importance of reviews also incentivizes fraudsters (or spammers) to write fake or spam reviews to secretly promote or demote some target products and services. Existing approaches to detecting spam reviews and reviewers employed review contents, reviewer behaviors, star rating patterns, and reviewer-product networks for detection. In this research, we further discovered that reviewers' posting rates (number of reviews written in a period of time) also follow an interesting distribution pattern, which has not been reported before. That is, their posting rates are bimodal. Multiple spammers also tend to collectively and actively post reviews to the same set of products within a short time frame, which we call co-bursting. Furthermore, we found some other interesting patterns in individual reviewers' temporal dynamics and their co-bursting behaviors with other reviewers. Inspired by these findings, we first propose a two-mode Labeled Hidden Markov Model to model spamming using only individual reviewers' review posting times. We then extend it to the Coupled Hidden Markov Model to capture both reviewer posting behaviors and co-bursting signals. Our experiments show that the proposed model significantly outperforms state-of-the-art baselines in identifying individual spammers. Furthermore, we propose a co-bursting network based on co-bursting relations, which helps detect groups of spammers more effectively than existing approaches.


title: Real-time estimation of drivable image area based on monocular vision

abstract: Camera-based estimation of drivable image areas is still in evolution. These systems have been developed for improved safety and convenience, without the need to adapt itself to the environment. Machine Vision is an important tool to identify the region that includes the road in images. Road detection is the major task of autonomous vehicle guidance. In this way, this work proposes a drivable region detection algorithm that generates the region of interest from a dynamic threshold search method and from a drag process (DP). Applying the DP to estimation of drivable image areas has not been done yet, making the concept unique. Our system was has been evaluated from real data obtained by intelligent platforms and tested in different types of image texture, which include occlusion case, obstacle detection and reactive navigation.


title: A controlled-attention view of working-memory capacity.

abstract: In 2 experiments the authors examined whether individual differences in working-memory (WM) capacity are related to attentional control. Experiment I tested high- and low-WM-span (high-span and low-span) participants in a prosaccade task, in which a visual cue appeared in the same location as a subsequent to-be-identified target letter, and in an antisaccade task, in which a target appeared opposite the cued location. Span groups identified targets equally well in the prosaccade task, reflecting equivalence in automatic orienting. However, low-span participants were slower and less accurate than high-span participants in the antisaccade task, reflecting differences in attentional control. Experiment 2 measured eye movements across a long antisaccade session. Low-span participants made slower and more erroneous saccades than did high-span participants. In both experiments, low-span participants performed poorly when task switching from antisaccade to prosaccade blocks. The findings support a controlled-attention view of WM capacity.


title: A Graph Rewriting Approach for Converting Asynchronous ROMs into Synchronous Ones

abstract: SUMMARY Most of FPGAs have Configurable Logic Blocks (CLBs) to implement combinational and sequential circuits and block RAMs to implement Random Access Memories (RAMs) and Read Only Memories (ROMs). Circuit design that minimizes the number of clock cycles is easy if we use asynchronous read operations. However, most of FPGAs support synchronous read operations, but do not support asynchronous read operations. The main contribution of this paper is to provide one of the potent approaches to resolve this problem. We assume that a circuit using asynchronous ROMs designed by a non-expert or quickly designed by an expert is given. Our goal is to convert this circuit with asynchronous ROMs into an equivalent circuit with synchronous ones. The resulting circuit with synchronous ROMs can be embedded into FPGAs. We also discuss several techniques to decrease the latency and increase the clock frequency of the resulting circuits.