title: OpenTuner: an extensible framework for program autotuning

abstract: Program autotuning has been shown to achieve better or more portable performance in a number of domains. However, autotuners themselves are rarely portable between projects, for a number of reasons: using a domain-informed search space representation is critical to achieving good results; search spaces can be intractably large and require advanced machine learning techniques; and the landscape of search spaces can vary greatly between different problems, sometimes requiring domain specific search techniques to explore efficiently. This paper introduces OpenTuner, a new open source framework for building domain-specific multi-objective program autotuners. OpenTuner supports fully-customizable configuration representations, an extensible technique representation to allow for domain-specific techniques, and an easy to use interface for communicating with the program to be autotuned. A key capability inside OpenTuner is the use of ensembles of disparate search techniques simultaneously; techniques that perform well will dynamically be allocated a larger proportion of tests. We demonstrate the efficacy and generality of OpenTuner by building autotuners for 7 distinct projects and 16 total benchmarks, showing speedups over prior techniques of these projects of up to 2.8x with little programmer effort.


title: Graph-Walk-based Selective Regression Testing of Web Applications Created with Google Web Toolkit

abstract: Modern web applications are usually based on JavaScript. Due to its loosely typed, dynamic nature, test execution is time expensive and costly. Techniques for regression testing and fault-localization as well as frameworks like the Google Web Toolkit (GWT) ease the development and testing process, but still require approaches to reduce the testing effort. In this paper, we investigate the efficiency of a specialized, graph-walk based selective regression testing technique that aims to detect code changes on the client side in order to determine a reduced set of web tests. To do this, we analyze web applications created with GWT on different precision levels and with varying lookaheads. We examine how these parameters affect the localization of client-side code changes, run time, memory consumption and the number of web tests selected for re-execution. In addition, we propose a dynamic heuristics which targets an analysis that is as exact as possible while reducing memory consumption. The results are partially applicable on non-GWT applications. In the context of web applications,we see that the efficiency relies to a great degree on both the structure of the application and the code modifications, which is why we propose further measures tailored to the results of our approach.


title: Cloud of Things: Integrating Internet of Things and cloud computing and the issues involved

abstract: With the trend going on in ubiquitous computing, everything is going to be connected to the Internet and its data will be used for various progressive purposes, creating not only information from it, but also, knowledge and even wisdom. Internet of Things (IoT) becoming so pervasive that it is becoming important to integrate it with cloud computing because of the amount of data IoT's could generate and their requirement to have the privilege of virtual resources utilization and storage capacity, but also, to make it possible to create more usefulness from the data generated by IoT's and develop smart applications for the users. This IoT and cloud computing integration is referred to as Cloud of Things in this paper. IoT's and cloud computing integration is not that simple and bears some key issues. Those key issues along with their respective potential solutions have been highlighted in this paper.


title: Notice of RetractionThe construction and empirical study of China's Financial Conditions Index in new situation

abstract: The paper constructe China's Financial Conditions Index and estimate the index based VAR methodology. The empirical results show that these variables play an important role in monetary policy and should be considered when the central bank implement monetary policy. Through the empirical analysis of Financial Conditions Index in the sample period, we confirm that the FCI can predict inflation rate well and China's monetary policy is against the economic cycle. It finds the FCI could serve as a auxiliary indicator of China's monetary policy.


BOOMERANG: Exploiting the Semantic Gap in Trusted Execution Environments.


title: Surgical management of pygopagus conjoined twins with spinal bifida

abstract: PURPOSE ::: Conjoined twins are a rare complication of 9 monozygotic twins and are associated with high perinatal mortality. Pygopagus are one of the rare types of conjoined twins with only a handful of cases reported in the literature. We present the case of one-and-half month-old male pygopagus conjoined twins, who were joined together dorsally in lower lumbar and sacral region and had spina bifida and shared a single thecal sac with combined weight of 6.14 kg. Spinal cord was separated at the level of the conus followed by duraplasty. They had uneventful recovery with normal 15 months follow-up. ::: ::: ::: CONCLUSION ::: Separation of conjoined twins is recommended in where this is feasible with the anticipated survival of both or one infant.


title: Software release planning: an evolutionary and iterative approach

abstract: Abstract To achieve higher flexibility and to better satisfy actual customer requirements, there is an increasing tendency to develop and deliver software in an incremental fashion. In adopting this process, requirements are delivered in releases and so a decision has to be made on which requirements should be delivered in which release. Three main considerations that need to be taken account of are the technical precedences inherent in the requirements, the typically conflicting priorities as determined by the representative stakeholders, as well as the balance between required and available effort. The technical precedence constraints relate to situations where one requirement cannot be implemented until another is completed or where one requirement is implemented in the same increment as another one. Stakeholder preferences may be based on the perceived value or urgency of delivered requirements to the different stakeholders involved. The technical priorities and individual stakeholder priorities may be in conflict and difficult to reconcile. This paper provides (i) a method for optimally allocating requirements to increments; (ii) a means of assessing and optimizing the degree to which the ordering conflicts with stakeholder priorities within technical precedence constraints; (iii) a means of balancing required and available resources for all increments; and (iv) an overall method called EVOLVE aimed at the continuous planning of incremental software development. The optimization method used is iterative and essentially based on a genetic algorithm. A set of the most promising candidate solutions is generated to support the final decision. The paper evaluates the proposed approach using a sample project.


title: Fast plane extraction in organized point clouds using agglomerative hierarchical clustering

abstract: Real-time plane extraction in 3D point clouds is crucial to many robotics applications. We present a novel algorithm for reliably detecting multiple planes in real time in organized point clouds obtained from devices such as Kinect sensors. By uniformly dividing such a point cloud into nonoverlapping groups of points in the image space, we first construct a graph whose node and edge represent a group of points and their neighborhood respectively. We then perform an agglomerative hierarchical clustering on this graph to systematically merge nodes belonging to the same plane until the plane fitting mean squared error exceeds a threshold. Finally we refine the extracted planes using pixel-wise region growing. Our experiments demonstrate that the proposed algorithm can reliably detect all major planes in the scene at a frame rate of more than 35Hz for 640×480 point clouds, which to the best of our knowledge is much faster than state-of-the-art algorithms.


title: Drone-Aided Communication as a Key Enabler for 5G and Resilient Public Safety Networks

abstract: Wireless networks comprising unmanned aerial vehicles can offer limited connectivity in a cost-effective manner to disaster-struck regions where terrestrial infrastructure might have been damaged. While these drones offer advantages such as rapid deployment to far-flung areas, their operations may be rendered ineffective by the absence of an adequate energy management strategy. This article considers the multi-faceted applications of these platforms and the challenges thereof in the networks of the future. In addition to providing an overview of the work done by researchers in determining the features of the air-to-ground channel, the article explores the use of drones in fields as diverse as military surveillance and network rehabilitation for disaster-struck areas. It also presents a case study that envisages a scenario in which drones operate alongside conventional wireless infrastructure, thereby allowing a greater number of users to establish a line-of-sight link for communication. This study investigates a power allocation strategy for the microwave base station and the small base stations operating at 28 GHz frequency band. The self-adaptive power control strategy for drones is dependent on the maximum allowable interference threshold and minimum data rate requirements. This study highlights the importance of incorporating the drones in the multi-tier heterogeneous network to extend the network coverage and capacity.


title: Colyseus: A Distributed Architecture for Online Multiplayer Games

abstract: This paper presents the design, implementation, and evaluation of Colyseus, a distributed architecture for interactive multiplayer games. Colyseus takes advantage of a game's tolerance for weakly consistent state and predictable workload to meet the tight latency constraints of game-play and maintain scalable communication costs. In addition, it provides a rich distributed query interface and effective prefetching subsystem to help locate and replicate objects before they are accessed at a node. We have implemented Colyseus and modified Quake II, a popular first person shooter game, to use it. Our measurements of Quake II and our own Colyseus-based game with hundreds of players shows that Colyseus effectively distributes game traffic across the participating nodes, allowing Colyseus to support low-latency game-play for an order of magnitude more players than existing single server designs, with similar per-node bandwidth costs.


title: BENEFITS AND CHALLENGES OF BUSINESS INTELLIGENCE ADOPTION IN SMALL AND MEDIUM-SIZED ENTERPRISES

abstract: Leveraging information is a key success factor for companies. Over the last two decades Business Intelligence (BI) has evolved to become a foundational cornerstone of enterprise decision support. However, prior research shows that small and medium-sized enterprises (SMEs), in particular, lag behind in the proliferation of BI. In this exploratory study we examine BI adoption within German SMEs in the state of Saxony (n = 214). We explore perceived benefits and challenges in their efforts to implement BI. By applying cluster analysis to these results we suggest four types of BI SMEs, each with an individual profile concerning potential benefits as well as a certain set of challenges that are to be expected when it comes to adopting BI solutions. Results can create value for enterprises that plan to implement a BI solution, BI consultants as well as BI suppliers.


An Efficient Convolutional Network for Human Pose Estimation


title: K-Geodetic Graphs and their Applications to Analysis across Different Scales of Dynamics of Complex Systems

abstract: This paper describes a new approach to the problem of the structural research of clusters based on the theory of geodetic and k-geodetic graphs. We firmly believe that this same approach can be used when solving problems of correlation between structural and spectral metrics in complex networks. So, what we want to point out is about the possibility of applying one theory of networks (the theory of k-geodetic networks) to the solution of problems in another type of networks (complex networks). The theory of geodetic graphs and their various modifications represents an important tool for the structural analysis of complex systems of transmission, processing, and analysis of information. In the case of large data sets, their stochastic dependence is described by large-dimensional correlation matrices. One of the problems of correlation analysis is the study of the structure of the correlation matrix. It is proved that such a structure is adequately described by geodetic graphs. The obtained structural data allow solving the choice problem of significant variables in multidimensional regression models.


title: Modeling Development and Disease with Organoids

abstract: Recent advances in 3D culture technology allow embryonic and adult mammalian stem cells to exhibit their remarkable self-organizing properties, and the resulting organoids reflect key structural and functional properties of organs such as kidney, lung, gut, brain and retina. Organoid technology can therefore be used to model human organ development and various human pathologies ‘in a dish.” Additionally, patient-derived organoids hold promise to predict drug response in a personalized fashion. Organoids open up new avenues for regenerative medicine and, in combination with editing technology, for gene therapy. The many potential applications of this technology are only beginning to be explored.


Medium Access Control Protocols in Mobile Ad Hoc Networks: Problems and Solutions


title: Step-by-step guide to critiquing research. Part 1: quantitative research

abstract: When caring for patients, it is essential that nurses are using the current best practice. To determine what this is, nurses must be able to read research critically. But for many qualified and student nurses, the terminology used in research can be difficult to understand, thus making critical reading even more daunting. It is imperative in nursing that care has its foundations in sound research, and it is essential that all nurses have the ability to critically appraise research to identify what is best practice. This article is a step-by-step approach to critiquing quantitative research to help nurses demystify the process and decode the terminology.


title: Hypergraph Convolution and Hypergraph Attention

abstract: Recently, graph neural networks have attracted great attention and achieved prominent performance in various research fields. Most of those algorithms have assumed pairwise relationships of objects of interest. However, in many real applications, the relationships between objects are in higher-order, beyond a pairwise formulation. To efficiently learn deep embeddings on the high-order graph-structured data, we introduce two end-to-end trainable operators to the family of graph neural networks, i.e., hypergraph convolution and hypergraph attention. Whilst hypergraph convolution defines the basic formulation of performing convolution on a hypergraph, hypergraph attention further enhances the capacity of representation learning by leveraging an attention module. With the two operators, a graph neural network is readily extended to a more flexible model and applied to diverse applications where non-pairwise relationships are observed. Extensive experimental results with semi-supervised node classification demonstrate the effectiveness of hypergraph convolution and hypergraph attention.


title: Oxymorons: The Myth of a U.S. Health Care System

abstract: Preface Acknowledgments About the Author 1. Wars, Strikes, Riots, and Acts of Congress The Accidental Patient The Accidental Provider Is There a Doctor in the Corporation? A Medical Chicken in Every Pot? I'm from the Government, and I'm Here to Audit You Back Alleys and "Integrated" Care Systems Chocolate and Vanilla Politics 2. Playing with the Boss's Money Ten Thousand Little Health Care Systems Life in the Data Mines Continuity Conundrum Stuck in Fifty Catatonic States Toward a New Federalism? Who Benefits from the Proliferation of Benefit Plans? 3. The HMO Will See You Now Diagnosis: Organizational Multiple Personality Disorder Data Speak Louder Than TV Ads MCO Multiple Personality Disorder, Deconstructed Who Buys an Ounce of Prevention? You Get What You Pay For 4. The Health Care Jobs Program The Managed Care Jobs Program Life on the Hospital Chain Gang Old South, New South Spoonfuls of Sugar 5. Chaos in the Clinic Having a Bad O'Hare Day From Here to Infinity Why It Is Called Medical "Practice". Rational Irrationality Misguidelines My Data Can Beat Up Your Data. From Infinity Back to Here 6. Vaporware.com Nightmare on Wall Street A Fix for Health Care's Permanent Love Triangle? Is There a Doctor in the Corporation.com? Whose Preferred Provider? Open Your E-mail and Say "Ah" Nightmare on Wall Street: The Remake 7. A "System" for the Uninsured A Not-for-Profit Hospital by Any Other Name Waiting for Godot's Reform Plan 8. A Simple Plan My Tax Break Is Your Tax Break ERISA (or Something Like It) for All The Benefits of Standard Benefits 9. Personal Effects References Index Contents viii


title: Classifying multi-category images using deep learning : A convolutional neural network model

abstract: This paper presents an image classification model using a convolutional neural network with Tensor Flow. Tensor Flow is a popular open source library for machine learning and deep neural networks. A multi-category image dataset has been considered for the classification. Conventional back propagation neural network has an input layer, hidden layer, and an output layer but convolutional neural network, has a convolutional layer, and a max pooling layer. We train this proposed classifier to calculate the decision boundary of the image dataset. The data in the real world is mostly in the form of unlabeled and unstructured format. These unstructured data may be image, sound and text data. Useful information cannot be easily derived from neural networks which are shallow i.e. the ones which have less number of hidden layers. We propose deep neural network based CNN classifier which has a large number of hidden layers and can derive meaningful information from images.


title: Passive UHF RFID-Based Localization Using Detection of Tag Interference on Smart Shelf

abstract: In this paper, we present a novel radio-frequency identification (RFID) smart shelf that accurately locates tagged objects using standard passive UHF RFID tags. This standard-based commercial off-the-shelf approach provides significant advantages over custom HF RFID and other near-field RFID approaches, including reduced tag costs, minimal infrastructure costs, and simple operation. In order to achieve accurate location sensing of objects sitting on the shelf, we utilize a novel localization algorithm that utilizes detected changes in a tag's readability to infer the presence of neighboring tags. According to our experimentation results, with a single RFID reader antenna for two wooden shelves of size 91 cm × 152 cm, our smart-shelf system estimates nine box-level object locations with an average error of just 18.48 cm, which is a 71% improvement in accuracy compared with the previously published k nearest neighbor (KNN) algorithm.


title: Clinical decision support systems for improving diagnostic accuracy and achieving precision medicine

abstract: As research laboratories and clinics collaborate to achieve precision medicine, both communities are required to understand mandated electronic health/medical record (EHR/EMR) initiatives that will be fully implemented in all clinics in the United States by 2015. Stakeholders will need to evaluate current record keeping practices and optimize and standardize methodologies to capture nearly all information in digital format. Collaborative efforts from academic and industry sectors are crucial to achieving higher efficacy in patient care while minimizing costs. Currently existing digitized data and information are present in multiple formats and are largely unstructured. In the absence of a universally accepted management system, departments and institutions continue to generate silos of information. As a result, invaluable and newly discovered knowledge is difficult to access. To accelerate biomedical research and reduce healthcare costs, clinical and bioinformatics systems must employ common data elements to create structured annotation forms enabling laboratories and clinics to capture sharable data in real time. Conversion of these datasets to knowable information should be a routine institutionalized process. New scientific knowledge and clinical discoveries can be shared via integrated knowledge environments defined by flexible data models and extensive use of standards, ontologies, vocabularies, and thesauri. In the clinical setting, aggregated knowledge must be displayed in user-friendly formats so that physicians, non-technical laboratory personnel, nurses, data/research coordinators, and end-users can enter data, access information, and understand the output. The effort to connect astronomical numbers of data points, including ‘-omics’-based molecular data, individual genome sequences, experimental data, patient clinical phenotypes, and follow-up data is a monumental task. Roadblocks to this vision of integration and interoperability include ethical, legal, and logistical concerns. Ensuring data security and protection of patient rights while simultaneously facilitating standardization is paramount to maintaining public support. The capabilities of supercomputing need to be applied strategically. A standardized, methodological implementation must be applied to developed artificial intelligence systems with the ability to integrate data and information into clinically relevant knowledge. Ultimately, the integration of bioinformatics and clinical data in a clinical decision support system promises precision medicine and cost effective and personalized patient care.


Estimation of Confidence Measures for Machine Translation


title: Logical structure analysis and generation for structured documents: a syntactic approach

abstract: This paper presents a syntactic method for sophisticated logical structure analysis that transforms document images with multiple pages and hierarchical structure into an electronic document based on SGML/XML. To produce a logical structure more accurately and quickly than previous works of which the basic units are text lines, the proposed parsing method takes text regions with hierarchical structure as input. Furthermore, we define a document model that is able to describe geometric characteristics and logical structure information of documents efficiently and present its automated creation method. Experimental results with 372 images scanned from the IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) show that the method has performed logical structure analysis successfully and generated a document model automatically. Particularly, the method generates SGML/XML documents as the result of structural analysis, so that it enhances the reusability of documents and independence of platform.


title: Prevalence of Parent-Reported ADHD Diagnosis and Associated Treatment Among U.S. Children and Adolescents, 2016

abstract: The purpose of this study is to estimate the national prevalence of parent-reported attention deficit/hyperactivity disorder (ADHD) diagnosis and treatment among U.S. children 2–17 years of age using the 2016 National Survey of Children’s Health (NSCH). The NSCH is a nationally representative, cross-sectional survey of parents regarding their children’s health that underwent a redesign before the 2016 data collection. It included indicators of lifetime receipt of an ADHD diagnosis by a health care provider, whether the child currently had ADHD, and receipt of medication and behavioral treatment for ADHD. Weighted prevalence estimates were calculated overall and by demographic and clinical subgroups (n = 45,736). In 2016, an estimated 6.1 million U.S. children 2–17 years of age (9.4%) had ever received an ADHD diagnosis. Of these, 5.4 million currently had ADHD, which was 89.4% of children ever diagnosed with ADHD and 8.4% of all U.S. children 2–17 years of age. Of children with current ADHD, almost two th...


title: PHOG-derived aesthetic measures applied to color photographs of artworks, natural scenes and objects

abstract: Previous research in computational aesthetics has led to the identification of multiple image features that, in combination, can be related to the aesthetic quality of images, such as photographs. Moreover, it has been shown that aesthetic artworks possess specific higher-order statistical properties, such as a scale-invariant Fourier spectrum, that can be linked to coding mechanisms in the human visual system. In the present work, we derive novel measures based on a PHOG representation of images for image properties that have been studied in the context of the aesthetic assessment of images previously. We demonstrate that a large dataset of colored aesthetic paintings of Western provenance is characterized by a specific combination of the PHOG-derived aesthetic measures (high self-similarity, moderate complexity and low anisotropy). In this combination, the artworks differ significantly from seven other datasets of photographs that depict various types of natural and man-made scenes, patterns and objects. To the best of our knowledge, this is the first time that these features have been derived and evaluated on a large dataset of different image categories.


title: Both aerobic endurance and strength training programmes improve cardiovascular health in obese adults.

abstract: Regular exercise training is recognized as a powerful tool to improve work capacity, endothelial function and the cardiovascular risk profile in obesity, but it is unknown which of high-intensity aerobic exercise, moderate-intensity aerobic exercise or strength training is the optimal mode of exercise. In the present study, a total of 40 subjects were randomized to high-intensity interval aerobic training, continuous moderate-intensity aerobic training or maximal strength training programmes for 12 weeks, three times/week. The high-intensity group performed aerobic interval walking/running at 85-95% of maximal heart rate, whereas the moderate-intensity group exercised continuously at 60-70% of maximal heart rate; protocols were isocaloric. The strength training group performed 'high-intensity' leg press, abdominal and back strength training. Maximal oxygen uptake and endothelial function improved in all groups; the greatest improvement was observed after high-intensity training, and an equal improvement was observed after moderate-intensity aerobic training and strength training. High-intensity aerobic training and strength training were associated with increased PGC-1alpha (peroxisome-proliferator-activated receptor gamma co-activator 1alpha) levels and improved Ca(2+) transport in the skeletal muscle, whereas only strength training improved antioxidant status. Both strength training and moderate-intensity aerobic training decreased oxidized LDL (low-density lipoprotein) levels. Only aerobic training decreased body weight and diastolic blood pressure. In conclusion, high-intensity aerobic interval training was better than moderate-intensity aerobic training in improving aerobic work capacity and endothelial function. An important contribution towards improved aerobic work capacity, endothelial function and cardiovascular health originates from strength training, which may serve as a substitute when whole-body aerobic exercise is contra-indicated or difficult to perform.


abstract: Spin-transfer torque magnetic random access memory (STT-MRAM) is a novel, magnetic memory technology that leverages the base platform established by an existing 100pnm node memory product called MRAM to enable a scalable nonvolatile memory solution for advanced process nodes. STT-MRAM features fast read and write times, small cell sizes of 6F2 and potentially even smaller, and compatibility with existing DRAM and SRAM architecture with relatively small associated cost added. STT-MRAM is essentially a magnetic multilayer resistive element cell that is fabricated as an additional metal layer on top of conventional CMOS access transistors. In this review we give an overview of the existing STT-MRAM technologies currently in research and development across the world, as well as some specific discussion of results obtained at Grandis and with our foundry partners. We will show that in-plane STT-MRAM technology, particularly the DMTJ design, is a mature technology that meets all conventional requirements for an STT-MRAM cell to be a nonvolatile solution matching DRAM and/or SRAM drive circuitry. Exciting recent developments in perpendicular STT-MRAM also indicate that this type of STT-MRAM technology may reach maturity faster than expected, allowing even smaller cell size and product introduction at smaller nodes.


title: DEVELOPMENT OF CUSTOMER RELATIONSHIP MANAGEMENT SYSTEM TO IMPROVE SERVICE QUALITY IN PT MANULIFE LIFE INSURANCE INDONESIA

abstract: Customer Relationship Management System (CRMS) Development in order to improve service quality in PT. AJ. Manulife Indonesia is done by comprehending the performance model of the company and the factors that are affecting the company performance improvement and the quality of the decision to be taken by top management. System Dynamic is a method that can be used to stimulate complex systems. System Dynamics approach is expected to build a model of corporate performance that can be used to evaluate the quality of service to customers so that they can make decisions quickly and accurately. This study proves that the model is built with standard models used as changing targets and it can simulate a target quality of service to customers by delivering current and future achievement. Achievement in the future is influenced by the value of achievement of SLA, Response Time, and Defect, where the greater value of control, the greater value of the correction rate so that the GAP will be smaller. Correction rate which is determined in this study was 10%, 20%, and 30% of GAP (CB)


title: On Spherically Symmetric Non-Static Space-Times Admitting Homothetic Motions

abstract: Spherically symmetric solutions admitting a homothetic Killing vector field (HKVF) either orthogonal, $\eta_{\bot}$, or parallel,$\eta_{||}$, to the 4-velocity vector field, $u^a$, are studied. New self-similar solution of Einstein's field equation is found in the case when HKVF is in a general form. Some physical properties of the obtained solution are examined.


title: ECNU: Using Traditional Similarity Measurements and Word Embedding for Semantic Textual Similarity Estimation

abstract: This paper reports our submissions to semantic textual similarity task, i.e., task 2 in Semantic Evaluation 2015. We built our systems using various traditional features, such as string-based, corpus-based and syntactic similarity metrics, as well as novel similarity measures based on distributed word representations, which were trained using deep learning paradigms. Since the training and test datasets consist of instances collected from various domains, three different strategies of the usage of training datasets were explored: (1) use all available training datasets and build a unified supervised model for all test datasets; (2) select the most similar training dataset and separately construct a individual model for each test set; (3) adopt multi-task learning framework to make full use of available training sets. Results on the test datasets show that using all datasets as training set achieves the best averaged performance and our best system ranks 15 out of 73.


title: An ensemble classification approach for handling spatio-temporal drifts in partially labeled data streams

abstract: The classification of streaming data requires learning in an environment where the distribution of the incoming data might change continuously. Stream classification methodologies need to adapt to these changes under limitations of time and memory resources. As such, it is not possible to expect all the samples in the stream to be labeled, as labeling is often time consuming and expensive. In this paper a new ensemble classification approach is proposed, which can handle Spatio-Temporal drifts in streams even when the labeling is limited. The proposed methodology uses a grid density clustering approach to track drifts in the spatial configuration of the data, and maintains a set of classifier models local to each cluster, to track its evolution over time. Structured weighted aggregation of the models across all clusters is performed to produce an overall effective prediction on a new sample. Additionally, a uniform sampling approach amenable to the grid representation of the clusters is proposed, which selects samples to be labeled while preserving the grid density information of the stream. This provides for better selection of representative samples to be labeled, for improved drift detection and handling, while maintaining the labeling budget. Experimental comparison with state of the art drift handling systems shows that the proposed methodology is able to give a high classification performance, with a manageable ensemble size and with only 10% of the samples labeled.


title: Fine-Grained Entity Type Classification by Jointly Learning Representations and Label Embeddings

abstract: Fine-grained entity type classification (FETC) is the task of classifying an entity mention to a broad set of types. Distant supervision paradigm is extensively used to generate training data for this task. However, generated training data assigns same set of labels to every mention of an entity without considering its local context. Existing FETC systems have two major drawbacks: assuming training data to be noise free and use of hand crafted features. Our work overcomes both drawbacks. We propose a neural network model that jointly learns entity mentions and their context representation to eliminate use of hand crafted features. Our model treats training data as noisy and uses non-parametric variant of hinge loss function. Experiments show that the proposed model outperforms previous state-of-the-art methods on two publicly available datasets, namely FIGER (GOLD) and BBN with an average relative improvement of 2.69% in micro-F1 score. Knowledge learnt by our model on one dataset can be transferred to other datasets while using same model or other FETC systems. These approaches of transferring knowledge further improve the performance of respective models.


title: Generalizing Agile Software Development Life Cycle

abstract: In last decade, various agile methods have been introduced and used by software industry. It has been observed that many practitioners are using hybrid of agile methods and traditional methods. The knowledge of agile software development process about the theoretical grounds, applicability in large development settings and connections to establish software engineering disciplines remain mostly in dark. It has been reported that it is difficult for average manager to implement agile method in the organization. Further, every agile method has its own development cycle that brings technological, managerial and environmental changes in organization. A proper roadmap of agile software development in the form of agile software development life cycle can be developed to address the aforesaid issues of agile software development process. Thus, there is strong need of agile software development life cycle that clearly defines the phases included in any agile method and also describes the artifacts of each phase. This generalization of agile software development life cycle provides the guideline for average developers about usability, suitability, applicability of agile methods.


title: Modeling Enterohepatic Circulation

abstract: Purpose of ReviewThe objective of the current analysis is to review the anatomy and physiology of the enterohepatic circulation (EHC); describe how those intricacies affect drug concentration profiles; present various pharmacokinetic models used in the literature to describe EHC concentration-time data; and provide a categorization of these models based on common features.Recent FindingsThe presence of EHC results in longer apparent drug half-lives and the appearance of multiple secondary peaks. Several empirical modeling strategies are present in the literature and these have increased in complexity as our understanding of the impact EHC has on pharmacokinetics, and as the increase in computing power has allowed these models to become more tractable. Although the intent of all these models is to capture the nuances of concentration-time profiles when EHC exists, they remain deficient in their mechanistic representation of the EHC process. In addition, limitations in biological sampling and experimental design present difficulties in our ability to distinguish among some of these models. Finally, the quantitative comparison of different models to literature examples is not possible. Nonetheless, our knowledge of the EHC process and the limitations of our models can be used to inform our interpretation of pharmacokinetic results presented in the literature.SummaryModeling strategies of EHC vary in the literature. This review article provides a basic review of the EHC process, relates those models to the EHC process, and presents a categorization and summary of modeling strategies implemented to represent EHC.


title: An architecture-level cache simulation framework supporting advanced PMA STT-MRAM

abstract: With integration density on-chip rocketing up, leakage power dominates the whole power budget of contemporary CMOS technology based memory, especially for SRAM based on-chip cache. To overcome the aggravating “power wall” issue, some emerging memory technologies such as STT-MRAM (Spin transfer torque magnetic RAM), PCRAM (Phase change RAM), and ReRAM(Resistive RAM) are proposed as promising candidates for next generation cache design. Although there are several existing simulation tools available for cache design, such as NVSim and CACTI, they either cannot support the most advanced PMA (Perpendicular magnetic anisotropy) STT-MRAM model or lack the ability for multi-banked large capacity cache simulation. In this paper, we propose an architecture level design framework for cache design from device level up to array structure level, which can support the most advanced PMA STT-MRAM technology. The simulation results are analyzed and compared with those produced by NVSim, which prove the correctness of our framework. The potential benefits of PMA STT-MRAM used as multi-banked L2 and L3 cache are also investigated in the paper. We believe that our framework will be helpful for computer architecture researchers to adopt the PMA STT-MRAM in on-chip cache design.


title: MARVEL: A Mixed Reality Learning Environment for Vocational Training in Mechatronics

abstract: Mechatronics is an area that merges multidisciplinary knowledge coming from mechanical engineering, electronics and computer technology. Vocational training in mechatronics requires the teaching of ‘multi-skills’ in various learning contexts, as well as a good blend of learning in the classroom and practical training in work surroundings. This paper presents the contribution of MARVEL to meet these target requirements. The aim of the MARVEL project is to implement and evaluate learning environments for mechatronics in vocational training, allowing students ubiquitous access to physical workshops and laboratory facilities from remote places. The project will cover concepts that merge real and virtual as well as local and remote worlds in real time. We describe a concept of experiential learning which proposes a ‘mix’ of virtual learning sessions (remote labs, simulations) in combination with learning in real labs or even at the workplace.


title: A Light-weight Solution to Preservation of Access Pattern Privacy in Un-trusted Clouds

abstract: Cloud computing is a new computing paradigm that is gaining increased popularity. More and more sensitive user data are stored in the cloud. The privacy of users' access pattern to the data should be protected to prevent un-trusted cloud servers from inferring users' private information or launching stealthy attacks. Meanwhile, the privacy protection schemes should be efficient as cloud users often use thin client devices to access the cloud. In this paper, we propose a lightweight scheme to protect the privacy of data access pattern. Comparing with existing state-of-the-art solutions, our scheme incurs less communication and computational overhead, requires significantly less storage space at the cloud user, while consuming similar storage space at the cloud server. Rigorous proofs and extensive evaluations have been conducted to demonstrate that the proposed scheme can hide the data access pattern effectively in the long run after a reasonable number of accesses have been made.


title: RPNet: an End-to-End Network for Relative Camera Pose Estimation

abstract: This paper addresses the task of relative camera pose estimation from raw image pixels, by means of deep neural networks. The proposed RPNet network takes pairs of images as input and directly infers the relative poses, without the need of camera intrinsic/extrinsic. While state-of-the-art systems based on SIFT + RANSAC, are able to recover the translation vector only up to scale, RPNet is trained to produce the full translation vector, in an end-to-end way. Experimental results on the Cambridge Landmark dataset show very promising results regarding the recovery of the full translation vector. They also show that RPNet produces more accurate and more stable results than traditional approaches, especially for hard images (repetitive textures, textureless images, etc). To the best of our knowledge, RPNet is the first attempt to recover full translation vectors in relative pose estimation.


title: The Concept of Flow

abstract: What constitutes a good life? Few questions are of more fundamental importance to a positive psychology. Flow research has yielded one answer, providing an understanding of experiences during which individuals are fully involved in the present moment. Viewed through the experiential lens of flow, a good life is one that is characterized by complete absorption in what one does. In this chapter, we describe the flow model of optimal experience and optimal development, explain how flow and related constructs have been measured, discuss recent work in this area, and identify some promising directions for future research.


title: Population studies of mortality.

abstract: Population Studies has become the principal outlet for demographic research on mortality. Many of the advances in the measurement of mortality in data-poor countries were reported in its pages. It has also published most of the influential articles which attempted to make a broad-scale assessment of the sources of mortality change. These include special attention to developments in England and Wales and Sri Lanka. Capitalizing on the widespread availability of demographic surveys, articles in the 1980s featured careful analyses of the demographic correlates of child mortality. Such studies have passed the point of diminishing returns, and declines in child mortality have focused increased attention on conditions among adults. Unfortunately, demography has not developed the means for measuring and analysing adult mortality in underdeveloped countries that are equivalent in their power to methods for studying child mortality.


title: Better Zero-Knowledge Proofs for Lattice Encryption and Their Application to Group Signatures

abstract: Lattice problems are an attractive basis for cryptographic systems because they seem to offer better security than discrete logarithm and factoring based problems. Efficient lattice-based constructions are known for signature and encryption schemes. However, the constructions known for more sophisticated schemes such as group signatures are still far from being practical. In this paper we make a number of steps towards efficient lattice-based constructions of more complex cryptographic protocols. First, we provide a more efficient way to prove knowledge of plaintexts for lattice-based encryption schemes. We then show how our new protocol can be combined with a proof of knowledge for Pedersen commitments in order to prove that the committed value is the same as the encrypted one. Finally, we make use of this to construct a new group signature scheme that is a “hybrid” in the sense that privacy holds under a lattice-based assumption while security is discrete-logarithm-based.


title: Dense Multi-path U-Net for Ischemic Stroke Lesion Segmentation in Multiple Image Modalities

abstract: Delineating infarcted tissue in ischemic stroke lesions is crucial to determine the extend of damage and optimal treatment for this life-threatening condition. However, this problem remains challenging due to high variability of ischemic strokes' location and shape. Recently, fully-convolutional neural networks (CNN), in particular those based on U-Net, have led to improved performances for this task. In this work, we propose a novel architecture that improves standard U-Net based methods in three important ways. First, instead of combining the available image modalities at the input, each of them is processed in a different path to better exploit their unique information. Moreover, the network is densely-connected (i.e., each layer is connected to all following layers), both within each path and across different paths, similar to HyperDenseNet. This gives our model the freedom to learn the scale at which modalities should be processed and combined. Finally, inspired by the Inception architecture, we improve standard U-Net modules by extending inception modules with two convolutional blocks with dilated convolutions of different scale. This helps handling the variability in lesion sizes. We split the 93 stroke datasets into training and validation sets containing 83 and 9 examples respectively. Our network was trained on a NVidia TITAN XP GPU with 16 GBs RAM, using ADAM as optimizer and a learning rate of 1$\times$10$^{-5}$ during 200 epochs. Training took around 5 hours and segmentation of a whole volume took between 0.2 and 2 seconds, as average. The performance on the test set obtained by our method is compared to several baselines, to demonstrate the effectiveness of our architecture, and to a state-of-art architecture that employs factorized dilated convolutions, i.e., ERFNet.


title: A zigbee-based home automation system

abstract: In recent years, the home environment has seen a rapid introduction of network enabled digital technology. This technology offers new and exciting opportunities to increase the connectivity of devices within the home for the purpose of home automation. Moreover, with the rapid expansion of the Internet, there is the added potential for the remote control and monitoring of such network enabled devices. However, the adoption of home automation systems has been slow. This paper identifies the reasons for this slow adoption and evaluates the potential of ZigBee for addressing these problems through the design and implementation of a flexible home automation architecture. A ZigBee based home automation system and Wi-Fi network are integrated through a common home gateway. The home gateway provides network interoperability, a simple and flexible user interface, and remote access to the system. A dedicated virtual home is implemented to cater for the system's security and safety needs. To demonstrate the feasibility and effectiveness of the proposed system, four devices, a light switch, radiator valve, safety sensor and ZigBee remote control have been developed and evaluated with the home automation system.


title: Dynamic metrics for java

abstract: In order to perform meaningful experiments in optimizing compilation and run-time system design, researchers usually rely on a suite of benchmark programs of interest to the optimization technique under consideration. Programs are described as numeric, memory-intensive, concurrent, or object-oriented, based on a qualitative appraisal, in some cases with little justification. We believe it is beneficial to quantify the behaviour of programs with a concise and precisely defined set of metrics, in order to make these intuitive notions of program behaviour more concrete and subject to experimental validation. We therefore define and measure a set of unambiguous, dynamic, robust and architecture-independent metrics that can be used to categorize programs according to their dynamic behaviour in five areas: size, data structure, memory use, concurrency, and polymorphism. A framework computing some of these metrics for Java programs is presented along with specific results demonstrating how to use metric data to understand a program's behaviour, and both guide and evaluate compiler optimizations.


Fake iris detection based on 3D structure of iris pattern


title: Interpretable&Explorable Approximations of Black Box Models

abstract: We propose Black Box Explanations through Transparent Approximations (BETA), a novel model agnostic framework for explaining the behavior of any black-box classifier by simultaneously optimizing for fidelity to the original model and interpretability of the explanation. To this end, we develop a novel objective function which allows us to learn (with optimality guarantees), a small number of compact decision sets each of which explains the behavior of the black box model in unambiguous, well-defined regions of feature space. Furthermore, our framework also is capable of accepting user input when generating these approximations, thus allowing users to interactively explore how the black-box model behaves in different subspaces that are of interest to the user. To the best of our knowledge, this is the first approach which can produce global explanations of the behavior of any given black box model through joint optimization of unambiguity, fidelity, and interpretability, while also allowing users to explore model behavior based on their preferences. Experimental evaluation with real-world datasets and user studies demonstrates that our approach can generate highly compact, easy-to-understand, yet accurate approximations of various kinds of predictive models compared to state-of-the-art baselines.


title: A High-Performance, Scalable Infrastructure for Large-Scale Active DNS Measurements

abstract: The domain name system (DNS) is a core component of the Internet. It performs the vital task of mapping human readable names into machine readable data (such as IP addresses, which hosts handle e-mail, and so on). The content of the DNS reveals a lot about the technical operations of a domain. Thus, studying the state of large parts of the DNS over time reveals valuable information about the evolution of the Internet. We collect a unique long-term data set with daily DNS measure-ments for all the domains under the main top-level domains (TLDs) on the Internet (including .com , .net , and .org , comprising 50% of the global DNS name space). This paper discusses the challenges of performing such a large-scale active measurement. These challenges include scaling the daily measurement to collect data for the largest TLD ( .com , with 123M names) and ensuring that a measurement of this scale does not impose an unacceptable burden on the global DNS infrastructure. The paper discusses the design choices we have made to meet these challenges and documents the design of the measurement system we implemented based on these choices. Two case studies related to cloud e-mail services illustrate the value of measuring the DNS at this scale. The data this system collects is valuable to the network research community. Therefore, we end this paper by discussing how we make the data accessible to other researchers.


title: A Novel Planar Reconfigurable Monopulse Antenna for Indoor Smart Wireless Access Points’ Application

abstract: In this paper, a novel planar reconfigurable monopulse antenna with compact size is proposed and studied for indoor smart wireless access points (WAPs) application. Circular polarization is achieved by using a dual-mode substrate integrated waveguide (SIW) slot antenna. Two diagonal ports are individually used to excite the SIW element antenna to achieve a 180° phase-shifting excitation. A reconfigurable $2 \times 2$ prototype is developed with the proposed element antenna, and it uses eight PIN diodes as four switches to implement the reconfiguration. Sum beams, difference beams in azimuth and elevation planes, and bidifference beams are generated. Measurement results verify the designed prototype, showing that the antenna has a null depth of over 30 dB and a peak sum beam gain of 12.29 dBi. The measured 10-dB return loss bandwidths are 5.72–5.96, 5.75–5.95, 5.73–5.97, and 5.76–5.94 GHz for the sum, elevation difference, azimuth difference and bidifference beams, respectively. And their measured 3-dB axial ratio (AR) bandwidths are 5.84–5.88, 5.86–5.90, 5.85–5.9, 5.85–5.9, and 5.84–5.91 GHz. The capability of reconfigurable monopulse beams ensures that the proposed antenna is a good candidate for smart WAP applications.


title: Empirically validated web page design metrics

abstract: A quantitative analysis of a large collection of expert-rated web sites reveals that page-level metrics can accurately predict if a site will be highly rated. The analysis also provides empirical evidence that important metrics, including page composition, page formatting, and overall page characteristics, differ among web site categories such as education, community, living, and finance. These results provide an empirical foundation for web site design guidelines and also suggest which metrics can be most important for evaluation via user studies.


title: Business models and tariff simulation in car-sharing services

abstract: The paper considers an important new and growing business in sustainable transportation, car-sharing services. This is, to our knowledge, the first comprehensive analysis of car-sharing services from the business model point of view. Specifically, we apply and introduce a standard and reproducible way to compare the business models of car-sharing companies. Our analysis results show that a crucial issue in defining car-sharing services is the creation of customized tariff plans. Thus, as a second contribution of our paper, we introduce a specific solution based on Monte Carlo simulation. This tool simulates the existing price and tariff policies or the introduction of new ones for different profiles of car-sharing users, according to different mobility needs and the traffic congestion of the urban area. As an example, we use our methodology to provide an in-depth description of the situation in the city of Turin, Italy.


title: A Robust System for Noisy Image Classification Combining Denoising Autoencoder and Convolutional Neural Network

abstract: Image classification, a complex perceptual task with many real life important applications, faces a major challenge in presence of noise. Noise degrades the performance of the classifiers and makes them less suitable in real life scenarios. To solve this issue, several researches have been conducted utilizing denoising autoencoder (DAE) to restore original images from noisy images and then Convolutional Neural Network (CNN) is used for classification. The existing models perform well only when the noise level present in the training set and test set are same or differs only a little. To fit a model in real life applications, it should be independent to level of noise. The aim of this study is to develop a robust image classification system which performs well at regular to massive noise levels. The proposed method first trains a DAE with low-level noise-injected images and a CNN with noiseless native images independently. Then it arranges these two trained models in three different combinational structures: CNN, DAE-CNN, and DAE-DAE-CNN to classify images corrupted with zero, regular and massive noises, accordingly. Final system outcome is chosen by applying the winner-takes-all combination on individual outcomes of the three structures. Although proposed system consists of three DAEs and three CNNs in different structure layers, the DAEs and CNNs are the copy of same DAE and CNN trained initially which makes it computationally efficient as well. In DAE-DAE-CNN, two identical DAEs are arranged in a cascaded structure to make the structure well suited for classifying massive noisy data while the DAE is trained with low noisy image data. The proposed method is tested with MNIST handwritten numeral dataset with different noise levels. Experimental results revealed the effectiveness of the proposed method showing better results than individual structures as well as the other related methods.


title: Substrate Integrated Waveguide (SIW) Monopulse Slot Antenna Array

abstract: A Ka-band compact single layer substrate integrated waveguide monopulse slot array antenna for the application of monopulse tracking system is designed, fabricated and measured. The feeding network as well as the monopulse comparator and the subarrays is integrated on the same dielectric with the size of 140 mmtimes130 mm. The bandwidth ( S11 < -10 dB) of the antenna is 7.39% with an operating frequency range of 30.80 GHz-33.14 GHz. The maximum gain at 31.5 GHz is 18.74 dB and the maximum null depth is -46.3 dB. The sum- and difference patterns of three planes: H-plane, E-plane and diagonal plane are measured and presented.


title: Geometric Generalization Based Zero-Shot Learning Dataset Infinite World: Simple Yet Powerful

abstract: Raven's Progressive Matrices are one of the widely used tests in evaluating the human test taker's fluid intelligence. Analogously, this paper introduces geometric generalization based zero-shot learning tests to measure the rapid learning ability and the internal consistency of deep generative models. Our empirical research analysis on state-of-the-art generative models discern their ability to generalize concepts across classes. In the process, we introduce Infinite World, an evaluable, scalable, multi-modal, light-weight dataset and Zero-Shot Intelligence Metric ZSI. The proposed tests condenses human-level spatial and numerical reasoning tasks to its simplistic geometric forms. The dataset is scalable to a theoretical limit of infinity, in numerical features of the generated geometric figures, image size and in quantity. We systematically analyze state-of-the-art model's internal consistency, identify their bottlenecks and propose a pro-active optimization method for few-shot and zero-shot learning.


title: AROMA: A Deep Multi-Task Learning Based Simple and Complex Human Activity Recognition Method Using Wearable Sensors

abstract: Human activity recognition (HAR) is a promising research issue in ubiquitous and wearable computing. However, there are some problems existing in traditional methods: 1) They treat HAR as a single label classification task, and ignore the information from other related tasks, which is helpful for the original task. 2) They need to predesign features artificially, which are heuristic and not tightly related to HAR task. To address these problems, we propose AROMA (human activity recognition using deep multi-task learning). Human activities can be divided into simple and complex activities. They are closely linked. Simple and complex activity recognitions are two related tasks in AROMA. For simple activity recognition task, AROMA utilizes a convolutional neural network (CNN) to extract deep features, which are task dependent and non-handcrafted. For complex activity recognition task, AROMA applies a long short-term memory (LSTM) network to learn the temporal context of activity data. In addition, there is a shared structure between the two tasks, and the object functions of these two tasks are optimized jointly. We evaluate AROMA on two public datasets, and the experimental results show that AROMA is able to yield a competitive performance in both simple and complex activity recognitions.


title: Linear Transformations for Cross-lingual Semantic Textual Similarity

abstract: Cross-lingual semantic textual similarity systems estimate the degree of the meaning similarity between two sentences, each in a different language. State-of-the-art algorithms usually employ machine translation and combine vast amount of features, making the approach strongly supervised, resource rich, and difficult to use for poorly-resourced languages. In this paper, we study linear transformations, which project monolingual semantic spaces into a shared space using bilingual dictionaries. We propose a novel transformation, which builds on the best ideas from prior works. We experiment with unsupervised techniques for sentence similarity based only on semantic spaces and we show they can be significantly improved by the word weighting. Our transformation outperforms other methods and together with word weighting leads to very promising results on several datasets in different languages.


Social media marketing and advertising


title: A2: Analog Malicious Hardware

abstract: While the move to smaller transistors has been a boon for performance it has dramatically increased the cost to fabricate chips using those smaller transistors. This forces the vast majority of chip design companies to trust a third party -- often overseas -- to fabricate their design. To guard against shipping chips with errors (intentional or otherwise) chip design companies rely on post-fabrication testing. Unfortunately, this type of testing leaves the door open to malicious modifications since attackers can craft attack triggers requiring a sequence of unlikely events, which will never be encountered by even the most diligent tester. In this paper, we show how a fabrication-time attacker can leverage analog circuits to create a hardware attack that is small (i.e., requires as little as one gate) and stealthy (i.e., requires an unlikely trigger sequence before effecting a chip's functionality). In the open spaces of an already placed and routed design, we construct a circuit that uses capacitors to siphon charge from nearby wires as they transition between digital values. When the capacitors fully charge, they deploy an attack that forces a victim flip-flop to a desired value. We weaponize this attack into a remotely-controllable privilege escalation by attaching the capacitor to a wire controllable and by selecting a victim flip-flop that holds the privilege bit for our processor. We implement this attack in an OR1200 processor and fabricate a chip. Experimental results show that our attacks work, show that our attacks elude activation by a diverse set of benchmarks, and suggest that our attacks evade known defenses.


title: Efficient visual event detection using volumetric features

abstract: This paper studies the use of volumetric features as an alternative to popular local descriptor approaches for event detection in video sequences. Motivated by the recent success of similar ideas in object detection on static images, we generalize the notion of 2D box features to 3D spatio-temporal volumetric features. This general framework enables us to do real-time video analysis. We construct a realtime event detector for each action of interest by learning a cascade of filters based on volumetric features that efficiently scans video sequences in space and time. This event detector recognizes actions that are traditionally problematic for interest point methods - such as smooth motions where insufficient space-time interest points are available. Our experiments demonstrate that the technique accurately detects actions on real-world sequences and is robust to changes in viewpoint, scale and action speed. We also adapt our technique to the related task of human action classification and confirm that it achieves performance comparable to a current interest point based human activity recognizer on a standard database of human activities.


title: Curve matching using the fast marching method

abstract: Common techniques for curve alignment find a solution in the form of a shortest network path by means of dynamic programming. In this paper we present an approach that employs Sethian’s Fast Marching Method to find the solution with sub-resolution accuracy and in consistence with the underlying continuous problem. We demonstrate how the method may be applied to compare closed curves, morph one curve into another, and compute curve averages. Our method is based on a local curve dissimilarity function F(t,s) that compares the two input curves \(\mathcal{C}_{1}(t)\) and \(\mathcal{C}_{2}(s)\) at given points t and s. In our experiments, we compare dissimilarity functions based on local curvature information and on shape contexts. We have tested the algorithm on a database of 110 sample curves by performing “best matches” experiments.


title: ASSOCIATION BETWEEN SOCIAL MEDIA USE AND DEPRESSION AMONG U.S. YOUNG ADULTS

abstract: BACKGROUND ::: Social media (SM) use is increasing among U.S. young adults, and its association with mental well-being remains unclear. This study assessed the association between SM use and depression in a nationally representative sample of young adults. ::: ::: ::: METHODS ::: We surveyed 1,787 adults ages 19 to 32 about SM use and depression. Participants were recruited via random digit dialing and address-based sampling. SM use was assessed by self-reported total time per day spent on SM, visits per week, and a global frequency score based on the Pew Internet Research Questionnaire. Depression was assessed using the Patient-Reported Outcomes Measurement Information System (PROMIS) Depression Scale Short Form. Chi-squared tests and ordered logistic regressions were performed with sample weights. ::: ::: ::: RESULTS ::: The weighted sample was 50.3% female and 57.5% White. Compared to those in the lowest quartile of total time per day spent on SM, participants in the highest quartile had significantly increased odds of depression (AOR = 1.66, 95% CI = 1.14-2.42) after controlling for all covariates. Compared with those in the lowest quartile, individuals in the highest quartile of SM site visits per week and those with a higher global frequency score had significantly increased odds of depression (AOR = 2.74, 95% CI = 1.86-4.04; AOR = 3.05, 95% CI = 2.03-4.59, respectively). All associations between independent variables and depression had strong, linear, dose-response trends. Results were robust to all sensitivity analyses. ::: ::: ::: CONCLUSIONS ::: SM use was significantly associated with increased depression. Given the proliferation of SM, identifying the mechanisms and direction of this association is critical for informing interventions that address SM use and depression.


title: Data processing and semantics for advanced internet of things (IoT) applications: modeling, annotation, integration, and perception

abstract: This tutorial presents tools and techniques for effectively utilizing the Internet of Things (IoT) for building advanced applications, including the Physical-Cyber-Social (PCS) systems. The issues and challenges related to IoT, semantic data modelling, annotation, knowledge representation (e.g. modelling for constrained environments, complexity issues and time/location dependency of data), integration, analysis, and reasoning will be discussed. The tutorial will describe recent developments on creating annotation models and semantic description frameworks for IoT data (e.g. such as W3C Semantic Sensor Network ontology). A review of enabling technologies and common scenarios for IoT applications from the data and knowledge engineering point of view will be discussed. Information processing, reasoning, and knowledge extraction, along with existing solutions related to these topics will be presented. The tutorial summarizes state-of-the-art research and developments on PCS systems, IoT related ontology development, linked data, domain knowledge integration and management, querying large-scale IoT data, and AI applications for automated knowledge extraction from real world data.


title: Empirical Study of Usage and Performance of Java Collections

abstract: Collection data structures have a major impact on the performance of applications, especially in languages such as Java, C#, or C++. This requires a developer to select an appropriate collection from a large set of possibilities, including different abstractions (e.g. list, map, set, queue), and multiple implementations. In Java, the default implementation of collections is provided by the standard Java Collection Framework (JCF). However, there exist a large variety of less known third-party collection libraries which can provide substantial performance benefits with minimal code changes. In this paper, we first study the popularity and usage patterns of collection implementations by mining a code corpus comprised of 10,986 Java projects. We use the results to evaluate and compare the performance of the six most popular alternative collection libraries in a large variety of scenarios. We found that for almost every scenario and JCF collection type there is an alternative implementation that greatly decreases memory consumption while offering comparable or even better execution time. Memory savings range from 60% to 88% thanks to reduced overhead and some operations execute 1.5x to 50x faster. We present our results as a comprehensive guideline to help developers in identifying the scenarios in which an alternative implementation can provide a substantial performance improvement. Finally, we discuss how some coding patterns result in substantial performance differences of collections.


title: Path planning with Inventory-driven Jump-Point-Search

abstract: In many navigational domains the traversability of cells is conditioned on the path taken. This is often the case in video-games, in which a character may need to acquire a certain object (i.e., a key or a flying suit) to be able to traverse specific locations (e.g., doors or high walls). In order for non-player characters to handle such scenarios we present invJPS, an "inventory-driven" pathfinding approach based on the highly successful grid-based Jump-Point-Search (JPS) algorithm. We show, formally and experimentally, that the invJPS preserves JPS's optimality guarantees and its symmetry breaking advantages in inventory-based variants of game maps.


title: On Multiplicative Integration with Recurrent Neural Networks

abstract: We introduce a general and simple structural design called Multiplicative Integration (MI) to improve recurrent neural networks (RNNs). MI changes the way in which information from difference sources flows and is integrated in the computational building block of an RNN, while introducing almost no extra parameters. The new structure can be easily embedded into many popular RNN models, including LSTMs and GRUs. We empirically analyze its learning behaviour and conduct evaluations on several tasks using different RNN models. Our experimental results demonstrate that Multiplicative Integration can provide a substantial performance boost over many of the existing RNN models.


Efficient Information Dissemination of 3 D Point Cloud Mapping Data for Autonomous Vehicles


title: Event-related brain dynamics in continuous sustained-attention tasks

abstract: Event-related brain dynamics of electroencephalographic (EEG) activity in a continuous compensatory tracking task (CTT) and in a continuous driving simulation were analyzed by independent component analysis (ICA) and time-frequency techniques. We showed that changes in the level of subject performance are accompanied by distinct changes in EEG spectrum of a class of bilateral posterior independent EEG components. During periods of high-error (drowsy) performance, tonic alpha band EEG power was significantly elevated, compared to that during periods of low-error (alert) performance. In addition, characteristic transient (phasic) alpha and other band increases and decreases followed critical task events, depending on current performance level. These performance-related and event-related spectral changes were consistently observed across subjects and sessions, and were remarkably similar across the two continuous sustained-attention tasks.


title: T-count Optimized Design of Quantum Integer Multiplication

abstract: Quantum circuits of many qubits are extremely difficult to realize; thus, the number of qubits is an important metric in a quantum circuit design. Further, scalable and reliable quantum circuits are based on Clifford + T gates. An efficient quantum circuit saves quantum hardware resources by reducing the number of T gates without substantially increasing the number of qubits. Recently, the design of a quantum multiplier is presented by Babu [1] which improves the existing works in terms of number of quantum gates, number of qubits, and delay. However, the recent design is not based on fault-tolerant Clifford + T gates. Also, it has large number of qubits and garbage outputs. Therefore, this work presents a T-count optimized quantum circuit for integer multiplication with only $4 \cdot n + 1$ qubits and no garbage outputs. The proposed quantum multiplier design saves the T-count by using a novel quantum conditional adder circuit. Also, where one operand to the controlled adder is zero, the conditional adder is replaced with a Toffoli gate array to further save the T gates. To have fair comparison with the recent design by Babu and get an actual estimate of the T-count, it is made garbageless by using Bennett's garbage removal scheme. The proposed design achieves an average T-count savings of $47.55\%$ compared to the recent work by Babu. Further, comparison is also performed with other recent works by Lin et. al. [2], and Jayashree et. al.[3]. Average T-count savings of $62.71\%$ and $26.30\%$ are achieved compared to the recent works by Lin et. al., and Jayashree et. al., respectively.


title: Complex Event Detection at Wire Speed with FPGAs

abstract: Complex event detection is an advanced form of data stream processing where the stream(s) are scrutinized to identify given event patterns. The challenge for many complex event processing (CEP) systems is to be able to evaluate event patterns on high-volume data streams while adhering to real-time constraints. To solve this problem, in this paper we present a hardware-based complex event detection system implemented on field-programmable gate arrays (FPGAs). By inserting the FPGA directly into the data path between the network interface and the CPU, our solution can detect complex events at gigabit wire speed with constant and fully predictable latency, independently of network load, packet size, or data distribution. This is a significant improvement over CPU-based systems and an architectural approach that opens up interesting opportunities for hybrid stream engines that combine the flexibility of the CPU with the parallelism and processing power of FPGAs.


title: Using Learning Analytics to Devise Interactive Personalised Nudges for Active Video Watching

abstract: Videos can be a powerful medium for acquiring soft skills, where learning requires contextualisation in personal experience and ability to see different perspectives. However, to learn effectively while watching videos, students need to actively engage with video content. We implemented interactive notetaking during video watching in an active video watching system (AVW) as a means to encourage engagement. This paper proposes a systematic approach to utilise learning analytics for the introduction of adaptive intervention - a choice architecture for personalised nudges in the AVW to extend learning. A user study was conducted and used as an illustration. By characterising clusters derived from user profiles, we identify different styles of engagement, such as parochial learning, habitual video watching, and self-regulated learning (which is the target ideal behaviour). To find opportunities for interventions, interaction traces in the AVW were used to identify video intervals with high user interest and relevant behaviour patterns that indicate when nudges may be triggered. A prediction model was developed to identify comments that are likely to have high social value, and can be used as examples in nudges. A framework for interactive personalised nudges was then conceptualised for the case study.


title: Toward an actualization of social intelligence in human and robot collaborative systems

abstract: As robot technology is evolving and creating a social community between humans and robots, it is necessary to research and develop a new type of intelligence, which we refer to as "social intelligence''. Social intelligence enables natural and socially appropriate interactions. Its importance is gaining a growing interest among not just the human-computer interaction researchers but also robot technology researchers and developers. This article discusses the definition, importance, and benefits of social intelligence in human and robot collaborative systems. The virtual social environment is employed to implement an experimental social intelligence system because of its low cost and high flexibility. Software robots (i.e. agents) with the social intelligence model have been implemented by featuring an emotion model and a personality model under the virtual environment. The social intelligence model that handles affective responses is based on the theories of personality, emotion, and human-media interaction such as cognitive appraisal theory and media equation. The experiment was conducted with the virtual learning collaborative system to examine the effect of the social intelligence model in the collaborative system. The data showed that the users had more positive impressions about the usefulness and the application and learning experience when the cooperative agent displayed some social responses with personality and emotions. It should be noted here that the cooperative agent did not provide any explicit assistance for the human user such as giving clues and showing answers, and yet the user's evaluation on the usefulness of the learning system was influenced by the social agent. The data also suggested that the cooperative agent contributed to the effectiveness of the learning system.


title: Abstract Parameterization and Parametric Design of Mannequins

abstract: Abstract This paper presents a novel feature based parameterization approach of human bodies from the unorganized cloud points and the parametric design method for generating new models based on the parameterization. The parameterization consists of two phases. First, the semantic feature extraction technique is applied to construct the feature wireframe of a human body from laser scanned 3D unorganized points. Secondly, the symmetric detail mesh surface of the human body is modeled. Gregory patches are utilized to generate G 1 continuous mesh surface interpolating the curves on feature wireframe. After that, a voxel-based algorithm adds details on the smooth G 1 continuous surface by the cloud points. Finally, the mesh surface is adjusted to become symmetric. Compared to other template fitting based approaches, the parameterization approach introduced in this paper is more efficient. The parametric design approach synthesizes parameterized sample models to a new human body according to user input sizing dimensions. It is based on a numerical optimization process. The strategy of choosing samples for synthesis is also introduced. Human bodies according to a wide range of dimensions can be generated by our approach. Different from the mathematical interpolation function based human body synthesis methods, the models generated in our method have the approximation errors minimized. All mannequins constructed by our approach have consistent feature patches, which benefits the design automation of customized clothes around human bodies a lot.


title: Investigating and promoting UX practice in industry: an experimental study

abstract: Abstract The efforts of addressing user experience (UX) in product development keep growing, as demonstrated by the proliferation of workshops and conferences bringing together academics and practitioners, who aim at creating interactive software able to satisfy their users. This special issue focuses on “Interplay between User Experience Evaluation and Software Development”, stating that the gap between human-computer interaction and software engineering with regard to usability has somewhat been narrowed. Unfortunately, our experience shows that software development organizations perform few usability engineering activities or none at all. Several authors acknowledge that, in order to understand the reasons of the limited impact of usability engineering and UX methods, and to try to modify this situation, it is fundamental to thoroughly analyze current software development practices, involving practitioners and possibly working from inside the companies. This article contributes to this research line by reporting an experimental study conducted with software companies. The study has confirmed that still too many companies either neglect usability and UX, or do not properly consider them. Interesting problems emerged. This article gives suggestions on how they may be properly addressed, since their solution is the starting point for reducing the gap between research and practice of usability and UX. It also provides further evidence on the value of the research method, called Cooperative Method Development, based on the collaboration of researchers and practitioners in carrying out empirical research; it has been used in a step of the performed study and has revealed to be instrumental for showing practitioners why to improve their development processes and how to do so.


title: Quantifying the Independence of Human Finger Movements: Comparisons of Digits, Hands, and Movement Frequencies

abstract: To determine whether other digits move when normal humans attempt to move just one digit, we asked 10 right-handed subjects to move one finger at a time while we recorded the motion of all five digits simultaneously with both a video motion analysis system and an instrumented glove. We quantified the independence of the digits to compare (1) the different digits, (2) the right versus the left hand, and (3) movements at a self-paced frequency versus externally paced movements at 3 Hz. We also quantified the degree to which motion occurred at the proximal, middle, or distal joint of each digit. Even when asked to move just one finger, normal human subjects produced motion in other digits. Movements of the thumb, index finger, and little finger typically were more highly individuated than were movements of the middle or ring fingers. Fingers of the dominant hand were not more independent than were those of the nondominant hand. Self-paced movements made at approximately 2 Hz were more highly individuated than were externally paced movements at 3 Hz. Angular motion tended to be greatest at the middle joint of each digit, with increased angular motion at the proximal and distal joints during 3 Hz movements. Simultaneous motion of noninstructed digits may result in part from passive mechanical connections between the digits, in part from the organization of multitendoned finger muscles, and in part from distributed neural control of the hand.


title: Virtual Babyz: Believable Agents with Narrative Intelligence

abstract: This paper describes the interactive narrative experiences in Babyz, an interactive entertainment product for the PC currently in development at PF Magic / Mindscape in San Francisco, to be released in October 1999. Babyz are believable agents designed and implemented in the tradition of Dogz and Catz, Your Virtual Petz. As virtual human characters, Babyz are more intelligent, expressive and communicative than their Petz predecessors, allowing for both broader and deeper narrative possibilities. Babyz are designed with behaviors to support entertaining short-term narrative experiences, as well as long-term emotional relationships and narratives.


SentiSummary : Sentiment Summarization for User Product Reviews


title: A New Topology and Control Strategy for a Hybrid Battery-Ultracapacitor Energy Storage System

abstract: This study investigates a new hybrid energy storage system (HESS), which consists of a battery bank and an ultra-capacitor (UC) bank, and a control strategy for this system. The proposed topology uses a bi-directional DC-DC converter with a lower power rating than those used in the traditional HESS topology. The proposed HESS has four operating modes, and the proposed control strategy chooses the appropriate operating mode and regulates the distribution of power between the battery bank and the UC bank. Additionally, the control system prevents surges during mode switching and ensures that both the battery bank and the bi-directional DC-DC converter operate within their power limits. The proposed HESS is used to improve the performance of an existing power-split hybrid electric vehicle (HEV). A method for calculating the parameters of the proposed HESS is presented. A simulation model of the proposed HESS and control strategy was developed, and a scaled-down experimental platform was constructed. The results of the simulations and the experiments provide strong evidence for the feasibility of the proposed topology and the control strategy. The performance of the HESS is not influenced by the power limits of the bi-directional DC-DC converter.


title: How Hard Can It Be? Designing and Implementing a

abstract: Networks have become multipath: mobile devices have multiple radio interfaces, datacenters have redundant paths andmultihoming is the normfor big server farms. Meanwhile, TCP is still only single-path. ::: ::: Is it possible to extend TCP to enable it to support multiple paths for current applications on today's Internet? The answer is positive. We carefully review the constraints--partly due to various types of middleboxes-- that influenced the design of Multipath TCP and show how we handled them to achieve its deployability goals. ::: ::: We report our experience in implementing Multipath TCP in the Linux kernel and we evaluate its performance. Our measurements focus on the algorithms needed to efficiently use paths with different characteristics, notably send and receive buffer tuning and segment reordering. We also compare the performance of our implementation with regular TCP on web servers. Finally, we discuss the lessons learned from designing MPTCP.


title: Deep Learning for Content-Based, Cross-Modal Retrieval of Videos and Music.

abstract: In the context of multimedia content, a modality can be defined as a type of data item such as text, images, music, and videos. Up to now, only limited research has been conducted on cross-modal retrieval of suitable music for a specified video or vice versa. Moreover, much of the existing research relies on metadata such as keywords, tags, or associated description that must be individually produced and attached posterior. This paper introduces a new content-based, cross-modal retrieval method for video and music that is implemented through deep neural networks. The proposed model consists of a two-branch network that extracts features from the two different modalities and embeds them into a single embedding space. We train the network via cross-modal ranking loss such that videos and music with similar semantics end up close together in the embedding space. In addition, to preserve inherent characteristics within each modality, the proposed single-modal structure loss was also used for training. Owing to the lack of a dataset to evaluate cross-modal video-music tasks, we constructed a large-scale video-music pair benchmark. Finally, we introduced reasonable quantitative and qualitative experimental protocols. The experimental results on our dataset are expected to be a baseline for subsequent studies of less-mature video-to-music and music-to video related tasks.


title: Object Detection in High-Resolution Remote Sensing Images Using Rotation Invariant Parts Based Model

abstract: In this letter, we propose a rotation invariant parts-based model to detect objects with complex shape in high-resolution remote sensing images. Specifically, the geospatial objects with complex shape are firstly divided into several main parts, and the structure information among parts is described and regulated in polar coordinates to achieve the rotation invariance on configuration. Meanwhile, the pose variance of each part relative to the object is also defined in our model. In encoding the features of the rotated parts and objects, a new rotation invariant feature is proposed by extending histogram oriented gradients. During the final detection step, a clustering method is introduced to locate the parts in objects, and that method can also be used to fuse the detection results. By this way, an efficient detection model is constructed and the experimental results demonstrate the robustness and precision of our proposed detection model.


title: Adaptive histogram equalization based fusion technique for hazy underwater image enhancement

abstract: Degradation of underwater images is an atmospheric phenomenon which is a result of scattering and absorption of light. In this paper, we have defined a fusion based approach to enhance the visibility of underwater images. Our method uses only one single hazy image to derive the contrast improved and colour corrected versions of the original image. Further, it removes the distortion and uplifts the visibility of the distant objects in the image by applying weight maps on each of the derived inputs. We have used multi-scale fusion technique to blend the inputs and weight maps together, ensuring that each fused image contributes its most significant feature into the final image. Our technique is simple and straightforward that effectively contribute in enhancing the quality and appearance of underwater hazy images.


Book Review: “Comprehensive Biochemistry. Vol. 14: Biological Oxidations”


title: Maneuver-Based Trajectory Planning for Highly Autonomous Vehicles on Real Road With Traffic and Driver Interaction

abstract: This paper presents the design and first test on a simulator of a vehicle trajectory-planning algorithm that adapts to traffic on a lane-structured infrastructure such as highways. The proposed algorithm is designed to run on a fail-safe embedded environment with low computational power, such as an engine control unit, to be implementable in commercial vehicles of the near future. The target platform has a clock frequency of less than 150 MHz, 150 kB RAM of memory, and a 3-MB program memory. The trajectory planning is performed by a two-step algorithm. The first step defines the feasible maneuvers with respect to the environment, aiming at minimizing the risk of a collision. The output of this step is a target group of maneuvers in the longitudinal direction (accelerating or decelerating), in the lateral direction (changing lanes), and in the combination of both directions. The second step is a more detailed evaluation of several possible trajectories within these maneuvers. The trajectories are optimized to additional performance indicators such as travel time, traffic rules, consumption, and comfort. The output of this module is a trajectory in the vehicle frame that represents the recommended vehicle state (position, heading, speed, and acceleration) for the following seconds.


title: Team Performance in Software Development: Research Results versus Agile Principles

abstract: This article reviews scientific studies of factors influencing colocated development teams' performance and proposes five factors that strongly affect performance. In the process, it compares these propositions with the Agile Manifesto's development principles. The Web extra at https://extras.computer.org/extra/mso2016040106s1.pdf details the sources and research methods the authors employed.


title: Flexible sensor for Mckibben pneumatic actuator

abstract: A flexible electro-conductive rubber sensor for measuring the length of a McKibben pneumatic actuator has been developed. Mckibben actuators are flexible, lightweight, and widely used to actuate power assisting devices for which compliance is required for safety. The actuator's length needs to be measured to control the devices accurately. However, the flexibility and lightweight properties might be lost if rigid sensors such as linear potentionmeters or encoders are directly attached to the actuators. To keep the desirable properties of McKibben actuators, compact and flexible sensors are necessary. In this paper, a flexible sensor using electro-conductive flexible rubber is proposed to measure the length of McKibben actuators. Using this sensor, a higher accuracy can be obtained by measuring the circumference displacement than directly measuring the axial displacement. The estimation accuracy is evaluated and the usefulness of the proposed method is verified by adopting to a multiple-link robot arm driven by the McKibben actuators.


title: Locally adaptive dimensionality reduction for indexing large time series databases

abstract: Similarity search in large time series databases has attracted much research interest recently. It is a difficult problem because of the typically high dimensionality of the data. The most promising solutions involve performing dimensionality reduction on the data, then indexing the reduced data with a multidimensional index structure. Many dimensionality reduction techniques have been proposed, including Singular Value Decomposition (SVD), the Discrete Fourier transform (DFT), and the Discrete Wavelet Transform (DWT). In this article, we introduce a new dimensionality reduction technique, which we call Adaptive Piecewise Constant Approximation (APCA). While previous techniques (e.g., SVD, DFT and DWT) choose a common representation for all the items in the database that minimizes the global reconstruction error, APCA approximates each time series by a set of constant value segments of varying lengths such that their individual reconstruction errors are minimal. We show how APCA can be indexed using a multidimensional index structure. We propose two distance measures in the indexed space that exploit the high fidelity of APCA for fast searching: a lower bounding Euclidean distance approximation, and a non-lower-bounding, but very tight, Euclidean distance approximation, and show how they can support fast exact searching and even faster approximate searching on the same index structure. We theoretically and empirically compare APCA to all the other techniques and demonstrate its superiority.


title: Regularizing RNNs by Stabilizing Activations

abstract: We stabilize the activations of Recurrent Neural Networks (RNNs) by penalizing the squared distance between successive hidden states' norms. ::: This penalty term is an effective regularizer for RNNs including LSTMs and IRNNs, improving performance on character-level language modeling and phoneme recognition, and outperforming weight noise and dropout. ::: We achieve competitive performance (18.6\% PER) on the TIMIT phoneme recognition task for RNNs evaluated without beam search or an RNN transducer. ::: With this penalty term, IRNN can achieve similar performance to LSTM on language modeling, although adding the penalty term to the LSTM results in superior performance. ::: Our penalty term also prevents the exponential growth of IRNN's activations outside of their training horizon, allowing them to generalize to much longer sequences.


title: Elliptic Curve Cryptography For EAACK Intrusion Detection System

abstract: Mobile Ad hoc Networks (MANET) have been highly vulnerable to attacks due to the dynamic nature of its network infrastructure. Among the various attacks, routing attacks do most of devastating damage to MANET. An Enhanced Adaptive Acknowledgment (EAACK) Intrusion detection system removes the malicious nodes from the network and re-route the traffic. Further improvement in the security can be achieved by implementing an encryption technique, but it increases the routing overheads in the network. To reduce routing overhead caused by cryptography methods without compromising security level in EAACK protocol, Elliptic curve cryptography (ECC) is used in this work. It is observed that routing overhead has reduced by 17-25%.


title: AptStore: Dynamic Storage Management for Hadoop

abstract: Typical Hadoop setups employ Direct Attached Storage (DAS) with compute nodes and uniform replication of data to sustain high I/O throughput and fault tolerance. However, not all data is accessed at the same time or rate. Thus, if a large replication factor is used to support higher throughput for popular data, it wastes storage by unnecessarily replicating unpopular data as well. Conversely, if less replication is used to conserve storage for the unpopular data, it means fewer replicas for even popular data and thus lower I/O throughput. We present Apt Store, a dynamic data management system for Hadoop, which aims to improve overall I/O throughput while reducing storage cost. We design a tiered storage that uses the standard DAS for popular data to sustain high I/O throughput, and network-attached enterprise filers for cost-effective, fault-tolerant, but lower-throughput storage for unpopular data. We design a file Popularity Prediction Algorithm (PPA) that analyzes file system audit logs and predicts the appropriate storage policy of each file, as well as use the information for transparent data movement between tiers. Our evaluation of Apt Store on a real cluster shows 21.3% improvement in application execution time over standard Hadoop, while trace driven simulations show 23.7% increase in read throughput and 43.4% reduction in the storage capacity requirement of the system.