title: Multi-view 3D reconstruction for scenes under the refractive plane with known vertical direction

abstract: Images taken from scenes under water suffer distortion due to refraction. While refraction causes magnification with mild distortion on the observed images, severe distortions in geometry reconstruction would be resulted if the refractive distortion is not properly handled. Different from the radial distortion model, the refractive distortion depends on the scene depth seen from each light ray as well as the camera pose relative to the refractive surface. Therefore, it's crucial to obtain a good estimate of scene depth, camera pose and optical center to alleviate the impact of refractive distortion. In this work, we formulate the forward and back projections of light rays involving a refractive plane for the perspective camera model by explicitly modeling refractive distortion as a function of depth. Furthermore, for cameras with an inertial measurement unit (IMU), we show that a linear solution to the relative pose and a closed-form solution to the absolute pose can be derived with known camera vertical directions. We incorporate our formulations with the general structure from motion framework followed by the patch-based multiview stereo algorithm to obtain a 3D reconstruction of the scene. We show through experiments that the explicit modeling of depth-dependent refractive distortion physically leads to more accurate scene reconstructions.


title: Preventing Shoulder-Surfing Attack with the Concept of Concealing the Password Objects' Information

abstract: Traditionally, picture-based password systems employ password objects (pictures/icons/symbols) as input during an authentication session, thus making them vulnerable to “shoulder-surfing” attack because the visual interface by function is easily observed by others. Recent software-based approaches attempt to minimize this threat by requiring users to enter their passwords indirectly by performing certain mental tasks to derive the indirect password, thus concealing the user’s actual password. However, weaknesses in the positioning of distracter and password objects introduce usability and security issues. In this paper, a new method, which conceals information about the password objects as much as possible, is proposed. Besides concealing the password objects and the number of password objects, the proposed method allows both password and distracter objects to be used as the challenge set’s input. The correctly entered password appears to be random and can only be derived with the knowledge of the full set of password objects. Therefore, it would be difficult for a shoulder-surfing adversary to identify the user’s actual password. Simulation results indicate that the correct input object and its location are random for each challenge set, thus preventing frequency of occurrence analysis attack. User study results show that the proposed method is able to prevent shoulder-surfing attack.


title: D numbers theory: a generalization of Dempster-Shafer evidence theory

abstract: Efficient modeling of uncertain information in real world is still an open issue. Dempster-Shafer evidence theory is one of the most commonly used methods. However, the Dempster-Shafer evidence theory has the assumption that the hypothesis in the framework of discernment is exclusive of each other. This condition can be violated in real applications, especially in linguistic decision making since the linguistic variables are not exclusive of each others essentially. In this paper, a new theory, called as D numbers theory (DNT), is systematically developed to address this issue. The combination rule of two D numbers is presented. An coefficient is defined to measure the exclusive degree among the hypotheses in the framework of discernment. The combination rule of two D numbers is presented. If the exclusive coefficient is one which means that the hypothesis in the framework of discernment is exclusive of each other totally, the D combination is degenerated as the classical Dempster combination rule. Finally, a linguistic variables transformation of D numbers is presented to make a decision. A numerical example on linguistic evidential decision making is used to illustrate the efficiency of the proposed D numbers theory.


title: Probe and Adapt: Rate Adaptation for HTTP Video Streaming At Scale

abstract: Today, the technology for video streaming over the Internet is converging towards a paradigm named HTTP-based adaptive streaming (HAS), which brings two new features. First, by using HTTP/TCP, it leverages network-friendly TCP to achieve both firewall/NAT traversal and bandwidth sharing. Second, by pre-encoding and storing the video in a number of discrete rate levels, it introduces video bitrate adaptivity in a scalable way so that the video encoding is excluded from the closed-loop adaptation. A conventional wisdom in HAS design is that since the TCP throughput observed by a client would indicate the available network bandwidth, it could be used as a reliable reference for video bitrate selection. We argue that this is no longer true when HAS becomes a substantial fraction of the total network traffic. We show that when multiple HAS clients compete at a network bottleneck, the discrete nature of the video bitrates results in difficulty for a client to correctly perceive its fair-share bandwidth. Through analysis and test bed experiments, we demonstrate that this fundamental limitation leads to video bitrate oscillation and other undesirable behaviors that negatively impact the video viewing experience. We therefore argue that it is necessary to design at the application layer using a "probe and adapt" principle for video bitrate adaptation (where "probe" refers to trial increment of the data rate, instead of sending auxiliary piggybacking traffic), which is akin, but also orthogonal to the transport-layer TCP congestion control. We present PANDA - a client-side rate adaptation algorithm for HAS - as a practical embodiment of this principle. Our test bed results show that compared to conventional algorithms, PANDA is able to reduce the instability of video bitrate selection by over 75% without increasing the risk of buffer underrun.


title: A simplified approach to midface aging.

abstract: We review herein our experience with subperiosteal midface-lifting under direct vision with a simple fixation technique. The technical aspects of the procedure are described in detail. A total of 121 patients underwent midface-lifting and meloplication with the 82/18 L-lactide/glycolide device (Coapt Endotine Midface ST 4.5; Coapt Technologies, Palo Alto, Calif) by both the senior (G.S.K.) and junior (R.N.H.) authors. The senior author’s experience included 110 patients over a 26-month period. Thirtytwo of these cases were isolated procedures. The other 78 were performed in conjunction with various procedures, most commonly rhytidectomy. There were no revisions during this period. Two cases of “puckering” were noted. Both were immediately corrected, one with fat injection and one with poly-L-lactic acid injection (Sculptra; Dermik Aesthetics, distributed by Besse Medical Supply, West Chester, Ohio). The junior author’s experience included 11 cases over an 8-month period. Two cases of asymmetry were noted. One was corrected with fat injection, and the other required revision. Subperiosteal midface-lifting and meloplication using the Coapt Endotine Midface ST 4.5 device is a simple, effective technique that can be quickly learned and applied.


title: Fluorescent calix[4]arene which responds to solvent polarity and metal ions

abstract: A calix[4]arene having two pyrene moieties on the lower rim has been synthesized; the ratio of the monomer vs. excimer emission was sensitively affected by solvent polarity and added metal ions.


DEVELOPMENT OF A SELF MICRO-EMULSIFYING TABLET OF CYCLOSPORINE-A BY THE LIQUISOLID COMPACT TECHNIQUE


title: Moving towards maturity: challenges to successful e-government implementation and diffusion

abstract: The implementation, diffusion and adoption of e-government in the public sector has been a topic that has been debated by the research community for some time. In particular, the limited adoption of e-government services is attributed to factors such as the heterogeneity of users, lack of user-orientation, the limited transformation of public sector and the mismatch between expectations and supply. In this editorial, we review theories and factors impacting implementation, diffusion and adoption of e-government. Most theories used in prior research follow mainstream information systems concepts, which can be criticized for not taking into account e-government specific characteristics. The authors argue that there is a need for e-government specific theories and methodologies that address the idiosyncratic nature of e-government as the well-known information systems concepts that are primarily developed for business contexts are not equipped to encapsulate the complexities surrounding e-government. Aspects like accountability, digital divide, legislation, public governance, institutional complexity and citizens' needs are challenging issues that have to be taken into account in e-government theory and practices. As such, in this editorial we argue that e-government should develop as an own strand of research, while information systems theories and concepts should not be neglected.


title: Speech enhancement based on neural networks improves speech intelligibility in noise for cochlear implant users

abstract: &NA; Speech understanding in noisy environments is still one of the major challenges for cochlear implant (CI) users in everyday life. We evaluated a speech enhancement algorithm based on neural networks (NNSE) for improving speech intelligibility in noise for CI users. The algorithm decomposes the noisy speech signal into time‐frequency units, extracts a set of auditory‐inspired features and feeds them to the neural network to produce an estimation of which frequency channels contain more perceptually important information (higher signal‐to‐noise ratio, SNR). This estimate is used to attenuate noise‐dominated and retain speech‐dominated CI channels for electrical stimulation, as in traditional n‐of‐m CI coding strategies. The proposed algorithm was evaluated by measuring the speech‐in‐noise performance of 14 CI users using three types of background noise. Two NNSE algorithms were compared: a speaker‐dependent algorithm, that was trained on the target speaker used for testing, and a speaker‐independent algorithm, that was trained on different speakers. Significant improvements in the intelligibility of speech in stationary and fluctuating noises were found relative to the unprocessed condition for the speaker‐dependent algorithm in all noise types and for the speaker‐independent algorithm in 2 out of 3 noise types. The NNSE algorithms used noise‐specific neural networks that generalized to novel segments of the same noise type and worked over a range of SNRs. The proposed algorithm has the potential to improve the intelligibility of speech in noise for CI users while meeting the requirements of low computational complexity and processing delay for application in CI devices. HighlightsAn algorithm for improving speech understanding in noise for cochlear implant users is evaluated.Significant improvements were found for stationary and non‐stationary noise types.It generalizes to a novel speaker and works over a range of signal‐to‐noise ratios.The small algorithmic delay makes it suitable for real‐time application.


EmojiGAN: learning emojis distributions with a generative model


title: Managing engineering systems with large state and action spaces through deep reinforcement learning

abstract: Abstract Decision-making for engineering systems management can be efficiently formulated using Markov Decision Processes (MDPs) or Partially Observable MDPs (POMDPs). Typical MDP/POMDP solution procedures utilize offline knowledge about the environment and provide detailed policies for relatively small systems with tractable state and action spaces. However, in large multi-component systems the dimensions of these spaces easily explode, as system states and actions scale exponentially with the number of components, whereas environment dynamics are difficult to be described explicitly for the entire system and may, often, only be accessible through computationally expensive numerical simulators. In this work, to address these issues, an integrated Deep Reinforcement Learning (DRL) framework is introduced. The Deep Centralized Multi-agent Actor Critic (DCMAC) is developed, an off-policy actor-critic DRL algorithm that directly probes the state/belief space of the underlying MDP/POMDP, providing efficient life-cycle policies for large multi-component systems operating in high-dimensional spaces. Apart from deep network approximators parametrizing complex functions with vast state spaces, DCMAC also adopts a factorized representation of the system actions, thus being able to designate individualized component- and subsystem-level decisions, while maintaining a centralized value function for the entire system. DCMAC compares well against Deep Q-Network and exact solutions, where applicable, and outperforms optimized baseline policies that incorporate time-based, condition-based, and periodic inspection and maintenance considerations.


title: Pain management in the Ehlers–Danlos syndromes

abstract: Chronic pain in the Ehlers-Danlos syndromes (EDS) is common and may be severe. According to one study, nearly 90% of patients report some form of chronic pain. Pain, which is often one of the first symptoms to occur, may be widespread or localized to one region such as an arm or a leg. Studies on treatment modalities are few and insufficient to guide management. The following is a discussion of the evidence regarding the underlying mechanisms of pain in EDS. The causes of pain in this condition are multifactorial and include joint subluxations and dislocations, previous surgery, muscle weakness, proprioceptive disorders, and vertebral instability. Affected persons may also present with generalized body pain, fatigue, headaches, gastrointestinal pain, temporomandibular joint pain, dysmenorrhea, and vulvodynia. Pain management strategies may be focused around treating the cause of the pain (e.g., dislocation of a joint, proprioceptive disorder) and minimizing the sensation of pain. Management strategies for chronic pain in EDS includes physical therapy, medications, as well as durable medical equipment such as cushions, compressive garments, and braces. The different modalities are discussed in this paper. © 2017 Wiley Periodicals, Inc.


title: Deep Neural Decision Trees

abstract: Deep neural networks have been proven powerful at processing perceptual data, such as images and audio. However for tabular data, tree-based models are more popular. A nice property of tree-based models is their natural interpretability. In this work, we present Deep Neural Decision Trees (DNDT) -- tree models realised by neural networks. A DNDT is intrinsically interpretable, as it is a tree. Yet as it is also a neural network (NN), it can be easily implemented in NN toolkits, and trained with gradient descent rather than greedy splitting. We evaluate DNDT on several tabular datasets, verify its efficacy, and investigate similarities and differences between DNDT and vanilla decision trees. Interestingly, DNDT self-prunes at both split and feature-level.


title: Generation of Overhead View Images by Estimating Intrinsic and Extrinsic Camera Parameters of Multiple Fish-Eye Cameras

abstract: In recent years, systems to support driving a car using cameras are increasing. As an example, a system to generate overhead view images is proposed and used to assist drivers. In this paper, we propose a method for generating overhead view images with small errors by estimating intrinsic and extrinsic camera parameters of multiple fish-eye cameras. The accuracy of generated images is evaluated quantitatively by experiments and effectiveness of the proposed method is shown.


title: Integrated coverage and connectivity configuration in wireless sensor networks

abstract: An effective approach for energy conservation in wireless sensor networks is scheduling sleep intervals for extraneous nodes, while the remaining nodes stay active to provide continuous service. For the sensor network to operate successfully, the active nodes must maintain both sensing coverage and network connectivity. Furthermore, the network must be able to configure itself to any feasible degrees of coverage and connectivity in order to support different applications and environments with diverse requirements. This paper presents the design and analysis of novel protocols that can dynamically configure a network to achieve guaranteed degrees of coverage and connectivity. This work differs from existing connectivity or coverage maintenance protocols in several key ways: 1) We present a Coverage Configuration Protocol (CCP) that can provide different degrees of coverage requested by applications. This flexibility allows the network to self-configure for a wide range of applications and (possibly dynamic) environments. 2) We provide a geometric analysis of the relationship between coverage and connectivity. This analysis yields key insights for treating coverage and connectivity in a unified framework: this is in sharp contrast to several existing approaches that address the two problems in isolation. 3) Finally, we integrate CCP with SPAN to provide both coverage and connectivity guarantees. We demonstrate the capability of our protocols to provide guaranteed coverage and connectivity configurations, through both geometric analysis and extensive simulations.


title: Virtual InfiniBand clusters for HPC clouds

abstract: High Performance Computing (HPC) employs fast interconnect technologies to provide low communication and synchronization latencies for tightly coupled parallel compute jobs. Contemporary HPC clusters have a fixed capacity and static runtime environments; they cannot elastically adapt to dynamic workloads, and provide a limited selection of applications, libraries, and system software. In contrast, a cloud model for HPC clusters promises more flexibility, as it provides elastic virtual clusters to be available on-demand. This is not possible with physically owned clusters. In this paper, we present an approach that makes it possible to use InfiniBand clusters for HPC cloud computing. We propose a performance-driven design of an HPC IaaS layer for InfiniBand, which provides throughput and latency-aware virtualization of nodes, networks, and network topologies, as well as an approach to an HPC-aware, multi-tenant cloud management system for elastic virtualized HPC compute clusters.


title: Analysis of the query logs of a web site search engine

abstract: A large number of studies have investigated the transaction log of general-purpose search engines such as Excite and AltaVista, but few studies have reported on the analysis of search logs for search engines that are limited to particular Web sites, namely, Web site search engines. In this article, we report our research on analyzing the search logs of the search engine of the Utah state government Web site. Our results show that some statistics, such as the number of search terms per query, of Web users are the same for general-purpose search engines and Web site search engines, but others, such as the search topics and the terms used, are considerably different. Possible reasons for the differences include the focused domain of Web site search engines and users' different information needs. The findings are useful for Web site developers to improve the performance of their services provided on the Web and for researchers to conduct further research in this area. The analysis also can be applied in e-government research by investigating how information should be delivered to users in government Web sites.


title: Developing Software with Scrum in a Small Cross-Organizational Project

abstract: In an action research study, we describe the application of the scrum software development process in a small cross-organizational development project. The stakeholders in the project report many of the benefits we have found in previous studies, such as increased overview of the project, flexibility and motivation. In addition, we have found that estimation can be challenging in cross-organizational projects due to the customer-provider relationship between the participating organizations.


title: Hybrid Precoding for mmWave Massive MIMO Systems With Partially Connected Structure

abstract: Hybrid precoding is widely studied in millimetre-wave (mmWave) massive MIMO systems due to low cost as well as low power consumption. In general, there are two kinds of hybrid precoding structures: one is fully connected structure (FCS), where each radio frequency (RF) chain is connected to all antennas, and the other is partially connected structure (PCS), where each RF chain is connected to a sub-array. In this paper, we investigate the optimal hybrid precoder design problem for mmWave massive MIMO systems based on PCS, since this kind of structure is more practical for antenna deployment. We first focus on the optimization of analog precoder (AP) and propose two AP design schemes for high signal-to-noise ratio (SNR) condition and low SNR condition, respectively. For each of the schemes, the original optimization problem is reformulated to single-stream optimal transmitter beamforming problem with per-antenna power constraint, which has an optimal solution. Then, the optimal digital precoder is obtained by water-filling algorithm after AP is determined. Moreover, upper bounds of the achievable data rate for the proposed schemes with closed-form expression are derived.


title: Planar Wide-Angle Scanning Phased Array With Pattern-Reconfigurable Windmill-Shaped Loop Elements

abstract: A wide-angle scanning planar phased array with pattern-reconfigurable windmill-shaped loop elements has been designed. Each element has a four-port windmill-shaped structure over a high-impedance surface, and it can steer the main beam in four different space quadrants by exciting one port at a time. A 16-element planar (4 × 4) array is fabricated and measured to demonstrate the wide-angle scanning performance of the array. The results show that the fabricated array is able to scan its main beam up to 72° and supports a 3-dB scanning coverage up to 92° in each quadrant. In addition, input amplitudes and phases for the array are optimized with a hybrid particle swarm optimization algorithm to enhance the array scanning performance.


Bayesian recommender systems : models and algorithms


title: Bundling mobile base station and wireless energy transfer: Modeling and optimization

abstract: Wireless energy transfer is a promising technology to fundamentally address energy and lifetime problems in a wireless sensor network (WSN). On the other hand, it has been well recognized that a mobile base station has significant advantages over a static one. In this paper, we study the interesting problem of co-locating the mobile base station on the wireless charging vehicle (WCV). The goal is to minimize energy consumption of the entire system while ensuring none of the sensor nodes runs out of energy. We develop a mathematical model for this complex problem. Instead of studying the general problem formulation (OPT-t), which is time-dependent, we show that it is sufficient to study a special subproblem (OPT-s) which only involves space-dependent variables. Subsequently, we develop a provably near-optimal solution to OPT-s. The novelty of this research mainly resides in the development of several solution techniques to tackle a complex problem that is seemingly intractable at first glance. In addition to addressing a challenging and interesting problem in a WSN, we expect the techniques developed in this research can be applied to address other related networking problems involving time-dependent movement, flow routing, and energy consumption.


title: Stigma, negative attitudes and discrimination towards mental illness within the nursing profession: a review of the literature

abstract: The aim of this paper was to review the existing literature pertaining to stigma, negative attitudes and discrimination towards mental illness, specifically as viewed through the lens of the nursing profession. The results of the literature review were synthesized and analysed, and the major themes drawn from this were found to correspond with Schulze's model identifying three positions that healthcare workers may assume in relation to stigma of mental illness: ‘stigmatizers’, ‘stigmatized’ and ‘de-stigmatizers’. In this paper, the nursing profession is examined from the perspectives of the first two major themes: the ‘stigmatizers’ and ‘stigmatized’. Their primary sub-themes are identified and discussed: (1) Nurses as ‘the stigmatizers’: (a) nurses' attitudes in general medical settings towards patients with psychiatric illness and (b) psychiatric nurses; (2) Nurses as ‘the stigmatized’: (a) nurses who have mental illness and (b) stigma within the profession against psychiatric nurses and/or psychiatry in general. The secondary and tertiary sub-themes are also identified and reviewed.


title: Twitter sentiment analysis with different feature extractors and dimensionality reduction using supervised learning algorithms

abstract: Twitter is an online micro-blogging platform which allows us to treasure trove about the current circumstance at any juncture in time. In this paper, we analyze the sentiments of huge amount of tweets generated from Twitter users which are stored in Twitter database. We have chosen accuracy as the evaluation criteria of classification methods, namely Naive Bayes and Support Vector Machine. Also we have used unigram and bigram as feature extractors along with Chi2 and Singular Value Decomposition for dimensionality reduction. Through tokenization, having several stages of pre-processing and several combinations of feature vectors and classification methods, we are able to achieve an accuracy of 89.61% when analyzing the sentiment of tweets.


title: Fraud detection using outlier predictor in health insurance data

abstract: In day today life, health insurance data collection plays major role for employers. In several countries misbehavior in health insurance is a major problem. Health insurance data fraud is an intentional act of misleading, hiding or misrepresenting information that makes profit to a single or group of members. These kind of violation leads to major loss for health insurance providing organisation. Hence Detecting fraudulent and abusive cases in health insurance is one of the most challenging problems. The aim of the project is fraud detection in health insurance data. In order to increase the accuracy of the framework, several methods are utilized, such as the pairwise comparison method of analytic hierarchical processing (AHP). Its mainly focus on a concrete problem of probabilistic outlier detection. Overcoming the existing drawback of time consuming, proactive and retrospective analysis are integrated together, which significantly reduces the time requirements for the fact-finding process.


title: Gradient based threshold free color filter array interpolation

abstract: Color Filter Array (CFA) interpolation is an integral part of image processing pipeline for single sensor digital cameras. Many CFA algorithms have been proposed over the years to improve resulting image quality. One such algorithm is the highly successful Directional Linear Minimum Mean-Square Error Estimation (DLMMSE) method. We make several observations on this algorithm and propose a new method to address those points. The proposed method yields visually pleasing results and outperforms all CFA interpolation algorithms that are included in a recent survey paper in terms of PSNR.


title: Customer perceived value, satisfaction and loyalty: the role of switching costs

abstract: It is a marketplace reality that marketing managers sometimes inflict switching costs on their customers, to inhibit them from defecting to new suppliers. In a competitive setting, such as the Internet market, where competition may be only one click away, has the potential of switching costs as an exit barrier and a binding ingredient of customer loyalty become altered? To address that issue, this article examines the moderating effects of switching costs on customer loyalty through both satisfaction and perceived-value measures. The results, evoked from a Web-based survey of online service users, indicate that companies that strive for customer loyalty should focus primarily on satisfaction and perceived value. The moderating effects of switching costs on the association of customer loyalty and customer satisfaction and perceived value are significant only when the level of customer satisfaction or perceived value is above average. In light of the major findings, the article sets forth strategic implications for customer loyalty in the setting of electronic commerce. © 2004 Wiley Periodicals, Inc.


title: Accident Detection and Smart Rescue System using Android Smartphone with Real-Time Location Tracking

abstract: A large number of deaths are caused by Traffic accidents worldwide. The global crisis of road safety can be seen by observing the significant number of deaths and injuries that are caused by road traffic accidents. In many situations the family members or emergency services are not informed in time. This results in delayed emergency service response time, which can lead to an individual’s death or cause severe injury. The purpose of this work is to reduce the response time of emergency services in situations like traffic accidents or other emergencies such as fire, theft/robberies and medical emergencies. By utilizing onboard sensors of a smartphone to detect vehicular accidents and report it to the nearest emergency responder available and provide real time location tracking for responders and emergency victims, will drastically increase the chances of survival for emergency victims, and also help save emergency services time and resources.


Loss of the Full Ocean Depth ROV Kaiko – Part 3: The Cause of Secondary Cable Fracture


title: Predictive Tool for Software Team Performance

abstract: When supervising software engineering team projects, having all team members contribute actively to the project is often a challenge. Most often than not, there will be teams having some members with limited or no contribution. Thus one of the key roles of a team leader and academic supervisor are to monitor who is contributing and who is falling behind. Assessing the progress information of each team member becomes vital. This is to introduce strategies that encourages ensure every member is contributing effectively and efficiently in a timely manner. This paper proposes a rubric solution that assesses the progress information of each team member and provides a formative performance feedback on how each member is contributing to the project. This will enable each team member to reflect on his or her performance, and hopefully self-regulate and put in the necessary contribution. At the same time, this will assist the team leader and academic supervisor in monitoring the team member.


Multi-Dimensional, Phrase-Based Summarization in Text Cubes


title: Communication Efficient Distributed Machine Learning with the Parameter Server

abstract: This paper describes a third-generation parameter server framework for distributed machine learning. This framework offers two relaxations to balance system performance and algorithm efficiency. We propose a new algorithm that takes advantage of this framework to solve non-convex non-smooth problems with convergence guarantees. We present an in-depth analysis of two large scale machine learning problems ranging from l1 -regularized logistic regression on CPUs to reconstruction ICA on GPUs, using 636TB of real data with hundreds of billions of samples and dimensions. We demonstrate using these examples that the parameter server framework is an effective and straightforward way to scale machine learning to larger problems and systems than have been previously achieved.


title: Rumination and worry as mediators of the relationship between self-compassion and depression and anxiety

abstract: The mediating effects of rumination (with brooding and reflection components) and worry were examined in the relation between self-compassion and depression and anxiety. Two hundred and seventy-one nonclinical undergraduates completed measures of self-compassion, rumination, worry, depression and anxiety. Results showed that for the relation between self-compassion and depression, only brooding (rumination) emerged as a significant mediator. For anxiety, both brooding and worrying emerged as significant mediators, but the mediating effect of worry was significantly greater than that of brooding. The present results suggest that one way via which self-compassion has buffering effects on depression and anxiety is through its positive effects on unproductive repetitive thinking.


title: A rapid learning algorithm for vehicle classification

abstract: A fast learning algorithm is introduced for real-time vehicle classification.A fast feature selection method for AdaBoost is presented by combining a sample's feature value with its class label.A rapid incremental learning algorithm of AdaBoost is designed. AdaBoost is a popular method for vehicle detection, but the training process is quite time-consuming. In this paper, a rapid learning algorithm is proposed to tackle this weakness of AdaBoost for vehicle classification. Firstly, an algorithm for computing the Haar-like feature pool on a 32×32 grayscale image patch by using all simple and rotated Haar-like prototypes is introduced to represent a vehicle's appearance. Then, a fast training approach for the weak classifier is presented by combining a sample's feature value with its class label. Finally, a rapid incremental learning algorithm of AdaBoost is designed to significantly improve the performance of AdaBoost. Experimental results demonstrate that the proposed approaches not only speed up the training and incremental learning processes of AdaBoost, but also yield better or competitive vehicle classification accuracies compared with several state-of-the-art methods, showing their potential for real-time applications.


title: iNICU – Integrated Neonatal Care Unit: Capturing Neonatal Journey in an Intelligent Data Way

abstract: Neonatal period represents first 28 days of life, which is the most vulnerable time for a child’s survival especially for the preterm babies. High neonatal mortality is a prominent and persistent problem across the globe. Non-availability of trained staff and infrastructure are the major recognized hurdles in the quality care of these neonates. Hourly progress growth charts and reports are still maintained manually by nurses along with continuous calculation of drug dosage and nutrition as per the changing weight of the baby. iNICU (integrated Neonatology Intensive Care Unit) leverages Beaglebone and Intel Edison based IoT integration with biomedical devices in NICU i.e. monitor, ventilator and blood gas machine. iNICU is hosted on IBM Softlayer based cloud computing infrastructure and map NICU workflow in Java based responsive web application to provide translational research informatics support to the clinicians. iNICU captures real time vital parameters i.e. respiration rate, heart rate, lab data and PACS amounting for millions of data points per day per child. Stream of data is sent to Apache Kafka layer which stores the same in Apache Cassandra NoSQL. iNICU also captures clinical data like feed intake, urine output, and daily assessment of child in PostgreSQL database. It acts as first Big Data hub (of both structured and unstructured data) of neonates across India offering temporal (longitudinal) data of their stay in NICU and allow clinicians in evaluating efficacy of their interventions. iNICU leverages drools based clinical rule based engine and deep learning based big data analytical model coded in R and PMML. iNICU solution aims to improve care time, fills skill gap, enable remote monitoring of neonates in rural regions, assists in identifying the early onset of disease, and reduction in neonatal mortality.


title: Geometric Hashing: An Overview

abstract: Geometric hashing, a technique originally developed in computer vision for matching geometric features against a database of such features, finds use in a number of other areas. Matching is possible even when the recognizable database objects have undergone transformations or when only partial information is present. The technique is highly efficient and of low polynomial complexity.


title: Modeling Purposeful Adaptive Behavior with the Principle of Maximum Causal Entropy

abstract: Predicting human behavior from a small amount of training examples is a challenging machine learning problem. In this thesis, we introduce the principle of maximum causal entropy, a general technique for applying information theory to decision-theoretic, game-theoretic, and control settings where relevant information is sequentially revealed over time. This approach guarantees decision-theoretic performance by matching purposeful measures of behavior (Abbeel & Ng, 2004), and/or enforces game-theoretic rationality constraints (Aumann, 1974), while otherwise being as uncertain as possible, which minimizes worst-case predictive log-loss (Grunwald & Dawid, 2003). ::: We derive probabilistic models for decision, control, and multi-player game settings using this approach. We then develop corresponding algorithms for efficient inference that include relaxations of the Bellman equation (Bellman, 1957), and simple learning algorithms based on convex optimization. We apply the models and algorithms to a number of behavior prediction tasks. Specifically, we present empirical evaluations of the approach in the domains of vehicle route preference modeling using over 100,000 miles of collected taxi driving data, pedestrian motion modeling from weeks of indoor movement data, and robust prediction of game play in stochastic multi-player games.


title: GERBIL: General Entity Annotator Benchmarking Framework

abstract: We present GERBIL, an evaluation framework for semantic entity annotation. The rationale behind our framework is to provide developers, end users and researchers with easy-to-use interfaces that allow for the agile, fine-grained and uniform evaluation of annotation tools on multiple datasets. By these means, we aim to ensure that both tool developers and end users can derive meaningful insights pertaining to the extension, integration and use of annotation applications. In particular, GERBIL provides comparable results to tool developers so as to allow them to easily discover the strengths and weaknesses of their implementations with respect to the state of the art. With the permanent experiment URIs provided by our framework, we ensure the reproducibility and archiving of evaluation results. Moreover, the framework generates data in machine-processable format, allowing for the efficient querying and post-processing of evaluation results. Finally, the tool diagnostics provided by GERBIL allows deriving insights pertaining to the areas in which tools should be further refined, thus allowing developers to create an informed agenda for extensions and end users to detect the right tools for their purposes. GERBIL aims to become a focal point for the state of the art, driving the research agenda of the community by presenting comparable objective evaluation results.


title: Supporting Drivers in Keeping Safe Speed and Safe Distance: The SASPENCE Subproject Within the European Framework Programme 6 Integrating Project PReVENT

abstract: This paper describes a novel driver-support system that helps to maintain the correct speed and headway (distance) with respect to lane curvature and other vehicles ahead. The system has been developed as part of the Integrating Project PReVENT under the European Framework Programme 6, which is named SAfe SPEed and safe distaNCE (SASPENCE). The application uses a detailed description of the situation ahead of the vehicle. Many sensors [radar, video camera, Global Positioning System (GPS) and accelerometers, digital maps, and vehicle-to-vehicle wireless local area network (WLAN) connections] are used, and state-of-the-art data fusion provides a model of the environment. The system then computes a feasible maneuver and compares it with the driver's behavior to detect possible mistakes. The warning strategies are based on this comparison. The system “talks” to the driver mainly via a haptic pedal or seat belt and “listens” to the driver mainly via the vehicle acceleration. This kind of operation, i.e., the comparison between what the system thinks is possible and what the driver appears to be doing, and the consequent dialog can be regarded as simple implementations of the rider-horse metaphor (H-metaphor). The system has been tested in several situations (driving simulator, hardware in the loop, and real road tests). Objective and subjective data have been collected, revealing good acceptance and effectiveness, particularly in awakening distracted drivers. The system intervenes only when a problem is actually detected in the headway and/or speed (approaching curves or objects) and has been shown to cause prompt reactions and significant speed correction before getting into really dangerous situations.


title: Test Case Generation Based on Invariant Extraction

abstract: Software testing is an effective way to find software errors. Generating a good test suite is the key. A program invariant is a property that is true at a particular program point or points. The property could reflect the program's execution over a test suite. Based on this point, we integrate the random test case generation technique and the invariant extraction technique, achieving automatic test case generation and selection. With the same invariants, compared with the traditional random test case generation technique, the experimental results show that the approach this paper describes can generate a smaller test suite.


Interval Logics and Their Decision Procedures - Part II: A Real-Time Interval Logic


title: Evaluation and management of children with holoprosencephaly.

abstract: Recent advances in genetics and neuroimaging have greatly contributed to our understanding of the spectrum of midline brain and craniofacial malformations known as holoprosencephaly. Neuroradiologic studies have provided detailed characteristics of four major types of holoprosencephaly: alobar, semilobar, lobar, and middle interhemispheric variant. Clinical studies in children with these types of holoprosencephaly have revealed a wide range of survival and neurologic outcomes. Motor and developmental dysfunctions correlate with the severity of the brain malformation in holoprosencephaly. These findings have implications in the management of medical problems associated with holoprosencephaly and overall prognostication.


title: Fast Object Detection in Compressed Video

abstract: Object detection in videos has drawn increasing attention since it is more practical in real scenarios. Most of the deep learning methods use CNNs to process each decoded frame in a video stream individually. However, the free of charge yet valuable motion information already embedded in the video compression format is usually overlooked. In this paper, we propose a fast object detection method by taking advantage of this with a novel Motion aided Memory Network (MMNet). The MMNet has two major advantages: 1) It significantly accelerates the procedure of feature extraction for compressed videos. It only need to run a complete recognition network for I-frames, i.e. a few reference frames in a video, and it produces the features for the following P frames (predictive frames) with a light weight memory network, which runs fast; 2) Unlike existing methods that establish an additional network to model motion of frames, we take full advantage of both motion vectors and residual errors that are freely available in video streams. To our best knowledge, the MMNet is the first work that investigates a deep convolutional detector on compressed videos. Our method is evaluated on the large-scale ImageNet VID dataset, and the results show that it is 3x times faster than single image detector R-FCN and 10x times faster than high-performance detector MANet at a minor accuracy loss.


title: VQA with no questions-answers training

abstract: Methods for teaching machines to answer visual questions have made significant progress in the last few years, but although demonstrating impressive results on particular datasets, these methods lack some important human capabilities, including integrating new visual classes and concepts in a modular manner, providing explanations for the answer and handling new domains without new examples. In this paper we present a system that achieves state-of-the-art results on the CLEVR dataset without any questions-answers training, utilizes real visual estimators and explains the answer. The system includes a question representation stage followed by an answering procedure, which invokes an extendable set of visual estimators. It can explain the answer, including its failures, and provide alternatives to negative answers. The scheme builds upon a framework proposed recently, with extensions allowing the system to deal with novel domains without relying on training examples.


title: Advances in Power Conversion and Drives for Shipboard Systems

abstract: This paper presents some of the key advances in power electronics pertaining to shipboard electric power system applications. The focus is on the emerging wide bandgap semiconductor devices, i.e., silicon carbide (SiC) and gallium nitride (GaN) devices, and their potential impact on future shipboard power conversion and drives. Their benefits on power converter efficiency and power density are explained through a case study of a medium-voltage (MV) class motor drive system. SiC and GaN also enable new applications, including solid-state transformers, while posing new design and application challenges such as gate drive, protection, and interaction with loads. In addition to device related topics, this paper also overviews other important advances in power electronics, including topology, control, passive components, thermal management, filters, and packaging. The significance of power electronics building blocks (PEBBs) concept for shipboard power system development is discussed. Recogniz- ing the growing complexity of shipboard power systems, some system-level technologies related to future MV direct current (dc) system architecture are highlighted.


title: Expanding the Technology Acceptance Model with the Inclusion of Trust, Social Influence, and Health Valuation to Determine the Predictors of German Users’ Willingness to Continue using a Fitness App: A Structural Equation Modeling Approach

abstract: ABSTRACTAccording to one market research, fitness or running apps are hugely popular in Germany. Such a trend prompts the question concerning the factors influencing German users’ intention to cont...


title: Crossbar-aware neural network pruning

abstract: Crossbar architecture has been widely adopted in neural network accelerators due to the efficient implementations on vector-matrix multiplication operations. However, in the case of convolutional neural networks (CNNs), the efficiency is compromised dramatically because of the large amounts of data reuse. Although some mapping methods have been designed to achieve a balance between the execution throughput and resource overhead, the resource consumption cost is still huge while maintaining the throughput. Network pruning is a promising and widely studied method to shrink the model size, whereas prior work for CNNs compression rarely considered the crossbar architecture and the corresponding mapping method and cannot be directly utilized by crossbar-based neural network accelerators. This paper proposes a crossbar-aware pruning framework based on a formulated $L_{0}$ -norm constrained optimization problem. Specifically, we design an $L_{0}$ -norm constrained gradient descent with relaxant probabilistic projection to solve this problem. Two types of sparsity are successfully achieved: 1) intuitive crossbar-grain sparsity and 2) column-grain sparsity with output recombination, based on which we further propose an input feature maps reorder method to improve the model accuracy. We evaluate our crossbar-aware pruning framework on the median-scale CIFAR10 data set and the large-scale ImageNet data set with VGG and ResNet models. Our method is able to reduce the crossbar overhead by 44%–72% with insignificant accuracy degradation. This paper significantly reduce the resource overhead and the related energy cost and provides a new co-design solution for mapping CNNs onto various crossbar devices with much better efficiency.


Stylometry with R : a suite of tools


title: Always-ON visual node with a hardware-software event-based binarized neural network inference engine

abstract: This work introduces an ultra-low-power visual sensor node coupling event-based binary acquisition with Binarized Neural Networks (BNNs) to deal with the stringent power requirements of always-on vision systems for IoT applications. By exploiting in-sensor mixed-signal processing, an ultra-low-power imager generates a sparse visual signal of binary spatial-gradient features. The sensor output, packed as a stream of events corresponding to the asserted gradient binary values, is transferred to a 4-core processor when the amount of data detected after frame difference surpasses a given threshold. Then, a BNN trained with binary gradients as input runs on the parallel processor if a meaningful activity is detected in a pre-processing stage. During the BNN computation, the proposed Event-based Binarized Neural Network model achieves a system energy saving of 17.8% with respect to a baseline system including a low-power RGB imager and a Binarized Neural Network, while paying a classification performance drop of only 3% for a real-life 3-classes classification scenario. The energy reduction increases up to 8x when considering a long-term always-on monitoring scenario, thanks to the event-driven behavior of the processing sub-system.


title: Group invariants in algebras of generalized functions

abstract: The invariance properties of Colombeau generalized functions under actions of smooth Lie transformation groups are studied. Several characterization results analogous to the smooth setting are derived and applications to generalized rotational invariance are given.


title: ON INTER-REALITY LITERACY: EMOTIONS AS PREDICTORS OF PERFORMANCE IN VIRTUAL WORLDS

abstract: Virtual worlds, set-up on the Internet, occur as a highly complex form of visual media. They foreshadow future developments, not only in leisure settings, but also in health care and business environments. The interaction between real-life and virtual worlds, i.e., inter-reality, has recently moved to the center of scientific interest (Bainbridge 2007). Particularly, the empirical assessment of the value of virtual embodiment and its outcomes is needed (Schultze 2010). Here, this paper aims to make a contribution. Reviewing prior media theories and corresponding conceptualizations such as presence, immersion, media literacy and emotions, we argue that in inter-reality, individual differences in perceiving and dealing with one’s own and other’s emotions influence an individual's performance. Providing construct operationalizations and model propositions, we suggest testing the theory in the context of competitive and socially interactive virtual worlds.


title: Active Mobile Robot Localization

abstract: Localization is the problem of determining the position of a mobile robot from sensor data. Most existing localization approaches are passive, i.e., they do not exploit the opportunity to control the robot’s effectors during localization. This paper proposes an active localization approach. The approach provides rational criteria for (1) setting the robot’s motion direction (exploration), and (2) determining the pointing direction of the sensors so as to most efficiently localize the robot. Furthermore, it is able to deal with noisy sensors and approximative world models. The appropriateness of our approach is demonstrated empirically using a mobile robot in a structured office environment.


title: registered trademark of AAAS. REVIEW Network Analysis in the Social Sciences

abstract: Over the past decade, there has been an explosion of interest in network research across the physical and social sciences. For social scientists, the theory of networks has been a gold mine, yielding explanations for social phenomena in a wide variety of disciplines from psychology to economics. Here, we review the kinds of things that social scientists have tried to explain using social network analysis and provide a nutshell description of the basic assumptions, goals, and explanatory mechanisms prevalent in the field. We hope to contribute to a dialogue among researchers from across the physical and social sciences who share a common interest in understanding the antecedents and consequences of network phenomena.


title: Information diffusion through blogspace

abstract: We study the dynamics of information propagation in environments of low-overhead personal publishing, using a large collection of WebLogs over time as our example domain. We characterize and model this collection at two levels. First, we present a macroscopic characterization of topic propagation through our corpus, formalizing the notion of long-running "chatter" topics consisting recursively of "spike" topics generated by outside world events, or more rarely, by resonances within the community. Second, we present a microscopic characterization of propagation from individual to individual, drawing on the theory of infectious diseases to model the flow. We propose, validate, and employ an algorithm to induce the underlying propagation network from a sequence of posts, and report on the results.


title: Block Compressed Sensing of Natural Images

abstract: Compressed sensing (CS) is a new technique for simultaneous data sampling and compression. In this paper, we propose and study block compressed sensing for natural images, where image acquisition is conducted in a block-by-block manner through the same operator. While simpler and more efficient than other CS techniques, the proposed scheme can sufficiently capture the complicated geometric structures of natural images. Our image reconstruction algorithm involves both linear and nonlinear operations such as Wiener filtering, projection onto the convex set and hard thresholding in the transform domain. Several numerical experiments demonstrate that the proposed block CS compares favorably with existing schemes at a much lower implementation cost.


title: Person tracking and following with 2D laser scanners

abstract: Having accurate knowledge of the positions of people around a robot provides rich, objective and quantitative data that can be highly useful for a wide range of tasks, including autonomous person following. The primary objective of this research is to promote the development of robust, repeatable and transferable software for robots that can automatically detect, track and follow people in their environment. The work is strongly motivated by the need for such functionality onboard an intelligent power wheelchair robot designed to assist people with mobility impairments. In this paper we propose a new algorithm for robust detection, tracking and following from laser data. We show that the approach is effective in various environments, both indoor and outdoor, and on different robot platforms (the intelligent power wheelchair and a Clearpath Husky). The method has been implemented in the Robot Operating System (ROS) framework and will be publicly released as a ROS package. We also describe and will release several datasets designed to promote the standardized evaluation of similar algorithms.


title: Extracting Rule RF in Educational Data Classification: From a Random Forest to Interpretable Refined Rules

abstract: To early detect in-trouble students in an academic credit system has been emerging in the educational data mining research arena. This problem has been taken into consideration with a multi-class educational data classification task. Although many existing supervised learning algorithms are available and able to provide us with many acceptable classification models, the interpretability of these models needs to be investigated so that they can be applied in practice. On the other hand, random forests have been examined and appeared to be an appropriate solution to effectively classify the students for early in-trouble student detection in a credit system. However, random forests are black-box ensemble models which lack a capability of explanation for the reasoning behind their prediction. Therefore, in this paper, we define a rule extraction algorithm named ExtractingRuleRF to derive an interpretable refined classification rule set from a random forest for a multi-class data classification task. The proposed algorithm follows a greedy approach with two phases: rule refinement and rule extraction. In the first phase, we prepare a ranked weighted rule set with more interpretability and equivalent classification power of the input random forest by retaining its classification scheme. In the second phase, our rule extraction process returns the best rules for the highest accuracy and/or a full coverage based on the priority of each ranked rule. Consequently, the theoretical analysis of the algorithm and experimental results on real educational data sets have shown that ExtractingRuleRF can produce a more effective and interpretable rule-based classification model than its corresponding random forest. Such a result helps our knowledge-based educational decision support with interpretable classification rules to be more practical.


title: Ten Steps to Making Evaluation Matter.

abstract: This paper proposes ten steps to make evaluations matter. The ten steps are a combination of the usual recommended practice such as developing program theory and implementing rigorous evaluation designs with a stronger focus on more unconventional steps including developing learning frameworks, exploring pathways of evaluation influence, and assessing spread and sustainability. Consideration of these steps can lead to a focused dialogue between program planners and evaluators and can result in more rigorously planned programs. The ten steps can also help in developing and implementing evaluation designs that have greater potential for policy and programmatic influence. The paper argues that there is a need to go beyond a formulaic approach to program evaluation design that often does not address the complexity of the programs. The complexity of the program will need to inform the design of the evaluation. The ten steps that are described in this paper are heavily informed by a Realist approach to evaluation. The Realist approach attempts to understand what is it about a program that makes it work.


title: Web corpora - the best possible solution for tracking rare phenomena in underresourced languages: clitics in Bosnian, Croatian and Serbian

abstract: Complex linguistic phenomena, such as Clitic Climbing in Bosnian, Croatian and Serbian, are often described intuitively, only from the perspective of the main tendency. In this paper, we argue that web corpora currently offer the best source of empirical material for studying Clitic Climbing in BCS. They thus allow the most accurate description of this phenomenon, as less frequent constructions can be tracked only in big, well-annotated data sources. We compare the properties of web corpora for BCS with traditional sources and give examples of studies on CC based on web corpora. Furthermore, we discuss problems related to web corpora and suggest some improvements for the future.


title: Intervention Strategies to Increase Self-efficacy and Self-regulation in Adaptive On-Line Learning. In

abstract: This research outline refers to the validation of interventional strategies to increase the learner’s motivation and self-efficacy in an on-line learning environment. Previous work in this area is mainly based on Keller’s ARCS model of instructional design and this study argues for an approach based on Bandura’s Social Cognitive Theory – especially the aspects of self-efficacy and self-regulation. The research plan envisages two phases: The first phase will extract rules for interventional strategy selection from expert teachers. The second phase aims to validate these rules by providing to the learner the selected strategy and observing the resulting behavior.


title: Learning and teaching programming: A review and discussion

abstract: In this paper we review the literature relating to the psychological/educational study of programming. We identify general trends comparing novice and expert programmers, programming knowledge and strategies, program generation and comprehension, and object-oriented versus procedural programming. (We do not cover research relating specifically to other programming styles.) The main focus of the review is on novice programming and topics relating to novice teaching and learning. Various problems experienced by novices are identified, including issues relating to basic program design, to algorithmic complexity in certain language features, to the “fragility” of novice knowledge, and so on. We summarise this material and suggest some practical implications for teachers. We suggest that a key issue that emerges is the distinction between effective and ineffective novices. What characterises effective novices? Is it possible to identify the specific deficits of ineffective novices and help them to become effec...


title: An Introduction to Independent Component Analysis: InfoMax and FastICA algorithms

abstract: This paper presents an introduction to independent component analysis (ICA). Unlike principal component analysis, which is based on the assumptions of uncorrelatedness and normality, ICA is rooted in the assumption of statistical independence. Foundations and basic knowledge necessary to understand the technique are provided hereafter. Also included is a short tutorial illustrating the implementation of two ICA algorithms (FastICA and InfoMax) with the use of the Mathematica software.


title: 340 GHz On-Chip 3-D Antenna With 10 dBi Gain and 80% Radiation Efficiency

abstract: This paper discusses the design methodologies of a 340 GHz on-chip 3-D antenna. Firstly, a high-gain and high-radiation efficiency substrate integrated waveguide (SIW) cavity backed on-chip antenna is designed using a standard 0.13- $\mu{\hbox{m}}$ SiGe BiCMOS technology. Then, a low-permittivity supporter and a dielectric resonator (DR) are vertically stacked on the proposed on-chip antenna, forming a 3-D Yagi-like antenna to further enhance the gain and radiation efficiency. The measurements showed that the proposed antenna achieved a peak gain of ${\sim}$ 10 dBi and radiation efficiency of ${\sim}$ 80% at 340 GHz; the impedance bandwidth is ${\sim}$ 12% with the use of dielectric resonator antenna (DRA) and the Yagi-like structure. The antenna size is ${\sim} {\hbox{0.7}}\times {\hbox{0.7}}\ {\hbox{mm}}^{2}$ .


title: Ensemble learning for data stream analysis

abstract: A comprehensive survey of ensemble approaches for data stream analysis.Taxonomy of ensemble algorithms for various data stream mining tasks.Discussion of open research problems and lines of future research. In many applications of information systems learning algorithms have to act in dynamic environments where data are collected in the form of transient data streams. Compared to static data mining, processing streams imposes new computational requirements for algorithms to incrementally process incoming examples while using limited memory and time. Furthermore, due to the non-stationary characteristics of streaming data, prediction models are often also required to adapt to concept drifts. Out of several new proposed stream algorithms, ensembles play an important role, in particular for non-stationary environments. This paper surveys research on ensembles for data stream classification as well as regression tasks. Besides presenting a comprehensive spectrum of ensemble approaches for data streams, we also discuss advanced learning concepts such as imbalanced data streams, novelty detection, active and semi-supervised learning, complex data representations and structured outputs. The paper concludes with a discussion of open research problems and lines of future research.


title: Users' satisfaction in recommendation systems for groups: an approach based on noncooperative games

abstract: A major difficulty in a recommendation system for groups is to use a group aggregation strategy to ensure, among other things, the maximization of the average satisfaction of group members. This paper presents an approach based on the theory of noncooperative games to solve this problem. While group members can be seen as game players, the items for potential recommendation for the group comprise the set of possible actions. Achieving group satisfaction as a whole becomes, then, a problem of finding the Nash equilibrium. Experiments with a MovieLens dataset and a function of arithmetic mean to compute the prediction of group satisfaction for the generated recommendation have shown statistically significant results when compared to state-of-the-art aggregation strategies, in particular, when evaluation among group members are more heterogeneous. The feasibility of this unique approach is shown by the development of an application for Facebook, which recommends movies to groups of friends.


title: Modeling Reportable Events as Turning Points in Narrative

abstract: We present novel experiments in modeling the rise and fall of story characteristics within narrative, leading up to the Most Reportable Event (MRE), the compelling event that is the nucleus of the story. We construct a corpus of personal narratives from the bulletin board website Reddit, using the organization of Reddit content into topic-specific communities to automatically identify narratives. Leveraging the structure of Reddit comment threads, we automatically label a large dataset of narratives. We present a change-based model of narrative that tracks changes in formality, affect, and other characteristics over the course of a story, and we use this model in distant supervision and selftraining experiments that achieve significant improvements over the baselines at the task of identifying MREs.


title: Financial incentives and the "performance of crowds"

abstract: The relationship between financial incentives and performance, long of interest to social scientists, has gained new relevance with the advent of web-based "crowd-sourcing" models of production. Here we investigate the effect of compensation on performance in the context of two experiments, conducted on Amazon's Mechanical Turk (AMT). We find that increased financial incentives increase the quantity, but not the quality, of work performed by participants, where the difference appears to be due to an "anchoring" effect: workers who were paid more also perceived the value of their work to be greater, and thus were no more motivated than workers paid less. In contrast with compensation levels, we find the details of the compensation scheme do matter---specifically, a "quota" system results in better work for less pay than an equivalent "piece rate" system. Although counterintuitive, these findings are consistent with previous laboratory studies, and may have real-world analogs as well.


title: SemEval-2015 Task 6: Clinical TempEval

abstract: Clinical TempEval 2015 brought the temporal information extraction tasks of past TempEval campaigns to the clinical domain. Nine sub-tasks were included, covering problems in time expression identification, event expression identification and temporal relation identification. Participant systems were trained and evaluated on a corpus of clinical notes and pathology reports from the Mayo Clinic, annotated with an extension of TimeML for the clinical domain. Three teams submitted a total of 13 system runs, with the best systems achieving near-human performance on identifying events and times, but with a large performance gap still remaining for temporal relations.


Procedural house generation: A method for dynamically generating floor plans


title: A new method to detect dissolved gases in transformer oil using NIR-IR spectroscopy

abstract: Dissolved gas analysis (DGA) is widely accepted as an effective technique to detect incipient faults within power transformers. Gases such as hydrogen, methane, acetylene, ethylene and ethane are generated as a result of insulation oil decomposition, while carbon monoxide and carbon dioxide are generated as a result of insulation paper decomposition. Laboratory-based gas chromatography (GC) in accordance to ASTM D3612 standard is globally accepted as a reliable technique to quantify dissolved gases in transformer oil samples. However, this technique incurs running cost and requires an expert to conduct the test. Furthermore, due to the complexity of the equipment, measurement is only performed in a laboratory environment and takes long time to get the results since the extraction of the oil sample from the transformer. To overcome these issues, various online DGA techniques have been developed and currently used by industry. However, these techniques are still considered in development stage with some limitations that have to be addressed to fully accept its results. This paper proposes a new method to estimate the concentration of various dissolved gases in transformer oil using near infrared-to-infrared (NIR-IR) spectroscopy that can be conducted instantly onsite with relatively inexpensive equipment. The proposed measuring technique does not call for expert person to conduct the measurements and has the potential to be implemented online. Results show a good correlation between oil spectral response and the concentration of each dissolved gas in the oil at particular wavelength range. Fuzzy logic model approach is employed to model this correlation and estimate the concentration of each dissolved gas.


title: Towards a Streaming Approach to the Mitigation of Covert Timing Channels

abstract: Leaking data using covert timing channels is considered as a critical threat in network communication. These type of channels use the time between inter-arrival packets to pass information between different processes, making it a very critical to design techniques to eliminate and mitigate such channels; hence, ensuring a more secure communication environment. This paper proposes a new online streaming approach to the mitigation of covert timing channels. The new approach eliminates covert timing channels while having a little impact on the overall Quality of Service (QoS). A classification-based strategy was used to test the performance of the proposed mitigation model. In fact, we showed that the classification accuracy of a classifier model trained and tested on a mitigated flow fell down to 50% compared to the same classifier model when trained and tested on the same flow before mitigation indicating that the classifier is no longer able to distinguish between covert and overt channels.


title: World influence and interactions of universities from Wikipedia networks

abstract: Abstract We present Wikipedia Ranking of World Universities (WRWU) based on analysis of networks of 24 Wikipedia editions collected in May 2017. With PageRank and CheiRank algorithms we determine ranking of universities averaged over cultural views of these editions. The comparison with the Shanghai ranking gives overlap of 60% for top 100 universities showing that WRWU gives more significance to their historical development. We show that the new reduced Google matrix algorithm allows to determine interactions between leading universities on a scale of ten centuries. This approach also determines the influence of specific universities on world countries. We also compare different cultural views of Wikipedia editions on significance and influence of universities. Graphical abstract


title: InteriorNet: Mega-scale Multi-sensor Photo-realistic Indoor Scenes Dataset

abstract: Datasets have gained an enormous amount of popularity in the computer vision community, from training and evaluation of Deep Learning-based methods to benchmarking Simultaneous Localization and Mapping (SLAM). Without a doubt, synthetic imagery bears a vast potential due to scalability in terms of amounts of data obtainable without tedious manual ground truth annotations or measurements. Here, we present a dataset with the aim of providing a higher degree of photo-realism, larger scale, more variability as well as serving a wider range of purposes compared to existing datasets. Our dataset leverages the availability of millions of professional interior designs and millions of production-level furniture and object assets -- all coming with fine geometric details and high-resolution texture. We render high-resolution and high frame-rate video sequences following realistic trajectories while supporting various camera types as well as providing inertial measurements. Together with the release of the dataset, we will make executable program of our interactive simulator software as well as our renderer available at https://interiornetdataset.github.io. To showcase the usability and uniqueness of our dataset, we show benchmarking results of both sparse and dense SLAM algorithms.


title: A Survey on Semantic Web Technologies for the Internet of Things

abstract: The Internet of Things is a vast and growing virtual universe that includes medical devices, automobiles, industrial systems and a variable number of consumer electronics devices. With some 100 billion devices expected to be connected to the Internet by 2025, providing interoperability among the things on the IoT is one of the most fundamental requirements to support tracking, object addressing and discovery as well as information representation, storage, and exchange. Applying semantic techniques to IoT can support Interoperability, effective data access, integration resource discovery reasoning and processing of data knowledge extraction. In this paper, we present an overview of semantic technologies such as Semantic Web of Things (SWoT), Ontologies, Resource Description Framework (RDF), RDF Schema, Web Ontology Language (OWL), SPARQL Protocol And RDF Query Language (SPARQL), Semantic annotation and reasoning widely deployed in IoT.


title: Real-time Implementation of YOLO+JPDA for Small Scale UAV Multiple Object Tracking

abstract: This paper describes the development of a real-time multiple object detection and tracking system for a small scale UAV. The YOLO deep learning visual object detection algorithm and JPDA multiple target detection algorithm, were selected and implemented. The theory and implementation details of these algorithms are presented. The performance analysis of the system is done on both public dataset and aerial videos taken by UAV.


title: A Novel Coding Scheme for Indexing Fingerprint Patterns ⋆

abstract: Indexing is the process of assigning a numerical value to a database entry in order to facilitate its rapid retrieval. Indexing a fingerprint database can reduce the search space and improve the response time of an identification system. We discuss a novel method for generating index codes for fingerprint images by using a small set of pre-determined reference fingerprints. In the proposed method, the match scores generated by comparing an input fingerprint with the reference fingerprints are subjected to a discretization function, which converts them into an index code. A search mechanism based on the Hamming distance identifies those index codes in the database that are similar to the code of the input image. The proposed technique has several advantages: it obviates the need to extract complex features from the fingerprint image; it utilizes the matcher that is already associated with a particular application; and it can be used to index any biometric database irrespective of the trait or matcher being used. Experimental results on two fingerprint databases (NIST-4 and WVU) indicate that the proposed encoding scheme generates index codes that are well-scattered thereby allowing noisy query images to be indexed correctly.


title: A New Reconstruction Filter for Undersampled Light Fields

abstract: This paper builds on previous research in the light field area of image-based rendering. We present a new reconstruction filter that significantly reduces the "ghosting" artifacts seen in undersampled light fields, while preserving important high-fidelity features such as sharp object boundaries and view-dependent reflectance. By improving the rendering quality achievable from undersampled light fields, our method allows acceptable images to be generated from smaller image sets. We present both frequency and spatial domain justifications for our techniques. We also present a practical framework for implementing the reconstruction filter in multiple rendering passes.


Influence of the Public Procurement Procedure Type on the Duration of Public Procurement


title: Information technology impacts on firm performance: an extension of Kohli and Devaraj (2003)

abstract: Despite the importance of investing in information technology, research on business value of information technology (BVIT) shows contradictory results, raising questions about the reasons for divergence. Kohli and Devaraj (2003) provided valuable insights into this issue based on a meta-analysis of 66 BVIT studies. This paper extends Kohli and Devaraj by examining the influences on BVIT through a meta-analysis of 303 studies published between 1990 and 2013. We found that BVIT increases when the study does not consider IT investment, does not use profitability measure of value, and employs primary data sources, fewer IT-related antecedents, and larger sample size. Considerations of IT alignment, IT adoption and use, and interorganizational IT strengthen the relationship between IT investment on BVIT, whereas the focus on environmental theories dampens the same relationship. However, the use of productivity measures of value, the number of dependent variables, the economic region, the consideration of IT assets and IT infrastructure or capability, and the consideration of IT sophistication do not affect BVIT. Finally, BVIT increases over time with IT progress. Implications for future research and practice are discussed.


title: Project management in research projects

abstract: Project Management (PM) is a set of activities which enables successful implementation of a project, where a project may be defined as involving a group of inter- related activities that are planned and then executed in a certain sequence to create a unique output (product or service) within a specific time frame. Research projects differ in many ways from development projects, the most significant being (lack of) clear requirements and (in)ability to plan an output from the start of the project. In terms of outputs, evaluation criteria for a research project must take into account such, we well as other "particularities"; for example, that proving that something can not be done may represent a positive result for a research project. This article describes particularities of research projects, and proposes adequately adjusted project management process and practices. It also gives examples and evaluation of successful application thereof in practice, in joint projects between academia and industry over the past five years.


Argumentation Theory: A Very Short Introduction


title: Architectural extensions for elliptic curve cryptography over GF(2/sup m/) on 8-bit microprocessors

abstract: We describe and analyze architectural extensions to accelerate the public key cryptosystem elliptic curve cryptography (ECC) on 8-bit microprocessors. We show that simple extensions of the data path suffice to efficiently support ECC over GF(2/sup m/). These extensions include an extended multiplier that generates results for both integer multiplications and multiplications in fields GF(2/sup m/) and a multiply-accumulate instruction for efficiently performing multiple precision multiplications. To our knowledge, this is the first paper that quantifies performance of standard NIST and SECG elliptic curves over GF(2/sup m/) on an 8-bit microprocessor equipped with a dual field multiplier. On the ATmegal28 microprocessor running at 8 MHz we measured an execution time of 0.29 s for a 163-bit ECC point multiplication over GF(2/sup m/), 0.81s for a 160-bit ECC point multiplication over GF(p), and 11 s for a 1024-bit RSA private key operation - the chosen key sizes provide equivalent security strength.


title: A Capacitor-Less CMOS Active Feedback Low-Dropout Regulator With Slew-Rate Enhancement for Portable On-Chip Application

abstract: A low-dropout regulator for on-chip application with active feedback and a slew-rate enhancement circuit to minimize compensation capacitance and speed up transient response is presented in this brief. The idea has been modeled and experimentally verified in a standard 0.35-?m CMOS process. The total compensation capacitance is 7 pF. From experimental results, the implemented regulator can operate from a supply voltage of 1.8-4.5 V with a minimum dropout voltage of 0.2 V at a maximum 100-mA load and I Q of 20 ?A.


Risk assessment of look‒alike, sound‒alike (LASA) medication errors in an Italian hospital pharmacy: A model based on the ‘Failure Mode and Effect Analysis’


title: Wideband Circularly Polarized E-Shaped Patch Antenna for Wireless Applications [Wireless Corner]

abstract: A new technique to achieve a circularly polarized probe-fed single-layer microstrip-patch antenna with a wideband axial ratio is proposed. The antenna is a modified form of the conventional E-shaped patch, used to broaden the impedance bandwidth of a basic patch antenna. By letting the two parallel slots of the E patch be unequal, asymmetry is introduced. This leads to two orthogonal currents on the patch and, hence, circularly polarized fields are excited. The proposed technique exhibits the advantage of the simplicity of the E-shaped patch design, which requires only the slot lengths, widths, and position parameters to be determined. Investigations of the effect of various dimensions of the antenna have been carried out via parametric analysis. Based on these investigations, a design procedure for a circularly polarized E-shaped patch was developed. A prototype has been designed, following the suggested procedure for the IEEE 802.11big WLAN band. The performance of the fabricated antenna was measured and compared with simulation results. Various examples with different substrate thicknesses and material types are presented and compared with the recently proposed circularly polarized U-slot patch antennas.


title: GLAD: Group Anomaly Detection in Social Media Analysis

abstract: Traditional anomaly detection on social media mostly focuses on individual point anomalies while anomalous phenomena usually occur in groups. Therefore, it is valuable to study the collective behavior of individuals and detect group anomalies. Existing group anomaly detection approaches rely on the assumption that the groups are known, which can hardly be true in real world social media applications. In this article, we take a generative approach by proposing a hierarchical Bayes model: Group Latent Anomaly Detection (GLAD) model. GLAD takes both pairwise and point-wise data as input, automatically infers the groups and detects group anomalies simultaneously. To account for the dynamic properties of the social media data, we further generalize GLAD to its dynamic extension d-GLAD. We conduct extensive experiments to evaluate our models on both synthetic and real world datasets. The empirical results demonstrate that our approach is effective and robust in discovering latent groups and detecting group anomalies.


title: Predictive Monitoring of Mobile Patients by Combining Clinical Observations With Data From Wearable Sensors

abstract: The majority of patients in the hospital are ambulatory and would benefit significantly from predictive and personalized monitoring systems. Such patients are well suited to having their physiological condition monitored using low-power, minimally intrusive wearable sensors. Despite data-collection systems now being manufactured commercially, allowing physiological data to be acquired from mobile patients, little work has been undertaken on the use of the resultant data in a principled manner for robust patient care, including predictive monitoring. Most current devices generate so many false-positive alerts that devices cannot be used for routine clinical practice. This paper explores principled machine learning approaches to interpreting large quantities of continuously acquired, multivariate physiological data, using wearable patient monitors, where the goal is to provide early warning of serious physiological determination, such that a degree of predictive care may be provided. We adopt a one-class support vector machine formulation, proposing a formulation for determining the free parameters of the model using partial area under the ROC curve, a method arising from the unique requirements of performing online analysis with data from patient-worn sensors. There are few clinical evaluations of machine learning techniques in the literature, so we present results from a study at the Oxford University Hospitals NHS Trust devised to investigate the large-scale clinical use of patient-worn sensors for predictive monitoring in a ward with a high incidence of patient mortality. We show that our system can combine routine manual observations made by clinical staff with the continuous data acquired from wearable sensors. Practical considerations and recommendations based on our experiences of this clinical study are discussed, in the context of a framework for personalized monitoring.


title: An Extended Orthogonal Variability Model for Metadata-Driven Multitenant Cloud Services

abstract: A metadata-driven architecture for multitenant cloud services is an important class of cloud computing architectures. It enables dynamic variability of services on a single instance for embracing diverse requirements among multiple tenants. It does not require to develop applications, but to generate applications through metadata Web APIs at runtime according to individual tenant's requirements. The variability among requirements makes the design of multitenant services complicated. To design the variability of multitenant services, this article proposed an extended model of OVM (Orthogonal Variability Model) to model variability and their dependencies at two levels: meta-level and base-level. We demonstrate the effectiveness of the extended OVM through a case study of the development of an application service on Force.com from Salesforce.com.


title: Adaptive Greedy Dictionary Selection for Web Media Summarization

abstract: Initializing an effective dictionary is an indispensable step for sparse representation. In this paper, we focus on the dictionary selection problem with the objective to select a compact subset of basis from original training data instead of learning a new dictionary matrix as dictionary learning models do. We first design a new dictionary selection model via $\ell _{2,0}$ norm. For model optimization, we propose two methods: one is the standard forward–backward greedy algorithm, which is not suitable for large-scale problems; the other is based on the gradient cues at each forward iteration and speeds up the process dramatically. In comparison with the state-of-the-art dictionary selection models, our model is not only more effective and efficient, but also can control the sparsity. To evaluate the performance of our new model, we select two practical web media summarization problems: 1) we build a new data set consisting of around 500 users, 3000 albums, and 1 million images, and achieve effective assisted albuming based on our model and 2) by formulating the video summarization problem as a dictionary selection issue, we employ our model to extract keyframes from a video sequence in a more flexible way. Generally, our model outperforms the state-of-the-art methods in both these two tasks.


title: Experimental analysis of AODV, DSDV and OLSR routing protocol for flying adhoc networks (FANETs)

abstract: In recent years the capability and role of Mobile Adhoc Networks have rapidly evolved. Their use in emergency, natural disaster, military battle fields and UAVs is getting very popular as a result of cutting edge technologies in networking and communication. Using the concept of MANET new networking paradigms like VANET and FANET have evolved. FANET is comparably new concept of MANET and it has capabilities to tackle with situations where traditional MANET cannot do so. Due to high mobility and fast topology change in FANET, this is highly challengeable for researcher to implement routing in FANETs. Routing protocols play a dominating role in enhancing the performance of adhoc networks. In this paper, experimental analysis is carried out on AODV, DSDV and OLSR routing protocol for FANET environment using NS2 simulator.


title: Prolog as the Fundament for Applications on the Semantic Web

abstract: htmlabstractThis article describes the experiences developing a Semantic Web application entirely in Prolog. The application, a demonstrator that provides access to multiple art collections and linking these using cultural heritage vocabularies, has won the first price in the ISWC-06 contest on SemanticWeb end-user applications. In this document we concentrate on the Prolog-based architecture, describing experiences and vital aspects of the design.


title: Reverse Engineering Mobile Application User Interfaces with REMAUI (T)

abstract: When developing the user interface code of a mobile application, in practice a big gap exists between the digital conceptual drawings of graphic artists and working user interface code. Currently, programmers bridge this gap manually, by reimplementing the conceptual drawings in code, which is cumbersome and expensive. To bridge this gap, we introduce the first technique to automatically Reverse Engineer Mobile Application User Interfaces (REMAUI). On a given input bitmap REMAUI identifies user interface elements such as images, texts, containers, and lists, via computer vision and optical character recognition (OCR) techniques. In our experiments on 488 screenshots of over 100 popular third-party Android and iOS applications, REMAUI-generated user interfaces were similar to the originals, both pixel-by-pixel and in terms of their runtime user interface hierarchies. REMAUI's average overall runtime on a standard desktop computer was 9 seconds.


title: ADVANCES IN NETWORK CLUSTERING AND BLOCKMODELING

abstract: The paper addresses the stability of the co-authorship networks in time. The analysis is done on the networks of Slovenian researchers in two time periods (1991-2000 and 2001-2010). Two researchers are linked if they published at least one scientific bibliographic unit in a given time period. As proposed by Kronegger et al. (2011), the global network structures are examined by generalized blockmodeling with the assumed multi-core--semi-periphery--periphery blockmodel type. The term core denotes a group of researchers who published together in a systematic way with each other. ::: The obtained blockmodels are comprehensively analyzed by visualizations and through considering several statistics regarding the global network structure. To measure the stability of the obtained blockmodels, different adjusted modified Rand and Wallace indices are applied. Those enable to distinguish between the splitting and merging of cores when operationalizing the stability of cores. Also, the adjusted modified indices can be used when new researchers occur in the second time period (newcomers) and when some researchers are no longer present in the second time period (departures). The research disciplines are described and clustered according to the values of these indices. Considering the obtained clusters, the sources of instability of the research disciplines are studied (e.g., merging or splitting of cores, newcomers or departures). Furthermore, the differences in the stability of the obtained cores on the level of scientific disciplines are studied by linear regression analysis where some personal characteristics of the researchers (e.g., age, gender), are also considered.


title: Age of Onset and Sexual Orientation in Transsexual Males and Females

abstract: ABSTRACT Introduction With regard to transsexual developments, onset age (OA) appears to be the starting point of different psychosexual pathways. Aim To explore differences between transsexual adults with an early vs. late OA. Methods Data were collected within the European Network for the Investigation of Gender Incongruence using the Dutch Biographic Questionnaire on Transsexualism (Biografische Vragenlijst voor Transseksuelen) and a self‐constructed score sheet according to the DSM‐IV‐TR ( Diagnostic and Statistical Manual, Fourth Edition, Text Revision ) criteria of Gender Identity Disorder (GID) and Gender Identity Disorder in Childhood (GIDC). One hundred seventy participants were included in the analyses. Main Outcome Measures Transsexual adults who, in addition to their GID diagnosis, also fulfilled criteria A and B of GIDC (“a strong cross‐gender identification,”“persistent discomfort about her or his assigned sex”) retrospectively were considered as having an early onset (EO). Those who fulfilled neither criteria A nor B of GIDC were considered as having a late onset (LO). Participants who only fulfilled criterion A or B of GIDC were considered a residual (RES) group. Results The majority of female to males (FtMs) appeared to have an early OA (EO = 60 [77.9%] compared to LO = 10 [13%] and to RES = 7 [9.1%]). Within male to females (MtFs), percentages of EO and LO developments were more similar (EO = 36 [38.7%], LO = 45 [48.4%], RES = 12 [12.9%]). FtMs presented to gender clinics at an earlier age than MtFs (28.04 to 36.75). The number of EO vs. LO transsexual adults differed from country to country (Belgium, Germany, the Netherlands, Norway). Conclusion OA has a discriminative value for transsexual developments and it would appear that retrospective diagnosis of GIDC criteria is a valid method of assessment. Differences in OA and sex ratio exist between European countries. Nieder TO, Herff M, Cerwenka S, Preuss WF, Cohen‐Kettenis PT, De Cuypere G, Hebold Haraldsen IR, and Richter‐Appelt H. Age of onset and sexual orientation in transsexual males and females. J Sex Med 2011;8:783–791.


title: ExB Text Summarizer

abstract: We present our state of the art multilingual text summarizer capable of single as well as multi-document text summarization. The algorithm is based on repeated application of TextRank on a sentence similarity graph, a bag of words model for sentence similarity and a number of linguistic pre- and post-processing steps using standard NLP tools. We submitted this algorithm for two different tasks of the MultiLing 2015 summarization challenge: Multilingual Singledocument Summarization and Multilingual Multi-document Summarization.


title: An unsupervised approach based on the generalized Gaussian model to automatic change detection in multitemporal SAR images

abstract: We present a novel automatic and unsupervised change-detection approach specifically oriented to the analysis of multitemporal single-channel single-polarization synthetic aperture radar (SAR) images. This approach is based on a closed-loop process made up of three main steps: (1) a novel preprocessing based on a controlled adaptive iterative filtering; (2) a comparison between multitemporal images carried out according to a standard log-ratio operator; and (3) a novel approach to the automatic analysis of the log-ratio image for generating the change-detection map. The first step aims at reducing the speckle noise in a controlled way in order to maximize the discrimination capability between changed and unchanged classes. In the second step, the two filtered multitemporal images are compared to generate a log-ratio image that contains explicit information on changed areas. The third step produces the change-detection map according to a thresholding procedure based on a reformulation of the Kittler-Illingworth (KI) threshold selection criterion. In particular, the modified KI criterion is derived under the generalized Gaussian assumption for modeling the distributions of changed and unchanged classes. This parametric model was chosen because it is capable of better fitting the conditional densities of classes in the log-ratio image. In order to control the filtering step and, accordingly, the effects of the filtering process on change-detection accuracy, we propose to identify automatically the optimal number of despeckling filter iterations [Step 1] by analyzing the behavior of the modified KI criterion. This results in a completely automatic and self-consistent change-detection approach that avoids the use of empirical methods for the selection of the best number of filtering iterations. Experiments carried out on two sets of multitemporal images (characterized by different levels of speckle noise) acquired by the European Remote Sensing 2 satellite SAR sensor confirm the effectiveness of the proposed unsupervised approach, which results in change-detection accuracies very similar to those that can be achieved by a manual supervised thresholding.