title: Specimen Box: A tangible interaction technique for world-fixed virtual reality displays

abstract: Object selection and manipulation in world-fixed displays such as CAVE-type systems are typically achieved with tracked input devices, which lack the tangibility of real-world interactions. Conversely, due to the visual blockage of the real world, head-mounted displays allow the use of many types of real world objects that can convey realistic haptic feedback. To bridge this gap, we propose Specimen Box, an interaction technique that allows users to naturally hold a plausible physical object while manipulating virtual content inside it. This virtual content is rendered based on the tracked position of the box in relation to the user's point of view. Specimen Box provides the weight and tactile feel of an actual object and does not occlude rendered objects in the scene. The end result is that the user sees the virtual content as if it exists inside the clear physical box. We hypothesize that the effect of holding a physical box, which is a valid part of the overall scenario, would improve user performance and experience. To verify this hypothesis, we conducted a user study which involved a cognitively loaded inspection task requiring extensive manipulation of the box. We compared Specimen Box to Grab-and-Twirl, a naturalistic bimanual manipulation technique that closely mimics the mechanics of our proposed technique. Results show that in our specific task, performance was significantly faster and rotation rate was significantly lower with Specimen Box. Further, performance of the control technique was positively affected by experience with Specimen Box.


title: Raman spectroscopy as a stress sensor in packaging: correct formulae for different sample surfaces

abstract: In the paper, RS (Raman Spectroscopy) is discussed as a stress sensor in packaging. This paper discusses the measurement of thermo-mechanical stress introduced in the Si chip by packaging. Examples are shown for Si on Cu substrate, flip-chip and PSGA samples. It is shown that the relation between the Raman shift and the stress tensor components depends on the crystallographic orientation of the surface of the sample, i.e. Raman measurements on a [001] surface of a chip give different results than measurements on a cross-section ((1-10) surface). The Raman data are compared with finite element calculations.


Devices based on Rotary Soft Fluidic Actuators-Concept and Prototyping


Image Denoising with Wavelet Based Thresholding


title: HeNet: A Deep Learning Approach on Intel® Processor Trace for Effective Exploit Detection

abstract: This paper presents HeNet, a hierarchical ensemble neural network, applied to classify hardware-generated control flow traces for malware detection. Deep learning-based malware detection has so far focused on analyzing executable files and runtime API calls. Static code analysis approaches face challenges due to obfuscated code and adversarial perturbations. Behavioral data collected during execution is more difficult to obfuscate but recent research has shown successful attacks against API call based malware classifiers. We investigate control flow based characterization of a program execution to build robust deep learning malware classifiers. HeNet consists of a low-level behavior model and a top-level ensemble model. The low-level model is a per-application behavior model, trained via transfer learning on a time-series of images generated from control flow trace of an execution. We use Intel® Processor Trace enabled processor for low overhead execution tracing and design a lightweight image conversion and segmentation of the control flow trace. The top-level ensemble model aggregates the behavior classification of all the trace segments and detects an attack. The use of hardware trace adds portability to our system and the use of deep learning eliminates the manual effort of feature engineering. We evaluate HeNet against real-world exploitations of PDF readers. HeNet achieves 100% accuracy and 0% false positive on test set, and higher classification accuracy compared to classical machine learning algorithms.


title: Optimal decentralized protocol for electric vehicle charging

abstract: Motivated by the power-grid-side challenges in the integration of electric vehicles, we propose a decentralized protocol for negotiating day-ahead charging schedules for electric vehicles. The overall goal is to shift the load due to electric vehicles to fill the overnight electricity demand valley. In each iteration of the proposed protocol, electric vehicles choose their own charging profiles for the following day according to the price profile broadcast by the utility, and the utility updates the price profile to guide their behavior. This protocol is guaranteed to converge, irrespective of the specifications (e.g., maximum charging rate and deadline) of electric vehicles. At convergence, the l 2 norm of the aggregated demand is minimized, and the aggregated demand profile is as “flat” as it can possibly be. The proposed protocol needs no coordination among the electric vehicles, hence requires low communication and computation capability. Simulation results demonstrate convergence to optimal collections of charging profiles within few iterations.


title: Statistical Dependency Analysis with Support Vector Machines

abstract: In this paper, we propose a method for analyzing word-word dependencies using deterministic bottom-up manner using Support Vector machines. We experimented with dependency trees converted from Penn treebank data, and achieved over 90% accuracy of word-word dependency. Though the result is little worse than the most up-to-date phrase structure based parsers, it looks satisfactorily accurate considering that our parser uses no information from phrase structures.


title: Water Quality Monitoring and Control for Aquaculture Based on Wireless Sensor Networks

abstract: We have designed and presented a wireless sensor network monitoring and control system for aquaculture. The system can detect and control water quality parameters of temperature, dissolved oxygen content, pH value, and water level in real-time. The sensor nodes collect the water quality parameters and transmit them to the base station host computer through ZigBee wireless communication standard. The host computer is used for data analysis, processing and presentation using LabVIEW software platform. The water quality parameters will be sent to owners through short messages from the base station via the Global System for Mobile (GSM) module for notification. The experimental evaluation of the network performance metrics of quality of communication link, battery performance and data aggregation was presented. The experimental results show that the system has great prospect and can be used to operate in real world environment for optimum control of aquaculture environment


title: Measurement, Modeling, and Analysis of the Mobile App Ecosystem

abstract: Mobile applications (apps) have been gaining popularity due to the advances in mobile technologies and the large increase in the number of mobile users. Consequently, several app distribution platforms, which provide a new way for developing, downloading, and updating software applications in modern mobile devices, have recently emerged. To better understand the download patterns, popularity trends, and development strategies in this rapidly evolving mobile app ecosystem, we systematically monitored and analyzed four popular third-party Android app marketplaces. Our study focuses on measuring, analyzing, and modeling the app popularity distribution and explores how pricing and revenue strategies affect app popularity and developers’ income. Our results indicate that unlike web and peer-to-peer file sharing workloads, the app popularity distribution deviates from commonly observed Zipf-like models. We verify that these deviations can be mainly attributed to a new download pattern, which we refer to as the clustering effect. We validate the existence of this effect by revealing a strong temporal affinity of user downloads to app categories. Based on these observations, we propose a new formal clustering model for the distribution of app downloads and demonstrate that it closely fits measured data. Moreover, we observe that paid apps follow a different popularity distribution than free apps and show how free apps with an ad-based revenue strategy may result in higher financial benefits than paid apps. We believe that this study can be useful to appstore designers for improving content delivery and recommendation systems, as well as to app developers for selecting proper pricing policies to increase their income.


title: Hybrid ensemble approach for classification

abstract: This paper presents a novel hybrid ensemble approach for classification in medical databases. The proposed approach is formulated to cluster extracted features from medical databases into soft clusters using unsupervised learning strategies and fuse the decisions using parallel data fusion techniques. The idea is to observe associations in the features and fuse the decisions made by learning algorithms to find the strong clusters which can make impact on overall classification accuracy. The novel techniques such as parallel neural-based strong clusters fusion and parallel neural network based data fusion are proposed that allow integration of various clustering algorithms for hybrid ensemble approach. The proposed approach has been implemented and evaluated on the benchmark databases such as Digital Database for Screening Mammograms, Wisconsin Breast Cancer, and Pima Indian Diabetics. A comparative performance analysis of the proposed approach with other existing approaches for knowledge extraction and classification is presented. The experimental results demonstrate the effectiveness of the proposed approach in terms of improved classification accuracy on benchmark medical databases.


Learning to Recognize Transient Sound Events using Attentional Supervision


title: Fake news as we feel it: perception and conceptualization of the term"fake news"in the media

abstract: In this article, we quantitatively analyze how the term"fake news"is being shaped in news media in recent years. We study the perception and the conceptualization of this term in the traditional media using eight years of data collected from news outlets based in 20 countries. Our results not only corroborate previous indications of a high increase in the usage of the expression"fake news", but also show contextual changes around this expression after the United States presidential election of 2016. Among other results, we found changes in the related vocabulary, in the mentioned entities, in the surrounding topics and in the contextual polarity around the term"fake news", suggesting that this expression underwent a change in perception and conceptualization after 2016. These outcomes expand the understandings on the usage of the term"fake news", helping to comprehend and more accurately characterize this relevant social phenomenon linked to misinformation and manipulation.


Topic Sentiment Joint Model with Word Embeddings


title: Fully Automatic Roadside Camera Calibration for Traffic Surveillance

abstract: This paper deals with automatic calibration of roadside surveillance cameras. We focus on parameters necessary for measurements in traffic-surveillance applications. Contrary to the existing solutions, our approach requires no a priori knowledge, and it works with a very wide variety of road settings (number of lanes, occlusion, quality of ground marking), as well as with practically unlimited viewing angles. The main contribution is that our solution works fully automatically-without any percamera or per-video manual settings or input whatsoever-and it is computationally inexpensive. Our approach uses tracking of local feature points and analyzes the trajectories in a manner based on cascaded Hough transform and parallel coordinates. An important assumption for the vehicle movement is that at least a part of the vehicle motion is approximately straight-we discuss the impact of this assumption on the applicability of our approach and show experimentally that this assumption does not limit the usability of our approach severely. We efficiently and robustly detect vanishing points, which define the ground plane and vehicle movement, except for the scene scale. Our algorithm also computes parameters for radial distortion compensation. Experiments show that the obtained camera parameters allow for measurements of relative lengths (and potentially speed) with ~2% mean accuracy. The processing is performed easily in real time, and typically, a 2-min-long video is sufficient for stable calibration.


title: Improving Fraud and Abuse Detection in General Physician Claims: A Data Mining Study

abstract: Background We aimed to identify the indicators of healthcare fraud and abuse in general physicians’ drug prescription claims, and to identify a subset of general physicians that were more likely to have committed fraud and abuse. Methods We applied data mining approach to a major health insurance organization dataset of private sector general physicians’ prescription claims. It involved 5 steps: clarifying the nature of the problem and objectives, data preparation, indicator identification and selection, cluster analysis to identify suspect physicians, and discriminant analysis to assess the validity of the clustering approach. Results Thirteen indicators were developed in total. Over half of the general physicians (54%) were ‘suspects’ of conducting abusive behavior. The results also identified 2% of physicians as suspects of fraud. Discriminant analysis suggested that the indicators demonstrated adequate performance in the detection of physicians who were suspect of perpetrating fraud (98%) and abuse (85%) in a new sample of data. Conclusion Our data mining approach will help health insurance organizations in low-and middle-income countries (LMICs) in streamlining auditing approaches towards the suspect groups rather than routine auditing of all physicians.


title: (CAD)$^2$RL: Real Single-Image Flight without a Single Real Image

abstract: Deep reinforcement learning has emerged as a promising and powerful technique for automatically acquiring control policies that can process raw sensory inputs, such as images, and perform complex behaviors. However, extending deep RL to real-world robotic tasks has proven challenging, particularly in safety-critical domains such as autonomous flight, where a trial-and-error learning process is often impractical. In this paper, we explore the following question: can we train vision-based navigation policies entirely in simulation, and then transfer them into the real world to achieve real-world flight without a single real training image? We propose a learning method that we call CAD$^2$RL, which can be used to perform collision-free indoor flight in the real world while being trained entirely on 3D CAD models. Our method uses single RGB images from a monocular camera, without needing to explicitly reconstruct the 3D geometry of the environment or perform explicit motion planning. Our learned collision avoidance policy is represented by a deep convolutional neural network that directly processes raw monocular images and outputs velocity commands. This policy is trained entirely on simulated images, with a Monte Carlo policy evaluation algorithm that directly optimizes the network's ability to produce collision-free flight. By highly randomizing the rendering settings for our simulated training set, we show that we can train a policy that generalizes to the real world, without requiring the simulator to be particularly realistic or high-fidelity. We evaluate our method by flying a real quadrotor through indoor environments, and further evaluate the design choices in our simulator through a series of ablation studies on depth prediction. For supplementary video see: this https URL


title: The Operationalization of"Fields"as WoS Subject Categories (WCs) in Evaluative Bibliometrics: The cases of"Library and Information Science"and"Science&Technology Studies"

abstract: Normalization of citation scores using reference sets based on Web-of-Science Subject Categories (WCs) has become an established ("best") practice in evaluative bibliometrics. For example, the Times Higher Education World University Rankings are, among other things, based on this operationalization. However, WCs were developed decades ago for the purpose of information retrieval and evolved incrementally with the database; the classification is machine-based and partially manually corrected. Using the WC"information science&library science"and the WCs attributed to journals in the field of"science and technology studies,"we show that WCs do not provide sufficient analytical clarity to carry bibliometric normalization in evaluation practices because of"indexer effects."Can the compliance with"best practices"be replaced with an ambition to develop"best possible practices"? New research questions can then be envisaged.


Toward optimal active learning through monte carlo estimation of error reduction


title: Administration of Lactobacillus helveticus NS8 improves behavioral, cognitive, and biochemical aberrations caused by chronic restraint stress

abstract: Increasing numbers of studies have suggested that the gut microbiota is involved in the pathophysiology of stress-related disorders. Chronic stress can cause behavioral, cognitive, biochemical, and gut microbiota aberrations. Gut bacteria can communicate with the host through the microbiota-gut-brain axis (which mainly includes the immune, neuroendocrine, and neural pathways) to influence brain and behavior. It is hypothesized that administration of probiotics can improve chronic-stress-induced depression. In order to examine this hypothesis, the chronic restraint stress depression model was established in this study. Adult specific pathogen free (SPF) Sprague-Dawley rats were subjected to 21 days of restraint stress followed by behavioral testing (including the sucrose preference test (SPT), elevated-plus maze test, open-field test (OFT), object recognition test (ORT), and object placement test (OPT)) and biochemical analysis. Supplemental Lactobacillus helveticus NS8 was provided every day during stress until the end of experiment, and selective serotonin reuptake inhibitor (SSRI) citalopram (CIT) served as a positive control. Results showed that L. helveticus NS8 improved chronic restraint stress-induced behavioral (anxiety and depression) and cognitive dysfunction, showing an effect similar to and better than that of CIT. L. helveticus NS8 also resulted in lower plasma corticosterone (CORT) and adrenocorticotropic hormone (ACTH) levels, higher plasma interleukin-10 (IL-10) levels, restored hippocampal serotonin (5-HT) and norepinephrine (NE) levels, and more hippocampal brain-derived neurotrophic factor (BDNF) mRNA expression than in chronic stress rats. Taken together, these results indicate an anti-depressant effect of L. helveticus NS8 in rats subjected to chronic restraint stress depression and that this effect could be due to the microbiota-gut-brain axis. They also suggest the therapeutic potential of L. helveticus NS8 in stress-related and possibly other kinds of depression.


title: Analytical BER Performance of M-QAM-OFDM Systems in the Presence of IQ Imbalance

abstract: It is well known that the performance of OFDM systems is severely affected by the inphase and quadraure (IQ) imbalances of down converter modulators in receivers. In this paper, the effects of IQ imbalance on the performance of the OFDM systems with rectangular M-QAM constellations are investigated and a closed form of bit error rate (BER) of these systems in additive white Gaussian noise (AWGN) channels is derived. Theoretical results show perfect matching with those obtained by simulations and they can be used to derive the OFDM system performance, without the need to run extensive simulations.


title: The kaleidoscope of effective gamification: deconstructing gamification in business applications

abstract: Developers of gamified business applications face the challenge of creating motivating gameplay strategies and creative design techniques to deliver subject matter not typically associated with games in a playful way. We currently have limited models that frame what makes gamification effective (i.e., engaging people with a business application). Thus, we propose a design-centric model and analysis tool for gamification: The kaleidoscope of effective gamification. We take a look at current models of game design, self-determination theory and the principles of systems design to deconstruct the gamification layer in the design of these applications. Based on the layers of our model, we provide design guidelines for effective gamification of business applications.


title: HERMES: Dynamic partitioning for distributed social network graph databases

abstract: Social networks are large graphs that require multiple graph database servers to store and manage them. Each database server hosts a graph partition with the objectives of balancing server loads, reducing remote traversals (edge-cuts), and adapting the partitioning to changes in the structure of the graph in the face of changing workloads. To achieve these objectives, a dynamic repartitioning algorithm is required to modify an existing partitioning to maintain good quality partitions while not imposing a significant overhead to the system. In this paper, we introduce a lightweight repartitioner, which dynamically modifies a partitioning using a small amount of resources. In contrast to the existing repartitioning algorithms, our lightweight repartitioner is e cient, making it suitable for use in a real system. We integrated our lightweight repartitioner into Hermes, which we designed as an extension of the open source Neo4j graph database system, to support workloads over partitioned graph data distributed over multiple servers. Using real-world social network data, we show that Hermes leverages the lightweight repartitioner to maintain high quality partitions and provides a 2 to 3 times performance improvement over the de-facto standard random hash-based partitioning.


title: Summarization Techniques for Pattern Collections in Data Mining

abstract: Discovering patterns from data is an important task in data mining. There exist techniques to find large collections of many kinds of patterns from data very efficiently. A collection of patterns can be regarded as a summary of the data. A major difficulty with patterns is that pattern collections summarizing the data well are often very large. ::: In this dissertation we describe methods for summarizing pattern collections in order to make them also more understandable. More specifically, we focus on the following themes: 1) Quality value simplifications. 2) Pattern orderings. 3) Pattern chains and antichains. 4) Change profiles. 5) Inverse pattern discovery.


title: πBox: A Platform for Privacy-Preserving Apps

abstract: We present πBox, a new application platform that prevents apps from misusing information about their users. To strike a useful balance between users' privacy and apps' functional needs, πBox shifts much of the responsibility for protecting privacy from the app and its users to the platform itself. To achieve this, πBox deploys (1) a sandbox that spans the user's device and the cloud, (2) specialized storage and communication channels that enable common app functionalities, and (3) an adaptation of recent theoretical algorithms for differential privacy under continual observation.We describe a prototype implementation of πBox and show how it enables a wide range of useful apps with minimal performance overhead and without sacrificing user privacy.


title: A SOM Based Approach to Skin Detection with Application in Real Time Systems

abstract: A large body of human image processing techniques use skin detection as a first primitive for subsequent feature extraction. Well established methods of colour modelling, such as histograms and Gaussian mixture models have enabled the construction of suitably accurate skin detectors. However such techniques are not ideal for use in adaptive real time environments. We describe methods of skin detection using a Self-Organising Map or SOM, and show performance comparable (94% accuracy on facial images) to conventional techniques. We also introduce the AXEON Learning Processor as the basis for a hardware skin detector, and outline the potential benefits of using this system in a demanding environment, such as filtering Internet traffic, to which conventional techniques are not best suited.


title: Visualization of fine-grained code change history

abstract: Conventional version control systems save code changes at each check-in. Recently, some development environments retain more fine-grain changes. However, providing tools for developers to use those histories is not a trivial task, due to the difficulties in visualizing the history. We present two visualizations of fine-grained code change history, which actively interact with the code editor: a timeline visualization, and a code history diff view. Our timeline and filtering options allow developers to navigate through the history and easily focus on the information they need. The code history diff view shows the history of any particular code fragment, allowing developers to move through the history simply by dragging the marker back and forth through the timeline to instantly see the code that was in the snippet at any point in the past. We augment the usefulness of these visualizations with richer editor commands including selective undo and search, which are all implemented in an Eclipse plug-in called “Azurite”. Azurite helps developers with answering common questions developers ask about the code change history that have been identified by prior research. In addition, many of users' backtracking tasks can be achieved using Azurite, which would be tedious or error-prone otherwise.


title: Accurate multi-view reconstruction using robust binocular stereo and surface meshing

abstract: This paper presents a new algorithm for multi-view reconstruction that demonstrates both accuracy and efficiency. Our method is based on robust binocular stereo matching, followed by adaptive point-based filtering of the merged point clouds, and efficient, high-quality mesh generation. All aspects of our method are designed to be highly scalable with the number of views. Our technique produces the most accurate results among current algorithms for a sparse number of viewpoints according to the Middlebury datasets. Additionally, we prove to be the most efficient method among non-GPU algorithms for the same datasets. Finally, our scaled-window matching technique also excels at reconstructing deformable objects with high-curvature surfaces, which we demonstrate with a number of examples.


title: Using mobile phones in English education in Japan

abstract: Abstract We present three studies in mobile learning. ::: ::: First, we polled 333 Japanese university students regarding their use of mobile devices. One hundred percent reported owning a mobile phone. Ninety-nine percent send e-mail on their mobile phones, exchanging some 200 e-mail messages each week. Sixty-six percent e-mail peers about classes; 44% e-mail for studying. In contrast, only 43% e-mail on PCs, exchanging an average of only two messages per week. Only 20% had used a personal digital assistant. ::: ::: Second, we e-mailed 100-word English vocabulary lessons at timed intervals to the mobile phones of 44 Japanese university students, hoping to promote regular study. Compared with students urged to regularly study identical materials on paper or Web, students receiving mobile e-mail learned more (P<0.05). Seventy-one percent of the subjects preferred receiving these lessons on mobile phones rather than PCs. Ninety-three percent felt this a valuable teaching method. ::: ::: Third, we created a Web site explaining English idioms. Student-produced animation shows each idiom's literal meaning; a video shows the idiomatic meaning. Textual materials include an explanation, script, and quiz. Thirty-one Japanese college sophomores evaluated the site using video-capable mobile phones, finding few technical difficulties, and rating highly its educational effectiveness.


title: Markov Games as a Framework for Multi-Agent Reinforcement Learning

abstract: In the Markov decision process (MDP) formalization of reinforcement learning, a single adaptive agent interacts with an environment defined by a probabilistic transition function. In this solipsis-tic view, secondary agents can only be part of the environment and are therefore fixed in their behavior. The framework of Markov games allows us to widen this view to include multiple adaptive agents with interacting or competing goals. This paper considers a step in this direction in which exactly two agents with diametrically opposed goals share an environment. It describes a Q-learning-like algorithm for finding optimal policies and demonstrates its application to a simple two-player game in which the optimal policy is probabilistic.


title: A survey on the state of healthcare upcoding fraud analysis and detection

abstract: From its infancy in the 1910s, healthcare group insurance continues to increase, creating a consistently rising burden on the government and taxpayers. The growing number of people enrolled in healthcare programs such as Medicare, along with the enormous volume of money in the healthcare industry, increases the appeal for and risk of fraudulent activities. One such fraud, known as upcoding, is a means by which a provider can obtain additional reimbursement by coding a certain provided service as a more expensive service than what was actually performed. With the proliferation of data mining techniques and the recent and continued availability of public healthcare data, the application of these techniques towards fraud detection, using this increasing cache of data, has the potential to greatly reduce healthcare costs through a more robust detection of upcoding fraud. Presently, there is a sizable body of healthcare fraud detection research available but upcoding fraud studies are limited. Audit data can be difficult to obtain, limiting the usefulness of supervised learning; therefore, other data mining techniques, such as unsupervised learning, must be explored using mostly unlabeled records in order to detect upcoding fraud. This paper is specific to reviewing upcoding fraud analysis and detection research providing an overview of healthcare, upcoding, and a review of the current data mining techniques used therein.


title: Value Added Services and Adoption of Mobile Payments

abstract: In this paper, we investigate the role of Value Added Services (VAS) in consumers' adoption of mobile payments. VAS are supplementary digital services offered in connection with mobile payments; in this study exemplified by receipts, loyalty cards, and coupons. A research model is derived from existing literature and empirically tested through an experimental design survey, in which the experimental group is exposed to a mobile payment solution with VAS, including receipts, loyalty cards, and coupons, and the control group is exposed to a mobile payment solution without VAS. Our findings show that intention to adopt mobile payments increases as VAS are introduced and that this increase comes from a positive change in Perceived Usefulness, compatibility, and Convenience.


Tumor angiogenesis: transfilter diffusion studies in the hamster by the transparent chamber technique.


title: Planar distributed structures with negative refractive index

abstract: Planar distributed periodic structures of microstrip-line and stripline types, which support left-handed (LH) waves are presented and their negative refractive index (NRI) properties are shown theoretically, numerically, and experimentally. The supported LH wave is fully characterized based on the composite right/left-handed transmission-line theory and the dispersion characteristics, refractive indexes, and Bloch impedance are derived theoretically. In addition, formulas to extract equivalent-circuit parameters from full-wave simulation are given. Open (microstrip) and closed (stripline) structures with a 5/spl times/5 mm/sup 2/ unit cell operating at approximately 4 GHz are designed and characterized by full-wave finite-element-method simulations. A 20 /spl times/ 6 unit-cell NRI lens structure interfaced with two parallel-plate waveguides is designed. The focusing/refocusing effect of the lens is observed by both circuit theory and full-wave simulations. Focusing in the NRI lens is also observed experimentally in excellent agreement with circuit theory and numerical predictions. This result represents the first experimental demonstration of NRI property using a purely distributed planar structure.


title: State-space architecture of the partitioned-block-based acoustic echo controller

abstract: Acoustic echo cancellation has traditionally employed basically all variants known from deterministic adaptive filter design, such as least mean-square (LMS), recursive least-squares (RLS), and frequency-domain adaptive filters (FDAF). More recently, a stochastic adaptive filter design based on the concept of acoustic state-space modeling of the echo path has been introduced to accommodate for an ever sought unification of adaptive filtering and adaptation control. The corresponding Kalman filter theory has been formulated for single-channel, multi-channel, and nonlinear echo cancellation problems. This paper closes an important gap by formulating the state-space model and the corresponding adaptive algorithm for the partitioned-block filtering structure which is especially relevant in practice. This structure allows for the use of significantly longer filter lengths in comparison to previous work, and for the flexible design and implementation of acoustic echo cancellers for widely differing acoustic conditions.


title: Smartphone information security awareness: A victim of operational pressures

abstract: Abstract Smartphone information security awareness describes the knowledge, attitude and behaviour that employees apply to the security of the organisational information that they access, process and store on their smartphone devices. The surge in the number of smartphone devices connecting to organisational systems and used to process organisational data has enabled a new level of operational efficiency. While employees are aware of the benefits they enjoy by bringing their personal devices into the workplace, managers too are aware of the benefits of having a constantly connected workforce. Unfortunately, those aware of the risks to information security do not share an equal level of enthusiasm. These devices are owned by employees who are not adequately skilled to configure the security settings for acceptable security of that information. Moreover, routine information security awareness programmes, even if applied, gradually fade into the daily rush of operations from the day they are completed. This paper explores the factors which influence these oscillating levels of information security awareness. By applying an adapted version of an awareness model from the domain of accident prevention, the factors which cause diminishing awareness levels are exposed. Subsequently, information security awareness emerges as a symptom of such factors. Through geometrical modelling of the boundaries and pressures that govern our daily operations, an awareness model emerges. This model ensures that organisations are better equipped to monitor their information security awareness position, their boundaries and the daily pressures affecting the organisation, thus allowing them to design better integrated policies and procedures to encourage safe operating limits. The model is evaluated using a theory evaluation framework through an expert review process.


title: Generating Natural Questions About an Image

abstract: There has been an explosion of work in the vision & language community during the past few years from image captioning to video transcription, and answering questions about images. These tasks have focused on literal descriptions of the image. To move beyond the literal, we choose to explore how questions about an image are often directed at commonsense inference and the abstract events evoked by objects in the image. In this paper, we introduce the novel task of Visual Question Generation (VQG), where the system is tasked with asking a natural and engaging question when shown an image. We provide three datasets which cover a variety of images from object-centric to event-centric, with considerably more abstract training data than provided to state-of-the-art captioning systems thus far. We train and test several generative and retrieval models to tackle the task of VQG. Evaluation results show that while such models ask reasonable questions for a variety of images, there is still a wide gap with human performance which motivates further work on connecting images with commonsense knowledge and pragmatics. Our proposed task offers a new challenge to the community which we hope furthers interest in exploring deeper connections between vision & language.


title: A laser-vision based obstacle detection and distance estimation for smart wheelchair navigation

abstract: The aim of the research is to present an approach of obstacle distance estimation and navigation for smart wheelchair. The smart wheelchair is an electric wheelchair equipped with camera and line laser to navigate and avoid an obstacle. The camera was used to capture images from the environment to sense the pathway condition. The line laser was used in combination with camera to recognize an obstacle in the pathway based on the shape of line laser image in certain angle. A blob method detection was applied on the line laser image to recognize the pattern of the detected obstacles. The line laser projector and camera were mounted in fixed-certain position to make sure a fixed relation between blobs-gaps and obstacle-to-wheelchair distance. A simple linear regression from 16 obtained data was used to represent this relation as the estimated obstacle distance. As a result, the average error between the estimation and actual distance was 1.25 cm from 7 data testing experiments. The experiments result indicates that the proposed method is able to estimate well the distance between wheelchair and obstacle. Later, the smart wheelchair needs to decide further action whether it should turn left, right or just walk straight when facing certain obstacle to avoid it.


title: Towards terascale knowledge acquisition

abstract: Although vast amounts of textual data are freely available, many NLP algorithms exploit only a minute percentage of it. In this paper, we study the challenges of working at the terascale. We present an algorithm, designed for the teraxale, for mining is-a relations that achieves similar performance to a state-of-the-art linguistically-rich method. We focus on the accuracy of these two systems as a function of processing time and corpus size.


title: Implementing lean in surgery – lessons and implications

abstract: The principles of lean production originating from the Toyota production system has spread from manufacturing to healthcare. Needless to say, this raises concern whether such principles are actually applicable where the product are humans in need of medical care and what are the consequences for the medical staff. ::: ::: The literature on lean does not suggest that lean should not be applicable in healthcare. Still this paper begins by discussing if the principles of lean from a theoretical perspective can be expected to work in healthcare. It is found that that it will be useful in certain types of settings but it is not a universal tool that may be applied to all aspects of healthcare. ::: ::: A case of lean in a surgical ward is presented and it is observed that patients have been split in to two flows, which can be described as a lean and a normal flow.


5 G MIMO Data for Machine Learning : Application to Beam-Selection using Deep Learning


Identifying Significance of Discrepancies in Radiology Reports


title: Design and verification of smart and scalable DC microgrids for emerging regions

abstract: Roughly 1.3 billion people in developing countries still live without access to reliable electricity. As expanding access using current technologies will accelerate global climate change, there is a strong need for novel solutions that displace fossil fuels and are financially viable for developing regions. A novel DC microgrid solution that is geared at maximizing efficiency and reducing system installation cost is described in this paper. Relevant simulation and experimental results, as well as a proposal for undertaking field-testing of the technical and economic viability of the microgrid system are presented.


title: Obstacle detection using U-disparity on quadratic road surfaces

abstract: This paper addresses the problem of detecting obstacles that protruding from the road. Traditionally, the road surface has been considered flat, and camera orientation is fixed. However, both assumptions are not strictly true in urban scenarios. The proposed algorithm employs a time-of-flight (ToF) camera. It allows dynamic pitch/roll angles, height variations and represents the ground as a quadratic surface. The range information given by the camera is represented in both Euclidean and disparity domains, so that their domain characteristics support each other to achieve accurate and efficient detection results. Gradient filtering of the disparity image presents Euclidean planner patches, with which outliers can be minimised during road fittings. Obstacle points are subsequently detected by the connect component labelling algorithm. Experimental results show that the proposed method can effectively segment and detect multiple obstacles and presents their bounding boxes in complex scenarios.


title: A Scripted Printable Quadrotor: Rapid Design and Fabrication of a Folded MAV

abstract: Robotic systems hold great promise to assist with household, educational, and research tasks, but the difficulties of designing and building such robots often are an inhibitive barrier preventing their development. This paper presents a framework in which simple robots can be easily designed and then rapidly fabricated and tested, paving the way for greater proliferation of robot designs. The Python package presented in this work allows for the scripted generation of mechanical elements, using the principles of hierarchical structure and modular reuse to simplify the design process. These structures are then manufactured using an origami-inspired method in which precision cut sheets of plastic film are folded to achieve desired geometries. Using these processes, lightweight, low cost, rapidly built quadrotors were designed and fabricated. Flight tests compared the resulting robots against similar micro air vehicles (MAVs) generated using other processes. Despite lower tolerance and precision, robots generated using the process presented in this work took significantly less time and cost to design and build, and yielded lighter, lower power MAVs.


title: Endosonographic Tumor Staging for Treatment Decision in Resectable Gastric Cancer

abstract: Background and Aims: Neoadjuvant preoperative chemotherapy is the standard of care in locally advanced resectable gastric cancer. Therefore, accurate locoregional staging is essential for treatment decision. EUS is believed to be the most performant diagnostic method for locoregional staging. However, it is questionable, if results from centers of excellence can be maintained in clinical routine. Methods : We retrospectively analyzed the data of 62 resectable gastric cancers staged by EUS during routine clinical work-up. Preoperative variables (tumor size and site, histological differentiation) were compared with the postoperative pathology. Results: 19 locally limited (T1-2, N0), and 43 locally advanced (T3-4, or N+ irrespective of T stage) were analyzed. The sensitivity of EUS for the detection of locally advanced disease was 93%, with a specificity of 78%. Conclusions: Even in daily routine practice, differentiation of locally limited and advanced disease with EUS can be performed with high sensitivity and good specificity. Therefore, EUS is an essential part of the diagnostic procedure in patients with gastric cancer.


title: High Directivity Quarter-Wave Microstrip Couplers with Periodic Floating-Conductors on Coupled Edges

abstract: This paper presents a new quarter-wavelength microstrip coupler with high directivity, which consists of a periodic sequence of floating metal strips in the slots on the inner edges. After describing the characteristics of the coupled-line, a 15-dB coupler is designed and a high directivity of 30 dB or more in theory is obtained over a considerably wide bandwidth. The validity of the design concept is confirmed by em-simulations and experiments.


title: The kinesiology of the thumb trapeziometacarpal joint.

abstract: To measure the motions of the trapeziometacarpal joint of the thumb quantitatively, a roentgenographic method was developed and tested using T-shaped metal markers, a special cassette-holder, and biplane roentgenograms. Two experiments were performed. In the first one, the metal markers were fixed to the trapezium and third metacarpal in ten cadaver specimens, and a fixed spatial relationship between the trapezium and the third metacarpal was identified roentgenographically. This relationship was that the reference axes of the trapezium were aligned at median angles of 48 degrees of flexion, 38 degrees of abduction, and 80 degrees of pronation with reference to the reference axes of the third metacarpal. In the second experiment, in the dominant hand of nine male and ten female subjects (average age, twenty-six years) T-shaped markers were fixed to the skin overlying the third metacarpal and the metacarpal and phalanges of the thumb. Using the same roentgenographic technique and coordinate systems employed in the first study, the average total motions of the trapeziometacarpal joint (determined as motions of the first metacarpal with reference to the third metacarpal) were 53 degrees of flexion-extension, 42 degrees of abduction-adduction, and 17 degrees of axial rotation (pronation-supination). In addition, six functional positions of the thumb were studied: rest, flexion, extension, abduction, tip pinch, and grasp. A position of adduction and flexion of the trapeziometacarpal joint was most common during thumb function, and both the trapeziometacarpal and metacarpophalangeal joints contributed to rotation of the thumb.


title: Newborn and infant footprint crease pattern extraction

abstract: Medical staff around the world must take reasonable steps to identify newborns and infants clearly, so as to prevent mix-ups, and to ensure the correct medication reaches the correct child. Footprints are frequently taken despite verification with footprints being challenging due to strong noise. The noise is introduced by the tininess of the structures, movement during capture, and the infant's rapid growth. In this article we address the image processing part of the problem and introduce a novel algorithm for the extraction of creases from infant footprints. The algorithm uses directional filtering on different resolution levels, morphological processing, and block-wise crease line reconstruction. We successfully test our method on noise-affected infant footprints taken from the same infants at different ages.


title: Measurement Error Mechanisms Matter: Agricultural Intensification with Farmer Misperceptions and Misreporting

abstract: The mechanism(s) that generate measurement error matter to inference. Survey measurement error is typically thought to represent simple misreporting correctable through improved measurement. But errors might also or alternatively reflect respondent misperceptions that materially affect the respondent decisions under study. We show analytically that these alternate data generating processes imply different appropriate regression specifications and have distinct effects on the bias in parameter estimates. We introduce a simple empirical technique to generate unbiased estimates under more general conditions and to apportion measurement error between misreporting and misperceptions in measurement error when one has both self-reported and objectively-measured observations of the same explanatory variable. We then apply these techniques to the longstanding question of agricultural intensification: do farmers increase input application rates per unit area as the size of the plots they cultivate decreases? Using nationally representative data from four sub-Saharan African countries, we find strong evidence that measurement error in plot size reflects a mixture of farmer misreporting and misperceptions. The results matter to inference around the intensification hypothesis and call into question whether more objective, precise measures are always preferable when estimating behavioral parameters.


title: Rust of Kalanchoe Caused by Puccinia benkei

abstract: Since summer in 1991, a new rust disease has occurred on leaves of potted plants of Kalanchoe blossfeldiana growing in greenhouses in Konosu, Saitama Prefecture. Dark brown pustules in lesions on the leaves were mainly composed of 2-celled teliospores, with a few 1-celled ones. Leaves of potted plants of K. blossfeldiana were artificially inoculated with basidiospores. Whitish spots appeared on the upper surface of the leaves eight to nine days after inoculation. Sixteen to 19 days after inoculation, dark brown pustules containing teliospores appeared in the lesions. The fungus was also able to infect Sedum kamtschaticum and Sedum sp. From morphological observation of the teliospores and from results of inoculation experiments, the causal fungus of the disease was identified as Puccinia benkei Kusano, a microcyclic rust. We propose ‘Sabi-byo’ and ‘Rust’ as a Japanese common name and an English common name of the new disease on Kalanchoe, respectively.


Fetch & Freight : Standard Platforms for Service Robot Applications


title: Sleep and Academic Performance in Undergraduates: A Multi-measure, Multi-predictor Approach

abstract: The present study examined the associations of sleep patterns with multiple measures of academic achievement of undergraduate university students and tested whether sleep variables emerged as significant predictors of subsequent academic performance when other potential predictors, such as class attendance, time devoted to study, and substance use are considered. A sample of 1654 (55% female) full-time undergraduates 17 to 25 yrs of age responded to a self-response questionnaire on sleep, academics, lifestyle, and well-being that was administered at the middle of the semester. In addition to self-reported measures of academic performance, a final grade for each student was collected at the end of the semester. Univariate analyses found that sleep phase, morningness/ eveningness preference, sleep deprivation, sleep quality, and sleep irregularity were significantly associated with at least two academic performance measures. Among 15 potential predictors, stepwise multiple regression analysis identified 5 significant predictors of end-of-semester marks: previous academic achievement, class attendance, sufficient sleep, night outings, and sleep quality (R 2 = 0.14 and adjusted R 2 = 0.14, F(5, 1234) = 40.99, p< .0001). Associations between academic achievement and the remaining sleep variables as well as the academic, well-being, and lifestyle variables lost significance in stepwise regression. Together with class attendance, night outings, and previous academic achievement, self-reported sleep quality and self-reported frequency of sufficient sleep were among the main predictors of academic performance, adding an independent and significant contribution, regardless of academic variables and lifestyles of the students. (Author correspondence: ana.allen@ua.pt)


Accelerating CT Iterative Reconstruction Using ADMM and Nesterov ’ s Methods


title: Statistical arbitrage and securities prices

abstract: This article introduces the concept of a statistical arbitrage opportunity (SAO). In a finite-horizon economy, a SAO is a zero-cost trading strategy for which (i) the expected payoff is positive, and (ii) the conditional expected payoff in each final state of the economy is nonnegative. Unlike a pure arbitrage opportunity, a SAO can have negative payoffs provided that the average payoff in each final state is nonnegative. If the pricing kernel in the economy is path independent, then no SAOs can exist. Furthermore, ruling out SAOs imposes a novel martingale-type restriction on the dynamics of securities prices. The important properties of the restriction are that it (1) is model-free, in the sense that it requires no parametric assumptions about the true equilibrium model, (2) can be tested in samples affected by selection biases, such as the peso problem, and (3) continues to hold when investors' beliefs are mistaken. The article argues that one can use the new restriction to empirically resolve the joint hypothesis problem present in the traditional tests of the efficient market hypothesis. Copyright 2003, Oxford University Press.


title: Automatic LQR Tuning Based on Gaussian Process Global Optimization

abstract: This paper proposes an automatic controller tuning framework based on linear optimal control combined with Bayesian optimization. With this framework, an initial set of controller gains is automatically improved according to a pre-defined performance objective evaluated from experimental data. The underlying Bayesian optimization algorithm is Entropy Search, which represents the latent objective as a Gaussian process and constructs an explicit belief over the location of the objective minimum. This is used to maximize the information gain from each experimental evaluation. Thus, this framework shall yield improved controllers with fewer evaluations compared to alternative approaches. A seven-degree-of-freedom robot arm balancing an inverted pole is used as the experimental demonstrator. Results of two- and four-dimensional tuning problems highlight the method's potential for automatic controller tuning on robotic platforms.


title: Bypassing Space Explosion in High-Speed Regular Expression Matching

abstract: Network intrusion detection and prevention systems commonly use regular expression (RE) signatures to represent individual security threats. While the corresponding deterministic finite state automata (DFA) for any one RE is typically small, the DFA that corresponds to the entire set of REs is usually too large to be constructed or deployed. To address this issue, a variety of alternative automata implementations that compress the size of the final automaton have been proposed such as extended finite automata (XFA) and delayed input DFA (D2 FA). The resulting final automata are typically much smaller than the corresponding DFA. However, the previously proposed automata construction algorithms do suffer from some drawbacks. First, most employ a "Union then Minimize" framework where the automata for each RE are first joined before minimization occurs. This leads to an expensive nondeterministic finite automata (NFA) to DFA subset construction on a relatively large NFA. Second, most construct the corresponding large DFA as an intermediate step. In some cases, this DFA is so large that the final automaton cannot be constructed even though the final automaton is small enough to be deployed. In this paper, we propose a "Minimize then Union" framework for constructing compact alternative automata focusing on the D2 FA. We show that we can construct an almost optimal final D2 FA with small intermediate parsers. The key to our approach is a space-and time-efficient routine for merging two compact D2 FA into a compact D2 FA. In our experiments, our algorithm runs on average 155 times faster and uses 1500 times less memory than previous algorithms. For example, we are able to construct a D2 FA with over 80 000 000 states using only 1 GB of main memory in only 77 min.


title: From today's augmented houses to tomorrow's smart homes: new directions for home automation research

abstract: A considerable amount of research has been carried out towards making long-standing smart home visions technically feasible. The technologically augmented homes made possible by this work are starting to become reality, but thus far living in and interacting with such homes has introduced significant complexity while offering limited benefit. As these technologies are increasingly adopted, the knowledge we gain from their use suggests a need to revisit the opportunities and challenges they pose. Synthesizing a broad body of research on smart homes with observations of industry and experiences from our own empirical work, we provide a discussion of ongoing and emerging challenges, namely challenges for meaningful technologies, complex domestic spaces, and human-home collaboration. Within each of these three challenges we discuss our visions for future smart homes and identify promising directions for the field.


title: SmartDroid: an automatic system for revealing UI-based trigger conditions in android applications

abstract: User interface (UI) interactions are essential to Android applications, as many Activities require UI interactions to be triggered. This kind of UI interactions could also help malicious apps to hide their sensitive behaviors (e.g., sending SMS or getting the user's device ID) from being detected by dynamic analysis tools such as TaintDroid, because simply running the app, but without proper UI interactions, will not lead to the exposure of sensitive behaviors. In this paper we focus on the challenging task of triggering a certain behavior through automated UI interactions. In particular, we propose a hybrid static and dynamic analysis method to reveal UI-based trigger conditions in Android applications. Our method first uses static analysis to extract expected activity switch paths by analyzing both Activity and Function Call Graphs, and then uses dynamic analysis to traverse each UI elements and explore the UI interaction paths towards the sensitive APIs. We implement a prototype system SmartDroid and show that it can automatically and efficiently detect the UI-based trigger conditions required to expose the sensitive behavior of several Android malwares, which otherwise cannot be detected with existing techniques such as TaintDroid.


title: Weakly-Supervised Spatial Context Networks

abstract: We explore the power of spatial context as a self-supervisory signal for learning visual representations. In particular, we propose spatial context networks that learn to predict a representation of one image patch from another image patch, within the same image, conditioned on their real-valued relative spatial offset. Unlike auto-encoders, that aim to encode and reconstruct original image patches, our network aims to encode and reconstruct intermediate representations of the spatially offset patches. As such, the network learns a spatially conditioned contextual representation. By testing performance with various patch selection mechanisms we show that focusing on object-centric patches is important, and that using object proposal as a patch selection mechanism leads to the highest improvement in performance. Further, unlike auto-encoders, context encoders [21], or other forms of unsupervised feature learning, we illustrate that contextual supervision (with pre-trained model initialization) can improve on existing pre-trained model performance. We build our spatial context networks on top of standard VGG_19 and CNN_M architectures and, among other things, show that we can achieve improvements (with no additional explicit supervision) over the original ImageNet pre-trained VGG_19 and CNN_M models in object categorization and detection on VOC2007.


Experiences in Malware Binary Deobfuscation


title: Theory and design of a 30-hp matrix converter

abstract: A description is given of the control strategy and hardware design for a 30-hp matrix converter (9-switch forced-commutation cycloconverter), which was developed for high-bandwidth four-quadrant-motor drive applications. The switching technique ensures very low distortion of the input currents with unity fundamental displacement factor. Experimental results are presented. >


title: A taxonomy of web search

abstract: Classic IR (information retrieval) is inherently predicated on users searching for information, the so-called "information need". But the need behind a web search is often not informational -- it might be navigational (give me the url of the site I want to reach) or transactional (show me sites where I can perform a certain transaction, e.g. shop, download a file, or find a map). We explore this taxonomy of web searches and discuss how global search engines evolved to deal with web-specific needs.


title: Forgery-Resistant Touch-based Authentication on Mobile Devices

abstract: Mobile devices store a diverse set of private user data and have gradually become a hub to control users' other personal Internet-of-Things devices. Access control on mobile devices is therefore highly important. The widely accepted solution is to protect access by asking for a password. However, password authentication is tedious, e.g., a user needs to input a password every time she wants to use the device. Moreover, existing biometrics such as face, fingerprint, and touch behaviors are vulnerable to forgery attacks. We propose a new touch-based biometric authentication system that is passive and secure against forgery attacks. In our touch-based authentication, a user's touch behaviors are a function of some random "secret". The user can subconsciously know the secret while touching the device's screen. However, an attacker cannot know the secret at the time of attack, which makes it challenging to perform forgery attacks even if the attacker has already obtained the user's touch behaviors. We evaluate our touch-based authentication system by collecting data from 25 subjects. Results are promising: the random secrets do not influence user experience and, for targeted forgery attacks, our system achieves 0.18 smaller Equal Error Rates (EERs) than previous touch-based authentication.


Point-of-Interest Recommendation Using Heterogeneous Link Prediction


title: User Interfaces for All: New Perspectives into Human-Computer Interaction

abstract: This chapter introduces the notion of User Interfaces for All, elaborates on the motivating rationale and examines its key implications on Human-Computer Interaction. The underlying vision of User Interfaces for All is to provide an approach for the development of computational environments catering for the broadest possible range of human abilities, skills, requirements and preferences. Consequently, User Interfaces for All should not be conceived as an effort to advance a single solution for everybody. Instead, it is a new perspective into Human-Computer Interaction, seeking to unfold and reveal challenges and insights, and to instrument appropriate solutions for alleviating the current obstacles to the access and use of advanced information technologies by the widest possible end-user population.


title: Mode-Seeking on Hypergraphs for Robust Geometric Model Fitting

abstract: In this paper, we propose a novel geometric model fitting method, called Mode-Seeking on Hypergraphs (MSH), to deal with multi-structure data even in the presence of severe outliers. The proposed method formulates geometric model fitting as a mode seeking problem on a hypergraph in which vertices represent model hypotheses and hyperedges denote data points. MSH intuitively detects model instances by a simple and effective mode seeking algorithm. In addition to the mode seeking algorithm, MSH includes a similarity measure between vertices on the hypergraph and a "weight-aware sampling" technique. The proposed method not only alleviates sensitivity to the data distribution, but also is scalable to large scale problems. Experimental results further demonstrate that the proposed method has significant superiority over the state-of-the-art fitting methods on both synthetic data and real images.


title: Approximation by superpositions of a sigmoidal function

abstract: In this paper we demonstrate that finite linear combinations of compositions of a fixed, univariate function and a set of affine functionals can uniformly approximate any continuous function ofn real variables with support in the unit hypercube; only mild conditions are imposed on the univariate function. Our results settle an open question about representability in the class of single hidden layer neural networks. In particular, we show that arbitrary decision regions can be arbitrarily well approximated by continuous feedforward neural networks with only a single internal, hidden layer and any continuous sigmoidal nonlinearity. The paper discusses approximation properties of other possible types of nonlinearities that might be implemented by artificial neural networks.


Figaro : An Object-Oriented Probabilistic Programming Language


title: Opportunities for network coding: to wait or not to wait

abstract: It has been well established that wireless network coding can significantly improve the efficiency of multihop wireless networks. However, in a stochastic environment, some of the packets might not have coding pairs, which limits the number of available coding opportunities. In this context, an important decision is whether to delay packet transmission in hope that a coding pair will be available in the future or transmit a packet without coding. This paper addresses this problem by establishing a stochastic dynamic framework whose objective is to minimize a long-run average cost. We identify an optimal control policy that minimizes the costs due to a combination of transmissions and packet delays. We show that the optimal policy would be stationary, deterministic, and threshold-type based on queue lengths. Our analytical approach is applicable for many cases of interest such as time-varying on/off channels. We further substantiate our results with simulation experiments for more generalized settings.


title: Optimized Computation Offloading Performance in Virtual Edge Computing Systems Via Deep Reinforcement Learning

abstract: To improve the quality of computation experience for mobile devices, mobile-edge computing (MEC) is a promising paradigm by providing computing capabilities in close proximity within a sliced radio access network (RAN), which supports both traditional communication and MEC services. Nevertheless, the design of computation offloading policies for a virtual MEC system remains challenging. Specifically, whether to execute a computation task at the mobile device or to offload it for MEC server execution should adapt to the time-varying network dynamics. This paper considers MEC for a representative mobile user in an ultradense sliced RAN, where multiple base stations (BSs) are available to be selected for computation offloading. The problem of solving an optimal computation offloading policy is modeled as a Markov decision process, where our objective is to maximize the long-term utility performance whereby an offloading decision is made based on the task queue state, the energy queue state as well as the channel qualities between mobile user and BSs. To break the curse of high dimensionality in state space, we first propose a double deep ${Q}$ -network (DQN)-based strategic computation offloading algorithm to learn the optimal policy without knowing a priori knowledge of network dynamics. Then motivated by the additive structure of the utility function, a ${Q}$ -function decomposition technique is combined with the double DQN, which leads to a novel learning algorithm for the solving of stochastic computation offloading. Numerical experiments show that our proposed learning algorithms achieve a significant improvement in computation offloading performance compared with the baseline policies.


title: BCI competition 2003-data sets Ib and IIb: feature extraction from event-related brain potentials with the continuous wavelet transform and the t-value scalogram

abstract: The t-CWT, a novel method for feature extraction from biological signals, is introduced. It is based on the continuous wavelet transform (CWT) and Student's t-statistic. Applied to event-related brain potential (ERP) data in brain- computer interface (BCI) paradigms, the method provides fully automated detection and quantification of the ERP components that best discriminate between two samples of EEG signals and are, therefore, particularly suitable for classification of single-trial ERPs. A simple and fast CWT computation algorithm is proposed for the transformation of large data sets and single trials. The method was validated in the BCI Competition 2003 , where it was a winner (provided best classification) on two data sets acquired in two different BCI paradigms, P300 speller and slow cortical potential (SCP) self-regulation. These results are presented here.


title: Recognizing Dynamic Scenes with Deep Dual Descriptor based on Key Frames and Key Segments

abstract: Abstract Dynamic scene recognition is a challenging problem in recognizing a collection of static appearances and dynamic patterns in moving scenes. While existing methods focus on reliable capturing of static patterns, few works have explored frame selection from a dynamic scene sequence and temporal modeling. In this paper, we propose dynamic scene recognition using a deep dual descriptor based on “key frames” and “key segments”. Key frames that reflect the feature distribution of the sequence with a small number are used for capturing salient static appearances. Key segments, which are captured from the area around each key frame, provide an additional discriminative power by dynamic patterns. To this end, two types of transferred convolutional neural network features are used in our approach. A fully connected layer is used to select the key frames and key segments, while the convolutional layer is used to describe them. The evaluation results on numerous public datasets demonstrated the state-of-the-art performance of the proposed method.


title: Robust Human Activity Recognition Using Smartphone Sensors via CT-PCA and Online SVM

abstract: Human activity recognition using either wearable devices or smartphones can benefit various applications including healthcare, fitness, smart home, etc. Instead of using wearable devices which are intrusive and require extra cost, we shall leverage on modern smartphones embedded with a variety of sensors. Due to the flexibility of using smartphones, the recognition accuracy will degrade with orientation, placement, and subject variations. In this paper, we propose a robust human activity recognition system in terms of orientation, placement, and subject variations based on coordinate transformation and principal component analysis (CT-PCA) and online support vector machine (OSVM). The proposed CT-PCA scheme is utilized to eliminate the effect of orientation variations. Experiments show that the proposed scheme significantly improves the activity recognition accuracy and outperforms the state-of-the-art methods on leave one orientation out experiments, which demonstrates the generalization ability of the proposed scheme on the data from unseen orientations. We also show the effectiveness of this scheme on placement and subject variations. However, the inherent difference of signal properties for different placement and subject dramatically reduces the recognition accuracy, especially for different placement. Thus, we present an efficient OSVM algorithm, that is, online-independent support vector machine (OISVM), which utilizes a small portion of data from the unseen placement or subject to online update the parameters of the SVM algorithm. The experimental results demonstrate the effectiveness of this OISVM algorithm on placement and subject variations.


title: Hydrogen from algal biomass: A review of production process

abstract: Multifariousness of biofuel sources has marked an edge to an imperative energy issue. Production of hydrogen from microalgae has been gathering much contemplation right away. But, mercantile production of microalgae biofuels considering bio-hydrogen is still not practicable because of low biomass concentration and costly down streaming processes. This review has taken up the hydrogen production by microalgae. Biofuels are the up and coming alternative to exhaustible, environmentally and unsafe fossil fuels. Algal biomass has been considered as an enticing raw material for biofuel production, these days photobioreactors and open-air systems are being used for hydrogen production from algal biomass. The formers allow the careful cultivation control whereas the latter ones are cheaper and simpler. A contemporary, encouraging optimization access has been included called algal cell immobilization on various matrixes which has resulted in marked increase in the productivity per volume of a reactor and addition of the hydrogen-production phase.


GPU-Accelerated Data Mining with Swarm Intelligence


title: Do We Need Binary Features for 3D Reconstruction?

abstract: Binary features have been incrementally popular in the past few years due to their low memory footprints and the efficient computation of Hamming distance between binary descriptors. They have been shown with promising results on some real time applications, e.g., SLAM, where the matching operations are relative few. However, in computer vision, there are many applications such as 3D reconstruction requiring lots of matching operations between local features. Therefore, a natural question is that is the binary feature still a promising solution to this kind of applications? To get the answer, this paper conducts a comparative study of binary features and their matching methods on the context of 3D reconstruction in a recently proposed large scale mutliview stereo dataset. Our evaluations reveal that not all binary features are capable of this task. Most of them are inferior to the classical SIFT based method in terms of reconstruction accuracy and completeness with a not significant better computational performance.


title: Encumbrance-free telepresence system with real-time 3D capture and display using commodity depth cameras

abstract: This paper introduces a proof-of-concept telepresence system that offers fully dynamic, real-time 3D scene capture and continuous-viewpoint, head-tracked stereo 3D display without requiring the user to wear any tracking or viewing apparatus. We present a complete software and hardware framework for implementing the system, which is based on an array of commodity Microsoft Kinect™color-plus-depth cameras. Novel contributions include an algorithm for merging data between multiple depth cameras and techniques for automatic color calibration and preserving stereo quality even with low rendering rates. Also presented is a solution to the problem of interference that occurs between Kinect cameras with overlapping views. Emphasis is placed on a fully GPU-accelerated data processing and rendering pipeline that can apply hole filling, smoothing, data merger, surface generation, and color correction at rates of up to 100 million triangles/sec on a single PC and graphics board. Also presented is a Kinect-based marker-less tracking system that combines 2D eye recognition with depth information to allow head-tracked stereo views to be rendered for a parallax barrier autostereoscopic display. Our system is affordable and reproducible, offering the opportunity to easily deliver 3D telepresence beyond the researcher's lab.


title: Clinical Decision Making of Nurses Working in Hospital Settings

abstract: This study analyzed nurses' perceptions of clinical decision making (CDM) in their clinical practice and compared differences in decision making related to nurse demographic and contextual variables. A cross-sectional survey was carried out with 2095 nurses in four hospitals in Norway. A 24-item Nursing Decision Making Instrument based on cognitive continuum theory was used to explore how nurses perceived their CDM when meeting an elective patient for the first time. Data were analyzed with descriptive frequencies, t-tests, Chi-Square test, and linear regression. Nurses' decision making was categorized into analytic-systematic, intuitive-interpretive, and quasi-rational models of CDM. Most nurses reported the use of quasi-rational models during CDM thereby supporting the tenet that cognition most often includes properties of both analysis and intuition. Increased use of intuitive-interpretive models of CDM was associated with years in present job, further education, male gender, higher age, and working in predominantly surgical units.


title: Wideband hybrid precoder for massive MIMO systems

abstract: This paper studies wideband hybrid precoder for downlink space-division multiple-access and orthogonal frequency-division multiple-access (SDMA-OFDMA) massive multi-input multi-output (MIMO) systems. We first derive an iterative algorithm to alternatingly optimize the phase-shifter based wideband analog precoder and low-dimensional digital precoders, then an efficient low-complexity non-iterative hybrid precoder proposes. Simulation results show that in wideband systems the performance of hybrid precoder is affected by the employed frequency-domain scheduling method and the number of available radio frequency (RF) chains, which can perform as well as narrowband hybrid precoder when greedy scheduling is employed and the number of RF chains is large.


title: The Distress Analysis Interview Corpus of human and computer interviews

abstract: The Distress Analysis Interview Corpus (DAIC) contains clinical interviews designed to support the diagnosis of psychological distress ::: conditions such as anxiety, depression, and post traumatic stress disorder. The interviews are conducted by humans, human controlled ::: agents and autonomous agents, and the participants include both distressed and non-distressed individuals. Data collected include audio ::: and video recordings and extensive questionnaire responses; parts of the corpus have been transcribed and annotated for a variety of ::: verbal and non-verbal features. The corpus has been used to support the creation of an automated interviewer agent, and for research on ::: the automatic identification of psychological distress.


title: Context-aware integrated development environment command recommender systems

abstract: Integrated development environments (IDEs) are complex applications that integrate multiple tools for creating and manipulating software project artifacts. To improve users' knowledge and the effectiveness of usage of the available functionality, the inclusion of recommender systems into IDEs has been proposed. We present a novel IDE command recommendation algorithm that, by taking into account the contexts in which a developer works and in which different commands are usually executed, is able to provide relevant recommendations. We performed an empirical comparison of the proposed algorithm with state-of-the-art IDE command recommenders on a real-world data set. The algorithms were evaluated in terms of precision, recall, F1, k-tail, and with a new evaluation metric that is specifically measuring the usefulness of contextual recommendations. The experiments revealed that in terms of the contextual relevance and usefulness of recommendations the proposed algorithm outperforms existing algorithms.


title: Robot as a Service in Cloud Computing

abstract: Service-oriented architecture and cloud computing are becoming a dominant computing paradigm, as all major computing companies are supporting this paradigm and more and more organizations are adopting this paradigm. Robotics and service-oriented robotics computing start to joint this new paradigm in the past five years and are now ready to participate in large scale. This paper reports our research on service-oriented robotics computing and our design, implementation, and evaluation of Robot as a Service (RaaS) unit. To fully qualify the RaaS as a cloud computing unit, we have kept our design to comply with the common service standards, development platforms, and execution infrastructure. We also keep the source code open and allow the community to configure the RaaS following the Web 2.0 principles of participation. Developers can add, remove, and modify the RaaS of their own. For this purpose, we have implemented our RaaS on Windows and Linux operating systems running on Atom and Core 2 Duo architectures. RaaS supports programming languages commonly used for service-oriented computing such as Java and C#. Special efforts have been made to support Microsoft Visual Programming Language (VPL) for graphic composition. We are working with high schools to use RaaS and VPL in robotics camps and robotics competitions.


title: Fisheries observers as enforcement assets: Lessons from the North Pacific

abstract: Fisheries observers can both collect scientific data and report on compliance with fisheries regulations. Observers are used only for scientific purposes in most US fisheries. However, North Pacific groundfish fisheries observers are required to report violations of fisheries regulations that they witness. Based on evaluation of historical enforcement incidents and survey data, this paper considers the effects of mandatory reporting in the North Pacific. The goal of this analysis is to determine whether and how observer enforcement in the North Pacific differs from observer enforcement in other US fisheries and to determine what benefits observer enforcement might yield if adopted in other fisheries. Mandatory observer reporting increases incident reports compared to fisheries without mandatory reporting requirements. Observers also report violation types, including illegal discard and retention, that otherwise are rarely identified by traditional dockside or at-sea enforcement resources. However, observer-reported cases are prosecuted less often than cases directly identified by fisheries enforcement officers. The reasons for lower prosecution rates are unclear but do not appear to result from incorrect identification of violations. In the light of recent studies indicating that noncompliance with fisheries regulations is an increasing concern in some fisheries due to decreasing total catch limits, this paper concludes that observers represent a unique enforcement resource that can facilitate detection and penalization of violations. However, increased prosecution of observer-reported data is needed to reduce incentives for noncompliance when observers are onboard. The substantial benefits of observer enforcement may outweigh concerns regarding data biases and observer safety that have limited observer reporting to date.


title: Principals' Leadership Styles and Teachers' Job Performance in Senior Secondary Schools in Ondo State, Nigeria

abstract: This study investigated principals' leadership styles and teachers' job performance in senior secondary schools in Ondo State, Nigeria. The study population comprised all the 281 secondary schools in the State. Out of this population, a sample of 240 senior secondary schools was taken and selected through the stratified random sampling technique. Out of the 7460 teachers (including principals) in the schools, a sample of 2040 teachers was selected through the stratified random sampling technique. This sample was made up of 240 principals and 1800 teachers. Two instruments were used to collect data for the study. These were the principals' leadership style questionnaire and the teachers' job performance questionnaire. The data collected were analyzed using frequency counts, percentages, correlation matrix and the t-test. It was found that the democratic leadership style was the most commonly used leadership style among principals of senior secondary schools in the State. Teachers' job performance was also found to be at a moderate level in the schools. Teachers' job performance was found to be better in schools having principals using autocratic leadership style than in schools having principals using democratic or laissez-faire leadership styles. It was recommended that school principals should imbibe a mixture of autocratic and democratic styles of leadership in their school administration in order to enhance better job performance among teachers. The use of the laissez-faire leadership style should be discouraged among school principals, as it could not bring a better job performance among teachers.


title: Assessing the security of Node.js platform

abstract: Node.js is a novel event-based network application platform which forces developers to use asynchronous programming interfaces for I/O operations. The native language for developing applications on this platform is JavaScript. Despite its young age the platform has attracted a significant community of developers and gained support from the industry. The Node.js community generally has a strong focus on the scalability of the platform. However, little research has been done on how the platform's design decisions affect the security of its applications. This paper outlines several possible security pitfalls to be aware of when using Node.js platform and server side JavaScript. We also describe two discovered vulnerabilities and give recommendations for developing and configuring secure and resilient web applications on the Node.js platform.


title: Hierarchy and adaptivity in segmenting visual scenes

abstract: Humans usually can effortlessly find coherent regions even in noisy visual images, a task that is crucial for object recognition. Computer algorithms have been less successful at doing this in natural viewing conditions, in part because early work on the problem used only local computations on the image. Now a new approach has been developed, based on an image segmentation strategy that analyses all salient regions of an image and builds them into a hierarchical structure. This method is faster and more accurate than previous approaches, but the resulting algorithm is relatively simple to use. It is demonstrated in action by using it to find items within a large database of objects that match a target item. Computer algorithms have not been successful at finding coherent regions in noisy visual images under natural viewing conditions. A new approach can now determine all salient regions of an image and build them into a hierarchical structure faster and more accurately than previous approaches. Finding salient, coherent regions in images is the basis for many visual tasks, and is especially important for object recognition. Human observers perform this task with ease, relying on a system in which hierarchical processing seems to have a critical role1. Despite many attempts, computerized algorithms2,3,4,5 have so far not demonstrated robust segmentation capabilities under general viewing conditions. Here we describe a new, highly efficient approach that determines all salient regions of an image and builds them into a hierarchical structure. Our algorithm, segmentation by weighted aggregation, is derived from algebraic multigrid solvers for physical systems6, and consists of fine-to-coarse pixel aggregation. Aggregates of various sizes, which may or may not overlap, are revealed as salient, without predetermining their number or scale. Results using this algorithm are markedly more accurate and significantly faster (linear in data size) than previous approaches.


title: AT HOME WITH NATURE Effects of “ Greenness ” on Children ’ s Cognitive Functioning

abstract: The nearby natural environment plays a far more significant role in the well-being of children residing in poor urban environments than has previously been recognized. Using a premove/postmove long...


An Analysis of Several Heuristics for the Traveling Salesman Problem


Use of suggestion methods to induce psychogenic non-epileptic seizures


title: Bi-GANs-ST for Perceptual Image Super-resolution

abstract: Image quality measurement is a critical problem for image super-resolution (SR) algorithms. Usually, they are evaluated by some well-known objective metrics, e.g., PSNR and SSIM, but these indices cannot provide suitable results in accordance with the perception of human being. Recently, a more reasonable perception measurement has been proposed in [1], which is also adopted by the PIRM-SR 2018 challenge. In this paper, motivated by [1], we aim to generate a high-quality SR result which balances between the two indices, i.e., the perception index and root-mean-square error (RMSE). To do so, we design a new deep SR framework, dubbed Bi-GANs-ST, by integrating two complementary generative adversarial networks (GAN) branches. One is memory residual SRGAN (MR-SRGAN), which emphasizes on improving the objective performance, such as reducing the RMSE. The other is weight perception SRGAN (WP-SRGAN), which obtains the result that favors better subjective perception via a two-stage adversarial training mechanism. Then, to produce final result with excellent perception scores and RMSE, we use soft-thresholding method to merge the results generated by the two GANs. Our method performs well on the perceptual image super-resolution task of the PIRM 2018 challenge. Experimental results on five benchmarks show that our proposal achieves highly competent performance compared with other state-of-the-art methods.


title: Design and implementation of smart environment monitoring and analytics in real-time system framework based on internet of underwater things and big data

abstract: The Internet of Things Technology (IoT) is developing so rapidly. One of IoT implementation is an Internet of Underwater Things (IoUT) which is used for u.nderwater monitoring. This paper discuss an integrated smart environment system framework based on IoUT and big data that consist of open platform that processes data from Remotely Operated Vehicle (ROV) and portable sensor with water parameters sensors such an input device to collect and save the data of oxidation-reduction potential, pH, electrical conductivity, total dissolve solid, salinity, dissolved oxygen, and temperature in monitored rivers temporary. Coral Reef Monitoring used for preventing coral bleaching by using underwater camera to take some pictures that will be sent to data center and then analyze it, Wireless Mess Network Access Point used as the way ROVs ‘talking’ to each other while doing a monitoring process in a wide river. Then all of the collected data from ROVs and portable sensor will be saved and analyzed to the data center platform based on Hadoop Multi-Node Cluster. The data will be visualized as a chart or a table and can be accessed world-wide We found that, the processing of the data used in the open platform to be relatively straightforward using a combination of SQL. However, the use of HDFS (Hadoop Distributed File System) as data center and more versatile frameworks such as Mapreduce, Hive, Spark, Redis and more flexible set component of features that particularly facilitate working with larger volumes and more heterogeneous data sources.


title: Multi-source heterogeneous Hakka culture heritage data management based on MongoDB

abstract: Hakka culture is an important part of China southern culture. There are many features of Hakka culture data collected through the digital and information technology, such as a diverse data type and format, unstructured data, and huge volume, which brings big difficulty to manage and use them. NoSQL technology has high availability and high scalability, which provides new methods for the storage and management of unstructured Hakka culture data. In this paper, MongoDB, A document-oriented NoSQL database, was lead into the management of unstructured Hakka culture data. The study focuses on multi-source Hakka culture heterogeneous data management strategy and data service method. Storage strategy of the multi-source heterogeneous Hakka culture data based on MongoDB are proposed and established. The sharding, indexing and query mechanism for Hakka culture database were explored. Taking Western Fujian Hakka Culture data as the example, Hakka culture data management prototype system was constructed.


title: Seven Capital Devices for the Future of Stroke Rehabilitation

abstract: Stroke is the leading cause of long-term disability for adults in industrialized societies. Rehabilitation's efforts are tended to avoid long-term impairments, but, actually, the rehabilitative outcomes are still poor. Novel tools based on new technologies have been developed to improve the motor recovery. In this paper, we have taken into account seven promising technologies that can improve rehabilitation of patients with stroke in the early future: (1) robotic devices for lower and upper limb recovery, (2) brain computer interfaces, (3) noninvasive brain stimulators, (4) neuroprostheses, (5) wearable devices for quantitative human movement analysis, (6) virtual reality, and (7) tablet-pc used for neurorehabilitation.


Control of a Nonholonomic Mobile Robot : Backstepping Kinematics into Dynamics


title: The cognitive mechanisms underlying deception: An event-related potential study

abstract: The cognitive view on deception proposes that lying comes with a cognitive cost. This view is supported by the finding that lying typically takes longer than truth telling. Event-related potentials (ERPs) provide a means to unravel the cognitive processes underlying this cost. Using a mock-crime design, the current study (n = 20) investigated the effects of deception on the Contingent Negative Variation (CNV), the Lateralized Readiness Potential (LRP), the Correct Response Negativity (CRN), and the stimulus-locked N200 and P300 components. In line with previous research, lying resulted in more errors, longer reaction times (RTs) and longer RT standard deviations compared to truthful responses. A marginally significant effect suggested a stronger CNV for the anticipation of lying compared to the anticipation of truth telling. There were no significant deception effects on the stimulus- and the response-locked LRPs. Unexpectedly, we found a significantly larger CRN for truth telling compared to lying. Additional analyses revealed an enhanced N200 and a decreased P300 for lying compared to truth telling. Our results support the cognitive load hypothesis for lying, yet are mixed regarding the response conflict hypothesis. Results are discussed with regard to the specific characteristics of our design and their theoretical and applied implications.


Statistical language modeling using the CMU-cambridge toolkit


title: A hierarchical naive Bayes mixture model for name disambiguation in author citations

abstract: Because of name variations, an author may have multiple names and multiple authors may share the same name. Such name ambiguity affects the performance of document retrieval, web search, database integration, and may cause improper attribution to authors. This paper presents a hierarchical naive Bayes mixture model, an unsupervised learning approach, for name disambiguation in author citations. This method partitions a collection of citations1 into clusters, with each cluster containing only citations authored by the same author, thus disambiguating authorship in citations to induce author name identities. Three types of citation features are used: co-author names, paper title words, and journal or proceeding title words. The approach is illustrated with 16 name datasets that are constructed based on the publication lists collected from author homepages and DBLP computer science bibliography.


Effective Requirements Development-A Comparison of Requirements Elicitation techniques


title: Edge Provisioning with Flexible Server Placement

abstract: We present $\sf {Tentacle}$ , a decision support framework to provision edge servers for online services providers (OSPs). $\sf {Tentacle}$ takes advantage of the increasingly flexible edge server placement, which is enabled by new technologies such as edge computing platforms, cloudlets and network function virtualization, to optimize the overall performance and cost of edge infrastructures. The key difference between $\sf {Tentacle}$ and traditional server placement approaches lies on that $\sf {Tentacle}$ can discover proper unforeseen edge locations which significantly improve the efficiency and reduce the cost of edge provisioning. We show how $\sf {Tentacle}$ effectively identifies promising edge locations which are close to a collection of users merely with inaccurate network distance estimation methods, e.g., geographic coordinate (GC) and network coordinate systems (NC). We also show how $\sf {Tentacle}$ comprehensively considers various pragmatic concerns in edge provisioning, such as traffic limits by law or ISP policy, edge site deployment and resource usage cost, over-provisioning for fault tolerance, etc., with a simple optimization model. We simulate $\sf {Tentacle}$ using real network data at global and county-wide scales. Measurement-driven simulations show that with a given cost budget $\sf {Tentacle}$ can improve user performance by around 10-45 percent at global scale networks and 15-35 percent at a country-wide scale network.


Education reforms in Indonesia in the twenty-first century