title: A Faster RCNN-Based Pedestrian Detection System

abstract: Pedestrian detection systems are receiving increasing attention in both industry and academia with the rapid development of autonomous automobiles which employ artificial intelligence. These systems must detect specific classes of objects such as pedestrians rather than generic objects. In this paper, we present a faster RCNN based pedestrian detection system which improves upon previous solutions. The proposed model takes arbitrary size images as inputs and generates bounding boxes and confidence scores for pedestrians. The system achieves good performance and is faster than the well known and frequently used methods in the literature.


title: The role of positive emotions in positive psychology. The broaden-and-build theory of positive emotions.

abstract: The mission of positive psychology is to understand and foster the factors that allow individuals, communities, and societies to flourish (Seligman & Csikszentmihalyi, 2000). What role do positive emotions play in this mission? On first consideration, the answer seems simple: Positive emotions serve as markers of flourishing, or optimal well-being. Certainly moments in people's lives characterized by experiences of positive emotions—such as joy, interest, contentment, love, and the like—are moments in which they are not plagued by negative emotions—such as anxiety, sadness, anger, and despair. Consistent with this intuition, the overall balance of people's positive and negative emotions has been shown to predict their judgments of subjective well-being (Diener, Sandvik, & Pavot, 1991). Building on this finding, Kahneman (1999) suggested that “objective happiness” can best be measured by tracking (and later aggregating) people's momentary experiences of good and bad feelings (but see Fredrickson, 2000c). According to these perspectives, positive emotions signal flourishing. But this is not the whole story: Positive emotions also produce flourishing. Moreover, they do so not simply within the present, pleasant moment but over the long term as well. The take-home message is that positive emotions are worth cultivating, not just as end states in themselves but also as a means to achieving psychological growth and improved well-being over time. ::: ::: A review of current perspectives on emotions, affect, and their respective functions provides an important backdrop. A selective review follows.


title: EXAMINING HEALTHCARE PROFESSIONALS' ACCEPTANCE OF ELECTRONIC MEDICAL RECORDS USING UTAUT

abstract: With the growing demand for digital information in health care, the electronic medical record (EMR) represents the foundation of health information technology. It is essential, however, in an industry still largely dominated by paper-based records, that such systems be accepted and used. This research evaluates registered nurses’, certified nurse practitioners and physician assistants’ acceptance of EMR’s as a means to predict, define and enhance use. The research utilizes the Unified Theory of Acceptance and Use of Technology (UTAUT) as the theoretical model, along with the Partial Least Square (PLS) analysis to estimate the variance. Overall, the findings indicate that UTAUT is able to provide a reasonable assessment of health care professionals’ acceptance of EMR’s with social influence a significant determinant of intention and use.


title: Unsupervised Induction of Contingent Event Pairs from Film Scenes

abstract: Human engagement in narrative is partially driven by reasoning about discourse relations between narrative events, and the expectations about what is likely to happen next that results from such reasoning. Researchers in NLP have tackled modeling such expectations from a range of perspectives, including treating it as the inference of the CONTINGENT discourse relation, or as a type of common-sense causal reasoning. Our approach is to model likelihood between events by drawing on several of these lines of previous work. We implement and evaluate different unsupervised methods for learning event pairs that are likely to be CONTINGENT on one another. We refine event pairs that we learn from a corpus of film scene descriptions utilizing web search counts, and evaluate our results by collecting human judgments of contingency. Our results indicate that the use of web search counts increases the average accuracy of our best method to 85.64% over a baseline of 50%, as compared to an average accuracy of 75.15% without web search.


title: Social Sensing: A New Approach to Understanding Our Socioeconomic Environments

abstract: The emergence of big data brings new opportunities for us to understand our socioeconomic environments. We use the term social sensing for such individual-level big geospatial data and the associated analysis methods. The word sensing suggests two natures of the data. First, they can be viewed as the analogue and complement of remote sensing, as big data can capture well socioeconomic features while conventional remote sensing data do not have such privilege. Second, in social sensing data, each individual plays the role of a sensor. This article conceptually bridges social sensing with remote sensing and points out the major issues when applying social sensing data and associated analytics. We also suggest that social sensing data contain rich information about spatial interactions and place semantics, which go beyond the scope of traditional remote sensing data. In the coming big data era, GIScientists should investigate theories in using social sensing data, such as data representativeness and quality,...


title: Touchless interaction with software in interventional radiology and surgery: a systematic literature review

abstract: PURPOSE ::: In this article, we systematically examine the current state of research of systems that focus on touchless human-computer interaction in operating rooms and interventional radiology suites. We further discuss the drawbacks of current solutions and underline promising technologies for future development. ::: ::: ::: METHODS ::: A systematic literature search of scientific papers that deal with touchless control of medical software in the immediate environment of the operation room and interventional radiology suite was performed. This includes methods for touchless gesture interaction, voice control and eye tracking. ::: ::: ::: RESULTS ::: Fifty-five research papers were identified and analyzed in detail including 33 journal publications. Most of the identified literature (62 %) deals with the control of medical image viewers. The others present interaction techniques for laparoscopic assistance (13 %), telerobotic assistance and operating room control (9 % each) as well as for robotic operating room assistance and intraoperative registration (3.5 % each). Only 8 systems (14.5 %) were tested in a real clinical environment, and 7 (12.7 %) were not evaluated at all. ::: ::: ::: CONCLUSION ::: In the last 10 years, many advancements have led to robust touchless interaction approaches. However, only a few have been systematically evaluated in real operating room settings. Further research is required to cope with current limitations of touchless software interfaces in clinical environments. The main challenges for future research are the improvement and evaluation of usability and intuitiveness of touchless human-computer interaction and the full integration into productive systems as well as the reduction of necessary interaction steps and further development of hands-free interaction.


title: Actuation and Tangible User Interfaces: the Vaucanson Duck, Robots, and Shape Displays

abstract: In the last decade, the vision of future interfaces has shifted from virtual reality to augmented and tangible user interfaces (UI) where virtual and physical (or "bits and atoms") co-exist in harmony. Recently, a growing number of designers and researchers have been taking the next logical step: creating interfaces where physical, tangible elements are not merely dynamically coupled to the digital attributes and information, but are themselves dynamic, self-reconfigurable devices that can change their physical properties depending on the state of the interfaces, the user, or the environment.A combination of the actuation, self-configuration, and tangibility can expand and enhance the design of tangible interfaces. In this paper, we present an overview of the use of actuation in user interfaces and discuss the rationality of building actuated interfaces. We then discuss actuated interfaces in detail based on our experience designing Lumen shape displays. Work on actuated interfaces is still in its infancy, projects are few and far between, so we consider this paper an invitation to discussion and hope it can help stimulate further research in this area.


title: Springrobot: a prototype autonomous vehicle and its algorithms for lane detection

abstract: This work presents the current status of the Springrobot autonomous vehicle project, whose main objective is to develop a safety-warning and driver-assistance system and an automatic pilot for rural and urban traffic environments. This system uses a high precise digital map and a combination of various sensors. The architecture and strategy for the system are briefly described and the details of lane-marking detection algorithms are presented. The R and G channels of the color image are used to form graylevel images. The size of the resulting gray image is reduced and the Sobel operator with a very low threshold is used to get a grayscale edge image. In the adaptive randomized Hough transform, pixels of the gray-edge image are sampled randomly according to their weights corresponding to their gradient magnitudes. The three-dimensional (3-D) parametric space of the curve is reduced to the two-dimensional (2-D) and the one-dimensional (1-D) space. The paired parameters in two dimensions are estimated by gradient directions and the last parameter in one dimension is used to verify the estimated parameters by histogram. The parameters are determined coarsely and quantization accuracy is increased relatively by a multiresolution strategy. Experimental results in different road scene and a comparison with other methods have proven the validity of the proposed method.


Situational , Transformational , and Transactional Leadership and Leadership Development


title: A 12-bit 1.6 GS/s interleaved SAR ADC with dual reference shifting and interpolation achieving 17.8 fJ/conv-step in 65nm CMOS

abstract: A 12-bit SAR ADC architecture with dual reference shifting and interpolation technique has been proposed and implemented with 8-way time interleaving in 65nm CMOS. The proposed technique converts 4 bits per SAR conversion cycle with reduced overhead, which is a key to achieve both high speed and resolution while maintaining low power consumption. The measured peak SNDR is 72dB and remains above 65.3dB at 1-GHz input frequency at sample rate of 1.6 GS/s. It achieves a record power efficiency of 17.8fJ/conv-step among the recently published high-speed/resolution ADCs.


title: In Search of a Dataset for Handwritten Optical Music Recognition: Introducing MUSCIMA++

abstract: Optical Music Recognition (OMR) has long been without an adequate dataset and ground truth for evaluating OMR systems, which has been a major problem for establishing a state of the art in the field. Furthermore, machine learning methods require training data. We analyze how the OMR processing pipeline can be expressed in terms of gradually more complex ground truth, and based on this analysis, we design the MUSCIMA++ dataset of handwritten music notation that addresses musical symbol recognition and notation reconstruction. The MUSCIMA++ dataset version 0.9 consists of 140 pages of handwritten music, with 91255 manually annotated notation symbols and 82261 explicitly marked relationships between symbol pairs. The dataset allows training and evaluating models for symbol classification, symbol localization, and notation graph assembly, both in isolation and jointly. Open-source tools are provided for manipulating the dataset, visualizing the data and further annotation, and the dataset itself is made available under an open license.


title: Design and Fabrication of an End-Launched Rectangular Waveguide Adapter Fed by a Coaxial Loop

abstract: An end-launched adapter combining a coaxial loop within a rectangular waveguide has been analyzed and designed. The accurate electromagnetic modeling of input impedance for the adapter design has been developed using a modal analysis method. Also, design parameters to improve the end-launched adapter have been investigated. Numerical and experimental results of the proposed model have been compared with the results of previous works, and verified by an electromagnetic simulator of High Frequency Structure Simulator (HFSS). The input voltage standing wave ratios (VSWRs) are smaller than 2 over the wide frequency band from 7.5 GHz to 10.5 GHz.


title: An SRAM Design in 65nm and 45nm Technology Nodes Featuring Read and Write-Assist Circuits to Expand Operating Voltage

abstract: This paper describes a 32Mb SRAM that has been designed and fabricated in a 65nm low-power CMOS technology. The design has also been migrated to 45nm bulk and SOI technologies. The 68mm die features read and write-assist circuit techniques that expand the operating voltage range and improve manufacturability across technology platforms


title: NVM neuromorphic core with 64k-cell (256-by-256) phase change memory synaptic array with on-chip neuron circuits for continuous in-situ learning

abstract: We demonstrate a neuromorphic core with 64k-cell phase change memory (PCM) synaptic array (256 axons by 256 dendrites) with in-situ learning capability. 256 configurable on-chip neuron circuits perform leaky integrate and fire (LIF) and synaptic weight update based on spike-timing dependent plasticity (STDP). 2T-1R PCM unit cell design separates LIF and STDP learning paths, minimizing neuron circuit size. The circuit implementation of STDP learning algorithm along with 2T-1R structure enables both LIF and STDP learning to operate asynchronously and simultaneously within the array, avoiding additional complication and power consumption associated with timing schemes. We show hardware demonstration of in-situ learning with large representational capacity, enabled by large array size and analog synaptic weights of PCM cells.


title: Human Age Estimation With Regression on Discriminative Aging Manifold

abstract: Recently, extensive studies on human faces in the human-computer interaction (HCI) field reveal significant potentials for designing automatic age estimation systems via face image analysis. The success of such research may bring in many innovative HCI tools used for the applications of human-centered multimedia communication. Due to the temporal property of age progression, face images with aging features may display some sequential patterns with low-dimensional distributions. In this paper, we demonstrate that such aging patterns can be effectively extracted from a discriminant subspace learning algorithm and visualized as distinct manifold structures. Through the manifold method of analysis on face images, the dimensionality redundancy of the original image space can be significantly reduced with subspace learning. A multiple linear regression procedure, especially with a quadratic model function, can be facilitated by the low dimensionality to represent the manifold space embodying the discriminative property. Such a processing has been evaluated by extensive simulations and compared with the state-of-the-art methods. Experimental results on a large size aging database demonstrate the effectiveness and robustness of our proposed framework.


A Convolutional Neural Network in Legal Question Answering


title: Learning multiple layers of representations

abstract: To achieve its impressive performance in tasks such as speech perception or object recognition, the brain extracts multiple levels of representation from the sensory input. Backpropagation was the first computationally efficient model of how neural networks could learn multiple layers of representation, but it required labeled training data and it did not work well in deep networks. The limitations of backpropagation learning can now be overcome by using multilayer neural networks that contain top-down connections and training them to generate sensory data rather than to classify it. Learning multilayer generative models might seem difficult, but a recent discovery makes it easy to learn nonlinear distributed representations one layer at a time.


title: Surface reconstruction from unorganized points

abstract: This thesis describes a general method for automatic reconstruction of accurate, concise, piecewise smooth surfaces from unorganized 3D points. Instances of surface reconstruction arise in numerous scientific and engineering applications, including reverse-engineering--the automatic generation of CAD models from physical objects. ::: Previous surface reconstruction methods have typically required additional knowledge, such as structure in the data, known surface genus, or orientation information. In contrast, the method outlined in this thesis requires only the 3D coordinates of the data points. From the data, the method is able to automatically infer the topological type of the surface, its geometry, and the presence and location of features such as boundaries, creases, and corners. ::: The reconstruction method has three major phases: (1) initial surface estimation, (2) mesh optimization, and (3) piecewise smooth surface optimization. A key ingredient in phase 3, and another principal contribution of this thesis, is the introduction of a new class of piecewise smooth representations based on subdivision. The effectiveness of the three-phase reconstruction method is demonstrated on a number of examples using both simulated and real data. ::: Phases 2 and 3 of the surface reconstruction method can also be used to approximate existing surface models. By casting surface approximation as a global optimization problem with an energy function that directly measures deviation of the approximation from the original surface, models are obtained that exhibit excellent accuracy to conciseness trade-offs. Examples of piecewise linear and piecewise smooth approximations are generated for various surfaces, including meshes, NURBS surfaces, CSG models, and implicit surfaces.


title: An Evaluation of Bimanual Gestures on the Microsoft HoloLens

abstract: We developed and evaluated two-handed gestures on the Microsoft HoloLens to manipulate augmented reality annotations through rotation and scale operations. We explore the design space of bimanual interactions on head-worn AR platforms, with the intention of dedicating two-handed gestures to rotation and scaling manipulations while reserving one-handed interactions to drawing annotations. In total, we implemented five techniques for rotation and scale manipulation gestures on the Microsoft HoloLens: three two-handed techniques, one technique for one-handed rotation and two-handed scale, and one baseline one-handed technique that represents standard HoloLens UI recommendations. Two of the bimanual interaction techniques involve axis separation for rotation whereas the third technique is fully 6DOF and modeled after the successful “spindle” approach from 3DUI literature. To evaluate our techniques, we conducted a study with 48 users. We recorded multiple performance metrics for each user on each technique, as well as user preferences. Results indicate that in spite of problems due to field-of-view limitations, certain two-handed techniques perform comparatively to the one-handed baseline technique in terms of accuracy and time. Furthermore, the best-performing two-handed technique outdid all other techniques in terms of overall user preference, demonstrating that bimanual gesture interactions can serve a valuable role in the UI toolbox on head-worn AR devices such as the HoloLens.


title: A Novel Soft-Switching Two-Switch Flyback Converter with a Wide Operating Range and Regenerative Clamping

abstract: A novel soft-switching two-switch flyback converter is proposed in this paper. This converter is composed of two active power switches, a flyback transformer, a blocking diode, and two passive regenerative clamping circuits. The proposed converter has the advantages of a low cost circuit configuration, a simple control scheme, a high efficiency, and a wide operating range. The circuit topology, analysis, design considerations, and experimental results of the new flyback converter are presented.


title: Intersubjectivity and sentiment: from language to knowledge

abstract: Intersubjectivity is an important concept in psychology and sociology. It refers to sharing conceptualizations through social interactions in a community and using such shared conceptualization as a resource to interpret things that happen in everyday life. In this work, we make use of intersubjectivity as the basis to model shared stance and subjectivity for sentiment analysis. We construct an intersubjectivity network which links review writers, terms they used, as well as the polarities of the terms. Based on this network model, we propose a method to learn writer embeddings which are subsequently incorporated into a convolutional neural network for sentiment analysis. Evaluations on the IMDB, Yelp 2013 and Yelp 2014 datasets show that the proposed approach has achieved the state-of-the-art performance.


title: ASAP: A Self-Adaptive Prediction System for Instant Cloud Resource Demand Provisioning

abstract: The promise of cloud computing is to provide computing resources instantly whenever they are needed. The state-of-art virtual machine (VM) provisioning technology can provision a VM in tens of minutes. This latency is unacceptable for jobs that need to scale out during computation. To truly enable on-the-fly scaling, new VM needs to be ready in seconds upon request. In this paper, We present an online temporal data mining system called ASAP, to model and predict the cloud VM demands. ASAP aims to extract high level characteristics from VM provisioning request stream and notify the provisioning system to prepare VMs in advance. For quantification issue, we propose Cloud Prediction Cost to encodes the cost and constraints of the cloud and guide the training of prediction algorithms. Moreover, we utilize a two-level ensemble method to capture the characteristics of the high transient demands time series. Experimental results using historical data from an IBM cloud in operation demonstrate that ASAP significantly improves the cloud service quality and provides possibility for on-the-fly provisioning.


title: Automatic HVAC Control with Real-time Occupancy Recognition and Simulation-guided Model Predictive Control in Low-cost Embedded System

abstract: Intelligent building automation systems can reduce the energy consumption of heating, ventilation and air-conditioning (HVAC) units by sensing the comfort requirements automatically and scheduling the HVAC operations dynamically. Traditional building automation systems rely on fairly inaccurate occupancy sensors and basic predictive control using oversimplified building thermal response models, all of which prevent such systems from reaching their full potential. Such limitations can now be avoided due to the recent developments in embedded system technologies, which provide viable low-cost computing platforms with powerful processors and sizeable memory storage in a small footprint. As a result, building automation systems can now efficiently execute highly-sophisticated computational tasks, such as real-time video processing and accurate thermal-response simulations. With this in mind, we designed and implemented an occupancy-predictive HVAC control system in a low-cost yet powerful embedded system (using Raspberry Pi 3) to demonstrate the following key features for building automation: (1) real-time occupancy recognition using video-processing and machine-learning techniques, (2) dynamic analysis and prediction of occupancy patterns, and (3) model predictive control for HVAC operations guided by real-time building thermal response simulations (using an on-board EnergyPlus simulator). We deployed and evaluated our system for providing automatic HVAC control in the large public indoor space of a mosque, thereby achieving significant energy savings.


title: Analysis and Optimization of Back EMF Waveform of a Flux-Switching Permanent Magnet Motor

abstract: Flux-switching permanent magnet (FSPM) motors have a doubly salient structure, the magnets being housed in the stator and the stator winding comprising concentrated coils. They have attracted considerable interest due to their essentially sinusoidal phase back electromotive force (EMF) waveform. However, to date, the inherent nature of this desirable feature has not been investigated in detail. Thus, a typical three-phase FSPM motor with 12 stator teeth and ten rotor poles is considered. It is found that, since there is a significant difference in the magnetic flux paths associated with the coils of each phase, this results in harmonics in the coil back EMF waveforms being cancelled, resulting in essentially sinusoidal phase back EMF waveforms. In addition, the influence of the rotor pole-arc on the phase back EMF waveform is evaluated by finite-element analysis, and an optimal pole-arc for minimum harmonic content in the back EMF is obtained and verified experimentally.


title: A rule-based approach to 3D terrain generation via texture splatting

abstract: A technique derived from the popular texture splatting approach for the automatic colouring and texturing of a given terrain mesh is presented in this publication. Utilizing colour rules, a simple syntax is created allowing for the generation of texture and colour values based on the elevation and angle of a given vertex. Through this combination of elevation and angle, complex features such as ridges, hills and mountains can be described.


FAKE NEWS IDENTIFICATION CS 229: MACHINE LEARNING : GROUP 621


title: An object-based convolutional neural network (OCNN) for urban land use classification

abstract: Urban land use information is essential for a variety of urban-related applications such as urban planning and regional administration. The extraction of urban land use from very fine spatial resolution (VFSR) remotely sensed imagery has, therefore, drawn much attention in the remote sensing community. Nevertheless, classifying urban land use from VFSR images remains a challenging task, due to the extreme difficulties in differentiating complex spatial patterns to derive high-level semantic labels. Deep convolutional neural networks (CNNs) offer great potential to extract high-level spatial features, thanks to its hierarchical nature with multiple levels of abstraction. However, blurred object boundaries and geometric distortion, as well as huge computational redundancy, severely restrict the potential application of CNN for the classification of urban land use. In this paper, a novel object-based convolutional neural network (OCNN) is proposed for urban land use classification using VFSR images. Rather than pixel-wise convolutional processes, the OCNN relies on segmented objects as its functional units, and CNN networks are used to analyse and label objects such as to partition within-object and between-object variation. Two CNN networks with different model structures and window sizes are developed to predict linearly shaped objects (e.g. Highway, Canal) and general (other non-linearly shaped) objects. Then a rule-based decision fusion is performed to integrate the class-specific classification results. The effectiveness of the proposed OCNN method was tested on aerial photography of two large urban scenes in Southampton and Manchester in Great Britain. The OCNN combined with large and small window sizes achieved excellent classification accuracy and computational efficiency, consistently outperforming its sub-modules, as well as other benchmark comparators, including the pixel-wise CNN, contextual-based MRF and object-based OBIA-SVM methods. The proposed method provides the first object-based CNN framework to effectively and efficiently address the complicated problem of urban land use classification from VFSR images.


title: An RC Oscillator With Comparator Offset Cancellation

abstract: A fully-integrated 18.5 kHz RC time-constant-based oscillator is designed in 65 nm CMOS for sleep-mode timers in wireless sensors. A comparator offset cancellation scheme achieves $4\times $ to $25\times $ temperature stability improvement, leading to an accuracy of ±0.18% to ±0.55% over −40 to 90 °C. Sub-threshold operation and low-swing oscillations result in ultra-low power consumption of 130 nW. The architecture also provides timing noise suppression, leading to $10\times $ reduction in long-term Allan deviation. It is measured to have a stability of 20 ppm or better for measurement intervals over 0.5 s. The oscillator also has a fast startup-time, with the period settling in 4 cycles.


title: Voxel-based fabrication through material property mapping: A design method for bitmap printing

abstract: We present a bitmap printing method and digital workflow using multi-material high resolution Additive Manufacturing (AM). Material composition is defined based on voxel resolution and used to fabricate?a design object?with locally varying material stiffness, aiming to?satisfy the design objective. In this workflow voxel resolution is set by the printer's native resolution, eliminating the need for slicing and path planning. Controlling geometry and material property variation at the resolution of the printer provides significantly greater control over structure-property-function relationships. To demonstrate the utility of the bitmap printing approach we apply it to the design of a?customized prosthetic socket. Pressure-sensing elements are concurrently fabricated with the socket, providing possibilities for evaluation of the socket's fit. The level of control demonstrated in this study?cannot be achieved using traditional CAD tools and volume-based AM workflows, implying that new CAD workflows must be developed in order to enable designers to harvest the capabilities of AM. Bitmap printing workflow enables digital fabrication in printer's native resolution.Voxel-based design and representation of objects for multi-material printing.Using 3D printed light guides, deformation of materials can be sensed.


title: Simulator sickness in virtual display gaming: a comparison of stereoscopic and non-stereoscopic situations

abstract: In this paper we compare simulator sickness symptoms produced by racing game in three different conditions. In the first experiment the participants played the Need for Speed car racing game with an ordinary 17" display and in the second and third experiments they used a head-worn virtual display for the game playing. The difference between experiments 2 and 3 was in the use of stereoscopy, as in the third experiment the car racing game was seen in stereoscopic three-dimensions. Our results indicated that there were no significant differences in sickness symptoms when we compared the ordinary display and the virtual display in non-stereoscopic mode. In stereoscopic condition the eye strain and disorientation symptoms were significantly elevated compared to the ordinary display. We conclude that using a virtual display as an accessory in a mobile device is a viable alternative, because the non-stereoscopic virtual display did not produce significantly more sickness symptoms compared to ordinary game playing.


title: Graphviz and Dynagraph – Static and Dynamic Graph Drawing Tools

abstract: Graphviz is a collection of software for viewing and manipulating abstract graphs. It provides graph visualization for tools and web sites in domains such as software engineering, networking, databases, knowledge representation, and bioinformatics. Hundreds of thousands of copies have been distributed under an open source license.


title: Datasets for AVC (H.264) and HEVC (H.265) evaluation of dynamic adaptive streaming over HTTP (DASH)

abstract: In this paper we present datasets for both trace-based simulation and real-time testbed evaluation of Dynamic Adaptive Streaming over HTTP (DASH). Our trace-based simulation dataset provides a means of evaluation in frameworks such as NS-2 and NS-3, while our testbed evaluation dataset offers a means of analysing the delivery of content over a physical network and associated adaptation mechanisms at the client. Our datasets are available in both H.264 and H.265 with encoding rates comparative to the representations and resolutions of content distribution providers such as Netflix, Hulu and YouTube. The goal of our dataset is to provide researchers with a sufficiently large dataset, in both number, and duration, of clips which provides a comparison between both encoding schemes. We provide options for evaluating not only different content and genres, but also the underlying encoding metrics, such as transmission cost, segment distribution (the range of the oscillation of the segment sizes) and associated delivery issues such as jitter and re-buffering. Finally, we also offer our datasets in a header-only compressed format, which allows researchers to download the entire dataset and uncompress locally, thus ensuring that our datasets are accessible both online via remote and local servers.


title: Visualising the Structure of Architectural Open Spaces Based on Shape Analysis

abstract: This paper proposes the application of some well known two-dimensional geometrical shape descriptors for the visualisation of the structure of architectural open spaces. The paper demonstrates the use of visibility measures such as distance to obstacles and amount of visible space to calculate shape descriptors such as convexity and skeleton of the open space. The aim of the paper is to indicate a simple, objective and quantifiable approach to understand the structure of open spaces otherwise impossible due to the complex construction of built structures.


title: Ultrafine three-dimensional (3D) laser lithographic fabrication of microneedle and its application to painless insertion and blood sampling inspired by mosquito

abstract: In the way of biomimetic research, we have developed a painless needle mimicking mosquito's proboscis. A real three-dimensional (3D) sharp and jagged needle shape elaborately like mosquito, however, was difficult to achieve as far as using conventional fabrication methods. For this purpose, a 3D laser lithography with ultrafine resolution of 200 nm (0.2 µm) is employed in this article. Unlike other reported sole needles, presented microneedle is divided into two half parts, each is shaped like eaves-trough and dependently moved. The combined halves act as one hollow microneedle, which can draw up human blood by capillary action. The resistive force during insertion can be reduced when the two halves are alternately advanced, which mimics the motion of mosquito's proboscis.


title: An empirical study on large scale text classification with skip-gram embeddings

abstract: We investigate the integration of word embeddings as classification features in the setting of large scale text classification. Such representations have been used in a plethora of tasks, however their application in classification scenarios with thousands of classes has not been extensively researched, partially due to hardware limitations. In this work, we examine efficient composition functions to obtain document-level from word-level embeddings and we subsequently investigate their combination with the traditional one-hot-encoding representations. By presenting empirical evidence on large, multi-class, multi-label classification problems, we demonstrate the efficiency and the performance benefits of this combination.


title: The Declarative / Procedural Model : A Neurobiological Model of Language Learning , Knowledge , and Use

abstract: Most research on the neurobiology of language focuses on language alone. However, new biological functions commonly make use of previously existing mechanisms. Thus, language likely relies on previously existing neurobiological substrates, whether or not these have become further specialized for language. The declarative/procedural model posits that language learning, storage, and use depend heavily on declarative and procedural memory. After all, most, if not all, of language must be learned, and these are the two most important learning and memory systems in the brain. Crucially, both systems are well-studied at many levels in humans and animals, leading to numerous independent predictions about language. Here, I first present background on the neurobiology of the two systems, then lay out ensuing predictions for language, and finally examine evidence testing these predictions. The evidence suggests that language indeed depends on declarative and procedural memory, moreover in interesting and informative ways that advance our understanding of the neurobiology of language.


title: Disparity estimation for fisheye images with an application to intermediate view synthesis

abstract: To obtain depth information from a stereo camera setup, a common way is to conduct disparity estimation between the two views; the disparity map thus generated may then also be used to synthesize arbitrary intermediate views. A straightforward approach to disparity estimation is block matching, which performs well with perspective data. When dealing with non-perspective imagery such as obtained from ultra wide-angle fisheye cameras, however, block matching meets its limits. In this paper, an adapted disparity estimation approach for fisheye images is introduced. The proposed method exploits knowledge about the fisheye projection function to transform the fisheye coordinate grid to a corresponding perspective mesh. Offsets between views can thus be determined more accurately, resulting in more reliable disparity maps. By re-projecting the perspective mesh to the fisheye domain, the original fisheye field of view is retained. The benefit of the proposed method is demonstrated in the context of intermediate view synthesis, for which both objectively evaluated as well as visually convincing results are provided.


Fraud Detection with Density Estimation Trees


Correlates, determinants, and effectiveness of childcare educators’ practices and behaviours on preschoolers’ physical activity and eating behaviours: a systematic review protocol


title: Aesthetic appraisal of product designs: Independent effects of typicality and arousal

abstract: Theories differ on how typicality and arousal influence aesthetic appraisal and whether these processes together interact or have independent effects on aesthetic appraisal. This research investigates the simultaneous effects of typicality and arousal on aesthetic appraisal for product designs by manipulating both processes separately: typicality by prototype deviation and arousal by colour saturation levels. We demonstrate that typicality has a curvilinear relationship with aesthetic appraisal. Additionally, arousal has a positive linear relationship with aesthetic appraisal of product designs. Moreover, arousal can influence aesthetic appraisal independent from typicality. [ABSTRACT FROM AUTHOR] Copyright of British Journal of Psychology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)


title: High thermal conductance AlN power module with hybrid integrated gate drivers and SiC trench MOSFETs for 2 kW single-phase PV inverter

abstract: Silicon carbide (SiC) trench MOSFET half-bridge and full-bridge power modules with hybrid integrated gate drivers on aluminium nitride (AlN) substrate were designed with electro-thermal co-simulation and soldered on an aluminium heatsink, achieving 0.3 K/W junction-to-heatsink thermal resistance. A 2 kW single-stage PV-inverter with active energy-buffer was built around the modules, achieving a weighted CEC efficiency of 95.4% and power density of 3.14 kW/l at a switching frequency of 100 kHz. The gate driver and bootstrap circuit are hybrid integrated on the power module for low parasitic gate-loop inductance. Electro-thermal co-simulation reveals a low transient temperature ripple of 6 K during the 60 Hz grid-frequency cycles at full load.


title: Algorithms for the multi-armed bandit problem

abstract: The stochastic multi-armed bandit problem is an important model for studying the explorationexploitation tradeo in reinforcement learning. Although many algorithms for the problem are well-understood theoretically, empirical conrmation of their eectiveness is generally scarce. This paper presents a thorough empirical study of the most popular multi-armed bandit algorithms. Three important observations can be made from our results. Firstly, simple heuristics such as -greedy and Boltzmann exploration outperform theoretically sound algorithms on most settings by a signicant margin. Secondly, the performance of most algorithms varies dramatically with the parameters of the bandit problem. Our study identies for each algorithm the settings where it performs well, and the settings where it performs poorly. These properties are not described by current theory, even though they can be exploited in practice in the design of heuristics. Thirdly, the algorithms’ performance relative each to other is aected only by the number of bandit arms and the variance of the rewards. This nding may guide the design of subsequent empirical evaluations. In the second part of the paper, we turn our attention to an important area of application of bandit algorithms: clinical trials. Although the design of clinical trials has been one of the principal practical problems motivating research on multi-armed bandits, bandit algorithms have never been evaluated as potential treatment allocation strategies. Using data from a real study, we simulate the outcome that a 2001-2002 clinical trial would have had if bandit algorithms had been used to allocate patients to treatments. We nd


title: word2vec Parameter Learning Explained

abstract: The word2vec model and application by Mikolov et al. have attracted a great amount of attention in recent two years. The vector representations of words learned by word2vec models have been shown to carry semantic meanings and are useful in various NLP tasks. As an increasing number of researchers would like to experiment with word2vec or similar techniques, I notice that there lacks a material that comprehensively explains the parameter learning process of word embedding models in details, thus preventing researchers that are non-experts in neural networks from understanding the working mechanism of such models. ::: This note provides detailed derivations and explanations of the parameter update equations of the word2vec models, including the original continuous bag-of-word (CBOW) and skip-gram (SG) models, as well as advanced optimization techniques, including hierarchical softmax and negative sampling. Intuitive interpretations of the gradient equations are also provided alongside mathematical derivations. ::: In the appendix, a review on the basics of neuron networks and backpropagation is provided. I also created an interactive demo, wevi, to facilitate the intuitive understanding of the model.


title: A unified formulation of honeycomb and diamond networks

abstract: Honeycomb and diamond networks have been proposed as alternatives to mesh and torus architectures for parallel processing. When wraparound links are included in honeycomb and diamond networks, the resulting structures can be viewed as having been derived via a systematic pruning scheme applied to the links of 2D and 3D tori, respectively. The removal of links, which is performed along a diagonal pruning direction, preserves the network's node-symmetry and diameter, while reducing its implementation complexity and VLSI layout area. In this paper, we prove that honeycomb and diamond networks are special subgraphs of complete 2D and 3D tori, respectively, and show this viewpoint to hold important implications for their physical layouts and routing schemes. Because pruning reduces the node degree without increasing the network diameter, the pruned networks have an advantage when the degree-diameter product is used as a figure of merit. Additionally, if the reduced node degree is used as an opportunity to increase the link bandwidths to equalize the costs of pruned and unpruned networks, a gain in communication performance may result.


title: Proposal: A Design of E-learning User Authentication System

abstract: The user authentication is very crucial in developing an e-learning system. Emerging standards for distance learning and education influence in a major way the development of e-learning systems. E-learning system must be secured against manipulation from the side of the students and also it protects user's privacy. This paper examines privacy and security issues associated with e-learning. It presents the basic principles behind privacy practices and legislation. Concrete solutions for improving the degree of security achievable are shown. A design of an e-learning user authentication system is introduced. This framework prevents manipulation from the side of the students during learning, thus allowing a reliable control of learning success.


title: Neural representations of events arise from temporal community structure

abstract: Research on event perception has focused on transient elevations in predictive uncertainty or surprise as the primary signal driving event segmentation. Here the authors report behavioral and neuroimaging evidence that suggests that event representations can emerge even in the absence of such cues. They propose that this learning occurs in a manner analogous to the learning of semantic categories.


title: AIDE: An Active Learning-Based Approach for Interactive Data Exploration

abstract: In this paper, we argue that database systems be augmented with an automated data exploration service that methodically steers users through the data in a meaningful way. Such an automated system is crucial for deriving insights from complex datasets found in many big data applications such as scientific and healthcare applications as well as for reducing the human effort of data exploration. Towards this end, we present AIDE, an Automatic Interactive Data Exploration framework that assists users in discovering new interesting data patterns and eliminate expensive ad-hoc exploratory queries. AIDE relies on a seamless integration of classification algorithms and data management optimization techniques that collectively strive to accurately learn the user interests based on his relevance feedback on strategically collected samples. We present a number of exploration techniques as well as optimizations that minimize the number of samples presented to the user while offering interactive performance. AIDE can deliver highly accurate query predictions for very common conjunctive queries with small user effort while, given a reasonable number of samples, it can predict with high accuracy complex disjunctive queries. It provides interactive performance as it limits the user wait time per iteration of exploration to less than a few seconds.


title: Measuring Online Trust of Websites: Credibility, Perceived Ease of Use, and Risk

abstract: As the use of the web for ecommerce and information access continues to expand, user trust of websites has come under inspection. Empirical study of online trust is constrained by the shortage of high-quality measures of general online trust. The development of such measures is a priority for MIS researchers. This paper presents a new instrument for use in studying trust of an individual in a given website. The instrument was tested with two different websites in an experiment conducted in a controlled setting. The items in the instrument were analyzed using a confirmatory factor analysis with a split-sample approach. This process resulted in an instrument of high reliability whose components are unidimensional and whose constructs show high convergent and discriminant validity.


title: Attributed Grammars for Joint Estimation of Human Attributes, Part and Pose

abstract: In this paper, we are interested in developing compositional models to explicit representing pose, parts and attributes and tackling the tasks of attribute recognition, pose estimation and part localization jointly. This is different from the recent trend of using CNN-based approaches for training and testing on these tasks separately with a large amount of data. Conventional attribute models typically use a large number of region-based attribute classifiers on parts of pre-trained pose estimator without explicitly detecting the object or its parts, or considering the correlations between attributes. In contrast, our approach jointly represents both the object parts and their semantic attributes within a unified compositional hierarchy. We apply our attributed grammar model to the task of human parsing by simultaneously performing part localization and attribute recognition. We show our modeling helps performance improvements on pose-estimation task and also outperforms on other existing methods on attribute prediction task.


title: CORRUGATED SUBSTRATE INTEGRATED WAVEGUIDE (SIW) ANTIPODAL LINEARLY TAPERED SLOT ANTENNA ARRAY FED BY QUASI-TRIANGULAR POWER DIVIDER

abstract: In this paper, a new conflguration of Tapered Slot Antenna (TSA) with improved radiation pattern is proposed and studied. This antenna is designed in the form of a substrate integrated waveguide (SIW) array with respect to side lobe level constraints. For side lobe reduction, a simple quasi-triangular distribution is proposed and is accomplished uniquely by means of 3dB power dividers. A 12-way series feed network with T-junction is designed and demonstrated. Radiation features of the antenna array are discussed to illustrate the accomplishment of a low side lobe level (i19dB) of the array. The proposed antenna demonstrates the ability of the SIW technology to achieve a very low side lobe in a simple, compact and planar structure.


title: Computational Cameras: Convergence of Optics and Processing

abstract: A computational camera uses a combination of optics and processing to produce images that cannot be captured with traditional cameras. In the last decade, computational imaging has emerged as a vibrant field of research. A wide variety of computational cameras has been demonstrated to encode more useful visual information in the captured images, as compared with conventional cameras. In this paper, we survey computational cameras from two perspectives. First, we present a taxonomy of computational camera designs according to the coding approaches, including object side coding, pupil plane coding, sensor side coding, illumination coding, camera arrays and clusters, and unconventional imaging systems. Second, we use the abstract notion of light field representation as a general tool to describe computational camera designs, where each camera can be formulated as a projection of a high-dimensional light field to a 2-D image sensor. We show how individual optical devices transform light fields and use these transforms to illustrate how different computational camera designs (collections of optical devices) capture and encode useful visual information.


title: Evaluating the impact of smart technologies on harbor’s logistics via BPMN modeling and simulation

abstract: Abstract A smart Information and Communication Technology (ICT) enables a synchronized interplay of different key factors, aligning infrastructures, consumers, and governmental policy-making needs. In the harbor’s logistics context, smart ICT has been driving a multi-year wave of growth. Although there is a standalone value in the technological innovation of a task, the impact of a new smart technology is unknown without quantitative analysis methods on the end-to-end process. In this paper, we first present a review of the smart ICT for marine container terminals, and then we propose to evaluate the impact of such smart ICT via business process model and notation (BPMN) modeling and simulation. The proposed approach is discussed in a real-world modeling and simulation analysis, made on a pilot terminal of the Port of Leghorn (Italy).


title: Blob Analysis of the Head and Hands: A Method for Deception Detection

abstract: Behavioral indicators of deception and behavioral state are extremely difficult for humans to analyze. Blob analysis, a method for analyzing the movement of the head and hands based on the identification of skin color is presented. This method is validated with numerous skin tones. A proof-of-concept study is presented that uses blob analysis to explore behavioral state identification in the detection of deception.


title: Automation, per se, is not job elimination: How artificial intelligence forwards cooperative human-machine coexistence

abstract: Recent advances in artificial intelligence (AI) and machine learning, combined with developments in neuromorphic hardware technologies and ubiquitous computing, promote machines to emulate human perceptual and cognitive abilities in a way that will continue the trend of automation for several upcoming decades. Despite the gloomy scenario of automation as a job eliminator, we argue humans and machines can cross-fertilise in a way that forwards a cooperative coexistence. We build our argument on three pillars: (i) the economic mechanism of automation, (ii) the dichotomy of ‘experience’ that separates the first-person perspective of humans from artificial learning algorithms, and (iii) the interdependent relationship between humans and machines. To realise this vision, policy makers have to implement alternative educational approaches that support lifelong training and flexible job transitions.


title: Quantized Densely Connected U-Nets for Efficient Landmark Localization

abstract: In this paper, we propose quantized densely connected U-Nets for efficient visual landmark localization. The idea is that features of the same semantic meanings are globally reused across the stacked U-Nets. This dense connectivity largely improves the information flow, yielding improved localization accuracy. However, a vanilla dense design would suffer from critical efficiency issue in both training and testing. To solve this problem, we first propose order-K dense connectivity to trim off long-distance shortcuts; then, we use a memory-efficient implementation to significantly boost the training efficiency and investigate an iterative refinement that may slice the model size in half. Finally, to reduce the memory consumption and high precision operations both in training and testing, we further quantize weights, inputs, and gradients of our localization network to low bit-width numbers. We validate our approach in two tasks: human pose estimation and face alignment. The results show that our approach achieves state-of-the-art localization accuracy, but using ~70% fewer parameters, ~98% less model size and saving ~75% training memory compared with other benchmark localizers. The code is available at https://github.com/zhiqiangdon/CU-Net.


title: Animal behaviour : a concise introduction

abstract: The biology of behaviour The machinery of behaviour The genetics and development of behaviour Movements and migration Eating and not being eaten Signals Fighting Sexual behaviour Altruism and social life Epilogue.


title: When Does Non-Negative Matrix Factorization Give a Correct Decomposition into Parts?

abstract: We interpret non-negative matrix factorization geometrically, as the problem of finding a simplicial cone which contains a cloud of data points and which is contained in the positive orthant. We show that under certain conditions, basically requiring that some of the data are spread across the faces of the positive orthant, there is a unique such simplicial cone. We give examples of synthetic image articulation databases which obey these conditions; these require separated support and factorial sampling. For such databases there is a generative model in terms of 'parts' and NMF correctly identifies the 'parts'. We show that our theoretical results are predictive of the performance of published NMF code, by running the published algorithms on one of our synthetic image articulation databases.


title: MRBAYES: Bayesian inference of phylogenetic trees

abstract: Summary: The program MRBAYES performs Bayesian inference of phylogeny using a variant of Markov chain Monte Carlo. Availability: MRBAYES, including the source code, documentation, sample data files, and an executable, is available at http://brahms.biology.rochester.edu/software.html.


title: Mobile big data fault-tolerant processing for ehealth networks

abstract: In daily life, people tend to use mobile networks for more accurate overall data. With intelligent mobile devices, almost all kinds of data can be collected automatically, which contributes directly to the blooming of eHealth. However, large amounts of data are also leading us into the era of big data, in which new data collection, transmission, and processing techniques are required. To ensure ubiquitous data collection, the scale of mobile eHealth networks has to be expanded. Also, networks will face more pressure to transmit large amounts of eHealth data. In addition, because the processing time increases with data volume, even powerful processors cannot always be regarded as efficient for big data. To solve these problems, in this article, an interests-based reduced variable neighborhood search (RVNS) queue architecture (IRQA) is proposed. In this three-layer architecture, a fault-tolerant mechanism based on interests matching is designed to ensure the completeness of eHealth data in the data gathering layer. Then the data integrating layer checks the accuracy of data, and also prepares for data processing. In the end, an RVNS queue is adopted for rapid data processing in the data analyzing layer. After processing with relevant rules, only valuable data will be reported to health care providers, which saves their effort to identify these data. Simulation shows that IRQA is steady and fast enough to process large amounts of data.


title: Cell biology of protein misfolding: The examples of Alzheimer's and Parkinson's diseases

abstract: The salutary intersection of fundamental cell biology with the study of disease is well illustrated by the emerging elucidation of neurodegenerative disorders. Novel mechanisms in cell biology have been uncovered through disease-orientated research; for example, the discovery of presenilin as an intramembrane aspartyl protease that processes many diverse proteins within the lipid bilayer. A common theme has arisen in this field: normally-soluble proteins accumulate, misfold and oligomerize, inducing cytotoxic effects that are particularly devastating in the post-mitotic milieu of the neuron.


title: GPU-accelerated real-time free-viewpoint DIBR for 3DTV

abstract: Multi-view 3D video may succeed stereo 3DTV in multimedia and TV applications. The MPEG committee has installed a special task force to establish a standard for multiview 3D video coding. Enabling free-viewpoint selection by the consumer is one of the major purposes of multi-view 3D encoded data. One focal point of our research work concerns an efficient implementation of the rendering part of stereoscopic free-viewpoint visualization, which is a computationally expensive task and significantly determines the final image quality. The free-viewpoint DIBR algorithm that is described in this article is implemented with an off-theshelf GPU that can be integrated in advanced 3DTV systems. We present the principal steps of a representative freeviewpoint DIBR, based on image warping and image artifact reduction techniques, and show the key differences between the reference software and our GPU implementation. One of those differences is the joint execution of signal processing blocks to share memory usage. Using a combination of the highly parallel programming architecture CUDA and a graphics API, we have achieved real-time performance executing 1080p HD multi-view video with a rendering quality that is comparable to the software implementation.


title: Using triggered operations to offload rendezvous messages

abstract: Historically, MPI implementations have had to choose between eager messaging protocols that require buffering and rendezvous protocols that sacrifice overlap and strong independent progress in some scenarios. The typical choice is to use an eager protocol for short messages and switch to a rendezvous protocol for long messages. If overlap and progress are desired, some implementations offer the option of using a thread. We propose an approach that leverages triggered operations to implement a long message rendezvous protocol that provides strong progress guarantees. The results indicate that a triggered operation based rendezvous can achieve better overlap than a traditional rendezvous implementation and less wasted band width than an eager long protocol.


title: Brief Announcement: MIC++: Accelerating Maximal Information Coefficient Calculation with GPUs and FPGAs

abstract: To discover relationships and associations between pairs of variables in large data sets have become one of the most significant challenges for bioinformatics scientists. To tackle this problem, maximal information coefficient (MIC) is widely applied as a measure of the linear or non-linear association between two variables. To improve the performance of MIC calculation, in this work we present MIC++, a parallel approach based on the heterogeneous accelerators including Graphic Processing Unit (GPU) and Field Programmable Gate Array (FPGA) engines, focusing on both coarse-grained and fine-grained parallelism. As the evaluation of MIC++, we have demonstrated the performance on the state-of-the-art GPU accelerators and the FPGA-based accelerators. Preliminary estimated results show that the proposed parallel implementation can significantly achieve more than 6X-14X speedup using GPU, and 4X-13X using FPGA-based accelerators.


title: Modelling the continuum as paradigm shift in recordkeeping and archiving processes , and beyond – a personal reflection

abstract: This article outlines the development of a records continuum model initially developed as a teaching tool to communicate evidence‐based approaches to archives and records management. The continuum is being used in Australia as a metaphor to assist in getting records management ‘right’ in recordkeeping environments built around electronic communications, and the model supports this endeavour. It extends the concept of the continuum beyond metaphor, representing the case for viewing it in its fuller spacetime meanings as a worldview. In this form, the continuum is potentially a technologically driven paradigm shift within all information management and systems practice. There is a new game developing and the concept of the continuum can help us re‐organise our knowledge for that game. This article will discuss the diversity of records management theory and practice. It will look at the meanings of the continuum and my own model of it, including the differences between a worldview and a detailed view. Three other continuum models are presented. A continuum ‘patrol and control’ strategy for analysis is outlined briefly, and represents a point at which my own approach to the continuum is taking off into more detailed practical considerations in records management education and training.


title: Comparison between traditional neural networks and radial basis function networks

abstract: The paper presents the properties of two types of neural networks: traditional neural networks and radial basis function (RBF) networks, both of which are considered as universal approximators. In this paper, the advantages and disadvantages of the two types of neural network architectures are analyzed and compared based on four different examples. The comparison results indicate approaches to be taken relative to the network model selection for practical applications.


title: Modeling of Adaptive Cyber Physical Systems using Aspect-oriented Approach

abstract: This paper proposes an aspect-oriented approach to modeling adaptive cyber physical system (CPS) using Petri nets. The core concerns of CPSs are described as device model and task model, and dynamic variations of system behaviors or environment conditions are extracted as crosscutting concerns. The models of runtime inspection as well as device adaptation and task adaptation are designed as aspects nets. For the device adaptation stra tegy, fault types are analyzed and the control loop concept is integrated to form the adaptation aspect model. For the task adaptation, a rescheduling method using PSO-Pareto algorithm to find the b est solution of the backup devices is proposed. Via well-defined rules, thes e aspect nets can be weaved with the core concern nets into a comprehensive adaptive CPS model. By theoretical analysis and a case study, we show the modeling approach is feasible and flexible, whic h simplifies the design of adaptive CPSs.


title: D3NER: biomedical named entity recognition using CRF‐biLSTM improved with fine‐tuned embeddings of various linguistic information

abstract: Motivation ::: Recognition of biomedical named entities in the textual literature is a highly challenging research topic with great interest, playing as the prerequisite for extracting huge amount of high-valued biomedical knowledge deposited in unstructured text and transforming them into well-structured formats. Long Short-Term Memory (LSTM) networks have recently been employed in various biomedical named entity recognition (NER) models with great success. They, however, often did not take advantages of all useful linguistic information and still have many aspects to be further improved for better performance. ::: ::: ::: Results ::: We propose D3NER, a novel biomedical named entity recognition (NER) model using conditional random fields and bidirectional long short-term memory improved with fine-tuned embeddings of various linguistic information. D3NER is thoroughly compared with seven very recent state-of-the-art NER models, of which two are even joint models with named entity normalization (NEN), which was proven to bring performance improvements to NER. Experimental results on benchmark datasets, i.e. the BioCreative V Chemical Disease Relation (BC5 CDR), the NCBI Disease and the FSU-PRGE gene/protein corpus, demonstrate the out-performance and stability of D3NER over all compared models for chemical, gene/protein NER and over all models (without NEN jointed, as D3NER) for disease NER, in almost all cases. On the BC5 CDR corpus, D3NER achieves F1 of 93.14 and 84.68% for the chemical and disease NER, respectively; while on the NCBI Disease corpus, its F1 for the disease NER is 84.41%. Its F1 for the gene/protein NER on FSU-PRGE is 87.62%. ::: ::: ::: Availability and implementation ::: Data and source code are available at: https://github.com/aidantee/D3NER. ::: ::: ::: Supplementary information ::: Supplementary data are available at Bioinformatics online.


title: Taichi: An Open-Source Computer Graphics Library

abstract: An ideal software system in computer graphics should be a combination of innovative ideas, solid software engineering and rapid development. However, in reality these requirements are seldom met simultaneously. In this paper, we present early results on an open-source library named Taichi (this http URL), which alleviates this practical issue by providing an accessible, portable, extensible, and high-performance infrastructure that is reusable and tailored for computer graphics. As a case study, we share our experience in building a novel physical simulation system using Taichi.


title: A Dynamic Graph-Based Scheduling and Interference Coordination Approach in Heterogeneous Cellular Networks

abstract: To meet the demand of increasing mobile data traffic and provide better user experience, heterogeneous cellular networks (HCNs) have become a promising solution to improve both the system capacity and coverage. However, due to dense self-deployment of small cells in a limited area, serious interference from nearby base stations may occur, which results in severe performance degradation. To mitigate downlink interference and utilize spectrum resources more efficiently, we present a novel graph-based resource allocation and interference management approach in this paper. First, we divide small cells into cell clusters, considering their neighborhood relationships in the scenario. Then, we develop another graph clustering scheme to group user equipment (UE) in each cell cluster into UE clusters with minimum intracluster interference. Finally, we utilize a proportional fairness scheduling scheme to assign subchannels to each UE cluster and allocate power using water-filling method. To show the efficacy and effectiveness of our proposed approach, we propose a dual-based approach to search for optimal solutions as the baseline for comparisons. Furthermore, we compare the graph-based approach with the state of the art and a distributed approach without interference coordination. The simulation results show that our graph-based approach reaches more than 90% of the optimal performance and achieves a significant improvement in spectral efficiency compared with the state of the art and the distributed approach both under cochannel and orthogonal deployments. Moreover, the proposed graph-based approach has low computation complexity, making it feasible for real-time implementation.


title: Near-eye light field holographic rendering with spherical waves for wide field of view interactive 3D computer graphics

abstract: Holograms display a 3D image in high resolution and allow viewers to focus freely as if looking through a virtual window, yet computer generated holography (CGH) hasn't delivered the same visual quality under plane wave illumination and due to heavy computational cost. Light field displays have been popular due to their capability to provide continuous focus cues. However, light field displays must trade off between spatial and angular resolution, and do not model diffraction. We present a light field-based CGH rendering pipeline allowing for reproduction of high-definition 3D scenes with continuous depth and support of intra-pupil view-dependent occlusion. Our rendering accurately accounts for diffraction and supports various types of reference illuminations for hologram. We avoid under- and over-sampling and geometric clipping effects seen in previous work. We also demonstrate an implementation of light field rendering plus the Fresnel diffraction integral based CGH calculation which is orders of magnitude faster than the state of the art [Zhang et al. 2015], achieving interactive volumetric 3D graphics. To verify our computational results, we build a see-through, near-eye, color CGH display prototype which enables co-modulation of both amplitude and phase. We show that our rendering accurately models the spherical illumination introduced by the eye piece and produces the desired 3D imagery at the designated depth. We also analyze aliasing, theoretical resolution limits, depth of field, and other design trade-offs for near-eye CGH.


title: A methodology and tool for conceptual designing a data warehouse from ontology-based sources

abstract: Ontologies become more popular in various domains. In database, they contribute largely in designing operational databases, that we call ontology-based databases (OBDB). An OBDB stores ontology and their instances in the same repository. Several DBMS propose solutions to manipulate and query this type of databases. As consequence, these operational OBDB become a candidate to feed data warehouses (DW). In this paper, we propose a new conceptual design methodology of DW from data residing in various OBDB that takes into account sources and decision maker requirements. As the result, a semantic DW model is generated. The presence of ontology within a DW facilitates its querying in semantic level and its future integration with other DW built using our methodology. Finally, we present a case tool, called S2RWC, supporting our proposal.


title: Highlight in Telepsychiatry and Behavioral Health Emergencies.

abstract: Carolinas HealthCare System is one of the largest freestanding psychiatric emergency departments in the country. It has grown from a small community mental health center in the 1930s, to one of the largest providers of emergency mental health services in the country. It offers services in person and via telepsychiatry to other emergency departments and primary care clinics. It decreased emergency room wait times and revolutionized where and how patients get their care. This has been the work of several groups from many disciplines. The transition from community mental health center to large-scale mental health emergency department has been a model for the rest of the country.


title: Esthetic restorations of maxillary anterior teeth with orthodontic treatment and porcelain laminate veneers: a case report

abstract: If orthodontists and restorative dentists establish the interdisciplinary approach to esthetic dentistry, the esthetic and functional outcome of their combined efforts will be greatly enhanced. This article describes satisfying esthetic results obtained by the distribution of space for restoration by orthodontic treatment and porcelain laminate veneers in uneven space between maxillary anterior teeth. It is proposed that the use of orthodontic treatment for re-distribution of the space and the use of porcelain laminate veneers to alter crown anatomy provide maximum esthetic and functional correction for patients with irregular interdental spacing.


title: Friction Force Microscopy Investigation of Elastic Instabilities in Nanolubricated Junctions

abstract: We present experimental results concerning the Friction Force Microscopy (FFM) investigation of smooth mica and Si surfaces lubricated by a model lubricant. We have studied the friction force as a function of normal load and sliding velocity, analyzing results in the framework of interfacial liquid structuring and drainage effects.© 2005 ASME


A Hybrid Bi-LSTM-CRF Model to Recognition of Disabilities from Biomedical Texts


title: A Multi-task Deep Learning Architecture for Maritime Surveillance using AIS Data Streams

abstract: In a world of global trading, maritime safety, security and efficiency are crucial issues. We propose a multi-task deep learning framework for vessel monitoring using Automatic Identification System (AIS) data streams. We combine recurrent neural networks with latent variable modeling and an embedding of AIS messages to a new representation space to jointly address key issues to be dealt with when considering AIS data streams: massive amount of streaming data, noisy data and irregular time-sampling. We demonstrate the relevance of the proposed deep learning framework on real AIS datasets for a three-task setting, namely trajectory reconstruction, anomaly detection and vessel type identification.


title: Learning Reconstruction-Based Remote Gaze Estimation

abstract: It is a challenging problem to accurately estimate gazes from low-resolution eye images that do not provide fine and detailed features for eyes. Existing methods attempt to establish the mapping between the visual appearance space to the gaze space. Different from the direct regression approach, the reconstruction-based approach represents appearance and gaze via local linear reconstruction in their own spaces. A common treatment is to use the same local reconstruction in the two spaces, i.e., the reconstruction weights in the appearance space are transferred to the gaze space for gaze reconstruction. However, this questionable treatment is taken for granted but has never been justified, leading to significant errors in gaze estimation. This paper is focused on the study of this fundamental issue. It shows that the distance metric in the appearance space needs to be adjusted, before the same reconstruction can be used. A novel method is proposed to learn the metric, such that the affinity structure of the appearance space under this new metric is as close as possible to the affinity structure of the gaze space under the normal Euclidean metric. Furthermore, the local affinity structure invariance is utilized to further regularize the solution to the reconstruction weights, so as to obtain a more robust and accurate solution. Effectiveness of the proposed method is validated and demonstrated through extensive experiments on different subjects.


title: Computerised electronic foetal heart rate monitoring in labour: automated contraction identification

abstract: The foetal heart rate (FHR) response to uterine contractions is crucial to detect foetal distress by electronic FHR monitoring during labour. We are developing a new automated system (OxSys) for decision support in labour, using the Oxford database of intrapartum FHR records. We describe here a novel technique for automated detection of uterus contractions. In addition, we present a comparison of the new method with four other computerised approaches. During training, OxSys achieved sensitivity above 95% and positive predictive value (PPV) of up to 90% for traces of good quality. During testing, OxSys achieved sensitivity = 87% and PPV = 75%. For comparison, a second clinical expert obtained sensitivity = 93% and PPV = 80%, and all other computerised approaches achieved lower values. It was concluded that the proposed method can be employed with confidence in our study on foetal health assessment in labour and future OxSys development.


title: Privacy Preserving Access Control in Service-Oriented Architecture

abstract: Service-oriented Architecture (SOA) comprises a number of loosely-coupled independent services, which collaborate, interact and share data to accomplish incoming requests. A service invocation can involve multiple services, where each service accesses, processes and shares the client's data. These interactions may share data with unauthorized services and violate client's privacy. The client has no means of identifying if a violation occurred because it has no control over the service invocations beyond its trust domain. Such interactions introduce new security challenges which are not present in traditional systems. This paper proposes a data-centric approach for privacy preserving access control in SOA. Benefits of the proposed approach include the ability to dynamically define access polices by the clients and control data access at the time of each service interaction. A realistic healthcare scenario is used to evaluate the implementation of the proposed solution which validates its viability.


title: Compact T-shaped dipole antenna for GPS application

abstract: In this paper, a development of compact T-shaped dipole antenna is investigated for Ground Positioning System (GPS) application. The proposed antenna is equipped by a balance-to-unbalanced (balun) circuit to transform the antenna into the unbalance circuit feedable from a coaxial cable. The balun circuit is made of a λ/4 element which is also used as a stand of the antenna. Prior to fabrication, the proposed antenna is analyzed through the simulation to achieve the optimum antenna geometry implementable for the realization. From the simulation result, the proposed compact T-shaped dipole antenna resonates at frequency of 1575MHz with the gain of 6.37dBi and the reflection coefficient of -11.93dB corresponds to voltage standing wave ratio (VSWR) value of 1.68 and the -10dB working bandwidth of 150MHz ranges from the frequency of 1500MHz to 1650MHz. Meanwhile, the realized compact T-shaped dipole antenna has the gain of 6.01dBi and the reflection coefficient of -13.74dB at frequency of 1575MHz which corresponds to VSWR value of 1.52 and the -10dB working bandwidth of 190MHz ranges from the frequency of 1470MHz to 1660MHz.


Signed Distance Fields: A Natural Representation for Both Mapping and Planning


title: Pattern Recognition using Artificial Neural Network

abstract: An artificial neural network (ANN) usually called neural network. It can be considered as a resemblance to a paradigm which is inspired by biological nervous system. In network the signals are transmitted by the means of connections links. The links possess an associated way which is multiplied along with the incoming signal. The output signal is obtained by applying activation to the net input NN are one of the most exciting and challenging research areas. As ANN mature into commercial systems, they are likely to be implemented in hardware. Their fault tolerance and reliability are therefore vital to the functioning of the system in which they are embedded. The pattern recognition system is implemented with Back propagation network and Hopfield network to remove the distortion from the input. The Hopfield network has high fault tolerance which supports this system to get the accurate output.


title: A Revocable Backup System (Extended Abstract)

abstract: We present a system which enables a user to remove a file from both the file system and all the backup tapes on which the file is stored. The ability to remove files from all backup tapes is desirable in many cases. Our system erases information from the backup tape without actually writing on the tape. This is achieved by applying cryptography in a new way: a block cipher is used to enable the system to "forget" information rather than protect it. Our system is easy to install and is transparent to the end user. Further, it introduces no slowdown in system performance and little slowdown in the backup procedure.


title: VisDB: database exploration using multidimensional visualization

abstract: Discusses how the VisDB system supports the query specification process by representing the result visually. The main idea behind the system stems from the view of relational database tables as sets of multidimensional data where the number of attributes corresponds to the number of dimensions. In such a view, it is often unclear. In this system, each display pixel represents one database item. Pixels are arranged and colored to indicate the item's relevance to a user query and to give a visual impression of the resulting data set. >


title: Bilingual Language Switching in Naming: Asymmetrical Costs of Language Selection

abstract: Abstract In an experimental study of language switching and selection, bilinguals named numerals in either their first or second language unpredictably. Response latencies (RTs) on switch trials (where the response language changed from the previous trial) were slower than on nonswitch trials. As predicted, the language-switching cost was consistently larger when switching to the dominant L 1 from the weaker L 2 than vice versa such that, on switch trials, L 1 responses were slower than in L 2 . This “paradoxical” asymmetry in the cost of switching languages is explained in terms of differences in relative strength of the bilingual's two languages and the involuntary persistence of the previous language set across an intended switch of language. Naming in the weaker language, L 2 , requires active inhibition or suppression of the stronger competitor language, L 1 ; the inhibition persists into the following (switch) trial in the form of “negative priming” of the L 1 lexicon as a whole.


title: Quantifying Mental Health Signals in Twitter

abstract: The ubiquity of social media provides a rich opportunity to enhance the data available to mental health clinicians and researchers, enabling a better-informed and better-equipped mental health field. We present analysis of mental health phenomena in publicly available Twitter data, demonstrating how rigorous application of simple natural language processing methods can yield insight into specific disorders as well as mental health writ large, along with evidence that as-of-yet undiscovered linguistic signals relevant to mental health exist in social media. We present a novel method for gathering data for a range of mental illnesses quickly and cheaply, then focus on analysis of four in particular: post-traumatic stress disorder (PTSD), depression, bipolar disorder, and seasonal affective disorder (SAD). We intend for these proof-of-concept results to inform the necessary ethical discussion regarding the balance between the utility of such data and the privacy of mental health related information.


title: DEEP-CARVING: Discovering visual attributes by carving deep neural nets

abstract: Most of the approaches for discovering visual attributes in images demand significant supervision, which is cumbersome to obtain. In this paper, we aim to discover visual attributes in a weakly supervised setting that is commonly encountered with contemporary image search engines.


title: Benchmarking of objective quality metrics for HDR image quality assessment

abstract: Recent advances in high dynamic range (HDR) capture and display technologies have attracted a lot of interest from scientific, professional, and artistic communities. As in any technology, the evaluation of HDR systems in terms of quality of experience is essential. Subjective evaluations are time consuming and expensive, and thus objective quality assessment tools are needed as well. In this paper, we report and analyze the results of an extensive benchmarking of objective quality metrics for HDR image quality assessment. In total, 35 objective metrics were benchmarked on a database of 20 HDR contents encoded with 3 compression algorithms at 4 bit rates, leading to a total of 240 compressed HDR images, using subjective quality scores as ground truth. Performance indexes were computed to assess the accuracy, monotonicity, and consistency of the metric estimation of subjective scores. Statistical analysis was performed on the performance indexes to discriminate small differences between metrics. Results demonstrated that metrics designed for HDR content, i.e., HDR-VDP-2 and HDR-VQM, are the most reliable predictors of perceived quality. Finally, our findings suggested that the performance of most full-reference metrics can be improved by considering non-linearities of the human visual system, while further efforts are necessary to improve performance of no-reference quality metrics for HDR content.


title: Support Vector Machines for Uplift Modeling

abstract: Uplift modeling is a branch of Machine Learning which aims to predict not the class itself, but the difference between the class variable behavior in two groups: treatment and control. Objects in the treatment group have been subject to some action, while objects in the control group have not. By including the control group it is possible to build a model which predicts the causal effect of the action for a given individual. In this paper we present a variant of Support Vector Machines designed specifically for uplift modeling. The SVM optimization task has been reformulated to explicitly model the difference in class behavior between two datasets. The model predicts whether a given object will have a positive, neutral or negative response to a given action, and by tuning a parameter of the model the analyst is able to influence the relative proportion of neutral predictions and thus the sensitivity of the model. We adapt the dual coordinate descent method to efficiently solve our optimization task. Finally the proposed method is compared experimentally with other uplift modeling approaches.


title: Judgment Under Emotional Certainty and Uncertainty: The Effects of Specific Emotions on Information Processing

abstract: The authors argued that emotions characterized by certainty appraisals promote heuristic processing, whereas emotions characterized by uncertainty appraisals result in systematic processing. The 1st st experiment demonstrated that the certainty associated with an emotion affects the certainty experienced in subsequent situations. The next 3 experiments investigated effects on processing of emotions associated with certainty and uncertainty. Compared with emotions associated with uncertainty, emotions associated with certainty resulted in greater reliance on the expertise of a source of a persuasive message in Experiment 2, more stereotyping in Experiment 3, and less attention to argument quality in Experiment 4. In contrast to previous theories linking valence and processing, these findings suggest that the certainty appraisal content of emotions is also important in determining whether people engage in systematic or heuristic processing.


title: Multi-scale Feature Extraction on Point-sampled Surfaces

abstract: We present a new technique for extracting line-type features on point-sampled geometry. Given an unstructured point cloud as input, our method first applies principal component analysis on local neighborhoods to classify points according to the likelihood that they belong to a feature. Using hysteresis thresholding, we then compute a minimum spanning graph as an initial approximation of the feature lines. To smooth out the features while maintaining a close connection to the underlying surface, we use an adaptation of active contour models. Central to our method is a multi-scale classification operator that allows feature analysis at multiple scales, using the size of the local neighborhoods as a discrete scale parameter. This significantly improves the reliability of the detection phase and makes our method more robust in the presence of noise. To illustrate the usefulness of our method, we have implemented a non-photorealistic point renderer to visualize point-sampled surfaces as line drawings of their extracted feature curves.


title: On electric fields in belted cables and 3-phase gas insulated cables

abstract: The electric field distribution in Belted Cables and 3-phase gas insulated cables (GIC) enclosed in a common ground enclosure is analyzed and presented in this paper. The electric fields in this type of GIC are analogous to those of 3-phase belted cables. Unlike 3-phase screened cables, the stress distribution in belted cables is not radial. With time varying voltages (power frequency voltage variations; quasi-static voltages) the stress distribution in the cable insulation changes, not being radial. The electric stress distribution is expected to show systematic, revolving effect. This revolving effect is important as the location of maximum stresses keep on shifting. Also, as a secondary effect, the revolving e-field results into electric-field-winds which can add to the particle movements in the gas insulated cables. The literature acknowledges that particle movements in the gas insulated system can have deleterious effect on the over all system insulation strength; which partly get impetus from e-field-winds. The stresses in such a cable are analyzed using open source software Finite Element Method Magnetics (FEMM). The results presented show that the field distribution over a cycle (of 50 Hz) at discrete time intervals are thought to be having a great educational value, giving a feel of electric field variation in (i) belted cables and (ii) GIC with common enclosure. The computed results of electric stress and potential distribution using FEMM are compared with available results based on CSM models to validate the present results where ever possible.


title: Multiplication of Negative Scenarios: the Approach Public Administrations Could Use at Drafting General Rules

abstract: This paper addresses problems that emerge when draft laws are created without due regard for the calculus of probability. Although the latter should be sine qua non for future legislation, legislators usually do not use it despite the legislation’s pro future orientation. The paper, based on Hume’s old “is-ought” problem (the impossibility to move from descriptive statements to prescriptive ones) and with the awareness that probability will not be used soon, offers a solution for the future legislation in the multiplication of (negative) scenarios, applied to different life questions. Despite the more and more “popular use” of regulatory impact assessments, smart regulation, probability and risk, public administrations as the major drafters of general legal rules usually do not even use the (much simpler) negative approach to gain better insight into problems, although it is per se the natural way of our thinking. A new view on probability through signs that fit into (unwanted, but known in advance) scenarios can also provide new answers regarding causality. The latter is based on signs, which is what evidence per se really means.


title: Real-Time Fully Incremental Scene Understanding on Mobile Platforms

abstract: We propose an online RGB-D based scene understanding method for indoor scenes running in real time on mobile devices. First, we incrementally reconstruct the scene via simultaneous localization and mapping and compute a three-dimensional (3-D) geometric segmentation by fusing segments obtained from each input depth image in a global 3-D model. We combine this geometric segmentation with semantic annotations to obtain a semantic segmentation in form of a semantic map. To accomplish efficient semantic segmentation, we encode the segments in the global model with a fast incremental 3-D descriptor and use a random forest to determine its semantic label. The predictions from successive frames are then fused to obtain a confident semantic class across time. As a result, the overall method achieves an accuracy that gets close to the most state-of-the-art 3-D scene understanding methods while being much more efficient, enabling real-time execution on low-power embedded systems.


title: Deep Transfer Learning for EEG-Based Brain Computer Interface

abstract: The electroencephalography classifier is the most important component of brain-computer interface based systems. There are two major problems hindering the improvement of it. First, traditional methods do not fully exploit multimodal information. Second, large-scale annotated EEG datasets are almost impossible to acquire because biological data acquisition is challenging and quality annotation is costly. Herein, we propose a novel deep transfer learning approach to solve these two problems. First, we model cognitive events based on EEG data by characterizing the data using EEG optical flow, which is designed to preserve multimodal EEG information in a uniform representation. Second, we design a deep transfer learning framework which is suitable for transferring knowledge by joint training, which contains a adversarial network and a special loss function. The experiments demonstrate that our approach, when applied to EEG classification tasks, has many advantages, such as robustness and accuracy.


title: Ultrasound to improve the safety of hyaluronic acid filler treatments

abstract: BACKGROUND ::: Hyaluronic acid fillers are known for a reliable safety profile, but complications do occur, even serious vascular adverse events. ::: ::: ::: OBJECTIVE ::: To improve the safety of hyaluronic acid filler treatments. ::: ::: ::: METHODS ::: Ultrasound is used to image hyaluronic acid fillers. ::: ::: ::: RESULTS ::: Before a filler treatment is performed with ultrasound, previous filler treatments can be brought in to sight and vascular mapping can be performed. In case of adverse events, the filler and the surrounding tissues are visible. Dislocation, abscesses, and vascular adverse events can be seen. Under ultrasound guidance, hyaluronidase can be injected directly into the filler deposit. ::: ::: ::: CONCLUSION ::: Ultrasound examination can be an important tool to improve the safety of hyaluronic acid filler treatments.


title: How should we regulate artificial intelligence?

abstract: Using artificial intelligence (AI) technology to replace human decision-making will inevitably create new risks whose consequences are unforeseeable. This naturally leads to calls for regulation, but I argue that it is too early to attempt a general system of AI regulation. Instead, we should work incrementally within the existing legal and regulatory schemes which allocate responsibility, and therefore liability, to persons. Where AI clearly creates risks which current law and regulation cannot deal with adequately, then new regulation will be needed. But in most cases, the current system can work effectively if the producers of AI technology can provide sufficient transparency in explaining how AI decisions are made. Transparency ex post can often be achieved through retrospective analysis of the technology's operations, and will be sufficient if the main goal is to compensate victims of incorrect decisions. Ex ante transparency is more challenging, and can limit the use of some AI technologies such as neural networks. It should only be demanded by regulation where the AI presents risks to fundamental rights, or where society needs reassuring that the technology can safely be used. Masterly inactivity in regulation is likely to achieve a better long-term solution than a rush to regulate in ignorance.This article is part of a discussion meeting issue 'The growing ubiquity of algorithms in society: implications, impacts and innovations'.