title: Learning Binary Residual Representations for Domain-specific Video Streaming

abstract: We study domain-specific video streaming. Specifically, we target a streaming setting where the videos to be streamed from a server to a client are all in the same domain and they have to be compressed to a small size for low-latency transmission. Several popular video streaming services, such as the video game streaming services of GeForce Now and Twitch, fall in this category. While conventional video compression standards such as H.264 are commonly used for this task, we hypothesize that one can leverage the property that the videos are all in the same domain to achieve better video quality. Based on this hypothesis, we propose a novel video compression pipeline. Specifically, we first apply H.264 to compress domain-specific videos. We then train a novel binary autoencoder to encode the leftover domain-specific residual information frame-by-frame into binary representations. These binary representations are then compressed and sent to the client together with the H.264 stream. In our experiments, we show that our pipeline yields consistent gains over standard H.264 compression across several benchmark datasets while using the same channel bandwidth.


title: Quantifying Spatial Heterogeneity in Urban Landscapes: Integrating Visual Interpretation and Object-Based Classification

abstract: Describing and quantifying the spatial heterogeneity of land cover in urban systems is crucial for developing an ecological understanding of cities. This paper presents a new approach to quantifying the fine-scale heterogeneity in urban landscapes that capitalizes on the strengths of two commonly used approaches—visual interpretation and object-based image analysis. This new approach integrates the ability of humans to detect pattern with an object-based image analysis that accurately and efficiently quantifies the components that give rise to that pattern. Patches that contain a mix of built and natural land cover features were first delineated through visual interpretation. These patches served as pre-defined boundaries for finer-scale segmentation and classification of within-patch land cover features which were classified using object-based image analysis. Patches were then classified based on the within-patch proportion cover of features. We applied this approach to the Gwynns Falls watershed in Baltimore, Maryland, USA. The object-based classification approach proved to be effective for classifying within-patch land cover features. The overall accuracy of the classification maps of 1999 and 2004 were 92.3% and 93.7%, respectively. This exercise demonstrates that by integrating visual interpretation with object-based classification, the fine-scale spatial heterogeneity in urban landscapes and land cover change can be described and quantified in a more efficient and ecologically meaningful way than either purely automated or visual methods alone. This new approach provides a tool that allows us to quantify the structure of the urban landscape including both built and non-built components that will better accommodate ecological research linking system structure to ecological processes.


title: Topological analysis of spin-torque driven magnetic skyrmion formation

abstract: We performed micromagnetic simulations to investigate the exact underlying topological mechanism of skyrmion generation by blowing magnetic chiral stripe domains with a spin current generated by the spin Hall effect. This analysis of the topological dynamics reveals that the generation of a magnetic skyrmion is typically mediated by the creation and annihilation of an antiskyrmion. The threshold current density needed to generate persistent skyrmions depends on the damping constant, perpendicular magnetic field, and size of the sample.


title: Reflections on craft: probing the creative process of everyday knitters

abstract: Crafters today blend age-old techniques such as weaving and pottery with new information and communication technologies such as podcasts, online instructions, and blogs. This intersection of tradition and modernity provides an interesting site for understanding the adoption of new technology. We present a qualitative study of seven knitters introduced to Spyn - a system that enables the association of digitally recorded messages with physical locations on knit fabric. We gave knitters Spyn in order to elicit their reflections on their craft practices and learn from their interactions with material, people, and technology. While creating artifacts for friends and loved ones, knitters expanded the creative and communicative potential of their craftwork: knitters envisioned travel journals in knitted potholders and sung lullabies in knitted hats. We describe how these unusual craft activities provide a useful lens onto contemporary technological appropriation.


title: Texture edge-guided depth recovery for structured light-based depth sensor

abstract: The emergence of depth sensor facilitates the real-time and low-cost depth capture. However, the quality of its depth map is still inadequate for further applications due to holes, noises and artifacts existing within its depth information. In this paper, we propose an iterative depth boundary refinement framework to recover Kinect depth map. We extract depth edges and detect the incorrect regions, and then re-fill the incorrect regions until the depth edges are consistent with color edges. In the incorrect region detection procedure, we propose a RGB-D data edge detection method inspired by the recently developed deep learning. In the depth in-painting procedure, we propose a priority-determined fill order in which the high confidence pixels and strong edges are assigned to high priority. The actual depth values are computed by using a weighted cost filter, in which color, spatial similarity measures and Gaussian error model are considered. Experimental results demonstrate that the proposed method provides sharp and clear edges for the Kinect depth, and depth edges are aligned with the color edges.


title: Or how to understand continuity in technological development

abstract: The calls for change are all around us. Modern society would need to dramatically shift to other production methods, other approaches to nature and environment, and new, improved regulations for economic activities. This paper does not challenge this need for more elegant production methods nor does it aim to criticize the importance of sustainability. What I will do, however, is question the concept of changing itself. More often than not, a need for change is accompanied with a claim of a paradigm shift actually already happening. I will argue that it is theoretically impossible to know whether one experiences a shift or not, as one would need to see in the future to actually proof the shift. With several examples from historical analysis, on management of river systems in the Netherlands and irrigation development in the Netherlands East Indies, this argument is sustained. It will become clear that pointing to actual shifts is not that straightforward, even when we know the outcome of the shift. There are clear changes to be detected in history, but when was the shift? Analysis of shifts will prove to be at least partly a function of the perceptions of the observer, the amount of time between the shift under study and the observer, and the length of the time frame applied in the analysis. Now being able to locate actual shifts may hamper, or at least leads to questions, about management of shifts in modern times.


Sensorless Field Oriented Control (FOC) for a Permanent Magnet Synchronous Motor (PMSM) Using a PLL Estimator and Field Weakening (FW)


title: Weakly Supervised Learning of Semantic Parsers for Mapping Instructions to Actions

abstract: The context in which language is used provides a strong signal for learning to recover its meaning. In this paper, we show it can be used within a grounded CCG semantic parsing approach that learns a joint model of meaning and context for interpreting and executing natural language instructions, using various types of weak supervision. The joint nature provides crucial benefits by allowing situated cues, such as the set of visible objects, to directly influence learning. It also enables algorithms that learn while executing instructions, for example by trying to replicate human actions. Experiments on a benchmark navigational dataset demonstrate strong performance under differing forms of supervision, including correctly executing 60% more instruction sets relative to the previous state of the art.


title: Efficient Decentralized Deep Learning by Dynamic Model Averaging

abstract: We propose an efficient protocol for decentralized training of deep neural networks from distributed data sources. The proposed protocol allows to handle different phases of model training equally well and to quickly adapt to concept drifts. This leads to a reduction of communication by an order of magnitude compared to periodically communicating state-of-the-art approaches. Moreover, we derive a communication bound that scales well with the hardness of the serialized learning problem. The reduction in communication comes at almost no cost, as the predictive performance remains virtually unchanged. Indeed, the proposed protocol retains loss bounds of periodically averaging schemes. An extensive empirical evaluation validates major improvement of the trade-off between model performance and communication which could be beneficial for numerous decentralized learning applications, such as autonomous driving, or voice recognition and image classification on mobile phones.


title: Boys’ Talk: Hindi, Moustaches and Masculinity in New Delhi

abstract: Since the 1990s, scholars working within the area of language and gender have increasingly considered the ways in which masculinity informs and structures everyday language practice. While the paradigms that frame scholarship on language and masculinity differ, with early studies focusing on differences between men’s talk and women’s talk (e.g., Johnstone 1990; Tannen 1990) and later studies seeking to explain how men’s talk is produced performatively through appeal to ideologies of gendered language (e.g., Cameron 1997), the research has left us with a trove of data regarding linguistic possibilities for the enactment of masculinity. Whether explicating the homophobic story-telling strategies of male friends in Britain (Coates 2007), the use of sentence-final particles by white-collar Japanese men (SturtzSreetharan 2006), or employments of the address term dude among American college-aged men (Kiesling 2004), linguistic research on masculinity has decisively demonstrated that ‘maleness’ is as much gained as it is given, with speakers reproducing, and often exploiting, ideological links between form and meaning in the production of a gendered subjectivity. The burgeoning body of literature on women’s appropriation of purportedly masculine forms of discourse has offered a kind of proof for this theoretical position, establishing the floating and hence endlessly flexible nature of the linguistic sign (e.g., Queen 2005; Matsumoto 2002; Tetreault 2002; McElhinny 1995).


title: AP16-OL7: A multilingual database for oriental languages and a language recognition baseline

abstract: We present the AP16-OL7 database which was released as the training and test data for the oriental language recognition (OLR) challenge on APSIPA 2016. Based on the database, a baseline system was constructed on the basis of the i-vector model. We report the baseline results evaluated in various metrics defined by the AP16-OLR evaluation plan and demonstrate that AP16-OL7 is a reasonable data resource for multilingual research.


title: Information retrieval in falktales using natural language processing

abstract: Our aim is to extract information about literary characters in unstructured texts. We employ natural language processing and reasoning on domain ontologies. The first task is to identify the main characters and the parts of the story where these characters are described or act. We illustrate the system in a scenario in the folktale domain. The system relies on a folktale ontology that we have developed based Propp's model for folktales morphology.


title: Deep Learning Model to Estimate Air Pollution Using M-BP to Fill in Missing Proxy Urban Data

abstract: Air quality has deteriorated rapidly in Hong Kong and China in the past two decades, with NO2 and PM2.5 levels frequently exceeding WHO safety guidelines. While poor air quality has clear public health impacts, there are very limited air quality monitoring (AQM) stations, severely constraining evidence-based air quality decision-making, leading to severe criticisms about the utility of the current official Air Quality Health Index to the public. Since air pollution is highly location-dependent, a city-wide deployment of traditional, highly sophisticated air quality monitors would be prohibitively expensive. In this paper, we propose a deep learning model to estimate air pollution throughout the city, utilizing the readily available urban data as proxy data. As with many big data driven approaches, the proxy data may be sparse/missing. We propose the M-BP algorithm to recover/fill in such missing data. Our results show that the proposed model gives better estimates compared with existing big data approaches.


title: Medical image semantic segmentation based on deep learning

abstract: The image semantic segmentation has been extensively studying. The modern methods rely on the deep convolutional neural networks, which can be trained to address this problem. A few years ago networks require the huge dataset to be trained. However, the recent advances in deep learning allow training networks on the small datasets, which is a critical issue for medical images, since the hospitals and research organizations usually do not provide the huge amount of data. In this paper, we address medical image semantic segmentation problem by applying the modern CNN model. Moreover, the recent achievements in deep learning allow processing the whole image per time by applying concepts of the fully convolutional neural network. Our qualitative and quantitate experiment results demonstrated that modern CNN can successfully tackle the medical image semantic segmentation problem.


title: Improving performance of recurrent neural network with relu nonlinearity

abstract: In recent years significant progress has been made in successfully training recurrent neural networks (RNNs) on sequence learning problems involving long range temporal dependencies. The progress has been made on three fronts: (a) Algorithmic improvements involving sophisticated optimization techniques, (b) network design involving complex hidden layer nodes and specialized recurrent layer connections and (c) weight initialization methods. In this paper, we focus on recently proposed weight initialization with identity matrix for the recurrent weights in a RNN. This initialization is specifically proposed for hidden nodes with Rectified Linear Unit (ReLU) non linearity. We offer a simple dynamical systems perspective on weight initialization process, which allows us to propose a modified weight initialization strategy. We show that this initialization technique leads to successfully training RNNs composed of ReLUs. We demonstrate that our proposal produces comparable or better solution for three toy problems involving long range temporal structure: the addition problem, the multiplication problem and the MNIST classification problem using sequence of pixels. In addition, we present results for a benchmark action recognition problem.


title: Proposing a comprehensive framework for analysis and engineering of mobile payment business models

abstract: Although in the era of third generation (3G) mobile networks technical hurdles are minor, the continuing failure of mobile payments (m-payments) withstands the endorsement by customers and service providers. A major reason is the uncommonly high interdependency of technical, human and market factors which have to be regarded and orchestrated cohesively to solve the problem. In this paper, we apply Business Model Ontology in order to develop an m-payment business model framework based on the results of a precedent multi case study analysis of 27 m-payment procedures. The framework is depicted with a system of morphological boxes and the interrelations between the associated characteristics. Representing any m-payment business model along with its market setting and influencing decisions as instantiations, the resulting framework enables researchers and practitioners for comprehensive analysis of existing and future models and provides a helpful tool for m-payment business model engineering.


title: Collaborating through Social Media to Create Health Awareness

abstract: This paper examines the role that individuals and organizations can play in creating awareness through the use of social media. In the healthcare context, creating cancer awareness is key in early detection of cancer as well as cancer prevention altogether. Using a multiple case study analysis, we are able to understand how collaboration through social media is used to promote such awareness. We find that social media is used to create an online community that drives the creation of cancer awareness in many different ways. This research has implications for organizations looking to use social media to promote awareness as it explains the building process and role of social media grassroots communities.


title: Shoulder actuation mechanisms for arm rehabilitation exoskeletons

abstract: Rehabilitation robots start to become an important tool in stroke rehabilitation. Compared to manual arm training, robot-supported training can be more intensive, of longer duration, repetitive and task-oriented. Therefore, these devices have the potential to improve the rehabilitation process in stroke patients. While in the past, most groups have been working with endeffector-based robots, exoskeleton robots become more and more important, mainly because they offer a better guidance of the single human joints, especially during movements with large ranges. Regarding the upper extremities, the shoulder is the most complex human joint and its actuation is, therefore, challenging. This paper deals with shoulder actuation principles for exoskeleton robots. First, a quantitative analysis of the human shoulder movement is presented. Based on that analysis two shoulder actuation principles that provide motion of the center of the glenohumeral joint are presented and evaluated.


title: Organization and nucleotide sequence of the human KIT (mast/stem cell growth factor receptor) proto-oncogene.

abstract: We have cloned and sequenced the human KIT proto-oncogene, which contains 21 exons and spans more than 34 kb of DNA on chromosome segment 4q12. We also establish physical linkage between the KIT gene and the related PDGFRA gene. The organization of the KIT gene is virtually identical to that of the homologous FMS gene, located on chromosome 5. Together, these data suggest that the KIT and PDGFRA genes on chromosome 4 and the FMS and PDGFRB genes on chromosome 5 arose by duplication of a common ancestral gene, followed by duplication of a chromosome.


title: A Quantitative Comparison of Calibration Methods for RGB-D Sensors Using Different Technologies

abstract: RGB-D (Red Green Blue and Depth) sensors are devices that can provide color and depth information from a scene at the same time. Recently, they have been widely used in many solutions due to their commercial growth from the entertainment market to many diverse areas (e.g., robotics, CAD, etc.). In the research community, these devices have had good uptake due to their acceptable levelofaccuracyformanyapplicationsandtheirlowcost,butinsomecases,theyworkatthelimitof their sensitivity, near to the minimum feature size that can be perceived. For this reason, calibration processes are critical in order to increase their accuracy and enable them to meet the requirements of such kinds of applications. To the best of our knowledge, there is not a comparative study of calibration algorithms evaluating its results in multiple RGB-D sensors. Speciﬁcally, in this paper, a comparison of the three most used calibration methods have been applied to three different RGB-D sensors based on structured light and time-of-ﬂight. The comparison of methods has been carried out by a set of experiments to evaluate the accuracy of depth measurements. Additionally, an object reconstruction application has been used as example of an application for which the sensor works at the limit of its sensitivity. The obtained results of reconstruction have been evaluated through visual inspection and quantitative measurements.


title: Text-dependent speaker verification: Classifiers, databases and RSR2015

abstract: Abstract The RSR2015 database, designed to evaluate text-dependent speaker verification systems under different durations and lexical constraints has been collected and released by the Human Language Technology (HLT) department at Institute for Infocomm Research (I2R) in Singapore. English speakers were recorded with a balanced diversity of accents commonly found in Singapore. More than 151 h of speech data were recorded using mobile devices. The pool of speakers consists of 300 participants (143 female and 157 male speakers) between 17 and 42 years old making the RSR2015 database one of the largest publicly available database targeted for text-dependent speaker verification. We provide evaluation protocol for each of the three parts of the database, together with the results of two speaker verification system: the HiLAM system, based on a three layer acoustic architecture, and an i-vector/PLDA system. We thus provide a reference evaluation scheme and a reference performance on RSR2015 database to the research community. The HiLAM outperforms the state-of-the-art i-vector system in most of the scenarios.


title: A Broadband Compact Microstrip Rat-Race Hybrid Using a Novel CPW Inverter

abstract: A new compact microstrip rat-race hybrid with an octave bandwidth employing a novel frequency-independent coplanar waveguide (CPW) phase inverter is reported in this paper. The 270deg branch of a conventional rat-race is replaced by a -90deg branch, which is realized by a 90deg microstrip line and the CPW phase inverter. A new microstrip-to-CPW transition is introduced for which a lumped-element model is devised to facilitate parameter optimization. The designed transition has an insertion loss less than 0.33 dB across the designed frequency band from 1.5 to 3.5 GHz. The footprint of the proposed design is reduced by 75% and shows almost 60% and 80% enhancements in the 0.5-dB mismatch bandwidth of amplitude and 10deg mismatch bandwidth of phase, respectively, when compared with the conventional implementation. The proposed hybrid can be fabricated using a conventional printed-circuit and plated thru-hole technologies


title: Word sense disambiguation of WordNet glosses

abstract: This paper presents a suite of methods and results for the semantic disambiguation of WordNet glosses. WordNet is a resource widely used in natural language processing and artificial intelligence. Intended and designed as a lexical database, WordNet exhibits some deficiencies when used as a knowledge base. By semantically disambiguating the words in the glosses, we add pointers from each word to its concept or synset, and this increases the connectivity between the WordNet concepts by approximately an order of magnitude. We show how lexical chains and other applications can be built on this richly connected WordNet. The semantic disambiguation of the WordNet glosses is performed using automatic methods based on a set of heuristics. The precision of the semantic annotation is improved by using voting between the disambiguation system described here and another WSD system. The entire WordNet 2.0 has been disambiguated with an overall precision of 86% and is available at http://xwn.hlt.utdallas.edu.


DeepFacade: A Deep Learning Approach to Facade Parsing


title: Current and Future Perspectives on the Development, Evaluation, and Application ofin SilicoApproaches for Predicting Toxicity

abstract: Exploiting non-testing approaches to predict toxicity early in the drug discovery development cycle is a helpful component in minimizing expensive drug failures due to toxicity being identified in late development or even during clinical trials. Changes in regulations in the industrial chemicals and cosmetics sectors in recent years have prompted a significant number of advances in the development, application, and assessment of non-testing approaches, such as (Q)SARs. Many efforts have also been undertaken to establish guiding principles for performing read-across within category and analogue approaches. This review offers a perspective, as taken from these sectors, of the current status of non-testing approaches, their evolution in light of the advances in high-throughput approaches and constructs such as adverse outcome pathways, and their potential relevance for drug discovery. It also proposes a workflow for how non-testing approaches could be practically integrated within testing and assessment stra...


title: WiFinger: talk to your smart devices with finger-grained gesture

abstract: In recent literatures, WiFi signals have been widely used to "sense" people's locations and activities. Researchers have exploited the characteristics of wireless signals to "hear" people's talk and "see" keystrokes by human users. Inspired by the excellent work of relevant scholars, we turn to explore the field of human-computer interaction using finger-grained gestures under WiFi environment. In this paper, we present Wi-Finger - the first solution using ubiquitous wireless signals to achieve number text input in WiFi devices. We implement a prototype of WiFinger on a commercial Wi-Fi infrastructure. Our scheme is based on the key intuition that while performing a certain gesture, the fingers of a user move in a unique formation and direction and thus generate a unique pattern in the time series of Channel State Information (CSI) values. WiFinger is deigned to recognize a set of finger-grained gestures, which are further used to realize continuous text input in off-the-shelf WiFi devices. As the results show, WiFinger achieves up to 90.4% average classification accuracy for recognizing 9 digits finger-grained gestures from American Sign Language (ASL), and its average accuracy for single individual number text input in desktop reaches 82.67% within 90 digits.


title: A Low-Profile Omnidirectional Circularly Polarized Antenna Using Planar Sector-Shaped Endfire Elements

abstract: A single-layer circularly polarized (CP) antenna with omnidirectional radiation in the azimuth plane has been designed and evaluated. The concept is the combination of multiple identical endfire CP antenna elements in a circular array configuration. The design of each element is based on the superposition of two closely spaced complementary current sources, which are, respectively, realized by a sectorial parallel-plate cavity and arc-shaped strip arms. A meandered double-sided parallel strip line is introduced to adjust the phase relationship between two orthogonal linear polarized components. All antenna elements can be simultaneously excited by adopting a single coaxial probe in the center of the overall structure. Take 5.8-GHz Industrial Scientific Medical (ISM) band applications (5.725–5.875 GHz), for example, a prototype with a diameter of $0.62\lambda _{0}$ and a low profile of $0.029\lambda _{0}$ was fabricated. Experimental results show that the designed antenna has a usable bandwidth from 5.68 to 5.91 GHz (the common overlapped bandwidth limited by the −10 dB $\vert S_{11}\vert $ and the 3-dB axial ratio). Within the operation band, good omnidirectivity and a wide coverage can be obtained.


title: Using Canny's criteria to derive a recursively implemented optimal edge detector

abstract: A highly efficient recursive algorithm for edge detection is presented. Using Canny's design [1], we show that a solution to his precise formulation of detection and localization for an infinite extent filter leads to an optimal operator in one dimension, which can be efficiently implemented by two recursive filters moving in opposite directions. In addition to the noise truncature immunity which results, the recursive nature of the filtering operations leads, with sequential machines, to a substantial saving in computational effort (five multiplications and five additions for one pixel, independent of the size of the neighborhood). The extension to the two-dimensional case is considered and the resulting filtering structures are implemented as two-dimensional recursive filters. Hence, the filter size can be varied by simply changing the value of one parameter without affecting the time execution of the algorithm. Performance measures of this new edge detector are given and compared to Canny's filters. Various experimental results are shown.


title: Recognizing facial expression: machine learning and application to spontaneous behavior

abstract: We present a systematic comparison of machine learning methods applied to the problem of fully automatic recognition of facial expressions. We report results on a series of experiments comparing recognition engines, including AdaBoost, support vector machines, linear discriminant analysis. We also explored feature selection techniques, including the use of AdaBoost for feature selection prior to classification by SVM or LDA. Best results were obtained by selecting a subset of Gabor filters using AdaBoost followed by classification with support vector machines. The system operates in real-time, and obtained 93% correct generalization to novel subjects for a 7-way forced choice on the Cohn-Kanade expression dataset. The outputs of the classifiers change smoothly as a function of time and thus can be used to measure facial expression dynamics. We applied the system to to fully automated recognition of facial actions (FACS). The present system classifies 17 action units, whether they occur singly or in combination with other actions, with a mean accuracy of 94.8%. We present preliminary results for applying this system to spontaneous facial expressions.


Spatial Big Data : Platforms , Analytics , and Science


title: An Apollonian fractal MIMO antenna for WLAN application

abstract: A MIMO antenna of size 40mm × 40mm × 1.6mm is proposed for WLAN applications. Antenna consists of four mushroom shaped Apollonian fractal planar monopoles having micro strip feed lines with edge feeding. It uses defective ground structure (DGS) to achieve good isolation. To achieve more isolation, the antenna elements are placed orthogonal to each other. Further, isolation can be increased using parasitic elements between the elements of antenna. Simulation is done to study reflection coefficient as well as coupling between input ports, directivity, peak gain, efficiency, impedance and VSWR. Results show that MIMO antenna has a bandwidth of 1.9GHZ ranging from 5 to 6.9 GHz, and mutual coupling of less than −20dB.


title: An Algorithm to Find Overlapping Community Structure in Networks

abstract: Recent years have seen the development of many graph clustering algorithms, which can identify community structure in networks. The vast majority of these only find disjoint communities, but in many real-world networks communities overlap to some extent. We present a new algorithm for discovering overlapping communities in networks, by extending Girvan and Newman's well-known algorithm based on the betweennesscentrality measure. Like the original algorithm, ours performs hierarchical clustering -- partitioning a network into any desired number of clusters -- but allows them to overlap. Experiments confirm good performance on randomly generated networks based on a known overlapping community structure, and interesting results have also been obtained on a range of real-world networks.


Consultants’ Use of Telepractice: Practitioner Survey, Issues, and Resources


title: Simulation of dynamic traffic control system based on wireless sensor network

abstract: The use of wireless sensor network in the smart traffic control systems is very beneficial and starting to be very promising in the design and implementation for such systems. It will help in saving people time and adapt the intersections traffic lights to the traffic loads from each direction. In this paper we present an intelligent traffic signals control system based on a wireless sensor network (WSN). It uses the vehicle queue length during red cycle to perform better control in the next green cycle. The main objective is to minimize the average waiting time that will reduce the queues length and do better traffic management based on the arrivals in each direction. The system also includes an approach to alert the people about the red light crossing to minimize the possibility of accidents due to red light crossing violations. The system was simulated and results are shown in the end of this paper.


title: Exploiting AIS Data for Intelligent Maritime Navigation: A Comprehensive Survey From Data to Methodology

abstract: The Automatic Identification System (AIS) tracks vessel movement by means of electronic exchange of navigation data between vessels, with onboard transceiver, terrestrial and/or satellite base stations. The gathered data contains a wealth of information useful for maritime safety, security and efficiency. This paper surveys AIS data sources and relevant aspects of navigation in which such data is or could be exploited for safety of seafaring, namely traffic anomaly detection, route estimation, collision prediction and path planning.


title: Epileptic seizure detection by analyzing EEG signals using different transformation techniques

abstract: Abstract Feature extraction and classification are still challenging tasks to detect ictal (i.e., seizure period) and interictal (i.e., period between seizures) EEG signals for the treatment and precaution of the epileptic seizure patient due to different stimuli and brain locations. Existing seizure and non-seizure feature extraction and classification techniques are not good enough for the classification of ictal and interictal EEG signals considering for their non-abruptness phenomena, inconsistency in different brain locations, type (general/partial) of seizures, and hospital settings. In this paper we present generic seizure detection approaches for feature extraction of ictal and interictal signals using various established transformations and decompositions. We extract a number of statistical features using novel ways from high frequency coefficients of the transformed/decomposed signals. The least square support vector machine is applied on the features for classifications. Results demonstrate that the proposed methods outperform the existing state-of-the-art methods in terms of classification accuracy, sensitivity, and specificity with greater consistence for the large size benchmark dataset in different brain locations.


title: Channel modeling for millimeter wave MIMO

abstract: The large amounts of bandwidth available in the millimeter (mm) wave band enable multiGigabit wireless networks with applications ranging from indoor multimedia networking to outdoor backhaul for picocellular networks. Carrier wavelengths in this band are an order of magnitude smaller than those for existing cellular and WiFi systems, resulting in a drastically different propagation geometry. Omnidirectional transmission is essentially infeasible because of the increased propagation loss at smaller wavelengths; on the other hand, highly directive transmission and reception with electronically steerable beams can be achieved using compact antenna arrays. Thus, in contrast to the rich scattering environment at lower carrier frequencies, a small number of paths are dominant for directional mm wave links. The small wavelength also implies that spatial multiplexing gains can be obtained even in Line of Sight (LoS), or more generally, sparse scattering, environments with antennas with moderate separation. In this paper, we examine the consequences of these observations for two scenarios. The first is a lamppost-based outdoor deployment, where we model fading due to ground and wall reflections, and examine MIMO techniques for combating fading. The second is for spatial multiplexing for an indoor link, where we model the number of eigenmodes as a function of form factor, and examine the effect of blockage.


title: An Anti-SMS-Spam Using CAPTCHA

abstract: Today sending spams has turned to be a major problem in the Internet. In last 20 years, the Internet and mobile communication growth in parallel. So the spams are also born on the mobile phones as the form of SMS (short message service) spams. In this paper a new method is proposed for filtering SMS spams using CAPTCHA (completely automatic public turing test to tell computer and human apart) systems. CAPTCHA systems are used to distinguish between human users and computer programs automatically.In this method, the picture of an object is sent as an SMS picture message. Also name of that object and name of three other objects are written in the SMS as a multiple-choose question. The user should select the name of that object from a list of four object names and send the number back in reply as an SMS text message. If the SMS sender can pass the CAPTCHA test, it will be identified that SMS is legitimate.


title: Direct vehicle-to-vehicle communication with infrastructure assistance in 5G network

abstract: Compared with today’s 4G wireless communication network, the next generation of wireless system should be able to provide a wider range of services with different QoS requirements. One emerging new service is to exploit cooperative driving to actively avoid accidents and improve traffic efficiency. A key challenge for cooperative driving is on vehicle-to-vehicle (V2V) communication which requires a high reliability and a low end-toend (E2E) latency. In order to meet these requirements, 5G should be evaluated by new key performance indicators (KPIs) rather than the conventional metric, as throughput in the legacy cellular networks. In this work, we exploit network controlled direct V2V communication for information exchange among vehicles. This communication process refers to packet transmission directly among vehicles without the involvement of network infrastructure in U-plane. In order to have a network architecture to enable direct V2V communication, the architecture of the 4G network is enhanced by deploying a new central entity with specific functionality for V2V communication. Moreover, a resource allocation scheme is also specifically designed to adapt to traffic model and service requirements of V2V communication. Last but not least, different technologies are considered and simulated in this work to improve the performance of direct V2V communication.


title: A privacy paradox: Social networking in the United States

abstract: Teenagers will freely give up personal information to join social networks on the Internet. Afterwards, they are surprised when their parents read their journals. Communities are outraged by the personal information posted by young people online and colleges keep track of student activities on and off campus. The posting of personal information by teens and students has consequences. This article will discuss the uproar over privacy issues in social networks by describing a privacy paradox; private versus public space; and, social networking privacy issues. It will finally discuss proposed privacy solutions and steps that can be taken to help resolve the privacy paradox.


title: 3. The Search to Be Human in Dangling Man

abstract: Bellow’s first novel, Dangling Man (1944), is about a man named Joseph who does not know how to integrate himself in American life without losing the spiritual value of his isolation from society. Influenced by the current milieu of the time of modernism and existentialism, Bellow presents an alienated hero who seeks to find meaning in a hostile environment. 1 Written as a diary, the novel’s principal audience is himself as he details the life of a young man who believes that spiritual enlightenment can be obtained by isolating himself from society in order to study the thinkers of the Enlightenment. As the months pass, Joseph erupts into anti-social behavior, quarrels with his friends and relatives, and succumbs to outbursts of paranoia and violent behavior. At the end of the novel he admits that his experiment has been a failure and that his search for meaning cannot be conducted in this manner. Reduced to the same common physical, social, and historical denominator as everyone else, Joseph joins the Army to live a life of regular hours and regimentation. Initial reviews of the novel were positive and focused on the theme of Joseph’s alienation from society: a “dangling hero” who faces the disillusionment of his age. 2 Subsequent criticisms have explored Joseph representing an alternate set of values for the modern age with his attempts to subvert the idea of an objective reality subscribed by collective assent. 3 Other critics have looked at how the work is a continuation of the European form of the novel or how it is a metaphor of America recovering from the Great Depression and World War II. 4 However, I want to focus on a theme that Joseph Baim raises in his criticism where he argues that Dangling Man should be conceived as a struggle between reason and nihilism with neither succeeding when the protagonist has an ultimate breakthrough in intuitive experience. 5 Continuing this theme of Baim’s, I will argue that Joseph’s search for spiritual fulfillment, to be “human” in the present age, unfortunately fails because, although he finally understands the gratuitous nature of life, he remains trapped within his own rationality and ultimately cannot escape from it – a legacy from his studies of the Enlightenment. The theme of the novel is recorded in the two interviews that Joseph has with his alter ego, whom he calls Tu As Raison Aussi. In these sessions, Joseph denies it is impossible to be “human” in the present age, although the present is not as bad as it has been made out to be: “It’s too easy to abjure it or detest it. Too narrow. Too cowardly.” 6 However, the “ideal constructions” of the past, noble characters like “the Humanistic full man, the courtly lover, the knight, the ecclesiastic,” can no longer serve as useful archetypes to guide people like Joseph in the present age (102). Ideal constructions are a product of belief and are sustainable only with the conviction that it is part of an enduring reality. To lose such a belief is to ask “what of the gap between the ideal construction and the real world, the truth?” (102). No answer is provided to Joseph. The


The Painting Fool Sees! New Projects with the Automated Painter


title: Communication issues in parallel computing across ATM networks

abstract: To support distributed parallel computing across asynchronous transfer mode networks, the interplay between communications software and the network architecture is critical. In particular, we can improve communication performance significantly by efficiently designing collective communication operations, such as multicast and reduction. >


title: Particle filters for mobile robot localization

abstract: This chapter investigates the utility of particle filters in the context of mobile robotics. In particular, we report results of applying particle filters to the problem of mobile robot localization, which is the problem of estimating a robot’s pose relative to a map of its environment. The localization problem is a key one in mobile robotics, because it plays a fundamental role in various successful mobile robot systems; see e.g., (Cox and Wilfong 1990, Fukuda, Ito, Oota, Arai, Abe, Tanake and Tanaka 1993, Hinkel and Knieriemen 1988, Leonard, Durrant-Whyte and Cox 1992, Rencken 1993, Simmons, Goodwin, Haigh, Koenig and O’Sullivan 1997, Weis, Wetzler and von Puttkamer 1994) and various chapters in (Borenstein, Everett and Feng 1996) and (Kortenkamp, Bonasso and Murphy 1998). Occasionally, it has been referred to as “the most fundamental problem to providing a mobile robot with autonomous capabilities” (Cox 1991).


title: Privacy Preserving Keyword Searches on Remote Encrypted Data

abstract: We consider the following problem: a user $\mathcal{U}$ wants to store his files in an encrypted form on a remote file server $\mathcal{S}$. Later the user $\mathcal{U}$ wants to efficiently retrieve some of the encrypted files containing (or indexed by) specific keywords, keeping the keywords themselves secret and not jeopardizing the security of the remotely stored files. For example, a user may want to store old e-mail messages encrypted on a server managed by Yahoo or another large vendor, and later retrieve certain messages while travelling with a mobile device. ::: ::: In this paper, we offer solutions for this problem under well-defined security requirements. Our schemes are efficient in the sense that no public-key cryptosystem is involved. Indeed, our approach is independent of the encryption method chosen for the remote files. They are also incremental, in that $\mathcal{U}$ can submit new files which are secure against previous queries but still searchable against future queries.


title: A collapsed variational Bayesian inference algorithm for latent Dirichlet allocation

abstract: Latent Dirichlet allocation (LDA) is a Bayesian network that has recently gained much popularity in applications ranging from document modeling to computer vision. Due to the large scale nature of these applications, current inference procedures like variational Bayes and Gibbs sampling have been found lacking. In this paper we propose the collapsed variational Bayesian inference algorithm for LDA, and show that it is computationally efficient, easy to implement and significantly more accurate than standard variational Bayesian inference for LDA.


title: DrugBank: a comprehensive resource for in silico drug discovery and exploration

abstract: DrugBank is a unique bioinformatics/cheminformatics resource that combines detailed drug (i.e. chemical) data with comprehensive drug target (i.e. protein) information. The database contains .4100 drug entries including .800 FDA approved small molecule and biotech drugs as well as .3200 experimental drugs. Additionally, .14 000 protein or drug target sequences are linked to these drug entries. Each DrugCard entry contains .80 data fields with half of the information being devoted to drug/chemical data and the other half devoted to drug target or protein data. Many data fields are hyperlinked to other databases (KEGG, PubChem, ChEBI, PDB, Swiss-Prot and GenBank) and a variety of structure viewing applets. The database is fully searchable supporting extensive text, sequence, chemical structure and relational query searches. Potential applications of DrugBank include in silico drug target discovery, drug design, drug docking or screening, drug metabolism prediction, drug interaction prediction and general pharmaceutical education. DrugBank is available at http:// redpoll.pharmacy.ualberta.ca/drugbank/.


title: Silhouette Smoothing for Real-Time Rendering of Mesh Surfaces

abstract: Coarse piecewise linear approximation of surfaces causes the undesirable polygonal appearance of silhouettes. We present an efficient method for smoothing the silhouettes of coarse triangle meshes using efficient 3D curve reconstruction and simple local remeshing. It does not assume the availability of a fine mesh and generates only a moderate amount of additional data at runtime. Furthermore, polygonal feature edges are also smoothed in a unified framework. Our method is based on a novel interpolation scheme over silhouette triangles, and this ensures that smooth silhouettes are faithfully reconstructed and always change continuously with respect to the continuous movement of the viewpoint or objects. We speed up computation with GPU assistance to achieve real-time rendering of coarse meshes with the smoothed silhouettes. Experiments show that this method outperforms previous methods for silhouette smoothing.


title: DESIGN OF DUAL-FEED DUAL-POLARIZED MICROSTRIP ANTENNA WITH HIGH ISOLATION AND LOW CROSS POLARIZATION

abstract: A broadband dual-feed dual-polarized microstrip antenna with low cross polarization and high isolation is presented. The dual- orthogonal linearly polarized mode is excited by two difierent feed mechanisms from a single circular radiating patch. One of the two modes is excited by a pair of L-shaped probes with a 180 - phase difierences, and the other is excited by an H-shaped aperture. The proposed design has a very simple antenna structure with a wide input impedance bandwidth (23.25% for Port 1 and 35% for Port 2) and also its two input port isolation is found to be as low as i40dB. Measured results of the fabricated antenna prototypes are also carried out to verify the simulation analysis.


title: 3D Deformation Using Moving Least Squares

abstract: We present a 3d deformation method based on Moving Least Squares that extends the work by Schaefer et al. [Schaefer et al. 2006] to the 3d setting. The user controls the deformation by manipulating a set of point handles. Locally, the deformation takes the form of either a rigid transformation or optionally a similarity transformation, and tends to preserve local features. Our derivation of the closed-form solution is based on singular value decomposition, and is applicable to deformation in arbitrary dimensions, as opposed to the planar case in [Schaefer et al. 2006]. Our prototype implementation allows interactive deformation of meshes of over 100k vertices. For the application of 3d mesh deformation, we further introduce a weighting scheme that determines the influence of point handles on vertices based on approximate mesh geodesics. In practice, the new scheme gives much better deformation results for limbed character models, compared with simple Euclidean distance based weighting. The new weighting scheme can be of use to the traditional skinny based deformation technique as well.


title: Biometrics based attendance checking using Principal Component Analysis

abstract: In recent years, there have been very promising applications of biometric systems to improve access control systems and security of data recording. Of all the biometric systems available, fingerprint verification is the most dominant in commercial application due to its excellent performance and low cost. In this study, it is attempted to implement a fingerprint and face detection and recognition biometrics system for the professors' attendance management thus replacing the current manual system. Proposed system provides faculty face recognition using Viola-Jones Face Detection Method and Principal Component Analysis (PCA) integrated with fingerprint verification using Arduino. Test results improved attendance system accuracy and automate faculty attendance system.


title: Internet of Things and LoRa™ Low-Power Wide-Area Networks: A survey

abstract: Nowadays there is a lot of effort on the study, analysis and finding of new solutions related to high density sensor networks used as part of the IoT (Internet of Things) concept. LoRa (Long Range) is a modulation technique that enables the long-range transfer of information with a low transfer rate. This paper presents a review of the challenges and the obstacles of IoT concept with emphasis on the LoRa technology. A LoRaWAN network (Long Range Network Protocol) is of the Low Power Wide Area Network (LPWAN) type and encompasses battery powered devices that ensure bidirectional communication. The main contribution of the paper is the evaluation of the LoRa technology considering the requirements of IoT. After introduction in Section II the main obstacles of IoT development are discussed, section III addresses the challenges and entails the need for solutions to different problems in WSN research. Section IV presents the LoRaWAN communication protocol architecture requirements while in Section V the LoRa modulation performance is evaluated and discussed. In conclusion LoRa can be considered a good candidate in addressing the IoT challenges.


title: Formal Query Generation for Question Answering over Knowledge Bases

abstract: Question answering (QA) systems often consist of several components such as Named Entity Disambiguation (NED), Relation Extraction (RE), and Query Generation (QG). In this paper, we focus on the QG process of a QA pipeline on a large-scale Knowledge Base (KB), with noisy annotations and complex sentence structures. We therefore propose SQG, a SPARQL Query Generator with modular architecture, enabling easy integration with other components for the construction of a fully functional QA pipeline. SQG can be used on large open-domain KBs and handle noisy inputs by discovering a minimal subgraph based on uncertain inputs, that it receives from the NED and RE components. This ability allows SQG to consider a set of candidate entities/relations, as opposed to the most probable ones, which leads to a significant boost in the performance of the QG component. The captured subgraph covers multiple candidate walks, which correspond to SPARQL queries. To enhance the accuracy, we present a ranking model based on Tree-LSTM that takes into account the syntactical structure of the question and the tree representation of the candidate queries to find the one representing the correct intention behind the question. SQG outperforms the baseline systems and achieves a macro F1-measure of 75% on the LC-QuAD dataset.


title: Establishment of an in vitro model of the human placental barrier by placenta slice culture and ussing chamber.

abstract: Our purpose was to establish an in vitro model of the human placental barrier based on placenta slice culture and Ussing chamber. The villous morphology, beta-human chorionic gonadotropin (β-hCG), mRNA and efflux function of P-glycoprotein (P-gp), and the permeability of the fluorescent marker were confirmed. The results showed that syncytiotrophoblast cells with abundant endoplasmic reticulum and mitochondria were covered with a dense microvillus in the placenta slice. The β-hCG secretion levels in the Ussing chamber were 274.13 ± 13.52 mIU/mL at 5 h, significantly higher than that in the incubator 95.2 ± 13.14 mIU/mL, and β-hCG continued to secrete for 48 h. P-gp mRNA was expressed in the placenta slice. The Rho123 apparent permeability coefficient (Papp) value from maternal side to the fetal side was 26.34 ± 1.87 nm/s, but it was significantly increased, to 289.55 ± 6.02 nm/s after adding verapamil. The Rho123 efflux value was >2. The fluorescein Papp value was (3.42 ± 0.24) × 10(-3) nm/s. In contrast, the fluorescein isothiocyanate-dextran (FD70) Papp value was (3.93 ± 0.08) × 10(-5) nm/s. This indicates that the placenta slice in the Ussing chamber had the activity of a placenta, and can act as a valuable in vitro model of placental barrier.


title: Development of a therapeutic exercise robot for wrist and forearm rehabilitation

abstract: In this study, design steps and system concept of a 3 DOF upper limb rehabilitation robot which is called Physiotherabot/WF are introduced. Functional requirements of design, corresponding design parameters and system concept of the robot were examined in detail. Physiotherabot/WF can perform pronation-supination movement for forearm, abduction-adduction and flexion-extension movements for wrist. It can perform passive, active assisted, isotonic, isometric and isokinetic exercises. In order to control the robot, Hybrid Impedance Control (HIC) method is used. The HIC performance is shown by simulation results.


title: Automatic line matching and 3D reconstruction of buildings from multiple views

abstract: This paper describes two developments in the automatic reconstruction of buildings from aerial images. The first is an algorithm for automatically matching line segments over multiple images. The algorithm employs geometric constraints based on the multi-view geometry together with photometric constraints derived from the line neighbourhood, and achieves a performance of better than 95% correct matches over three views. The second development is a method for automatically computing a piecewise planar reconstruction based on the matched lines. The novelty here is that a planar facet hypothesis can be generated from a single 3D line, using an inter-image homography applied to the line neighbourhood. The algorithm has successfully generated near complete roof reconstructions from multiple images. This work has been carried out as part of the EC IMPACT project. A summary of the project is included.


title: Rebellion of the Cadres: The 1967 Implosion of the Chinese Party-State

abstract: AbstractAccounts of the tumultuous initial phase of the Cultural Revolution portray party-state cadres primarily as targets of a popular insurgency. Cadres in Party and government organs in fact were themselves in widespread rebellion against their superiors after October 1966, and rebel cadres were a major force in the national wave of power seizures that destroyed the civilian state in early 1967. The rebellion was a form of bureaucratic politics in a setting characterized by rapidly shifting signals and high uncertainty, in which the rebels’ motives were generated after the onset of the Cultural Revolution. Cadres played a central role in the destruction of the political institutions to which their vested interests were inextricably linked.


title: An effective grouping method for privacy-preserving bike sharing data publishing

abstract: Bike sharing programs are eco-friendly transportation systems that are widespread in smart city environments. In this paper, we study the problem of privacy-preserving bike sharing microdata publishing. Bike sharing systems collect visiting information along with user identity and make it public by removing the user identity. Even after excluding user identification, the published bike sharing dataset will not be protected against privacy disclosure risks. An adversary may arrange published datasets based on bike’s visiting information to breach a user’s privacy. In this paper, we propose a grouping based anonymization method to protect published bike sharing dataset from linking attacks. The proposed Grouping method ensures that the published bike sharing microdata will be protected from disclosure risks. Experimental results show that our approach can protect user privacy in the released datasets from disclosure risks and can keep more data utility compared with existing methods.


title: What studies of family home movies can teach us about autistic infants: A literature review

abstract: Abstract The current study reviewed all prior studies conducted on family home movies of infants who would be later diagnosed with autism spectrum disorder (ASD). Out of 41 original reports found since 1975, we retained 18 studies (317 films, maximum), sorted according to their methodological design using a quality grid. In the first 2 years of life, signs that differentiated children with ASD from children with developmental delays were as follows: less of a response to their name, less looking at others, lower eye contact quality and quantity, less positive facial expression and intersubjective behaviors (e.g., showing shared attention). Studies focusing on regression confirmed the clinical validity of the phenomena. We conclude that findings from home movies studies along with prospective studies have created the bases for identification of infants and toddlers at risk of developing ASD before the 18–24-month period, despite early diagnosis of autism remains a complex challenge.


title: Boosting and Maximum Likelihood for Exponential Models

abstract: We derive an equivalence between AdaBoost and the dual of a convex optimization problem, showing that the only difference between minimizing the exponential loss used by AdaBoost and maximum likelihood for exponential models is that the latter requires the model to be normalized to form a conditional probability distribution over labels. In addition to establishing a simple and easily understood connection between the two methods, this framework enables us to derive new regularization procedures for boosting that directly correspond to penalized maximum likelihood. Experiments on UCI datasets support our theoretical analysis and give additional insight into the relationship between boosting and logistic regression.


title: Writer identification using curvature-free features

abstract: Feature engineering takes a very important role in writer identification which has been widely studied in the literature. Previous works have shown that the joint feature distribution of two properties can improve the performance. The joint feature distribution makes feature relationships explicit instead of roping that a trained classifier picks up a non-linear relation present in the data. In this paper, we propose two novel and curvature-free features: run-lengths of local binary pattern (LBPruns) and cloud of line distribution (COLD) features for writer identification. The LBPruns is the joint distribution of the traditional run-length and local binary pattern (LBP) methods, which computes the run-lengths of local binary patterns on both binarized and gray scale images. The COLD feature is the joint distribution of the relation between orientation and length of line segments obtained from writing contours in handwritten documents. Our proposed LBPruns and COLD are textural-based curvature-free features and capture the line information of handwritten texts instead of the curvature information. The combination of the LBPruns and COLD features provides a significant improvement on the CERUG data set, handwritten documents on which contain a large number of irregular-curvature strokes. The results of proposed features evaluated on other two widely used data sets (Firemaker and IAM) demonstrate promising results.


Multi-Language Image Description with Neural Sequence Models


Search-based Neural Structured Learning for Sequential Question Answering


title: Latent Regression Forest: Structured Estimation of 3D Articulated Hand Posture

abstract: In this paper we present the Latent Regression Forest (LRF), a novel framework for real-time, 3D hand pose estimation from a single depth image. In contrast to prior forest-based methods, which take dense pixels as input, classify them independently and then estimate joint positions afterwards, our method can be considered as a structured coarse-to-fine search, starting from the centre of mass of a point cloud until locating all the skeletal joints. The searching process is guided by a learnt Latent Tree Model which reflects the hierarchical topology of the hand. Our main contributions can be summarised as follows: (i) Learning the topology of the hand in an unsupervised, data-driven manner. (ii) A new forest-based, discriminative framework for structured search in images, as well as an error regression step to avoid error accumulation. (iii) A new multi-view hand pose dataset containing 180K annotated images from 10 different subjects. Our experiments show that the LRF out-performs state-of-the-art methods in both accuracy and efficiency.


title: Predicting the Biological Effects of Mobile Phone Radiation Absorbed Energy Linked to the MRI-obtained Structure

abstract: The nature of an electromagnetic field is not the same outside and inside a biological subject. Numerical bioelectromagnetic simulation methods for penetrating electromagnetic fields facilitate the calculation of field components in biological entities. Calculating energy absorbed from known sources, such as mobile phones when placed near the head, is a prerequisite for studying the biological influence of an electromagnetic field. Such research requires approximate anatomical models which are used to calculate the field components and absorbed energy. In order to explore the biological effects in organs and tissues, it is necessary to establish a relationship between an analogous anatomical model and the real structure. We propose a new approach in exploring biological effects through combining two different techniques: 1) numerical electromagnetic simulation, which is used to calculate the field components in a similar anatomical model and 2) Magnetic Resonance Imaging (MRI), which is used to accurately locate sites with increased absorption. By overlapping images obtained by both methods, we can precisely locate the spots with maximum absorption effects. This way, we can detect the site where the most pronounced biological effects are to be expected. This novel approach successfully overcomes the standard limitations of working with analogous anatomical models.


title: Chronic lymphocytic leukaemia

abstract: Chronic lymphocytic leukaemia (CLL) is a malignancy of CD5+ B cells that is characterized by the accumulation of small, mature-appearing lymphocytes in the blood, marrow and lymphoid tissues. Signalling via surface immunoglobulin, which constitutes the major part of the B cell receptor, and several genetic alterations play a part in CLL pathogenesis, in addition to interactions between CLL cells and other cell types, such as stromal cells, T cells and nurse-like cells in the lymph nodes. The clinical progression of CLL is heterogeneous and ranges from patients who require treatment soon after diagnosis to others who do not require therapy for many years, if at all. Several factors, including the immunoglobulin heavy-chain variable region gene (IGHV) mutational status, genomic changes, patient age and the presence of comorbidities, should be considered when defining the optimal management strategies, which include chemotherapy, chemoimmunotherapy and/or drugs targeting B cell receptor signalling or inhibitors of apoptosis, such as BCL-2. Research on the biology of CLL has profoundly enhanced our ability to identify patients who are at higher risk for disease progression and our capacity to treat patients with drugs that selectively target distinctive phenotypic or physiological features of CLL. How these and other advances have shaped our current understanding and treatment of patients with CLL is the subject of this Primer. Research on the biology of chronic lymphocytic leukaemia (CLL) — a malignancy of CD5+ B cells — has profoundly enhanced the identification of patients who are at high risk of disease progression and the treatment of patients with drugs that target the distinctive features of CLL. This Primer highlights these advances, as well as the epidemiology, genetics and immunobiology of CLL.


title: Surgical anatomy of the midcheek: facial layers, spaces, and the midcheek segments.

abstract: This article describes the surgical anatomy of the midcheek. The article's key points: (1) Structurally, the midcheek is formed by the convergence of three components: the lid-cheek segment, the malar segment, and the nasolabial segment. (2) The midcheek skeleton provides the attachment for the muscles and ligaments of both the lower lid and the upper lip. (3) The midcheek contains proportionally more spaces and fewer ligaments than other parts of the face. (4) Each midcheek segment overlies a specific facial space. (5) The lid-cheek contour transition does not have a fixed relationship to the inferior orbital rim. (6) The youthful lid-cheek transition is above the orbital rim and the aged is below the rim. (7) Facial nerve branches course in predictable locations.


title: Connecting Devices to Cookies via Filtering, Feature Engineering, and Boosting

abstract: We present a supervised machine learning system capable of matching internet devices to web cookies through filtering, feature engineering, binary classification, and post processing. The system builds a reasonably sized training and testing data set through filtering and feature engineering. We build 415 features in total. Some of these features were engineered to be O(n) time, stand alone classifiers for this problem. Other features use various natural language processing (NLP) techniques. Meta features are created by ridge regression and Adaboost. Then binary classification through two different gradient boosting (XGBoost with logarithmic loss) models is performed. A post processing pipeline connects devices and cookies in a way that maximizes F_0.5 score. Our machine learning system obtained a private F_0.5 score of 0.849562 for a final rank of 12th/340 on the ICDM 2015: Drawbridge Cross-Device Connections challenge.


title: ARtalet: Tangible User Interface Based Immersive Augmented Reality Authoring Tool for Digilog Book

abstract: A Digilog Book is an augmented reality (AR) based next generation publication supporting both sentimental analog emotions and immersive digital contents to improve a user’s experience. This paper enhances the Digilog Book authoring tool, ARtalet. This is a tangible user interface based immersive AR authoring tool providing an intuitive non-programming based authoring methods using a 3D user interface in an AR environment. As novel authoring functions, we propose 3D object trajectory manipulation, real-time deformation, and audio/vibration feedback authoring functions to enhance a user’s experience and interest. The ARtalet can be applicable to other Digilog application authoring, including posters, pictures, newspapers, and sign boards*.


title: Notice of RetractionA practical distributed electronic voting system

abstract: In accordance with Chinese national state and the practical needs, a distributed electronic voting system CCS-RI favorable for the voters to vote and verify at the nearby place has been put forward by referring and learning from the foreign advanced and mature systematic plan. It using the multi-levels verification has increased the voting credibility; meanwhile, the arrangement of distributed voting/counting at the voting place has been beneficial for smooth implementation of the large-scale voting at the grass-roots level.


title: Towards Music Structural Segmentation across Genres: Features, Structural Hypotheses, and Annotation Principles

abstract: This article faces the problem of how different audio features and segmentation methods work with different music genres. A new annotated corpus of Chinese traditional Jingju music is presented. We incorporate this dataset with two existing music datasets from the literature in an integrated retrieval system to evaluate existing features, structural hypotheses, and segmentation algorithms outside a Western bias. A harmonic-percussive source separation technique is introduced to the feature extraction process and brings significant improvement to the segmentation. Results show that different features capture the structural patterns of different music genres in different ways. Novelty- or homogeneity-based segmentation algorithms and timbre features can surpass the investigated alternatives for the structure analysis of Jingju due to their lack of harmonic repetition patterns. Findings indicate that the design of audio features and segmentation algorithms as well as the consideration of contextual information related to the music corpora should be accounted dependently in an effective segmentation system.


title: Making Classification Competitive for Deep Metric Learning

abstract: Deep metric learning aims to learn a function mapping image pixels to embedding feature vectors that model the similarity between images. The majority of current approaches are non-parametric, learning the metric space directly through the supervision of similar (pairs) or relatively similar (triplets) sets of images. A difficult challenge for training these approaches is mining informative samples of images as the metric space is learned with only the local context present within a single mini-batch. Alternative approaches use parametric metric learning to eliminate the need for sampling through supervision of images to proxies. Although this simplifies optimization, such proxy-based approaches have lagged behind in performance. In this work, we demonstrate that a standard classification network can be transformed into a variant of proxy-based metric learning that is competitive against non-parametric approaches across a wide variety of image retrieval tasks. We address key challenges in proxy-based metric learning such as performance under extreme classification and describe techniques to stabilize and learn higher dimensional embeddings. We evaluate our approach on the CAR-196, CUB-200-2011, Stanford Online Product, and In-Shop datasets for image retrieval and clustering. Finally, we show that our softmax classification approach can learn high-dimensional binary embeddings that achieve new state-of-the-art performance on all datasets evaluated with a memory footprint that is the same or smaller than competing approaches.


title: PVRNet: Point-View Relation Neural Network for 3D Shape Recognition

abstract: Three-dimensional (3D) shape recognition has drawn much research attention in the field of computer vision. The advances of deep learning encourage various deep models for 3D feature representation. For point cloud and multi-view data, two popular 3D data modalities, different models are proposed with remarkable performance. However the relation between point cloud and views has been rarely investigated. In this paper, we introduce Point-View Relation Network (PVRNet), an effective network designed to well fuse the view features and the point cloud feature with a proposed relation score module. More specifically, based on the relation score module, the point-single-view fusion feature is first extracted by fusing the point cloud feature and each single view feature with point-singe-view relation, then the pointmulti- view fusion feature is extracted by fusing the point cloud feature and the features of different number of views with point-multi-view relation. Finally, the point-single-view fusion feature and point-multi-view fusion feature are further combined together to achieve a unified representation for a 3D shape. Our proposed PVRNet has been evaluated on ModelNet40 dataset for 3D shape classification and retrieval. Experimental results indicate our model can achieve significant performance improvement compared with the state-of-the-art models.


title: Scaffolding and Achievement in Problem-Based and Inquiry Learning : A Response to Kirschner , Sweller , and Clark ( 2006 )

abstract: Many innovative approaches to education such as problem-based learning (PBL) and inquiry learning (IL) situate learning in problem-solving or investigations of complex phenomena. Kirschner, Sweller, and Clark (2006) grouped these approaches together with unguided discovery learning. However, the problem with their line of argument is that IL and PBL approaches are highly scaffolded. In this article, we first demonstrate that Kirschner et al. have mistakenly conflated PBL and IL with discovery learning. We then present evidence demonstrating that PBL and IL are powerful and effective models of learning. Far from being contrary to many of the principles of guided learning that Kirschner et al. discussed, both PBL and IL employ scaffolding extensively thereby reducing the cognitive load and allowing students to learn in complex domains. Moreover, these approaches to learning address important goals of education that include content knowledge, epistemic practices, and soft skills such as collaboration and sel...


title: "What happens if..."Learning to Predict the Effect of Forces in Images

abstract: What happens if one pushes a cup sitting on a table toward the edge of the table? How about pushing a desk against a wall? In this paper, we study the problem of understanding the movements of objects as a result of applying external forces to them. For a given force vector applied to a specific location in an image, our goal is to predict long-term sequential movements caused by that force. Doing so entails reasoning about scene geometry, objects, their attributes, and the physical rules that govern the movements of objects. We design a deep neural network model that learns long-term sequential dependencies of object movements while taking into account the geometry and appearance of the scene by combining Convolutional and Recurrent Neural Networks. Training our model requires a large-scale dataset of object movements caused by external forces. To build a dataset of forces in scenes, we reconstructed all images in SUN RGB-D dataset in a physics simulator to estimate the physical movements of objects caused by external forces applied to them. Our Forces in Scenes (ForScene) dataset contains 65,000 object movements in 3D which represent a variety of external forces applied to different types of objects. Our experimental evaluations show that the challenging task of predicting long-term movements of objects as their reaction to external forces is possible from a single image. The code and dataset are available at: http://allenai.org/plato/forces.


title: Generation quiz with genetic algorithm based on bloom's taxonomy classification in serious game based virtual environments

abstract: Learning with seorius game based virtual environments provide immersive and motivation. One aspect of learning is evaluation. But evaluation provides anxiety on the learner, as question that is too difficult. In this paper will describe how to generate questions in evaluation(quiz) based on bloom taxonomy with composition 25:50:25 for the types of questions C1, C2, and C3. The question generation used genetic algorithms and implemented in the virtual learning environment (VLE) through SLOODLE for 5th grades math course. System testing used compare genetic algorithm (GA) with random method. The result is the question composition of the generation of GA better than the generation of random method.


On deterministic packet marking


title: ICtCp Versus Y'CbCr: Evaluation of ICtCp Color Space and an Adaptive Reshaper for HDR and WCG

abstract: New emerging technologies in the field of multimedia, such as high dynamic range and wide color gamut, challenge the performance of existing image and video data processing algorithms, especially in terms of visual data compression and its faithful representation. High dynamic range and wide color gamut offer to capture and to represent a scene with extended amount of details in both, luminance and chrominance, respectively. This paper presents and evaluates possible solutions to cope with such extended amount of visual information while delivering a faithful representation of visual information. More particularly, two approaches proposing novel color space, a constant intensity color difference signal representation called ICtCp, and novel compression optimization algorithm, reshaper, are described and subjectively evaluated. The results of the conducted experiments show competitive performance of two proposed approaches, ICtCp and reshaper, allowing 10% bitrate reduction while preserving the perceived quality.


title: Azithromycin Inhibits the Replication of Zika Virus

abstract: Background: The emergence of Zika virus (ZIKV) is associated to dramatic complications in fetuses and neonates. As there is no vaccine and no drug to prevent and treat ZIKV infections, there is an urgent need to have active drugs against ZIKV that can be used during pregnancy. Large screening strategies suggested that azithromycin (AZ) has an in vitro activity against ZIKV, we provide additional data supporting this hypothesis. ::: Methods: We tested the efficacy of AZ on ZIKV-infected Vero cells at a concentration that can be reached in vivo in amniotic fluid. We conducted two experiments with addition to infected cells of a single dose or multi doses of 50 mg/L of AZ, and analyzed ZIKV replication by immunofluorescence assay (IFA) and by measuring viral RNA loads at different times up to 96 h post-infection (hpi). ::: Results: Addition of a single dose of 50 mg/L of AZ prevented replication of ZIKV during 48 hpi; after 48 hpi, ZIKV replication was detected by IFA but viral RNA loads remained lower than in untreated infected cells. ZIKV replication was inhibited by addition of multi doses of 50 mg/L of AZ. ::: Conclusions: Our data confirm the in vitro activity of AZ against ZIKV. Since there will be no active specific drugs and vaccine available soon against ZIKV, AZ might be the first compound that could prevent and treat ZIKV infections, with the advantages of being an approved and safe drug usable during pregnancy.


Persistent , Protected and Cached : Building Blocks for Main Memory Data Stores


title: Fuzzy Interpolation Rational Bezier Curve

abstract: Recently, interpolation methods especially in curves design are widely used for modeling data points. In this paper a new interpolation method for modeling fuzzy data using fuzzy interpolation rational Bezier curves is introduced based on fuzzy set theory. This method is able to model uncertainty data with definition of fuzzy data via fuzzy number concept. The fuzzy interpolation method is modeled using fuzzy interpolation rational cubic Bezier curve (in further will be referred as FIRCBC). For fuzzy n-data cases, segments curve constructed in order to interpolate fuzzy data piece wisely. For the illustration as hypothetical example, FIRCBC has been applied in verification of offline handwriting signature where the signatures become fuzzy cases.


title: Automated hedcut illustration using isophotes

abstract: In this work, we present an automated system for creating hedcut illustrations, portraits rendered using small image feature aligned dots (stipples). We utilize edge detection and shading cues from the input photograph to direct stipple placement within the image. Both image edges and isophotes are extracted as a means of describing the image feature and shading information. Edge features and isophotes are then assigned different priorities, with isophotes being assigned the highest priority to enhance the depth perception within the hedcut portrait. Priority assignment dictates the stipple alignment and spacing. Finally, stipple size is based on the number of points and intensity and the gradient magnitude of the input image.


title: On- and Off-Topic Classification and Semantic Annotation of User-Generated Software Requirements

abstract: Users prefer natural language software requirements because of their usability and accessibility. When they describe their wishes for software development, they often provide off-topic information. We therefore present an automated approach for identifying and semantically annotating the on-topic parts of the given descriptions. It is designed to support requirement engineers in the requirement elicitation process on detecting and analyzing requirements in user-generated content. Since no lexical resources with domain-specific information about requirements are available, we created a corpus of requirements written in controlled language by instructed users and uncontrolled language by uninstructed users. We annotated these requirements regarding predicate-argument structures, conditions, priorities, motivations and semantic roles and used this information to train classifiers for information extraction purposes. The approach achieves an accuracy of 92% for the on- and off-topic classification task and an F1-measure of 72% for the semantic annotation.


title: Sparse Camera Network for Visual Surveillance -- A Comprehensive Survey

abstract: Technological advances in sensor manufacture, communication, and computing are stimulating the development of new applications that are transforming traditional vision systems into pervasive intelligent camera networks. The analysis of visual cues in multi-camera networks enables a wide range of applications, from smart home and ofﬁce automation to large area surveillance and trafﬁc surveillance. While dense camera networks - in which most cameras have large overlapping ﬁelds of view - are well ::: studied, we are mainly concerned with sparse camera networks. A sparse camera network undertakes large area surveillance using as few cameras as possible, and most cameras have non-overlapping ﬁelds of view with one another. The task is challenging due to the lack of knowledge about the topological structure of the network, variations in the appearance and motion of speciﬁc tracking targets in different views, and the difﬁculties of understanding composite events in the network. In this review paper, we present a comprehensive survey of recent research results to address the problems of intra-camera tracking, topological structure learning, target appearance modeling, and global activity understanding in sparse camera networks. A number of current open research issues are discussed.


title: Moving beyond the direct impact of using CRM systems on frontline employees' service performance: The mediating role of adaptive behaviour

abstract: Despite substantial investments in customer‐relationship‐management (CRM) systems, companies continue to experience pain rather than profit. Meanwhile, the concept of “adaptive behaviour” of frontline employees has received little attention in the literature related to CRM systems in which the frontline employees are the primary users. In this study, we propose that with the aid of CRM systems, individual employees are able to immediately access information about customers and service offerings, thus enabling their adaptive behaviours to provide personalized service to individual customers. Based on coping theory, we develop a CRM system‐driven adaptive behaviour model that explains how CRM systems facilitate individual employees' service performance by enabling adaptive behaviour during their service encounters. Multisourced data from a financial company in China largely support our proposed model, showing that employees' postadoption of CRM systems (routinization and infusion of use) enables interpersonal and offering adaptive behaviours, which in turn enhance employees' service performance. In addition, forming a postadoption behaviour of CRM systems relies on the frequent use. We discuss the theoretical and practical implications of adaptive behaviour in service encounters with the aid of CRM systems.


title: Development of a microcontroller based electrical impedance tomography system

abstract: Electrical Impedance Tomography (EIT) is one of the growing and optimistic imaging modality for clinical imaging with such advantages as: non-invasive, low cost, ease of use and practicality, this method has been used in many areas of science and research. This paper consists of designing and development of hardware for EIT system using microcontroller MSP430G2 and AVR ATMEGA 16. A 16-electrode phantom is developed and studied with an instrumentation for EIT. Using serial communication data applies to PC for image reconstruction using EIDORS.


title: Origin Tracking $$+$$+ Text Differencing $$=$$= Textual Model Differencing

abstract: In textual modeling, models are created through an intermediate parsing step which maps textual representations to abstract model structures. Therefore, the identify of elements is not stable across different versions of the same model. Existing model differencing algorithms, therefore, cannot be applied directly because they need to identify model elements across versions. In this paper we present Textual Model Diff tmdiff, a technique to support model differencing for textual languages. tmdiff requires origin tracking during text-to-model mapping to trace model elements back to the symbolic names that define them in the textual representation. Based on textual alignment of those names, tmdiff can then determine which elements are the same across revisions, and which are added or removed. As a result, tmdiff brings the benefits of model differencing to textual languages.


title: Effective Bug Triage Based on Historical Bug-Fix Information

abstract: For complex and popular software, project teams could receive a large number of bug reports. It is often tedious and costly to manually assign these bug reports to developers who have the expertise to fix the bugs. Many bug triage techniques have been proposed to automate this process. In this paper, we describe our study on applying conventional bug triage techniques to projects of different sizes. We find that the effectiveness of a bug triage technique largely depends on the size of a project team (measured in terms of the number of developers). The conventional bug triage methods become less effective when the number of developers increases. To further improve the effectiveness of bug triage for large projects, we propose a novel recommendation method called Bug Fixer, which recommends developers for a new bug report based on historical bug-fix information. Bug Fixer constructs a Developer-Component-Bug (DCB) network, which models the relationship between developers and source code components, as well as the relationship between the components and their associated bugs. A DCB network captures the knowledge of "who fixed what, where". For a new bug report, Bug Fixer uses a DCB network to recommend to triager a list of suitable developers who could fix this bug. We evaluate Bug Fixer on three large-scale open source projects and two smaller industrial projects. The experimental results show that the proposed method outperforms the existing methods for large projects and achieves comparable performance for small projects.


title: An Analysis of Machine- and Human-Analytics in Classification

abstract: In this work, we present a study that traces the technical and cognitive processes in two visual analytics applications to a common theoretic model of soft knowledge that may be added into a visual analytics process for constructing a decision-tree model. Both case studies involved the development of classification models based on the "bag of features" approach. Both compared a visual analytics approach using parallel coordinates with a machine-learning approach using information theory. Both found that the visual analytics approach had some advantages over the machine learning approach, especially when sparse datasets were used as the ground truth. We examine various possible factors that may have contributed to such advantages, and collect empirical evidence for supporting the observation and reasoning of these factors. We propose an information-theoretic model as a common theoretic basis to explain the phenomena exhibited in these two case studies. Together we provide interconnected empirical and theoretical evidence to support the usefulness of visual analytics.


title: A very low-power CMOS mixed-signal IC for implantable pacemaker applications

abstract: Low power consumption is crucial for medical implant devices. A single-chip, very-low-power interface IC used in implantable pacemaker systems is presented. It contains amplifiers, filters, ADCs, battery management system, voltage multipliers, high voltage pulse generators, programmable logic and timing control. A few circuit techniques are proposed to achieve nanopower circuit operations within submicron CMOS process. Subthreshold transistor designs and switched-capacitor circuits are widely used. The 200 k transistor IC occupies 49 mm/sup 2/, is fabricated in a 0.5-/spl mu/m two-poly three-metal multi-V/sub t/ process, and consumes 8 /spl mu/W.


title: The fifth dialog state tracking challenge

abstract: Dialog state tracking - the process of updating the dialog state after each interaction with the user - is a key component of most dialog systems. Following a similar scheme to the fourth dialog state tracking challenge, this edition again focused on human-human dialogs, but introduced the task of cross-lingual adaptation of trackers. The challenge received a total of 32 entries from 9 research groups. In addition, several pilot track evaluations were also proposed receiving a total of 16 entries from 4 groups. In both cases, the results show that most of the groups were able to outperform the provided baselines for each task.


Deep HyperNEAT : Evolving the Size and Depth of the Substrate Evolutionary


title: Max-Violation Perceptron and Forced Decoding for Scalable MT Training

abstract: While large-scale discriminative training has triumphed in many NLP problems, its definite success on machine translation has been largely elusive. Most recent efforts along this line are not scalable (training on the small dev set with features from top 100 most frequent words) and overly complicated. We instead present a very simple yet theoretically motivated approach by extending the recent framework of “violation-fixing perceptron”, using forced decoding to compute the target derivations. Extensive phrase-based translation experiments on both Chinese-to-English and Spanish-to-English tasks show substantial gains in BLEU by up to +2.3/+2.0 on dev/test over MERT, thanks to 20M+ sparse features. This is the first successful effort of large-scale online discriminative training for MT.


title: Cloud-Computing in Banking Influential Factors, Benefits and Risks from a Decision Maker's Perspective

abstract: In 2008 Gartner Group listed "Cloud-Computing (CC)" in its Hype-Cycle (Gartner, 2008). Since these days, enterprises in different industries discuss the utilization of cloud-computing for their own benefit. The banking industry traditionally is heavily dependent on information technology. Therefore, it can be assumed that cloudcomputing could be of particular interesting for banks. This paper investigates the use of cloud-computing in German banks and the associated benefits and risks as senior management perceives them. Grounded in the TOE-Framework ten expert interviews with senior decision makers German banks have been conducted to evaluate the decision criteria pro and con cloud computing. Several factors influencing the cloud-computing decision have been detected, amongst them the technology supporting infrastructure, government regulations and security and compliance requirements. Furthermore the financial benefits came up as the most important perceived benefit and government regulation (esp. privacy/security regulations) are the most important risks perceived by senior management.


title: Detection of Flow Based Anomaly in OpenFlow Controller: Machine Learning Approach in Software Defined Networking

abstract: Software Defined Networking (SDN) has come to prominence in recent years and demonstrates an enormous potential in shaping the future of networking by separating control plane from data plane. OpenFlow is the first and most widely used protocol that makes this separation possible in the first place. As a newly emerged technology, SDN has its inherent security threats that can be eliminated or at least mitigated by securing the OpenFlow controller that manages flow control in SDN. SDN provides us a chance to strengthen our network security by decoupling its control plane and data plane. At this level, there also exists some certain risk, which is associated with this technology. In this research, a flow based anomaly detection method in OpenFlow controller have been approached by using machine-learning algorithms in SDN architecture. In order to improve the classifier performance, some feature selection methods namely Info Gain, Gain Ratio, CFS Subset Evaluator, Symmetric Uncertainty, and Chi-square test have been applied as a processing of dataset. A full dataset of 41 features and a reduced dataset has experimented. A dataset with feature selection ensures the highest accuracy of nearly 82% with Random Forest classifier using Gain Ratio feature selection Evaluator. Experimental results ratify that machine-learning approach with feature selection method indices strong potential for the detection of flow based anomaly in OpenFlow controller.


title: Study of maneuvering target tracking algorithm based on Kalman filter and ANFIS

abstract: Although Kalman filtering algorithm has been widely used in the maneuvering target tracking, conventional Kalman filtering algorithm always fails to track the maneuvering target as the target changes its movement state suddenly. In order to overcome its disadvantages, an improved Kalman filtering algorithm that based on the adaptive neural fuzzy inference system (ANFIS) is proposed in this paper. In the improved algorithm, the covariance matrix of Kalman residual is gainer and the measurement noise covariance can be updated in real-time by ANFIS module. Finally, the comparison and analysis of the experiment results between the original Kalman filtering algorithm and the improved one has been carried out. The experiment results show that the tracking error is obviously reduced and the accuracy is significantly boosted after the original Kalman filtering algorithm was substituted by the improved one.


title: Dynamic Deformation Analysis of Power Transformer Windings in Short-Circuit Fault by FEM

abstract: This paper presents the investigations of short-circuit current, electromagnetic force, and transient dynamic response of windings deformation including mechanical stress, strain, and displacements for an oil-immersed-type 220-kV power transformer. The worst-case fault with three-phase short-circuit happening simultaneously is assumed. A considerable leakage magnetic field excited by short-circuit current can produce the dynamical electromagnetic force to act on copper disks in each winding. The two-dimensional finite element method (FEM) is employed to obtain the electromagnetic force and its dynamical characteristics in axial and radial directions. In addition, to calculate the windings deformation accurately, we measured the nonlinear elasticity characteristic of spacer and built three-dimensional FE kinetic model to analyze the axial dynamic deformation. The results of dynamic mechanical stress and strain induced by combining of short-circuit force and prestress are useful for transformer design and fault diagnosis.