title: Predictive control for agile semi-autonomous ground vehicles using motion primitives

abstract: This paper presents a hierarchical control framework for the obstacle avoidance of autonomous and semi-autonomous ground vehicles. The high-level planner is based on motion primitives created from a four-wheel nonlinear dynamic model. Parameterized clothoids and drifting maneuvers are used to improve vehicle agility. The low-level tracks the planned trajectory with a nonlinear Model Predictive Controller. The first part of the paper describes the proposed control architecture and methodology. The second part presents simulative and experimental results with an autonomous and semi-autonomous ground vehicle traveling at high speed on an icy surface.


title: The development of a symptom questionnaire for assessing virtual reality viewing using a head-mounted display.

abstract: PURPOSE ::: Virtual reality devices, including virtual reality head-mounted displays, are becoming increasingly accessible to the general public as technological advances lead to reduced costs. However, there are numerous reports that adverse effects such as ocular discomfort and headache are associated with these devices. To investigate these adverse effects, questionnaires that have been specifically designed for other purposes such as investigating motion sickness have often been used. The primary purpose of this study was to develop a standard questionnaire for use in investigating symptoms that result from virtual reality viewing. In addition, symptom duration and whether priming subjects elevates symptom ratings were also investigated. ::: ::: ::: METHODS ::: A list of the most frequently reported symptoms following virtual reality viewing was determined from previously published studies and used as the basis for a pilot questionnaire. The pilot questionnaire, which consisted of 12 nonocular and 11 ocular symptoms, was administered to two groups of eight subjects. One group was primed by having them complete the questionnaire before immersion; the other group completed the questionnaire postviewing only. Postviewing testing was carried out immediately after viewing and then at 2-min intervals for a further 10 min. ::: ::: ::: RESULTS ::: Priming subjects did not elevate symptom ratings; therefore, the data were pooled and 16 symptoms were found to increase significantly. The majority of symptoms dissipated rapidly, within 6 min after viewing. Frequency of endorsement data showed that approximately half of the symptoms on the pilot questionnaire could be discarded because <20% of subjects experienced them. ::: ::: ::: CONCLUSIONS ::: Symptom questionnaires to investigate virtual reality viewing can be administered before viewing, without biasing the findings, allowing calculation of the amount of change from pre- to postviewing. However, symptoms dissipate rapidly and assessment of symptoms needs to occur in the first 5 min postviewing. Thirteen symptom questions, eight nonocular and five ocular, were determined to be useful for a questionnaire specifically related to virtual reality viewing using a head-mounted display.


title: Deep Learning for Isotropic Super-Resolution from Non-Isotropic 3D Electron Microscopy

abstract: The most sophisticated existing methods to generate 3D isotropic super-resolution (SR) from non-isotropic electron microscopy (EM) are based on learned dictionaries. Unfortunately, none of the existing methods generate practically satisfying results. For 2D natural images, recently developed super-resolution methods that use deep learning have been shown to significantly outperform the previous state of the art. We have adapted one of the most successful architectures (FSRCNN) for 3D super-resolution, and compared its performance to a 3D U-Net architecture that has not been used previously to generate super-resolution. We trained both architectures on artificially downscaled isotropic ground truth from focused ion beam milling scanning EM (FIB-SEM) and tested the performance for various hyperparameter settings.


title: Transfer representation-learning for anomaly detection

abstract: We evaluate transfer representation-learning for ::: anomaly detection using convolutional neural ::: networks by: (i) transfer learning from pretrained ::: networks, and (ii) transfer learning from ::: an auxiliary task by defining sub-categories of ::: the normal class. We empirically show that both ::: approaches offer viable representations for the ::: task of anomaly detection, without explicitly imposing ::: a prior on the data.


title: Gratitude and hedonic and eudaimonic well-being in Vietnam war veterans.

abstract: Little information exists on the contribution of psychological strengths to well-being in persons with post-traumatic stress disorder (PTSD). Data from other populations suggest that gratitude, defined as the positive experience of thankfulness for being the recipient of personal benefits, may have salutary effects on everyday functioning. We investigated whether dispositional gratitude predicted daily hedonic and eudaimonic well-being in combat veterans with and without PTSD. We also examined associations between daily gratitude and daily well-being across time. Veterans with PTSD, compared to those without PTSD, exhibited significantly lower dispositional gratitude; no differences were found on daily gratitude. Dispositional gratitude predicted greater daily positive affect, percentage of pleasant days over the assessment period, daily intrinsically motivating activity, and daily self-esteem over and above effects attributable to PTSD severity and dispositional negative and positive affect in the PTSD group but not the non-PTSD group. Daily gratitude was uniquely associated with each dimension of daily well-being in both groups. Although preliminary, these results provide support for the further investigation of gratitude in trauma survivors.


title: Visualizing Translation Variation: Shakespeare’s Othello

abstract: Recognized as great works of world literature, Shakespeare's poems and plays have been translated into dozens of languages for over 300 years. Also, there are many re-translations into the same language, for example, there are more than 60 translations of Othello into German. Every translation is a different interpretation of the play. These large quantities of translations reflect changing culture and express individual thought by the authors. They demonstrate wide connections between different world regions today, and reveal a retrospective view of their cultural, intercultural, and linguistic histories. Researchers from Arts and Humanities at Swansea University are collecting a large number of translations ofWilliamShakespeare's Othello. In this paper, we have developed an interactive visualization system to present, analyze and explore the variations among these different translations. Our system is composed of two parts: the structure-aware Treemap for document selection and meta data analysis, and Focus + Context parallel coordinates for in-depth document comparison and exploration. In particular, we want to learn more about which content varies highly with each translation, and which content remains stable. We also want to form hypotheses as to the implications behind these variations. Our visualization is evaluated by the domain experts from Arts and Humanities.


title: Deep People Counting in Extremely Dense Crowds

abstract: People counting in extremely dense crowds is an important step for video surveillance and anomaly warning. The problem becomes especially more challenging due to the lack of training samples, severe occlusions, cluttered scenes and variation of perspective. Existing methods either resort to auxiliary human and face detectors or surrogate by estimating the density of crowds. Most of them rely on hand-crafted features, such as SIFT, HOG etc, and thus are prone to fail when density grows or the training sample is scarce. In this paper we propose an end-to-end deep convolutional neural networks (CNN) regression model for counting people of images in extremely dense crowds. Our method has following characteristics. Firstly, it is a deep model built on CNN to automatically learn effective features for counting. Besides, to weaken influence of background like buildings and trees, we purposely enrich the training data with expanded negative samples whose ground truth counting is set as zero. With these negative samples, the robustness can be enhanced. Extensive experimental results show that our method achieves superior performance than the state-of-the-arts in term of the mean and variance of absolute difference.


title: Can We Increase Psychological Well-Being? The Effects of Interventions on Psychological Well-Being: A Meta-Analysis of Randomized Controlled Trials

abstract: BACKGROUND ::: There is a rapidly growing interest in psychological well-being (PWB) as outcome of interventions. Ryff developed theory-based indicators of PWB that are consistent with a eudaimonic perspective of happiness. Numerous interventions have been developed with the aim to increase PWB. However, the effects on PWB measured as coherent outcome have not been examined across studies yet. This meta-analysis of randomized controlled trials of behavioral interventions aims to answer the question whether it is possible to enhance PWB. ::: ::: ::: METHODS ::: A systematic literature search was performed in PsycINFO, Cochrane and Web of Science. To be included, studies had to be randomized controlled trials of behavioral interventions with psychological well-being as primary or secondary outcome measure, measured with either Ryff's Psychological Well-Being Scales or the Mental Health Continuum-Short Form. The meta-analysis was performed using a random effects model. From the 2,298 articles found, 27 met the inclusion criteria. The included studies involved 3,579 participants. ::: ::: ::: RESULTS ::: We found a moderate effect (Cohen's d = 0.44; z = 5.62; p < .001). Heterogeneity between the studies was large (Q (26) = 134.12; p < .001; I2 = 80.62). At follow-up after two to ten months, a small but still significant effect size of 0.22 was found. There was no clear indication of publication bias. Interventions were more effective in clinical groups and when they were delivered individually. Effects were larger in studies of lower quality. ::: ::: ::: CONCLUSIONS ::: It appears to be possible to improve PWB with behavioral interventions. The results are promising for the further development and implementation of interventions to promote PWB. Delivering interventions face-to-face seems to be the most promising option. We recommend to keep including clinical groups in the research of psychological well-being. Heterogeneity is a limitation of the study and there is need for more high-quality studies.


title: Creating Open Government Ecosystems: A Research and Development Agenda

abstract: In this paper, we propose to view the concept of open government from the perspective of an ecosystem, a metaphor often used by policy makers, scholars, and technology gurus to convey a sense of the interdependent social systems of actors, organizations, material infrastructures, and symbolic resources that can be created in technology-enabled, information-intensive social systems. We use the concept of an ecosystem to provide a framework for considering the outcomes of a workshop organized to generate a research and development agenda for open government. The agenda was produced in discussions among participants from the government (at the federal, state, and local levels), academic and civil sector communities at the Center for Technology in Government (CTG) at the University at Albany, SUNY in April 2011. The paper begins by discussing concepts central to understanding what is meant by an ecosystem and some principles that characterize its functioning. We then apply this metaphor more directly to government, proposing that policymakers engage in strategic ecosystems thinking, which means being guided by the goal of explicitly and purposefully constructing open government ecosystems. From there, we present the research agenda questions essential to the development of this new view of government's interaction with users and organizations. Our goal is to call attention to some of the fundamental ways in which government must change in order to evolve from outdated industrial bureaucratic forms to information age networked and interdependent systems.


title: Accurate reconstruction of image stimuli from human fMRI based on the decoding model with capsule network architecture

abstract: In neuroscience, all kinds of computation models were designed to answer the open question of how sensory stimuli are encoded by neurons and conversely, how sensory stimuli can be decoded from neuronal activities. Especially, functional Magnetic Resonance Imaging (fMRI) studies have made many great achievements with the rapid development of the deep network computation. However, comparing with the goal of decoding orientation, position and object category from activities in visual cortex, accurate reconstruction of image stimuli from human fMRI is a still challenging work. In this paper, the capsule network (CapsNet) architecture based visual reconstruction (CNAVR) method is developed to reconstruct image stimuli. The capsule means containing a group of neurons to perform the better organization of feature structure and representation, inspired by the structure of cortical mini column including several hundred neurons in primates. The high-level capsule features in the CapsNet includes diverse features of image stimuli such as semantic class, orientation, location and so on. We used these features to bridge between human fMRI and image stimuli. We firstly employed the CapsNet to train the nonlinear mapping from image stimuli to high-level capsule features, and from high-level capsule features to image stimuli again in an end-to-end manner. After estimating the serviceability of each voxel by encoding performance to accomplish the selecting of voxels, we secondly trained the nonlinear mapping from dimension-decreasing fMRI data to high-level capsule features. Finally, we can predict the high-level capsule features with fMRI data, and reconstruct image stimuli with the CapsNet. We evaluated the proposed CNAVR method on the dataset of handwritten digital images, and exceeded about 10% than the accuracy of all existing state-of-the-art methods on the structural similarity index (SSIM).


title: Organizational Capability in the Public Sector: A Configurational Approach

abstract: This paper brings together resource-based theory and contingency theory to analyse organizational capability in the public sector. Fuzzy-set Qualitative Comparative Analysis is used to identify configurations of organizational attributes (department size, structural complexity, agencification, personnel instability, use of temporary employees), associated with high and low organizational capability in UK central government departments. Findings identify a single core configuration of organizational attributes associated with high capability departments - low structural complexity and personnel stability. Two core configurations are associated with low capability departments – personnel instability, and the combination of structural complexity and departmental agencification. Based on the configurations evident in successful and struggling organizations, discussion illuminates potential organizational design strategies to improve public sector organizational capability.


title: Providing accident detection in vehicular networks through OBD-II devices and Android-based smartphones

abstract: The increasing activity in the Intelligent Transportation Systems (ITS) area faces a strong limitation: the slow pace at which the automotive industry is making cars "smarter". On the contrary, the smartphone industry is advancing quickly. Existing smartphones are endowed with multiple wireless interfaces and high computational power, being able to perform a wide variety of tasks. By combining smartphones with existing vehicles through an appropriate interface we are able to move closer to the smart vehicle paradigm, offering the user new functionalities and services when driving. In this paper we propose an Android-based application that monitors the vehicle through an On Board Diagnostics (OBD-II) interface, being able to detect accidents. Our proposed application estimates the G force experienced by the passengers in case of a frontal collision, which is used together with airbag triggers to detect accidents. The application reacts to positive detection by sending details about the accident through either e-mail or SMS to pre-defined destinations, immediately followed by an automatic phone call to the emergency services. Experimental results using a real vehicle show that the application is able to react to accident events in less than 3 seconds, a very low time, validating the feasibility of smartphone based solutions for improving safety on the road.


Knowledge-Based Recommender Systems


title: A constraint-based approach to solving games on infinite graphs

abstract: We present a constraint-based approach to computing winning strategies in two-player graph games over the state space of infinite-state programs. Such games have numerous applications in program verification and synthesis, including the synthesis of infinite-state reactive programs and branching-time verification of infinite-state programs. Our method handles games with winning conditions given by safety, reachability, and general Linear Temporal Logic (LTL) properties. For each property class, we give a deductive proof rule that --- provided a symbolic representation of the game players --- describes a winning strategy for a particular player. Our rules are sound and relatively complete. We show that these rules can be automated by using an off-the-shelf Horn constraint solver that supports existential quantification in clause heads. The practical promise of the rules is demonstrated through several case studies, including a challenging "Cinderella-Stepmother game" that allows infinite alternation of discrete and continuous choices by two players, as well as examples derived from prior work on program repair and synthesis.


title: When accuracy hurts, and when it helps: A test of the empathic accuracy model in marital interactions

abstract: This study tested predictions from W. Ickes and J. A. Simpson’s (1997, 2001) empathic accuracy model. Married couples were videotaped as they tried to resolve a problem in their marriage. Both spouses then viewed a videotape of the interaction, recorded the thoughts and feelings they had at specific time points, and tried to infer their partner’s thoughts and feelings. Consistent with the model, when the partner’s thoughts and feelings were relationship-threatening (as rated by both the partners and by trained observers), greater empathic accuracy on the part of the perceiver was associated with pre-to-posttest declines in the perceiver’s feelings of subjective closeness. The reverse was true when the partner’s thoughts and feelings were nonthreatening. Exploratory analyses revealed that these effects were partially mediated through observer ratings of the degree to which partners tried to avoid the discussion issue. Previous research has uncovered an apparent paradox about the relation between empathic accuracy and relationship quality. The results of some studies indicate that empathic understanding is


title: Alternative expression analysis by RNA sequencing

abstract: Alternative expression analysis by sequencing (ALEXA-seq) aligns RNA-seq reads from different cell types to a database of alternative expression sequence features and quantifies isoforms that are differentially expressed between samples.


title: Building An Open-source Robotic Stereotaxic Instrument

abstract: This protocol includes the designs and software necessary to upgrade an existing stereotaxic instrument to a robotic (CNC) stereotaxic instrument for around $1,000 (excluding a drill), using industry standard stepper motors and CNC controlling software. Each axis has variable speed control and may be operated simultaneously or independently. The robot's flexibility and open coding system (g-code) make it capable of performing custom tasks that are not supported by commercial systems. Its applications include, but are not limited to, drilling holes, sharp edge craniotomies, skull thinning, and lowering electrodes or cannula. In order to expedite the writing of g-coding for simple surgeries, we have developed custom scripts that allow individuals to design a surgery with no knowledge of programming. However, for users to get the most out of the motorized stereotax, it would be beneficial to be knowledgeable in mathematical programming and G-Coding (simple programming for CNC machining). The recommended drill speed is greater than 40,000 rpm. The stepper motor resolution is 1.8°/Step, geared to 0.346°/Step. A standard stereotax has a resolution of 2.88 μm/step. The maximum recommended cutting speed is 500 μm/sec. The maximum recommended jogging speed is 3,500 μm/sec. The maximum recommended drill bit size is HP 2.


Sharing in the host–guest relationship: perspectives on the Airbnb hospitality experience


title: Web page classification based on context to the content extraction of articles

abstract: Now-a-days, World Wide Web is growing as a huge repository with high source of voluminous and heterogeneous information which continues to expand in size and complexity. But still Web pages are observed unstructured, semi-structured or asynchronous, which contains excess irrelevant information like advertisement, sponsored links, headers, footers etc. Hence guiding on to fetch particular documents over the Internet is well supported by search engines, on giving appropriate keywords in form of queries, or either by catalogues generation, which organize documents into hierarchical file structure. But maintenance of such catalogues manually is more difficult, due to the huge data residing on the Web; hence it becomes necessary build some techniques for auto-categorization of documents. Auto-categorizations scales the retrieval of more relevant information by crawling through web page contents and comparing with the keywords which exact matches the behavioral of any particular category. The prime objective of this research is to identify the category in which the article falls, which enrich the reader's knowledge with direct access the content or locate to the relevant titles. The paper describes a model to perform categorization on article related to child development and parenting's contexts, which starts with catalog generation through identification & analysis carried with the consideration of category keyword along with relevant information been extracted from various sources over web to achieve the classification with expected accuracy.


title: Sodium Survey of the usage and functionality of salt as an ingredient in UK manufactured food products

abstract: The Food and Drink Federation carried out a survey in 1999 to clarify the usage and functionality of salt as an ingredient in UK manufactured branded food products. The results demonstrate the availability of a choice of labelled products, with a wide range of sodium content within product categories. Salt serves a variety of purposes in manufactured foods particularly as a preservative and flavouring. Many UK manufacturers have implemented recipe alterations to reduce the sodium content of their products or are keeping this possibility under review. In many product categories, there are constraints to reducing the sodium content of products, including detrimental effects on product safety, quality and consumer acceptance. The industry is responsive to consumer demand and is committed to providing nutrition labelling information about sodium content, when practicable, on the pack.


title: More Guidelines Than Rules: CSRF Vulnerabilities from Noncompliant OAuth 2.0 Implementations

abstract: OAuth 2.0 provides an open framework for the authorization of users across the web. While the standard enumerates mandatory security protections for a variety of attacks, many embodiments of this standard allow these protections to be optionally implemented. In this paper, we analyze the extent to which one particularly dangerous vulnerability, Cross Site Request Forgery, exists in real-world deployments. We crawl the Alexa Top 10,000 domains, and conservatively identify that 25i?ź% of websites using OAuth appear vulnerable to CSRF attacks. We then perform an in-depth analysis of four high-profile case studies, which reveal not only weaknesses in sample code provided in SDKs, but also inconsistent implementation of protections among services provided by the same company. From these data points, we argue that protection against known and sometimes subtle security vulnerabilities can not simply be thrust upon developers as an option, but instead must be strongly enforced by Identity Providers before allowing web applications to connect.


title: Language modeling for bag-of-visual words image categorization

abstract: In this paper, we propose two ways of improving image classification based on bag-of-words representation [25]. Two shortcomings of this representation are the loss of the spatial information of visual words and the presence of noisy visual words due to the coarseness of the vocabulary building process. On the one hand, we propose a new representation of images that goes further in the analogy with textual data: visual sentences, that allows us to "read" visual words in a certain order, as in the case of text. We can therefore consider simple spatial relations between words. We also present a new image classification scheme that exploits these relations. It is based on the use of language models, a very popular tool from speech and text analysis communities. On the other hand, we propose new techniques to eliminate useless words, one based on geometric properties of the keypoints, the other on the use of probabilistic Latent Semantic Analysis (pLSA). Experiments show that our techniques can significantly improve image classification, compared to a classical Support Vector Machine-based classification.


Hedonic Hotspots: Generating Sensory Pleasure in the Brain


title: Application of NARMA L2 controller for load frequency control of multi-area power system

abstract: Most important problem in an interconnected area is to maintain frequency in a tolerable limit. In this paper study of automatic load frequency control is carried out for a multi-area power system using an intelligent controller. NARMA-L2 controller is used for investigating the dynamic response of the frequency and Tie-line power in the interconnected area. By using the conventional controller the performance is improved, but it is slow. The efficiency of an intelligent controller for frequency control is carried by using MATLAB software. The Peak-overshoot, Peak-undershoot, Settling-time of the responses carried out by the trained controller is better than the conventional controller.


title: Multi-class Emotions Classification by Sentic Levels as Features in Sentiment Analysis

abstract: Sentiment Analysis has become a critical research area in recent days and pervasive in real life. Considering the identification of Emotions from textual content, we propose the Hourglass of Emotions as the feature that comes from the intensity of affective dimensions and combination thereof. Thus, based on a news dataset labeled with six primary Emotions, we intend to solve the Multi-class Classification Problem comparing decomposition methods - One against All and One Against One - and several aggregation methods. As base classifiers algorithms, we adopted Support Vector Machine, Naive Bayes, Decision Tree and Random Forests. Anchored on the results, we found that it is feasible to use this new set of features. The combination of Support Vector Machine and WENG pairwise coupling method was the best one, producing an accuracy of 55.91%.


title: Bootstrapping incremental dialogue systems from minimal data: the generalisation power of dialogue grammars

abstract: We investigate an end-to-end method for automatically inducing task-based dialogue systems from small amounts of unannotated dialogue data. It combines an incremental semantic grammar - Dynamic Syntax and Type Theory with Records (DS-TTR) - with Reinforcement Learning (RL), where language generation and dialogue management are a joint decision problem. The systems thus produced are incremental: dialogues are processed word-by-word, shown previously to be essential in supporting natural, spontaneous dialogue. We hypothesised that the rich linguistic knowledge within the grammar should enable a combinatorially large number of dialogue variations to be processed, even when trained on very few dialogues. Our experiments show that our model can process 74% of the Facebook AI bAbI dataset even when trained on only 0.13% of the data (5 dialogues). It can in addition process 65% of bAbI+, a corpus we created by systematically adding incremental dialogue phenomena such as restarts and self-corrections to bAbI. We compare our model with a state-of-the-art retrieval model, MemN2N. We find that, in terms of semantic accuracy, MemN2N shows very poor robustness to the bAbI+ transformations even when trained on the full bAbI dataset.


title: Load-balancing routing for wireless access networks

abstract: The widespread use of wireless devices presents new challenges for network operators, who need to provide service to ever larger numbers of mobile end users, while ensuring quality-of-service guarantees. We describe a new distributed routing algorithm that performs dynamic load-balancing for wireless access networks. The algorithm constructs a load-balanced backbone tree, which simplifies routing and avoids per-destination state for routing and per-flow state for QoS reservations. We evaluate the performance of the algorithm using several metrics including adaptation to mobility, degree of load-balance, bandwidth blocking rate, and convergence speed. We find that the algorithm achieves better network utilization by lowering bandwidth blocking rates than other methods.


title: FoXtrot: Distributed structural and value XML filtering

abstract: Publish/subscribe systems have emerged in recent years as a promising paradigm for offering various popular notification services. In this context, many XML filtering systems have been proposed to efficiently identify XML data that matches user interests expressed as queries in an XML query language like XPath. However, in order to offer XML filtering functionality on an Internet-scale, we need to deploy such a service in a distributed environment, avoiding bottlenecks that can deteriorate performance. In this work, we design and implement FoXtrot, a system for filtering XML data that combines the strengths of automata for efficient filtering and distributed hash tables for building a fully distributed system. Apart from structural-matching, performed using automata, we also discuss different methods for evaluating value-based predicates. We perform an extensive experimental evaluation of our system, FoXtrot, on a local cluster and on the PlanetLab network and demonstrate that it can index millions of user queries, achieving a high indexing and filtering throughput. At the same time, FoXtrot exhibits very good load-balancing properties and improves its performance as we increase the size of the network.


title: Role of calcium in phototoxicity of 2-butylamino-2-demethoxy-hypocrellin A to human gastric cancer MGC-803 cells

abstract: After incubation with 2-butylamino-2-demethoxy-hypocrellin A (2-BA-2-DMHA), photodynamically induced change in the cytoplasmic free calcium concentration ([Ca 2+ ]i) and its effect on cell damage were investigated in human gastric cancer (MGC-803). Fluorescence spectrophotometry measurement indicated that the photosensitization of MGC-803 by 2-BA-2-DMHA caused an increase in intracellular calcium [Ca 2+ ]i, and this increase in [Ca 2+ ]i showed a dependence on the concentration of 2-BA-2-DMHA, light dose and extracellular [Ca 2+ ]e. This phenomenon of intracellular calcium accumulation was further confirmed by using laser scanning confocal microscopy (LSCM). Furthermore, the results from MTTassay and flow cytometry analysis suggested that chelation of extracellular calcium by EGTA or intracellular calcium by BAPTA could inhibit photodynamically induced cell killing, while increase of [Ca 2+ ]i by thapsigargin (TG), a highly specific inhibitor of the Ca 2+ -ATPase, or by A23187, a calcium ionophore could enhance this action. Meanwhile, the nucleus morphology was also investigated by fluorescence microscopy. The results indicated that the increase in intracellular Ca 2+ concentration was responsible


title: Bi-Shifting Auto-Encoder for Unsupervised Domain Adaptation

abstract: In many real-world applications, the domain of model learning (referred as source domain) is usually inconsistent with or even different from the domain of testing (referred as target domain), which makes the learnt model degenerate in target domain, i.e., the test domain. To alleviate the discrepancy between source and target domains, we propose a domain adaptation method, named as Bi-shifting Auto-Encoder network (BAE). The proposed BAE attempts to shift source domain samples to target domain, and also shift the target domain samples to source domain. The non-linear transformation of BAE ensures the feasibility of shifting between domains, and the distribution consistency between the shifted domain and the desirable domain is constrained by sparse reconstruction between them. As a result, the shifted source domain is supervised and follows similar distribution as target domain. Therefore, any supervised method can be applied on the shifted source domain to train a classifier for classification in target domain. The proposed method is evaluated on three domain adaptation scenarios of face recognition, i.e., domain adaptation across view angle, ethnicity, and imaging sensor, and the promising results demonstrate that our proposed BAE can shift samples between domains and thus effectively deal with the domain discrepancy.


title: Debt Accumulation and Its Implications for Growth and Poverty

abstract: Rising levels of debt and debt servicing, falling rates of investment, declining growth rates of output and employment, and sharp increase in poverty sum up the disappointing performance of Pakistan’s economy over the last decade. By the end of the fiscal year 2001, external debt had increased to $30 billion, and the ratio of external debt and the present value of debt servicing stood at 64.0 and 80.0 percent of GDP respectively. Even though the ratio of debt servicing to export earnings and debt servicing to total foreign exchange earnings have declined because of debt rescheduling, still they were 37.4 and 23.3 percent, respectively, in 2000-01. Prior to debt rescheduling, the two ratios were 55.4 and 34.9 percent in 1997-98. The average growth rate of GDP has been less than 4 percent, with a declining trend, and unemployment rate has increased from less than 5 percent to 7.8 percent over the last decade. The real wages have fallen, and one-third of the population is unable to meet its nutritional requirements. While a number of factors including the inconsistency of the economic policies of successive governments, Structural Adjustment and Stabilisation Programmes of the IMF, and corruption have been responsible for this state of affairs, debt accumulation to alarming proportions is also a major cause of such performance.


title: Stepped bend substrate integrated waveguide to rectangular waveguide transitions

abstract: A new wideband transition between substrate integrated waveguide (SIW) and rectangular waveguide (RWG) that resembles a right angle waveguide E-bend at Ku/K band is presented. The transition has removable but stable mounting, requires only PCB fabrication and has adaptable quality and bandwidth characteristics depending on the number of substrate layers used.


title: The immunopathogenesis of seropositive rheumatoid arthritis: from triggering to targeting

abstract: Understanding the gradual development of disease in patients with seropositive rheumatoid arthritis offers new opportunities for prevention and early therapy.


title: Physical activity in outpatients with mental disorders: status, measurement and social cognitive determinants of health behavior change

abstract: Introduction ::: Physical activity (PA) can play an important role in improving the mental and physical health in patients with mental disorders but is not well studied in this population. The aim of this study was to assess the status of PA in outpatients with mental disorders, compare the convergence of self-rating and accelerometer measurement and examine the influence of social cognitive variables from the Motivation-Volition (MoVo) model and clinical measures on PA.


title: ÖZCAN, ET. AL.: A LARGE-SCALE DATABASE OF IMAGES AND CAPTIONS 1 A Large-Scale Database of Images and Captions for Automatic Face Naming

abstract: We present a large scale database of images and captions, designed for supporting research on how to use captioned images from the Web for training visual classifiers. It consists of more than 125,000 images of celebrities from different fields downloaded from the Web. Each image is associated to its original text caption, extracted from the html page the image comes from. We coin it FAN-Large, for Face And Names Large scale database. Its size and deliberate high level of noise makes it to our knowledge the largest and most realistic database supporting this type of research. The dataset and its annotations are publicly available and can be obtained from http://www.vision. ee.ethz.ch/~calvin/fanlarge/. We report results on a thorough assessment of FAN-Large using several existing approaches for name-face association, and present and evaluate new contextual features derived from the caption. Our findings provide important cues on the strengths and limitations of existing approaches.


title: Suppression of leakage magnetic field from a wireless power transfer system using ferrimagnetic material and metallic shielding

abstract: This paper describes a method to suppress the leakage magnetic field from a wireless power transfer (WPT) system through the use of a ferrimagnetic material and metallic shielding. To demonstrate the advantages of the coil structure with the ferrimagnetic material and metallic shielding, magnetic field distributions and the electrical performance of three different coil structures are investigated via 3D electromagnetic (EM) field solver and SPICE simulation. Results show that the suggested method considerably reduces the leakage magnetic field in the vicinity of the WPT system without significant loss of electrical performance. The simulation results of the suggested coil structure are experimentally verified with a 100 W-class WPT system for an LED TV.


title: Spatial-Temporal Graph Convolutional Networks for Sign Language Recognition

abstract: The recognition of sign language is a challenging task with an important role in society to facilitate the communication of deaf persons. We propose a new approach of Spatial-Temporal Graph Convolutional Network to sign language recognition based on the human skeletal movements. The method uses graphs to capture the signs dynamics in two dimensions, spatial and temporal, considering the complex aspects of the language. Additionally, we present a new dataset of human skeletons for sign language based on ASLLVD to contribute to future related studies.


title: Memory-Efficient Adaptive Optimization for Large-Scale Learning

abstract: Adaptive gradient-based optimizers such as AdaGrad and Adam are among the methods of choice in modern machine learning. These methods maintain second-order statistics of each parameter, thus doubling the memory footprint of the optimizer. In behemoth-size applications, this memory overhead restricts the size of the model being used as well as the number of examples in a mini-batch. We describe a novel, simple, and flexible adaptive optimization method with sublinear memory cost that retains the benefits of per-parameter adaptivity while allowing for larger models and mini-batches. We give convergence guarantees for our method and demonstrate its effectiveness in training very large deep models.


title: Real-Time or Near Real-Time Persisting Daily Healthcare Data Into HDFS and ElasticSearch Index Inside a Big Data Platform

abstract: Mayo Clinic (MC) healthcare generates a large number of HL7 V2 messages—0.7–1.1 million on weekends and 1.7–2.2 million on business days at present. With multiple RDBMS-based systems, such a large volume of HL7 messages still cannot be real-time or near-real-time stored, analyzed, and retrieved for enterprise-level clinic and nonclinic usage. To determine if Big Data technology coupled with ElasticSearch technology can satisfy MC daily healthcare needs for HL7 message processing, a BigData platform was developed to contain two identical Hadoop clusters (TDH1.3.2 version)—each containing an ElasticSearch cluster and instances of a storm topology—MayoTopology for processing HL7 messages on MC ESB queues into an ElasticSearch index and the HDFS. The implemented BigData platform can process 62 ± 4 million HL7 messages per day while the ElasticSearch index can provide ultrafast free-text searching at a speed level of 0.2-s per query on an index containing a dataset of 25 million HL7-derived-JSON-documents. The results suggest that the implemented BigData platform exceeds MC enterprise-level patient-care needs.


title: Underwater robotics: out of the research laboratory and into the field

abstract: The development of two new classes of commercial underwater robotic vehicles-deep diving work-class remotely operated vehicles and survey-class autonomous vehicles-is being driven by the needs of deep water oil production and deep ocean telecommunication cable operations. The paper presents a survey of the present state and future directions of commercial underwater robotics, examines principal technical challenges, and outlines new enabling technologies for commercial underwater robotic vehicles.


title: Development of wideband digital array radar

abstract: As the further development of narrowband digital array radar, wideband digital array radar(WDAR) get more and more research all over the world, for it not only has the advantages of narrowband digital array radar such as compact structure, large dynamic range and flexible digital beam control ability, but also can do high resolution processing such as target imaging. In this paper, based on studying some existing experimental platforms of WDAR, the authors present the basic architecture and function modules of WDAR, analyze the characteristics of WDAR compared with conventional radar, and then some key technologies of WDAR are concisely discussed, the last section conclude the paper.


title: SemEval-2010 Task 10: Linking Events and Their Participants in Discourse

abstract: In this paper, we describe the SemEval-2010 shared task on "Linking Events and Their Participants in Discourse". This task is a variant of the classical semantic role labelling task. The novel aspect is that we focus on linking local semantic argument structures across sentence boundaries. Specifically, the task aims at linking locally uninstantiated roles to their co-referents in the wider discourse context (if such co-referents exist). This task is potentially beneficial for a number of NLP applications and we hope that it will not only attract researchers from the semantic role labelling community but also from co-reference resolution and information extraction.


title: Efficient sampling for better OSN data provisioning

abstract: Data concerning the users and usage of Online Social Networks (OSNs) has become available externally, from public resources (e.g., user profiles), participation in OSNs (e.g., establishing relationships and recording transactions such as user updates) and APIs of the OSN provider (such as the Twitter API). APIs let OSN providers monetize the release of data while helping control measurement load, e.g. by providing samples with different cost-granularity tradeoffs. To date, this approach has been more suited to releasing transactional data, with graphical data still being obtained by resource intensive methods such a graph crawling. In this paper, we propose a method for OSNs to provide samples of the user graph of tunable size, in non-intersecting increments, with sample selection that can be weighted to enhance accuracy when estimating different features of the graph.


title: Factors influencing intention to use mobile technologies forlearning among technical universities students

abstract: Mobile technologies offer more than just calling. There are number of innovative features that are crucial ::: and impactful to educational purpose. Despite the importance of understanding factors influencing mobile ::: technologies for learning, this area has received limited research. Thus, the aim of this study is to ::: investigate factors influencing intention to use m-learning among students at Malaysian technical ::: universities. The research framework for this research has been adopted from unified theory of ::: acceptance and use of technology (UTAUT) to achieve research objectives. A total of 400 set of data was ::: collected from four Malaysia technical universities using purposeful structured random sampling and ::: self-administrated survey questionnaire. AMOS was performed to test whether the model of its good fit to ::: test hypotheses formulated. The results revealed that performance expectancy and self-management of ::: learning are significant related to behavioral intention to use m-learning while effort expectancy, social ::: influence and perceived playfulness do not indicate significant relationship with intention to use mobile ::: technologies for leaming among students at Malaysia technical universities. The findings of this study ::: added new understanding as regard to factors influencing students' intention to use m-learning in higher ::: educatioli


title: Trapped ions interacting with laser fields: a perturbative analysis without rotating wave approximation

abstract: The Hamiltonian describing a single ion placed in a potential trap in interaction with a laser beam is studied by means of a suitable perturbative approach. It is shown, in particular, that the rotating wave approximation does not provide the correct expression, at the first perturbative order, of the evolution operator of the system.


title: Dimensionality Reduction for Fast Similarity Search in Large Time Series Databases

abstract: The problem of similarity search in large time series databases has attracted much attention recently. It is a non-trivial problem because of the inherent high dimensionality of the data. The most promising solutions involve first performing dimensionality reduction on the data, and then indexing the reduced data with a spatial access method. Three major dimensionality reduction techniques have been proposed: Singular Value Decomposition (SVD), the Discrete Fourier transform (DFT), and more recently the Discrete Wavelet Transform (DWT). In this work we introduce a new dimensionality reduction technique which we call Piecewise Aggregate Approximation (PAA). We theoretically and empirically compare it to the other techniques and demonstrate its superiority. In addition to being competitive with or faster than the other methods, our approach has numerous other advantages. It is simple to understand and to implement, it allows more flexible distance measures, including weighted Euclidean queries, and the index can be built in linear time.


title: Metamorphic Testing: A New Approach for Generating Next Test Cases

abstract: In software testing, a set of test cases is constructed according to some predefined selection criteria. The software is then examined against these test cases. Three interesting observations have been made on the current artifacts of software testing. Firstly, an error-revealing test case is considered useful while a successful test case which does not reveal software errors is usually not further investigated. Whether these successful test cases still contain useful information for revealing software errors has not been properly studied. Secondly, no matter how extensive the testing has been conducted in the development phase, errors may still exist in the software [5]. These errors, if left undetected, may eventually cause damage to the production system. The study of techniques for uncovering software errors in the production phase is seldom addressed in the literature. Thirdly, as indicated by Weyuker in [6], the availability of test oracles is pragmatically unattainable in most situations. However, the availability of test oracles is generally assumed in conventional software testing techniques. In this paper, we propose a novel test case selection technique that derives new test cases from the successful ones. The selection aims at revealing software errors that are possibly left undetected in successful test cases which may be generated using some existing strategies. As such, the proposed technique augments the effectiveness of existing test selection strategies. The technique also helps uncover software errors in the production phase and can be used in the absence of test oracles.


title: Hacker's toolbox: Detecting software-based 802.11 evil twin access points

abstract: The usage of public Wi-Fi hotspots has become a common routine in our everyday life. They are ubiquitous and offer fast and budget-friendly connectivity for various client devices. However, they are exposed to a severe security threat: since 802.11 identifiers (SSID, BSSID) can be easily faked, an attacker can setup an evil twin, i.e., an access point (AP) that users are unable to distinguish from a legitimate one. Once a user connects to the evil twin, he inadvertently creates a playground for various attacks such as collection of sensitive data (e.g., credit card information, passwords) or man-in-the-middle attacks even on encrypted traffic. It is particularly alarming that this security flaw has led to the development of several tools that are freely available, easy to use and allow mounting the attack from commodity client devices such as laptops, smartphones or tablets without attracting attention. In this paper we provide a detailed overview of tools that have been developed (or can be misused) to set up evil twin APs. We inspect them thoroughly in order to identify characteristics that allow them to be distinguished from legitimate hardware-based access points. Our analysis has discovered three methods for detecting software-based APs. These exploit accuracy flaws due to emulation of hardware behavior or peculiarities of the client Wi-Fi hardware they operate on. Our evaluation with 60 hardware APs and a variety of tools on different platforms reveals enormous potential for reliable detection. Furthermore, our methods can be performed on typical client hardware within a short period of time without even connecting to a potentially untrustworthy access point.


title: Safe Local Exploration for Replanning in Cluttered Unknown Environments for Micro-Aerial Vehicles

abstract: In order to enable microaerial vehicles (MAVs) to assist in complex, unknown, unstructured environments, they must be able to navigate with guaranteed safety, even when faced with a cluttered environment they have no prior knowledge of. While trajectory-optimization-based local planners have been shown to perform well in these cases, prior work either does not address how to deal with local minima in the optimization problem or solves it by using an optimistic global planner. We present a conservative trajectory-optimization-based local planner, coupled with a local exploration strategy that selects intermediate goals. We perform extensive simulations to show that this system performs better than the standard approach of using an optimistic global planner and also outperforms doing a single exploration step when the local planner is stuck. The method is validated through experiments in a variety of highly cluttered environments including a dense forest. These experiments show the complete system running in real time fully onboard an MAV, mapping and replanning at 4 Hz.


title: Effects of naloxone on the hering-breuer apnea in sleeping kittens.

abstract: The modulatory role of endogenous opiates on the Hering -Breuer inflation reflex was examined in 11 newborn kittens, aged 10-31 days, during active and quite sleep. The Hering -Breuer apnea duration was significantly shortened by naloxone (1 mg/kg, intraperitoneally; P less than 0.05). This effect was abolished by 100% O2 breathing. The duration of apnea, and its shortening by naloxone, did not differ significantly in the two sleep states. Hering -Breuer apnea is a result of the inspiratory inhibition and expiratory excitation of medullary neurons in response to lung stretch; the apnea is terminated by the opposing influences of chemoreceptors, which respond to hypercapnia and hypoxia. The results suggest that opioid influences on the Hering -Breuer reflex are due to an opioid modulation of the carotid body discharge in hypoxia, or of its central integration, and that sleep state is not implicated in such modulation in kittens.


title: Bayesian Network Analysis of Signaling Networks: A Primer

abstract: High-throughput proteomic data can be used to reveal the connectivity of signaling networks and the influences between signaling molecules. We present a primer on the use of Bayesian networks for this task. Bayesian networks have been successfully used to derive causal influences among biological signaling molecules (for example, in the analysis of intracellular multicolor flow cytometry). We discuss ways to automatically derive a Bayesian network model from proteomic data and to interpret the resulting model.


title: Printed Multi-Band MIMO Antenna Systems and Their Performance Metrics [Wireless Corner]

abstract: Multiple-input-multiple-output (MIMO) antenna systems are a key enabling technology for modern fourth-generation (4G) wireless systems. The need for higher data rates for multimedia applications within the limited bandwidth and power levels led the way to the use of multiple antennas at the receiver and transmitter ends. Printed MIMO antenna systems, supporting multiple bands, including the lower band of the 4G wireless standard (LTE), pose a challenge in terms of available size. In this work, we start by defining the new required metrics to characterize MIMO antenna systems. We then present several recent printed multi-band MIMO antenna systems, along with some isolation-enhancement mechanisms that are used in these closely packed antennas.


title: Cross-domain recommendation: an embedding and mapping approach

abstract: Data sparsity is one of the most challenging problems for recommender systems. One promising solution to this problem is cross-domain recommendation, i.e., leveraging feedbacks or ratings from multiple domains to improve recommendation performance in a collective manner. In this paper, we propose an Embedding and Mapping framework for Cross-Domain Recommendation, called EMCDR. The proposed EMCDR framework distinguishes itself from existing crossdomain recommendation models in two aspects. First, a multi-layer perceptron is used to capture the nonlinear mapping function across domains, which offers high flexibility for learning domain-specific features of entities in each domain. Second, only the entities with sufficient data are used to learn the mapping function, guaranteeing its robustness to noise caused by data sparsity in single domain. Extensive experiments on two cross-domain recommendation scenarios demonstrate that EMCDR significantly outperforms state-of-the-art cross-domain recommendation methods.


title: Cognitive differences between anxious, normal, and ADHD children on a dichotic listening task.

abstract: To compare the performance of children with anxiety disorders with that of children with attention-deficit hyperactivity disorder (ADHD) and normal controls in the processing of emotional information. A total of 57 children ages 8 to 11 years (18 anxious, 20 ADHD, 19 normal control) were administered a dichotic listening task for the detection of words and emotions. Comparisons of overall performance, false alarms, and a sensitivity index (which took false alarms into account) were done using repeated measures analyses of variance. Anxious children made fewer false alarms for emotion targets compared to both ADHD children and normal controls, and fewer false alarms for words compared to normal controls. When controlling for false alarms, their performance exceeded that of both ADHD children and normal controls. There were no group differences in correct responses. Performance on a dichotic listening task differentiates anxious, ADHD, and normal children, particularly when listening for emotional targets. Further studies using this task may therefore elucidate differences in the processing of words and emotions between these three groups of children.


title: Robust positioning a mobile robot with active beacon sensors

abstract: With the development of service robots and with the emerging concept of the ubiquitous world, localization of a mobile robot has become a popular issue. Even though several localization schemes have been previously, none of them is free from the economical constraints placed on commercial robots. To implement a real time commercial localization system, a new absolute position estimation method for a mobile robot in indoor environment is proposed in this paper. Design and implementation of the localization system comes from the usage of active beacon systems each of which is composed of an RFID receiver and an ultra-sonic transmitter. The RFID receiver gets the synchronization signal from the mobile robot and the ultra-sonic transmitter sends out a short-term signal to be used for measuring the distance from the beacon to the mobile robot. The position of a mobile robot in a three dimensional space can be calculated basically from the distance information from three beacons which have their own absolute position information. One of the interesting problems comes from the static localization scheme. That is, the collision avoidance of surrounding objects can be planned and controlled efficiently if the velocity of the moving object can be estimated. The main contribution of this paper is developing a dynamic localization scheme to be used for the moving objects, which is a new application in the field of active beacon sensors. In the dynamic localization scheme, sensor fusion techniques and extended Kalman filter have been used to improve the estimation accuracy. Experimental results demonstrate the effectiveness and feasibility of this scheme.


title: Exploring Gamification Techniques and Applications for Sustainable Tourism

abstract: Tourism is perceived as an appropriate solution for pursuing sustainable economic growth due to its main characteristics. In the context of sustainable tourism, gamification can act as an interface between tourists (clients), organisations (companies, NGOs, public institutions) and community, an interface built in a responsible and ethical way. The main objective of this study is to identify gamification techniques and applications used by organisations in the hospitality and tourism industry to improve their sustainable activities. The first part of the paper examines the relationship between gamification and sustainability, highlighting the links between these two concepts. The second part identifies success stories of gamification applied in hospitality and tourism and reviews gamification benefits by analysing the relationship between tourism organisations and three main tourism stakeholders: tourists, tourism employees and local community. The analysis is made in connection with the main pillars of sustainability: economic, social and environmental. This study is positioning the role of gamification in the tourism and hospitality industry and further, into the larger context of sustainable development.


title: A word distributed representation based framework for large-scale short text classification

abstract: With the development of internet, there are billions of short texts generated each day. However, the accuracy of large scale short text classification is poor due to the data sparseness. Traditional methods used to use external dataset to enrich the representation of document and solve the data sparsity problem. But external dataset which matches the specific short texts is hard to find. In this paper, we propose a framework to solve the data sparsity problem without using external dataset. Our framework deal with large scale short text by making the most of semantic similarity of words which learned from the training short texts. First, we learn word distributed representation and measure the word semantic similarity from the training short texts. Then, we propose a method which enrich the document representation by using the word semantic similarity information. At last, we build classifiers based on the enriched representation. We evaluate our framework on both the benchmark dataset(Standford Sentiment Treebank) and the large scale Chinese news title dataset which collected by ourselves. For the benchmark dataset, using our framework can improve 3% classification accuracy. The result we tested on the large scale Chinese news title dataset shows that our framework achieve better result with the increase of the training set size.


title: A Large-Scale Study of Online Shopping Behavior

abstract: The continuous growth of e-commerce has stimulated great interest in generating theories and models for online consumer behavior. While studies on online consumer behavior are widespread, research on relating Internet browsing activities to online shopping behavior are scarce. This paper provides an exploratory analysis on the relationship between online browsing habits and consumers' pre-shopping effort, as one of the indicators of shopping behavior. The data used in this study was extracted from 88,637 users with more than half a million shopping instances from two large online retailers, Amazon and Walmart. Our findings provide insights for scholars to form hypotheses and design models or theories to explain online consumer behavior. Practitioners may also use the results of this study to make strategic decisions.


title: SiGe BiCMOS X-band transceiver-chip for phased-array systems

abstract: In this paper, the authors present an X-band core chip in a production-ready 0.25 μm SiGe BiCMOS technology, suited for implementation in a transmit/receive module for future phased-array antennas. The very compact chip (3 × 3 mm2) incorporates separate circuits for the receive and transmit paths with a very accurate 6 bit phase shifting and amplitude variation capability. Once mounted in a QFN package, the core chip achieves an excellent gain of 18 dB between 8 GHz and 12 GHz, good linearity with a P1dB, in exceeding −20 dBm and noise figure below 10 dB for the receiving path. In transmit mode, the P1dB, out is in excess of 13 dBm between 8 GHz and 12 GHz and even 17 dBm between 8 GHz and 10 GHz, which is sufficient to drive an external high power amplifier. Additionally, an on-chip digital control is implemented as well as analog and mixed-signal functionality. The chip supports pulsed and continuous wave operation. The latter consumes 790 mW in Tx or 330 mW in Rx mode, with a supply voltage of 3.3 V for the RF components and 2.5 V for the digital as well as analog and mixed-signal components. The overall achieved performance makes this X-band core chip very well suited for implementation in next generation X-band transmit/receive modules for phased-array antenna systems.


title: Winner-take-all autoencoders

abstract: In this paper, we propose a winner-take-all method for learning hierarchical sparse representations in an unsupervised fashion. We first introduce fully-connected winner-take-all autoencoders which use mini-batch statistics to directly enforce a lifetime sparsity in the activations of the hidden units. We then propose the convolutional winner-take-all autoencoder which combines the benefits of convolutional architectures and autoencoders for learning shift-invariant sparse representations. We describe a way to train convolutional autoencoders layer by layer, where in addition to lifetime sparsity, a spatial sparsity within each feature map is achieved using winner-take-all activation functions. We will show that winner-take-all autoencoders can be used to to learn deep sparse representations from the MNIST, CIFAR-10, ImageNet, Street View House Numbers and Toronto Face datasets, and achieve competitive classification performance.


title: Reconfigurable computing: architectures and design methods

abstract: Reconfigurable computing is becoming increasingly attractive for many applications. This survey covers two aspects of reconfigurable computing: architectures and design methods. The paper includes recent advances in reconfigurable architectures, such as the Alters Stratix II and Xilinx Virtex 4 FPGA devices. The authors identify major trends in general-purpose and special-purpose design methods. It is shown that reconfigurable computing designs are capable of achieving up to 500 times speedup and 70% energy savings over microprocessor implementations for specific applications.


title: Biomarkers for predicting efficacy of PD-1/PD-L1 inhibitors

abstract: Programmed cell death protein 1/programmed cell death ligand 1 (PD-1/PD-L1) is a negative modulatory signaling pathway for activation of T cell. It is acknowledged that PD-1/PD-L1 axis plays a crucial role in the progression of tumor by altering status of immune surveillance. As one of the most promising immune therapy strategies, PD-1/PD-L1 inhibitor is a breakthrough for the therapy of some refractory tumors. However, response rate of PD-1/PD-L1 inhibitors in overall patients is unsatisfactory, which limits the application in clinical practice. Therefore, biomarkers which could effectively predict the efficacy of PD-1/PD-L1 inhibitors are crucial for patient selection. Biomarkers reflecting tumor immune microenvironment and tumor cell intrinsic features, such as PD-L1 expression, density of tumor infiltrating lymphocyte (TIL), tumor mutational burden, and mismatch-repair (MMR) deficiency, have been noticed to associate with treatment effect of anti-PD-1/anti-PD-L1 therapy. Furthermore, gut microbiota, circulating biomarkers, and patient previous history have been found as valuable predictors as well. Therefore establishing a comprehensive assessment framework involving multiple biomarkers would be meaningful to interrogate tumor immune landscape and select sensitive patients.


A Large Vocabulary Continuous Speech Recognition System for Indonesian Language


title: Multi-layer alpha blending

abstract: We introduce multi-layer alpha blending, a novel solution to real-time order-independent transparency that operates in a single rendering pass and in bounded memory. The main contribution of our method is a new scalable approximation for the compositing equation that makes possible to easily trade off better image quality for more memory and lower performance. We demonstrate improved image quality and performance over previously published methods, while also reducing memory requirements.


title: Fast road detection and tracking in aerial videos

abstract: We propose a fast approach for detecting and tracking a specific road in aerial videos. It combines adaptive Gaussian Mixture Models (GMMs) to describe road colour distributions, and homography based tracking to track road geometries, where an efficient technique is developed to estimate homography transformations between two frames. Experiments are conducted on videos captured by our unmanned aerial vehicles. All the results demonstrate the effectiveness of our proposed method. We test 1755 frames from 5 videos. Our approach can achieve 0.032 seconds per frame and 2.64% segmentation error for images with 908 × 513 resolutions, on average.


title: The Nature of Creativity

abstract: Like E. Paul Torrance, my colleagues and I have tried to understand the nature of creativity, to assess it, and to improve instruction by teaching for creativity as well as teaching students to think creatively. This article reviews our investment theory of creativity, propulsion theory of creative contributions, and some of the data we have collected with regard to creativity. It also describes the propulsion theory of creative contributions. Finally, it draws some conclusions.


Detection of Plunger Movement in DC Solenoids


title: Using Big Data for Predicting Freshmen Retention

abstract: Traditional research in student retention is survey-based, relying on data collected from questionnaires, which is not optimal for proactive prediction and real-time decision (student intervention) support. Machine learning approaches have their own limitations. Therefore, in this research, we propose a big data approach to formulating a predictive model. We used commonly available (student demographic and academic) data in academic institutions augmented by derived implicit social networks from students’ university smart card transactions. Furthermore, we applied a sequence learning method to infer students’ campus integration from their purchasing behaviors. Since student retention data is highly imbalanced, we built a new ensemble classifier to predict students at-risk of dropping out. For model evaluation, we use a real-world dataset of smart card transactions from a large educational institution. The experimental results show that the addition of campus integration and social behavior features refined using the ensemble method significantly improve prediction accuracy and recall.


title: Applicability of Fractional Slot Axial Flux Permanent Magnet Synchronous Machines in the Field Weakening Region

abstract: In this paper, a complete sensitivity analysis of the optimal parameters for the axial flux permanent magnet synchronous machines working in the field weakening region is implemented. Thanks to the presence of a parameterized accurate analytical model, it is possible to obtain all the required parameters of the machine. The two goals of the ideal design are to maximize the power density: $P_{\text{density}}$ and the ratio of maximal to rated speed: $n_{\max}/n_r$ , which is an inductance related parameter keeping the efficiency at the target speed above 90 $\%$ . Different slots/poles/phases combinations are studied to reveal the optimum combination for each phase. This paper has studied the effect of the ratio of number of stator slots to number of rotor poles on the $P_{\text{density}}$ and $n_{\max}/n_r$ . It is shown that a low value of this parameter results in a better $P_{\text{density}}$ and $n_{\max}/n_r$ . The effect of the outer diameter, and the inner to outer diameter ratio are studied with respect to the two design goals. In addition, a comparison between the finite and the theoretical infinite speed designs is implemented. A complete 3D finite element validation has proven the robustness of the analytical model.


title: A 5-DOF rotation-symmetric parallel manipulator with one unconstrained tool rotation

abstract: This paper introduces a novel 5-DOF parallel manipulator with a rotation-symmetric arm system. The manipulator is unorthodox since one degree of freedom of its manipulated platform is unconstrained. Such a manipulator is still useful in a wide range of applications utilizing a rotation-symmetric tool. The manipulator workspace is analyzed for singularities and collisions. The rotation-symmetric arm system leads to a large positional workspace in relation to the footprint of the manipulator. With careful choice of structural parameters, the rotational workspace of the tool is also sizeable.


title: Contextual RNN-GANs for Abstract Reasoning Diagram Generation

abstract: Understanding, predicting, and generating object motions and transformations is a core problem in artificial intelligence. Modeling sequences of evolving images may provide better representations and models of motion and may ultimately be used for forecasting, simulation, or video generation. Diagrammatic Abstract Reasoning is an avenue in which diagrams evolve in complex patterns and one needs to infer the underlying pattern sequence and generate the next image in the sequence. For this, we develop a novel Contextual Generative Adversarial Network based on Recurrent Neural Networks (Context-RNN-GANs), where both the generator and the discriminator modules are based on contextual history (modeled as RNNs) and the adversarial discriminator guides the generator to produce realistic images for the particular time step in the image sequence. We evaluate the Context-RNN-GAN model (and its variants) on a novel dataset of Diagrammatic Abstract Reasoning, where it performs competitively with 10th-grade human performance but there is still scope for interesting improvements as compared to college-grade human performance. We also evaluate our model on a standard video next-frame prediction task, achieving improved performance over comparable state-of-the-art.


title: Three-dimensional modeling of arc root rotation by external magnetic field in nontransferred thermal plasma torches

abstract: A three-dimensional (3-D) transient numerical model has been developed to investigate the arc root rotation driven by an external magnetic field and its influences on the thermal plasma characteristics in the nontransferred plasma torches with rod-type cathode (RTC) and well-type cathode (WTC). The 3-D distributions of electric current density are obtained from a current continuity equation along with the generalized Ohm's law, while the magnetic fields induced by the arc, superimposed on the external field, are calculated by a magnetic vector potential equation. The coupled interactions between the arc and the plasma flow are described in the framework of time-dependent magnetohydrodynamic (MHD) equations in conjunction with a K-/spl epsiv/ turbulence model. Numerical simulations have been focused on finding the magnetically driven rotating velocities of the anode arc root for the RTC torch and the cathode arc root for the WTC torch, respectively. The external application of magnetic field turns out to be a practical method for rotating the arc root rapidly to reduce the electrode erosion in the typical torch operation. The 3-D simulations also reveal that a large swirling motion is induced by the external magnetic field, thereby the distribution of plasma temperature is helically distorted. In addition, it is shown for the RTC torch that the rotation velocity of arc root rises in proportion to the square root of external field strength and that it increases with input current but decreases with gas flow rate.


title: Twisted String Actuation Systems: A Study of the Mathematical Model and a Comparison of Twisted Strings

abstract: This paper presents an improved mathematical model of a twisted string transmission system. The proposed mathematical model has been validated experimentally and provided a better match with the practical data in comparison with the conventional model. Translational transmission systems based on twisted strings coupled with electric motors can compose light-weight, compact, and mechanically simple actuators that can be used in various robotic applications. An extensive experimental study on the performance of different types of strings during twisting is presented. The drawbacks and advantages of each type of strings, along with some of their properties, are discussed.


Emotional Gratification in Entertainment Experience . Why Viewers of Movies and TV Series Find it Rewarding to


title: Overweight Among Primary School-age Children in Malaysia

abstract: This study is a secondary data analysis from the National Health Morbidity Survey III, a population-based study conducted in 2006. A total of 7,749 children between 7 and 12 years old were recruited into the study. This study seeks to report the prevalence of overweight (including obesity) children in Malaysia using international cut-off point and identify its associated key social determinants. The results show that the overall prevalence of overweight children in Malaysia was 19.9%. The urban residents, males, Chinese, those who are wealthy, have overweight or educated guardians showed higher prevalence of overweight. In multivariable analysis, higher likelihood of being overweight was observed among those with advancing age (OR=1.15), urban residents (OR=1.16, 95% CI: 1.01-1.36), the Chinese (OR=1.45, 95% CI: 1.19-1.77), boys (OR=1.23, 95% CI: 1.08-1.41), and those who came from higher income family. In conclusion, one out of five of 7-12 year-old-children in Malaysia were overweight. Locality of residence, ethnicity, gender, guardian education, and overweight guardian were likely to be the predictors of this alarming issue. Societal and public health efforts are needed in order to reduce the burden of disease associated with obesity.


title: Exponential natural evolution strategies

abstract: The family of natural evolution strategies (NES) offers a principled approach to real-valued evolutionary optimization by following the natural gradient of the expected fitness. Like the well-known CMA-ES, the most competitive algorithm in the field, NES comes with important invariance properties. In this paper, we introduce a number of elegant and efficient improvements of the basic NES algorithm. First, we propose to parameterize the positive definite covariance matrix using the exponential map, which allows the covariance matrix to be updated in a vector space. This new technique makes the algorithm completely invariant under linear transformations of the underlying search space, which was previously achieved only in the limit of small step sizes. Second, we compute all updates in the natural coordinate system, such that the natural gradient coincides with the vanilla gradient. This way we avoid the computation of the inverse Fisher information matrix, which is the main computational bottleneck of the original NES algorithm. Our new algorithm, exponential NES (xNES), is significantly simpler than its predecessors. We show that the various update rules in CMA-ES are closely related to the natural gradient updates of xNES. However, xNES is more principled than CMA-ES, as all the update rules needed for covariance matrix adaptation are derived from a single principle. We empirically assess the performance of the new algorithm on standard benchmark functions


title: Mobile Phone Based Clinical Microscopy for Global Health Applications

abstract: Light microscopy provides a simple, cost-effective, and vital method for the diagnosis and screening of hematologic and infectious diseases. In many regions of the world, however, the required equipment is either unavailable or insufficiently portable, and operators may not possess adequate training to make full use of the images obtained. Counterintuitively, these same regions are often well served by mobile phone networks, suggesting the possibility of leveraging portable, camera-enabled mobile phones for diagnostic imaging and telemedicine. Toward this end we have built a mobile phone-mounted light microscope and demonstrated its potential for clinical use by imaging P. falciparum-infected and sickle red blood cells in brightfield and M. tuberculosis-infected sputum samples in fluorescence with LED excitation. In all cases resolution exceeded that necessary to detect blood cell and microorganism morphology, and with the tuberculosis samples we took further advantage of the digitized images to demonstrate automated bacillus counting via image analysis software. We expect such a telemedicine system for global healthcare via mobile phone -- offering inexpensive brightfield and fluorescence microscopy integrated with automated image analysis -- to provide an important tool for disease diagnosis and screening, particularly in the developing world and rural areas where laboratory facilities are scarce but mobile phone infrastructure is extensive.


title: Interpreting CNNs via Decision Trees

abstract: This paper aims to quantitatively explain the rationales of each prediction that is made by a pre-trained convolutional neural network (CNN). We propose to learn a decision tree, which clarifies the specific reason for each prediction made by the CNN at the semantic level. I.e., the decision tree decomposes feature representations in high conv-layers of the CNN into elementary concepts of object parts. In this way, the decision tree tells people which object parts activate which filters for the prediction and how much each object part contributes to the prediction score. Such semantic and quantitative explanations for CNN predictions have specific values beyond the traditional pixel-level analysis of CNNs. More specifically, our method mines all potential decision modes of the CNN, where each mode represents a typical case of how the CNN uses object parts for prediction. The decision tree organizes all potential decision modes in a coarse-to-fine manner to explain CNN predictions at different fine-grained levels. Experiments have demonstrated the effectiveness of the proposed method.


title: TILES: classifying contextual information for mobile tourism applications

abstract: Purpose – The design of context‐aware mobile applications can be improved through a clear and in‐depth understanding of context and how it can be used to meet users' requirements. Using tourism as a case application, this paper aims to address the lack of understanding of context and tourists' goals.Design/methodology/approach – This is achieved through a literature review of existing research and focus groups to gather information needs for tasks commonly executed by tourists.Findings – This paper proposes the TILES (temporal, identity, location, environmental and social) model to define and classify five main contextual types, and properties associated with each type for tourism‐related applications. The TILES model (with 32 factors) derived from the analysis of the literature review is refined through inputs from two focus groups to incorporate an additional ten factors.Research implications/limitations – The TILES model can be generalised to support domains other than tourism, such as medical and edut...


title: Rising Food Prices, Food Price Volatility, and Political Unrest

abstract: Do food prices cause political unrest? Throughout history, riots appear to have frequently broken out as a consequence of high food prices. This paper studies the impact of food prices on political unrest using monthly data on food prices at the international level. Because food prices and political unrest are jointly determined, the incidence of natural disasters in a given month is used in an attempt to identify the causal relationship between food prices and political unrest. Empirical results indicate that between January 1990 and January 2011, food price increases have led to increased political unrest, whereas food price volatility has been associated with decreases in political unrest. These findings are consistent with those of the applied microeconomics literature on the welfare impacts of food prices.


title: Emergence of KPC-2-Producing Pseudomonas aeruginosa Sequence Type 463 Isolates in Hangzhou, China

abstract: Thirty-nine Klebsiella pneumoniae carbapenemase (KPC)-producing Pseudomonas aeruginosa isolates, all exhibiting high-level resistance to carbapenems and other β-lactam antibiotics, were isolated in Hangzhou, China. Molecular epidemiology analysis indicated the presence of two dominant clones, namely, clones A and B, both of which belong to sequence type 463 (ST463). A genetic environment analysis demonstrated that both clones harbor an ISKpn8 transposase, bla(KPC-2), and an ISKpn6-like transposase. These findings depict the features of clonal expansion and transmission of KPC-2-producing P. aeruginosa strains in Hangzhou, China.


title: Benchmark of a large scale database for facial beauty prediction

abstract: Due to its indefinite evaluation criterion, facial beauty analysis has shown its challenges in pattern recognition and biometric recognition. Plenty of methods have been designed for solving this problem, but there are still various limitations. Most of the available research results in this field are achieved on a small-scale facial beauty database which is difficult in modeling the structure information for facial beauty. And majority of the presented facial beauty prediction algorithm requires burden landmark or expensive optimization procedure. In this paper, we have established a large scale facial beauty database named LSFBD, which is a new database for facial beauty analysis. The LSFBD database contains 20000 labeled images, which has 10000 unconstrained male subjects and 10000 unconstrained female subjects separately, and all images are verified with well-designed rating with average scores and standard deviation. In this database, manually adding the extreme beauty images make the distribution more reasonable and the result more accurate. CRBM or Eigenfaces results are presented in the paper as benchmark for evaluating the task of facial beauty prediction.


title: Reading Literary Fiction Improves Theory of Mind

abstract: Understanding others’ mental states is a crucial skill that enables the complex social relationships that characterize human societies. Yet little research has investigated what fosters this skill, which is known as Theory of Mind (ToM), in adults. We present five experiments showing that reading literary fiction led to better performance on tests of affective ToM (experiments 1 to 5) and cognitive ToM (experiments 4 and 5) compared with reading nonfiction (experiments 1), popular fiction (experiments 2 to 5), or nothing at all (experiments 2 and 5). Specifically, these results show that reading literary fiction temporarily enhances ToM. More broadly, they suggest that ToM may be influenced by engagement with works of art.


title: Socioeconomic Status Modifies the Sex Difference in Spatial Skill

abstract: We examined whether the male spatial advantage varies across children from different socioeconomic (SES) groups. In a longitudinal study, children were administered two spatial tasks requiring mental transformations and a syntax comprehension task in the fall and spring of second and third grades. Boys from middle-and high-SES backgrounds outperformed their female counterparts on both spatial tasks, whereas boys and girls from a low-SES group did not differ in their performance level on these tasks. As expected, no sex differences were found on the verbal comprehension task. Prior studies have generally been based on the assumption that the male spatial advantage reflects ability differences in the population as a whole. Our finding that the advantage is sensitive to variations in SES provides a challenge to this assumption, and has implications for a successful explanation of the sex-related difference in spatial skill.


title: Requirements for tools for ambiguity identification and measurement in natural language requirements specifications

abstract: This paper proposes a two-step approach to identifying ambiguities in natural language (NL) requirements specifications (RSs). In the first step, a tool would apply a set of ambiguity measures to a RS in order to identify potentially ambiguous sentences in the RS. In the second step, another tool would show what specifically is potentially ambiguous about each potentially ambiguous sentence. The final decision of ambiguity remains with the human users of the tools. The paper describes several requirements-identification experiments with several small NL RSs using four prototypes of the first tool based on linguistic instruments and resources of different complexity and a manual mock-up of the second tool.


title: Copyright © 200x Inderscience Enterprises Ltd. Enhanced SEA algorithm and fingerprint classification

abstract: This paper proposes the Enhanced Shrinking and Expanding Algorithm (ESEA) with a new categorisation method. The ESEA overcomes anomalies in the original Shrinking and Expanding Algorithm (SEA) which fails to locate Singular Points (SPs) in many cases. Experimental results show that the accuracy rate of the ESEA reaches 94.7%, a 32.5% increase from the SEA. In the proposed fingerprint categorisation method, each fingerprint will be assigned to a specific subclass. The search for a specific fingerprint can therefore be performed only on specific subclasses containing a small portion of a large fingerprint database, which will save enormous computational time.


A Decision Framework for Blockchain Platforms for IoT and Edge Computing


Maximum a posteriori Based Decoding for CTC Acoustic Models.


title: Circumferential Traveling Wave Slot Array on Cylindrical Substrate Integrated Waveguide (CSIW)

abstract: Traveling wave slot array on cylindrical substrate integrated waveguide (CSIW) is designed, fabricated and measured at K-band. CSIW is formed by wrapping the substrate integrated waveguide (SIW) around the cylinder in the circumferential direction. 16 element longitudinal slot array on the broad wall of single CSIW is designed by the Elliot's design procedure. The spacings between the slot elements are determined to reduce the half power beam width (HPBW) and to obtain good matching at 25 GHz. A 4 × 16 slot array is formed using 4 CSIW array each having 16 traveling wave longitudinal slots and the array is fed by 1 × 4 SIW power divider structure. About 10 ° beam steering is achieved when frequency is swept from 24 to 26 GHz. Gain of the antenna is 14 dB. Very good agreement between the simulated and measured results is obtained.


title: Convolutional Neural Networks for Diabetic Retinopathy

abstract: Abstract The diagnosis of diabetic retinopathy (DR) through colour fundus images requires experienced clinicians to identify the presence and significance of many small features which, along with a complex grading system, makes this a difficult and time consuming task. In this paper, we propose a CNN approach to diagnosing DR from digital fundus images and accurately classifying its severity. We develop a network with CNN architecture and data augmentation which can identify the intricate features involved in the classification task such as micro-aneurysms, exudate and haemorrhages on the retina and consequently provide a diagnosis automatically and without user input. We train this network using a high-end graphics processor unit (GPU) on the publicly available Kaggle dataset and demonstrate impressive results, particularly for a high-level classification task. On the data set of 80,000 images used our proposed CNN achieves a sensitivity of 95% and an accuracy of 75% on 5,000 validation images.


title: Mirror mirror on the ceiling: flexible wireless links for data centers

abstract: Modern data centers are massive, and support a range of distributed applications across potentially hundreds of server racks. As their utilization and bandwidth needs continue to grow, traditional methods of augmenting bandwidth have proven complex and costly in time and resources. Recent measurements show that data center traffic is often limited by congestion loss caused by short traffic bursts. Thus an attractive alternative to adding physical bandwidth is to augment wired links with wireless links in the 60 GHz band. ::: We address two limitations with current 60 GHz wireless proposals. First, 60 GHz wireless links are limited by line-of-sight, and can be blocked by even small obstacles. Second, even beamforming links leak power, and potential interference will severely limit concurrent transmissions in dense data centers. We propose and evaluate a new wireless primitive for data centers, 3D beamforming, where 60 GHz signals bounce off data center ceilings, thus establishing indirect line-of-sight between any two racks in a data center. We build a small 3D beamforming testbed to demonstrate its ability to address both link blockage and link interference, thus improving link range and number of concurrent transmissions in the data center. In addition, we propose a simple link scheduler and use traffic simulations to show that these 3D links significantly expand wireless capacity compared to their 2D counterparts.


title: VLSI Implementation of Fully Parallel LTE Turbo Decoders

abstract: Turbo codes facilitate near-capacity transmission throughputs by achieving a reliable iterative forward error correction. However, owing to the serial data dependence imposed by the logarithmic Bahl–Cocke–Jelinek–Raviv algorithm, the limited processing throughputs of the conventional turbo decoder implementations impose a severe bottleneck upon the overall throughputs of real-time communication schemes. Motivated by this, we recently proposed a floating-point fully parallel turbo decoder (FPTD) algorithm, which eliminates the serial data dependence, allowing parallel processing and hence significantly reducing the number of clock cycles required. In this paper, we conceive a technique for reducing the critical datapath of the FPTD, and we propose a novel fixed-point version as well as its very large scale integration (VLSI) implementation. We also propose a novel technique, which allows the FPTD to also decode shorter frames employing compatible interleaver patterns. We strike beneficial tradeoffs amongst the latency, core area, and energy consumption by investigating the minimum bit widths and techniques for message log-likelihood ratio scaling and state metric normalization. Accordingly, the design flow and design tradeoffs considered in this paper are also applicable to other fixed-point implementations of error correction decoders. We demonstrate that upon using Taiwan Semiconductor Manufacturing Company (TSMC) 65-nm low-power technology for decoding the longest long-term evolution frames (6144 b) received over an additive white Gaussian noise channel having $E_{b}/N_{0}=1~ \text {dB}$ , the proposed fixed-point FPTD VLSI achieves a processing throughput of 21.9 Gb/s and a processing latency of $0.28~ \mu \text {s}$ . These results are 17.1 times superior to those of the state-of-the-art benchmarker. Furthermore, the proposed fixed-point FPTD VLSI achieves an energy consumption of $2.69~ \mu \text {J}$ /frame and a normalized core area of $5~ \text {mm}^{\vphantom {R^{'}}2}/\text {Gb/s}$ , which are 34% and 23% lower than those of the benchmarker, respectively.


title: Simulink Modeling and Design of an Efficient Hardware-Constrained FPGA-Based PMSM Speed Controller

abstract: The aim of this paper is to present a holistic approach to modeling and field programmable gate array (FPGA) implementation of a permanent magnet synchronous motor (PMSM) speed controller. The whole system is modeled in the Matlab Simulink environment. The controller is then translated to discrete time and remodeled using System Generator blocks, directly synthesizable into FPGA hardware. The algorithm is further refined and factorized to take into account hardware constraints, so as to fit into a low-cost FPGA, without significantly increasing the execution time. The resulting controller is then integrated together with sensor interfaces and analysis tools and implemented into an FPGA device. Experimental results validate the controller and verify the design.


Pastry: Scalable, distributed object location and routing for large-scale peer-to-peer systems


title: Passive technologies for future large-scale photonic integrated circuits on silicon: polarization handling, light non-reciprocity and loss reduction

abstract: Newly developed photonic components could allow large-scale photonic integrated circuits to be built on silicon substrates. Daoxin Dai from Zhejiang University, China, alongside co-workers from the University of California, USA, have proposed several new optical technologies for use in photonic integrated circuits, which substitute or work alongside electrical circuits in optical devices. The researchers have designed new ultrashort polarization-handling devices that split high-intensity beams of light, and a ring optical isolator that reduces reflections. The team have also created a new waveguide based on silicon nitride that can guide optical waves with a minimal loss of energy. These new technologies will allow scientists to construct higher performance, more compact optical devices.


Automatic Creation of a Sentence Aligned Sinhala-Tamil Parallel Corpus.


title: A spatial-temporal hole filling approach with background modeling and texture synthesis for 3D video

abstract: This paper presents a hole-filling method based on spatial-temporal background information for view synthesis in 3D Video. The process of obtaining stationary background information is updated with a new background codebook in order to extract reliable temporal background modeling. An estimation of local background value is conducted to discriminate an adaptively boundary between the background region and the foreground region as well as to update the information about the hole region. The holes are filled by combining the spatial background information and the temporal background information. In addition, an exemplar-based inpainting method is used to fill the rest of holes, in which a priority function using background depth information is defined to determine the order in which the holes are filled. The experimental results demonstrated that the proposed algorithm synthesized satisfactory views regardless of video characteristics and type of hole region.