title: Role of Procedural Justice, Organizational Commitment and Job Satisfaction on job Performance: The Mediating Effects of Organizational Citizenship Behavior

abstract: The study examines the impact of procedural justice, organizational commitment, job satisfaction on employeeperformance, and the potential mediating role played by organization citizenship behaviors in that process. Thismodel was tested using a sample of 70 employees embedded in 2 groups from 15 branches of a large, syariah bankin Malang. The sample is taken using proportional random sampling. Data is collected directly from respondentsusing questionnaires and technical data analysis using GeSCA.The study results showed that both procedural justice and organizational commitment positively affectedOrganizational Citizenship Behavior. Organizational commitments do positive influence job performance. Jobsatisfaction did not positively influence Organizational Citizenship Behavior and job performance. OrganizationalCitizenship behavior positively influences job performance. Organizational Citizenship behavior acted as a partialmediator between procedural justice, organizational commitment, and job performance. A number of suggestionson managerial theory and implementation were proposed.


title: IoT based smart home automation system using sensor node

abstract: In recent years, the advancements in Information and Communication Technology (ICT) are mainly focused on the Internet of Things (IoT). In a real-world scenario, IoT based services improve the domestic environment and are used in various applications. Home automation based IoT is versatile and popular applications. In home automation, all home appliances are networked together and able to operate without human involvement. Home automation gives a significant change in humans life which gives smart operating of home appliances. This motivated us to develop a new solution which controls some home appliances like light, fan, door cartons, energy consumption, and level of the Gas cylinder using various sensors like LM35, IR sensors, LDR module, Node MCU ESP8266, and Arduino UNO. The proposed solution uses the sensor and detects the presence or absence of a human object in the housework accordingly. Our solution also provides information about the energy consumed by the house owner regularly in the form of message. Also, it checks, the level of gas in the gas cylinder if it reaches lesser than the threshold, it automatically books the gas and sends a reference number as a message to the house owner. The proposed solution is deployed and tested for various conditions. Finally, in this paper, the working model of our proposed solution is developed as a prototype and explained as a working model.


title: Cannabidiol (CBD) as an Adjunctive Therapy in Schizophrenia: A Multicenter Randomized Controlled Trial

abstract: Objective:Research in both animals and humans indicates that cannabidiol (CBD) has antipsychotic properties. The authors assessed the safety and effectiveness of CBD in patients with schizophrenia.Method:In an exploratory double-blind parallel-group trial, patients with schizophrenia were randomized in a 1:1 ratio to receive CBD (1000 mg/day; N=43) or placebo (N=45) alongside their existing antipsychotic medication. Participants were assessed before and after treatment using the Positive and Negative Syndrome Scale (PANSS), the Brief Assessment of Cognition in Schizophrenia (BACS), the Global Assessment of Functioning scale (GAF), and the improvement and severity scales of the Clinical Global Impressions Scale (CGI-I and CGI-S).Results:After 6 weeks of treatment, compared with the placebo group, the CBD group had lower levels of positive psychotic symptoms (PANSS: treatment difference=−1.4, 95% CI=−2.5, −0.2) and were more likely to have been rated as improved (CGI-I: treatment difference=−0.5, 95% CI=−0....


title: A Density-Based Clustering Method for Urban Scene Mobile Laser Scanning Data Segmentation

abstract: The segmentation of urban scene mobile laser scanning (MLS) data into meaningful street objects is a great challenge due to the scene complexity of street environments, especially in the vicinity of street objects such as poles and trees. This paper proposes a three-stage method for the segmentation of urban MLS data at the object level. The original unorganized point cloud is first voxelized, and all information needed is stored in the voxels. These voxels are then classified as ground and non-ground voxels. In the second stage, the whole scene is segmented into clusters by applying a density-based clustering method based on two key parameters: local density and minimum distance. In the third stage, a merging step and a re-assignment processing step are applied to address the over-segmentation problem and noise points, respectively. We tested the effectiveness of the proposed methods on two urban MLS datasets. The overall accuracies of the segmentation results for the two test sites are 98.3% and 97%, thereby validating the effectiveness of the proposed method.


title: Inter-Rater Reliability of Total Body Score-A Scale for Quantification of Corpse Decomposition

abstract: The degree of body decomposition can be quantified using Total Body Score (TBS), a scale frequently used in taphonomic or entomological studies of decomposition. Here, the inter-rater reliability of the scale is analyzed. The study was made on 120 laymen, which were trained in the use of the scale. Participants scored decomposition of pig carcasses from photographs. It was found that the scale, when used by different people, gives homogeneous results irrespective of the user qualifications (the Krippendorff's alfa for all participants was 0.818). The study also indicated that carcasses in advanced decomposition receive significantly less accurate scores. Moreover, it was found that scores for cadavers in mosaic decomposition (i.e., representing signs of at least two stages of decomposition) are less accurate. These results demonstrate that the scale may be regarded as inter-rater reliable. Some propositions for refinement of the scale were also discussed.


Emotion in Human-Computer Interaction


title: The Cyberhand: on the design of a cybernetic prosthetic hand intended to be interfaced to the peripheral nervous system

abstract: The objective of the project described in this paper is the development of a cybernetic prosthesis, replicating as much as possible the sensory-motor capabilities of the natural hand. The human hand is not only an effective tool but also an ideal instrument to acquire information from the external environment. The development of a truly human-like artificial hand is probably the most widely known paradigm of "bionics". The Cyberhand Project aims to obtain a cybernetic prosthetic hand interfaced to the peripheral nervous system. In particular this paper is focused on the hand mechanisms design and it presents preliminary results in developing the three fingered anthropomorphic hand prototype and its sensory system.


title: Curiosity and pleasure

abstract: We discuss the hypothesis that acquisition of knowledge is a deeply rooted psychological need. But so is the desire for fast decisions and for minimizing cognitive efforts. There is a controversy between maximizing knowledge rationally for decision making or using Tversky and Kahneman heuristic mechanisms. Here we explore a basic aspect of learning, does it bring pleasure? We report experimental results showing that acquisition of knowledge is hedonically pleasing. Thus, the satisfaction of curiosity through acquiring knowledge brings pleasure and could improve decision making. Such a mechanism would confirm the hypothesis that curiosity is a fundamental and ancient motivation.


title: Dynamic quest plot generation using Petri net planning

abstract: In most cases, the story of popular RPG games is designed by professional designers as a main content. However, manual design of game content has limitation in the quantitative aspect. Manual story generation requires a large amount of time and effort. Because gamers want more diverse and rich content, so it is not easy to satisfy the needs with manual design. PCG (Procedural Content Generation) is to automatically generate the content of the game. In this paper, we propose a quest generation engine using Petri net planning. As a combination of Petri-net modules a quest, a quest plot is created. The proposed method is applied to a commercial game platform to show the feasibility.


title: Serious Game Evaluation as a Meta-Game.

abstract: Purpose – This paper aims to briefly outline the seamless evaluation approach and its application during an evaluation of ORIENT, a serious game aimed at young adults.Design/methodology/approach – In this paper, the authors detail a unobtrusive, embedded evaluation approach that occurs within the game context, adding value and entertainment to the player experience whilst accumulating useful data for the development team.Findings – The key result from this study was that during the “seamless evaluation” approach, users were unaware that they had been participating in an evaluation, with instruments enhancing rather than detracting from the in‐role game experience.Practical implications – This approach, seamless evaluation, was devised in response to player expectations, perspectives and requirements, recognising that in the evaluation of games the whole process of interaction including its evaluation must be enjoyable and fun for the user.Originality/value – Through using seamless evaluation, the authors ...


title: Robust Voltage Control of Floating Interleaved Boost Converter for Fuel Cell Systems

abstract: In this paper, a robust voltage control is designed for a floating interleaved boost converter with high voltage gain. The proposed controller has an inner loop based on super-twisting sliding mode algorithm, which has continuous control signal and the sliding surface is defined for the inductor current. The reference current value is generated by active disturbance rejection control (ADRC) algorithm in the outer loop, based on the output voltage error. The stability of the sliding mode inner loop and the ADRC outer loop are proven using Lyapunov stability theorem and Routh–Hurwitz criteria, respectively. The robustness of the proposed controller is analyzed in depth, and validated by the simulations and experimental results obtained with a 100 W prototype converter.


title: Electronic books versus adult readers: effects on children's emergent literacy as a function of social class

abstract: We compared the effects of children’s reading of an educational electronic storybook on their emergent literacy with those of being read the same story in its printed version by an adult. We investigated 128 5- to 6-year-old kindergarteners; 64 children from each of two socio-economic status (SES) groups: low (LSES) and middle (MSES). In each group, children were randomly assigned to one of three subgroups. The two intervention groups included three book reading sessions each; children in one group individually read the electronic book; in the second group, the children were read the same printed book by an adult; children in the third group, which served as a control, received the regular kindergarten programme. Pre- and post-intervention emergent literacy measures included vocabulary, word recognition and phonological awareness. Compared with the control group, the children’s vocabulary scores in both intervention groups improved following reading activity. Children from both interventions groups and both SES groups showed a similarly good level of story comprehension. In both SES groups, compared with the control group, children’s phonological awareness and word recognition did not improve following both reading interventions. Implications for future research and for education are discussed.


The Effectiveness of Theory of Mind Training On the Social Skills of Children with High Functioning Autism Spectrum Disorders


title: Quaternion Convolutional Neural Networks

abstract: Neural networks in the real domain have been studied for a long time and achieved promising results in many vision tasks for recent years. However, the extensions of the neural network models in other number fields and their potential applications are not fully-investigated yet. Focusing on color images, which can be naturally represented as quaternion matrices, we propose a quaternion convolutional neural network (QCNN) model to obtain more representative features. In particular, we redesign the basic modules like convolution layer and fully-connected layer in the quaternion domain, which can be used to establish fully-quaternion convolutional neural networks. Moreover, these modules are compatible with almost all deep learning techniques and can be plugged into traditional CNNs easily. We test our QCNN models in both color image classification and denoising tasks. Experimental results show that they outperform the real-valued CNNs with same structures.


title: Supervising the New with the Old: Learning SFM from SFM

abstract: Recent work has demonstrated that it is possible to learn deep neural networks for monocular depth and ego-motion estimation from unlabelled video sequences, an interesting theoretical development with numerous advantages in applications. In this paper, we propose a number of improvements to these approaches. First, since such self-supervised approaches are based on the brightness constancy assumption, which is valid only for a subset of pixels, we propose a probabilistic learning formulation where the network predicts distributions over variables rather than specific values. As these distributions are conditioned on the observed image, the network can learn which scene and object types are likely to violate the model assumptions, resulting in more robust learning. We also propose to build on dozens of years of experience in developing handcrafted structure-from-motion (SFM) algorithms. We do so by using an off-the-shelf SFM system to generate a supervisory signal for the deep neural network. While this signal is also noisy, we show that our probabilistic formulation can learn and account for the defects of SFM, helping to integrate different sources of information and boosting the overall performance of the network.


title: Burst Image Deblurring Using Permutation Invariant Convolutional Neural Networks

abstract: We propose a neural approach for fusing an arbitrary-length burst of photographs suffering from severe camera shake and noise into a sharp and noise-free image. Our novel convolutional architecture has a simultaneous view of all frames in the burst, and by construction treats them in an order-independent manner. This enables it to effectively detect and leverage subtle cues scattered across different frames, while ensuring that each frame gets a full and equal consideration regardless of its position in the sequence. We train the network with richly varied synthetic data consisting of camera shake, realistic noise, and other common imaging defects. The method demonstrates consistent state of the art burst image restoration performance for highly degraded sequences of real-world images, and extracts accurate detail that is not discernible from any of the individual frames in isolation.


title: HANDS18: Methods, Techniques and Applications for Hand Observation

abstract: This report outlines the proceedings of the Fourth International Workshop on Observing and Understanding Hands in Action (HANDS 2018). The fourth instantiation of this workshop attracted significant interest from both academia and the industry. The program of the workshop included regular papers that are published as the workshop's proceedings, extended abstracts, invited posters, and invited talks. Topics of the submitted works and invited talks and posters included novel methods for hand pose estimation from RGB, depth, or skeletal data, datasets for special cases and real-world applications, and techniques for hand motion re-targeting and hand gesture recognition. The invited speakers are leaders in their respective areas of specialization, coming from both industry and academia. The main conclusions that can be drawn are the turn of the community towards RGB data and the maturation of some methods and techniques, which in turn has led to increasing interest for real-world applications.


title: Automatic player detection, labeling and tracking in broadcast soccer video

abstract: In this paper, we present a method to perform automatic multiple player detection, unsupervised labeling and efficient tracking in broadcast soccer videos. Player detection is to determine the players' positions and scales. It is achieved by combining the ability of dominant color based background subtraction and a boosting detector with Haar features. We then collect hundreds of player samples with the player detector, and learn codebook based player appearance model by unsupervised clustering algorithm. A player can be labeled as one of four types: two teams, referee or outlier. The learning capability enables the method to be generalized well to different videos without any manually initialization. Based on detection and labeling, we perform multiple player tracking with Markov chain Monte Carlo (MCMC) data association. Some data driven dynamics are proposed to improve the Markov chain's efficiency, such as label and motion consistent and track length. The testing results on FIFA World Cup 2006 videos demonstrate that our method can reach high detection and labeling precision, and reliably tracking in cases of scenes such as player occlusion, moderate camera motion and pose variation.


Modeling of Brushless DC Motor with Various Loading Conditions for Electric Vehicle Application


title: Decision guidance models for microservices: service discovery and fault tolerance

abstract: Introducing a microservice system is a challenging task and requires the exploration and documentation of several related areas of design. Exploration and documentation of software architecture design is supported by decision guidance models in software architecture. In this paper, we present such guidance models for several microservice system design areas, including service discovery and fault tolerance. The presented models have been created based on existing microservice literature and have been validated and refined in design workshops with business partners as part of a technical action research (TAR) study.


title: TutorialBank: A Manually-Collected Corpus for Prerequisite Chains, Survey Extraction and Resource Recommendation

abstract: The field of Natural Language Processing (NLP) is growing rapidly, with new research published daily along with an abundance of tutorials, codebases and other online resources. In order to learn this dynamic field or stay up-to-date on the latest research, students as well as educators and researchers must constantly sift through multiple sources to find valuable, relevant information. To address this situation, we introduce TutorialBank, a new, publicly available dataset which aims to facilitate NLP education and research. We have manually collected and categorized over 6,300 resources on NLP as well as the related fields of Artificial Intelligence (AI), Machine Learning (ML) and Information Retrieval (IR). Our dataset is notably the largest manually-picked corpus of resources intended for NLP education which does not include only academic papers. Additionally, we have created both a search engine and a command-line tool for the resources and have annotated the corpus to include lists of research topics, relevant resources for each topic, prerequisite relations among topics, relevant sub-parts of individual resources, among other annotations. We are releasing the dataset and present several avenues for further research.


title: An adjustable scotch yoke mechanism for robotic dolphin

abstract: This paper describes the design, construction, and performance analysis of an adjustable Scotch yoke mechanism mimicking the dorsoventral movement for dolphin-like robots. Since dolphins propel themselves by vertical oscillations following a sinusoidal path with alterable amplitudes, a two- motor-driven Scotch yoke mechanism is adopted as the main propulsor to generate sinusoidal oscillations, where leading screw mechanism and rack and pinion mechanism actuated by the minor motor are incorporated to independently change the length of the crank actuated by the major motor. Meanwhile, the output of the Scotch yoke, i.e., reciprocating motion, is converted into the up-and-down oscillation via rack and gear transmission. A motion control scheme based on the novel Scotch yoke is then formed and applied to achieve desired propulsion. Preliminary tests in a robotics context finally confirm the feasibility of the developed mechanism in mechanics and propulsion.


title: Simple low-dimensional features approximating NCC-based image matching

abstract: This paper proposes new low-dimensional image features that enable images to be very efficiently matched. Image matching is one of the key technologies for many vision-based applications, including template matching, block motion estimation, video compression, stereo vision, image/video near-duplicate detection, similarity join for image/video database, and so on. Normalized cross correlation (NCC) is one of widely used method for image matching with preferable characteristics such as robustness to intensity offsets and contrast changes, but it is computationally expensive. The proposed features, derived by the method of Lagrange multipliers, can provide upper-bounds of NCC as a simple dot product between two low-dimensional feature vectors. By using the proposed features, NCC-based image matching can be effectively accelerated. The matching performance with the proposed features is demonstrated using an image database obtained from actual broadcast videos. The new features are shown to outperform other methods: multilevel successive elimination algorithm (MSEA), discrete cosine transform (DCT) coefficients, and histograms, achieving very high precision while only slightly sacrificing recall.


title: Domain-Independent Optimistic Initialization for Reinforcement Learning

abstract: In Reinforcement Learning (RL), it is common to use optimistic initialization of value functions to encourage exploration. However, such an approach generally depends on the domain, viz., the scale of the rewards must be known, and the feature representation must have a constant norm. We present a simple approach that performs optimistic initialization with less dependence on the domain.


title: Think different: increasing online community participation using uniqueness and group dissimilarity

abstract: Online communities can help people form productive relationships. Unfortunately, this potential is not always fulfilled: many communities fail, and designers don't have a solid understanding of why. We know community activity begets activity. The trick, however, is to inspire participation in the first place. Social theories suggest methods to spark positive community participation. We carried out a field experiment that tested two such theories. We formed discussion communities around an existing movie recommendation web site, manipulating two factors: (1) similarity-we controlled how similar group members' movie ratings were; and (2) uniqueness-we told members how their movie ratings (with respect to a discussion topic) were unique within the group. Both factors positively influenced participation. The results offer a practical success story in applying social science theory to the design of online communities.


title: Natural Language Generation from Pictographs

abstract: We present a Pictograph-to-Text translation system for people with Intellectual or Developmental Disabilities (IDD). The system translates pictograph messages, consisting of one or more pictographs, into Dutch text using WordNet links and an ngram language model. We also provide several pictograph input methods assisting the users in selecting the appropriate pictographs.


title: Opamp-sharing MDAC design for pipelined successive-stage of a 1.8V 80MS/s 14-bit pipelined ADC

abstract: A design of opamp-sharing multiplying digital-to-analog converter (MDAC) used in the successive stages of an 80MS/s 14-bit pipelined analog-to-digital converter (ADC) with 1.8V supply voltage is presented in this paper. Opamp-sharing structure of the paper is proposed to achieve low-power operation, and SC-CMFB (switch capacitor-common mode feedback) circuit further reduces power consumption. The gain-boost structure of the amplifier is used to meet the precision requirement of the MDAC. The memory effect is completely eliminated with clock-resetting phase. The circuit design is implemented in the Chartered 0.18um CMOS process and the simulation results show that the designed MDAC could meet performance requirements of the pipelined ADC, consuming 10.5mW power.


Medical Students’ Perception on the Use of QR Code Versus Traditional Pen-and-Paper as an Attendance Record Tool in Medical School


title: Game Development Software Engineering Process Life Cycle: A Systematic Review

abstract: Software game is a kind of application that is used not only for entertainment, but also for serious purposes that can be applicable to different domains such as education, business, and health care. Multidisciplinary nature of the game development processes that combine sound, art, control systems, artificial intelligence (AI), and human factors, makes the software game development practice different from traditional software development. However, the underline software engineering techniques help game development to achieve maintainability, flexibility, lower effort and cost, and better design. The purpose of this study is to assesses the state of the art research on the game development software engineering process and highlight areas that need further consideration by researchers. In the study, we used a systematic literature review methodology based on well-known digital libraries. The largest number of studies have been reported in the production phase of the game development software engineering process life cycle, followed by the pre-production phase. By contrast, the post-production phase has received much less research activity than the pre-production and production phases. The results of this study suggest that the game development software engineering process has many aspects that need further attention from researchers; that especially includes the postproduction phase.


title: A probabilistic computational model of cross-situational word learning

abstract: Words are the essence of communication: They are the building blocks of any language. Learning the meaning of words is thus one of the most important aspects of language acquisition: Children must first learn words before they can combine them into complex utterances. Many theories have been developed to explain the impressive efficiency of young children in acquiring the vocabulary of their language, as well as the developmental patterns observed in the course of lexical acquisition. A major source of disagreement among the different theories is whether children are equipped with special mechanisms and biases for word learning, or their general cognitive abilities are adequate for the task. We present a novel computational model of early word learning to shed light on the mechanisms that might be at work in this process. The model learns word meanings as probabilistic associations between words and semantic elements, using an incremental and probabilistic learning mechanism, and drawing only on general cognitive abilities. The results presented here demonstrate that much about word meanings can be learned from naturally occurring child-directed utterances (paired with meaning representations), without using any special biases or constraints, and without any explicit developmental changes in the underlying learning mechanism. Furthermore, our model provides explanations for the occasionally contradictory child experimental data, and offers predictions for the behavior of young word learners in novel situations.


title: Stock Market Declines and Liquidity*

abstract: Consistent with recent theoretical models where binding capital constraints lead to sudden liquidity dry-ups, we find that negative market returns decrease stock liquidity, especially during times of tightness in the funding market. The asymmetric effect of changes in aggregate asset values on liquidity and commonality in liquidity cannot be fully explained by changes in demand for liquidity or volatility effects. We document interindustry spillover effects in liquidity, which are likely to arise from capital constraints in the market making sector. We also find economically significant returns to supplying liquidity following periods of large drops in market valuations. Copyright (c) 2009 the American Finance Association.


title: An Integrative Contingency Model of Software Project Risk Management

abstract: Drawing both from the IS literature on software project risk management and the contingency research in Organization Theory literature, the present study develops an integrative contingency model of software project risk management. Adopting a profile deviation perspective of fit, the outcome of a software development project (Performance) is hypothesized to be influenced by the fit between the project's risk (Risk Exposure) and how project risk is managed (Risk Management Profile). The research model was tested with longitudinal data obtained from project leaders and key users of 75 software projects. The results support the contingency model proposed and suggest that in order to increase project performance a project's risk management profile needs to vary according to the project's risk exposure. Specifically, high-risk projects were found to call for high information processing capacity approaches in their management. However, the most appropriate management approach was found to depend on the performance criterion used. When meeting project budgets was the performance criterion, successful high-risk projects had high levels of internal integration, as well as high levels of formal planning. When system quality was the performance criterion, successful high-risk projects had high levels of user participation.


title: Fundamental Principles of Deception in Genetic Search

abstract: Abstract This paper presents several theorems concerning the nature of deception and the central role that deception plays in function optimization using genetic algorithms. A simple proof is offered which shows that the only problems which pose challenging optimization tasks are problems that involve some degree of deception and which result in conflicting k-arm bandit competitions between hyperplanes. The concept of a deceptive attractor is introduced and shown to be more general than the deceptive optimum found in the deceptive functions that have been constructed to date. Also introduced are the concepts of fully deceptive problems as well as less strict consistently deceptive problems. A proof is given showing that deceptive attractors must have a complementary bit pattern to that found in the binary representation of the global optimum if a function is to be either fully deceptive or consistently deceptive. Some empirical results are presented which demonstrate different methods of dealing with deception and poor linkage during genetic search.


title: Vehicle type classification via adaptive feature clustering for traffic surveillance video

abstract: Vehicle type classification has become an important part of intelligent traffic. However traditional methods can not deal with the varying situations in the reality. In this paper, a novel method is proposed to handle this task in the real road traffic surveillance video. In order to distinguish different vehicles, we categorize vehicles into three types: compact cars, mid-size cars, and heavy-duty vehicles. For a certain video, our method has four steps. First, a deep convolutional neural network is used to detect vehicles in the candidate region and a data set would be generated. Second, the main features of vehicles can be extracted using a fully-connected network. Also, for the sake of higher accuracy, weak labels given by pre-trained extreme learning machine (ELM) are fused into the final features, adding prior information proportionally. Third, K-means is implemented to learn three vehicle-type cluster centers adaptively. Finally, vehicle type will be recognized according to the closest distance principal. Experimental results show that the recognition rate outperforms other traditional methods, verifying the feasibility and effectiveness of the proposed method.


title: YellowFin and the Art of Momentum Tuning

abstract: Hyperparameter tuning is one of the most time-consuming workloads in deep learning. State-of-the-art optimizers, such as AdaGrad, RMSProp and Adam, reduce this labor by adaptively tuning an individual learning rate for each variable. Recently researchers have shown renewed interest in simpler methods like momentum SGD as they may yield better results. Motivated by this trend, we ask: can simple adaptive methods, based on SGD perform as well or better? We revisit the momentum SGD algorithm and show that hand-tuning a single learning rate and momentum makes it competitive with Adam. We then analyze its robustness to learning rate misspecification and objective curvature variation. Based on these insights, we design YellowFin, an automatic tuner for momentum and learning rate in SGD. YellowFin optionally uses a negative-feedback loop to compensate for the momentum dynamics in asynchronous settings on the fly. We empirically show YellowFin can converge in fewer iterations than Adam on ResNets and LSTMs for image recognition, language modeling and constituency parsing, with a speedup of up to $3.28$x in synchronous and up to $2.69$x in asynchronous settings.


title: StoryBank: mobile digital storytelling in a development context

abstract: Mobile imaging and digital storytelling currently support a growing practice of multimedia communication in the West. In this paper we describe a project which explores their benefit in the East, to support non-textual information sharing in an Indian village. Local audiovisual story creation and sharing activities were carried out in a one month trial, using 10 customized cameraphones and a digital library of stories represented on a village display. The findings show that the system was usable by a cross-section of the community and valued for its ability to express a mixture of development and community information in an accessible form. Lessons for the role of HCI in this context are also discussed.


title: Evaluation of LoRa LPWAN technology for remote health and wellbeing monitoring

abstract: Low power consumption, low transceiver chip cost and large coverage area are the main characteristics of the low power wide area networks (LPWAN) technologies. We expect that LPWAN can be part of enabling new human-centric health and wellness monitoring applications. Therefore in this work we study the indoor performance of one LPWAN technology, namely LoRa, by the means of real-life measurements. The measurements were conducted using the commercially available equipment in the main campus of the University of Oulu, Finland, which has an indoor area spanning for over 570 meters North to South and over 320 meters East to West. The measurements were executed for a sensor node operating close to human body that was periodically reporting the sensed data to a base station. The obtained results show that when using 14 dBm transmit power and the largest spreading factor of 12 for the 868 MHz ISM band, the whole campus area can be covered. Measured packet success delivery ratio was 96.7 % without acknowledgements and retransmissions.


title: Vehicle-to-vehicle safety messaging in DSRC

abstract: This paper studies the design of layer-2 protocols for a vehicle to send safety messages to other vehicles. The target is to send vehicle safety messages with high reliability and low delay. The communication is one-to-many, local, and geo-significant. The vehicular communication network is ad-hoc, highly mobile, and with large numbers of contending nodes. The messages are very short, have a brief useful lifetime, but must be received with high probability. For this environment, this paper explores the efficacy of rapid repetition of broadcast messages. This paper proposes several random access protocols for medium access control. The protocols are compatible with the Dedicated Short Range Communications (DSRC) multi-channel architecture. Analytical bounds on performance of the proposed protocols are derived. Simulations are conducted to assess the reception reliability and channel usage of the protocols. The sensitivity of the protocol performance is evaluated under various offered traffic and vehicular traffic flows. The results show our approach is feasible for vehicle safety messages in DSRC.


title: Comparison of Behavioral Intervention and Sensory-Integration Therapy in the Treatment of Challenging Behavior

abstract: The objective of the current study was to compare the effects of sensory-integration therapy (SIT) and a behavioral intervention on rates of challenging behavior (including self-injurious behavior) in four children diagnosed with Autism Spectrum Disorder. For each of the participants a functional assessment was conducted to identify the variables maintaining challenging behavior. Results of these assessments were used to design function-based behavioral interventions for each participant. Recommendations for the sensory-integration treatment were designed by an Occupational Therapist, trained in the use of sensory-integration theory and techniques. The sensory-integration techniques were not dependent on the results of the functional assessments. The study was conducted within an alternating treatments design, with initial baseline and final best treatment phase. For each participant, results demonstrated that the behavioral intervention was more effective than the sensory integration therapy in the treatment of challenging behavior. In the best treatment phase, the behavioral intervention alone was implemented and further reduction was observed in the rate of challenging behavior. Analysis of saliva samples revealed relatively low levels of cortisol and very little stress-responsivity across the SIT condition and the behavioral intervention condition, which may be related to the participants’ capacity to perceive stress in terms of its social significance.


title: Virtual Reality Exposure Therapy for the Treatment of Posttraumatic Stress Disorder Following September 11, 2001

abstract: Objective: This preliminary study endeavored to evaluate the use of virtual reality (VR) enhanced exposure therapy for the treatment of posttraumatic stress disorder (PTSD) consequent to the World Trade Center attacks of September 11,2001. Method: Participants were assigned to a VR treatment (N = 13) or a waitlist control (N = 8) group and were mostly middle-aged, male disaster workers. All participants were diagnosed with PTSD according to DSM-IV-TR criteria using the Clinician-Administered PTSD Scale (CAPS). The study was conducted between February 2002 and August 2005 in offices located in outpatient buildings of a hospital campus. Results: Analysis of variance showed a significant interaction of time by group (p <.01) on CAPS scores, with a between-groups posttreatment effect size of 1.54. The VR group showed a significant decline in CAPS scores compared with the waitlist group (p <.01). Conclusions: Our preliminary data suggest that VR is an effective treatment tool for enhancing exposure therapy for both civilians and disaster workers with PTSD and may be especially useful for those patients who cannot engage in imaginal exposure therapy.


title: Annotating and sketching on 3D web models

abstract: This paper reports on our progress and findings in building a Web annotation system for non-immersive 3D virtual environments. Over the last two years, we developed and tested two systems for collaborating designers to comment on virtual 3D models. Our first system, Redliner [12] lets design team members browse and leave text annotations on surfaces in three-dimensional models. Experience with Redliner, including two user evaluations in different settings, led us to develop Space Pen [13], a second annotation system with improved interaction capabilities. It goes beyond the post-it note metaphor, allowing users to draw in and on the virtual environment.


title: Deep Learning Hierarchical Representations for Image Steganalysis

abstract: Nowadays, the prevailing detectors of steganographic communication in digital images mainly consist of three steps, i.e., residual computation, feature extraction, and binary classification. In this paper, we present an alternative approach to steganalysis of digital images based on convolutional neural network (CNN), which is shown to be able to well replicate and optimize these key steps in a unified framework and learn hierarchical representations directly from raw images. The proposed CNN has a quite different structure from the ones used in conventional computer vision tasks. Rather than a random strategy, the weights in the first layer of the proposed CNN are initialized with the basic high-pass filter set used in the calculation of residual maps in a spatial rich model (SRM), which acts as a regularizer to suppress the image content effectively. To better capture the structure of embedding signals, which usually have extremely low SNR (stego signal to image content), a new activation function called a truncated linear unit is adopted in our CNN model. Finally, we further boost the performance of the proposed CNN-based steganalyzer by incorporating the knowledge of selection channel. Three state-of-the-art steganographic algorithms in spatial domain, e.g., WOW, S-UNIWARD, and HILL, are used to evaluate the effectiveness of our model. Compared to SRM and its selection-channel-aware variant maxSRMd2, our model achieves superior performance across all tested algorithms for a wide variety of payloads.


title: Formant smoothing for quality improvement of post-laryngectomised speech reconstruction

abstract: In this paper, we use the voice samples recorded from laryngectomised patients to develop a novel method for speech enhancement and regeneration of natural sounding speech for laryngectomees. By leveraging recent advances in computational methods for speech reconstruction, our proposed method takes advantages of both non-training and training-based approaches to improve the quality of reconstructed speech for voice-impaired individuals. Since the proposed method has been developed based on the samples obtained from post-laryngectomised patients (and not based on the characteristics of other alternative modes of speech such as whispers and pseudo-whispers), it can address the limitations of current computational methods to some extent. Furthermore, by focusing on English vowels, objective evaluations are carried out to show the efficiency of the proposed method.


title: Optimization With Parity Constraints: From Binary Codes to Discrete Integration

abstract: Many probabilistic inference tasks involve summations over exponentially large sets. Recently, it has been shown that these problems can be reduced to solving a polynomial number of MAP inference queries for a model augmented with randomly generated parity constraints. By exploiting a connection with max-likelihood decoding of binary codes, we show that these optimizations are computationally hard. Inspired by iterative message passing decoding algorithms, we propose an Integer Linear Programming (ILP) formulation for the problem, enhanced with new sparsification techniques to improve decoding performance. By solving the ILP through a sequence of LP relaxations, we get both lower and upper bounds on the partition function, which hold with high probability and are much tighter than those obtained with variational methods.


Raltegravir: is a 400 mg once-daily dose enough?


title: A Pattern Mining Approach to Sensor-Based Human Activity Recognition

abstract: Recognizing human activities from sensor readings has recently attracted much research interest in pervasive computing due to its potential in many applications, such as assistive living and healthcare. This task is particularly challenging because human activities are often performed in not only a simple (i.e., sequential), but also a complex (i.e., interleaved or concurrent) manner in real life. Little work has been done in addressing complex issues in such a situation. The existing models of interleaved and concurrent activities are typically learning-based. Such models lack of flexibility in real life because activities can be interleaved and performed concurrently in many different ways. In this paper, we propose a novel pattern mining approach to recognize sequential, interleaved, and concurrent activities in a unified framework. We exploit Emerging Pattern-a discriminative pattern that describes significant changes between classes of data-to identify sensor features for classifying activities. Different from existing learning-based approaches which require different training data sets for building activity models, our activity models are built upon the sequential activity trace only and can be applied to recognize both simple and complex activities. We conduct our empirical studies by collecting real-world traces, evaluating the performance of our algorithm, and comparing our algorithm with static and temporal models. Our results demonstrate that, with a time slice of 15 seconds, we achieve an accuracy of 90.96 percent for sequential activity, 88.1 percent for interleaved activity, and 82.53 percent for concurrent activity.


title: A NEW METHOD FOR WEB SITE PROTECTION AND COMPRESSION

abstract: Protection of the Web contents is of great importance in the field of web design. In this paper, a program called Web Encrypt 2, to provide protection and compression of web sites is presented. It is based on a modification of the trigraph algorithm and has several improvements in reference to the already existing software. Detailed description of the features and a comparative analysis of the proposed program are given.


title: Game-theoretic approach towards network security: A review

abstract: Advancements in information technology has increased the use of internet. With the pervasiveness of internet, network security has become critical issue in every organization. Network attacks results in massive amount of loss in terms of money, reputation and data confidentiality. Reducing or eliminating the negative effects of any intrusion is a fundamental issue of network security. The network security problem can be represented as a game between the attacker or intruder and the network administrator where both the players try to attain maximum outcome. The network administrator tries to defend the attack and the attacker tries to overcome it and attack the system. Thus network security can be enforced using game theoretic approach. This paper presents a review of game theoretic solutions developed for network security.


title: Growing a Brain: Fine-Tuning by Increasing Model Capacity

abstract: CNNs have made an undeniable impact on computer vision through the ability to learn high-capacity models with large annotated training sets. One of their remarkable properties is the ability to transfer knowledge from a large source dataset to a (typically smaller) target dataset. This is usually accomplished through fine-tuning a fixed-size network on new target data. Indeed, virtually every contemporary visual recognition system makes use of fine-tuning to transfer knowledge from ImageNet. In this work, we analyze what components and parameters change during fine-tuning, and discover that increasing model capacity allows for more natural model adaptation through fine-tuning. By making an analogy to developmental learning, we demonstrate that growing a CNN with additional units, either by widening existing layers or deepening the overall network, significantly outperforms classic fine-tuning approaches. But in order to properly grow a network, we show that newly-added units must be appropriately normalized to allow for a pace of learning that is consistent with existing units. We empirically validate our approach on several benchmark datasets, producing state-of-the-art results.


Neurohormonal mechanisms in emotional inhibition of milk ejection.


title: Learning to Compose with Professional Photographs on the Web

abstract: Photo composition is an important factor affecting the aesthetics in photography. However, it is a highly challenging task to model the aesthetic properties of good compositions due to the lack of globally applicable rules to the wide variety of photographic styles. Inspired by the thinking process of photo taking, we formulate the photo composition problem as a view finding process which successively examines pairs of views and determines their aesthetic preferences. We further exploit the rich professional photographs on the web to mine unlimited high-quality ranking samples and demonstrate that an aesthetics-aware deep ranking network can be trained without explicitly modeling any photographic rules. The resulting model is simple and effective in terms of its architectural design and data sampling method. It is also generic since it naturally learns any photographic rules implicitly encoded in professional photographs. The experiments show that the proposed view finding network achieves state-of-the-art performance with sliding window search strategy on two image cropping datasets.


title: Automatic Evaluation of Text Coherence: Models and Representations

abstract: This paper investigates the automatic evaluation of text coherence for machine-generated texts. We introduce a fully-automatic, linguistically rich model of local coherence that correlates with human judgments. Our modeling approach relies on shallow text properties and is relatively inexpensive. We present experimental results that assess the predictive power of various discourse representations proposed in the linguistic literature. Our results demonstrate that certain models capture complementary aspects of coherence and thus can be combined to improve performance.


title: AC/DC TCP: Virtual Congestion Control Enforcement for Datacenter Networks

abstract: Multi-tenant datacenters are successful because tenants can seamlessly port their applications and services to the cloud. Virtual Machine (VM) technology plays an integral role in this success by enabling a diverse set of software to be run on a unified underlying framework. This flexibility, however, comes at the cost of dealing with out-dated, inefficient, or misconfigured TCP stacks implemented in the VMs. This paper investigates if administrators can take control of a VM's TCP congestion control algorithm without making changes to the VM or network hardware. We propose AC/DC TCP, a scheme that exerts fine-grained control over arbitrary tenant TCP stacks by enforcing per-flow congestion control in the virtual switch (vSwitch). Our scheme is light-weight, flexible, scalable and can police non-conforming flows. In our evaluation the computational overhead of AC/DC TCP is less than one percentage point and we show implementing an administrator-defined congestion control algorithm in the vSwitch (i.e., DCTCP) closely tracks its native performance, regardless of the VM's TCP stack.


title: ENVIRONMENTALLY SUSTAINABLE BUSINESS PROCESS MANAGEMENT – DEVELOPING A GREEN BPM READINESS MODEL

abstract: During the last half decade, various theories, concepts, and models have been established to make organizations more environmentally sustainable. The IS community had their share with the Green IS/IT domain. As it turned out most of these concepts took a very functional approach, not making a good fit for business process oriented organizations. To fill this gap the research stream of Green Business Process Management (GBPM) arose. In this paper we develop and discuss various capabilities that organizations should have in order to actually take advantage of GBPM. We call these capabilities GBPM readiness.


title: Local Trinary Patterns for human action recognition

abstract: We present a novel action recognition method which is based on combining the effective description properties of Local Binary Patterns with the appearance invariance and adaptability of patch matching based methods. The resulting method is extremely efficient, and thus is suitable for real-time uses of simultaneous recovery of human action of several lengths and starting points. Tested on all publicity available datasets in the literature known to us, our system repeatedly achieves state of the art performance. Lastly, we present a new benchmark that focuses on uncut motion recognition in broadcast sports video.


title: A New Algorithm of Fully-automatic Red-eye Removal

abstract: The redeye effect is typically formed in consumer photos taken with a built-in camera flash. The paper presents a new algorithm of the automatic removal of red-eye from digital photos. The proposed algorithm can remove red-eye automatically without manual intervention. First, Detecting faces by using the AdaBoost algorithm. Second, the red-eyes are located by using operations of segmentation, morphology and geometric constraint. Finally, correcting the located red-eyes completely. The experimental results are satisfied with high correction rates, relatively low computational complexity and robustness.


title: Torque mode control of a Bowden cable-driven assistive system

abstract: A cable-driven actuating system is proposed in this paper. The proposed system is attractive for rehabilitation systems because the weight of the actuator is not imposed on the human body. Since the end-effector and the actuators are connected by Bowden cables, the humans are allowed to freely move in a certain range while being assisted. However, it is a challenge to account for the variable friction of the Bowden cable and the inertia and friction of the actuator in the design of control algorithms. In this paper, a hierarchical control strategy is adopted to control the proposed cable-driven actuating system. To determine the reference trajectory of the motor in real-time, a sensor fusion method based on a kinematic Kalman filter with MEMS accelerometers is proposed. By the proposed control methods, the cable-driven actuating system realizes a precise force-mode actuation. The system performance is evaluated by experiments.


title: Understanding kangaroo care and its benefits to preterm infants

abstract: The holding of an infant with ventral skin-to-skin contact typically in an upright position with the swaddled infant on the chest of the parent, is commonly referred to as kangaroo care (KC), due to its simulation of marsupial care. It is recommended that KC, as a feasible, natural, and cost-effective intervention, should be standard of care in the delivery of quality health care for all infants, regardless of geographic location or economic status. Numerous benefits of its use have been reported related to mortality, physiological (thermoregulation, cardiorespiratory stability), behavioral (sleep, breastfeeding duration, and degree of exclusivity) domains, as an effective therapy to relieve procedural pain, and improved neurodevelopment. Yet despite these recommendations and a lack of negative research findings, adoption of KC as a routine clinical practice remains variable and underutilized. Furthermore, uncertainty remains as to whether continuous KC should be recommended in all settings or if there is a critical period of initiation, dose, or duration that is optimal. This review synthesizes current knowledge about the benefits of KC for infants born preterm, highlighting differences and similarities across low and higher resource countries and in a non-pain and pain context. Additionally, implementation considerations and unanswered questions for future research are addressed. kangaroo care, skin-to-skin contact, infant, preterm, review


title: DEUCE: Write-Efficient Encryption for Non-Volatile Memories

abstract: Phase Change Memory (PCM) is an emerging Non Volatile Memory (NVM) technology that has the potential to provide scalable high-density memory systems. While the non-volatility of PCM is a desirable property in order to save leakage power, it also has the undesirable effect of making PCM main memories susceptible to newer modes of security vulnerabilities, for example, accessibility to sensitive data if a PCM DIMM gets stolen. PCM memories can be made secure by encrypting the data. Unfortunately, such encryption comes with a significant overhead in terms of bits written to PCM memory, causing half of the bits in the line to change on every write, even if the actual number of bits being written to memory is small. Our studies show that a typical writeback modifies, on average, only 12% of the bits in the cacheline. Thus, encryption causes almost a 4x increase in the number of bits written to PCM memories. Such extraneous bit writes cause significant increase in write power, reduction in write endurance, and reduction in write bandwidth. To provide the benefit of secure memory in a write efficient manner this paper proposes Dual Counter Encryption (DEUCE). DEUCE is based on the observation that a typical writeback only changes a few words, so DEUCE reencrypts only the words that have changed. We show that DEUCE reduces the number of modified bits per writeback for a secure memory from 50% to 24%, which improves performance by 27% and increases lifetime by 2x.


title: Consciousness: a unique way of processing information

abstract: In this article, I argue that consciousness is a unique way of processing information, in that: it produces information, rather than purely transmitting it; the information it produces is meaningful for us; the meaning it has is always individuated. This uniqueness allows us to process information on the basis of our personal needs and ever-changing interactions with the environment, and consequently to act autonomously. Three main basic cognitive processes contribute to realize this unique way of information processing: the self, attention and working memory. The self, which is primarily expressed via the central and peripheral nervous systems, maps our body, the environment, and our relations with the environment. It is the primary means by which the complexity inherent to our composite structure is reduced into the “single voice” of a unique individual. It provides a reference system that (albeit evolving) is sufficiently stable to define the variations that will be used as the raw material for the construction of conscious information. Attention allows for the selection of those variations in the state of the self that are most relevant in the given situation. Attention originates and is deployed from a single locus inside our body, which represents the center of the self, around which all our conscious experiences are organized. Whatever is focused by attention appears in our consciousness as possessing a spatial quality defined by this center and the direction toward which attention is focused. In addition, attention determines two other features of conscious experience: periodicity and phenomenal quality. Self and attention are necessary but not sufficient for conscious information to be produced. Complex forms of conscious experiences, such as the various modes of givenness of conscious experience and the stream of consciousness, need a working memory mechanism to assemble the basic pieces of information selected by attention.


Harassment Detection on Twitter using Conversations


title: PYIN: A fundamental frequency estimator using probabilistic threshold distributions

abstract: We propose the Probabilistic YIN (PYIN) algorithm, a modification of the well-known YIN algorithm for fundamental frequency (F0) estimation. Conventional YIN is a simple yet effective method for frame-wise monophonic F0 estimation and remains one of the most popular methods in this domain. In order to eliminate short-term errors, outputs of frequency estimators are usually post-processed resulting in a smoother pitch track. One shortcoming of YIN is that such post-processing cannot fall back on alternative interpretations of the signal because the method outputs precisely one estimate per frame. To address this problem we modify YIN to output multiple pitch candidates with associated probabilities (PYIN Stage 1). These probabilities arise naturally from a prior distribution on the YIN threshold parameter. We use these probabilities as observations in a hidden Markov model, which is Viterbi-decoded to produce an improved pitch track (PYIN Stage 2). We demonstrate that the combination of Stages 1 and 2 raises recall and precision substantially. The additional computational complexity of PYIN over YIN is low. We make the method freely available online ::: 1 ::: as an open source C++ library for Vamp hosts.


title: A survey of approaches for verifying model transformations

abstract: As with other software development artifacts, model transformations are not bug-free and so must be systematically verified. Their nature, however, means that transformations require specialist verification techniques. This paper brings together current research on model transformation verification by classifying existing approaches along two dimensions. Firstly, we present a coarse-grained classification based on the technical details of the approach (e.g., testing, theorem proving, model checking). Secondly, we present a finer-grained classification which categorizes approaches according to criteria such as level of formality, transformation language, properties verified. The purpose of the survey is to bring together research in model transformation verification to act as a resource for the community. Furthermore, based on the survey, we identify a number of trends in current and past research on model transformation verification.


title: Simulation and analysis of SVPWM based VSI for wind energy systems

abstract: Space Vector Pulse Width Modulation (SVPWM) technique is extensively used to control the three-phase voltage source inverter (VSI) of wind energy systems. The main advantages of SVPWM method are fixed switching frequency, low harmonic content and higher DC bus utilization. In terms of the use of the DC bus voltage compared to conventional PWM method provides 15.15% more efficient usage. This paper focuses on SVPWM controlled three-phase VSI simulated under MATLAB/SIMULINK.


title: Active Learning for Regression Using Greedy Sampling

abstract: Abstract Regression problems are pervasive in real-world applications. Generally a substantial amount of labeled samples are needed to build a regression model with good generalization ability. However, many times it is relatively easy to collect a large number of unlabeled samples, but time-consuming or expensive to label them. Active learning for regression (ALR) is a methodology to reduce the number of labeled samples, by selecting the most beneficial ones to label, instead of random selection. This paper proposes two new ALR approaches based on greedy sampling (GS). The first approach (GSy) selects new samples to increase the diversity in the output space, and the second (iGS) selects new samples to increase the diversity in both input and output spaces. Extensive experiments on 10 UCI and CMU StatLib datasets from various domains, and on 15 subjects on EEG-based driver drowsiness estimation, verified their effectiveness and robustness.


title: Unsupervised Deep Generative Adversarial Hashing Network

abstract: Unsupervised deep hash functions have not shown satisfactory improvements against their shallow alternatives, and usually require supervised pretraining to avoid overfitting. In this paper, we propose a new deep unsupervised hashing function, called HashGAN, which efficiently obtains binary representation of input images without any supervised pretraining. HashGAN consists of three networks, a generator, a discriminator and an encoder. By sharing the parameters of the encoder and discriminator, we benefit from the adversarial loss as a data-dependent regularization in training our deep hash function. Moreover, a novel hashing loss function is introduced for real images, which results in minimum entropy, uniform frequency, consistent and independent hash bits. Furthermore, we employ a collaborative loss in training our model, enforcing similar random inputs and hash bits for synthesized images. In our experiments, HashGAN outperforms the previous unsupervised hash functions in image retrieval and achieves the state-of-the-art performance in image clustering on benchmark datasets. We also provide an ablation study, showing the contribution of each component in our loss function.


title: Graph Cuts and Efficient N-D Image Segmentation

abstract: Combinatorial graph cut algorithms have been successfully applied to a wide range of problems in vision and graphics. This paper focusses on possibly the simplest application of graph-cuts: segmentation of objects in image data. Despite its simplicity, this application epitomizes the best features of combinatorial graph cuts methods in vision: global optima, practical efficiency, numerical robustness, ability to fuse a wide range of visual cues and constraints, unrestricted topological properties of segments, and applicability to N-D problems. Graph cuts based approaches to object extraction have also been shown to have interesting connections with earlier segmentation methods such as snakes, geodesic active contours, and level-sets. The segmentation energies optimized by graph cuts combine boundary regularization with region-based properties in the same fashion as Mumford-Shah style functionals. We present motivation and detailed technical description of the basic combinatorial optimization framework for image segmentation via s/t graph cuts. After the general concept of using binary graph cut algorithms for object segmentation was first proposed and tested in Boykov and Jolly (2001), this idea was widely studied in computer vision and graphics communities. We provide links to a large number of known extensions based on iterative parameter re-estimation and learning, multi-scale or hierarchical approaches, narrow bands, and other techniques for demanding photo, video, and medical applications.


title: Learning Spatial Knowledge for Text to 3D Scene Generation

abstract: We address the grounding of natural language to concrete spatial constraints, and inference of implicit pragmatics in 3D environments. We apply our approach to the task of text-to-3D scene generation. We present a representation for common sense spatial knowledge and an approach to extract it from 3D scene data. In text-to3D scene generation, a user provides as input natural language text from which we extract explicit constraints on the objects that should appear in the scene. The main innovation of this work is to show how to augment these explicit constraints with learned spatial knowledge to infer missing objects and likely layouts for the objects in the scene. We demonstrate that spatial knowledge is useful for interpreting natural language and show examples of learned knowledge and generated 3D scenes.


title: Conducted EMI modeling and reduction in a flyback switched mode power supply

abstract: In the design of power electronics systems, electromagnetic compatibility (EMC) should be well attended as well as electrical, thermal and mechanical issues. This paper provides a characterization approach for conducted electromagnetic interference (EMI) in switching mode power supplies. The EMI model outlined in this paper considers main parasitics of the components to forecast EMI phenomena. The experimental results validate that the derived model can roughly predict EMI. To attenuate EMI in the prototype converter, spreading spectrum is employed and its influence on EMI spectra is presented via measurement.


title: Constructing Knowledge Graphs of Depression

abstract: Knowledge Graphs have been shown to be useful tools for integrating multiple medical knowledge sources, and to support such tasks as medical decision making, literature retrieval, determining healthcare quality indicators, co-morbodity analysis and many others. A large number of medical knowledge sources have by now been converted to knowledge graphs, covering everything from drugs to trials and from vocabularies to gene-disease associations. Such knowledge graphs have typically been generic, covering very large areas of medicine. (e.g. all of internal medicine, or arbitrary drugs, arbitrary trials, etc.). This has had the effect that such knowledge graphs become prohibitively large, hampering both efficiency for machines and usability for people. In this paper we show how we use multiple large knowledge sources to construct a much smaller knowledge graph that is focussed on single disease (in our case major depression disorder). Such a disease-centric knowledge-graph makes it more convenient for doctors (in our case psychiatric doctors) to explore the relationship among various knowledge resources and to answer realistic clinical queries (This paper is an extended version of [1].).


title: Performance Analysis of Millimeter-Wave Phased Array Antennas in Cellular Handsets

abstract: This letter discusses the usage of high-gain steerable antenna arrays operating at millimeter-wave (mmWave) frequencies for future cellular networks (5G). Based on the probable outline of the 5G networks, a method for characterizing phased array antennas in cellular handsets has been introduced. For analyzing the performance, the total scan pattern of the array configuration together with its respective coverage efficiency are essential to consider in order to compare different antenna designs and topology approaches with each other. Two design approaches and subarray schemes of these have been considered in order to illustrate the relevance of such a characterization method. The results show the importance of evaluating potential array antennas in such manners. The method can be applied to much more complex system models, where polarization diversity, hand and body effect, and statistical modeling of the channel may be included.


title: Performance improvement of QPSK modem implemented in FPGA

abstract: This paper proposes a Quadrature Phase Shift Keying (QPSK) using two different methods. QPSK is one of the forms of Phase Shift Keying (PSK) modulation scheme. Generally a conventional QPSK modulator with Direct Digital Synthesizer (DDS) and arithmetic multiplier separates base band signal into I and Q phase which consumes low throughput with complexity in hardware implementation. Hence to generate high throughput QPSK modulator, the first proposal uses an up and down accumulator for carrier generator instead of DDS and arithmetic multiplier is modified as BOOTH multiplier. The second proposed method will produce the QPSK signal, which is based on stored QPSK phase data in ROM, which eliminates completely the DDS and multiplier blocks of the modulator.


title: Substrate integrated waveguide (SIW) linear phase filter

abstract: A planar four-pole linear phase filter centered at 10GHz based on substrate integrated waveguide (SIW) is proposed. The filter is composed of four side-by-side horizontally oriented SIW cavities, which are coupled in turn by evanescent waveguide sections with three direct coupling and one cross coupling between the first and fourth SIW cavities. The SIW cavities are fed by microstrips through coupling slots. A curve-fitting technique is adopted to improve the efficiency of the design process. The measured results are presented and compared with the results simulated by a high-frequency structure simulator. Good agreement between the simulated and measured results is observed.


title: The design of communication convertor based on CAN bus

abstract: With the plentiful application of various electronic equipments and control devices which used in industry, the communication networks based on field bus are produced. Connection between CAN bus and normal bus RS-232 which is used in existing serial communication, could realize to construct network with multidrop and long-distance communication. Method of conversion between RS-232 and CAN bus is discussed and a communication convertor with new design and debug is introduced in the paper. In order to conquer the illogicalities or conflictions brought from bus configuration and communication protocol of various controller, it is chosen unattached bus controller SJA1000 and singlechip AT89C51 to constitute CAN bus brainpower node in the convertor; made use of MAX232 to complete the level conversion from 232 to micro controller interface card TTL; provided differential sending capability of bus and differential incepting capability of CAN controller. RS-232 with the excellences of working in ISM (industry, science, medicine) domains and having built-in UART (asynchronism serial receival) interface, combined with CAN, could realize data communication between PC serial interface and CAN bus fleetly and exactly. It was examined through debug that there are some advantages of long direct communication distances, high communication velocity, simple configuration, low cost with the design. And it could replace the CAN interface card with high price in current market for reasons of simple configuration and low-cost.


title: Enhanced CNN Based Electron Microscopy Image Segmentation

abstract: Abstract Detecting the neural processes like axons and dendrites needs high quality SEM images. This paper proposes an approach using perceptual grouping via a graph cut and its combinations with Convolutional Neural Network (CNN) to achieve improved segmentation of SEM images. Experimental results demonstrate improved computational efficiency with linear running time.


title: Case studies of spinal deformities in ornamental koi, Cyprinus carpio L

abstract: This is a study of vertebral deformities in ornamental koi based on computed radiography and skeletons cleaned by dermestid beetles (Dermestes maculatus). All koi developed gradual onset of swimming abnormalities as adults. Extensive intervertebral osteophyte formation correlated with age of fish and was associated with hindquarter paresis in one koi. Vertebral compression and fusion were the most common spinal deformities occurring at multiple sites, similar to findings in other farmed fish. Site-specific spinal deformities were thought to develop due to differences in swimming behaviour and rates of vertebral growth. One koi had offspring with spinal deformities. Spinal deformities are significant problems in both European and Australian food fish hatcheries. The heritability of vertebral deformities in farmed fish is reportedly low unless there is concurrent poor husbandry or nutritional deficiencies. The specific aetiologies for vertebral deformities in koi in this study could not be ascertained. Current knowledge on spinal deformities in the better studied European food fish species suggests multifactorial aetiologies. Future research should include prospective longitudinal studies of larger numbers of koi from hatch and consideration of all potential risk factors such as husbandry, nutrition, temperature, photoperiod and genetics.


title: An Integrated, Conditional Model of Information Extraction and Coreference with Applications to Citation Matching

abstract: Although information extraction and coreference resolution appear together in many applications, most current systems perform them as ndependent steps. This paper describes an approach to integrated inference for extraction and coreference based on conditionally-trained undirected graphical models. We discuss the advantages of conditional probability training, and of a coreference model structure based on graph partitioning. On a data set of research paper citations, we show significant reduction in error by using extraction uncertainty to improve coreference citation matching accuracy, and using coreference to improve the accuracy of the extracted fields.


PACIS ) 2014 EXPLORING MOBILE PAYMENT ADOPTION IN CHINA


Parallel implementation of Apriori algorithms on the Hadoop-MapReduce platform - An evaluation of literature


title: Daughter cyst sign.

abstract: The daughter cyst sign is a specific indicator of an uncomplicated ovarian cyst and pathologically represents a stimulated ovarian follicle. This finding must be differentiated from an ectopic pregnancy in a patient who has the potential to become pregnant. We report an uncomplicated ovarian cyst in a 3-year-old female with McCune-Albright syndrome and precocious puberty mimicking an ectopic pregnancy.


title: AppGuard – Enforcing User Requirements on Android

abstract: The success of Android phones makes them a prominent target for malicious software, in particular since the Android permission system turned out to be inadequate to protect the user against security and privacy threats. This work presents AppGuard, a powerful and flexible system for the enforcement of user-customizable security policies on untrusted Android applications. AppGuard does not require any changes to a smartphone's firmware or root access. Our system offers complete mediation of security-relevant methods based on callee-site inline reference monitoring. We demonstrate the general applicability of AppGuard by several case studies, e.g., removing permissions from overly curious apps as well as defending against several recent real-world attacks on Android phones. Our technique exhibits very little space and runtime overhead. AppGuard is publicly available, has been invited to the Samsung Apps market, and has had more than 500,000 downloads so far.


title: 3D Surface Reconstruction by Pointillism

abstract: The objective of this work is to infer the 3D shape of an object from a single image. We use sculptures as our training and test bed, as these have great variety in shape and appearance. ::: To achieve this we build on the success of multiple view geometry (MVG) which is able to accurately provide correspondences between images of 3D objects under varying viewpoint and illumination conditions, and make the following contributions: first, we introduce a new loss function that can harness image-to-image correspondences to provide a supervisory signal to train a deep network to infer a depth map. The network is trained end-to-end by differentiating through the camera. Second, we develop a processing pipeline to automatically generate a large scale multi-view set of correspondences for training the network. Finally, we demonstrate that we can indeed obtain a depth map of a novel object from a single image for a variety of sculptures with varying shape/texture, and that the network generalises at test time to new domains (e.g. synthetic images).


title: Multi-Source Domain Adaptation with Mixture of Experts

abstract: We propose a mixture-of-experts approach for unsupervised domain adaptation from multiple sources. The key idea is to explicitly capture the relationship between a target example and different source domains. This relationship, expressed by a point-to-set metric, determines how to combine predictors trained on various domains. The metric is learned in an unsupervised fashion using meta-training. Experimental results on sentiment analysis and part-of-speech tagging demonstrate that our approach consistently outperforms multiple baselines and can robustly handle negative transfer.


title: Objects2action: Classifying and Localizing Actions without Any Video Example

abstract: The goal of this paper is to recognize actions in video without the need for examples. Different from traditional zero-shot approaches we do not demand the design and specification of attribute classifiers and class-to-attribute mappings to allow for transfer from seen classes to unseen classes. Our key contribution is objects2action, a semantic word embedding that is spanned by a skip-gram model of thousands of object categories. Action labels are assigned to an object encoding of unseen video based on a convex combination of action and object affinities. Our semantic embedding has three main characteristics to accommodate for the specifics of actions. First, we propose a mechanism to exploit multiple-word descriptions of actions and objects. Second, we incorporate the automated selection of the most responsive objects per action. And finally, we demonstrate how to extend our zero-shot approach to the spatio-temporal localization of actions in video. Experiments on four action datasets demonstrate the potential of our approach.


title: Guaranteed-quality mesh generation for curved surfaces

abstract: For several commonly-used solution techniques for partial differential equations, the first step is to divide the problem region into simply-shaped elements, creating a mesh. We present a technique for creating high-quality triangular meshes for regions on curved surfaces. This technique is an extension of previous methods we developed for regions in the plane. For both flat and curved surfaces, the resulting meshes are guaranteed to exhibit the following properties: (1) internal and external boundaries are respected, (2) element shapes are guaranteed—all elements are triangles with angles between 30 and 120 degrees (with the exception of badly shaped elements that may be required by the specified boundary), and (3) element density can be controlled, producing small elements in “interesting” areas and large elements elsewhere. An additional contribution of this paper is the development of a practical generalization of Delaunay triangulation to curved surfaces.


title: Guided-Wave Properties of Mode-Selective Transmission Line

abstract: The so-called mode-selective transmission line or simply “MSTL” is studied theoretically and experimentally. This low-loss and low-dispersion transmission line operates with a frequency-dependent mode-switching behavior. This self-adaptive mode-selective guided-wave structure begins with the propagation of electromagnetic waves over the lower frequency range in the form of a quasi-TEM fundamental mode similar to the microstrip line case, then followed by a fundamental quasi-TE10 mode with reference to rectangular waveguide over the higher frequency region. To gain insight into the physical mechanism and fundamental features of this mode-selective transmission line, a detailed semi-analytical hybrid-mode analysis is developed through the application of a method of lines. This method allows accurate and effective modeling of MSTL guided-wave properties. Propagation characteristics of this proposed mode-agile structure in terms of dispersion, modal, and loss properties are examined, which leads to the establishment of some basic MSTL design guidelines. Numerical results confirm the expected mode conversion and low-loss behavior through the observation of field evolutions along the structure. For experimental verification, a set of MSTL prototypes are fabricated on two different substrates through dissimilar fabrication processes. Measurements are carried out from dc-to-500 GHz using a vector network analyzer. Excellent agreement between theoretical and experimental results is observed. It is confirmed that the low-dispersion and low-loss behavior of MSTL makes it an outstanding integrated waveguide in support of high-performance super-broadband signal transmission and/or ultra-fast pulse propagation in a fully-integrated platform.


title: Radar Signal Processing for Jointly Estimating Tracks and Micro-Doppler Signatures

abstract: The aim of the radar systems is to collect information about their surroundings. In many scenarios besides static targets there are numerous moving objects with very different characteristics, such as extent, movement behavior or micro-Doppler spread. It would be most desirable to have algorithms that extract all information on static and moving object automatically, without a system operator. In this paper, we present measurements conducted with a commercially available high-resolution multi-channel linear frequency-modulated continuous-wave radar and algorithms that do not only produce radar images but a description of the scenario on a higher level. After conventional spectrum estimation and thresholding, we present a clustering stage that combines individual detections and generates representations of each target individually. This stage is followed by a Kalman filter based multi-target tracking block. The tracker allows us to follow each target and collect its properties over time. With this method of jointly estimating tracks and characteristics of each individual target in a scenario, inputs for classifiers can be generated. Which, in turn, will be able to generate information that could be used for driver assistance or alarm trigger systems.


title: Collaborative Filtering with Low Regret

abstract: There is much empirical evidence that item-item collaborative filtering works well in practice. Motivated to understand this, we provide a framework to design and analyze various recommendation algorithms. The setup amounts to online binary matrix completion, where at each time a random user requests a recommendation and the algorithm chooses an entry to reveal in the user's row. The goal is to minimize regret, or equivalently to maximize the number of +1 entries revealed at any time. We analyze an item-item collaborative filtering algorithm that can achieve fundamentally better performance compared to user-user collaborative filtering. The algorithm achieves good "cold-start" performance (appropriately defined) by quickly making good recommendations to new users about whom there is little information.


title: Software Engineers vs. Machine Learning Algorithms: An Empirical Study Assessing Performance and Reuse Tasks

abstract: Several papers have recently contained reports on applying machine learning (ML) to the automation of software engineering (SE) tasks, such as project management, modeling and development. However, there appear to be no approaches comparing how software engineers fare against machine-learning algorithms as applied to specific software development tasks. Such a comparison is essential to gain insight into which tasks are better performed by humans and which by machine learning and how cooperative work or human-in-the-loop processes can be implemented more effectively. In this paper, we present an empirical study that compares how software engineers and machine-learning algorithms perform and reuse tasks. The empirical study involves the synthesis of the control structure of an autonomous streetlight application. Our approach consists of four steps. First, we solved the problem using machine learning to determine specific performance and reuse tasks. Second, we asked software engineers with different domain knowledge levels to provide a solution to the same tasks. Third, we compared how software engineers fare against machine-learning algorithms when accomplishing the performance and reuse tasks based on criteria such as energy consumption and safety. Finally, we analyzed the results to understand which tasks are better performed by either humans or algorithms so that they can work together more effectively. Such an understanding and the resulting human-in-the-loop approaches, which take into account the strengths and weaknesses of humans and machine-learning algorithms, are fundamental not only to provide a basis for cooperative work in support of software engineering, but also, in other areas.


Gastrocystoplasty in Azotemic Canines


title: Supply and Threshold Voltage Scaling for Low Power CMOS

abstract: This paper investigates the effect of lowering the supply and threshold voltages on the energy efficiency of CMOS circuits. Using a first-order model of the energy and delay of a CMOS circuit, we show that lowering the supply and threshold voltage is generally advantageous, especially when the transistors are velocity saturated and the nodes have a high activity factor, In fact, for modern submicron technologies, this simple analysis suggests optimal energy efficiency at supply voltages under 0.5 V. Other process and circuit parameters have almost no effect on this optimal operating point. If there is some uncertainty in the value of the threshold or supply voltage, however, the power advantage of this very low voltage operation diminishes. Therefore, unless active feedback is used to control the uncertainty, in the future the supply and threshold voltage will not decrease drastically, but rather will continue to scale down to maintain constant electric fields.


title: FastSum: Fast and Accurate Query-based Multi-document Summarization

abstract: We present a fast query-based multi-document summarizer called FastSum based solely on word-frequency features of clusters, documents and topics. Summary sentences are ranked by a regression SVM. The summarizer does not use any expensive NLP techniques such as parsing, tagging of names or even part of speech information. Still, the achieved accuracy is comparable to the best systems presented in recent academic competitions (i.e., Document Understanding Conference (DUC)). Because of a detailed feature analysis using Least Angle Regression (LARS), FastSum can rely on a minimal set of features leading to fast processing times: 1250 news documents in 60 seconds.


title: Behavior-Based Formation Control of Swarm Robots

abstract: Swarm robotics is a specific research field of multirobotics where a large number of mobile robots are controlled in a coordinated way. Formation control is one of the most challenging goals for the coordination control of swarm robots. In this paper, a behavior-based control design approach is proposed for two kinds of important formation control problems: efficient initial formation and formation control while avoiding obstacles. In this approach, a classification-based searching method for generating large-scale robot formation is presented to reduce the computational complexity and speed up the initial formation process for any desired formation. The behavior-based method is applied for the formation control of swarm robot systems while navigating in an unknown environment with obstacles. Several groups of experimental results demonstrate the success of the proposed approach. These methods have potential applications for various swarm robot systems in both the simulation and the practical environments.


title: The Internet in face‐to‐face higher education: Can interactive learning improve academic achievement?

abstract: Recent research on e-learning shows that blended learning is more effective than face-to-face learning. However, a clear empirical response has not been given to the cause of such improvement. Using a data set of 9044 students at two Catalan universities and a quasi-experimental approach, two possible hypotheses identified in previous research are studied. The results show that the principal cause of the improvement is not, in itself, the increase in time spent online for educational purposes. Rather, increasing the time devoted to studying online is only useful when it takes place as some form of interactive learning. The educational implications of these results are discussed. [ABSTRACT FROM AUTHOR]


title: A Multidimensional Critical State Analysis for Detecting Intrusions in SCADA Systems

abstract: A relatively new trend in Critical Infrastructures (e.g., power plants, nuclear plants, energy grids, etc.) is the massive migration from the classic model of isolated systems, to a system-of-systems model, where these infrastructures are intensifying their interconnections through Information and Communications Technology (ICT) means. The ICT core of these industrial installations is known as Supervisory Control And Data Acquisition Systems (SCADA). Traditional ICT security countermeasures (e.g., classic firewalls, anti-viruses and IDSs) fail in providing a complete protection to these systems since their needs are different from those of traditional ICT. This paper presents an innovative approach to Intrusion Detection in SCADA systems based on the concept of Critical State Analysis and State Proximity. The theoretical framework is supported by tests conducted with an Intrusion Detection System prototype implementing the proposed detection approach.


title: Controlling privacy with trust-aware link prediction in online social networks

abstract: In real life, the strength of the relationship among people indicates the level of trust. People tend to trust their friends, family members' opinions rather than unfamiliar one. Since this is a human characteristic and the phenomena of social network services which allow users to post their information, diaries, albums, and experiences, the need of identifying reliable people within social networks become a fundamental goal to prevent unreliable users accessing these rich information. In this study, we propose a capacity-based algorithm that adapts Advogato trust metric for identifying people of trust based on weighted relationships. Our method finds all possible trustworthy users who are connected to a target user in his/her personal network and prevents unreliable users from impacting on a personal network. We present experimental results that show how our algorithm performs better than existing alternatives.


title: Fast algorithms for optimal coalition formation in federated clouds

abstract: In this paper, we formulate the optimal coalition formation in federated clouds as an integer linear programming problem under the cloud service brokerage model proposed by Mashayekhy et al [10]. Then we propose a fast polynomial time greedy algorithm to find a near optimal coalition. The profit generated by the federation obtained using the greedy algorithm is within a negligible 0.06 percent of the optimal on an average. The greedy algorithm finds a federation 200 times faster on an average when compared with the Merge-Split algorithm. The payoff distribution within a federation is determined using exact Banzhaf index computation whereas the Merge-Split algorithm arrives at a payoff using an estimate of Banzhaf values. By computing the payoff distribution after the federation formation, we are able to achieve 66x speedup when compared with the Merge-Split algorithm.


title: An improved box-counting method for image fractal dimension estimation

abstract: Fractal dimension (FD) is a useful feature for texture segmentation, shape classification, and graphic analysis in many fields. The box-counting approach is one of the frequently used techniques to estimate the FD of an image. This paper presents an efficient box-counting-based method for the improvement of FD estimation accuracy. A new model is proposed to assign the smallest number of boxes to cover the entire image surface at each selected scale as required, thereby yielding more accurate estimates. The experiments using synthesized fractional Brownian motion images, real texture images, and remote sensing images demonstrate this new method can outperform the well-known differential boxing-counting (DBC) method.


title: Analysis of packet loss and delay variation on QoE for H.264 andWebM/VP8 Codecs

abstract: The popularity of multimedia services over Internet has increased in the recent years. These services include Video on Demand (VoD) and mobile TV which are predominantly growing, and the user expectations towards the quality of videos are gradually increasing. Different video codec’s are used for encoding and decoding. Recently Google has introduced the VP8 codec which is an open source compression format. It is introduced to compete with existing popular codec namely H.264/AVC developed by ITU-T Video Coding Expert Group (VCEG), as by 2016 there will be a license fee for H.264. In this work we compare the performance of H.264/AVC and WebM/VP8 in an emulated environment. NetEm is used as an emulator to introduce delay/delay variation and packet loss. We have evaluated the user perception of impaired videos using Mean Opinion Score (MOS) by following the International Telecommunication Union (ITU) Recommendations Absolute Category Rating (ACR) and analyzed the results using statistical methods. It was found that both video codec’s exhibit similar performance in packet loss, But in case of delay variation H.264 codec shows better results when compared to WebM/VP8. Moreover along with the MOS ratings we also studied the effect of user feelings and online video watching experience impacts on their perception.


title: AC resistance calculation methods and practical design considerations when using litz wire

abstract: This paper reviews and compares two easily applicable methods used to calculate the AC resistance and an optimal strand diameter for a litz wire. The methods can be implemented in mathematical software, such as MATLAB, by using the equations and program listings given in this paper. The practical limitations of the litz wire are discussed based on the results of the methods. The AC to DC resistance ratios are plotted as a function of various strand diameters with respect to skin depth, which allows the approximation of litz wire AC resistance with a small calculation effort. The analysis and results presented in this paper are verified by measurements and FEM simulations with various litz wire configurations.