title: Lengthening of embolus and copulatory duct: a review of an evolutionary trend in the spider family Sparassidae (Araneae)

abstract: The phenomenon of lengthening copulatory structures in the spider family Sparassidae is reviewed. One can distinguish between a diversifying type and a lengthening type, but admitting that there may be combinations and transitions between these cases. Some 55% of 662 species examined show clearly that the embolus and/or copulatory ducts are lengthened in comparison with the ancestral species, whereas only in 8% there is no noteworthy lengthening of these structures. Different types of lengthening are recognised: 'tegular coil', 'distal coil', 'distal screw', the irregular or combination type, and the so-called 'functional lengthening'. In these types uniformity of the copulatory structures prevails, although diversifying elements may occur albeit rarely. Combined morphological changes, occurring in the course of evolutionary length- ening, are considered as being dependent on functional constraints. The position of the embolus' tip may play an important role in this context. Understanding of functional and evolutionary aspects may enlighten possible mechanisms which trigger the phenomenon.


title: Predicting Air Compressor Failures with Echo State Networks

abstract: Modern vehicles have increasing amounts of data streaming continuously on-board their controller area networks. These data are primarily used for controlling the vehicle and for feedback to the dri ...


title: Adapting Markov Decision Process for Search Result Diversification

abstract: In this paper we address the issue of learning diverse ranking models for search result diversification. Typical methods treat the problem of constructing a diverse ranking as a process of sequential document selection. At each ranking position, the document that can provide the largest amount of additional information to the users is selected, because the search users usually browse the documents in a top-down manner. Thus, to select an optimal document for a position, it is critical for a diverse ranking model to capture the utility of information the user have perceived from the preceding documents. Existing methods usually calculate the ranking scores (e.g., the marginal relevance) directly based on the query and the selected documents, with heuristic rules or handcrafted features. The utility the user perceived at each of the ranks, however, is not explicitly modeled. In this paper, we present a novel diverse ranking model on the basis of continuous state Markov decision process (MDP) in which the user perceived utility is modeled as a part of the MDP state. Our model, referred to as MDP-DIV, sequentially takes the actions of selecting one document according to current state, and then updates the state for the chosen of the next action. The transition of the states are modeled in a recurrent manner and the model parameters are learned with policy gradient. Experimental results based on the TREC benchmarks showed that MDP-DIV can significantly outperform the state-of-the-art baselines.


title: Undecidability of static analysis

abstract: Static analysis of programs is indispensable to any software tool, environment, or system that requires compile-time information about the semantics of programs. With the emergence of languages like C and LISP, static analysis of programs with dynamic storage and recursive data structures has become a field of active research. Such analysis is difficult, and the static-analysis community has recognized the need for simplifying assumptions and approximate solutions. However, even under the common simplifying assumptions, such analyses are harder than previously recognized. Two fundamental static-analysis problems are may alias and must alias . The former is not recursive (is undecidable), and the latter is not recursively enumerable (is uncomputable), even when all paths are executable in the program being analyzed for languages with if statements, loops, dynamic storage, and recursive data structures.


title: A hybrid bundle adjustment/pose-graph approach to VSLAM/GPS fusion for low-capacity platforms

abstract: We focus on the real-time fusion of monocular visual SLAM with GPS data in order to obtain city-scale, georeferenced pose estimations and reconstructions. Recently, GPS/VSLAM fusion through constrained local key-frame based Bundle Adjustment (BA) using Barrier Term Optimization (BTO) has proven to be (to the best of our knowledge) the most robust and accurate method. However, this approach requires a higher number of cameras to be considered in the optimization: in practice, more than 30 cameras are necessary, while a typical vision-only BA can succeed with as few as 10 cameras. This problem dimensionality makes the method unsuitable for autonomous embedded platforms of low computational capacity (e.g. MAVs). In this paper, we present a hybrid constrained BA/pose-graph approach using BTO, which is motivated by theoretical observations about covariance changes as a function of the gauge. We show that our method has desirable properties that allows its successful use in a BTO context, and present two different formulations. The experimental validation of our method shows that both our formulations reduce the computational cost in comparison with constrained BA using BTO, without any significant loss of precision. In particular, our first formulation yields a 60% reduction in execution time.


title: Adaptive Control of Duty Cycling in Energy-Harvesting Wireless Sensor Networks

abstract: Increasingly many wireless sensor network deployments are using harvested environmental energy to extend system lifetime. Because the temporal profiles of such energy sources exhibit great variability due to dynamic weather patterns, an important problem is designing an adaptive duty-cycling mechanism that allows sensor nodes to maintain their power supply at sufficient levels (energy neutral operation) by adapting to changing environmental conditions. Existing techniques to address this problem are minimally adaptive and assume a priori knowledge of the energy profile. While such approaches are reasonable in environments that exhibit low variance, we find that it is highly inefficient in more variable scenarios. We introduce a new technique for solving this problem based on results from adaptive control theory and show that we achieve better performance than previous approaches on a broader class of energy source data sets. Additionally, we include a tunable mechanism for reducing the variance of the node's duty cycle over time, which is an important feature in tasks such as event monitoring. We obtain reductions in variance as great as two-thirds without compromising task performance or ability to maintain energy neutral operation.


title: Continuous versions of firefly algorithm: a review

abstract: Firefly algorithm is a swarm based metaheuristic algorithm designed for continuous optimization problems. It works by following better solutions and also with a random search mechanism. It has been successfully used in different problems arising in different disciplines and also modified for discrete problems. Unlike its easiness to understand and to implement; its effectiveness is highly affected by the parameter values. In addition modifying the search mechanism may give better performance. Hence different modified versions are introduced to overcome its limitations and increase its performance. In this paper, the modifications done on firefly algorithm for continuous optimization problems will be reviewed with a critical analysis. A detailed discussion on the modifications with possible future works will also be presented. In addition a comparative study will be conducted using forty benchmark problems with different dimensions based on ten base functions. The result shows that some of the modified versions produce superior results with a tradeoff of high computational time. Hence, this result will help practitioners to decide which modified version to apply based on the computational resource available and the sensitivity of the problem.


title: Analysis of complex network methods for extractive automatic text summarization

abstract: Automatic text summarization is an important research area in the domain of information systems. It aims to create a compressed version of documents, which should cover all the significant contents and overall meaning. In extractive text summarization, sentences are scored on various of features. A large number of features network based have been proposed by researchers in the past literatures. This paper reviews all the features that use metrics and concept of complex network for scoring sentences. The experiment results on single feature and combinations of various features we proposed are discussed. Quantitative and qualitative aspects were considered in our assessment performing on the DUC 2002 data sets.


title: Power Aware High Performance Computing: Challenges and Opportunities for Application and System Developers — Survey & Tutorial

abstract: Power and energy consumption are seen of one of the most critical design factor for any next generation large-scale HPC system. The price centers have to pay for energy is shifting the budgets from investment to operating costs, leading to scenarios in which the sizes of systems will be determined by their power needs, rather by the initial hardware cost. As a consequence, virtually all funding agencies for HPC projects around the world have set aggressive goals for peak power requirements in future machines. Yet, with today's HPC architectures and systems, these goals are still far out of reach: they will only be achievable through a complex set of mechanisms at all levels of hardware and software, from buildings and infrastructure to software control and all the way to microarchitectural solutions. All of these mechanisms will ultimately impact the application developer. On future HPC systems, running a code efficiently (as opposed to purely with high performance) will be a major requirement for every user. This work accompanies the tutorial “Power Aware High Performance Computing: Challenges and Opportunities for Application and system Developers” and captures the key aspects discussed. We will review existing literature to discuss the challenges caused by power and energy constraints, present available approaches in hardware and software, highlight impacts on HPC center and infrastructure design as well as operations, and ultimately show how this shift in paradigm from “cycle awareness” to “power awareness” will impact application development.


title: Crowd-based Smart Parking: A Case Study for Mobile Crowdsourcing

abstract: An increasing number of mobile applications aim to enable “smart cities” by harnessing contributions from citizens armed with mobile devices that have sensing ability. However, there are few generally recognized guidelines for developing and deploying crowdsourcing-based solutions in mobile environments. This paper considers the design of a crowdsourcing-based smart parking system as a specific case study in an attempt to explore the basic design principles applicable to an array of similar applications. Through simulations, we show that the strategies behind crowdsourcing can heavily influence the utility of such applications. Equally importantly, we show that tolerating a certain level of freeriding increases the social benefits while maintaining quality of service level offered. Our findings provide designers with a better understanding of mobile crowdsourcing features and help guide successful designs.


COSINE: Community-Preserving Social Network Embedding from Information Diffusion Cascades


title: NEUZZ: Efficient Fuzzing with Neural Program Smoothing

abstract: Fuzzing has become the de facto standard technique for finding software vulnerabilities. However, even state-of-the-art fuzzers are not very efficient at finding hard-to-trigger software bugs. Most popular fuzzers use evolutionary guidance to generate inputs that can trigger different bugs. Such evolutionary algorithms, while fast and simple to implement, often get stuck in fruitless sequences of random mutations. Gradient-guided optimization presents a promising alternative to evolutionary guidance. Gradient-guided techniques have been shown to significantly outperform evolutionary algorithms at solving high-dimensional structured optimization problems in domains like machine learning by efficiently utilizing gradients or higher-order derivatives of the underlying function. However, gradient-guided approaches are not directly applicable to fuzzing as real-world program behaviors contain many discontinuities, plateaus, and ridges where the gradient-based methods often get stuck. We observe that this problem can be addressed by creating a smooth surrogate function approximating the discrete branching behavior of target program. In this paper, we propose a novel program smoothing technique using surrogate neural network models that can incrementally learn smooth approximations of a complex, real-world program's branching behaviors. We further demonstrate that such neural network models can be used together with gradient-guided input generation schemes to significantly improve the fuzzing efficiency. Our extensive evaluations demonstrate that NEUZZ significantly outperforms 10 state-of-the-art graybox fuzzers on 10 real-world programs both at finding new bugs and achieving higher edge coverage. NEUZZ found 31 unknown bugs that other fuzzers failed to find in 10 real world programs and achieved 3X more edge coverage than all of the tested graybox fuzzers for 24 hours running.


title: Selective Convolutional Descriptor Aggregation for Fine-Grained Image Retrieval

abstract: Deep convolutional neural network models pre-trained for the ImageNet classification task have been successfully adopted to tasks in other domains, such as texture description and object proposal generation, but these tasks require annotations for images in the new domain. In this paper, we focus on a novel and challenging task in the pure unsupervised setting: fine-grained image retrieval. Even with image labels, fine-grained images are difficult to classify, let alone the unsupervised retrieval task. We propose the Selective Convolutional Descriptor Aggregation (SCDA) method. SCDA firstly localizes the main object in fine-grained images, a step that discards the noisy background and keeps useful deep descriptors. The selected descriptors are then aggregated and dimensionality reduced into a short feature vector using the best practices we found. SCDA is unsupervised, using no image label or bounding box annotation. Experiments on six fine-grained datasets confirm the effectiveness of SCDA for fine-grained image retrieval. Besides, visualization of the SCDA features shows that they correspond to visual attributes (even subtle ones), which might explain SCDA's high mean average precision in fine-grained retrieval. Moreover, on general image retrieval datasets, SCDA achieves comparable retrieval results with state-of-the-art general image retrieval approaches.


title: The pretesting effect: Do unsuccessful retrieval attempts enhance learning?

abstract: Testing previously studied information enhances long-term memory, particularly when the information is successfully retrieved from memory. The authors examined the effect of unsuccessful retrieval attempts on learning. Participants in 5 experiments read an essay about vision. In the test condition, they were asked about embedded concepts before reading the passage; in the extended study condition, they were given a longer time to read the passage. To distinguish the effects of testing from attention direction, the authors emphasized the tested concepts in both conditions, using italics or bolded keywords or, in Experiment 5, by presenting the questions but not asking participants to answer them before reading the passage. Posttest performance was better in the test condition than in the extended study condition in all experiments—a pretesting effect— even though only items that were not successfully retrieved on the pretest were analyzed. The testing effect appears to be attributable, in part, to the role unsuccessful tests play in enhancing future learning.


title: The sum of its parts: reducing sparsity in click estimation with query segments

abstract: The critical task of predicting clicks on search advertisements is typically addressed by learning from historical click data. When enough history is observed for a given query-ad pair, future clicks can be accurately modeled. However, based on the empirical distribution of queries, sufficient historical information is unavailable for many query-ad pairs. The sparsity of data for new and rare queries makes it difficult to accurately estimate clicks for a significant portion of typical search engine traffic. In this paper we provide analysis to motivate modeling approaches that can reduce the sparsity of the large space of user search queries. We then propose methods to improve click and relevance models for sponsored search by mining click behavior for partial user queries. We aggregate click history for individual query words, as well as for phrases extracted with a CRF model. The new models show significant improvement in clicks and revenue compared to state-of-the-art baselines trained on several months of query logs. Results are reported on live traffic of a commercial search engine, in addition to results from offline evaluation.


title: A research on effectiveness of Facebook advertising on enhancing purchase intention of consumers

abstract: To identify consumer perceptions towards social media advertising approach and relationship to developing brand image for companies.Examine how Facebook advertising impacts user likelihood of elaboration in regards to generate further messages from a brand. The present study aimed to examine the effectiveness of advertisements in enhancing consumers' purchasing intention on Facebook in 2013. It is an applied study in terms of its goals, and a descriptive survey one in terms of methodology. The statistical population included all undergraduate students in Cypriot universities. An 11-item researcher-made questionnaire was used to compare and analyze the effectiveness of advertisements. Data analysis was carried out using SPSS17, the parametric statistical method of t-test, and the non-parametric Friedman test. The results of the study showed that Facebook advertising significantly affected brand image and brand equity, both of which factors contributed to a significant change in purchasing intention.


title: On lq estimation of sparse inverse covariance

abstract: Recently, major attention has been given to penalized log-likelihood estimators for sparse precision (inverse covariance) matrices. The penalty is responsible for inducing sparsity, and a very common choice is the convexl1 norm. However, it is not always the case that the best estimator is achieved with this penalty. So, to improve sparsity and reduce biases associated with thel1 norm, one must move to non-convex penalties such as thelq (0 q < 1). In this paper we introduce the resulting non-concave lq penalized log-likelihood problem, and derive the corresponding optimality conditions. A novel cyclic descent algorithm is presented for penalized log-likelihood optimization, and we show how the derived conditions can be used to reduce algorithm computation. We illustrate by comparing reconstruction quality over the range 0 q 1 for several experiments.


Timeline extraction using distant supervision and joint inference


title: Procedural rhythmic character animation : an interactive Chinese lion dance

abstract: **************** **************** **************** **************** **************** ************* The creation of a stylistic animation through the use of high-level controls has always been a design goal for computer animation software. In this paper, we propose a procedural animation system, called rhythmic character animation playacting (RhyCAP), which allows a designer tointeractively direct animated characters by adjusting rhythmic parameters such as tempo, exaggeration, and timing. The motions thus generated reflect the intention of the director and also adapt to environmental obstacle constraints. We use a sequence of martialart steps in the performance of a Chinese lion dance to illustrate the effectiveness of the system. The animation is generated by composition of common motion elements, concisely represented in an action graph. We have implemented an animation control program that allows Chinese lion dance to be choreographed interactively. This authoring tool also serves as a useful means for preserving this part of world cultural heritage. Copyright # 2006 John Wiley & Sons, Ltd.


User Modeling and Tweets Recommendation Based on Wikipedia Concept Graph ∗


title: A case of de novo duplication of 15q24-q26.3

abstract: Distal duplication, or trisomy 15q, is an extremely rare chromosomal disorder characterized by prenatal and postnatal overgrowth, mental retardation, and craniofacial malformations. Additional abnormalities typically include an unusually short neck, malformations of the fingers and toes, scoliosis and skeletal malformations, genital abnormalities, particularly in affected males, and, in some cases, cardiac defects. The range and severity of symptoms and physical findings may vary from case to case, depending upon the length and location of the duplicated portion of chromosome 15q. Most reported cases of duplication of the long arm of chromosome 15 frequently have more than one segmental imbalance resulting from unbalanced translocations involving chromosome 15 and deletions in another chromosome, as well as other structural chromosomal abnormalities. We report a female newborn with a de novo duplication, 15q24-q26.3, showing intrauterine overgrowth, a narrow asymmetric face with down-slanting palpebral fissures, a large, prominent nose, and micrognathia, arachnodactyly, camptodactyly, congenital heart disease, hydronephrosis, and hydroureter. Chromosomal analysis showed a 46,XX,inv(9)(p12q13),dup(15)(q24q26.3). Array comparative genomic hybridization analysis revealed a gain of 42 clones on 15q24-q26.3. This case represents the only reported patient with a de novo 15q24-q26.3 duplication that did not result from an unbalanced translocation and did not have a concomitant monosomic component in Korea.


Deeper inside pagerank


title: Factors influencing to the implementation success of big data analytics: A systematic literature review

abstract: Big data analytics (BDA) readiness factors have been widely researched; nevertheless, few have investigated the impact and success factors of BDA implementation in the organizational context. The relevance and quality of BDA outcomes have been a significant concern to the organizational leaders in supporting them for strategic decision-making. To that end, the objective of this study is twofold. Firstly, it investigates the factors that influencing the success of BDA implementation for effective decision-making. Secondly, this study adds to the body of knowledge in the information system (IS) domain, especially with its focus on BDA implementation packages. Based on 18 selected papers, this review has established 10 influencing factors that may influence the success of BDA implementation, therefore, contribute to the practice and research of BDA domain and its effectiveness towards the organizational performance enhancement.


title: Deep Convolutional Neural Network for Image Deconvolution

abstract: Many fundamental image-related problems involve deconvolution operators. Real blur degradation seldom complies with an ideal linear convolution model due to camera noise, saturation, image compression, to name a few. Instead of perfectly modeling outliers, which is rather challenging from a generative model perspective, we develop a deep convolutional neural network to capture the characteristics of degradation. We note directly applying existing deep neural networks does not produce reasonable results. Our solution is to establish the connection between traditional optimization-based schemes and a neural network architecture where a novel, separable structure is introduced as a reliable support for robust deconvolution against artifacts. Our network contains two submodules, both trained in a supervised manner with proper initialization. They yield decent performance on non-blind image deconvolution compared to previous generative-model based methods.


title: Character-based movie summarization

abstract: A decent movie summary is helpful for movie producer to promote the movie as well as audience to capture the theme of the movie before watching the whole movie. Most exiting automatic movie summarization approaches heavily rely on video content only, which may not deliver ideal result due to the semantic gap between computer calculated low-level features and human used high-level understanding. In this paper, we incorporate script into movie analysis and propose a novel character-based movie summarization approach, which is validated by modern film theory that what actually catches audiences' attention is the character. We first segment scenes in the movie by analysis and alignment of script and movie. Then we conduct substory discovery and content attention analysis based on the scent analysis and character interaction features. Given obtained movie structure and content attention value, we calculate movie attraction scores at both shot and scene levels and adopt this as criterion to generate movie summary. The promising experimental results demonstrate that character analysis is effective for movie summarization and movie content understanding.


title: Enterprise systems implementation and accounting benefits

abstract: Purpose – The aim of this paper is to examine the accounting benefits involved in adopting enterprise systems, but also to comprehend the underlying causes that motivate their adoption.Design/methodology/approach – This paper presents evidence from a survey of 73 companies which implemented ES on the related benefits to their accounting information provision and practices.Findings – Empirical evidence confirms a number of benefits derived from the application of ES that focus on the following dimensions: organisation, operations, management and IT infrastructure. These benefits are related both to the reasons leading to the implementation of ES and to the relevant selection of ES modules.Originality/value – The corresponding results from this survey can then be used as a basis for establishing the best approach in order to fully exploit the potential of future ES applications in accounting practice.


title: A review on the platform design, dynamic modeling and control of hybrid UAVs

abstract: This article presents a review on the platform design, dynamic modeling and control of hybrid Unmanned Aerial Vehicles (UAVs). For now, miniature UAVs which have experienced a tremendous development are dominated by two main types, i.e., fixed-wing UAV and Vertical Take-Off and Landing (VTOL) UAV, each of which, however, has its own inherent limitations on such as flexibility, payload, axnd endurance. Enhanced popularity and interest are recently gained by a newer type of UAVs, named hybrid UAV that integrates the beneficial features of both conventional ones. In this paper, a technical overview of the recent advances of the hybrid UAV is presented. More specifically, the hybrid UAV's platform design together with the associated technical details and features are introduced first. Next, the work on hybrid UAV's flight dynamics modeling is then categorized and explained. As for the flight control system design for the hybrid UAV, several flight control strategies implemented are discussed and compared in terms of theory, linearity and implementation.


title: [Munchausen syndrome in a 66-year-old patient]

abstract: The study of a 66 year old woman's medical chart revealed many discordant elements, leading to the diagnosis of Munchausen's syndrome. The patient had been treated over a period of 20 years at the Medical polyclinic for a variety of symptoms concerning virtually all organs. Her son's chart similarly contains discordant elements, rising the suspicion of a Munchausen Syndrome "by proxy". With this case report, historical aspects and the therapeutic challenge of this syndrome are outlined.


title: KnowSim: A Document Similarity Measure on Structured Heterogeneous Information Networks

abstract: As a fundamental task, document similarity measure has broad impact to document-based classification, clustering and ranking. Traditional approaches represent documents as bag-of-words and compute document similarities using measures like cosine, Jaccard, and dice. However, entity phrases rather than single words in documents can be critical for evaluating document relatedness. Moreover, types of entities and links between entities/words are also informative. We propose a method to represent a document as a typed heterogeneous information network (HIN), where the entities and relations are annotated with types. Multiple documents can be linked by the words and entities in the HIN. Consequently, we convert the document similarity problem to a graph distance problem. Intuitively, there could be multiple paths between a pair of documents. We propose to use the meta-path defined in HIN to compute distance between documents. Instead of burdening user to define meaningful meta paths, an automatic method is proposed to rank the meta-paths. Given the meta-paths associated with ranking scores, an HIN-based similarity measure, KnowSim, is proposed to compute document similarities. Using Freebase, a well-known world knowledge base, to conduct semantic parsing and construct HIN for documents, our experiments on 20Newsgroups and RCV1 datasets show that KnowSim generates impressive high-quality document clustering.


title: Cyberbullying and self-esteem in Australian adults

abstract: Cyberbullying research is currently focused on identifying personal factors which increase the risk of an individual being involved in the behaviour. Recent findings indicate that within the web of cyberbullying culture a large group of individuals are both cyberbullies and victims. This group of cyberbully/victims has been shown to differ from pure cyberbullies or victims on various factors during adolescence; particularly self-esteem. However, little research to date has investigated cyberbullying behaviour in adults. The current study examined the prevalence of cyberbully typologies and their relationship with self-esteem within a convenience sample of 164 Australian young adults (72% being females; 17-25 years). Results found that the largest group identified were cyberbully/victims (62%), followed by individuals not involved (17%), cyberbullies (11%) and cybervictims (10%) respectively. The ratio of males and females in each of the four cyberbully typologies was similar. Contrary to previous research, all four cyberbully typologies reported similar levels of self-esteem. These findings suggest that research should examine cyberbullying behaviour across all age groups to determine if this is related to different factors in adolescence compared to adulthood. Limitations and future recommendations are discussed.


title: BIDaaS: Blockchain Based ID As a Service

abstract: Blockchain technology has been known as the underlying technology of cryptocurrencies, but nowadays it is further considered as a functional technology for improving existing technologies and creating new applications previously never practical. In this paper, we are focused on utilizing blockchain technology to introduce a new ID as a service (IDaaS) for digital identity management. The proposed blockchain-based ID as a service (BIDaaS) is explained with one practical example that shows how the proposed BIDaaS works as an identity and authentication management infrastructure for mobile users of a mobile telecommunication company.


title: Surface EMG-Based Inter-Session Gesture Recognition Enhanced by Deep Domain Adaptation

abstract: High-density surface electromyography (HD-sEMG) is to record muscles’ electrical activity from a restricted area of the skin by using two dimensional arrays of closely spaced electrodes. This technique allows the analysis and modelling of sEMG signals in both the temporal and spatial domains, leading to new possibilities for studying next-generation muscle-computer interfaces (MCIs). sEMG-based gesture recognition has usually been investigated in an intra-session scenario, and the absence of a standard benchmark database limits the use of HD-sEMG in real-world MCI. To address these problems, we present a benchmark database of HD-sEMG recordings of hand gestures performed by 23 participants, based on an 8 × 16 electrode array, and propose a deep-learning-based domain adaptation framework to enhance sEMG-based inter-session gesture recognition. Experiments on NinaPro, CSL-HDEMG and our CapgMyo dataset validate that our approach outperforms state-of-the-arts methods on intra-session and effectively improved inter-session gesture recognition.


title: Preparation of an Unconstrained Vietnamese Online Handwriting Database and Recognition Experiments by Recurrent Neural Networks

abstract: This paper presents our attempts to collect and analyze unconstrained Vietnamese online handwriting text patterns by pen-based computers. Totally, our database contains over 120,000 strokes from more than 140,000 characters, which is one of the largest Vietnamese online handwriting pattern databases currently. For building and analyzing our database, we made a collection tool, a line segmentation tool, and a delayed stroke detection tool. Moreover, we investigated some statistical information from personal information of writers. In order to solve the unconstrained handwriting recognition problem, we conducted experiments using Bidirectional Long Short-Term Memory (BLSTM) networks. BLSTM network is architecture of Recurrent Neural Network (RNN) and applied recently for many related problems. The performance of BLSTM network on our database is nearly 80% of accuracy even though this database contains many delayed strokes. In near future, we are going to avail our database for research purposes, as it would be the fundamental for the handwriting recognition research.


title: SIW magic tee fed by printed Ridge Gap Waveguide design

abstract: Magic Tee is a four port passive device presented more than 50 years ago. It is deployed in many applications such as duplexers and antenna feeding structures. This type of devices is developed and implemented through different guiding structures. Two major modern guiding structures are the Substrate integrated waveguide (SIW) and the Ridge Gap Waveguide (RGW). This paper introduces a compact magic Tee design implemented by a mixed technology of SIW and RGW. The traditional magic tee contains three ports in one plane, while the fourth port exists in another plane. This results in a large size of the complete structure. This work proposes mixing two different guiding structures in one design in order to overcome this problem. The final results of the magic Tee with the RGW transition shows a good matching and isolation levels for the required bandwidth.


title: Profiler: integrated statistical analysis and visualization for data quality assessment

abstract: Data quality issues such as missing, erroneous, extreme and duplicate values undermine analysis and are time-consuming to find and fix. Automated methods can help identify anomalies, but determining what constitutes an error is context-dependent and so requires human judgment. While visualization tools can facilitate this process, analysts must often manually construct the necessary views, requiring significant expertise. We present Profiler, a visual analysis tool for assessing quality issues in tabular data. Profiler applies data mining methods to automatically flag problematic data and suggests coordinated summary visualizations for assessing the data in context. The system contributes novel methods for integrated statistical and visual analysis, automatic view suggestion, and scalable visual summaries that support real-time interaction with millions of data points. We present Profiler's architecture --- including modular components for custom data types, anomaly detection routines and summary visualizations --- and describe its application to motion picture, natural disaster and water quality data sets.


The Influence of IS Affordances on Work Practices in Health Care: A Relational Coordination Approach


title: A new multitool for hand-assisted advanced laparoscopic surgery (HALS)

abstract: BACKGROUND ::: Hand-assisted laparoscopic surgery (HALS), which has become possible due to the introduction of effective hand-access devices, facilitates the execution of major operations. Over the past 2 years, we have been evaluating various designs for small instruments that can be used by the internal assisting hand. Here we report on the development, testing, and evaluation of a small reusable multitool that hangs from the little finger of the internal assisting hand of the surgeon when not in use. ::: ::: ::: METHODS ::: The Dundee Multitool (DMT) was designed to enable the internal deployment (by thumb extrusion) of a small dissecting forceps (pickup), needle driver, and scissors. The multitool hangs from the little finger of the internal assisting hand when not in use. The instrument was subjected to beta testing in the laboratory using HALS trainers. When testing was completed, it was used in major HALS operations after approval by the hospital and consent of the individual patients. ::: ::: ::: RESULTS ::: The DMT was found to work well. The various active instruments could be extruded from the casing with ease and functioned well in picking up tissues, intracorporeal suturing/tying, and the cutting of sutures and ligatures. Both in laboratory experiments and during clinical evaluation, suturing was easy when the active driving of the needle though the tissues was carried out by the external hand. The multitool needle driver, held by the internal hand as an assisting instrument in conjunction with active needle driving through the tissue edges by the dominant (external) hand, improved suturing efficiency (i.e., reduced execution time) by 30% when compared to total laparoscopic suturing. ::: ::: ::: CONCLUSIONS ::: We have described a novel little-finger-hung multitool for HALS surgery that deploys with ease at any one time. It incorporates a needle driver, a dissecting forceps, and a suture scissors. The good functionality of the multitool has been confirmed by both laboratory experiments and clinical evaluation.


title: An interactive tool for manual, semi-automatic and automatic video annotation

abstract: The aim of annotation tools is to relieve the user from the burden of the manual annotation as much as possible.We developed an interactive video annotation tool that supports manual, semi-automatic and automatic annotations.The tool integrates several computer vision algorithms in an interactive and incremental learning framework.A quantitative and qualitative evaluation of the proposed tool on a challenging case study domain is presented.The use of the semi-automatic and automatic modality drastically reduces the human effort. The annotation of image and video data of large datasets is a fundamental task in multimedia information retrieval and computer vision applications. The aim of annotation tools is to relieve the user from the burden of the manual annotation as much as possible. To achieve this ideal goal, many different functionalities are required in order to make the annotations process as automatic as possible. Motivated by the limitations of existing tools, we have developed the iVAT: an interactive Video Annotation Tool. It supports manual, semi-automatic and automatic annotations through the interaction of the user with various detection algorithms. To the best of our knowledge, it is the first tool that integrates several computer vision algorithms working in an interactive and incremental learning framework. This makes the tool flexible and suitable to be used in different application domains. A quantitative and qualitative evaluation of the proposed tool on a challenging case study domain is presented and discussed. Results demonstrate that the use of the semi-automatic, as well as the automatic, modality drastically reduces the human effort while preserving the quality of the annotations.


title: Developed Dijkstra shortest path search algorithm and simulation

abstract: This paper presents an improved Dijkstra shortest path search algorithm. The algorithm considers the different types of weights, set the priority target search region, reduce the large number of meaningless operations, in order to improve search efficiency. We do some relevant testing based on the shortest distance and shortest time through simulation. The simulation show that the improved Dijkstra shortest path search algorithm can reduce the computation time.


title: Discrete Route/Trajectory Decision Making Problems

abstract: The paper focuses on composite multistage decision making problems which are targeted to design a route/trajectory from an initial decision situation (origin) to goal (destination) decision situation(s). Automobile routing problem is considered as a basic physical metaphor. The problems are based on a discrete (combinatorial) operations/states design/solving space (e.g., digraph). The described types of discrete decision making problems can be considered as intelligent design of a route (trajectory, strategy) and can be used in many domains: (a) education (planning of student educational trajectory), (b) medicine (medical treatment), (c) economics (trajectory of start-up development). Several types of the route decision making problems are described: (i) basic route decision making, (ii) multi-goal route decision making, (iii) multi-route decision making, (iv) multi-route decision making with route/trajectory change(s), (v) composite multi-route decision making (solution is a composition of several routes/trajectories at several corresponding domains), and (vi) composite multi-route decision making with coordinated routes/trajectories. In addition, problems of modeling and building the design spaces are considered. Numerical examples illustrate the suggested approach. Three applications are considered: educational trajectory (orienteering problem), plan of start-up company (modular three-stage design), and plan of medical treatment (planning over digraph with two-component vertices).


title: The cysteine protease CEP1, a key executor involved in tapetal programmed cell death, regulates pollen development in Arabidopsis.

abstract: Tapetal programmed cell death (PCD) is a prerequisite for pollen grain development in angiosperms, and cysteine proteases are the most ubiquitous hydrolases involved in plant PCD. We identified a papain-like cysteine protease, CEP1, which is involved in tapetal PCD and pollen development in Arabidopsis thaliana. CEP1 is expressed specifically in the tapetum from stages 5 to 11 of anther development. The CEP1 protein first appears as a proenzyme in precursor protease vesicles and is then transported to the vacuole and transformed into the mature enzyme before rupture of the vacuole. cep1 mutants exhibited aborted tapetal PCD and decreased pollen fertility with abnormal pollen exine. A transcriptomic analysis revealed that 872 genes showed significantly altered expression in the cep1 mutants, and most of them are important for tapetal cell wall organization, tapetal secretory structure formation, and pollen development. CEP1 overexpression caused premature tapetal PCD and pollen infertility. ELISA and quantitative RT-PCR analyses confirmed that the CEP1 expression level showed a strong relationship to the degree of tapetal PCD and pollen fertility. Our results reveal that CEP1 is a crucial executor during tapetal PCD and that proper CEP1 expression is necessary for timely degeneration of tapetal cells and functional pollen formation.


title: Electrically Small Frequency-Agile PIFA-as-a-Package for Portable Wireless Devices

abstract: A reconfigurable electrically small capacitively loaded PIFA-as-a-package (PIFA-AAP) is developed to demonstrate the potential utility of reconfigurable antenna technologies to miniature and/or portable UHF wireless devices. The scalable PIFA-AAP concept involves simply integrating the antenna and the device package to maximize the effective area of the antenna given the physical constraints of the application. An elegant approach to frequency-agility is developed using commercial-off-the-shelf solid state switches, overcoming the key weakness of extreme environmental sensitivity inherent to any electrically small antenna. The measured performance of a 25 times 50 times 9-mm PIFA-AAP includes near-contiguous tuning coverage between 407.8 and 463.1 MHz with a total realized gain of better than -10 dBi across the tuning range. The measured bandwidth of our proof-of-concept frequency-agile PIFA-AAP is benchmarked against the Wheeler-Chu-Mclean fundamental limit.


title: Learning Foreign Language through an Interactive Multimedia Program: An Experimental Study on the Effects of the Relevance Component of the ARCS Model

abstract: This experimental study investigated effects of intrinsic motivation and embedded relevance enhancement within a computer-based interactive multimedia (CBIM) lesson for English as a foreign language (EFL) learners. Subjects, categorized as having a higher or lower level of intrinsic motivation, were randomly assigned to learn concepts related to criticism using a CBIM program featuring English language text, videos, and exercises either with or without enhanced relevance components. Two dependent variables, comprehension, as measured by a posttest, and perceptions of motivation, as measured by the Modified Instructional Material Motivation Survey (MIMMS), were assessed after students completed the CBIM program. Two-way ANOVA was used to analyze the collected data. The findings indicated that (a) the use of relevance enhancement strategies facilitated students’ language learning regardless of learners’ level of intrinsic motivation, (b) more highly intrinsically motivated students performed better regardless of the specific treatments they received, (c) the effects of the two variables were additive; intrinsically motivated students who learned from the program with embedded instructional strategies performed the best overall, and (d) there was no significant interaction between the two variables.


title: Krylov Subspace Descent for Deep Learning

abstract: In this paper, we propose a second order optimization method to learn models where both the dimensionality of the parameter space and the number of training samples is high. In our method, we construct on each iteration a Krylov subspace formed by the gradient and an approximation to the Hessian matrix, and then use a subset of the training data samples to optimize over this subspace. As with the Hessian Free (HF) method of Martens (2010), the Hessian matrix is never explicitly constructed, and is computed using a subset of data. In practice, as in HF, we typically use a positive definite substitute for the Hessian matrix such as the Gauss-Newton matrix. We investigate the effectiveness of our proposed method on deep neural networks, and compare its performance to widely used methods such as stochastic gradient descent, conjugate gradient descent and L-BFGS, and also to HF. Our method leads to faster convergence than either L-BFGS or HF, and generally performs better than either of them in cross-validation accuracy. It is also simpler and more general than HF, as it does not require a positive semidefinite approximation of the Hessian matrix to work well nor the setting of a damping parameter. The chief drawback versus HF is the need for memory to store a basis for the Krylov subspace.


title: VideoSense: towards effective online video advertising

abstract: With Internet delivery of video content surging to an unprecedented level, online video advertising is becoming increasingly pervasive. In this paper, we present a novel advertising system for online video service called VideoSense, which automatically associates the most relevant video ads with online videos and seamlessly inserts the ads at the most appropriate positions within each individual video. Unlike most current video-oriented sites that only display a video ad at the beginning or the end of a video, VideoSense aims to embed more contextually relevant ads at less intrusive positions within the video stream. Given an online video, VideoSense is able to detect a set of candidate ad insertion points based on content discontinuity and attractiveness, select a list of relevant candidate ads ranked according to global textual relevance, and compute local visual-aural relevance between each pair of insertion points and ads. To support contextually relevant and less intrusive advertising, the ads are expected to be inserted at the positions with highest discontinuity and lowest attractiveness, while the overall global and local relevance is maximized. We formulate this task as a nonlinear 0-1 integer programming problem and embed these rules as constraints. The experiments have proved the effectiveness of VideoSense for online video advertising.


title: Literature review of brain computer interface (BCI) using Electroencephalogram signal

abstract: The system based on brain computer interface has been developed from past 30 years and now grown into the vast and diverse field. The first brain activity signal is recorded by Berger in year 1942. The electrical signal which are generated by brain activity is also known as Electroencephalogram (EEG). The system which depends on Electroencephalogramshould capable to provide more controlled variable commands to the user. The control capability of this signal have been depend on the electrodes and the noise reduction technique used for preparing the system. Now a days the electrodes are getting better and have very large range of selection criteria depends on application such as the gel based electrodes are used to obtain the high sensitivity for better resolution. Recently various sensors (electrodes) are introduced for producing high quality brain signal with less distortion compareto gel based electrodes. The signal acquisition system are also getting better by using the op-amp based filter circuits and composite instrumentation amplifier integrated circuit. The aim of BCI is to control the robotics machinery with better accuracy. The main use of BCI is to control the wheelchair or robotic limb movement for the disable person and can be used to accessing security system. The BCI can be used to discover the emotions which is also use to control the surrounding environment such as controlling the interior of the car or house.


“They Need To Say Sorry:” Anti-Racism in First Graders’ Racial Learning


title: A Comparative Study on Neural Network Based Soccer Result Prediction

abstract: This study mainly remarks the efficiency of black-box modeling capacity of neural networks in the case of forecasting soccer match results, and opens up several debates on the nature of prediction and selection of input parameters. The selection of input parameters is a serious problem in soccer match prediction systems based on neural networks or statistical methods. Several input vector suggestions are implemented in literature which is mostly based on direct data from weekly charts. Here in this paper, two different input vector parameters have been tested via learning vector quantization networks in order to emphasize the importance of input parameter selection. The input vector parameters introduced in this study are plain and also meaningful when compared to other studies. The results of different approaches presented in this study are compared to each other, and also compared with the results of other neural network approaches and statistical methods in order to give an idea about the successful prediction performance. The paper is concluded with discussions about the nature of soccer match forecasting concept that may draw the interests of researchers willing to work in this area.


title: Non-GNSS radio frequency navigation

abstract: There are many situations in which global navigation satellite systems (GNSS) such as the global positioning system (GPS) cannot provide adequate navigation performance (such as indoors or in urban canyons). This paper describes the technical challenges of non-GNSS radio frequency navigation, with particular emphasis on signals of opportunity (i.e., signals that are intended for purposes other than navigation). Advantages and disadvantages of signal of opportunity navigation are described, along with the dominant issues that must be dealt with in order to make such systems a practical reality.


title: Lagrangian decomposition algorithm for allocating marketing channels

abstract: In this paper, we formulate a new problem related to the well-known influence maximization in the context of computational advertising. Our new problem considers allocating marketing channels (e.g., TV, newspaper, and websites) to advertisers from the view point of a match maker, which was not taken into account in previous studies on the influence maximization. The objective of the problem is to find an allocation such that each advertiser can influence some given number of customers while the slots of marketing channels are limited. ::: ::: We propose an algorithm based on the Lagrangian decomposition. We empirically show that our algorithm computes better quality solutions than existing algorithms, scales up to graphs of 10M vertices, and performs well particularly in a parallel environment.


title: Analysis and Performance of a Smart Antenna for 2.45-GHz Single-Anchor Indoor Positioning

abstract: This paper presents the theoretical analysis and the experimental evaluation of a new switched beam antenna designed to operate at 2.45 GHz. The antenna enables direction of arrival estimation using six directional planar elements arranged to form a platonic solid geometry. It also supports polarization diversity, and it is suitable for single-anchor indoor positioning applications. We adopt the Cramer-Rao bound to study the estimation accuracy of the proposed antenna in absolute 2-D target positioning using received signal strength measurements. First, we describe the design principles for the radiators, we provide an extensive characterization of the switched antenna prototype, and we discuss positioning applications. We then report experimental data that support the results of the theoretical analysis and show consistency between theoretical expectation and the measurements. Finally, we discuss results from proof-of-concept operative indoor positioning example, showing an average localization error as low as 1.7 m.


title: Neutralizing Cross-Site Scripting Attacks Using Open Source Technologies

abstract: Exploiting the security vulnerabilities in web browsers, web applications and firewalls is a fundamental trait of cross-site scripting (XSS) attacks. Majority of web population with basic web awareness are vulnerable and even expert web users may not notice the attack to be able to respond in time to neutralize the ill effects of attack. Due to their subtle nature, a victimized server, a compromised browser, an impersonated email or a hacked web application tends to keep this form of attacks alive even in the present times. XSS attacks severely offset the benefits offered by Internet based services thereby impacting the global internet community. This paper focuses on defense, detection and prevention mechanisms to be adopted at various network doorways to neutralize XSS attacks using open source tools.


title: A broadband dual-polarized magneto-electric dipole antenna

abstract: A new type of dual-polarized planar antenna excited by two Gamma-shaped strips is designed and simulated. The antenna has about 66.7% (SWRLt2) impedance bandwidth and the isolation is less than -32.5 dB over the whole impedance bandwidth. The antenna yields the maximum gain of 9.8 dBi. And the radiation pattern across the operating bandwidth is stable.


title: A Novel 1D-Convolution Accelerator for Low-Power Real-time CNN processing on the Edge

abstract: With the rise of deep learning, the demand for real-time edge intelligence is greater than ever. Current algorithm and hardware realizations often focus on the cloud paradigm and maintain the assumption that the entire frames data is available in large batches. As a result, obtaining real-time AI inference at the edge has been a tough goal due to tight-latency awareness as well as streaming nature of the data. There is an inherent need for novel architectures that can realize latency-aware agile deep learning algorithms at the edge. This paper introduces a novel joint algorithm architecture approach to enable real-time low-power Convolutional Neural Network (CNN) processing on edge devices. The core of the proposed approach is utilizing 1D dimensional convolution with an architecture that can truly benefit from the algorithm optimization. On the algorithm side, we present a novel training and inference based on 1D convolution. On the architecture side, we present a novel data flow architecture with the capability of performing on-the-fly 1D convolution over the pixel stream. Our results on Xilinx Zynq-7000 FPGA for SqueezeNet demonstrates only 2% lost in accuracy while maintaining real-time processing of 60 frames per second with only 1.73W power consumption. The Dynamic power consumption is 7.3X lower than regular 2D convolution CNN for performing the same frame rate, and 4.3X less than Nvidia Jetson TX2 total power, performing only 30 frame per second.


title: A vision-based approach for autonomous landing

abstract: Monocular vision is frequently used in Micro Air Vehicles for many tasks such autonomous navigation, tracking, search and autonomous landing. To address this problem and in the context of autonomous landing of a MAV on a platform, we use a template-based matching in an image pyramid scheme in combination with an edge detector. Thus, the landing zone is localised via image processing in a frame-to-frame basis. Images are captured by the MAV's onboard camera of the MAV and processed with a multi-scale image processing strategy to detect the landing zone at different scales. We assessed our approach in real-time experiments using a Parrot Bebop 2.0 in outdoors and at different heights.


title: Effect of a limited-enforcement intelligent tutoring system in dermatopathology on student errors, goals and solution paths

abstract: Objectives: Determine effects of a limited-enforcement intelligent tutoring system in dermatopathology on student errors, goals and solution paths. Determine if limited enforcement in a medical tutoring system inhibits students from learning the optimal and most efficient solution path. Describe the type of deviations from the optimal solution path that occur during tutoring, and how these deviations change over time. Determine if the size of the problem-space (domain scope), has an effect on learning gains when using a tutor with limited enforcement. Methods: Analyzed data mined from 44 pathology residents using SlideTutor-a Medical Intelligent Tutoring System in Dermatopathology that teaches histopathologic diagnosis and reporting skills based on commonly used diagnostic algorithms. Two subdomains were included in the study representing sub-algorithms of different sizes and complexities. Effects of the tutoring system on student errors, goal states and solution paths were determined. Results: Students gradually increase the frequency of steps that match the tutoring system's expectation of expert performance. Frequency of errors gradually declines in all categories of error significance. Student performance frequently differs from the tutor-defined optimal path. However, as students continue to be tutored, they approach the optimal solution path. Performance in both subdomains was similar for both errors and goal differences. However, the rate at which students progress toward the optimal solution path differs between the two domains. Tutoring in superficial perivascular dermatitis, the larger and more complex domain was associated with a slower rate of approximation towards the optimal solution path. Conclusions: Students benefit from a limited-enforcement tutoring system that leverages diagnostic algorithms but does not prevent alternative strategies. Even with limited enforcement, students converge toward the optimal solution path.


title: Advantages of Geometric Algebra Over Complex Numbers in the Analysis of Networks With Nonsinusoidal Sources and Linear Loads

abstract: An alternative circuit analysis technique is used to study networks with nonsinusoidal sources and linear loads. In contrast to the technique developed by Steinmetz, this method is supported by geometric algebra instead of the algebra of complex numbers, uses multivectors in place of phasors and is performed in the GN domain instead of the frequency domain. The advantages of this method over the present technique involve: determining the flow of current and power quantities in the circuit, validating the results using the principle of conservation of energy, discerning and revealing other forms of reactive power generation, and the ability to design compensators with great flexibility. The power equation is composed of the active power and the CN -power representing the nonactive power. All the CN-power terms are sorted into reactive power terms due to phase shift, reactive power terms due to harmonic interactions and degrading power terms which determine the new quantity called degrading power. This decomposition shows that estimating these quantities is intricate. It also displays the power equation's functionality for power factor improvement. The geometric addition of power quantities is not pre-established but results from applying the established norm and yields the new quantity called net apparent power.


title: A RESURF-Enhanced p-Channel Trench SOI LDMOS With Ultralow Specific on-Resistance

abstract: A low specific on-resistance ( \(R_{{{\rm \mathrm{{\scriptstyle ON}},sp}}}\) ) silicon-on-insulator (SOI) p-channel LDMOS (pLDMOS) with an enhanced reduced surface field (RESURF) effect and self-shielding effect of the back-gate (BG) bias is proposed and investigated. It features an oxide trench and the p-drift region surrounding the trench, which is built on the n-SOI layer. In the OFF-state, first, the extended trench gate also acts as a gate field plate; second, the p-drift and the n-SOI layer forms a folded RESURF structure. Both increase the doping dose of the p-drift and modulate electric field (E-field) distribution; third, the oxide trench not only reduces the device pitch but also enhances the E-field strength. All of them result in a low \(R_{\rm {\scriptstyle {ON,SP}}}\) and a high breakdown voltage (BV) with a reduced device pitch. The free charges induced on the SOI/buried oxide (BOX) interface not only enhance the E-field strength in the BOX but also effectively shield the influence of BG bias effect in a wide range. The proposed pLDMOS achieves state-of-the-art improvement in the tradeoff between BV and \(R_{\rm {\scriptstyle {ON,SP}}}\) . Compared with the p-top SOI pLDMOS, the proposed device reduces the \(R_{\rm {\scriptstyle {ON,SP}}}\) by 79% at the same BV. A strong immunity to the BG bias effect is demonstrated and analyzed in detail.


title: Coverage Analysis for Dense Heterogeneous Networks with Cooperative NOMA

abstract: In a heterogeneous cellular network (HetNet) consisting of $M$ tiers of densely-deployed base stations (BSs), consider that each of the BSs in the HetNet that are associated with multiple users is able to simultaneously schedule and serve two users in a downlink time slot by performing the (power-domain) non-orthogonal multiple access (NOMA) scheme. This paper aims at the preliminary study on the downlink coverage performance of the HetNet with the non-cooperative and the proposed cooperative NOMA schemes. First, we study the coverage probability of the NOMA users for the non-cooperative NOMA scheme in which no BSs are coordinated to jointly transmit the NOMA signals for a particular cell and the coverage probabilities of the two NOMA users of the BSs in each tier are derived. We show that the coverage probabilities can be largely reduced if allocated transmit powers for the NOMA users are not satisfied with some constraints. Next, we study and derive the coverage probabilities for the proposed cooperative NOMA scheme in which the void BSs that are not tagged by any users are coordinated to enhance the far NOMA user in a particular cell. Our analyses show that cooperative NOMA can significantly improve the coverage of all NOMA users as long as the transmit powers for the NOMA users are properly allocated.


title: Analyzing Locally Coordinated Cyber-Physical Attacks for Undetectable Line Outages

abstract: This paper offers the pertinent studies on the locally coordinated cyber-physical attacks that would use the incomplete network information and could cause undetectable transmission line outages. First, power line reactances in the targeted local areas are estimated and the remaining power system is modeled equivalently. Next, a mathematical model in the mixed-integer linear programming form is presented for analyzing the attacks, which would cause undetectable line outages while maximizing the system risk resulting from the power flow redistribution. An efficient greedy search-based heuristic method is developed in order to offer satisfactory solutions for real-time applications. Last, the proposed model for the locally coordinated cyber-physical attacks and the corresponding solution strategies are verified by the case studies based on a six-bus system and the two-area IEEE RTS-96 system.


title: Modeling rolling gaits of a snake robot

abstract: Successful deployment of a snake robot in search and rescue tasks requires the capability of generating controls which can adapt to unknown environments in real-time. However, available motion generation techniques can be computationally expensive and lack the ability to adapt to the surroundings. This work considers modeling the rolling motion of a snake robot by applying the Bellows model with computation reduction techniques. One benefit of this is that controllers are defined with physically meaningful parameters, which in turn allows for higher level control of the robot. Another benefit is that it allows controllers to be defined by “composing shapes”, which enables developing controllers that can adapt to the surroundings. Using shape composition, we implemented a novel gait, named rolling hump, which forms a contour-fitting hump to negotiate obstacles. The efficacy of a snake robot climbing over obstacles by using the rolling hump is experimentally evaluated. An autonomous control strategy is presented and realized in simulation.


title: Toward a Dynamic Learning Perspective of Entrepreneurship

abstract: This conceptual article introduces a dynamic learning perspective of entrepreneurship that builds upon existing “dominant” theoretical approaches to understanding entrepreneurial activity. As many aspects of entrepreneurial learning remain poorly understood, this article maps out and extends current boundaries of thinking regarding how entrepreneurs learn. It presents key conclusions from emergent empirical and conceptual work on the subject and synthesizes a broad range of contributory adult, management, and individual learning literature to develop a robust and integrated thematic conceptualization of entrepreneurial learning. Three distinctive, interrelated elements of entrepreneurial learning are proposed—dynamic temporal phases, interrelated processes, and overarching characteristics. The article concludes by demonstrating how a “learning lens” can be applied to create further avenues for research in entrepreneurship from a learning perspective.


title: Fast Pedestrian Detection Using Estimation of Feature Information Based on Integral Image

abstract: This paper enhances the speed of a pedestrian detection using an estimation of feature information based on integral image. Pedestrian model or input image should be resized to the size of various pedestrians. In case that the size of pedestrian model would be changed, pedestrian models with respect to the size of pedestrians should be required. Reducing the size of pedestrian model, however, deteriorates the quality of the model information. Since various features according to the size of pedestrian models should be extracted, repetitive feature extractions spend the most time in overall process of pedestrian detection. In order to enhance the processing time of feature extraction, this paper proposes the fast extraction of pedestrian features based on the estimate of integral image. The efficiency of the proposed method is evaluated by comparative experiments with the Channel Feature and Adaboost training using INRIA person dataset.


title: Recognizing actions using depth motion maps-based histograms of oriented gradients

abstract: In this paper, we propose an effective method to recognize human actions from sequences of depth maps, which provide additional body shape and motion information for action recognition. In our approach, we project depth maps onto three orthogonal planes and accumulate global activities through entire video sequences to generate the Depth Motion Maps (DMM). Histograms of Oriented Gradients (HOG) are then computed from DMM as the representation of an action video. The recognition results on Microsoft Research (MSR) Action3D dataset show that our approach significantly outperforms the state-of-the-art methods, although our representation is much more compact. In addition, we investigate how many frames are required in our framework to recognize actions on the MSR Action3D dataset. We observe that a short sub-sequence of 30-35 frames is sufficient to achieve comparable results to that operating on entire video sequences.


title: Shift-Invariant and Sampling Spaces Associated With the Fractional Fourier Transform Domain

abstract: Shift-invariant spaces play an important role in sampling theory, multiresolution analysis, and many other areas of signal and image processing. A special class of the shift-invariant spaces is the class of sampling spaces in which functions are determined by their values on a discrete set of points. One of the vital tools used in the study of sampling spaces is the Zak transform. The Zak transform is also related to the Poisson summation formula and a common thread between all these notions is the Fourier transform. In this paper, we extend some of these notions to the fractional Fourier transform (FrFT) domain. First, we introduce two definitions of the discrete fractional Fourier transform and two semi-discrete fractional convolutions associated with them. We employ these definitions to derive necessary and sufficient conditions pertaining to FrFT domain, under which integer shifts of a function form an orthogonal basis or a Riesz basis for a shift-invariant space. We also introduce the fractional Zak transform and derive two different versions of the Poisson summation formula for the FrFT. These extensions are used to obtain new results concerning sampling spaces, to derive the reproducing-kernel for the spaces of fractional band-limited signals, and to obtain a new simple proof of the sampling theorem for signals in that space. Finally, we present an application of our shift-invariant signal model which is linked with the problem of fractional delay filtering.


title: An adaptive software development process model

abstract: The concept of agile process models has gained great popularity in software (SW) development community in past few years. Agile models promote fast development. This property has certain drawbacks, such as poor documentation and bad quality. Fast development promotes use of agile process models in small-scale projects. This paper modifies and evaluates extreme programming (XP) process model and proposes a novel adaptive process mode based on these modifications.


title: Learning to Sketch with Shortcut Cycle Consistency

abstract: To see is to sketch -- free-hand sketching naturally builds ties between human and machine vision. In this paper, we present a novel approach for translating an object photo to a sketch, mimicking the human sketching process. This is an extremely challenging task because the photo and sketch domains differ significantly. Furthermore, human sketches exhibit various levels of sophistication and abstraction even when depicting the same object instance in a reference photo. This means that even if photo-sketch pairs are available, they only provide weak supervision signal to learn a translation model. Compared with existing supervised approaches that solve the problem of D(E(photo)) ->sketch, where E($\cdot$) and D($\cdot$) denote encoder and decoder respectively, we take advantage of the inverse problem (e.g., D(E(sketch)) ->photo), and combine with the unsupervised learning tasks of within-domain reconstruction, all within a multi-task learning framework. Compared with existing unsupervised approaches based on cycle consistency (i.e., D(E(D(E(photo)))) ->photo), we introduce a shortcut consistency enforced at the encoder bottleneck (e.g., D(E(photo)) ->photo) to exploit the additional self-supervision. Both qualitative and quantitative results show that the proposed model is superior to a number of state-of-the-art alternatives. We also show that the synthetic sketches can be used to train a better fine-grained sketch-based image retrieval (FG-SBIR) model, effectively alleviating the problem of sketch data scarcity.


Predicting the Popularity of News Articles


title: The dynamic sustainability framework: addressing the paradox of sustainment amid ongoing change

abstract: BACKGROUND ::: Despite growth in implementation research, limited scientific attention has focused on understanding and improving sustainability of health interventions. Models of sustainability have been evolving to reflect challenges in the fit between intervention and context. ::: ::: ::: DISCUSSION ::: We examine the development of concepts of sustainability, and respond to two frequent assumptions -'voltage drop,' whereby interventions are expected to yield lower benefits as they move from efficacy to effectiveness to implementation and sustainability, and 'program drift,' whereby deviation from manualized protocols is assumed to decrease benefit. We posit that these assumptions limit opportunities to improve care, and instead argue for understanding the changing context of healthcare to continuously refine and improve interventions as they are sustained. Sustainability has evolved from being considered as the endgame of a translational research process to a suggested 'adaptation phase' that integrates and institutionalizes interventions within local organizational and cultural contexts. These recent approaches locate sustainability in the implementation phase of knowledge transfer, but still do not address intervention improvement as a central theme. We propose a Dynamic Sustainability Framework that involves: continued learning and problem solving, ongoing adaptation of interventions with a primary focus on fit between interventions and multi-level contexts, and expectations for ongoing improvement as opposed to diminishing outcomes over time. ::: ::: ::: SUMMARY ::: A Dynamic Sustainability Framework provides a foundation for research, policy and practice that supports development and testing of falsifiable hypotheses and continued learning to advance the implementation, transportability and impact of health services research.


title: The Basic AI Drives

abstract: One might imagine that AI systems with harmless goals will be harmless. This paper instead shows that intelligent systems will need to be carefully designed to prevent them from behaving in harmful ways. We identify a number of “drives” that will appear in sufficiently advanced AI systems of any design. We call them drives because they are tendencies which will be present unless explicitly counteracted. We start by showing that goal-seeking systems will have drives to model their own operation and to improve themselves. We then show that self-improving systems will be driven to clarify their goals and represent them as economic utility functions. They will also strive for their actions to approximate rational economic behavior. This will lead almost all systems to protect their utility functions from modification and their utility measurement systems from corruption. We also discuss some exceptional systems which will want to modify their utility functions. We next discuss the drive toward self-protection which causes systems try to prevent themselves from being harmed. Finally we examine drives toward the acquisition of resources and toward their efficient utilization. We end with a discussion of how to incorporate these insights in designing intelligent technology which will lead to a positive future for humanity.


title: Mixed-Signal Production Test: A Measurement Principle Perspective

abstract: This article, based on a tutorial the author presented at ITC 2008, is an overview and introduction to mixed-signal production test. The article focuses on the fundamental techniques and procedures in production test and explores key issues confronting the industry.


title: Integrating motivational, social, and contextual work design features: A meta-analytic summary and theoretical extension of the work design literature.

abstract: The authors developed and meta-analytically examined hypotheses designed to test and extend work design theory by integrating motivational, social, and work context characteristics. Results from a summary of 259 studies and 219,625 participants showed that 14 work characteristics explained, on average, 43% of the variance in the 19 worker attitudes and behaviors examined. For example, motivational characteristics explained 25% of the variance in subjective performance, 2% in turnover perceptions, 34% in job satisfaction, 24% in organizational commitment, and 26% in role perception outcomes. Beyond motivational characteristics, social characteristics explained incremental variances of 9% of the variance in subjective performance, 24% in turnover intentions, 17% in job satisfaction, 40% in organizational commitment, and 18% in role perception outcomes. Finally, beyond both motivational and social characteristics, work context characteristics explained incremental variances of 4% in job satisfaction and 16% in stress. The results of this study suggest numerous opportunities for the continued development of work design theory and practice.


title: Handwritten Bangla Basic and Compound character recognition using MLP and SVM classifier

abstract: Abstract — A novel approach for recognition of handwritten compound Bangla characters, along with the Basic characters of Bangla alphabet, is presented here. Compared to English like Roman script, one of the major stumbling blocks in Optical Character Recognition (OCR) of handwritten Bangla script is the large number of complex shaped character classes of Bangla alphabet. In addition to 50 basic character classes, there are nearly 160 complex shaped compound character classes in Bangla alphabet. Dealing with such a large varieties of handwritten characters with a suitably designed feature set is a challenging problem. Uncertainty and imprecision are inherent in handwritten script. Moreover, such a large varieties of complex shaped characters, some of which have close resemblance, makes the problem of OCR of handwritten Bangla characters more difficult. Considering the complexity of the problem, the present approach makes an attempt to identify compound character classes from most frequently to less frequently occured ones, i.e., in order of importance. This is to develop a frame work for incrementally increasing the number of learned classes of compound characters from more frequently occurred ones to less frequently occurred ones along with Basic characters. On experimentation, the technique is observed produce an average recognition rate of 79.25% using MLP and 80.510% using SVM after three fold cross validation of data with future scope of improvement and extension.


title: Intelligent Approaches to interact with Machines using Hand Gesture Recognition in Natural way: A Survey

abstract: Hand gestures recognition (HGR) is one of the main areas of research for the engineers, scientists and bioinformatics. HGR is the natural way of Human Machine interaction and today many researchers in the academia and industry are working on different application to make interactions more easy, natural and convenient without wearing any extra device. HGR can be applied from games control to vision enabled robot control, from virtual reality to smart home systems. In this paper we are discussing work done in the area of hand gesture recognition where focus is on the intelligent approaches including soft computing based methods like artificial neural network, fuzzy logic, genetic algorithms etc. The methods in the preprocessing of image for segmentation and hand image construction also taken into study. Most researchers used fingertips for hand detection in appearance based modeling. Finally the comparison of results given by different researchers is also presented.


title: Tamil phoneme classification using contextual features and discriminative models

abstract: The speech recognition systems may be designed based on any one of the sub-word unit phoneme, tri-phone and syllable. The phonemes are a set of base-forms for representing the unique sounds in a particular language. In supervised phoneme classification, the segmentation of phoneme, features and class label are given and the goal is to classify the phoneme. Phoneme classification and recognition can be useful in applications such as spoken document retrieval, named entity extraction, out-of-vocabulary detection, language identification, and spoken term detection. In trained speech, each phoneme occurs clearly in speech waveform. In spontaneous speech, due to co-articulation effect, influence of adjacent phonemes is present in each phoneme where left and right context frame information plays vital role in accurate phoneme classification. In the proposed work, three discriminative classifiers like Multilayer Perceptron, Naive Bayes and Support Vector Machine are used to classify 25 phonemes of Tamil language. The approximate boundaries of phoneme identified using Spectral Transition Measure (STM). After segmentation, Mel Frequency Cepstral Co-Efficient (MFCC) of 9 frames including 4 left context frames, 1 centre frame corresponding to the phoneme and 4 right context frames are extracted and used as input to classifiers. Tamil word dataset prepared to cover 25 phonemes of the language. The performance of the classifiers are analysed and results are presented.


title: A sparse matrix vector multiply accelerator for support vector machine

abstract: Sparse matrix vector multiplication (SpMV) is a linear algebra construct commonly found in machine learning (ML) algorithms, such as support vector machine (SVM). We profiled a popular SVM software (libSVM) on an energy-efficient microserver and a high-performance server for real-world ML datasets, and observed that SpMV dominates runtime. We propose a novel SpMV algorithm tailored for ML and a hardware accelerator architecture design based on this algorithm. Our evaluations show that the proposed algorithm and hardware accelerator achieves significant efficiency improvements over the conventional SpMV algorithm used in libSVM.


title: A Review of Charge Scheduling of Electric Vehicles in Smart Grid

abstract: Electric vehicles (EVs) are being introduced by different manufacturers as an environment-friendly alternative to vehicles with internal combustion engines, with several benefits. The number of EVs is expected to grow rapidly in the coming years. However, uncoordinated charging of these vehicles can put a severe stress on the power grid. The problem of charge scheduling of EVs is an important and challenging problem and has seen significant research activity in the last few years. This review covers the recent works done in the area of scheduling algorithms for charging EVs in smart grid. The works are first classified into two broad classes of unidirectional versus bidirectional charging, and then, each class is further classified based on whether the scheduling is centralized or distributed and whether any mobility aspects are considered or not. It then reviews the key results in this field following the classification proposed. Some interesting research challenges that can be addressed are also identified.


title: 7kW GaN W-band transmitter

abstract: Recent advances in high power millimeter wave (mmW) Gallium Nitride (GaN) technology has enabled the development of a high power solid state W-band transmitter with an unprecedented 7kW Continuous Wave (CW) output power. The transmitter coherently sums a total of 8,192 1W+ GaN Power Amplifier (PA) Monolithic Microwave Integrated Circuits (MMICs) using spatial combining. This accomplishment represents a two-orders-of-magnitude improvement in W-band solid state power generation over previously published results.


title: Handwritten document image segmentation into text lines and words

abstract: Two novel approaches to extract text lines and words from handwritten document are presented. The line segmentation algorithm is based on locating the optimal succession of text and gap areas within vertical zones by applying Viterbi algorithm. Then, a text-line separator drawing technique is applied and finally the connected components are assigned to text lines. Word segmentation is based on a gap metric that exploits the objective function of a soft-margin linear SVM that separates successive connected components. The algorithms tested on the benchmarking datasets of ICDAR07 handwriting segmentation contest and outperformed the participating algorithms.


title: CBT for depression: a pilot RCT comparing mobile phone vs. computer

abstract: This paper reports the results of a pilot randomized controlled trial comparing the delivery modality (mobile phone/tablet or fixed computer) of a cognitive behavioural therapy intervention for the treatment of depression. The aim was to establish whether a previously validated computerized program (The Sadness Program) remained efficacious when delivered via a mobile application. 35 participants were recruited with Major Depression (80% female) and randomly allocated to access the program using a mobile app (on either a mobile phone or iPad) or a computer. Participants completed 6 lessons, weekly homework assignments, and received weekly email contact from a clinical psychologist or psychiatrist until completion of lesson 2. After lesson 2 email contact was only provided in response to participant request, or in response to a deterioration in psychological distress scores. The primary outcome measure was the Patient Health Questionnaire 9 (PHQ-9). Of the 35 participants recruited, 68.6% completed 6 lessons and 65.7% completed the 3-months follow up. Attrition was handled using mixed-model repeated-measures ANOVA. Both the Mobile and Computer Groups were associated with statistically significantly benefits in the PHQ-9 at post-test. At 3 months follow up, the reduction seen for both groups remained significant. These results provide evidence to indicate that delivering a CBT program using a mobile application, can result in clinically significant improvements in outcomes for patients with depression. Australian New Zealand Clinical Trials Registry ACTRN 12611001257954


title: Multiplexing signals in reinforcement learning with internal models and dopamine

abstract: A fundamental challenge for computational and cognitive neuroscience is to understand how reward-based learning and decision-making are made and how accrued knowledge and internal models of the environment are incorporated. Remarkable progress has been made in the field, guided by the midbrain dopamine reward prediction error hypothesis and the underlying reinforcement learning framework, which does not involve internal models (‘model-free’). Recent studies, however, have begun not only to address more complex decision-making processes that are integrated with model-free decision-making, but also to include internal models about environmental reward structures and the minds of other agents, including model-based reinforcement learning and using generalized prediction errors. Even dopamine, a classic model-free signal, may work as multiplexed signals using model-based information and contribute to representational learning of reward structure.


title: Design of coil structure achieving uniform magnetic field distribution for wireless charging platform

abstract: Wireless power transfer technology via electromagnetic coupling has been widely used in the planar contactless charging platform. In this paper, a current distribution is deduced to ensure axial component of magnetic field is uniformly distributed in the charging region and zero outside the region. Based on continuous current distributions that can generate even axial component of magnetic field, two transmitting coil structures are proposed. The uniformity of the axial component of magnetic field distribution is confirmed by using the simulation tools. In the case of multiple coils in parallel, the magnetic field distribution near the margin of the charging region changes greatly. This paper analyzed the changes of magnetic field in the case of two coils in parallel, and compared the magnetic field distribution with different coil structures.


title: A survey of Binary similarity and distance measures

abstract: The binary feature vector is one of the most common representations of patterns and measuring similarity and distance measures play a critical role in many problems such as clustering, classification, etc. Ever since Jaccard proposed a similarity measure to classify ecological species in 1901, numerous binary similarity and distance measures have been proposed in various fields. Applying appropriate measures results in more accurate data analysis. Notwithstanding, few comprehensive surveys on binary measures have been conducted. Hence we collected 76 binary similarity and distance measures used over the last century and reveal their correlations through the hierarchical clustering technique.


title: Hearing loss and falls: A systematic review and meta-analysis

abstract: BACKGROUND ::: Falls are a devastating condition in older individuals. Identifying potentially modifiable risk factors such as hearing loss would provide a substantial public health benefit. ::: ::: ::: OBJECTIVE ::: To evaluate the current evidence for an association between hearing loss and falls risk. ::: ::: ::: DATA SOURCES ::: A systematic search of PubMed, Cumulative Index to Nursing and Allied Health Literature, Embase, Scopus, Web of Science, and Cochrane databases was performed in July 2014. ::: ::: ::: STUDY ELIGIBILITY ::: Studies were eligible for inclusion if they were published in the peer-reviewed literature. All studies used a predetermined definition of hearing loss. Main outcomes and measurements were fall hospitalization records or self-reports of falls by structured interview or validated questionnaires. ::: ::: ::: STUDY APPRAISAL AND SYNTHESIS ::: Two investigators independently reviewed the literature related to hearing loss, falls, and older adults. We pooled effect sizes from across the studies and performed a meta-analysis to compute an overall effect size. ::: ::: ::: RESULTS AND LIMITATIONS ::: Twelve eligible studies were identified. The odds of falling were 2.39 times greater among older adults with hearing loss than older adults with normal hearing (pooled odds ratio 2.39, 95% confidence interval [CI]: 2.11-2.68). In sensitivity analyses, we restricted the meta-analysis to studies where hearing loss was audiometrically defined (N = 6) and observed hearing loss to be associated with a 69% increase in the odds of falling (pooled odds ratio 1.69, 95% CI: 1.18-2.19). When we further limited to studies that also performed multivariate regression analyses (N = 4), the overall effect size did not appreciably change (pooled odds ratio 1.72, 95% CI: 1.07-2.37). We observed a potential positive publication bias in the literature. Limitations of the systematic review and meta-analysis are the cross-sectional designs of most studies and the heterogeneity across studies (Q = 631, P < .05, I2 = 98.1%). ::: ::: ::: CONCLUSIONS AND RELEVANCE ::: In the published literature, hearing loss is associated with a significantly increased odds of falling in older adults. These findings need to be interpreted in light of the potential for positive publication bias in the literature on this topic. ::: ::: ::: LEVEL OF EVIDENCE ::: NA Laryngoscope, 126:2587-2596, 2016.


title: Modelling User Trust and Mobile Payment Adoption : A Conceptual Framework

abstract: The proliferation of new technologies and the changing dynamics of industry competition have spurred growth in innovative production, marketing and consumption. The need for convenience has also fuelled enormous interest in the use of mobile payment innovations, which in turn creates security challenges for users. Indications are that mobile phones and handheld devices should have made more inroads in businesses and societies as in most advanced societies are at today [1]. Instead, there has been a slow acceptance of mobile and handheld devices as alternative payment systems, especially in developing countries, despite the efforts of key players such as banks, mobile network operators, and mobile payment service providers (MPSP). This paper, therefore, is a conceptual design to examine security factors influencing the acceptance of mobile payment systems in Malaysia. This paper will examine the impact of security dimensions on trust and how these impacts on trust influence users' intention to use mobile payment systems. We will also highlight several research, practitioner and policy implications.


title: Analysis of misinformation containment in online social networks

abstract: With their blistering expansion in recent years, popular online social sites such as Twitter, Facebook and Bebo, have become not only one of the most effective channels for viral marketing but also the major news sources for many people nowadays. Alongside these promising features, however, comes the threat of misinformation propagation which can lead to undesirable effects. Due to the sheer number of online social network (OSN) users and the highly clustered structures commonly shared by these kinds of networks, there is a substantial challenge to efficiently contain viral spread of misinformation in large-scale social networks. In this paper, we focus on how to limit viral propagation of misinformation in OSNs. Particularly, we study a set of problems, namely the @b"T^I-Node Protectors problems, which aim to find the smallest set of highly influential nodes from which disseminating good information helps to contain the viral spread of misinformation, initiated from a set of nodes I, within a desired fraction (1-@b) of the nodes in the entire network in T time steps. For this family of problems, we analyze and present solutions including their inapproximability results, greedy algorithms that provide better lower bounds on the number of selected nodes, and a community-based method for solving these problems. We further conduct a number of experiments on real-world traces, and the empirical results show that our proposed methods outperform existing alternative approaches in finding those important nodes that can help to contain the spread of misinformation effectively.


title: WebCQ-detecting and delivering information changes on the web

abstract: WebCQ is a prototype system for large-scale Web information monitoring and delivery. It makes heavy use of the structure present in hypertext and the concept of continual queries. In this paper we discuss both mechanisms that WebCQ uses to discover and detect changes to the World Wide Web (the Web) pages e ciently, and the methods to notify users of interesting changes with a personalized customization. The WebCQ system consists of four main components: a change detection robot that discovers and detects changes, a proxy cache service that reduces communication tra cs to the original information servers, a personalized presentation tool that highlights changes detected by WebCQ sentinels, and a change noti cation service that delivers fresh information to the right users at the right time. A salient feature of our change detection robot is its ability to support various types of web page sentinels for detecting, presenting, and delivering interesting changes to web pages. This paper describes the WebCQ system with an emphasis on general issues in designing and engineering a large-scale information change monitoring system on the Web.