title: Colonic transit studies: normal values for adults and children with comparison of radiological and scintigraphic methods

abstract: The sitz or plastic marker study for colonic transit has been around for many years. It is applicable where an X-ray machine exists, is widely used and is accepted as the gold standard for diagnosing constipation. Recently, radiopharmaceutical methods have been developed. The theme of this review is their possible roles in the assessment of paediatric bowel motility disorders in patients presenting to paediatric surgeons. This review presents data on total and segmental transit in normal adults and children and comparing the two techniques in adults. Reliability and reproducibility are presented. Normative data for colonic transit in adults and children are discussed and parameters for assessing abnormal transit are reviewed. Normal colonic transit takes 20–56 h. Plastic marker studies are more readily accessible, but the assessment may be misleading with current methods. Plastic markers show faster transit than scintigraphy. It is difficult to compare the two techniques because methods of reporting are different. Using scintigraphy, repeatability is good. Separation of normal from slow transit in the ascending colon is apparent at 24 and 48 h, but the determination of transit through the distal colon/rectum in adults may require studies of more than 7 days. In conclusion, plastic marker studies and scintigraphy show similar transit rates in young adults and children. However, scintigraphy has advantages of allowing transit through the stomach and small intestine to be measured and has proved useful in the diagnostic workup of children with intractable constipation.


title: Street art gangs: location based hybrid reality game

abstract: We present a location based mixed reality game called Street Art Gangs that we have developed to explore the playful appropriation of the hybrid reality comprising of a city center and its detailed virtual replica represented as a 3D virtual model. In the real streets SAG is played with a mobile phone app that allows tagging predefined locations around the city to claim their ownership and busting nearby players of competing gangs. The virtual game world is viewed with a PC app that allows observing the current owners of taggable locations, the locations of other players, and the locations of patrolling virtual policemen busting players. We have developed two incremental versions of SAG that have been evaluated with tournaments in the wild. We conceptualize the findings of the tournaments with de Souza e Silva's theoretical framework for hybrid reality games. Our findings suggest that players preferred to play the game on real city streets while the added value of the virtual game world remained marginal. The size of the game both in terms of area and the number of taggable locations turned out to have a major impact on gameplay.


title: A distributed cyber-based information distillation and control architecture for wireless healthcare systems

abstract: Cyber-physical space, characterized by the seamless integration of wired and wireless networking technologies, embedded computer systems, sensor and actuator technologies, enable ordinary people to richly interact with their environment. The health-care domain presents many promising applications for cyber-physical space, such as patient information management, real-time emergency reporting, elder living assistance, etc. However, although many component technologies needed for realizing such a health-care based cyber-physical space are available, a robust and effective architecture with novel management protocols implemented which synthesizes these separated components to cooperatively work in a predicted way is missing. In this paper, we propose a distributed, and effective heterogeneous architecture to make up such a gap, emphasizing on patient information retrieval,real-time monitoring and instant reactions.


title: Moment-Based Bound on Peak-to-Average Power Ratio and Reduction with Unitary Matrix

abstract: Reducing Peak-to-Average Power Ratio (PAPR) is a significant task in OFDM systems. To evaluate the efficiency of PAPR-reducing methods, the complementary cumulative distribution function (CCDF) of PAPR is often used. In the situation where the central limit theorem can be applied, an approximate form of the CCDF has been obtained. On the other hand, in general situations, the bound of the CCDF has been obtained under some assumptions. In this paper, we derive the bound of the CCDF with no assumption about modulation schemes. Therefore, our bound can be applied with any codewords and that our bound is written with fourth moments of codewords. Further, we propose a method to reduce the bound with unitary matrices. With this method, it is shown that our bound is closely related to the CCDF of PAPR.


title: SBEED: Convergent Reinforcement Learning with Nonlinear Function Approximation

abstract: When function approximation is used, solving the Bellman optimality equation with stability guarantees has remained a major open problem in reinforcement learning for decades. The fundamental difficulty is that the Bellman operator may become an expansion in general, resulting in oscillating and even divergent behavior of popular algorithms like Q-learning. In this paper, we revisit the Bellman equation, and reformulate it into a novel primal-dual optimization problem using Nesterov's smoothing technique and the Legendre-Fenchel transformation. We then develop a new algorithm, called Smoothed Bellman Error Embedding, to solve this optimization problem where any differentiable function class may be used. We provide what we believe to be the first convergence guarantee for general nonlinear function approximation, and analyze the algorithm's sample complexity. Empirically, our algorithm compares favorably to state-of-the-art baselines in several benchmark control problems.


title: A Joint Model of Language and Perception for Grounded Attribute Learning

abstract: As robots become more ubiquitous and capable, it becomes ever more important for untrained users to easily interact with them. Recently, this has led to study of the language grounding problem, where the goal is to extract representations of the meanings of natural language tied to the physical world. We present an approach for joint learning of language and perception models for grounded attribute induction. The perception model includes classifiers for physical characteristics and a language model based on a probabilistic categorial grammar that enables the construction of compositional meaning representations. We evaluate on the task of interpreting sentences that describe sets of objects in a physical workspace, and demonstrate accurate task performance and effective latent-variable concept induction in physical grounded scenes.


MOBILE PHONE ANTENNA USING SLOTTED GROUND STRUCTURE


title: Ergonomics principles associated with laparoscopic surgeon injury/illness.

abstract: OBJECTIVE ::: This study sought to investigate the prevalence of laparoscopic surgeon injury/illness symptoms and evaluate associations between symptoms and operating room ergonomics. ::: ::: ::: BACKGROUND ::: Although laparoscopic procedures significantly benefit patients in terms of decreased recovery times and improved outcomes, they contribute to mental fatigue and musculoskeletal problems among surgeons. A variety of ergonomic interventions and applications are implemented by surgeons to reduce health problems. Currently, there is a gap in knowledge regarding a surgeon's individual assessment of the operating room, an assessment that, in turn, would prompt the implementation of these interventions. ::: ::: ::: METHOD ::: A new survey instrument solicited information from surgeons (N = 61) regarding surgeon demographics, perception, frequency of operating room equipment adjustment, and self-reported symptoms. Surgeons responded to questions addressing safety, ergonomics, and fatigue in the operating room, using a 5-point Likert-type scale that included the option undecided. ::: ::: ::: RESULTS ::: Surgeons who responded undecided were more likely to experience symptoms of injury/illness than respondents who were able to assess the features of their operating rooms. Symptoms were experienced by 100% of participants. The most prevalent symptoms were neck stiffness, back stiffness, and back pain. ::: ::: ::: CONCLUSION ::: This study supports hypotheses that surgeons are experiencing body part discomfort and indicators of fatigue that may be associated with performing laparoscopy. Results suggest that awareness, knowledge, and utilization of ergonomic principles could protect surgeons against symptoms that lead to occupational injury. ::: ::: ::: APPLICATION ::: The purpose of this brief report is to convey the importance of ergonomic principles in the operating room, specific to laparoscopic surgery and surgeon injury/illness symptoms.


title: A Miniaturized CPW-Fed Antipodal Vivaldi Antenna With Enhanced Radiation Performance for Wideband Applications

abstract: A miniaturized coplanar waveguide (CPW)-fed antipodal Vivaldi antenna (AVA) is presented in this letter. Elliptically shaped strip conductors are used to lower the antenna operating frequency. With two pairs of tapered slots and circularly shaped loads, the radiation performance in lower operating band can be greatly enhanced. The fabricated prototype of this proposed antenna with a size of $90 \times 93.5 \times 0.8~\hbox{mm}^{3}$ was measured to confirm the simulated results. The wide measured impedance band from 1.32 GHz to more than 17 GHz for $\hbox {VSWR} can be obtained. Meanwhile, the good measured radiation patterns with gain $3.5\hbox {--} 9.3~\hbox{dBi}$ in end-fire direction achieve in the whole measured working band.


title: Phosphatidylserine Increases IKBKAP Levels in Familial Dysautonomia Cells

abstract: Familial Dysautonomia (FD) is an autosomal recessive congenital neuropathy that results from abnormal development and progressive degeneration of the sensory and autonomic nervous system. The mutation observed in almost all FD patients is a point mutation at position 6 of intron 20 of the IKBKAP gene; this gene encodes the IκB kinase complex-associated protein (IKAP). The mutation results in a tissue-specific splicing defect: Exon 20 is skipped, leading to reduced IKAP protein expression. Here we show that phosphatidylserine (PS), an FDA-approved food supplement, increased IKAP mRNA levels in cells derived from FD patients. Long-term treatment with PS led to a significant increase in IKAP protein levels in these cells. A conjugate of PS and an omega-3 fatty acid also increased IKAP mRNA levels. Furthermore, PS treatment released FD cells from cell cycle arrest and up-regulated a significant number of genes involved in cell cycle regulation. Our results suggest that PS has potential for use as a therapeutic agent for FD. Understanding its mechanism of action may reveal the mechanism underlying the FD disease.


title: Working and Environmental Factors on Job Burnout: A Cross-sectional Study Among Nurses

abstract: BACKGROUND ::: Burnout is a problem that impacts on the staff management costs and on the patient care quality. ::: ::: ::: OBJECTIVE ::: This work aimed to investigate some psychosocial factors related to burnout. Specifically, we explored the sample characteristics for moderate/high emotional exhaustion, cynicism and professional inefficacy, as well as the relationship between both working and environmental variables and burnout. ::: ::: ::: METHOD ::: A cross-sectional study involving 307 nurses from one Italian hospital was carried out. A self-reported questionnaire was used to collect data. Data analysis was performed by using SPSS 19.0. ::: ::: ::: RESULTS ::: The results showed that there was a significant difference between nurses with low and moderate/high burnout in all the three components in almost all the examined organizational variables. In addition, we found that the aspects of working life had a significant impact on the three dimensions of burnout. ::: ::: ::: CONCLUSIONS ::: The findings of this study not only can provide useful basis for future research in the field, but also can offer practical suggestions for improving nursing practice and promote effective workplace, thus reducing the risk burnout among nurses.


Character-based Decoding in Tree-to-Sequence Attention-based Neural Machine Translation.


title: Supervised Locally Linear Embedding

abstract: Locally linear embedding (LLE) is a recently proposed method for unsupervised nonlinear dimensionality reduction. It has a number of attractive features: it does not require an iterative algorithm, and just a few parameters need to be set. Two extensions of LLE to supervised feature extraction were independently proposed by the authors of this paper. Here, both methods are unified in a common framework and applied to a number of benchmark data sets. Results show that they perform very well on high-dimensional data which exhibits a manifold structure.


title: A New Clustering Approach for Anomaly Intrusion Detection

abstract: Recent advances in technology have made our work easier compare to earlier times. Computer network is growing day by day but while discussing about the security of computers and networks it has always been a major concerns for organizations varying from smaller to larger enterprises. It is true that organizations are aware of the possible threats and attacks so they always prepare for the safer side but due to some loopholes attackers are able to make attacks. Intrusion detection is one of the major fields of research and researchers are trying to find new algorithms for detecting intrusions. Clustering techniques of data mining is an interested area of research for detecting possible intrusions and attacks. This paper presents a new clustering approach for anomaly intrusion detection by using the approach of K-medoids method of clustering and its certain modifications. The proposed algorithm is able to achieve high detection rate and overcomes the disadvantages of K-means algorithm.


title: Bubbling menus: a selective mechanism for accessing hierarchical drop-down menus

abstract: This paper introduces bubbling menus, a new design for cascading drop-down menus. Bubbling menus combine the bubble cursor [10] with directional mouse-gesture techniques to facilitate the access of certain items in a menu, such as frequently selected items. Through an extensive iterative design process, we explore bubbling menus in the context of adaptive and customizable user interfaces. Unlike other adaptation and customization techniques such as split menus, bubbling menus do not disrupt the original structure of menus and enable the activation of menus far from a menu bar. Results from two evaluation studies presented in the paper show that bubbling menus provide an effective alternative to accelerate menu selections tasks.


title: Predicting Instructor's Intervention in MOOC forums

abstract: Instructor intervention in student discussion forums is a vital component in Massive Open Online Courses (MOOCs), where personalized interaction is limited. This paper introduces the problem of predicting instructor interventions in MOOC forums. We propose several prediction models designed to capture unique aspects of MOOCs, combining course information, forum structure and posts content. Our models abstract contents of individual posts of threads using latent categories, learned jointly with the binary intervention prediction problem. Experiments over data from two Coursera MOOCs demonstrate that incorporating the structure of threads into the learning problem leads to better predictive performance.


title: Receiving right/wrong feedback: Consequences for learning

abstract: Prior work suggests that receiving feedback that one’s response was correct or incorrect (right/wrong feedback) does not help learners, as compared to not receiving any feedback at all (Pashler, Cepeda, Wixted, & Rohrer, 2005). In three experiments we examined the generality of this conclusion. Right/ wrong feedback did not aid error correction, regardless of whether participants learned facts embedded in prose (Experiment 1) or translations of foreign vocabulary (Experiment 2). While right/wrong feedback did not improve the overall retention of correct answers (Experiments 1 and 2), it facilitated retention of low-confidence correct answers (Experiment 3). Reviewing the original materials was very useful to learners, but this benefit was similar after receiving either right/wrong feedback or no feedback (Experiments 1 and 2). Overall, right/wrong feedback conveys some information to the learner, but is not nearly as useful as being told the correct answer or having the chance to review the to-be-learned materials.


title: Twitter Sentiment Analysis Using a Modified Naïve Bayes Algorithm

abstract: Microblogging has emerged as a popular platform and a powerful communication tool among people nowadays. A clear majority of people share their opinions about various aspects of their lives online every day. Thus, microblogging websites offer rich sources of data in order to perform sentiment analysis and opinion mining. Because microblogging has emerged relatively recently there are only some research works which are devoted to this field. In this paper, the focus is on performing the task of sentiment analysis using Twitter which is one of the most popular microblogging platforms. Twitter is a very popular microblogging site where its users write status messages called tweets to express themselves. These status updates mostly express their opinions about various topics. The objective of this paper is to build a system that can classify these Twitter status updates as positive, negative, or neutral with respect to any query term thereby giving an idea about the overall sentiment of the people towards that topic. This type of sentiment analysis is useful for advertisers, consumers researching a service or product, companies, governments, marketers, or any organization who are researching public opinion.


title: A Comparison of Named Entity Recognition Tools Applied to Biographical Texts

abstract: Named entity recognition (NER) is a popular domain of natural language processing. For this reason, many tools exist to perform this task. Amongst other points, they differ in the processing method they rely upon, the entity types they can detect, the nature of the text they can handle, and their input/output formats. This makes it difficult for a user to select an appropriate NER tool for a specific situation. In this article, we try to answer this question in the context of biographic texts. For this matter, we first constitute a new corpus by annotating 247 Wikipedia articles. We then select 4 publicly available, well known and free for research NER tools for comparison: Stanford NER, Illinois NET, OpenCalais NER WS and Alias-i LingPipe. We apply them to our corpus, assess their performances and compare them. When considering overall performances, a clear hierarchy emerges: Stanford has the best results, followed by LingPipe, Illionois and OpenCalais. However, a more detailed evaluation performed relatively to entity types and article categories highlights the fact their performances are diversely influenced by those factors. This complementarity opens an interesting perspective regarding the combination of these individual tools in order to improve performance.


title: Dynamic Cascades for Face Detection

abstract: In this paper, we propose a novel method, called "dynamic cascade", for training an efficient face detector on massive data sets. There are three key contributions. The first is a new cascade algorithm called "dynamic cascade ", which can train cascade classifiers on massive data sets and only requires a small number of training parameters. The second is the introduction of a new kind of weak classifier, called "Bayesian stump", for training boost classifiers. It produces more stable boost classifiers with fewer features. Moreover, we propose a strategy for using our dynamic cascade algorithm with multiple sets of features to further improve the detection performance without significant increase in the detector's computational cost. Experimental results show that all the new techniques effectively improve the detection performance. Finally, we provide the first large standard data set for face detection, so that future researches on the topic can be compared on the same training and testing set.


title: Attack Analysis Results for Adversarial Engagement 1 of the DARPA Transparent Computing Program

abstract: This report presents attack analysis results of the first adversarial engagement event stream for the first engagement of the DARPA TC program conducted in October 2016. The analysis was performed by Stony Brook University and University of Illinois at Chicago. The findings in this report are obtained without prior knowledge of the attacks conducted.


title: Amazon and whole foods: follow the strategy (and the money)

abstract: Checking out the recent Amazon acquisition of Whole Foods.


title: Multimedia Services in Cloud-Based Vehicular Networks

abstract: Research into the requirements for mobile services has seen a growing interest in the fields of cloud technology and vehicular applications. Integrating cloud computing and storage with vehicles is a way to increase accessibility to multimedia services, and inspire myriad potential applications and research topics. This paper presents an overview of the characteristics of cloud computing, and introduces the basic concepts of vehicular networks. An architecture for multimedia cloud computing is proposed to suit subscription service mechanisms. The tendency to equip vehicles with advanced and embedded devices such as diverse sensors increases the capabilities of vehicles to provide computation and collection of multimedia content in the form of the vehicular network. Then, the taxonomy of cloud-based vehicular networks is addressed from the standpoint of the service relationship between the cloud computing and vehicular networks. In this paper, we identify the main considerations and challenges for cloud based vehicular networks regarding multimedia services, and propose potential research directions to make multimedia services achievable. More specifically, we quantitatively evaluate the performance metrics of these researches. For example, in the proposed broadcast storm mitigation scheme for vehicular networks, the packet delivery ratio and the normalized throughput can both achieve about 90%, making the proposed scheme a useful candidate for multimedia data exchange. Moreover, in the video uplinking scenarios, the proposed scheme is favorably compared with two well-known schedulers, M-LWDF and EXP, with the performance much closer to the optimum.


title: Automatic Gloss Finding for a Knowledge Base using Ontological Constraints

abstract: While there has been much research on automatically constructing structured Knowledge Bases (KBs), most of it has focused on generating facts to populate a KB. However, a useful KB must go beyond facts. For example, glosses (short natural language definitions) have been found to be very useful in tasks such as Word Sense Disambiguation. However, the important problem of Automatic Gloss Finding, i.e., assigning glosses to entities in an initially gloss-free KB, is relatively unexplored. We address that gap in this paper. In particular, we propose GLOFIN, a hierarchical semi-supervised learning algorithm for this problem which makes effective use of limited amounts of supervision and available ontological constraints. To the best of our knowledge, GLOFIN is the first system for this task. Through extensive experiments on real-world datasets, we demonstrate GLOFIN's effectiveness. It is encouraging to see that GLOFIN outperforms other state-of-the-art SSL algorithms, especially in low supervision settings. We also demonstrate GLOFIN's robustness to noise through experiments on a wide variety of KBs, ranging from user contributed (e.g., Freebase) to automatically constructed (e.g., NELL). To facilitate further research in this area, we have made the datasets and code used in this paper publicly available.


title: A decentralized optimization method for energy saving of HVAC systems

abstract: Improving the control strategy of building HVAC (heating, ventilation, and air-conditioning) systems can lead to significant energy savings while preserving human comfort requirements. This paper proposes a decentralized optimization method for optimal setpoint control for HVAC system energy saving. The HVAC system is divided into multiple subsystems and a decentralized optimization algorithm is introduced based on the local information. With limited knowledge of its neighboring subsystems, each subsystem can achieve the local/global optimal solution of the original optimization problem, which may be nonconvex and includes both discrete and continuous decision variables. Simulation results demonstrate the effectiveness of the proposed method.


title: A taxonomy of network and computer attacks

abstract: Attacks over the years have become both increasingly numerous and sophisticated. This paper focuses on the provisioning of a method for the analysis and categorisation of both computer and network attacks, thus providing assistance in combating new attacks, improving computer and network security as well as providing consistency in language when describing attacks. Such a taxonomy is designed to be useful to information bodies such as CERTs (Computer Emergency Response Teams) who have to handle and categorise an every increasing number of attacks on a daily basis. Information bodies could use the taxonomy to communicate more effectively as the taxonomy would provide a common classification scheme. The proposed taxonomy consists of four dimensions which provide a holistic taxonomy in order to deal with inherent problems in the computer and network attack field. The first dimension covers the attack vector and the main behaviour of the attack. The second dimension allows for classification of the attack targets. Vulnerabilities are classified in the third dimension and payloads in the fourth. Finally, to demonstrate the usefulness of this taxonomy, a case study applies the taxonomy to a number of well known attacks.


title: Eleven new boreal species of Inocybe with nodulose spores

abstract: The diversity and characteristics of species within Inocybe section Inocybe Singer were investigated in Finland. As a result, 11 species are described here as new: Inocybe acutoides, I. amicta, I. boreocarelica, I. bufonia, I. ericetorum, I. kittilensis, I. lapponica, I. paludicola, I. porcorum, I. pseudoteraturgus, and I. silvae-herbaceae. Detailed macroscopic and microscopic descriptions, habitat information, and ITS sequence data are included. The new species are compared with each other and with similar species within the section, and a key is provided for the European boreal species. The types of Inocybe acuta, I. alpigenes, I. borealis, I. cicatricata, I. giacomi, I. heterochrominea, I. hinnulea, I. johannae, I. proximella, I. purpureobadia, I. putilla, I. striaepes, I. taxocystis, I. teraturgus, I. tetragonospora, and I. umbrinodisca are treated. The often poorly understood Inocybe acuta, I. borealis, I. giacomi, and I. proximella, as well as the recently described I. purpureobadia, are characterised on the basis of our own material. Inocybe borealis, I. giacomi, and I. purpureobadia are reported from the boreal zone for the first time. Inocybe borealis appears to be a remarkably variable species. Inocybe striata and I. umbrinodisca are reduced to synonyms of I. proximella, I. heterochrominea is reduced to a synonym of I. borealis, and I. tetragonospora to a synonym of I. alpigenes. In addition, Inocybe hinnulea and I. striaepes are reduced to synonyms of I. giacomi, and the previously supposed synonymy of I. johannae with I. giacomi is supported by ITS analysis.


title: Class Specific TF-IDF Boosting for Short-text Classification

abstract: Proper formulation of features plays an important role in short-text classification tasks as the amount of text available is very little. In literature, Term Frequency - Inverse Document Frequency (TF-IDF) is commonly used to create feature vectors for such tasks. However, TF-IDF formulation does not utilize the class information available in supervised learning. For classification problems, if it is possible to identify terms that can strongly distinguish among classes, then more weight can be given to those terms during feature construction phase. This may result in improved classifier performance with the incorporation of extra class label related information. We propose a supervised feature construction method to classify tweets, based on the actionable information that might be present, posted during different disaster scenarios. Improved classifier performance for such classification tasks can be helpful in the rescue and relief operations. We used three benchmark datasets containing tweets posted during Nepal and Italy earthquakes in 2015 and 2016 respectively. Experimental results show that the proposed method obtains better classification performance on these benchmark datasets


title: Recent Advances in the Development of Cardiovascular Biomarkers

abstract: Cardiovascular diseases (CVD) are initiated by endothelial dysfunction and resultant expression of adhesion molecules for inflammatory cells.1–3 Inflammatory cells secrete cytokines/chemokines and growth factors and promote CVD.4 Additionally, vascular cells themselves produce and secrete several factors, some of which can be useful for the early diagnosis and evaluation of disease severity of CVD.5–7 Among vascular cells, abundant vascular smooth muscle cells (VSMCs) secrete a variety of humoral factors that affect vascular functions in an autocrine/paracrine manner.8,9 Among these factors, we reported that CyPA (cyclophilin A) is secreted mainly from VSMCs in response to Rho-kinase activation and excessive reactive oxygen species (ROS).10–12 Additionally, extracellular CyPA augments ROS production, damages vascular functions,13 and promotes CVD.8,14–24 Importantly, a recent study in ATVB demonstrated that ambient air pollution increases serum levels of inflammatory cytokines.25 Moreover, Bell et al26 reported an association of air pollution exposure with high-density lipoprotein (HDL) cholesterol and particle number. In a large, multiethnic cohort study of men and women free of prevalent clinical CVD, they found that higher concentrations of PM2.5 over a 3-month time period was associated with lower HDL particle number, and higher annual concentrations of black carbon were associated with lower HDL cholesterol. Together with the authors’ previous work on biomarkers of oxidative stress, they provided evidence for potential pathways that may explain the link between air pollution exposure and acute cardiovascular events.25,26 The objective of this review is to highlight the novel research in the field of biomarkers for CVD. ::: ::: Endothelial dysfunction induces the development of atherosclerotic disease.3,27,28 In the maintenance of endothelial function and vascular homeostasis, AMPK (AMP-activated protein kinase) and its downstream signaling of endothelial …


title: An Evolutionary Upgrade of Cognitive Load Theory: Using the Human Motor System and Collaboration to Support the Learning of Complex Cognitive Tasks

abstract: Cognitive load theory is intended to provide instructional strategies derived from experimental, cognitive load effects. Each effect is based on our knowledge of human cognitive architecture, primarily the limited capacity and duration of a human working memory. These limitations are ameliorated by changes in long-term memory associated with learning. Initially, cognitive load theory's view of human cognitive architecture was assumed to apply to all categories of information. Based on Geary’s (Educational Psychologist 43, 179–195 2008; 2011) evolutionary account of educational psychology, this interpretation of human cognitive architecture requires amendment. Working memory limitations may be critical only when acquiring novel information based on culturally important knowledge that we have not specifically evolved to acquire. Cultural knowledge is known as biologically secondary information. Working memory limitations may have reduced significance when acquiring novel information that the human brain specifically has evolved to process, known as biologically primary information. If biologically primary information is less affected by working memory limitations than biologically secondary information, it may be advantageous to use primary information to assist in the acquisition of secondary information. In this article, we suggest that several cognitive load effects rely on biologically primary knowledge being used to facilitate the acquisition of biologically secondary knowledge. We indicate how incorporating an evolutionary view of human cognitive architecture can provide cognitive load researchers with novel perspectives of their findings and discuss some of the practical implications of this view.


3D Thermal Imaging: Fusion of Thermography and Depth Cameras


title: Keyword Spotting in Online Handwritten Documents Containing Text and Non-text Using BLSTM Neural Networks

abstract: Spotting keywords in handwritten documents without transcription is a valuable method as it allows one to search, index, and classify such documents. In this paper we show that keyword spotting based on bi-directional Long Short-Term Memory (BLSTM) recurrent neural nets can successfully be applied on online handwritten documents with non-text content. It even works without preprocessing steps such as text vs. non-text distinction and text line extraction. We also propose a modification that can improve the precision with little effort.


title: General problem solving with category theory

abstract: This paper proposes a formal cognitive framework for problem solving based on category theory. We introduce cognitive categories, which are categories with exactly one morphism between any two objects. Objects in these categories are interpreted as states and morphisms as transformations between states. Moreover, cognitive problems are reduced to the specification of two objects in a cognitive category: an outset (i.e. the current state of the system) and a goal (i.e. the desired state). Cognitive systems transform the target system by means of generators and evaluators. Generators realize cognitive operations over a system by grouping morphisms, whilst evaluators group objects as a way to generalize outsets and goals to partially defined states. Meta-cognition emerges when the whole cognitive system is self-referenced as sub-states in the cognitive category, whilst learning must always be considered as a meta-cognitive process to maintain consistency. Several examples grounded in basic AI methods are provided as well.


title: Searching for intellectual turning points: Progressive knowledge domain visualization

abstract: This article introduces a previously undescribed method progressively visualizing the evolution of a knowledge domain's cocitation network. The method first derives a sequence of cocitation networks from a series of equal-length time interval slices. These time-registered networks are merged and visualized in a panoramic view in such a way that intellectually significant articles can be identified based on their visually salient features. The method is applied to a cocitation study of the superstring field in theoretical physics. The study focuses on the search of articles that triggered two superstring revolutions. Visually salient nodes in the panoramic view are identified, and the nature of their intellectual contributions is validated by leading scientists in the field. The analysis has demonstrated that a search for intellectual turning points can be narrowed down to visually salient nodes in the visualized network. The method provides a promising way to simplify otherwise cognitively demanding tasks to a search for landmarks, pivots, and hubs.


title: Rearrangement with Nonprehensile Manipulation Using Deep Reinforcement Learning

abstract: Rearranging objects on a tabletop surface by means of nonprehensile manipulation is a task which requires skillful interaction with the physical world. Usually, this is achieved by precisely modeling physical properties of the objects, robot, and the environment for explicit planning. In contrast, as explicitly modeling the physical environment is not always feasible and involves various uncertainties, we learn a nonprehensile rearrangement strategy with deep reinforcement learning based on only visual feedback. For this, we model the task with rewards and train a deep Q-network. Our potential field-based heuristic exploration strategy reduces the amount of collisions which lead to suboptimal outcomes and we actively balance the training set to avoid bias towards poor examples. Our training process leads to quicker learning and better performance on the task as compared to uniform exploration and standard experience replay. We demonstrate empirical evidence from simulation that our method leads to a success rate of 85%, show that our system can cope with sudden changes of the environment, and compare our performance with human level performance.


title: The vocabulary problem in human-system communication

abstract: In almost all computer applications, users must enter correct words for the desired objects or actions. For success without extensive training, or in first-tries for new targets, the system must recognize terms that will be chosen spontaneously. We studied spontaneous word choice for objects in five application-related domains, and found the variability to be surprisingly large. In every case two people favored the same term with probability


title: F.: Comparison of supervised learning methods for spike time coding in spiking neural networks

abstract: In this review we focus our attention on supervised learning methods for spike time coding in Spiking Neural Networks (SNNs). This study is motivated by recent experimental results regarding information coding in biological neural systems, which suggest that precise timing of individual spikes may be essential for efficient computation in the brain. We are concerned with the fundamental question: What paradigms of neural temporal coding can be implemented with the recent learning methods? In order to answer this question, we discuss various approaches to the learning task considered. We shortly describe the particular learning algorithms and report the results of experiments. Finally, we discuss the properties, assumptions and limitations of each method. We complete this review with a comprehensive list of pointers to the literature.


title: Assessment of the Efficacy of Cryolipolysis on Saddlebags: A Prospective Study of 53 Patients

abstract: Background: Cryolipolysis is a noninvasive subcutaneous fat removal technique. Its efficacy has been demonstrated on various fatty areas but not yet on saddlebags. The main objective of this study was to demonstrate the efficacy, patient tolerance, and safety of cryolipolysis on the saddlebags. Methods: This prospective study enrolled 53 patients with saddlebags. Patients with a history of liposuction or other surgical procedure on the saddlebag area and those on diet pills were excluded. The primary endpoint was a decrease in fat thickness at 3 and 6 months, as assessed by thigh circumference measurement and by ultrasound evaluation of subcutaneous fat. Pain associated with cryolipolysis was assessed using a visual analogue scale. Body mass index at the different time points and adverse events were recorded. All patients completed a satisfaction questionnaire at the end of the study. Results: At 6 months, there was a mean decrease of 5.63 cm in thigh circumference; the mean decrease in fat layer thickness measured by ultrasound was 1.31 cm. The satisfaction questionnaire showed that 93.75 percent of patients were satisfied with the results. The mean visual analogue scale score was 1.66 of 10 after the session. Reversible skin changes such as postprocedure postinflammatory hyperpigmentation were observed in 8.33 percent of patients. Conclusions: Cryolipolysis is an effective technique for reducing saddlebag fat and is well tolerated by patients. A substantial risk of skin lesions, including postinflammatory hyperpigmentation that resolved after a few months, was observed. Cryolipolysis is a good alternative to liposuction in women with moderate, well-localized saddlebags. CLINICAL QUESTION/LEVEL OF EVIDENCE: Therapeutic, IV.


title: Challenges with applying vulnerability prediction models

abstract: Vulnerability prediction models (VPM) are believed to hold promise for providing software engineers guidance on where to prioritize precious verification resources to search for vulnerabilities. However, while Microsoft product teams have adopted defect prediction models, they have not adopted vulnerability prediction models (VPMs). The goal of this research is to measure whether vulnerability prediction models built using standard recommendations perform well enough to provide actionable results for engineering resource allocation. We define 'actionable' in terms of the inspection effort required to evaluate model results. We replicated a VPM for two releases of the Windows Operating System, varying model granularity and statistical learners. We reproduced binary-level prediction precision (~0.75) and recall (~0.2). However, binaries often exceed 1 million lines of code, too large to practically inspect, and engineers expressed preference for source file level predictions. Our source file level models yield precision below 0.5 and recall below 0.2. We suggest that VPMs must be refined to achieve actionable performance, possibly through security-specific metrics.


title: WTRP - wireless token ring protocol

abstract: The wireless token ring protocol (WTRP) is a novel medium access control (MAC) protocol for wireless local area networks (WLANs). In contrast with IEEE 802.11 networks, WTRP guarantees quality of service (QoS) in terms of bounded latency and reserved bandwidth, which are critical in many real-time applications. Compared to 802.11, WTRP improves efficiency by reducing the number of retransmissions due to collisions, and it is more fair as all stations use the channel for the same amount of time. Stations take turns transmitting and give up the right to transmit after a specified amount of time. WTRP is a distributed protocol that supports many topologies, as not all stations need to be connected to each other or to a central station. WTRP is robust against single node failures, and recovers gracefully from multiple simultaneous faults. WTRP is suitable for interaccess point coordination in ITS DSRC, safety-critical vehicle-to-vehicle communications, and home networking, and provides extensions to other networks and Mobile IP.


title: Externalizing Metaphors: Anxiety and High‐Functioning Autism

abstract: TOPIC: The intent of this article is to explore the efficacy of both the literal and concrete externalization aspects within narrative therapy, and the implementation of interactive metaphors as a combined psychotherapeutic approach for decreasing anxiety with people who present with high-functioning autism. ::: ::: PURPOSE: The purpose of this exploratory article is to propose the use of externalizing metaphors as a treatment modality as a potentially useful way to engage clients. Specifically, a three-step process of change is described, which allows for concretizing affective states and experiences, and makes use of visual strengths of people presenting with an autism spectrum disorder. ::: ::: SOURCE: A selective review was conducted of significant works regarding the process of change in narrative therapy, with particular emphasis on metaphors. Works were selected based on their relevance to the current paper and included both published works (searched via Psyc-INFO) and materials from narrative training sessions. ::: ::: CONCLUSIONS: Further research is needed to address the testable hypotheses resulting from the current model. This line of research would not only establish best practices in a population for which there is no broadly accepted treatment paradigm, but would also contribute to the larger fields of abnormal psychology, emotion regulation, and cognitive psychology by further elucidating the complex ways these systems interact.


title: Comparative study of encryption algorithm over big data in cloud systems

abstract: Big data refers to data that is too large and complex to be processed. Big data handles voluminous amount of structured, semi structured and unstructured data with standard tools. Big Data also refers to the data where the volume, velocity or variety of data. It combines the historic data with the present data to predict the outcomes. In this regard, providing security for these data is a challenging task. Apache Hadoop was one of the tool designed to handle big data. Apache Hadoop along with other software products were used to process and interpret the results of big data. Hadoop includes various main components like Map reduce and HDFS for handling big data. Cloud computing is the technology that provides the online data storage. But here providing security is the key issue. In this paper, an integrated approach is introduced to encrypt and decrypt the data before sending on cloud. To achieve better performance and security performance analysis on different techniques can be applied based on different parameters.


title: Feedback control of cumuliform cloud formation based on computational fluid dynamics

abstract: Clouds play an important role for creating realistic images of outdoor scenes. In order to generate realistic clouds, many methods have been developed for modeling and animating clouds. One of the most effective approaches for synthesizing realistic clouds is to simulate cloud formation processes based on the atmospheric fluid dynamics. Although this approach can create realistic clouds, the resulting shapes and motion depend on many simulation parameters and the initial status. Therefore, it is very difficult to adjust those parameters so that the clouds form the desired shapes. This paper addresses this problem and presents a method for controlling the simulation of cloud formation. In this paper, we focus on controlling cumuliform cloud formation. The user specifies the overall shape of the clouds. Then, our method automatically adjusts parameters during the simulation in order to generate clouds forming the specified shape. Our method can generate realistic clouds while their shapes closely match to the desired shape.


title: Device-free indoor localization and tracking through Human-Object Interactions

abstract: Device-free indoor localization aims to localize people without requiring them to carry any devices or being actively involved in the localizing process. It underpins a wide range of applications including older people surveillance, intruder detection and indoor navigation. However, in a cluttered environment such as a residential home, the Received Signal Strength Indicator (RSSI) is heavily obstructed by furniture or metallic appliances, thus reducing the localization accuracy. This environment is important to observe as human-object interaction (HOI) events, detected by pervasive sensors, can potentially reveal people's interleaved locations during daily living activities, such as watching TV, opening the fridge door. This paper aims to enhance the performance of commercial off-the-shelf (COTS) RFID-based localization system by leveraging HOI contexts in a furnished home. Specifically, we propose a general Bayesian probabilistic framework to integrate both RSSI signals and HOI events to infer the most likely location and trajectory. Experiments conducted in a residential house demonstrate the effectiveness of our proposed method, in which we can localize a resident with average 95% accuracy and track a moving subject with 0.58m mean error distance.


ELIZA—a computer program for the study of natural language communication between man and machine


title: Automatic knowledge extraction from documents

abstract: Access to a large amount of knowledge is critical for success at answering open-domain questions for DeepQA systems such as IBM Watson™. Formal representation of knowledge has the advantage of being easy to reason with, but acquisition of structured knowledge in open domains from unstructured data is often difficult and expensive. Our central hypothesis is that shallow syntactic knowledge and its implied semantics can be easily acquired and can be used in many areas of a question-answering system. We take a two-stage approach to extract the syntactic knowledge and implied semantics. First, shallow knowledge from large collections of documents is automatically extracted. Second, additional semantics are inferred from aggregate statistics of the automatically extracted shallow knowledge. In this paper, we describe in detail what kind of shallow knowledge is extracted, how it is automatically done from a large corpus, and how additional semantics are inferred from aggregate statistics. We also briefly discuss the various ways extracted knowledge is used throughout the IBM DeepQA system.


title: Converting pairing-based cryptosystems from composite-order groups to prime-order groups

abstract: We develop an abstract framework that encompasses the key properties of bilinear groups of composite order that are required to construct secure pairing-based cryptosystems, and we show how to use prime-order elliptic curve groups to construct bilinear groups with the same properties. In particular, we define a generalized version of the subgroup decision problem and give explicit constructions of bilinear groups in which the generalized subgroup decision assumption follows from the decision Diffie-Hellman assumption, the decision linear assumption, and/or related assumptions in prime-order groups. ::: ::: We apply our framework and our prime-order group constructions to create more efficient versions of cryptosystems that originally required composite-order groups. Specifically, we consider the Boneh-Goh-Nissim encryption scheme, the Boneh-Sahai-Waters traitor tracing system, and the Katz-Sahai-Waters attribute-based encryption scheme. We give a security theorem for the prime-order group instantiation of each system, using assumptions of comparable complexity to those used in the composite-order setting. Our conversion of the last two systems to prime-order groups answers a problem posed by Groth and Sahai.


title: Isolation Improvement in a Dual-Band Dual-Element MIMO Antenna System Using Capacitively Loaded Loops

abstract: A dual-band dual-element multiple-input-multiple-output (MIMO) antenna system with enhanced isolation is proposed. The MIMO antenna system is based on printed 4-shaped antenna elements. Dual band isolation is achieved by using an array of printed capacitively loaded loops (CLLs) on the top side of the board for high band isolation improvement and a complementary CLL structure on the GND plane of the antenna for lower band isolation improvement. The lower band of operation covers 827{853MHz and the higher band covers 2.3{ 2.98GHz. Two prototypes were investigated to access the efiect of the isolation mechanism. Measured isolation improvement of 10dB was observed in the lower operating band while the improvement in the higher band was approximately 2.5dB. The isolation improvement was at the expense of 5% reduction in e-ciency. The measured gain patterns as well MIMO flgures of merits such as the correlation factor, TARC and MEG were investigated as well.


title: Video Imagination from a Single Image with Transformation Generation

abstract: In this work, we focus on a challenging task: synthesizing multiple imaginary videos given a single image. Major problems come from high dimensionality of pixel space and the ambiguity of potential motions. To overcome those problems, we propose a new framework that produce imaginary videos by transformation generation. The generated transformations are applied to the original image in a novel volumetric merge network to reconstruct frames in imaginary video. Through sampling different latent variables, our method can output different imaginary video samples. The framework is trained in an adversarial way with unsupervised learning. For evaluation, we propose a new assessment metric RIQA. In experiments, we test on 3 datasets varying from synthetic data to natural scene. Our framework achieves promising performance in image quality assessment. The visual inspection indicates that it can successfully generate diverse five-frame videos in acceptable perceptual quality.


title: A survey of online failure prediction methods

abstract: With the ever-growing complexity and dynamicity of computer systems, proactive fault management is an effective approach to enhancing availability. Online failure prediction is the key to such techniques. In contrast to classical reliability methods, online failure prediction is based on runtime monitoring and a variety of models and methods that use the current state of a system and, frequently, the past experience as well. This survey describes these methods. To capture the wide spectrum of approaches concerning this area, a taxonomy has been developed, whose different approaches are explained and major concepts are described in detail.


title: Development of a Repetitive Wave Erection Marx Generator

abstract: A repetitive ten-stage wave erection Marx generator is developed to investigate the electrical characteristics of such compact devices and potentially provide an economical approach to realize the miniaturization of intense electron beam accelerators. Compact design has been made for the generator in order to achieve a proper stray capacitance of the spark gap electrode with respect to the ground in each stage because these proper grounded stray capacitances are critical for obtaining a good wave erection process. This generator is initially resistively isolated for single-shot tests and then changed to inductively isolated for repetitive operation. In single-shot experiments, the generator is tested to be able to deliver a high-voltage pulse of 210 kV and a rise time of about 5 ns on a 90-Ω dummy load at a charging voltage of 40 kV. This result agrees basically with that of the PSpice circuit simulation, which adopts a self-breakdown spark gap model. The preliminary experimental results of repetitive operation show that at a charging voltage of 30 kV, the generator can operate at 8.5 Hz without gas blow-off from the internal spark gaps, producing an output pulse of 150 kV and a rise time of less than 20 ns. Differences in the output pulse waveforms between resistively and inductively isolated configurations are analyzed.


title: GoMiner: a resource for biological interpretation of genomic and proteomic data

abstract: We have developed GoMiner, a program package that organizes lists of 'interesting' genes (for example, under- and overexpressed genes from a microarray experiment) for biological interpretation in the context of the Gene Ontology. GoMiner provides quantitative and statistical output files and two useful visualizations. The first is a tree-like structure analogous to that in the AmiGO browser and the second is a compact, dynamically interactive 'directed acyclic graph'. Genes displayed in GoMiner are linked to major public bioinformatics resources.


title: Automatic Text Summarization System for Punjabi Language

abstract: This paper concentrates on single document multi news Punjabi extractive summarizer. Although lot of research is going on in field of multi document news summarization systems but not even a single paper was found in literature for single document multi news summarization for any language. It is first time that this system has been developed for Punjabi language and is available online at: http://pts.learnpunjabi.org/. Punjab is one of Indian states and Punjabi is its official language. Punjabi is under resourced language. Various linguistic resources for Punjabi were also developed first time as part of this project like Punjabi noun morph, Punjabi stemmer and Punjabi named entity recognition, Punjabi keywords identification, normalization of Punjabi nouns etc. A Punjabi document (like single page of Punjabi E-news paper) can have hundreds of multi news of varying length. Based on compression ratio selected by user, this system starts by extracting headlines of each news, lines just next to headlines and other important lines depending upon their importance. Selection of sentences is on the basis of statistical and linguistic features of sentences. This system comprises of two main steps: Pre Processing and Processing phase. Pre Processing phase represents the Punjabi text in structured way. In processing phase, different features deciding the importance of sentences are determined and calculated. Some of the statistical features are Punjabi keywords identification, relative sentence length feature and numbered data feature. Various linguistic features for selecting important sentences in summary are: Punjabi-headlines identification, identification of lines just next to headlines, identification of Punjabi-nouns, identification of Punjabi-proper-nouns, identification of common-English-Punjabi-nouns, identification of Punjabi-cue-phrases and identification of title-keywords in sentences. Scores of sentences are determined from sentence-feature-weight equation. Weights of features are determined using mathematical regression. Using regression, feature values of some Punjabi documents which are manually summarized are treated as independent input values and their corresponding dependent output values are provided. In the training phase, manually summaries of fifty news-documents are made by giving fuzzy scores to the sentences of those documents and then regression is applied for finding values of feature-weights and then average values of feature-weights are calculated. High scored sentences in proper order are selected for final summary. In final summary, sentences coherence is maintained by properly ordering the sentences in the same order as they appear in the input text at the selective compression ratios. This extractive Punjabi summarizer is available online.


title: Anomaly detection in dynamic networks: a survey

abstract: Anomaly detection is an important problem with multiple applications, and thus has been studied for decades in various research domains. In the past decade there has been a growing interest in anomaly detection in data represented as networks, or graphs, largely because of their robust expressiveness and their natural ability to represent complex relationships. Originally, techniques focused on anomaly detection in static graphs, which do not change and are capable of representing only a single snapshot of data. As real-world networks are constantly changing, there has been a shift in focus to dynamic graphs, which evolve over time.


title: Robust method for outdoor localization of a mobile robot using received signal strength in low power wireless networks

abstract: This paper deals with localization of a mobile robot using received signal strength indicator (RSSI) in low power IEEE 802.15.4 conform wireless communication in an outdoor environment. Hardware modifications are derived to reduce the radio irregularity and to increase the uniqueness of the measured RSSI to distance. A novel algorithm is elaborated allowing for sub meter accuracy. It accounts for the noise and implicitly models the uncertainty. Additionally it is robust to node failures. To further improve the accuracy a particle filter is employed to perform probabilistic sensor fusion of odometry, ultrasonic and RSSI sensors and a map. The suitability of this approach is shown with real measurements achieving a mean positioning error of 0.32 m.


title: DILIGENT: Towards a fine-grained methodology for Distributed, Loosely-controlled and evolving Engineering of oNTologies

abstract: Ontology engineering processes in truly distributed settings like the Semantic Web or global peer-to-peer systems may not be adequately supported by conventional, centralized ontology engineering methodologies. In this paper, we present our work towards the DILIGENT methodology, which is intended to support domain experts in a distributed setting to engineer and evolve ontologies with the help of a fine-grained methodological approach based on Rhetorical Structure Theory, viz. the DILIGENT model of ontology engineering by argumentation.


Signed Network Embedding in Social Media


Malaria and the mobile and migrant population in Cambodia: a population movement framework to inform strategies for malaria control and elimination


Diurnal Change of Photosynthesis and Its Relation to Yield in Soybean Cultivars.


title: Deep Neural Networks with Inexact Matching for Person Re-Identification

abstract: Person Re-Identification is the task of matching images of a person across multiple camera views. Almost all prior approaches address this challenge by attempting to learn the possible transformations that relate the different views of a person from a training corpora. Then, they utilize these transformation patterns for matching a query image to those in a gallery image bank at test time. This necessitates learning good feature representations of the images and having a robust feature matching technique. Deep learning approaches, such as Convolutional Neural Networks (CNN), simultaneously do both and have shown great promise recently. In this work, we propose two CNN-based architectures for Person Re-Identification. In the first, given a pair of images, we extract feature maps from these images via multiple stages of convolution and pooling. A novel inexact matching technique then matches pixels in the first representation with those of the second. Furthermore, we search across a wider region in the second representation for matching. Our novel matching technique allows us to tackle the challenges posed by large viewpoint variations, illumination changes or partial occlusions. Our approach shows a promising performance and requires only about half the parameters as a current state-of-the-art technique. Nonetheless, it also suffers from false matches at times. In order to mitigate this issue, we propose a fused architecture that combines our inexact matching pipeline with a state-of-the-art exact matching technique. We observe substantial gains with the fused model over the current state-of-the-art on multiple challenging datasets of varying sizes, with gains of up to about 21%.


title: Removing Structural Noise in Handwriting Images using Deep Learning

abstract: Since handwriting recognition is very sensitive to structural noise, like superimposed objects such as straight lines and other marks, it is necessary to remove noise in a preprocessing stage before recognition. Although numerous denoising approaches have been proposed, it remains a challenge. The difficulties are due to non-locality of structural noise and hard discernment between spurious and the meaningful regions. We propose a supervised approach using deep learning to remove structural noise. Specifically, we generalize the deep autoencoder into the deep denoising autoencoder (DDAE), which consists in training a neural network with noisy and clean pairs to minimize cross-entropy error. Inspired by recurrent neural networks, we introduce feedback loop from the output to enhance the "repaired" image well in the reconstruction stage in our framework. We test the DDAE model on three handwritten image data sets, and show advantages over Wiener filter, robust Boltzmann machines and deep autoencoder.


title: Byte Code Engineering

abstract: The term “Java” is used to denote two different concepts: the language itself and the related execution environment, the Java Virtual Machine (JVM), which executes byte code instructions. Several research projects deal with byte code-generating compilers or the implementation of new features via byte code transformations. Examples are code optimization, the implementation of parameterized types for Java, or the adaptation of run-time behavior through load-time transformations. Many programmers are doing this by implementing their own specialized byte code manipulation tools, which are, however, restricted in the range of their reusability. Therefore, we have developed a general purpose framework for the static analysis and dynamic creation or transformation of byte code. In this paper we present its main features and possible application areas.


title: A New Hybrid Consensus Protocol: Deterministic Proof Of Work

abstract: The Decentralized-Consistent-Scale (DCS) Triangle defines three dimensions that illustrate the tradeoffs of the blockchain consensus mechanism. In this paper, we propose a new hybrid consensus protocol, called Deterministic Proof of Work (DPoW), which can reach high levels of scalability and consistency without significant reduction to decentralization. Our protocol introduces a Map-reduce PoW mining algorithm to perform alongside Practical Byzantine Fault Tolerance (PBFT) verification, which together allow for transactions to be confirmed immediately, largely improving scalability. In addition, the protocol is designed such that forking cannot occur, ensuring strong consistency and security against a multitude of attacks. The Map-reduce PoW mining process ensures that no single entity can control the network, guaranteeing decentralization. We analyzed the security of our protocol by evaluating the possibility of double spending attacks, and furthermore, conducted experiments which demonstrate our claims.


title: Investigating The Adoption of Knowledge Management in a Non-Govermental Organization in Malaysia

abstract: Non-governmental organizations use knowledge ::: extensively in humanitarian efforts, engagements with organizations concerning the welfare of the people and other activities. The knowledge in nongovernmental organizations are mostly tacit. ::: Nevertheless, little is known about knowledge ::: management practices in non-governmental ::: organizations.The aim of this study is to investigate the adoption of knowledge management in a nongovernmental organization.This study adopts a case study research design. Data was collected through a survey sent to a non-governmental organization.The questionnaire was based on the four pillars of knowledge management namely management and organization, people and culture, content and processes and infrastructure. A total of 31 respondents from one NGO participated in the survey.The data was analyzed using descriptive analyses.The findings indicate that knowledge is widely acknowledge as important in non-governmental organizations.To a certain extent, knowledge management principles have been practiced in the organization.However, non-governmental organizations have yet to establish an organization wide knowledge management strategy.The study provides an understanding of how knowledge management is perceived by the non-governmental organizations and provide valuable insights to the practice of knowledge management in nongovernmental organizations.


title: Social Networking Sites: Their Users and Social Implications - A Longitudinal Study

abstract: The rapid adoption of social networking sites (SNSs) raises important questions about the social implications of such usage. Drawing on unique longitudinal data, surveying a representative sample of Norwegian online users (N = 2,000, age 15–75 years) in 3 annual waves (2008, 2009, and 2010), this study found a significantly higher score among SNS users in comparison to nonusers in 3 out of 4 social capital dimensions: face-to-face interactions, number of acquaintances, and bridging capital. However, SNS-users, and in particular males, reported more loneliness than nonusers. Furthermore, cluster analyses identified 5 distinct types of SNS users: Sporadics, Lurkers, Socializers, Debaters, and Advanced. Results indicate that Socializers report higher levels of social capital compared to other user types. © 2012 Wiley Periodicals, Inc.


title: Aspects of Rumor Spreading on a Microblog Network

abstract: Rumors have been studied for several decades in social and psychological fields, where most studies were theory-driven and relied on surveys due to difficulties in gathering data. Rumor research is now gaining new perspectives, because online social media enable researchers to examine closely various kinds of information dissemination on the Internet. In this paper, we review social psychology literature on rumors and try to identify the key differences in the dissemination of rumors and non-rumors. The insights from this study can shed light on improving automatic classification of rumors and better comprehending rumor theories in online social media.


Improving Sequence-to-Sequence Constituency Parsing


title: Estimation of linear stochastic systems over a queueing network

abstract: In this paper, we consider the standard state estimation problem over a congested packet-based network. The network is modeled as a queue with a single server processing the packets. This provides a framework to consider the effect of packet drops, packet delays and bursty losses on state estimation. We use a modified Kalman Filter with buffer to cope with delayed packets. We analyze the stability of the estimates with varying buffer length and queue size. We use high order Markov chains for our analysis. Simulation examples are presented to illustrate the theory.


title: Multi-Level Recurrent Residual Networks for Action Recognition

abstract: Most existing Convolutional Neural Networks(CNNs) used for action recognition are either difficult to optimize or underuse crucial temporal information. Inspired by the fact that the recurrent model consistently makes breakthroughs in the task related to sequence, we propose a novel Multi-Level Recurrent Residual Networks(MRRN) which incorporates three recognition streams. Each stream consists of a Residual Networks(ResNets) and a recurrent model. The proposed model captures spatiotemporal information by employing both alternative ResNets to learn spatial representations from static frames and stacked Simple Recurrent Units(SRUs) to model temporal dynamics. Three distinct-level streams learned low-, mid-, high-level representations independently are fused by computing a weighted average of their softmax scores to obtain the complementary representations of the video. Unlike previous models which boost performance at the cost of time complexity and space complexity, our models have a lower complexity by employing shortcut connection and are trained end-to-end with greater efficiency. MRRN displays significant performance improvements compared to CNN-RNN framework baselines and obtains comparable performance with the state-of-the-art, achieving 51.3% on HMDB-51 dataset and 81.9% on UCF-101 dataset although no additional data.


title: The Enterprise Ontology

abstract: This is a comprehensive description of the Enterprise Ontology, a collection of terms and definitions relevant to business enterprises. We state its intended purposes, describe how we went about building it, define all the terms and describe our experiences in converting these into formal definitions. We then describe how we used the Enterprise Ontology and give an evaluation which compares the actual uses with original purposes. We conclude by summarising what we have learned. The Enterprise Ontology was developed within the Enterprise Project, a collaborative effort to provide a framework for enterprise modelling. The ontology was built to serve as a basis for this framework which includes methods and a computer tool set for enterprise modelling. We give an overview of the Enterprise Project, elaborate on the intended use of the ontology, and give a brief overview of the process we went through to build it. The scope of the Enterprise Ontology covers those core concepts required for the project, which will appeal to a wider audience. We present natural language definitions for all the terms, starting with the foundational concepts (e.g. entity, relationship, actor). These are used to define the main body of terms, which are divided into the following subject areas: activities, organisation, strategy and marketing. We review some of the things learned during the formalisation process of converting the natural language definitions into Ontolingua. We identify and propose solutions for what may be general problems occurring in the development of a wide range of ontologies in other domains. We then characterise in general terms the sorts of issues that will be faced when converting an informal ontology into a formal one. Finally, we describe our experiences in using the Enterprise Ontology. We compare these with the intended uses, noting our successes and failures. We conclude with an overall evaluation and summary of what we have learned.


title: De-Constructing TLS 1.3

abstract: SSL/TLS is one of the most widely deployed cryptographic protocols on the Internet. It is used to protect the confidentiality and integrity of transmitted data in various client-server applications. The currently specified version is TLSi?ź1.2, and its security has been analyzed extensively in the cryptographic literature. The IETF working group is actively developing a new version, TLSi?ź1.3, which is designed to address several flaws inherent to previous versions. ::: ::: In this paper, we analyze the security of a slightly modified version of the current TLS 1.3 draft. We do not encrypt the server's certificate. Our security analysis is performed in the constructive cryptography framework. This ensures that the resulting security guarantees are composable and can readily be used in subsequent protocol steps, such as password-based user authentication over a TLS-based communication channel in which only the server is authenticated. Most steps of our proof hold in the standard model, with the sole exception that the key derivation function HKDF is used in a way that has a proof only in the random-oracle model. Beyond the technical results on TLSi?ź1.3, this work also exemplifies a novel approach towards proving the security of complex protocols by a modular, step-by-step decomposition, in which smaller sub-steps are proved in isolation and then the security of the protocol follows by the composition theorem.


title: Effect of Co-curricular Activities on Development of Social Skills of Children with Intellectual Disability

abstract: Background: Social skills are an integral part of an individual's survival in a society. Children with intellectual disability show significant limitations both in intellectual functioning and adaptive behavior as expected of children of similar age preventing the typical acquisition of adaptive social skills. Such skills are learned by continuous interaction with others through engaging in some activities. By participating in co-curricular activities their communication skills could be enhanced and they could express themselves in a better way. Objective: This study has been conducted to find out the effect of co-curricular activities on development of social skills of children with intellectual disability. Sample: The subjects N=8, (Six boys and two girls, IQ ranging from 35-49) were selected from Govt. Rehabilitation Institute for Intellectual Disabilities (GRIID), Chandigarh based on set inclusion and exclusion criteria. Design: Using purposive sampling and utilizing single group, pre and post tests experimental method. Tool: The pre and post tests were conducted by making use of Social Skill Assessment Tool developed by the researcher . Results: The intervention programme was conducted for 20 sessions to perform three co-curricular activities i.e. dance for 10 sessions, play (throwing the ball into the bucket) for 5 sessions and thumb painting for another 5 sessions. Duration of each session was 45 minutes on daily basis except for weekends and gazetted holidays. Based on the result of post test performance, the collected data was analyzed quantitatively by using t-test. In the analysis, it was found that the mean scores of pre-test and post-test were 27.12 and 50.75 respectively and the calculated t value was 10.88 whereas the table value of t was 3.49, which was less than the calculated value. Hence, null hypotheses was rejected at p<0.01 level of significance. The statistical analysis shows that there is a highly significant effect of co-curricular activities on development of social skills of children with intellectual disability. Conclusion: Based on the above findings, the researcher in the present study has explored that co-curricular activities help in improving the social skills in children with intellectual disability. Keyword: Social skills, co-curricular activities, children with intellectual disability


Continuously Improving Natural Language Understanding for Robotic Systems through Semantic Parsing, Dialog, and Multi-modal Perception


title: Acetabular Labrum

abstract: Acetabular labral tears are a mechanical cause of hip pain. Hip MR imaging should be performed on 1.5-T or 3-T magnets using small field-of-view and high-resolution imaging. The following should be used in the assessment for labral tear abnormalities on MR arthrography: labral morphology and contrast extension into the labral substance or between the labral base and acetabulum. Description of the labral tear and extent of tear is useful for hip arthroscopists. Understanding the pitfalls around the acetabular labral complex helps avoids misinterpretation of labral tears.


title: A survey of context-aware middleware

abstract: This paper provides a survey of a chosen set of context-aware middleware systems, and categorises their properties and use according to a taxonomy. An overview of each system is provided, as well as descriptions of the different properties.


title: Coordinated development of leading biomass pretreatment technologies.

abstract: For the first time, a single source of cellulosic biomass was pretreated by leading technologies using identical analytical methods to provide comparative performance data. In particular, ammonia explosion, aqueous ammonia recycle, controlled pH, dilute acid, flowthrough, and lime approaches were applied to prepare corn stover for subsequent biological conversion to sugars through a Biomass Refining Consortium for Applied Fundamentals and Innovation (CAFI) among Auburn University, Dartmouth College, Michigan State University, the National Renewable Energy Laboratory, Purdue University, and Texas A&M University. An Agricultural and Industrial Advisory Board provided guidance to the project. Pretreatment conditions were selected based on the extensive experience of the team with each of the technologies, and the resulting fluid and solid streams were characterized using standard methods. The data were used to close material balances, and energy balances were estimated for all processes. The digestibilities of the solids by a controlled supply of cellulase enzyme and the fermentability of the liquids were also assessed and used to guide selection of optimum pretreatment conditions. Economic assessments were applied based on the performance data to estimate each pretreatment cost on a consistent basis. Through this approach, comparative data were developed on sugar recovery from hemicellulose and cellulose by the combined pretreatment and enzymatic hydrolysis operations when applied to corn stover. This paper introduces the project and summarizes the shared methods for papers reporting results of this research in this special edition of Bioresource Technology.


title: Dual-Band Helical Filters Based on Nonuniform Pitch Helical Resonators

abstract: A helical resonator with two-sectional different pitches is proposed in this paper. The frequency ratios of the first two resonant modes of the helical resonator can be tuned to desirable values in a certain range by adjusting the pitches. The proposed resonator is applied to design dual-band bandpass helical filters. To achieve control over dual-band filtering characteristics, a feeding structure with step-rectangular plate and a coupling window with three apertures are also proposed to realize the expected external quality factors and coupling coefficients in the two bands. To validate the design, a second order and a fourth order dual-band helical filters are fabricated and measured. The measured results show good agreement with simulation ones.


title: Spherical Random Features for Polynomial Kernels

abstract: Compact explicit feature maps provide a practical framework to scale kernel methods to large-scale learning, but deriving such maps for many types of kernels remains a challenging open problem. Among the commonly used kernels for nonlinear classification are polynomial kernels, for which low approximation error has thus far necessitated explicit feature maps of large dimensionality, especially for higher-order polynomials. Meanwhile, because polynomial kernels are unbounded, they are frequently applied to data that has been normalized to unit l2 norm. The question we address in this work is: if we know a priori that data is normalized, can we devise a more compact map? We show that a putative affirmative answer to this question based on Random Fourier Features is impossible in this setting, and introduce a new approximation paradigm, Spherical Random Fourier (SRF) features, which circumvents these issues and delivers a compact approximation to polynomial kernels for data on the unit sphere. Compared to prior work, SRF features are less rank-deficient, more compact, and achieve better kernel approximation, especially for higher-order polynomials. The resulting predictions have lower variance and typically yield better classification accuracy.


title: Towards a unified Media-User Typology (MUT): A meta-analysis and review of the research literature on media-user typologies

abstract: Considering the increasingly complex media landscape and diversity of use, it is important to establish a common ground for identifying and describing the variety of ways in which people use new media technologies. Characterising the nature of media-user behaviour and distinctive user types is challenging and the literature offers little guidance in this regard. Hence, the present research aims to classify diverse user behaviours into meaningful categories of user types, according to the frequency of use, variety of use and content preferences. To reach a common framework, a review of the relevant research was conducted. An overview and meta-analysis of the literature (22 studies) regarding user typology was established and analysed with reference to (1) method, (2) theory, (3) media platform, (4) context and year, and (5) user types. Based on this examination, a unified Media-User Typology (MUT) is suggested. This initial MUT goes beyond the current research literature, by unifying all the existing and various user type models. A common MUT model can help the Human-Computer Interaction community to better understand both the typical users and the diversification of media-usage patterns more qualitatively. Developers of media systems can match the users' preferences more precisely based on an MUT, in addition to identifying the target groups in the developing process. Finally, an MUT will allow a more nuanced approach when investigating the association between media usage and social implications such as the digital divide.


title: Using simple agent-based modeling to inform and enhance neighborhood walkability

abstract: Pedestrian-friendly neighborhoods with proximal destinations and services encourage walking and decrease car dependence, thereby contributing to more active and healthier communities. Proximity to key destinations and services is an important aspect of the urban design decision making process, particularly in areas adopting a transit-oriented development (TOD) approach to urban planning, whereby densification occurs within walking distance of transit nodes. Modeling destination access within neighborhoods has been limited to circular catchment buffers or more sophisticated network-buffers generated using geoprocessing routines within geographical information systems (GIS). Both circular and network-buffer catchment methods are problematic. Circular catchment models do not account for street networks, thus do not allow exploratory ‘what-if’ scenario modeling; and network-buffering functionality typically exists within proprietary GIS software, which can be costly and requires a high level of expertise to operate. This study sought to overcome these limitations by developing an open-source simple agent-based walkable catchment tool that can be used by researchers, urban designers, planners, and policy makers to test scenarios for improving neighborhood walkable catchments. A simplified version of an agent-based model was ported to a vector-based open source GIS web tool using data derived from the Australian Urban Research Infrastructure Network (AURIN). The tool was developed and tested with end-user stakeholder working group input. The resulting model has proven to be effective and flexible, allowing stakeholders to assess and optimize the walkability of neighborhood catchments around actual or potential nodes of interest (e.g., schools, public transport stops). Users can derive a range of metrics to compare different scenarios modeled. These include: catchment area versus circular buffer ratios; mean number of streets crossed; and modeling of different walking speeds and wait time at intersections. The tool has the capacity to influence planning and public health advocacy and practice, and by using open-access source software, it is available for use locally and internationally. There is also scope to extend this version of the tool from a simple to a complex model, which includes agents (i.e., simulated pedestrians) ‘learning’ and incorporating other environmental attributes that enhance walkability (e.g., residential density, mixed land use, traffic volume).


title: Simple high‐pressure cell for neutron scattering

abstract: A high‐pressure cell, capable of 8 kbar, is developed for neutron scattering. It can be used with ILL type orange cryostats to obtain a temperature as low as 1.5 K. The simple seal design described here can easily be adopted to other high‐pressure applications.


title: Constant Time Weighted Median Filtering for Stereo Matching and Beyond

abstract: Despite the continuous advances in local stereo matching for years, most efforts are on developing robust cost computation and aggregation methods. Little attention has been seriously paid to the disparity refinement. In this work, we study weighted median filtering for disparity refinement. We discover that with this refinement, even the simple box filter aggregation achieves comparable accuracy with various sophisticated aggregation methods (with the same refinement). This is due to the nice weighted median filtering properties of removing outlier error while respecting edges/structures. This reveals that the previously overlooked refinement can be at least as crucial as aggregation. We also develop the first constant time algorithm for the previously time-consuming weighted median filter. This makes the simple combination ``box aggregation + weighted median'' an attractive solution in practice for both speed and accuracy. As a byproduct, the fast weighted median filtering unleashes its potential in other applications that were hampered by high complexities. We show its superiority in various applications such as depth up sampling, clip-art JPEG artifact removal, and image stylization.


title: Experiments with a Higher-Order Projective Dependency Parser

abstract: We present experiments with a dependency parsing model defined on rich factors. Our model represents dependency trees with factors that include three types of relations between the tokens of a dependency and their children. We extend the projective parsing algorithm of Eisner (1996) for our case, and train models using the averaged perceptron. Our experiments show that considering higher-order information yields significant improvements in parsing accuracy, but comes at a high cost in terms of both time and memory consumption. In the multilingual exercise of the CoNLL-2007 shared task (Nivre et al., 2007), our system obtains the best accuracy for English, and the second best accuracies for Basque and Czech.


title: Aggregate Query Answering under Uncertain Schema Mappings

abstract: Recent interest in managing uncertainty in data integration has led to the introduction of probabilistic schema mappings and the use of probabilistic methods to answer queries across multiple databases using two semantics: by-table and by-tuple. In this paper, we develop three possible semantics for aggregate queries: the range, distribution, and expected value semantics, and show that these three semantics combine with the by-table and by-tuple semantics in six ways. We present algorithms to process COUNT, AVG, SUM, MIN, and MAX queries under all six semantics and develop results on the complexity of processing such queries under all six semantics. We show that computing COUNT is in PTIME for all six semantics and computing SUM is in PTIME for all but the by-tuple/distribution semantics. Finally, we show that AVG, MIN, and MAX are PTIME computable for all by-table semantics and for the by-tuple/range semantics.We developed a prototype implementation and experimented with both real-world traces and simulated data. We show that, as expected, naive processing of aggregates does not scale beyond small databases with a small number of mappings. The results also show that the polynomial time algorithms are scalable up to several million tuples as well as with a large number of mappings.


title: Auto-patching DOM-based XSS at scale

abstract: DOM-based cross-site scripting (XSS) is a client-side code injection vulnerability that results from unsafe dynamic code generation in JavaScript applications, and has few known practical defenses. We study dynamic code evaluation practices on nearly a quarter million URLs crawled starting from the the Alexa Top 1000 websites. Of 777,082 cases of dynamic HTML/JS code generation we observe, 13.3% use unsafe string interpolation for dynamic code generation — a well-known dangerous coding practice. To remedy this, we propose a technique to generate secure patches that replace unsafe string interpolation with safer code that utilizes programmatic DOM construction techniques. Our system transparently auto-patches the vulnerable site while incurring only 5.2 − 8.07% overhead. The patching mechanism requires no access to server-side code or modification to browsers, and thus is practical as a turnkey defense.


title: Mobilizing Informational Social Capital in Cyber Space: Online Social Network Structural Properties and Knowledge Sharing

abstract: Online networks can be construed as social networks in which people engage in interactions, build relationships, share information, and request and extend assistance to each other using electronic communication technologies. Is social capital embedded in online networks? How is such social capital mobilized (i.e., shared)? What structural properties of cyber networks are associated with mobilization of social capital? These questions have drawn the attention of researchers in the areas of social networks, social capital, and online communication. Our research is an initial effort to touch upon these three questions. Whereas most previous research on both conventional and online social networks has favored analysis of either egocentric (i.e., individuals) or bounded (i.e., groups or organizations) networks as the primary unit of analysis, this study investigates online discussion forums that span formal boundaries of organizations, examining their structural properties and patterns of information exchange. We employ a network-based approach to the study of social capital, postulating that mobilization of social capital is contingent on social network properties. Using data from professional online forums devoted to knowledge management, we find that two network properties, core–periphery structure and centralization, are related to the mobilization of informational social capital in online networks. Implications and limitations of this study are discussed and suggestions for future research are provided.


title: Dyna, an integrated architecture for learning, planning, and reacting

abstract: Dyna is an AI architecture that integrates learning, planning, and reactive execution. Learning methods are used in Dyna both for compiling planning results and for updating a model of the effects of the agent's actions on the world. Planning is incremental and can use the probabilistic and ofttimes incorrect world models generated by learning processes. Execution is fully reactive in the sense that no planning intervenes between perception and action. Dyna relies on machine learning methods for learning from examples---these are among the basic building blocks making up the architecture---yet is not tied to any particular method. This paper briefly introduces Dyna and discusses its strengths and weaknesses with respect to other architectures.


title: Think to Move: a Neuromagnetic Brain-Computer Interface (BCI) System for Chronic Stroke

abstract: Background and Purpose— Stroke is a leading cause of long-term motor disability among adults. Present rehabilitative interventions are largely unsuccessful in improving the most severe cases of motor impairment, particularly in relation to hand function. Here we tested the hypothesis that patients experiencing hand plegia as a result of a single, unilateral subcortical, cortical or mixed stroke occurring at least 1 year previously, could be trained to operate a mechanical hand orthosis through a brain-computer interface (BCI). Methods— Eight patients with chronic hand plegia resulting from stroke (residual finger extension function rated on the Medical Research Council scale=0/5) were recruited from the Stroke Neurorehabilitation Clinic, Human Cortical Physiology Section of the National Institute for Neurological Disorders and Stroke (NINDS) (n=5) and the Clinic of Neurology of the University of Tubingen (n=3). Diagnostic MRIs revealed single, unilateral subcortical, cortical or mixed lesions in all pati...


title: Word Embeddings for Speech Recognition

abstract: Speech recognition systems have used the concept of states as a way to decompose words into sub-word units for decades. As the number of such states now reaches the number of words used to train acoustic models, it is interesting to consider approaches that relax the assumption that words are made of states. We present here an alternative construction, where words are projected into a continuous embedding space where words that sound alike are nearby in the Euclidean sense. We show how embeddings can still allow to score words that were not in the training dictionary. Initial experiments using a lattice rescoring approach and model combination on a large realistic dataset show improvements in word error rate.


Vertex Domination in t-Norm Fuzzy Graphs


Syllable and Meter in Neural Network generated


title: Image segmentation using seeded region growing

abstract: Image segmentation is the process of clustering pixels into salient image regions (i.e) regions corresponding to individual surfaces, objects or natural parts of objects. Image segmentation plays a vital role in image analysis and computer vision applications. Several general-purpose algorithms and techniques have been developed for image segmentation. Segmentation process should be stopped when region of interest is separated from the input image. Based on the application, region of interest may differ and hence none of the segmentation algorithm satisfies the global applications. Thus segmentation still remains a challenging area for researchers. This paper presents a comparison of some literature on color image segmentation based on region growing and merging algorithm. Finally an automatic seeded region growing algorithm is proposed for segmenting color images.


title: A hybrid solution for privacy preserving medical data sharing in the cloud environment

abstract: Storing and sharing of medical data in the cloud environment, where computing resources including storage is provided by a third party service provider, raise serious concern of individual privacy for the adoption of cloud computing technologies. Existing privacy protection researches can be classified into three categories, i.e., privacy by policy, privacy by statistics, and privacy by cryptography. However, the privacy concerns and data utilization requirements on different parts of the medical data may be quite different. The solution for medical dataset sharing in the cloud should support multiple data accessing paradigms with different privacy strengths. The statistics or cryptography technology alone cannot enforce the multiple privacy demands, which blocks their application in the real-world cloud. This paper proposes a practical solution for privacy preserving medical record sharing for cloud computing. Based on the classification of the attributes of medical records, we use vertical partition of medical dataset to achieve the consideration of different parts of medical data with different privacy concerns. It mainly includes four components, i.e., (1) vertical data partition for medical data publishing, (2) data merging for medical dataset accessing, (3) integrity checking, and (4) hybrid search across plaintext and ciphertext, where the statistical analysis and cryptography are innovatively combined together to provide multiple paradigms of balance between medical data utilization and privacy protection. A prototype system for the large scale medical data access and sharing is implemented. Extensive experiments show the effectiveness of our proposed solution. Proposed a hybrid solution for privacy preserving data sharing in cloud environment.Different methods are innovatively combined to support multiple paradigms of medical data sharing with different privacy strengths.The experimental evaluations are reported based on the implementation of four basic components and a real world case study.