title: Prototype of automatic accident detection and management in vehicular environment using VANET and IoT

abstract: The increase in the population of cities and the number of vehicles leads to increase in congestion on roads and accidents. The lack of quick medical service on road accidents is the major cause of death. In such situation, an automatic accident detection can help to save the loss of life. In this paper, a prototype is designed for an automatic accident detection using Vehicular Adhoc Network (VANET) and Internet of Things (IoT). The application is able to detect accident and severity of the emergency level with the help of mechanical and medical sensors deployed in the vehicle. In case of emergency, the message is sent to a hospital using VANET, where our central server is able to find out the location of the accident and nearest medical center. It sends a message for an ambulance after detecting basic information. In order to clear the path on the way to accident's location, the client application on the ambulance generates alert messages.


title: Design of circular dual and quad ridge horn antennas for millimeter wave applications

abstract: The design of circular dual and quad ridged horn antennas covering the 18 to 45 GHz band is discussed. An exponential taper and blending fillet for the ridge profile are presented to minimize 3 dB beamwidth variation while maintaining high boresight gain for both dual and quad ridge horns. Boresight gain for a quad ridge design is greater than 15 dBi, while boresight gain greater than 17.9 dBi is shown for a dual ridge circular horn design. Both horns have axial 3 dB beamwidth variation less than 5 degrees over the entire frequency band.


Robust Malware Detection using Integrated Static and Dynamic Analysis


title: Keeping Authorities "Honest or Bust" with Decentralized Witness Cosigning

abstract: The secret keys of critical network authorities - such as time, name, certificate, and software update services - represent high-value targets for hackers, criminals, and spy agencies wishing to use these keys secretly to compromise other hosts. To protect authorities and their clients proactively from undetected exploits and misuse, we introduce CoSi, a scalable witness cosigning protocol ensuring that every authoritative statement is validated and publicly logged by a diverse group of witnesses before any client will accept it. A statement S collectively signed by W witnesses assures clients that S has been seen, and not immediately found erroneous, by those W observers. Even if S is compromised in a fashion not readily detectable by the witnesses, CoSi still guarantees S's exposure to public scrutiny, forcing secrecy-minded attackers to risk that the compromise will soon be detected by one of the W witnesses. Because clients can verify collective signatures efficiently without communication, CoSi protects clients' privacy, and offers the first transparency mechanism effective against persistent man-in-the-middle attackers who control a victim's Internet access, the authority's secret key, and several witnesses' secret keys. CoSi builds on existing cryptographic multisignature methods, scaling them to support thousands of witnesses via signature aggregation over efficient communication trees. A working prototype demonstrates CoSi in the context of timestamping and logging authorities, enabling groups of over 8,000 distributed witnesses to cosign authoritative statements in under two seconds.


title: Analysis of rewards on reward-based crowdfunding platforms

abstract: Today, crowdfunding has emerged as a popular means for fundraising. Among various crowdfunding platforms, reward-based ones are the most well received. However, to the best knowledge of the authors, little research has been performed on rewards. In this paper, we analyze a Kickstarter dataset, which consists of approximately 3K projects and 30K rewards. The analysis employs various statistical methods, including Pearson correlation tests, Kolmogorov-Smirnow test and Kaplan-Meier estimation, to study the relationships between various reward characteristics and project success. We find that projects with more rewards, with limited offerings and late-added rewards are more likely to succeed.


title: Reusing Scientific Data: How Earthquake Engineering Researchers Assess the Reusability of Colleagues’ Data

abstract: Investments in cyberinfrastructure and e-Science initiatives are motivated by the desire to accelerate scientific discovery. Always viewed as a foundation of science, data sharing is appropriately seen as critical to the success of such initiatives, but new technologies supporting increasingly data-intensive and collaborative science raise significant challenges and opportunities. Overcoming the technical and social challenges to broader data sharing is a common and important research objective, but increasing the supply and accessibility of scientific data is no guarantee data will be applied by scientists. Before reusing data created by others, scientists need to assess the data's relevance, they seek confidence the data can be understood, and they must trust the data. Using interview data from earthquake engineering researchers affiliated with the George E. Brown, Jr. Network for Earthquake Engineering Simulation (NEES), we examine how these scientists assess the reusability of colleagues' experimental data for model validation.


title: SemanticTalk: Software for Visualizing Brainstorming Sessions and Thematic Concept Trails on Document Collections

abstract: In this demonstration we introduce a technology to support knowledge structuring processes already at the time of their creation by building up concept structures in real time. Our focus was set on the design of a minimal invasive system, which ideally requires no human interaction and thus gives the maximum freedom to the participants of a knowledge creation or exchange processes. The system captures and displays spoken dialogs as well as text documents for further use in knowledge engineer's tools.


title: Fundamental Locomotive Activity Time Efficiency with Differently Positioning Drive-Axis Wheelchairs Among Elders

abstract: This study examined time efficiency in wheelchair locomotive activities among four different wheelchairs propelled by elders with arms and/or legs. Sixteen elder manual wheelchair users propelled her/his own wheelchair and three wheelchairs (the main drive-axis wheels positioning in front, middle, and rear, respectively) in the test of seven wheelchair locomotive activities of daily living. A Mixed-Model ANOVA and Bonferroni post hoc test (p < .05) were employed to determine the time efficiency among four wheelchairs and three groups. The results demonstrated better time efficiency resulted from propelling the mid drive-axis and/or rear drive-axis wheelchairs; using arms and legs simultaneously propelling wheelchairs was more time efficient than using the arms or legs only in the selected locomotive activities.


title: Discriminating gender on Chinese microblog: A study of online behaviour, writing style and preferred vocabulary

abstract: As user attributes are useful for applications such as personalized recommendation, adverting and so on, user attribute predication on Twitter has attracted intensive attentions in recent years. Although Chinese micro-blogging services are different from Twitter on various aspects such as language, user behaviours and so on, few efforts have been made on Chinese micro-blogging services. In this paper, we propose a gender prediction model for Chinese microblog which exploits features including online behaviour, writing style, and preferred vocabulary. Experimental results on Sina Weibo, which is one of the most popular micro-blogging services in China, show that our model achieves the state-of-the-art accuracy 94.3%. We also find significant distinctions between male and female microblog users on online behaviour, writing style and preferred vocabulary, which would be helpful for improving personalized applications. Keywords—gender prediction, Chinese microblog, user behaviour analysis.


title: Capacity of Neural Networks for Lifelong Learning of Composable Tasks

abstract: We investigate neural circuits in the exacting setting that (i) the acquisition of a piece of knowledge can occur from a single interaction, (ii) the result of each such interaction is a rapidly evaluatable subcircuit, (iii) hundreds of thousands of such subcircuits can be acquired in sequence without substantially degrading the earlier ones, and (iv) recall can be in the form of a rapid evaluation of a composition of subcircuits that have been so acquired at arbitrary different earlier times.We develop a complexity theory, in terms of asymptotically matching upper and lower bounds, on the capacity of a neural network for executing, in this setting, the following action, which we call {\it association}: Each action sets up a subcircuit so that the excitation of a chosen set of neurons A will in future cause the excitation of another chosen set B.% As model of computation we consider the neuroidal model, a fully distributed model in which the quantitative resources n, the neuron numbers, d, the number of other neurons each neuron is connected to, and k, the inverse of the maximum synaptic strength, are all accounted for.A succession of experiences, possibly over a lifetime, results in the realization of a complex set of subcircuits. The composability requirement constrains the model to ensure that, for each association as realized by a subcircuit, the excitation in the triggering set of neurons A is quantitatively similar to that in the triggered set B, and also that the unintended excitation in the rest of the system is negligible. These requirements ensure that chains of associations can be triggeredWe first analyze what we call the Basic Mechanism, which uses only direct connections between neurons in the triggering set A and the target set B. We consider random networks of n neurons with expected number d of connections to and from each. We show that in the composable context capacity growth is limited by d^2, a severe limitation if the network is sparse, as it is in cortex. We go on to study the Expansive Mechanism, that additionally uses intermediate relay neurons which have high synaptic weights. For this mechanism we show that the capacity can grow as dn, to within logarithmic factors. From these two results it follows that in the composable regime, for the realistic cortical estimate of d=n^{\frac{1}{2}, superlinear capacity of order n^{\frac{3}{2}} in terms of the neuron numbers can be realized by the Expansive Mechanism, instead of the linear order n to which the Basic Mechanism is limited. More generally, for both mechanisms, we establish matching upper and lower bounds on capacity in terms of the parameters n, d, and the inverse maximum synaptic strength k.The results as stated above assume that in a set of associations, a target B can be triggered by at most one set A. It can be shown that the capacities are similar if the number m of As that can trigger a B is greater than one but small, but become severely constrained if m exceeds a certain threshold.


title: Console gaming across generations: exploring intergenerational interactions in collocated console gaming

abstract: In this paper, we examine the intergenerational gaming practices of four generations of console gamers, from ages 3 to 83 and, in particular, the roles that gamers of different generations take on when playing together in groups. Our data highlight the extent to which existing gaming technologies support interactions within collocated intergenerational groups, and our analysis reveals a more generationally flexible suite of roles in these computer-mediated interactions than have been documented by previous studies of more traditional collocated, intergenerational interactions. Finally, we offer implications for game designers who wish to make console games more accessible to intergenerational groups.


title: An improved ensemble approach for imbalanced classification problems

abstract: Classification of imbalanced data is a challenging task in machine learning, as most classification approaches tend to bias towards the majority class, even though the minority class is often the one of greater importance. Consequently, methods that are capable of boosting the classification accuracy on the minority class are sought after. In this paper, we propose an improved ensemble approach for imbalanced classification. Our algorithm is based on undersampling of the majority class to create balanced object subspaces, on which individual classifiers are trained. As not all generated classifiers will be useful for the ensemble construction, we carry out a pruning procedure to discard irrelevant models. This classifier selection is based on a diversity measure to identify mutually complementary classifiers. The remaining predictors are combined using a trained fuser based on discriminants. Extensive experimental results on several benchmark datasets demonstrate our proposed method to adequately address class imbalance and to (statistically) outperform several state-of-the-art classifier ensembles dedicated to imbalanced classification.


title: Mindfulness and trauma: implications for treatment

abstract: Mindfulness, originally a construct used in Eastern spiritual and philosophical traditions, has found new utility in psychotherapy practice. Mindfulness practice has been recently applied to treatments of several psychological and health related problems, and research is showing successful outcomes in psychological interventions incorporating mindfulness practices. Several schools of psychotherapy have theorized why mindfulness may be an effective intervention. One population which would theoretically be benefited by mindfulness practice in treatment consists of those individuals who have experienced traumatic events and are exhibiting post-traumatic stress disorder and/or related correlates of past trauma. The present paper gives a general review of the application of mindfulness to clinical psychology interventions. Additionally, we explain how mindfulness is applicable to our integrative behavioral approach to treating trauma and its sequelae. Specifically, this paper will (a) give a general overview of the conceptions and applications of mindfulness to psychology and psychotherapy and provide a brief account of the concepts origins in eastern traditions; (b) discuss the theoretical conceptualization of clinical problems that may relate to the long-term correlates of trauma; (c) describe how mindfulness, acceptance and the therapeutic relationship can address trauma symptoms and discuss a modified treatment approach for trauma survivors that incorporates mindfulness and acceptance practices into traditional exposure treatment.


title: The Forgotten Ones? The Validity of Consideration and Initiating Structure in Leadership Research.

abstract: This study provided a meta-analysis of the relationship of the Ohio State leadership behaviors— Consideration and Initiating Structure—with leadership. Overall, 163 independent correlations for Consideration and 159 correlations for Initiating Structure were analyzed. Results revealed that both Consideration (.48) and Initiating Structure (.29) have moderately strong, nonzero relations with leadership outcomes. Consideration was more strongly related to follower satisfaction (leader satisfaction, job satisfaction), motivation, and leader effectiveness, and Initiating Structure was slightly more strongly related to leader job performance and group– organization performance. Validities did vary by leadership measure, but in most cases validities generalized regardless of the measure used. Overall, the results provide important support for the validity of Initiating Structure and Consideration in leadership research.


title: A New Classification Framework to Evaluate the Entity Profiling on the Web: Past, Present and Future

abstract: Recently, we have witnessed entity profiling (EP) becoming increasingly one of the most important topics in information extraction, personalized applications, and web data analysis. EP aims to identify, extract, and represent a compact summary of valuable information about an entity based on the data related to it. To determine how EP systems have developed, during the last few years, this article reviews EP systems through a survey of the literature, from 2000 to 2015. To fulfill this aim, we introduce a comparison framework to compare and classify EP systems. Our comparison framework is composed of thirteen criteria that include: profiling source, the entity being modeled, the information that constitutes the profile, representation schema, profile construction technique, scale, scope/target domain, language, updating mechanism, enrichment technique, dynamicity, evaluation method, and application among others. Then, using the comparison framework, we discuss the recent development of the field and list some of the open problems and main trends that have emerged in EP to provide a proper guideline for researchers to develop or use robust profiling systems with suitable features according to their needs.


title: A Live, Multiple-Representation Probabilistic Programming Environment for Novices

abstract: We present a live, multiple-representation novice environment for probabilistic programming based on the Infer.NET language. When compared to a text-only editor in a controlled experiment on 16 participants, our system showed a significant reduction in keystrokes during introductory probabilistic programming exercises, and subsequently, a significant improvement in program description and debugging tasks as measured by task time, keystrokes and deletions.


title: Bottom-up and top-down reasoning with convolutional latent-variable models

abstract: Convolutional neural nets (CNNs) have demonstrated remarkable performance in recent history. Such approaches tend to work in a "unidirectional" bottom-up feed-forward fashion. However, biological evidence suggests that feedback plays a crucial role, particularly for detailed spatial understanding tasks. This work introduces "bidirectional" architectures that also reason with top-down feedback: neural units are influenced by both lower and higher-level units. We do so by treating units as latent variables in a global energy function. We call our models convolutional latent-variable models (CLVMs). From a theoretical perspective, CLVMs unify several approaches for recognition, including CNNs, generative deep models (e.g., Boltzmann machines), and discriminative latent-variable models (e.g., DPMs). ::: From a practical perspective, CLVMs are particularly well-suited for multi-task learning. We describe a single architecture that simultaneously achieves state-of-the-art accuracy for tasks spanning both high-level recognition (part detection/localization) and low-level grouping (pixel segmentation). Bidirectional reasoning is particularly helpful for detailed low-level tasks, since they can take advantage of top-down feedback. Our architectures are quite efficient, capable of processing an image in milliseconds. We present results on benchmark datasets with both part/keypoint labels and segmentation masks (such as PASCAL and LFW) that demonstrate a significant improvement over prior art, in both speed and accuracy.


title: Graph Convolutional Neural Networks for Web-Scale Recommender Systems

abstract: Recent advancements in deep neural networks for graph-structured data have led to state-of-the-art performance on recommender system benchmarks. However, making these methods practical and scalable to web-scale recommendation tasks with billions of items and hundreds of millions of users remains an unsolved challenge. Here we describe a large-scale deep recommendation engine that we developed and deployed at Pinterest. We develop a data-efficient Graph Convolutional Network (GCN) algorithm, which combines efficient random walks and graph convolutions to generate embeddings of nodes (i.e., items) that incorporate both graph structure as well as node feature information. Compared to prior GCN approaches, we develop a novel method based on highly efficient random walks to structure the convolutions and design a novel training strategy that relies on harder-and-harder training examples to improve robustness and convergence of the model. We also develop an efficient MapReduce model inference algorithm to generate embeddings using a trained model. Overall, we can train on and embed graphs that are four orders of magnitude larger than typical GCN implementations. We show how GCN embeddings can be used to make high-quality recommendations in various settings at Pinterest, which has a massive underlying graph with 3 billion nodes representing pins and boards, and 17 billion edges. According to offline metrics, user studies, as well as A/B tests, our approach generates higher-quality recommendations than comparable deep learning based systems. To our knowledge, this is by far the largest application of deep graph embeddings to date and paves the way for a new generation of web-scale recommender systems based on graph convolutional architectures.


title: Low cost solution to remotely refresh display devices using LED

abstract: White LED offers advantageous properties such as high brightness, low power consumption and long lifetime. While the use of white LEDs for communication has been proposed, but it is very difficult to install many such “visible light communication” solutions in public places because of high cost of the specialized components required to assemble these solutions. This paper demonstrates an end to end system that can remotely refresh displays in a reasonable time using LED lights for communication keeping the design cost as low as possible. The transmission scheme involves transmitting differential signals via a pair of low cost high brightness LEDs. The scheme works at the pixel level and not with compressed images and is targeted at applications where a few pixels need to be refreshed at a time and which can tolerate higher bit error rates.


title: Roots of increased health care inequality in New York.

abstract: During the 1970s, New York city experienced an epidemic of housing destruction by contagious fire and building abandonment. This epidemic was triggered by reductions in municipal services, especially fire control, in the poor areas of high population density and aging housing. The rapid loss of low rent housing led to a wave of forced internal migration of the poor within the city and overcrowding of areas adjacent to the burned out ones. The spread of overcrowding spread the high fire incidence. The massive upheaval resulted in destruction of social networks and in degraded living conditions. Public health, as measured by many indices such as disease incidence, substance abuse incidence, infant mortality, and incidence of homicide, deteriorated. This deterioration caused increased demand for hospital health care, especially emergency service. The data on average stay length in the poorest areas in comparison with wealthier areas hints that the overloading of the voluntary hospitals in the poorest areas has caused care rationing and greater inequality in access to care.


title: Neural methods for dynamic branch prediction

abstract: This article presents a new and highly accurate method for branch prediction. The key idea is to use one of the simplest possible neural methods, the perceptron, as an alternative to the commonly used two-bit counters. The source of our predictor's accuracy is its ability to use long history lengths, because the hardware resources for our method scale linearly, rather than exponentially, with the history length. We describe two versions of perceptron predictors, and we evaluate these predictors with respect to five well-known predictors. We show that for a 4 KB hardware budget, a simple version of our method that uses a global history achieves a misprediction rate of 4.6p on the SPEC 2000 integer benchmarks, an improvement of 26p over gshare. We also introduce a global/local version of our predictor that is 14p more accurate than the McFarling-style hybrid predictor of the Alpha 21264. We show that for hardware budgets of up to 256 KB, this global/local perceptron predictor is more accurate than Evers' multicomponent predictor, so we conclude that ours is the most accurate dynamic predictor currently available. To explore the feasibility of our ideas, we provide a circuit-level design of the perceptron predictor and describe techniques that allow our complex predictor to operate quickly. Finally, we show how the relatively complex perceptron predictor can be used in modern CPUs by having it override a simpler, quicker Smith predictor, providing IPC improvements of 15.8p over gshare and 5.7p over the McFarling hybrid predictor.


title: Parsing Time: Learning to Interpret Time Expressions

abstract: We present a probabilistic approach for learning to interpret temporal phrases given only a corpus of utterances and the times they reference. While most approaches to the task have used regular expressions and similar linear pattern interpretation rules, the possibility of phrasal embedding and modification in time expressions motivates our use of a compositional grammar of time expressions. This grammar is used to construct a latent parse which evaluates to the time the phrase would represent, as a logical parse might evaluate to a concrete entity. In this way, we can employ a loosely supervised EM-style bootstrapping approach to learn these latent parses while capturing both syntactic uncertainty and pragmatic ambiguity in a probabilistic framework. We achieve an accuracy of 72% on an adapted TempEval-2 task -- comparable to state of the art systems.


title: Design of RFID near-field focusing circular patch array antenna at 2.4GHz with applications

abstract: In this paper, a planar circular patch array antenna that features electric fields focused in the near-field zone for 2.4 GHz radio frequency identification (RFID) applications is presented. This array antenna is implemented by using six circular patch elements and a microstrip feeding network with a proper input phase for each element. The array antenna is capable of enhancing electric field distributions in the near zone to charge and interrogate dipole-like passive tags near the array antenna by capacitive coupling. Readable range is measured for up to 12 tags lined up closely with 1 cm spacing. Applications using the proposed array antenna with an RFID reader by Sunlit are discussed.


title: Palmprint authentication using a symbolic representation of images

abstract: A new branch of biometrics, palmprint authentication, has attracted increasing amount of attention because palmprints are abundant of line features so that low resolution images can be used. In this paper, we propose a new texture based approach for palmprint feature extraction, template representation and matching. An extension of the SAX (Symbolic Aggregate approXimation), a time series technology, to 2D data is the key to make this new approach effective, simple, flexible and reliable. Experiments show that by adopting the simple feature of grayscale information only, this approach can achieve an equal error rate of 0.3%, and a rank one identification accuracy of 99.9% on a 7752 palmprint public database. This new approach has very low computational complexity so that it can be efficiently implemented on slow mobile embedded platforms. The proposed approach does not rely on any parameter training process and therefore is fully reproducible. What is more, besides the palmprint authentication, the proposed 2D extension of SAX may also be applied to other problems of pattern recognition and data mining for 2D images.


title: Frontal Fibrosing Alopecia: Role of Dermoscopy in Differential Diagnosis

abstract: Frontal fibrosing alopecia (FFA) is more common in postmenopausal women, but it can occur in younger women. Some authors consider FFA to be a distinct frontal variant of lichen planopilaris. From a clinical point of view, this relatively uncommon condition is characterized by progressive frontotemporal recession due to inflammatory destruction of hair follicles. Dermoscopy can be very useful, as the differential diagnosis between traction alopecia, alopecia areata, FFA and cicatricial marginal alopecia may be difficult. It is not clear whether or not treatment alters the natural history of the disease – the disease stabilized with time in most of the patients with or without continuing treatment. Here we report a case of a 50-year-old woman with FFA and discuss the relevance of dermoscopy in the differential diagnosis of this disease.


title: Short circuit behavior of the Bi-mode Insulated Gate Transistor (BIGT)

abstract: The BIGT is a Reverse Conducting IGBT device which combines functionality of a high power IGBT and fast diode on a single chip. In this paper, an investigation of the IGBT mode short circuit performance of the BIGT is carried out. The behavior of the BIGT under IGBT mode short circuit conditions is illustrated by measurements on a 3.3kV 1500A HiPak1 module and then supported by circuit and detailed device physics simulations. The short circuit conditions of type II and type III are discussed, with the variations introduced due to the gate control of the BIGT when operated in reverse conducting mode. Comparison to standard IGBTs with Soft-Punch-Through (SPT) technology in a HiPak2 module is also provided.


title: A two-view based multilayer feature graph for robot navigation

abstract: To facilitate scene understanding and robot navigation in a modern urban area, we design a multilayer feature graph (MFG) based on two views from an on-board camera. The nodes of an MFG are features such as scale invariant feature transformation (SIFT) feature points, line segments, lines, and planes while edges of the MFG represent different geometric relationships such as adjacency, parallelism, collinearity, and coplanarity. MFG also connects the features in two views and the corresponding 3D coordinate system. Building on SIFT feature points and line segments, MFG is constructed using feature fusion which incrementally, iteratively, and extensively verifies the aforementioned geometric relationships using random sample consensus (RANSAC) framework. Physical experiments show that MFG can be successfully constructed in urban area and the construction method is demonstrated to be very robust in identifying feature correspondence.


title: A Syntactic Resource for Thai: CG Treebank

abstract: This paper presents Thai syntactic resource: Thai CG treebank, a categorial approach of language resources. Since there are very few Thai syntactic resources, we designed to create treebank based on CG formalism. Thai corpus was parsed with existing CG syntactic dictionary and LALR parser. The correct parsed trees were collected as preliminary CG treebank. It consists of 50,346 trees from 27,239 utterances. Trees can be split into three grammatical types. There are 12,876 sentential trees, 13,728 noun phrasal trees, and 18,342 verb phrasal trees. There are 17,847 utterances that obtain one tree, and an average tree per an utterance is 1.85.


title: TrustICE: Hardware-Assisted Isolated Computing Environments on Mobile Devices

abstract: Mobile devices have been widely used to process sensitive data and perform important transactions. It is a challenge to protect secure code from a malicious mobile OS. ARM TrustZone technology can protect secure code in a secure domain from an untrusted normal domain. However, since the attack surface of the secure domain will increase along with the size of secure code, it becomes arduous to negotiate with OEMs to get new secure code installed. We propose a novel TrustZone-based isolation framework named TrustICE to create isolated computing environments (ICEs) in the normal domain. TrustICE securely isolates the secure code in an ICE from an untrusted Rich OS in the normal domain. The trusted computing base (TCB) of TrustICE remains small and unchanged regardless of the amount of secure code being protected. Our prototype shows that the switching time between an ICE and the Rich OS is less than 12 ms.


title: Fuzzy feature selection

abstract: In fuzzy classi"er systems the classi"cation is obtained by a number of fuzzy If}Then rules including linguistic terms such as Low and High that fuzzify each feature. This paper presents a method by which a reduced linguistic (fuzzy) set of a labeled multi-dimensional data set can be identi"ed automatically. After the projection of the original data set onto a fuzzy space, the optimal subset of fuzzy features is determined using conventional search techniques. The applicability of this method has been demonstrated by reducing the number of features used for the classi"cation of four real-world data sets. This method can also be used to generate an initial rule set for a fuzzy neural network. ( 1999 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.


title: Single phase transformerless inverter topologies for grid-tied photovoltaic system: A review

abstract: Grid-tied inverters are the key components of distributed generation system because of their function as an effective interface between renewable energy sources and utility. Recently, there has been an increasing interest in the use of transformerless inverter for low-voltage single-phase grid-tied photovoltaic (PV) system due to higher efficiency, lower cost, smaller size and weight when compared to the ones with transformer. However, the leakage current issues of transformerless inverter, which depends on the topology structure and modulation scheme, have to be addressed very carefully. This review focuses on the transformerless topologies, which are classified into three basic groups based on the decoupling method and leakage current characteristics. Different topologies under the three classes are presented, compared and evaluated based on leakage current, component ratings, advantages, and disadvantages. An examination of demand for the inverter, the utility grid, and the PV module are presented. A performance comparison in MATLAB/Simulink environment is done among different topologies. Also an analysis has been presented to select a better topology. Finally, based on the analysis and simulation results, a comparison table has been presented. Furthermore, some important experimental parameters have been summarized.


title: Spreadsheet Property Detection With Rule-assisted Active Learning

abstract: Spreadsheets are a critical and widely-used data management tool. Converting spreadsheet data into relational tables would bring benefits to a number of fields, including public policy, public health, and economics. Research to date has focused on designing domain-specific languages to describe transformation processes or automatically converting a specific type of spreadsheets. To handle a larger variety of spreadsheets, we have to identify various spreadsheet properties, which correspond to a series of transformation programs that contribute towards a general framework that converts spreadsheets to relational tables. In this paper, we focus on the problem of spreadsheet property detection. We propose a hybrid approach of building a variety of spreadsheet property detectors to reduce the amount of required human labeling effort. Our approach integrates an active learning framework with crude, easy-to-write, user-provided rules to save human labeling effort by generating additional high-quality labeled data especially in the initial training stage. Using a bagging-like technique, Our approach can also tolerate lower-quality user-provided rules. Our experiments show that when compared to a standard active learning approach, we reduced the training data needed to reach the performance plateau by 34-44% when a human provides relatively high-quality rules, and by a comparable amount with low-quality rules. A study on a large-scale web-crawled spreadsheet dataset demonstrates that it is crucial to detect a variety of spreadsheet properties in order to transform a large portion of the spreadsheets into a relational form.


title: Route-over vs mesh-under routing in 6LoWPAN

abstract: Transmission of IPv6 packets over Low-power Wireless Personal Area Networks (6LoWPAN) was considered nearly impractical once. The size of IPv6 packets is much larger than the packet size of the IEEE 802.15.4 data link layer. 6LoWPAN implements an adaptation layer between network and data link layers. Main purpose of the adaptation layer is to fragment and reassemble IPv6 packets. Implementation of the adaptation layer enhances the routing/forwarding decision of packets both network and adaptation layers. We can divide the routing scheme in 6LoWPAN into two categories: the mesh-under and the route-over, based on the routing decision taken on adaptation layer or network layer respectively. In this paper we perform an analytical comparison between these two schemes in terms of the packet/fragment arrival probability, the total number of transmissions and the total delay between source and destination. We also compare the selective fragment retransmission mechanism between mesh-under and route-over schemes.


title: Distributional Semantics in Technicolor

abstract: Our research aims at building computational models of word meaning that are perceptually grounded. Using computer vision techniques, we build visual and multimodal distributional models and compare them to standard textual models. Our results show that, while visual models with state-of-the-art computer vision techniques perform worse than textual models in general tasks (accounting for semantic relatedness), they are as good or better models of the meaning of words with visual correlates such as color terms, even in a nontrivial task that involves nonliteral uses of such words. Moreover, we show that visual and textual information are tapping on different aspects of meaning, and indeed combining them in multimodal models often improves performance.


title: Use of Fillers in Rhinoplasty

abstract: Surgical rhinoplasty is the one of the most common cosmetic procedures in Asians. But there are limitations, such as down time, high cost, and a steep learning curve. Most complications are implant related. A safer and less invasive procedure is rhinoplasty using fillers. Good knowledge of the nasal anatomy is essential for rhinoplasty using fillers. Knowledge of nerves, blood supply, and injection plane allows avoiding complications. There are several planes in the nose. The deep fatty layer is recommended for injection, because it is wide and loose and there are less important neurovascular structures. Botulinum toxin also can be used for noninvasive rhinoplasty.


title: APPLICATION OF WIRELESS SENSOR NETWORKS FOR GREENHOUSE PARAMETER CONTROL IN PRECISION AGRICULTURE

abstract: The technological development in Wireless Sensor Networks made it possible to use in monitoring and control of greenhouse parameter in precision agriculture. In last decades there have been tremendous advancements in technology for agriculture and growth of final yield. Due to uneven natural distribution of rain water it is very crucial for farmers to monitor and control the equal distribution of water to all crops in the whole farm or as per the requirement of the crop. There is no ideal irrigation method available which may be suitable for all weather conditions, soil structure and variety of crops cultures. Green house technology may be the best solution for this solution. All the parameters of greenhouse require a detailed analysis in order to choose the correct method. It is observed that farmers have to bear huge financial loss because of wrong prediction of weather and incorrect irrigation method to crops. In this contest with the evolution in wireless sensor technologies and miniaturized sensor devices, it is possible to uses them for automatic environment monitoring and controlling the parameters of greenhouse, for Precision Agriculture (PA) application. In this paper, we have proposed and analyse the use of Programmable System on Chip Technology (PSoC) as a part of Wireless Sensor Networks (WSN) to monitor and control various parameter of green house.


ARE YOU UP FOR THE CHALLENGE? TOWARDS THE DEVELOPMENT OF A BIG DATA CAPABILITY ASSESSMENT MODEL


title: A picture is worth a thousand words: A content analysis of Facebook profile photographs

abstract: This research examines identity construction and gender roles in social networking sites by studying and comparing the profile photographs of male and female Facebook users. Specifically, the number of photos in the profile album and the content of the main profile picture are studied by coding specific pictures, and determining if the content and amount of profile pictures differ significantly by gender. Participants include male and female Facebook users between the ages of 18 and 23 who are currently enrolled in a college or university. Profile pictures tended to be inactive, posed, appropriate, and only including the subject. The content and amount of Facebook profile photographs also did not significantly vary by gender. Implications of these findings, as well as suggestions for future research, are discussed.


title: Computing large matchings in planar graphs with fixed minimum degree

abstract: In this paper, we present algorithms that compute large matchings in planar graphs with fixed minimum degree. The algorithms give a guarantee on the size of the computed matching and run in linear time. Thus they are faster than the best known algorithm for computing maximum matchings in general graphs and in planar graphs, which run in O(nm) and O(n^1^.^1^8^8) time, respectively. For the class of planar graphs with minimum degree 3, the bounds we achieve are known to be the best possible. Further, we discuss how minimum degree 5 can be used to obtain stronger bounds on the matching size.


title: Blind detection for MIMO systems with low-resolution ADCs using supervised learning

abstract: This paper considers a multiple-input-multiple-output (MIMO) system with low-resolution analog-to-digital converters (ADCs). In this system, we propose a novel detection framework that performs data symbol detection without explicitly knowing channel state information at a receiver. The underlying idea of the proposed framework is to exploit supervised learning. Specifically, during channel training, the proposed approach sends a sequence of data symbols as pilots so that the receiver learns a nonlinear function that is determined by both a channel matrix and a quantization function of the ADCs. During data transmission, the receiver uses the learned nonlinear function to detect which data symbols were transmitted. In this context, we propose two blind detection methods to determine the nonlinear function from the training-data set. We also provide an analytical expression for the symbol-vector-error probability of the MIMO systems with one-bit ADCs when employing the proposed framework. Simulations demonstrate the performance improvement of the proposed framework compared to existing detection techniques.


title: Improving multi-target tracking via social grouping

abstract: We address the problem of multi-person data-association-based tracking (DAT) in semi-crowded environments from a single camera. Existing tracklet-association-based methods using purely visual cues (like appearance and motion information) show impressive results but rely on heavy training, a number of tuned parameters, and sophisticated detectors to cope with visual ambiguities within the video and low-level processing errors. In this work, we consider clustering dynamics to mitigate such ambiguities. This leads to a general optimization framework that adds social grouping behavior (SGB) to any basic affinity model. We formulate this as a nonlinear global optimization problem to maximize the consistency of visual and grouping cues for trajectories in both tracklet-tracklet linking space and tracklet-grouping assignment space. We formulate the Lagrange dual and solve it using a two-stage iterative algorithm, employing the Hungarian algorithm and K-means clustering. We build SGB upon a simple affinity model and show very promising performance on two publicly available real-world datasets with different tracklet extraction methods.


BEHIND ADVERTISING : THE LANGUAGE OF PERSUASION Kenechukwu


title: Global, Regional, and National Burden of Rheumatic Heart Disease, 1990–2015

abstract: BACKGROUND ::: Rheumatic heart disease remains an important preventable cause of cardiovascular death and disability, particularly in low-income and middle-income countries. We estimated global, regional, and national trends in the prevalence of and mortality due to rheumatic heart disease as part of the 2015 Global Burden of Disease study. ::: ::: ::: METHODS ::: We systematically reviewed data on fatal and nonfatal rheumatic heart disease for the period from 1990 through 2015. Two Global Burden of Disease analytic tools, the Cause of Death Ensemble model and DisMod-MR 2.1, were used to produce estimates of mortality and prevalence, including estimates of uncertainty. ::: ::: ::: RESULTS ::: We estimated that there were 319,400 (95% uncertainty interval, 297,300 to 337,300) deaths due to rheumatic heart disease in 2015. Global age-standardized mortality due to rheumatic heart disease decreased by 47.8% (95% uncertainty interval, 44.7 to 50.9) from 1990 to 2015, but large differences were observed across regions. In 2015, the highest age-standardized mortality due to and prevalence of rheumatic heart disease were observed in Oceania, South Asia, and central sub-Saharan Africa. We estimated that in 2015 there were 33.4 million (95% uncertainty interval, 29.7 million to 43.1 million) cases of rheumatic heart disease and 10.5 million (95% uncertainty interval, 9.6 million to 11.5 million) disability-adjusted life-years due to rheumatic heart disease globally. ::: ::: ::: CONCLUSIONS ::: We estimated the global disease prevalence of and mortality due to rheumatic heart disease over a 25-year period. The health-related burden of rheumatic heart disease has declined worldwide, but high rates of disease persist in some of the poorest regions in the world. (Funded by the Bill and Melinda Gates Foundation and the Medtronic Foundation.).


title: Online Resource Allocation Using Decompositional Reinforcement Learning

abstract: This paper considers a novel application domain for reinforcement learning: that of "autonomic computing," i.e. selfmanaging computing systems. RL is applied to an online resource allocation task in a distributed multi-application computing environment with independent time-varying load in each application. The task is to allocate servers in real time so as to maximize the sum of performance-based expected utility in each application. This task may be treated as a composite MDP, and to exploit the problem structure, a simple localized RL approach is proposed, with better scalability than previous approaches. The RL approach is tested in a realistic prototype data center comprising real servers, real HTTP requests, and realistic time-varying demand. This domain poses a number of major challenges associated with live training in a real system, including: the need for rapid training, exploration that avoids excessive penalties, and handling complex, potentially non-Markovian system effects. The early results are encouraging: in overnight training, RL performs as well as or slightly better than heavily researched model-based approaches derived from queuing theory.


title: Vehicle tracking using ultrasonic sensors & joined particle weighting

abstract: In recent years, driver-assistance systems have emerged as one major possibility to increase comfort and safety in road traffic. Still, cost is one major hindrance to the widespread use of safety systems such as lane-change or blind spot warning. To facilitate the widespread adoption of such safety systems, thus increasing safety for all traffic participants, the use of cost-efficient components is of crucial importance. Within this work we investigate the use of cost-efficient, widely used ultrasonic sensors for the tracking of passing-by vehicles at high velocities. Therefore, a particle filter with some mixture tracking capabilities is implemented to fuse the signals from 6 us-sensors. The main focus of this work lies on the development of a more detailed sensor model that is used in this particle filter. Additionally, a strategy to take into account object-visibility w.r.t. the different sensors is outlined. The derived concept is evaluated experimentally in real road traffic. The applicability of the tracking result in context of lane-change-decision-aid and blind-spot-surveillance systems is analyzed.


title: The biologic significance of bacterial encapsulation.

abstract: Capsules are important determinants of the behavior of bacteria within the animal host. To survive within the host, bacteria must be able to evade a diverse array of defense mechanisms that include complement-mediated bacteriolysis, uptake and killing by phagocytes as well as cell-mediated immune mechanisms. In this chapter the manner in which bacterial capsules, particularly those of Escherichia coli, enable the organism to survive in this hostile environment will be detailed.


Multimodal Attention in Recurrent Neural Networks for Visual Question Answering


title: Theory of Financial Risk and Derivative Pricing: From Statistical Physics to Risk Management

abstract: Foreword Preface 1. Probability theory: basic notions 2. Maximum and addition of random variables 3. Continuous time limit, Ito calculus and path integrals 4. Analysis of empirical data 5. Financial products and financial markets 6. Statistics of real prices: basic results 7. Non-linear correlations and volatility fluctuations 8. Skewness and price-volatility correlations 9. Cross-correlations 10. Risk measures 11. Extreme correlations and variety 12. Optimal portfolios 13. Futures and options: fundamental concepts 14. Options: hedging and residual risk 15. Options: the role of drift and correlations 16. Options: the Black and Scholes model 17. Options: some more specific problems 18. Options: minimum variance Monte-Carlo 19. The yield curve 20. Simple mechanisms for anomalous price statistics Index of most important symbols Index.


title: Motion artifact removal for wearable ECG using stationary wavelet multi-resolution analysis

abstract: Wearable electrocardiogram (ECG) is attracting much attention in daily healthcare applications. From the view-point of long-term use, it is desired that the electrodes are non-contact with the human body. In this study, we propose an algorithm using the stationary wavelet transform (SWT) to remove motion artifact superimposed on ECG signal when using non-contact capacitively coupling electrodes. In addition, we confirmed that the proposed algorithm performed well on the ECG signals including a premature ventricular contraction (PVC) using MIT-BIH Arrhythmia Database. As a result, the correlation coefficients of ECG signals with respect to the clean ones have been improved from 0.61 to 0.84 on median before and after motion artifact removal, which demonstrates the validity of the proposed SWT-based algorithm.


title: Non-Orthogonal Multiple Access in Multi-Cell Networks: Theory, Performance, and Practical Challenges

abstract: Non-orthogonal multiple access (NOMA) is a potential enabler for the development of 5G and beyond wireless networks. By allowing multiple users to share the same time and frequency, NOMA can scale up the number of served users, increase the spectral efficiency, and improve user-fairness compared to existing orthogonal multiple access (OMA) techniques. While single-cell NOMA has drawn significant attention recently, much less attention has been given to multi-cell NOMA. This article discusses the opportunities and challenges of NOMA in a multi-cell environment. As the density of base stations and devices increases, inter-cell interference becomes a major obstacle in multi-cell networks. As such, identifying techniques that combine interference management approaches with NOMA is of great significance. After discussing the theory behind NOMA, this paper provides an overview of the current literature and discusses key implementation and research challenges, with an emphasis on multi-cell NOMA.


title: Exploratory Analysis of Spatial and Temporal Data: A Systematic Approach

abstract: Exploratory data analysis (EDA) is about detecting and describing patterns, trends, and relations in data, motivated by certain purposes of investigation. As something relevant is detected in data, new questions arise, causing specific parts to be viewed in more detail. So EDA has a significant appeal: it involves hypothesis generation rather than mere hypothesis testing. The authors describe in detail and systemize approaches, techniques, and methods for exploring spatial and temporal data in particular. They start by developing a general view of data structures and characteristics and then build on top of this a general task typology, distinguishing between elementary and synoptic tasks. This typology is then applied to the description of existing approaches and technologies, resulting not just in recommendations for choosing methods but in a set of generic procedures for data exploration. Professionals practicing analysis will profit from tested solutions - illustrated in many examples - for reuse in the catalogue of techniques presented. Students and researchers will appreciate the detailed description and classification of exploration techniques, which are not limited to spatial data only. In addition, the general principles and approaches described will be useful for designers of new methods for EDA.


title: Experimental Control and Design of Low-Frequency Bias Networks for Dynamically Biased Amplifiers

abstract: To reach the video bandwidth requirements on the supply paths, power amplifiers with dynamic bias schemes are constrained to reduce the values of the low-frequency (LF) decoupling capacitors on their bias lines. This can entail a decrease of the LF stability margins, among other negative effects. In this work, a methodology is proposed to experimentally monitor and control the dominant poles that govern the LF dynamics of both gate and drain bias lines from dc to high compression power. A specific topology for the bias observation accesses allows a consistent characterization in large-signal regimes. An automatic procedure to trace root contours versus four control parameters is also developed. The complete approach can be used to optimize the design of the bias lines in terms of video bandwidth, relative stability margins, and bias voltage transfer function characteristics. Moreover, the design can account for the effect of the large-signal RF drive on these LF performances. The methodology is exemplified and validated in a demonstrator prototype specifically built for that purpose.


title: A Machine Learning Approach to Test Data Generation: A Case Study in Evaluation of Gene Finders

abstract: Programs for gene prediction in computational biology are examples of systems for which the acquisition of authentic test data is difficult as these require years of extensive research. This has lead to test methods based on semiartificially produced test data, often produced by ad hoctechniques complemented by statistical models such as Hidden Markov Models (HMM). The quality of such a test method depends on how well the test data reflect the regularities in known data and how well they generalize these regularities. So far only very simplified and generalized, artificial data sets have been tested, and a more thorough statistical foundation is required. ::: ::: We propose to use logic-statistical modelling methods for machine-learning for analyzing existing and manually marked up data, integrated with the generation of new, artificial data. More specifically, we suggest to use the PRISM system developed by Sato and Kameya. Based on logic programming extended with random variables and parameter learning, PRISM appears as a powerful modelling environment, which subsumes HMMs and a wide range of other methods, all embedded in a declarative language. We illustrate these principles here, showing parts of a model under development for genetic sequences and indicate first initial experiments producing test data for evaluation of existing gene finders, exemplified by GENSCAN, HMMGene and genemark.hmm.


title: Adsorption of human IgG on to poly(N-isopropylacrylamide)-based polymer particles

abstract: Thermosensitive poly(N-isopropylacrylamide)-based polymer particles were synthesised, and screened for the adsorption of human immunoglobulin G (hIgG). At pH 9 the adsorption on microgel particles was strongly affected by temperature, approximately 40 mg hIgG/g support (90% of initial hIgG) being adsorbed at 40°C but only 10% of initial hIgG at 25°C. At pH 5 the maximum adsorbed amount (20 mg hIgG/g support) was similar for both temperatures. The adsorption of hIgG on to charged poly(methyl methacrylate)/poly(N-isopropylacrylamide) core-shell latexes was negligible (5–10 mg hIgG/g support) at the same temperature and pH conditions. The lower adsorption of hIgG onto the core-shell particles is explained by steric interactions due to the small size of the shell.


title: A Stochastic Graph Evolution Framework for Robust Multi-Target Tracking

abstract: Maintaining the stability of tracks on multiple targets in video over extended time periods remains a challenging problem. A few methods which have recently shown encouraging results in this direction rely on learning context models or the availability of training data. However, this may not be feasible in many application scenarios. Moreover, tracking methods should be able to work across different scenarios (e.g. multiple resolutions of the video) making such context models hard to obtain. In this paper, we consider the problem of long-term tracking in video in application domains where context information is not available a priori, nor can it be learned online. We build our solution on the hypothesis that most existing trackers can obtain reasonable short-term tracks (tracklets). By analyzing the statistical properties of these tracklets, we develop associations between them so as to come up with longer tracks. This is achieved through a stochastic graph evolution step that considers the statistical properties of individual tracklets, as well as the statistics of the targets along each proposed long-term track. On multiple real-life video sequences spanning low and high resolution data, we show the ability to accurately track over extended time periods (results are shown on many minutes of continuous video).


title: Features for Content-Based Audio Retrieval

abstract: Abstract Today, a large number of audio features exists in audio retrieval for different purposes, such as automatic speech recognition, music information retrieval, audio segmentation, and environmental sound retrieval. The goal of this chapter is to review latest research in the context of audio feature extraction and to give an application-independent overview of the most important existing techniques. We survey state-of-the-art features from various domains and propose a novel taxonomy for the organization of audio features. Additionally, we identify the building blocks of audio features and propose a scheme that allows for the description of arbitrary features. We present an extensive literature survey and provide more than 200 references to relevant high-quality publications.


title: SCUBA: Secure Code Update By Attestation in sensor networks

abstract: This paper presents SCUBA (Secure Code Update By Attestation), for detecting and recovering compromised nodes in sensor networks. The SCUBA protocol enables the design of a sensor network that can detect compromised nodes without false negatives, and either repair them through code updates, or revoke the compromised nodes. The SCUBA protocol represents a promising approach for designing secure sensor networks by proposing a first approach for automatic recovery of compromised sensor nodes. The SCUBA protocol is based on ICE (Indisputable Code Execution), a primitive we introduce to dynamically establish a trusted code base on a remote, untrusted sensor node.


title: Fluid Simulation by the Smoothed Particle Hydrodynamics Method: A Survey

abstract: This paper presents a survey of Smoothed Particle Hydrodynamics (SPH) and its use in computational fluid dynamics. As a truly mesh-free particle method based upon the Lagrangian formulation, SPH has been applied to a variety of different areas in science, computer graphics and engineering. It has been established as a popular technique for fluid based simulations, and has been extended to successfully simulate various phenomena such as multi-phase flows, rigid and elastic solids, and fluid features such as air bubbles and foam. Various aspects of the method will be discussed: Similarities, advantages and disadvantages in comparison to Eulerian methods; Fundamentals of the SPH method; The use of SPH in fluid simulation; The current trends in SPH. The paper ends with some concluding remarks about the use of SPH in fluid simulations, including some of the more apparent problems, and a discussion on prospects for future work.


title: A real-time underwater object detection algorithm for multi-beam forward looking sonar

abstract: Abstract A novel algorithm for the detection of underwater man-made objects in forward-looking sonar imagery is proposed. The algorithm takes advantage of the integral-image representation to quickly compute features, and progressively reduces the computational load by working on smaller portions of the image along the detection process phases. By adhering to the proposed scheme, real-time detection on sonar data onboard an autonomous vehicle is made possible. The proposed method does not require training data, as it dynamically takes into account environmental characteristics of the sensed sonar data. The proposed approach has been implemented and integrated into the software system of the Gemellina autonomous surface vehicle, and is able to run in real time. The validity of the proposed approach is demonstrated on real experiments carried out at sea with the Gemellina autonomous surface vehicle.


title: Training Load Monitoring in Team Sports: A Novel Framework Separating Physiological and Biomechanical Load-Adaptation Pathways

abstract: There have been considerable advances in monitoring training load in running-based team sports in recent years. Novel technologies nowadays offer ample opportunities to continuously monitor the activities of a player. These activities lead to internal biochemical stresses on the various physiological subsystems; however, they also cause internal mechanical stresses on the various musculoskeletal tissues. Based on the amount and periodization of these stresses, the subsystems and tissues adapt. Therefore, by monitoring external loads, one hopes to estimate internal loads to predict adaptation, through understanding the load-adaptation pathways. We propose a new theoretical framework in which physiological and biomechanical load-adaptation pathways are considered separately, shedding new light on some of the previously published evidence. We hope that it can help the various practitioners in this field (trainers, coaches, medical staff, sport scientists) to align their thoughts when considering the value of monitoring load, and that it can help researchers design experiments that can better rationalize training-load monitoring for improving performance while preventing injury.


title: Biochemical composition of amniotic fluid and extraembryonic coelomic fluid in the first trimester of pregnancy

abstract: OBJECTIVE ::: To determine the biochemical composition of amniotic fluid and extraembryonic coelomic fluid between 8 and 12 weeks gestation. ::: ::: ::: DESIGN ::: Prospective observational study. ::: ::: ::: SUBJECTS ::: 40 women with a normal pregnancy between 7 and 12 weeks gestation having termination of pregnancy. ::: ::: ::: INTERVENTIONS ::: Before termination the women had a transvaginal ultrasound guided amniocentesis. Pure samples of amniotic fluid and extraembryonic coelomic fluid were obtained from each woman and standard biochemical variables were measured in each fluid sample immediately after collection. ::: ::: ::: RESULTS ::: Levels of sodium, potassium and bicarbonate were significantly higher in amniotic fluid whilst chloride, urea, bilirubin, protein, albumin, glucose, creatinine, calcium and phosphate were present in higher concentrations in extraembryonic coelomic fluid. All differences in concentration were significant (P less than 0.05; unpaired t-test). No relation was demonstrated between electrolyte concentrations in amniotic fluid or coelomic fluid and stage of gestation. ::: ::: ::: CONCLUSIONS ::: Amniotic fluid and extraembryonic coelomic fluid have a widely differing biochemical composition. The biological significance of these differences remains unexplained.


title: Dependency-Based Construction of Semantic Space Models

abstract: Traditionally, vector-based semantic space models use word co-occurrence counts from large corpora to represent lexical meaning. In this article we present a novel framework for constructing semantic spaces that takes syntactic relations into account. We introduce a formalization for this class of models, which allows linguistic knowledge to guide the construction process. We evaluate our framework on a range of tasks relevant for cognitive science and natural language processing: semantic priming, synonymy detection, and word sense disambiguation. In all cases, our framework obtains results that are comparable or superior to the state of the art.


title: Efficient similarity joins for near-duplicate detection

abstract: With the increasing amount of data and the need to integrate data from multiple data sources, one of the challenging issues is to identify near-duplicate records efficiently. In this article, we focus on efficient algorithms to find a pair of records such that their similarities are no less than a given threshold. Several existing algorithms rely on the prefix filtering principle to avoid computing similarity values for all possible pairs of records. We propose new filtering techniques by exploiting the token ordering information; they are integrated into the existing methods and drastically reduce the candidate sizes and hence improve the efficiency. We have also studied the implementation of our proposed algorithm in stand-alone and RDBMS-based settings. Experimental results show our proposed algorithms can outperform previous algorithms on several real datasets.


title: Hyperbolically Discounted Temporal Difference Learning

abstract: Hyperbolic discounting of future outcomes is widely observed to underlie choice behavior in animals. Additionally, recent studies (Kobayashi & Schultz, 2008) have reported that hyperbolic discounting is observed even in neural systems underlying choice. However, the most prevalent models of temporal discounting, such as temporal difference learning, assume that future outcomes are discounted exponentially. Exponential discounting has been preferred largely because it can be expressed recursively, whereas hyperbolic discounting has heretofore been thought not to have a recursive definition. In this letter, we define a learning algorithm, hyperbolically discounted temporal difference (HDTD) learning, which constitutes a recursive formulation of the hyperbolic model.


title: On Erasure Coding for Distributed Storage and Streaming Communications

abstract: The work presented in this thesis revolves around erasure correction coding, as applied to distributed data storage and real-time streaming communications. First, we examine the problem of allocating a given storage budget over a set of nodes for maximum reliability. ::: The objective is to find an allocation of the budget that maximizes the probability of successful recovery by a data collector accessing a random subset of the nodes. ::: This optimization problem is challenging in general because of its combinatorial nature, despite its simple formulation. ::: We study several variations of the problem, assuming different allocation models and access models, and determine the optimal allocation and the optimal symmetric allocation (in which all nonempty nodes store the same amount of data) for a variety of cases. ::: Although the optimal allocation can have nonintuitive structure and can be difficult to find in general, our results suggest that, as a simple heuristic, reliable storage can be achieved by spreading the budget maximally over all nodes when the budget is large, and spreading it minimally over a few nodes when it is small. ::: Coding would therefore be beneficial in the former case, while uncoded replication would suffice in the latter case. Second, we study how distributed storage allocations affect the recovery delay in a mobile setting. ::: Specifically, two recovery delay optimization problems are considered for a network of mobile storage nodes: ::: the maximization of the probability of successful recovery by a given deadline, and the minimization of the expected recovery delay. ::: We show that the first problem is closely related to the earlier allocation problem, and solve the second problem completely for the case of symmetric allocations. ::: It turns out that the optimal allocations for the two problems can be quite different. ::: In a simulation study, we evaluated the performance of a simple data dissemination and storage protocol for mobile delay-tolerant networks, and observed that the choice of allocation can have a significant impact on the recovery delay under a variety of scenarios. Third, we consider a real-time streaming system where messages created at regular time intervals at a source are encoded for transmission to a receiver over a packet erasure link; ::: the receiver must subsequently decode each message within a given delay from its creation time. ::: For erasure models containing a limited number of erasures per coding window, per sliding window, and containing erasure bursts whose maximum length is sufficiently short or long, we show that a time-invariant intrasession code asymptotically achieves the maximum message size among all codes that allow decoding under all admissible erasure patterns. ::: For the bursty erasure model, we also show that diagonally interleaved codes derived from specific systematic block codes are asymptotically optimal over all codes in certain cases. ::: We also study an i.i.d. erasure model in which each transmitted packet is erased independently with the same probability; ::: the objective is to maximize the decoding probability for a given message size. ::: We derive an upper bound on the decoding probability for any time-invariant code, and show that the gap between this bound and the performance of a family of time-invariant intrasession codes is small when the message size and packet erasure probability are small. ::: In a simulation study, these codes performed well against a family of random time-invariant convolutional codes under a number of scenarios. Finally, we consider the joint problems of routing and caching for named data networking. ::: We propose a backpressure-based policy that employs virtual interest packets to make routing and caching decisions. ::: In a packet-level simulation, the proposed policy outperformed a basic protocol that combines shortest-path routing with least-recently-used (LRU) cache replacement.


title: Leakage-resilient identity-based encryption scheme

abstract: We present an identity-based encryption in the relative-leakage model. In our construction, we firstly make use of a notion of new cryptographic primitive, identity-based hash proof system(IB-HPS), to construct a leakage-resilient IB-HPS based on a simple assumption and also prove its security by the variant of dual system encryption. Then, based on our IB-HPS scheme, we construct directly an efficient leakage-resilient identity-based encryption(IBE) scheme in the model.


title: Wrist-worn heartbeat monitoring system based on bio-impedance analysis

abstract: In this paper, a bio-impedance analysis (BIA) based wrist-worn heartbeat monitoring system is proposed. The system is able to estimate heart rate from a subject's wrist with only four electrodes. The design is achieved with a standard BIA device and off-the-shelf components for signal conditioning. The measured heartbeat-related impedance signal is compared with a reference heart rate signal obtained from piezoelectric finger pulse transducer. The BIA results agree with the reference, which validates the feasibility of the proposed system. To the best of our knowledge, this is the first reported BIA heartbeat monitoring system in the wristband configuration.


title: TraceBoK: Toward a Software Requirements Traceability Body of Knowledge

abstract: This paper introduces the idea of building a body of knowledge (BoK) to gather the better practices in software requirements traceability that could bring major benefits for analyzing, managing, and implementing software changes impact. The implementation of traceability in the organizations is still a challenge even many studies have been conducted on the subject. The aim of this work is to evaluate traceability approaches focusing on software product traceability by a systematic review. We conducted a series of interviews with practitioners to complement the evaluation. Based on this study, we categorize the approaches, and we propose a body of knowledge (Bok) on traceability requirements TraceBoK. This BoK intends to assist software engineers to decide how to define an implementation of traceability in the development and maintenance of software products. Experts in traceability have evaluated this BoK, and we notice that it meets practitioners needs. However, some improvements must be done in the future versions, and we have made TraceBoK available at tracebok.org. We hope that the proposed BoK helps practitioners to apply better and understand traceability in both academic and industry areas.


title: The underlying theory of project management is obsolete

abstract: In prior literature, it has been generally seen that there is no explicit theory of project management. We contend that it is possible to precisely point out the underlying theoretical foundation of project management as espoused in the PMBOK Guide by PMI and mostly applied in practice. This foundation can be divided into a theory of project and a theory of management. We link theories to the body of ::: knowledge by comparing prescriptions derived from theory to prescriptions presented in the PMBOK. Secondly, we show, by a comparison to competing theories and by an analysis of anomalies (deviations from assumptions or outcomes as implied in the body of knowledge) observed in project management practice, that this foundation is obsolete and has to be substituted by a wider and more powerful theoretical foundation.


title: Sensorless control strategy for salient-pole PMSM based on extended EMF in rotating reference frame

abstract: This paper presents a novel sensorless control strategy for a salient-pole permanent-magnet synchronous motor (PMSM). A new model of a salient-pole PMSM using an extended electromotive force (EMF) in the rotating reference frame is utilized to estimate both position and speed. The extended EMF is estimated by a least-order observer, and the estimation position error is obtained from the extended EMF. Both estimated position and speed are corrected so that the position error becomes zero. The proposed system is very simple and the design procedure is easy and clear. Several experimental drive tests are demonstrated and the experimental results show the effectiveness of the proposed sensorless control system.


title: Novel Transformerless Grid-Connected Power Converter With Negative Grounding for Photovoltaic Generation System

abstract: This paper proposes a novel transformerless grid-connected power converter with negative grounding for a photovoltaic generation system. The negative terminal of the solar cell array can be directly connected to the ground in the proposed grid-connected power converter to avoid the transparent conducting oxide corrosion that occurs in some types of thin-film solar cell array. The proposed grid-connected power converter consists of a dc-dc power converter and a dc-ac inverter. The salient features of the proposed power converter are that some power electronic switches are simultaneously used in both the dc-dc power converter and dc-ac inverter, and only two power electronic switches operate at high switching frequency at the same time (one is in the dc-dc power converter and the other is in the dc-ac inverter). The leakage current of the photovoltaic generation system is reduced because the negative terminal of the solar cell array is connected directly to the ground. Finally, a prototype was developed to verify the performance of the proposed grid-connected power converter. The experimental results show that the performance of the proposed grid-connected power converter is as expected.


title: Weighted Joint Bilateral Filter with Slope Depth Compensation Filter for Depth Map Refinement

abstract: In this paper, we propose a new refinement filter for depth maps. The filter convolutes a depth map by a jointly computed kernel on a natural image with a weight map. We call the filter weighted joint bilateral filter. The filter fits an outline of an object in the depth map to the outline of the object in the natural image, and it reduces noises. An additional filter of slope depth compensation filter removes blur across object boundary. The filter set’s computational cost is low and is independent of depth ranges. Thus we can refine depth maps to generate accurate depth map with lower cost. In addition, we can apply the filters for various types of depth map, such as computed by simple block matching, Markov random field based optimization, and Depth sensors. Experimental results show that the proposed filter has the best performance of improvement of depth map accuracy, and the proposed filter can perform real-time refinement.


title: Voltage-Loop Power-Stage Transfer Functions With MOSFET Delay for Boost PWM Converter Operating in CCM

abstract: Open-loop transfer functions can be used to create closed-loop models of pulsewidth-modulated (PWM) converters. The closed-loop small-signal model can be used to design a controller for the switching converter with well-known linear control theory. The dynamics of the power stage for boost PWM dc-dc converter operating in continuous-conduction mode (CCM) are studied. The transfer functions from output current to output voltage, from duty cycle to output voltage including MOSFET delay, and from input voltage to output voltage are derived. The derivations are performed using an averaged linear circuit small-signal model of the boost converter for CCM. Experimental Bode plots and step responses were used to test the accuracy of the derived transfer functions. The theoretical and experimental responses were in excellent agreement, confirming the validity of the derived transfer functions


title: Single-Molecule SERS Spectroscopy

abstract: It has long been the dream of chemists to examine a single molecule and monitor its structural changes. The observation of a single molecule and its individual properties and structural transformations can provide useful insight into the nature of processes that cannot be studied in an ensemble of molecules due to averaging. Moreover, high-throughput structurally selective detection of single molecules and quantification of matter by counting single molecules represent the ultimate limit in chemical analysis and trace detection. Today, single-molecule studies are a topic of rapidly growing scientific and practical interest in many fields, such as chemistry, physics, life sciences, and nanotechnology [1, 2, 3]. Photons have been identified as nearly perfect tools for detecting and studying single molecules. For example, the first single-molecule detection under ambient conditions was achieved using laser-induced fluorescence [4]. However, particularly under ambient conditions, there is a limited amount of molecular information that can be obtained from a fluorescence signal. Raman spectroscopy as a vibrational technique provides a high degree of structural information but the extremely small cross section of the effect, typically ∼ 10−30 cm to 10−25 cm, with the larger values occurring only under favorable resonance Raman conditions, precludes the use of Raman spectroscopy as a method for single-molecule detection. For example, the number of Stokes photons can be estimated as the product of Raman cross section and excitation intensity: Assuming a Raman line with a scattering cross section of 10−29 cm and 100mW excitation light focused to 1μm, a single molecule scatters ∼ 10−4 photons per second, which means that one must wait more than an hour for a Stokes photon from a single molecule. In comparison, fluorescence cross sections are ∼ 10−16 cm. Estimated enhancement factors for the Raman signals in SERS started from modest factors of 10 to 10 in the initial SERS experiments [5, 6]. For excitation laser wavelengths in resonance with the absorption band of the target molecule, surface-enhanced resonance Raman scattering (SERRS)


title: Subpixel rendering: from font rendering to image subsampling [Applications Corner]

abstract: Subpixel rendering technologies take advantage of the subpixel structure of a display to increase the apparent resolution and to improve the display quality of text, graphics, or images. These techniques can potentially improve the apparent resolution because a single pixel on color liquid crystal display (LCD) or organic light-emitting diode (OLED) displays consists of several independently controllable colored subpixels. Applications of subpixel rendering are font rendering and image/video subsampling. By controlling individual subpixel values of neighboring pixels, it is possible to microshift the apparent position of a line to give greater details of text. Similarly, since the individual selectable components are increased threefold by controlling subpixels rather than pixels, subpixel-based subsampling can potentially improve the apparent resolution of a down-scaled image. However, the increased apparent luminance resolution often comes at the price of color fringing artifacts. A major challenge is to suppress chrominance distortion while maintaining apparent luminance sharpness. This column introduces subpixel arrangement in color displays, how subpixel rendering works, and several practical subpixel rendering applications in font rendering and image subsampling.


title: Towards Accurate Distant Supervision for Relational Facts Extraction

abstract: Distant supervision (DS) is an appealing learning method which learns from existing relational facts to extract more from a text corpus. However, the accuracy is still not satisfying. In this paper, we point out and analyze some critical factors in DS which have great impact on accuracy, including valid entity type detection, negative training examples construction and ensembles. We propose an approach to handle these factors. By experimenting on Wikipedia articles to extract the facts in Freebase (the top 92 relations), we show the impact of these three factors on the accuracy of DS and the remarkable improvement led by the proposed approach.


title: Does the use of social networking sites increase children's risk of harm?

abstract: Although research findings have been equivocal as to whether the use of social networking sites (SNSs) increases experiences of online risk among children, the affordances of SNS lend support to this possibility, attracting much policy and public concern. The present article examines whether the use of such services increases the risks that children and young people encounter by analyzing data from a random stratified sample of approximately 1000 internet-using children aged 9-16 years in each of 25 European countries. Four hypotheses were formulated and tested. The first hypothesis, namely that children who use social networking sites will encounter more risks online than those who do not, is supported by the data. The second hypothesis stated that SNS users with more digital competence will encounter more online risk than those with less competence; this was also supported, despite being counter to common assumptions. Thirdly, we hypothesized that SNS users with more risky SNS practices (e.g. a public profile, displaying identifying information, with a very large number of contacts) will encounter more online risk than those with fewer risky practices: this too was supported by the data; thus what matters for risk is how SNS are used, a useful point for awareness-raising initiatives. The fourth hypothesis stated that SNS users with more digital competence in using the internet will experience less harm associated with online risk. The data did not support this hypothesis, since digital competence did not reduce the probability of children saying that they have been bothered or upset by something on the internet. Finally, the study found that, although this had not been predicted, whether or not risks are experienced as harmful depends on the specific relation between risks and platforms (website, instant messaging, gaming or social networking). We call on future research to explore how particular affordances sustain particular communicative conditions and, in turn, are responded to differently by children. The research and policy implications of the findings are discussed.


title: Adaptive Path Planning for Depth Constrained Bathymetric Mapping with an Autonomous Surface Vessel

abstract: This paper describes the design, implementation and testing of a suite of algorithms to enable depth constrained autonomous bathymetric (underwater topography) mapping by an Autonomous Surface Vessel (ASV). Given a target depth and a bounding polygon, the ASV will find and follow the intersection of the bounding polygon and the depth contour as modeled online with a Gaussian Process (GP). This intersection, once mapped, will then be used as a boundary within which a path will be planned for coverage to build a map of the Bathymetry. Methods for sequential updates to GP's are described allowing online fitting, prediction and hyper-parameter optimisation on a small embedded PC. New algorithms are introduced for the partitioning of convex polygons to allow efficient path planning for coverage. These algorithms are tested both in simulation and in the field with a small twin hull differential thrust vessel built for the task.


title: MATLAB script for analyzing and visualizing scanline data

abstract: Scanline surveys consist of directional and qualitative measurements of rock discontinuities. These surveys are used in geologic and engineering investigations of fractured rock masses. This paper introduces a new MATLAB script developed for visualizing results from scanline surveys as traces in 2D and disks in 3D. The script is also able to cluster orientation data and to present statistical summaries and to reflect the change in degree of rock brokenness along the scanline. Advantages of this new script are that it can present undulating discontinuities as wavy surfaces and different discontinuity properties using color codes. An intensity rose diagram is utilized to visualize interdependency of certain properties and orientation. This new script has a potential for preprocessing vast amounts of scanline and oriented drill core logging data before using it in 3D discontinuity network modeling. The script is demonstrated using data concerning rock fracturing gathered from a dimension stone quarry in Southern Finland.


title: Pavlov's dog associative learning demonstrated on synaptic-like organic transistors

abstract: In this letter, we present an original demonstration of an associative learning neural network inspired by the famous Pavlov's dogs experiment. A single nanoparticle organic memory field effect transistor NOMFET is used to implement each synapse. We show how the physical properties of this dynamic memristive device can be used to perform low-power write operations for the learning and implement short-term association using temporal coding and spike-timing-dependent plasticity-based learning. An electronic circuit was built to validate the proposed learning scheme with packaged devices, with good reproducibility despite the complex synaptic-like dynamic of the NOMFET in pulse regime.


Practical and mathematical aspects of the problem of reconstructing objects from radiographs


Ideological Zig-Zag: Official Explanations for the Great Terror, 1936–1938


title: SuggestBot: using intelligent task routing to help people find work in wikipedia

abstract: Member-maintained communities ask their users to perform tasks the community needs. From Slashdot, to IMDb, to Wikipedia, groups with diverse interests create community-maintained artifacts of lasting value (CALV) that support the group's main purpose and provide value to others. Said communities don't help members find work to do, or do so without regard to individual preferences, such as Slashdot assigning meta-moderation randomly. Yet social science theory suggests that reducing the cost and increasing the personal value of contribution would motivate members to participate more.We present SuggestBot, software that performs intelligent task routing (matching people with tasks) in Wikipedia. SuggestBot uses broadly applicable strategies of text analysis, collaborative filtering, and hyperlink following to recommend tasks. SuggestBot's intelligent task routing increases the number of edits by roughly four times compared to suggesting random articles. Our contributions are: 1) demonstrating the value of intelligent task routing in a real deployment; 2) showing how to do intelligent task routing; and 3) sharing our experience of deploying a tool in Wikipedia, which offered both challenges and opportunities for research.


title: Understanding optimal caching and opportunistic caching at "the edge" of information-centric networks

abstract: A formal framework is presented for the characterization of cache allocation models in Information-Centric Networks (ICN). The framework is used to compare the performance of optimal caching everywhere in an ICN with opportunistic caching of content only near its consumers. This comparison is made using the independent reference model adopted in all prior studies, as well as a new model that captures non-stationary reference locality in space and time. The results obtained analytically and from simulations show that optimal caching throughout an ICN and opportunistic caching at the edge routers of an ICN perform comparably the same. In addition caching content opportunistically only near its consumers is shown to outperform the traditional on-path caching approach assumed in most ICN architectures in an unstructured network with arbitrary topology represented as a random geometric graph.


title: Interactive Elicitation of Knowledge on Feature Relevance Improves Predictions in Small Data Sets

abstract: Providing accurate predictions is challenging for machine learning algorithms when the number of features is larger than the number of samples in the data. Prior knowledge can improve machine learning models by indicating relevant variables and parameter values. Yet, this prior knowledge is often tacit and only available from domain experts. We present a novel approach that uses interactive visualization to elicit the tacit prior knowledge and uses it to improve the accuracy of prediction models. The main component of our approach is a user model that models the domain expert's knowledge of the relevance of different features for a prediction task. In particular, based on the expert's earlier input, the user model guides the selection of the features on which to elicit user's knowledge next. The results of a controlled user study show that the user model significantly improves prior knowledge elicitation and prediction accuracy, when predicting the relative citation counts of scientific documents in a specific domain.


title: Engagement vs performance: using electronic portfolios to predict first semester engineering student retention

abstract: As providers of higher education begin to harness the power of big data analytics, one very fitting application for these new techniques is that of predicting student attrition. The ability to pinpoint students who might soon decide to drop out of a given academic program allows those in charge to not only understand the causes for this undesired outcome, but it also provides room for the development of early intervention systems. While making such inferences based on academic performance data alone is certainly possible, we claim that in many cases there is no substantial correlation between how well a student performs and his or her decision to withdraw. This is specially true when the overall set of students has a relatively similar academic performance. To address this issue, we derive measurements of engagement from students' electronic portfolios and show how these features can be effectively used to augment the quality of predictions.


title: Does the IEEE 802.11 MAC protocol work well in multihop wireless ad hoc networks

abstract: The IEEE 802.11 MAC protocol is the standard for wireless LANs; it is widely used in testbeds and simulations for wireless multihop ad hoc networks. However, this protocol was not designed for multihop networks. Although it can support some ad hoc network architecture, it is not intended to support the wireless mobile ad hoc network, in which multihop connectivity is one of the most prominent features. In this article we focus on the following question: can the IEEE 802.11 MAC protocol function well in multihop networks? By presenting several serious problems encountered in an IEEE 802.11-based multihop network and revealing the in-depth cause of these problems, we conclude that the current version of this wireless LAN protocol does not function well in multihop ad hoc networks. We thus doubt whether the WaveLAN-based system is workable as a mobile ad hoc testbed.


An Anti-spam Filter Combination Framework for Text-and-Image Emails through Incremental Learning


title: Cardinality Estimation: An Experimental Survey

abstract: Data preparation and data profiling comprise many both basic and complex tasks to analyze a dataset at hand and extract metadata, such as data distributions, key candidates, and functional dependencies. Among the most important types of metadata is the number of distinct values in a column, also known as the zeroth-frequency moment. Cardinality estimation itself has been an active research topic in the past decades due to its many applications. The aim of this paper is to review the literature of cardinality estimation and to present a detailed experimental study of twelve algorithms, scaling far beyond the original experiments. ::: ::: First, we outline and classify approaches to solve the problem of cardinality estimation - we describe their main idea, error-guarantees, advantages, and disadvantages. Our experimental survey then compares the performance all twelve cardinality estimation algorithms. We evaluate the algorithms' accuracy, runtime, and memory consumption using synthetic and real-world datasets. Our results show that different algorithms excel in different in categories, and we highlight their trade-offs.


title: Nature based solution for improving mental health and well‐being in urban areas

abstract: The general disproportion of urban development and the socio-economical crisis in Serbia, followed by a number of acute and chronic stressors, as well as years of accumulated trauma, prevented the parallel physical, mental and social adaptation of society as a whole. These trends certainly affected the quality of mental health and well-being, particularly on the vulnerable urban population, increasing the absolute number of people with depression, stress and psychosomatic disorders. This study was pioneering in Serbia and was conducted in collaboration with the Faculty of Forestry, the Institute of Mental Health and the Botanical Garden in Belgrade, in order to understand how spending time and performing horticulture therapy in specially designed urban green environments can improve mental health. The participants were psychiatric patients (n=30), users of the day hospital of the Institute who were randomly selected for the study, and the control group, assessed for depression, anxiety and stress before and after the intervention, using a DASS21 scale. During the intervention period the study group stayed in the Botanical garden and participated in a special programme of horticulture therapy. In order to exclude any possible "special treatment'' or ''placebo effect", the control group was included in occupational art therapy while it continued to receive conventional therapy. The test results indicated that nature based therapy had a positive influence on the mental health and well-being of the participants. Furthermore, the difference in the test results of the subscale stress before and after the intervention for the study group was F1.28 = 5.442 and p<;.05. According to socio demographic and clinical variables, the interesting trend was recorded on the subscale of anxiety showing that the male participants in the study group were more anxious, with the most pronounced inflection noted on this scale after treatment. The results of this study have shown that recuperation from stress, depression and anxiety was possible and much more complete when participants were involved in horticulture therapy as a nature-based solution for improving mental health.