title: Reactive high-level behavior synthesis for an Atlas humanoid robot

abstract: In this work, we take a step towards bridging the gap between the theory of formal synthesis and its application to real-world, complex, robotic systems. In particular, we present an end-to-end approach for the automatic generation of code that implements high-level robot behaviors in a verifiably correct manner, including reaction to the possible failures of low-level actions. We start with a description of the system defined a priori. Thus, a non-expert user need only specify a high-level task. We automatically construct a formal specification, in a fragment of Linear Temporal Logic (LTL), that encodes the system's capabilities and constraints, the task, and the desired reaction to low-level failures. We then synthesize a reactive mission plan that is guaranteed to satisfy the formal specification, i.e., achieve the task's goals or correctly react to failures. Lastly, we automatically generate a state machine that instantiates the synthesized symbolic plan in software. We showcase our approach using Team ViGIR's software and Atlas humanoid robot and present lab experiments, thus demonstrating the application of formal synthesis techniques to complex robotic systems. The proposed approach has been implemented and open-sourced as a collection of Robot Operating System (ROS) packages, which are adaptable to other systems.


title: Scene matching based visual SLAM navigation for small unmanned aerial vehicle

abstract: A scene matching based visual SLAM (simultaneous localization and mapping) navigation algorithm is proposed for SUAV (small unmanned aerial vehicle) which described by EKF (Extended Kalman Filtering). Firstly, a scene matching method with weighted Hausdorff distance was introduced for waypoints accurate abstraction. On this foundation, the SUAV's nonlinear state model was analyzed to establish nonlinear relationship model between the measurement and the waypoints, and then the state of the model was predicted and estimated to deal with data association and extend the state for new waypoints. Last, through continuously predicting and estimating, the algorithm located the SUAV accurately by visual information. Simulation results show that the proposed algorithm could effectively reduce the estimation error of navigation system and improves the positioning accuracy for SUAV.


title: The Research Object Suite of Ontologies: Sharing and Exchanging Research Data and Methods on the Open Web

abstract: Research in life sciences is increasingly being conducted in a digital and online environment. In particular, life scientists have been pioneers in embracing new computational tools to conduct their investigations. To support the sharing of digital objects produced during such research investigations, we have witnessed in the last few years the emergence of specialized repositories, e.g., DataVerse and FigShare. Such repositories provide users with the means to share and publish datasets that were used or generated in research investigations. While these repositories have proven their usefulness, interpreting and reusing evidence for most research results is a challenging task. Additional contextual descriptions are needed to understand how those results were generated and/or the circumstances under which they were concluded. Because of this, scientists are calling for models that go beyond the publication of datasets to systematically capture the life cycle of scientific investigations and provide a single entry point to access the information about the hypothesis investigated, the datasets used, the experiments carried out, the results of the experiments, the people involved in the research, etc. In this paper we present the Research Object (RO) suite of ontologies, which provide a structured container to encapsulate research data and methods along with essential metadata descriptions. Research Objects are portable units that enable the sharing, preservation, interpretation and reuse of research investigation results. The ontologies we present have been designed in the light of requirements that we gathered from life scientists. They have been built upon existing popular vocabularies to facilitate interoperability. Furthermore, we have developed tools to support the creation and sharing of Research Objects, thereby promoting and facilitating their adoption.


title: Using and Reporting the Delphi Method for Selecting Healthcare Quality Indicators: A Systematic Review

abstract: OBJECTIVE ::: Delphi technique is a structured process commonly used to developed healthcare quality indicators, but there is a little recommendation for researchers who wish to use it. This study aimed 1) to describe reporting of the Delphi method to develop quality indicators, 2) to discuss specific methodological skills for quality indicators selection 3) to give guidance about this practice. ::: ::: ::: METHODOLOGY AND MAIN FINDING ::: Three electronic data bases were searched over a 30 years period (1978-2009). All articles that used the Delphi method to select quality indicators were identified. A standardized data extraction form was developed. Four domains (questionnaire preparation, expert panel, progress of the survey and Delphi results) were assessed. Of 80 included studies, quality of reporting varied significantly between items (9% for year's number of experience of the experts to 98% for the type of Delphi used). Reporting of methodological aspects needed to evaluate the reliability of the survey was insufficient: only 39% (31/80) of studies reported response rates for all rounds, 60% (48/80) that feedback was given between rounds, 77% (62/80) the method used to achieve consensus and 57% (48/80) listed quality indicators selected at the end of the survey. A modified Delphi procedure was used in 49/78 (63%) with a physical meeting of the panel members, usually between Delphi rounds. Median number of panel members was 17(Q1:11; Q3:31). In 40/70 (57%) studies, the panel included multiple stakeholders, who were healthcare professionals in 95% (38/40) of cases. Among 75 studies describing criteria to select quality indicators, 28 (37%) used validity and 17(23%) feasibility. ::: ::: ::: CONCLUSION ::: The use and reporting of the Delphi method for quality indicators selection need to be improved. We provide some guidance to the investigators to improve the using and reporting of the method in future surveys.


title: Design and development of a flexible and elastic UWB wearable antenna on PDMS substrate

abstract: This paper proposed design and development of a flexible UWB wearable antenna using flexible and elastic polymer substrate. Polydimethylsiloxane (PDMS) was chosen to be used as flexible substrate for the proposed antenna which is a kind of silicone elastic, it has attractive mechanical and electrical properties such as flexibility, softness, water resistance low permittivity and transparency. The proposed antenna consists of a rectangular patch with two steps notches in the upper side of the patch, resulting in a more compact and increase in the bandwidth. In addition, the proposed antenna has an elliptical slot for an enhancement of the bandwidth and gain. The bottom side edges of the patch have been truncated to provide an additional surface current path. The proposed UWB wearable antenna functions from 2.5 GHz to 12.4 GHz frequency range and it was successfully designed and the simulated result showed that the return loss was maintained less than -10 dB and VSWR kept less than 2 over the entire desired frequency range (2.5 GHz – 12.4 GHz). The gain of the proposed antenna varies with frequency and the maximum gain recorded is 4.56 dB at 6.5 GHz. Simultaneously, The radiation patterns of the proposed antenna are also presented. The performance of the antenna under bending condition is comparable with the normal condition's performance.


title: Dynamic Cultural Influences on Neural Representations of the Self

abstract: People living in multicultural environments often encounter situations which require them to acquire different cultural schemas and to switch between these cultural schemas depending on their immediate sociocultural context. Prior behavioral studies show that priming cultural schemas reliably impacts mental processes and behavior underlying self-concept. However, less well understood is whether or not cultural priming affects neurobiological mechanisms underlying the self. Here we examined whether priming cultural values of individualism and collectivism in bicultural individuals affects neural activity in cortical midline structures underlying self-relevant processes using functional magnetic resonance imaging. Biculturals primed with individualistic values showed increased activation within medial prefrontal cortex (MPFC) and posterior cingulate cortex (PCC) during general relative to contextual self-judgments, whereas biculturals primed with collectivistic values showed increased response within MPFC and PCC during contextual relative to general self-judgments. Moreover, degree of cultural priming was positively correlated with degree of MPFC and PCC activity during culturally congruent self-judgments. These findings illustrate the dynamic influence of culture on neural representations underlying the self and, more broadly, suggest a neurobiological basis by which people acculturate to novel environments.


title: Dynamic objects detection through visual odometry and stereo-vision: a study of inaccuracy and improvement sources

abstract: Road safety, whatever the considered environment, relies heavily on the ability to detect and track moving objects from a moving point of view. In order to achieve such a detection, the vehicle's ego-motion must first be estimated and compensated. This issue is crucial to complete a fully autonomous vehicle; this is why several approaches have already been proposed. This study presents a method, based solely on visual information that implements such a process. Information from stereo-vision and motion is derived to extract the vehicle's ego-motion. Ego-motion extraction algorithm is thoroughly evaluated in terms of precision and uncertainty. Given those statistical attributes, a method for dynamic objects detection is presented. This method relies on 3D image registration and residual displacement field evaluation. This method is then evaluated on several real and synthetic data sequences. It will be shown that it allows a reliable and early detection, even in hard cases (e.g. occlusions,...). Given a few additional factors (detectable motion range), overall performances can be derived from visual odometry performances.


title: 25 years of time series forecasting

abstract: Abstract We review the past 25 years of research into time series forecasting. In this silver jubilee issue, we naturally highlight results published in journals managed by the International Institute of Forecasters ( Journal of Forecasting 1982–1985 and International Journal of Forecasting 1985–2005). During this period, over one third of all papers published in these journals concerned time series forecasting. We also review highly influential works on time series forecasting that have been published elsewhere during this period. Enormous progress has been made in many areas, but we find that there are a large number of topics in need of further development. We conclude with comments on possible future research directions in this field.


An analysis of data quality dimensions


title: On the Behaviour of Deviant Communities in Online Social Networks

abstract: On-line social networks are complex ensembles of inter-linked communities that interact on different topics. Some communities are characterized by what are usually referred to as deviant behaviors, conducts that are commonly considered inappropriate with respect to the society's norms or moral standards. Eating disorders, drug use, and adult content consumption are just a few examples. We refer to such communities as deviant networks. It is commonly believed that such deviant networks are niche, isolated social groups, whose activity is well separated from the mainstream social-media life. According to this assumption, research studies have mostly considered them in isolation. In this work we focused on adult content consumption networks, which are present in many on-line social media and in the Web in general. We found that few small and densely connected communities are responsible for most of the content production. Differently from previous work, we studied how such communities interact with the whole social network. We found that the produced content flows to the rest of the network mostly directly or through bridge-communities, reaching at least 450 times more users. We also show that a large fraction of the users can be inadvertently exposed to such content through indirect content resharing. We also discuss a demographic analysis of the producers and consumers networks. Finally, we show that it is easily possible to identify a few core users to radically uproot the diffusion process. We aim at setting the basis to study deviant communities in context.


title: ECG-based heartbeat classification for arrhythmia detection

abstract: HighlightsSurveys the feature description methods, and the learning algorithms employed.Also surveys the ECG signal preprocessing and the heartbeat segmentation techniques.Description of databases used for methods evaluation indicated by the AAMI standard.Discussion of limitations and drawbacks of the methods in the literature.Concluding remarks and future challenges are also pointed out. An electrocardiogram (ECG) measures the electric activity of the heart and has been widely used for detecting heart diseases due to its simplicity and non-invasive nature. By analyzing the electrical signal of each heartbeat, i.e., the combination of action impulse waveforms produced by different specialized cardiac tissues found in the heart, it is possible to detect some of its abnormalities. In the last decades, several works were developed to produce automatic ECG-based heartbeat classification methods. In this work, we survey the current state-of-the-art methods of ECG-based automated abnormalities heartbeat classification by presenting the ECG signal preprocessing, the heartbeat segmentation techniques, the feature description methods and the learning algorithms used. In addition, we describe some of the databases used for evaluation of methods indicated by a well-known standard developed by the Association for the Advancement of Medical Instrumentation (AAMI) and described in ANSI/AAMI EC57:1998/(R)2008 (ANSI/AAMI, 2008). Finally, we discuss limitations and drawbacks of the methods in the literature presenting concluding remarks and future challenges, and also we propose an evaluation process workflow to guide authors in future works.


title: Chat Detection in an Intelligent Assistant: Combining Task-oriented and Non-task-oriented Spoken Dialogue Systems

abstract: Recently emerged intelligent assistants on smartphones and home electronics (e.g., Siri and Alexa) can be seen as novel hybrids of domain-specific task-oriented spoken dialogue systems and open-domain non-task-oriented ones. To realize such hybrid dialogue systems, this paper investigates determining whether or not a user is going to have a chat with the system. To address the lack of benchmark datasets for this task, we construct a new dataset consisting of 15; 160 utterances collected from the real log data of a commercial intelligent assistant (and will release the dataset to facilitate future research activity). In addition, we investigate using tweets and Web search queries for handling open-domain user utterances, which characterize the task of chat detection. Experiments demonstrated that, while simple supervised methods are effective, the use of the tweets and search queries further improves the F1-score from 86.21 to 87.53.


title: A Java Based Architecture of P2P-Grid Middleware

abstract: During the last decade there has been a huge interest in Grid technologies, and numerous Grid projects have been initiated with various visions of the Grid. While all these visions have the same goal of resource sharing, they differ in the functionality that a Grid supports, characterization, programming environments, etc. We present a new Grid system dedicated to dealing with data issues, called DGET (Data Grid Environment and Tools). DGET is characterized by its peerto- peer communication system and entity-based architecture, therefore, taking advantage of the main functionality of both systems; P2P and Grid. DGET is currently under development and a prototype implementing the main components is in its first phase of testing. In this paper we limit our description to the system architectural features and to the main differences with other systems. Keywords: Grid Computing, Peer to Peer, Peer to Peer Grid


title: Gaze point estimation on curved display by using session level calibration for flat screen displays

abstract: The advantages of curved screen displays find their place in populations everyday life, therefore it is important to adapt the existing eye tracking systems for estimating the gaze point on a curved screen. In this paper we present a curved screen based gaze point estimation model. This model provides methods for estimating the gaze point position by eye rotation angles, and also transforms flat screen based relative coordinates into curved screen based relative coordinates. The model was validated by using a flat screen based eye tracking system for a gaze point estimation on a curved screen. The eye rotation angle, distance to the screen and gaze point location’s influence were modelled in this paper as well. The results of these experiments prove the up to 4% increase of gaze point estimation accuracy by using the proposed model, by comparing the flat screen mode with the curved screen. However the results found are significant only if the distance to the screen is less than 0.5 of curved screen radius.


title: Mining Temporal Patterns in Time Interval-Based Data

abstract: Sequential pattern mining is an important subfield in data mining. Recently, applications using time interval-based event data have attracted considerable efforts in discovering patterns from events that persist for some duration. Since the relationship between two intervals is intrinsically complex, how to effectively and efficiently mine interval-based sequences is a challenging issue. In this paper, two novel representations, endpoint representation and endtime representation, are proposed to simplify the processing of complex relationships among event intervals. Based on the proposed representations, three types of interval-based patterns: temporal pattern, occurrence-probabilistic temporal pattern, and duration-probabilistic temporal pattern, are defined. In addition, we develop two novel algorithms, Temporal Pattern Miner (TPMiner) and Probabilistic Temporal Pattern Miner (P-TPMiner), to discover three types of interval-based sequential patterns. We also propose three pruning techniques to further reduce the search space of the mining process. Experimental studies show that both algorithms are able to find three types of patterns efficiently. Furthermore, we apply proposed algorithms to real datasets to demonstrate the effectiveness and validate the practicability of proposed patterns.


OPTIMAL PLACEMENT OF PHASOR MEASUREMENT UNITS FOR POWER SYSTEM OBSERVABILITY


title: Project Governance, Project Performance, and the Mediating Role of Project Quality and Project Management Risk: An Agency Theory Perspective

abstract: Although project governance has garnered the attention of academicians, the area remains underdeveloped. This study measures the influence of project governance on project performance directly as well as through the mediation of project management risk and project quality and with moderation of project leadership. The survey yielded 354 responses from 132 Pakistani software firms, while SEM was applied using AMOS-21 for data analysis. This study finds that project governance has a significant positive influence on project performance directly and through mediation, whereas the moderation is insignificant. The findings will help not only Pakistan’s software firms but also those in developing countries to improve their projects’ performance through effective project governance.


title: On the expressive power of deep architectures

abstract: Deep architectures are families of functions corresponding to deep circuits. Deep Learning algorithms are based on parametrizing such circuits and tuning their parameters so as to approximately optimize some training objective. Whereas it was thought too difficult to train deep architectures, several successful algorithms have been proposed in recent years. We review some of the theoretical motivations for deep architectures, as well as some of their practical successes, and propose directions of investigations to address some of the remaining challenges.


title: Ranking with Large Margin Principle: Two Approaches

abstract: We discuss the problem of ranking k instances with the use of a "large margin" principle. We introduce two main approaches: the first is the "fixed margin" policy in which the margin of the closest neighboring classes is being maximized — which turns out to be a direct generalization of SVM to ranking learning. The second approach allows for k - 1 different margins where the sum of margins is maximized. This approach is shown to reduce to v-SVM when the number of classes k - 2. Both approaches are optimal in size of 2l where l is the total number of training examples. Experiments performed on visual classification and "collaborative filtering" show that both approaches outperform existing ordinal regression algorithms applied for ranking and multi-class SVM applied to general multi-class classification.


title: Smart Contracts Using Blockly: Representing a Purchase Agreement Using a Graphical Programming Language

abstract: This research addresses the issue that in-depth programming knowhow is needed to read and write smart contracts. The goal was making the creation of smart contracts accessible to non-computer experts by the use of a graphical programming language (Blockly). We used modularization to capture the complexity of legal contracts and developed a mapping process to transform the graphical representation to the smart contract programming language Solidity. We applied our approach to legal purchase agreements and proved the practicality of our solution and explored its limitations. A prototype was built to show the feasibility of our approach. Our industry partner challenged the prototype by applying it to the contract creation process. We consider our work as the first step towards an application of smart contracts in the non-IT world and outside the today's expert shaped ecosystem of blockchain specialists. Several continuative research questions have been derived from our finding and are listed at the end of this paper.


title: A survey on Data Analytic Platforms for Internet of Things

abstract: Due to wide availability of programmable, off-the-shelf sensors and advancements in Radio Frequency Identification Technologies as well as Wireless Sensor Networks, the Internet of Things (IoT) is making our lives smarter than ever before. The abundant growth in sensors triggers the spontaneous generation of IoT data at colossal scale which is referred to as big data. Processing, analyzing this data in a secure, timely and efficient manner has become the sine qua non for the successful working of IoT paradigm and thereby emanating the need of Data Analytic Platforms in IoT based smart systems. The data analytic platform performs analytics over massive data and provides crucial baseline for decision and planning in the IoT sphere. The fully matured cloud computing technology best fit the scenario of IoT and big data analytics offering powerful resources to carry out analytical and management tasks. The paper focuses on various big data analytic platforms for IoT in cloud computing domain.


title: A Fuzzy Ontology and SVM–Based Web Content Classification System

abstract: The volume of adult content on the world wide web is increasing rapidly. This makes an automatic detection of adult content a more challenging task, when eliminating access to ill-suited websites. Most pornographic webpage–filtering systems are based on n-gram, naive Bayes, K-nearest neighbor, and keyword-matching mechanisms, which do not provide perfect extraction of useful data from unstructured web content. These systems have no reasoning capability to intelligently filter web content to classify medical webpages from adult content webpages. In addition, it is easy for children to access pornographic webpages due to the freely available adult content on the Internet. It creates a problem for parents wishing to protect their children from such unsuitable content. To solve these problems, this paper presents a support vector machine (SVM) and fuzzy ontology–based semantic knowledge system to systematically filter web content and to identify and block access to pornography. The proposed system classifies URLs into adult URLs and medical URLs by using a blacklist of censored webpages to provide accuracy and speed. The proposed fuzzy ontology then extracts web content to find website type (adult content, normal, and medical) and block pornographic content. In order to examine the efficiency of the proposed system, fuzzy ontology, and intelligent tools are developed using Protege 5.1 and Java, respectively. Experimental analysis shows that the performance of the proposed system is efficient for automatically detecting and blocking adult content.


title: Extraction of correlated gene clusters from multiple genomic data by generalized kernel canonical correlation analysis

abstract: Motivation: Am ajor issue in computational biology is the reconstruction of pathways from several genomic datasets, such as expression data, protein interaction data and phylogenetic profiles. As a first step toward this goal, it is important to investigate the amount of correlation which exists between these data. Method: We present new methods to measure the correlation between several heterogeneous datasets, and to extract sets of genes which share similarities with respect to multiple biological attributes. The originality of our approach is the extension of the concept of correlation for non-vectorial data, which is made possible by the use of generalized kernel canonical correlation analysis (KCCA), and the method we propose to extract groups of genes responsible for the detected correlations. Moreover, two variants of KCCA are proposed when more than two datasets are available. Result: These methods are successfully tested on their ability to recognize operons in the Escherichia coli genome, from the comparison of three datasets corresponding to functional relationships between genes in metabolic pathways, geometrical relationships along the chromosome, and co-expression relationships as observed by gene expression data. Contact: yoshi@kuicr.kyoto-u.ac.jp


title: Practical Lessons from Predicting Clicks on Ads at Facebook

abstract: Online advertising allows advertisers to only bid and pay for measurable user responses, such as clicks on ads. As a consequence, click prediction systems are central to most online advertising systems. With over 750 million daily active users and over 1 million active advertisers, predicting clicks on Facebook ads is a challenging machine learning task. In this paper we introduce a model which combines decision trees with logistic regression, outperforming either of these methods on its own by over 3%, an improvement with significant impact to the overall system performance. We then explore how a number of fundamental parameters impact the final prediction performance of our system. Not surprisingly, the most important thing is to have the right features: those capturing historical information about the user or ad dominate other types of features. Once we have the right features and the right model (decisions trees plus logistic regression), other factors play small roles (though even small improvements are important at scale). Picking the optimal handling for data freshness, learning rate schema and data sampling improve the model slightly, though much less than adding a high-value feature, or picking the right model to begin with.


title: Scalable serializable snapshot isolation for multicore systems

abstract: Since 1990's, Snapshot Isolation (SI) has been widely studied, and it was successfully deployed in commercial and open-source database engines. Berenson et al. showed that data consistency can be violated under SI. Recently, a new class of Serializable SI algorithms (SSI) has been proposed to achieve serializable execution while still allowing concurrency between reads and updates.


title: Neuro-Fuzzy and Soft Computing-A Computational Approach to Learning and Machine Intelligence [Book Review]

abstract: Included in Prentice Hall's MATLAB Curriculum Series, this text provides a comprehensive treatment of the methodologies underlying neuro-fuzzy and soft computing. The book places equal emphasis on theoretical aspects of covered methodologies, empirical observations, and verifications of various applications in practice.


title: Hierarchical Fog-Cloud Computing for IoT Systems: A Computation Offloading Game

abstract: Fog computing, which provides low-latency computing services at the network edge, is an enabler for the emerging Internet of Things (IoT) systems. In this paper, we study the allocation of fog computing resources to the IoT users in a hierarchical computing paradigm including fog and remote cloud computing services. We formulate a computation offloading game to model the competition between IoT users and allocate the limited processing power of fog nodes efficiently. Each user aims to maximize its own quality of experience (QoE), which reflects its satisfaction of using computing services in terms of the reduction in computation energy and delay. Utilizing a potential game approach, we prove the existence of a pure Nash equilibrium and provide an upper bound for the price of anarchy. Since the time complexity to reach the equilibrium increases exponentially in the number of users, we further propose a near-optimal resource allocation mechanism and prove that in a system with $N$ IoT users, it can achieve an $\epsilon$-Nash equilibrium in $O(N/\epsilon)$ time. Through numerical studies, we evaluate the users' QoE as well as the equilibrium efficiency. Our results reveal that by utilizing the proposed mechanism, more users benefit from computing services in comparison to an existing offloading mechanism. We further show that our proposed mechanism significantly reduces the computation delay and enables low-latency fog computing services for delay-sensitive IoT applications.


title: Wodel: a domain-specific language for model mutation

abstract: Model-Driven Engineering (MDE) is a software engineering paradigm that uses models as main assets in all development phases. While many languages for model manipulation exist (e.g., for model transformation or code generation), there is a lack of frameworks to define and apply model mutations. A model mutant is a variation of an original model, created by specific model mutation operations. Model mutation has many applications, for instance, in the areas of model transformation testing, model-based testing or education. In this paper, we present a domain-specific language, called W odel , for the specification and generation of model mutants. W odel is domain-independent, as it can be used to generate mutants of models conforming to arbitrary metamodels. Its development environment is extensible, permitting the incorporation of post-processors for different applications. As an example, we show an application consisting on the automated generation of exercises for particular domains (automata, class diagrams, electronic circuits, etc.).


title: 3D modeling, distance and gradient computation for motion planning: A direct GPGPU approach

abstract: The Kinect sensor and KinectFusion algorithm have revolutionized environment modeling. We bring these advances to optimization-based motion planning by computing the obstacle and self-collision avoidance objective functions and their gradients directly from the KinectFusion model on the GPU without ever transferring any model to the CPU. Based on this, we implement a proof-of-concept motion planner which we validate in an experiment with a 19-DOF humanoid robot using real data from a tabletop work space. The summed-up time from taking the first look at the scene until the planned path avoiding an obstacle on the table is executed is only three seconds.


Ach : IPC for Real-Time Robot Control


title: A personalized graph-based document ranking model using a semantic user profile

abstract: The overload of the information available on the web, held with the diversity of the user information needs and the ambiguity of their queries have led the researchers to develop personalized search tools that return only documents that meet the user profile representing his main interests and needs We present in this paper a personalized document ranking model based on an extended graph-based distance measure that exploits a semantic user profile derived from a predefined web ontology (ODP) The measure is based on combining Minimum Common Supergraph (MCS) and Maximum Common Subgraph (mcs) between graphs representing respectively the document and the user profile We extend this measure in order to take into account a semantic recovery between the document and the user profile through common concepts and cross links connecting the two graphs Results show the effectiveness of our personalized graph-based ranking model compared to Yahoo search results.


title: A new noise reduction method of speech signal with running spectrum filtering

abstract: We propose a new noise reduction method for speech signals using running spectrum filtering (RSF) and new musical noise reduction. RSF, which uses FIR filtering, is realized in the modulation frequency domain and it can eliminate noise components effectively using running spectra of speech. Since some musical noise happens on RSF, a new post processing is also introduced for musical noise reduction. The report shows that the proposed method is well applied for speech enhancement. It also indicates that segmental SNR is improved and audibility evaluation shows a higher score than conventional processing.


title: Proposing a new term weighting scheme for text categorization

abstract: In text categorization, term weighting methods assign appropriate weights to the terms to improve the classification performance. In this study, we propose an effective term weighting scheme, i.e. tf.rf, and investigate several widely-used unsupervised and supervised term weighting methods on two popular data collections in combination with SVM and kNN algorithms. From our controlled experimental results, not all supervised term weighting methods have a consistent superiority over unsupervised term weighting methods. Specifically, the three supervised methods based on the information theory, i.e. tf.χ2, tf.ig and tf.or, perform rather poorly in all experiments. On the other hand, our proposed tf.rf achieves the best performance consistently and outperforms other methods substantially and significantly. The popularly-used tf.idf method has not shown a uniformly good performance with respect to different data corpora.


title: Facebook and YouTube addiction: The usage pattern of Malaysian students

abstract: This paper explores the usage of Facebook and YouTube among Malaysian students and the possibility of internet addiction in order to determine the effect of using social media in their social and academic lives. Data was collected from 667 Facebook users and 1056 YouTube users. Examining Young's [1]Internet addiction scale among the students revealed that 18% of Facebook users and 22% of YouTube users are addicted, and they spend more than two hours on Facebook and YouTube per day. They use Facebook for information, maintain relationships, academic learning, product inquiry, and meeting people, while YouTube is used for entertainment, information, academic learning, and product inquiry. These results create awareness for instructors and academic institution using YouTube videos and Facebook as complementary tools for teaching. They should be aware of the potential for compulsive and addicted users to be distracted from prescribed videos to unrelated materials.


title: Gait asymmetry in patients with Parkinson’s disease and elderly fallers: when does the bilateral coordination of gait require attention?

abstract: While it is known that certain pathologies may impact on left–right symmetry of gait, little is known about the mechanisms that contribute to gait symmetry or how high in the hierarchy of the control of gait symmetry is regulated in humans. To assess the contribution of cognitive function to gait symmetry, we measured gait asymmetry (GA) in three subject groups, patients with Parkinson’s disease (PD, n = 21), idiopathic elderly fallers (n = 15), and healthy elderly controls (n = 11). All subjects walked under two walking conditions: usual walking and dual tasking (cognitive loading) condition. For each subject, the swing time (SW) was calculated and averaged across strides for the left and right feet (SWL and SWR). GA was defined as: \( 100 \times {\left| {\ln ({\text{SWR}}/{\text{SWL}})} \right|.} \) For both the PD patients and the elderly fallers GA values were significantly higher during the usual walking condition, as compared with the control group (P < 0.01). In addition, for both the PD patients and the elderly fallers, GA significantly increased when they walked and performed a dual task, compared with the usual walking condition (P < 0.003). In contrast, dual tasking did not affect the GA of the healthy controls (P = 0.518). GA was associated with gait speed and gait variability, but no correlations were found between GA and the asymmetry of the classic PD motor symptoms. Thus, the results suggest that the ability to generate a steady, rhythmic walk with a bilaterally coordinated gait does not rely heavily on mental attention and cognitive resources in healthy older adults. In contrast, however, when gait becomes impaired and less automatic, GA apparently relies on cognitive input and attention.


title: Rookie: A unique approach for exploring news archives

abstract: News archives are an invaluable primary source for placing current events in historical context. But current search engine tools do a poor job at uncovering broad themes and narratives across documents. We present Rookie: a practical software system which uses natural language processing (NLP) to help readers, reporters and editors uncover broad stories in news archives. Unlike prior work, Rookie's design emerged from 18 months of iterative development in consultation with editors and computational journalists. This process lead to a dramatically different approach from previous academic systems with similar goals. Our efforts offer a generalizable case study for others building real-world journalism software using NLP.


title: Antimicrobial Activity of Some Essential Oils—Present Status and Future Perspectives

abstract: Extensive documentation on the antimicrobial properties of essential oils and their constituents has been carried out by several workers. Although the mechanism of action of a few essential oil components has been elucidated in many pioneering works in the past, detailed knowledge of most of the compounds and their mechanism of action is still lacking. This knowledge is particularly important for the determination of the effect of essential oils on different microorganisms, how they work in combination with other antimicrobial compounds, and their interaction with food matrix components. Also, recent studies have demonstrated that nanoparticles (NPs) functionalized with essential oils have significant antimicrobial potential against multidrug- resistant pathogens due to an increase in chemical stability and solubility, decreased rapid evaporation and minimized degradation of active essential oil components. The application of encapsulated essential oils also supports their controlled and sustained release, which enhances their bioavailability and efficacy against multidrug-resistant pathogens. In the recent years, due to increasingly negative consumer perceptions of synthetic preservatives, interest in essential oils and their application in food preservation has been amplified. Moreover, the development of resistance to different antimicrobial agents by bacteria, fungi, viruses, parasites, etc. is a great challenge to the medical field for treating the infections caused by them, and hence, there is a pressing need to look for new and novel antimicrobials. To overcome these problems, nano-encapsulation of essential oils and exploiting the synergies between essential oils, constituents of essential oils, and antibiotics along with essential oils have been recommended as an answer to this problem. However, less is known about the interactions that lead to additive, synergistic, or antagonistic effects. A contributing role of this knowledge could be the design of new and more potent antimicrobial blends, and understanding of the interplay between the components of crude essential oils. This review is written with the purpose of giving an overview of current knowledge about the antimicrobial properties of essential oils and their mechanisms of action, components of essential oils, nano-encapsulated essential oils, and synergistic combinations of essential oils so as to find research areas that can facilitate applications of essential oils to overcome the problem of multidrug-resistant micro-organisms.


title: Fast Learning in Networks of Locally-Tuned Processing Units

abstract: We propose a network architecture which uses a single internal layer of locally-tuned processing units to learn both classification tasks and real-valued function approximations (Moody and Darken 1988). We consider training such networks in a completely supervised manner, but abandon this approach in favor of a more computationally efficient hybrid learning method which combines self-organized and supervised learning. Our networks learn faster than backpropagation for two reasons: the local representations ensure that only a few units respond to any given input, thus reducing computational overhead, and the hybrid learning rules are linear rather than nonlinear, thus leading to faster convergence. Unlike many existing methods for data analysis, our network architecture and learning rules are truly adaptive and are thus appropriate for real-time use.


title: Retail Luxury Strategy: Assembling Charisma through Art and Magic

abstract: Luxury retail strategy differs from other retail strategies not merely in distinctive formulations of product, price, distribution, and appeals to customer distinction. Instead, it increasingly stands or falls on the legitimacy of a charismatic creative director. The director offers an aesthetic brand ideology. Luxury retail draws on the principles of art and magic to assemble the charismatic persona of the creative director and to diffuse his aesthetic ideology to the brand. Moreover, luxury retail strategy enlists magical and aesthetic principles within and without the store to achieve these ends. Finally, retail luxury is producer rather than consumer oriented and seeks to generate awe rather than community. This strategy appears to be to some extent a response to legitimacy crises provoked by recent strategic extensions of luxury brands into mass marketing. We offer some implications for marketing in which the charisma of a key personage is at stake.


title: Learning Symbolic Representations for Planning with Parameterized Skills

abstract: A critical capability required for generally intelligent robot behavior is the ability to sequence motor skills to reach a goal. This requires a (typically abstract) representation that supports goal-directed planning, which raises the question of how to construct such a representation. Previous work has addressed this question in the context of simple black-box motor skills, which are insufficiently flexible to support the wide range of behavior required of intelligent robots. We now extend that work to include parametrized motor skills, where a robot must both select an action to execute and also decide how to parametrize it. We show how to construct a representation suitable for planning with parametrized motor skills, and specify conditions which are sufficient to separate the selection of motor skills from the parametrization of those skills. Our method results in a simple discrete abstract representation for planning followed by a parameter selection process that operates on a fixed plan. We first demonstrate learning this representation in a virtual domain based on Angry Birds and then learn an abstract symbolic representation for a robot manipulation task.


title: Semi-Supervised Multi-Organ Segmentation via Deep Multi-Planar Co-Training

abstract: In multi-organ segmentation of abdominal CT scans, most existing fully supervised deep learning algorithms require lots of voxel-wise annotations, which are usually difficult, expensive, and slow to obtain. In comparison, massive unlabeled 3D CT volumes are usually easily accessible. Current mainstream works to address the semi-supervised biomedical image segmentation problem are mostly graph-based. By contrast, deep network based semi-supervised learning methods have not drawn much attention in this field. In this work, we propose Deep Multi-Planar Co-Training (DMPCT), whose contributions can be divided into two folds: 1) The deep model is learned in a co-training style which can mine consensus information from multiple planes like the sagittal, coronal, and axial planes; 2) Multi-planar fusion is applied to generate more reliable pseudo-labels, which alleviates the errors occurring in the pseudo-labels and thus can help to train better segmentation networks. Experiments are done on our newly collected large dataset with 100 unlabeled cases as well as 210 labeled cases where 16 anatomical structures are manually annotated by four radiologists and confirmed by a senior expert. The results suggest that DMPCT significantly outperforms the fully supervised method by more than 4% especially when only a small set of annotations is used.


title: Mixup-Based Acoustic Scene Classification Using Multi-Channel Convolutional Neural Network

abstract: Audio scene classification, the problem of predicting class labels of audio scenes, has drawn lots of attention during the last several years. However, it remains challenging and falls short of accuracy and efficiency. Recently, Convolutional Neural Network (CNN)-based methods have achieved better performance with comparison to the traditional methods. Nevertheless, conventional single channel CNN may fail to consider the fact that additional cues may be embedded in the multi-channel recordings. In this paper, we explore the use of Multi-channel CNN for the classification task, which aims to extract features from different channels in an end-to-end manner. We conduct the evaluation compared with the conventional CNN and traditional Gaussian Mixture Model-based methods. Moreover, to improve the classification accuracy further, this paper explores the using of mixup method. In brief, mixup trains the neural network on linear combinations of pairs of the representation of audio scene examples and their labels. By employing the mixup approach for data argumentation, the novel model can provide higher prediction accuracy and robustness in contrast with previous models, while the generalization error can also be reduced on the evaluation data.


title: A grey-box approach for automated GUI-model generation of mobile applications

abstract: As the mobile platform continues to pervade all aspects of human activity, and mobile applications, or mobile apps for short, on this platform tend to be faulty just like other types of software, there is a growing need for automated testing techniques for mobile apps. Modelbased testing is a popular and important testing approach that operates on a model of an app's behavior. However, such a model is often not available or of insufficient quality. To address this issue, we present a novel grey-box approach for automatically extracting a model of a given mobile app. In our approach, static analysis extracts the set of events supported by the Graphical User Interface (GUI) of the app. Then dynamic crawling reverse-engineers a model of the app, by systematically exercising these events on the running app. We also present a tool implementing this approach for the Android platform. Our empirical evaluation of this tool on several Android apps demonstrates that it can efficiently extract compact yet reasonably comprehensive models of high quality for such apps.


title: A full-wave analysis of shielded microstrip line-to-line transitions

abstract: A rigorous procedure is used to analyze several microstrip line-to-line transitions in a shielded multilayer structure. The transitions studied include edge-coupled lines, overlay-coupled lines, and coupled-to-single lines. A power conservation check based on a rigorous Poynting vector analysis is used to determine the accuracy of the numerical convergence. The results of power distributions and coupling coefficients of the line-to-line transitions are studied parametrically to identify the properties and applications of each transition. >


title: Improved adaptive Gaussian mixture model for background subtraction

abstract: Background subtraction is a common computer vision task. We analyze the usual pixel-level approach. We develop an efficient adaptive algorithm using Gaussian mixture probability density. Recursive equations are used to constantly update the parameters and but also to simultaneously select the appropriate number of components for each pixel.


title: DustCart, an autonomous robot for door-to-door garbage collection: From DustBot project to the experimentation in the small town of Peccioli

abstract: We report on the design and the experimental results of DustCart, a wheeled autonomous robot for door-to-door garbage collection. DustCart is able to navigate in urban environments avoiding static and dynamic obstacles and to interact with human users. The robot is managed by an Ambient Intelligence system (AmI) through a wireless connection: it navigates to collect garbage bags to the houses of users and then moves to discharge the collected waste to a dedicated area. The architecture, navigation and localization systems are described along with the results achieved in different urban sites. In particular, a localization approach based on optical beacons was used and guaranteed position errors sufficient for a safe robot navigation. We report also the first results of a long-term experimentation of the DustCart robot in Peccioli, a small town of Tuscany (Italy). This can be considered as a first step in using robotics in the everyday life of a real town for providing a real service.


title: Soft Similarity and Soft Cosine Measure: Similarity of Features in Vector Space Model

abstract: We show how to consider similarity between features for calculation of similarity of objects in the Vector Space Model (VSM) for machine learning algorithms and other classes of methods that involve similarity between objects. Unlike LSA, we assume that similarity between features is known (say, from a synonym dictionary) and does not need to be learned from the data.We call the proposed similarity measure soft similarity. Similarity between features is common, for example, in natural language processing: words, n-grams, or syntactic n-grams can be somewhat different (which makes them different features) but still have much in common: for example, words “play” and “game” are different but related. When there is no similarity between features then our soft similarity measure is equal to the standard similarity. For this, we generalize the well-known cosine similarity measure in VSM by introducing what we call “soft cosine measure”. We propose various formulas for exact or approximate calculation of the soft cosine measure. For example, in one of them we consider for VSM a new feature space consisting of pairs of the original features weighted by their similarity. Again, for features that bear no similarity to each other, our formulas reduce to the standard cosine measure. Our experiments show that our soft cosine measure provides better performance in our case study: entrance exams question answering task at CLEF. In these experiments, we use syntactic n-grams as features and Levenshtein distance as the similarity between n-grams, measured either in characters or in elements of n-grams.


title: Design of efficient hydroponic nutrient solution control system using soft computing based solution grading

abstract: This paper presents an efficient hydroponic nutrient solution control system whose system parameters are optimized using genetic algorithm. A novel mamdani fuzzy inference system (FIS) that grades the quality of solution for a given set of control parameters has been used as its fitness function. The FIS evaluation function has been designed using expert opinion from researchers at Murugappa Chettiar Research Centre, India. To evaluate the performance of the proposed algorithm, a virtual hydroponic nutrient control system with a solution monitoring unit was designed using Labview. The designed algorithm demonstrated better convergence efficiency and resource utilization compared to conventional error function based nutrient solution control systems.


title: Embedding-based News Recommendation for Millions of Users

abstract: It is necessary to understand the content of articles and user preferences to make effective news recommendations. While ID-based methods, such as collaborative filtering and low-rank factorization, are well known for making recommendations, they are not suitable for news recommendations because candidate articles expire quickly and are replaced with new ones within short spans of time. Word-based methods, which are often used in information retrieval settings, are good candidates in terms of system performance but have issues such as their ability to cope with synonyms and orthographical variants and define "queries" from users' historical activities. This paper proposes an embedding-based method to use distributed representations in a three step end-to-end manner: (i) start with distributed representations of articles based on a variant of a denoising autoencoder, (ii) generate user representations by using a recurrent neural network (RNN) with browsing histories as input sequences, and (iii) match and list articles for users based on inner-product operations by taking system performance into consideration. The proposed method performed well in an experimental offline evaluation using past access data on Yahoo! JAPAN's homepage. We implemented it on our actual news distribution system based on these experimental results and compared its online performance with a method that was conventionally incorporated into the system. As a result, the click-through rate (CTR) improved by 23% and the total duration improved by 10%, compared with the conventionally incorporated method. Services that incorporated the method we propose are already open to all users and provide recommendations to over ten million individual users per day who make billions of accesses per month.


title: Adenosine enhances IL-10 secretion by human monocytes.

abstract: Adenosine is a potent endogenous antiinflammatory agent released by cells under metabolically unfavorable conditions. Its effects on the production of IL-10 by human monocytes were presently investigated. Pre-incubation with adenosine dose-dependently enhanced IL-10 release by TNF stimulated human monocytes (+29, +58, and +116% at 1, 10, and 100 muM, respectively.) Adenosine also significantly enhanced IL-10 production after hydrogen peroxide and LPS stimulation and dose-dependently inhibited TNF secretion. Pre-incubation was not mandatory to achieve these effects, since addition of adenosine at the time of or 30 min after the stimulus led to the same results. Blocking IL-10 with anti-IL-10 mAbs partially restored adenosine-induced TNF inhibition. The enhanced IL-10 production was not observed when cells were preincubated with adenosine A1 or A2 receptor agonists (R-phenylisopropyladenosine, 5'-N-ethylcarboxamido-adenosine, and 2-chloroadenosine) and was not affected by pretreatment with theophyllin, an antagonist of both A1 and A2 receptors, or with dipyridamole, an inhibitor of adenosine cellular uptake. In conclusion, adenosine, in the submillimolar concentration range, increases IL-10 secretion by stimulated monocytes. This phenomenon participates in TNF inhibition, a known property of adenosine, but is not mediated through the occupancy of A1 or A2 receptors. This may represent a novel antiinflammatory property of adenosine by which it could modulate inflammation and limit ischemia-reperfusion injury.


title: Enabling PHP software engineering research in Rascal

abstract: Today, PHP is one of the most popular programming languages, and is commonly used in the open source community and in industry to build large application frameworks and web applications. In this paper, we discuss our ongoing work on PHP AiR, a framework for PHP Analysis in Rascal. PHP AiR is focused especially on program analysis and empirical software engineering, and is being used actively and effectively in work on evaluating PHP feature usage and system evolution, on program analysis for refactoring and security validation, and on source code metrics. We describe the requirements and design decisions for PHP AiR, summarize current research using PHP AiR, discuss lessons learned, and briefly sketch future work. We describe a framework in Rascal for PHP software engineering research, PHP AiR.We report results of using PHP AiR for empirical studies and program analysis.We examine the design decisions and tradeoffs encountered building PHP AiR.


title: Modern Heat Extraction Systems for Power Traction Machines—A Review

abstract: This paper presents a review of modern cooling system employed for the thermal management of power traction machines. Various solutions for heat extractions are described: high thermal conductivity insulation materials, spray cooling, high thermal conductivity fluids, combined liquid and air forced convection, and loss mitigation techniques.


title: Towards brain-activity-controlled information retrieval: Decoding image relevance from MEG signals

abstract: We hypothesize that brain activity can be used to control future information retrieval systems. To this end, we conducted a feasibility study on predicting the relevance of visual objects from brain activity. We analyze both magnetoencephalographic (MEG) and gaze signals from nine subjects who were viewing image collages, a subset of which was relevant to a predetermined task. We report three findings: i) the relevance of an image a subject looks at can be decoded from MEG signals with performance significantly better than chance, ii) fusion of gaze-based and MEG-based classifiers significantly improves the prediction performance compared to using either signal alone, and iii) non-linear classification of the MEG signals using Gaussian process classifiers outperforms linear classification. These findings break new ground for building brain-activity-based interactive image retrieval systems, as well as for systems utilizing feedback both from brain activity and eye movements.


title: Clearing the clouds: a study of emerging scale-out workloads on modern hardware

abstract: Emerging scale-out workloads require extensive amounts of computational resources. However, data centers using modern server hardware face physical constraints in space and power, limiting further expansion and calling for improvements in the computational density per server and in the per-operation energy. Continuing to improve the computational resources of the cloud while staying within physical constraints mandates optimizing server efficiency to ensure that server hardware closely matches the needs of scale-out workloads. In this work, we introduce CloudSuite, a benchmark suite of emerging scale-out workloads. We use performance counters on modern servers to study scale-out workloads, finding that today's predominant processor micro-architecture is inefficient for running these workloads. We find that inefficiency comes from the mismatch between the workload needs and modern processors, particularly in the organization of instruction and data memory systems and the processor core micro-architecture. Moreover, while today's predominant micro-architecture is inefficient when executing scale-out workloads, we find that continuing the current trends will further exacerbate the inefficiency in the future. In this work, we identify the key micro-architectural needs of scale-out workloads, calling for a change in the trajectory of server processors that would lead to improved computational density and power efficiency in data centers.


title: Neuroimaging cognitive reappraisal in clinical populations to define neural targets for enhancing emotion regulation. A systematic review

abstract: Abstract Reduced capacity to cognitively regulate emotional responses is a common impairment across major neuropsychiatric disorders. Brain systems supporting one such strategy, cognitive reappraisal of emotion, have been investigated extensively in the healthy population, a research focus that has led to influential meta-analyses and literature reviews. However, the emerging literature on neural substrates underlying cognitive reappraisal in clinical populations is yet to be systematically reviewed. Therefore, the goal of the current review was to summarize the literature on cognitive reappraisal and highlight common and distinct neural correlates of impaired emotion regulation in clinical populations. We performed a two-stage systematic literature search, selecting 32 studies on cognitive reappraisal in individuals with mood disorders (n = 12), anxiety disorders (n = 14), addiction (n = 2), schizophrenia (n = 2), and personality disorders (n = 5). Comparing findings across these disorders allowed us to determine underlying mechanisms that were either disorder-specific or common across disorders. Results showed that across clinical populations, individuals consistently demonstrated reduced recruitment of the ventrolateral prefrontal cortex (vlPFC) and dorsolateral prefrontal cortex (dlPFC) during downregulation of negative emotion, indicating that there may be a core deficit in selection, manipulation and inhibition during reappraisal. Further, in individuals with mood disorders, amygdala responses were enhanced during downregulation of emotion, suggesting hyperactive bottom-up responses or reduced modulatory capacity. In individuals with anxiety disorders, however, emotion regulation revealed reduced activity in the dorsal anterior cingulate cortex (dACC) and inferior/superior parietal cortex, possibly indicating a deficit in allocation of attention. The reviewed studies thus provide evidence for both disorder-specific and common deficits across clinical populations. These findings highlight the role of distinct neural substrates as targets for developing/assessing novel therapeutic approaches that are geared towards cognitive regulation of emotion, as well as the importance of transdiagnostic research to identify both disorder specific and core mechanisms.


title: The construction of next-generation matrices for compartmental epidemic models

abstract: The basic reproduction number ℛ0 is arguably the most important quantity in infectious disease epidemiology. The next-generation matrix (NGM) is the natural basis for the definition and calculation...


Exact Recovery of Sparsely Used Overcomplete Dictionaries.


title: A brief instrument to measure patients' overall satisfaction with primary care physicians.

abstract: BACKGROUND AND OBJECTIVES: A brief and psychometrically sound scale to measure patients’ overall satisfaction with their primary care physicians would be useful in studies where a longer instrument is impractical. The purpose of this study was to develop and examine the psychometrics of a brief instrument to measure patients’ overall satisfaction with their primary care physicians. METHODS: Research participants included 535 outpatients (between 18–75 years old, 66% female) who completed a mailed survey that included 10 items for measuring overall satisfaction with their primary care physician who was named on the survey. Patients were also asked about their perceptions of physician empathy, preventive tests recommended by the physician (colonoscopy, mammogram, and prostate-specific antigen (PSA) for age and gender appropriate patients) and demographic information. RESULTS: Factor analysis of the patient satisfaction items resulted in one prominent component. Corrected item-total score correlations of the patient satisfaction scale ranged from 0.85 to 0.96; correlation between patient satisfaction scores and patient perception of physician empathy was 0.93, and correlation with recommending the physician to family and friends was 0.92. Criterion-related validity coefficients were mostly in the 0.80s and 0.90s. Patient satisfaction scores were significantly higher for those whose physicians recommended preventive tests (colonoscopy, mammogram, and PSA—compliance rates >.80). Cronbach’s coefficient alpha for patient satisfaction scale was 0.98. CONCLUSIONS: Empirical evidence supported the validity and reliability of a brief patient satisfaction scale that has utility in the assessments of educational programs aimed at improving patient satisfaction, medical services, and patient outcomes in primary care settings.


title: The application of deep learning in communication signal modulation recognition

abstract: Automated Modulation Classification (AMC) has been applied in various emerging areas such as cognitive radio (CR). We also notice that Deep Learning (DL) is a powerful classification tool that has gained great popularity in various field. This article focuses on DL and aims at using it to solve communications problems. We propose a new data conversion algorithm in order to gain a better classification accuracy of communication signal modulation. This paper will show that our new method will bring significant improvement in signal modulation classification accuracy. Besides, AlexNet and GoogLeNet, two well-known DL network models, ResNet and VGG, will be utilized in this task to compare with each other.


title: 3D scattered data approximation with adaptive compactly supported radial basis functions

abstract: We develop an adaptive RBF fitting procedure for a high quality approximation of a set of points scattered over a piecewise smooth surface. We use compactly supported RBFs whose centers are randomly chosen from the points. The randomness is controlled by the point density and surface geometry. For each RBF, its support size is chosen adoptively according to surface geometry at a vicinity of the RBF center. All these lead to a noise-robust high quality approximation of the set. We also adapt our basic technique for shape reconstruction from registered range scans by taking into account measurement confidences. Finally, an interesting link between our RBF fitting procedure and partition of unity approximations is established and discussed.


title: A unifying framework for detecting outliers and change points from non-stationary time series data

abstract: We are concerned with the issues of outlier detection and change point detection from a data stream. In the area of data mining, there have been increased interest in these issues since the former is related to fraud detection, rare event discovery, etc., while the latter is related to event/trend by change detection, activity monitoring, etc. Specifically, it is important to consider the situation where the data source is non-stationary, since the nature of data source may change over time in real applications. Although in most previous work outlier detection and change point detection have not been related explicitly, this paper presents a unifying framework for dealing with both of them on the basis of the theory of on-line learning of non-stationary time series. In this framework a probabilistic model of the data source is incrementally learned using an on-line discounting learning algorithm, which can track the changing data source adaptively by forgetting the effect of past data gradually. Then the score for any given data is calculated to measure its deviation from the learned model, with a higher score indicating a high possibility of being an outlier. Further change points in a data stream are detected by applying this scoring method into a time series of moving averaged losses for prediction using the learned model. Specifically we develop an efficient algorithms for on-line discounting learning of auto-regression models from time series data, and demonstrate the validity of our framework through simulation and experimental applications to stock market data analysis.


title: Effects of E-Customer Satisfaction and E-Trust on E-Loyalty: Mongolian Online Shopping Behavior

abstract: E-commerce is an emerging industry in Mongolia. Its development has a high urban-rural gap. The study summarized experiences and observations from countries with advanced e-commerce and proposed a conceptual model. Applying expectancy confirmation theory (ECT) and the concept of e-SERVQUAL (NetQual), the study surveyed 305 effective samples and verified the relationships among expectation, service quality, disconfirmation, e-customer satisfaction, e-trust, and e-loyalty. These conclusions can be applied to developing and facilitating e-commerce in Mongolia.


title: Wireless Power Transfer for Electric Vehicle Applications

abstract: Wireless power transfer (WPT) using magnetic resonance is the technology which could set human free from the annoying wires. In fact, the WPT adopts the same basic theory which has already been developed for at least 30 years with the term inductive power transfer. WPT technology is developing rapidly in recent years. At kilowatts power level, the transfer distance increases from several millimeters to several hundred millimeters with a grid to load efficiency above 90%. The advances make the WPT very attractive to the electric vehicle (EV) charging applications in both stationary and dynamic charging scenarios. This paper reviewed the technologies in the WPT area applicable to EV wireless charging. By introducing WPT in EVs, the obstacles of charging time, range, and cost can be easily mitigated. Battery technology is no longer relevant in the mass market penetration of EVs. It is hoped that researchers could be encouraged by the state-of-the-art achievements, and push forward the further development of WPT as well as the expansion of EV.


title: 'Long autonomy or long delay?' The importance of domain in opinion mining

abstract: Nowadays, people do not only navigate the web, but they also contribute contents to the Internet. Among other things, they write their thoughts and opinions in review sites, forums, social networks, blogs and other websites. These opinions constitute a valuable resource for businesses, governments and consumers. In the last years, some researchers have proposed opinion extraction systems, mostly domain-independent ones, to automatically extract structured representations of opinions contained in those texts. In this work, we tackle this task in a domain-oriented approach, defining a set of domain-specific resources which capture valuable knowledge about how people express opinions on a given domain. These resources are automatically induced from a set of annotated documents. Some experiments were carried out on three different domains (user-generated reviews of headphones, hotels and cars), comparing our approach to other state-of-the-art, domain-independent techniques. The results confirm the importance of the domain in order to build accurate opinion extraction systems. Some experiments on the influence of the dataset size and an example of aggregation and visualization of the extracted opinions are also shown.


title: An Ensemble Classifier with Random Projection for Predicting Protein-Protein Interactions Using Sequence and Evolutionary Information

abstract: Identifying protein-protein interactions (PPIs) is crucial to comprehend various biological processes in cells. Although high-throughput techniques generate many PPI data for various species, they are only a petty minority of the entire PPI network. Furthermore, these approaches are costly and time-consuming and have a high error rate. Therefore, it is necessary to design computational methods for efficiently detecting PPIs. In this study, a random projection ensemble classifier (RPEC) was explored to identify novel PPIs using evolutionary information contained in protein amino acid sequences. The evolutionary information was obtained from a position-specific scoring matrix (PSSM) generated from PSI-BLAST. A novel feature fusion scheme was then developed by combining discrete cosine transform (DCT), fast Fourier transform (FFT), and singular value decomposition (SVD). Finally, via the random projection ensemble classifier, the performance of the presented approach was evaluated on Yeast, Human, and H. pylori PPI datasets using 5-fold cross-validation. Our approach achieved high prediction accuracies of 95.64%, 96.59%, and 87.62%, respectively, effectively outperforming other existing methods. Generally speaking, our approach is quite promising and supplies a practical and effective method for predicting novel PPIs.


title: Consistent hashing and random trees: distributed caching protocols for relieving hot spots on the World Wide Web

abstract: We describe a family of caching protocols for distrib-uted networks that can be used to decrease or eliminate the occurrence of hot spots in the network. Our protocols are particularly designed for use with very large networks such as the Internet, where delays caused by hot spots can be severe, and where it is not feasible for every server to have complete information about the current state of the entire network. The protocols are easy to implement using existing network protocols such as TCP/IP, and require very little overhead. The protocols work with local control, make efficient use of existing resources, and scale gracefully as the network grows. Our caching protocols are based on a special kind of hashing that we call consistent hashing. Roughly speaking, a consistent hash function is one which changes minimally as the range of the function changes. Through the development of good consistent hash functions, we are able to develop caching protocols which do not require users to have a current or even consistent view of the network. We believe that consistent hash functions may eventually prove to be useful in other applications such as distributed name servers and/or quorum systems.


title: Thing broker: a twitter for things

abstract: In the Web of Things, standard web technologies and protocols are used to represent and communicate with physical and virtual things. One challenge toward this vision is integrating things with different characteristics, protocols, interfaces and constraints while maintaining the simplicity and flexibility required for a variety of applications. In this paper we present the Thing Broker, a core platform for Web of Things that provides RESTFul interfaces to things using a Twitter-based set of abstractions and communication model. We present the key abstractions, a reference implementation and explain how a typical WoT application can be created using Thing Broker. We finish with a preliminary evaluation and draw some lessons from our experiences.


title: N.: Data-flow anti-patterns: Discovering data-flow errors in workflows

abstract: Despite the abundance of analysis techniques to discover control-flow errors in workflow designs, there is hardly any support for data-flow verification. Most techniques simply abstract from data, while data dependencies can be the source of all kinds of errors. This paper focuses on the discovery of data-flow errors in workflows. We present an analysis approach that uses so-called "anti-patterns" expressed in terms of a temporal logic. Typical errors include accessing a data element that is not yet available or updating a data element while it may be read in a parallel branch. Since the anti-patterns are expressed in terms of temporal logic, the well-known, stable, adaptable, and effective model-checking techniques can be used to discover data-flow errors. Moreover, our approach enables a seamless integration of control flow and data-flow verification.


title: Scaling blockchain for the energy sector

abstract: p.p1 {margin: 0.0px 0.0px 0.0px 0.0px; font: 10.0px Helvetica}Blockchain is a distributed ledger technology enabling digital transactions without theneed for central governance. Once transactions a ...


title: Full-range adaptive cruise control based on supervised adaptive dynamic programming

abstract: The paper proposes a supervised adaptive dynamic programming (SADP) algorithm for a full-range adaptive cruise control (ACC) system, which can be formulated as a dynamic programming problem with stochastic demands. The suggested ACC system has been designed to allow the host vehicle to drive both in highways and in Stop and Go (SG) urban scenarios. The ACC system can autonomously drive the host vehicle to a desired speed and/or a given distance from the target vehicle in both operational cases. Traditional adaptive dynamic programming (ADP) is a suitable tool to address the problem but training usually suffers from low convergence rates and hardly achieves an effective controller. A SADP algorithm which introduces the concept of inducing region is here introduced to overcome such training drawbacks. The SADP algorithm performs very well in all simulation scenarios and always better than more traditional controllers. The conclusion is that the proposed SADP algorithm is an effective control methodology able to effectively address the full-range ACC problem.


title: The full spectrum of human naive T cells

abstract: Naive T cells have long been regarded as a developmentally synchronized and fairly homogeneous and quiescent cell population, the size of which depends on age, thymic output and prior infections. However, there is increasing evidence that naive T cells are heterogeneous in phenotype, function, dynamics and differentiation status. Current strategies to identify naive T cells should be adjusted to take this heterogeneity into account. Here, we provide an integrated, revised view of the naive T cell compartment and discuss its implications for healthy ageing, neonatal immunity and T cell reconstitution following haematopoietic stem cell transplantation. Evidence is increasing that naive T cells are heterogeneous in phenotype, function, dynamics and differentiation status. Here, van den Broek et al. provide a revised view of the naive T cell compartment and then discuss the implications for ageing, neonatal immunity and T cell reconstitution following haematopoietic stem cell transplantation.


title: Deep Back-Projection Networks for Super-Resolution

abstract: The feed-forward architectures of recently proposed deep super-resolution networks learn representations of low-resolution inputs, and the non-linear mapping from those to high-resolution output. However, this approach does not fully address the mutual dependencies of low- and high-resolution images. We propose Deep Back-Projection Networks (DBPN), that exploit iterative up- and downsampling layers, providing an error feedback mechanism for projection errors at each stage. We construct mutually-connected up- and down-sampling stages each of which represents different types of image degradation and high-resolution components. We show that extending this idea to allow concatenation of features across up- and downsampling stages (Dense DBPN) allows us to reconstruct further improve super-resolution, yielding superior results and in particular establishing new state of the art results for large scaling factors such as 8A— across multiple data sets.


title: Assessing Sustainability of Mixed Use Neighbourhoods through Residents’ Travel Behaviour and Perception: The Case of Nagpur, India

abstract: Mixed land-use development is the integration of different land-use functions like residential, commercial, recreational, and institutional in an urban sector or a neighbourhood. Integrating transport and land-use mix is one of the goals of planning policies around the world. Prior studies mention the benefits of mixed land-use development towards creating sustainable environment, but do not specify the proportion of the mix of compatible land uses. This study attempts to assess the sustainability of the neighbourhoods with mixed land-use in the context of the Nagpur city, India. Residents’ travel behaviour in twelve neighbourhoods is studied by means of indicators namely trip lengths, mode of travel, vehicle ownership, and travel expenses. To investigate the users’ insight, the study further examines residents’ perception with the help of parameters such as safety, satisfaction, pollution, and mix. The sustainability indices are computed for both residents’ travel behaviour and perception, for each neighbourhood. The study revealed that neighbourhoods with high and moderate land-use mix are sustainable with travel behaviour. Residents’ perception sustainability index indicates neighbourhoods with moderate land-use mix are more sustainable than those with high and low land-use mixed neighbourhoods. This study advocates stakeholders’ insight and the proportion of mix in land-use planning decisions.


title: Face alignment with Cascaded Bidirectional LSTM Neural Networks

abstract: Face alignment is an important issue in many computer vision problems. The key problem is to find the nonlinear mapping from face image or feature to landmark locations. In this paper, we propose a novel cascaded approach with bidirectional Long Short Term Memory (LSTM) neural networks to approximate this nonlinear mapping. The cascaded structure is used to reduce the complexity of this problem and accelerate the algorithm by conducting the coarse-to-fine search. In each cascaded module, features of landmarks are delivered as inputs into the bidirectional LSTM network. The depth of the network guarantees the ability to learn highly complex mapping. The recurrent connections in LSTM explore the relationships of different landmarks and ensure that the shape of the face is maintained. On several challenging public databases, our approach achieves state-of-the-art performances.


title: Augmented virtual environments (AVE): dynamic fusion of imagery and 3D models

abstract: An augmented virtual environment (AVE) fuses dynamic imagery with 3D models. The AVE provides a unique approach to visualize and comprehend multiple streams of temporal data or images. Models are used as a 3D substrate for the visualization of temporal imagery, providing improved comprehension of scene activities. The core elements of AVE systems include model construction, sensor tracking, real-time video/image acquisition, and dynamic texture projection for 3D visualization. This paper focuses on the integration of these components and the results that illustrate the utility and benefits of the resulting augmented virtual environment.


title: Bridging the Gap between Social Animal and Unsocial Machine: A Survey of Social Signal Processing

abstract: Social Signal Processing is the research domain aimed at bridging the social intelligence gap between humans and machines. This paper is the first survey of the domain that jointly considers its three major aspects, namely, modeling, analysis, and synthesis of social behavior. Modeling investigates laws and principles underlying social interaction, analysis explores approaches for automatic understanding of social exchanges recorded with different sensors, and synthesis studies techniques for the generation of social behavior via various forms of embodiment. For each of the above aspects, the paper includes an extensive survey of the literature, points to the most important publicly available resources, and outlines the most fundamental challenges ahead.


title: DrugBank 3.0: a comprehensive resource for ‘Omics’ research on drugs

abstract: DrugBank (http://www.drugbank.ca) is a richly annotated database of drug and drug target information. It contains extensive data on the nomenclature, ontology, chemistry, structure, function, action, pharmacology, pharmacokinetics, metabolism and pharmaceutical properties of both small molecule and large molecule (biotech) drugs. It also contains comprehensive information on the target diseases, proteins, genes and organisms on which these drugs act. First released in 2006, DrugBank has become widely used by pharmacists, medicinal chemists, pharmaceutical researchers, clinicians, educators and the general public. Since its last update in 2008, DrugBank has been greatly expanded through the addition of new drugs, new targets and the inclusion of more than 40 new data fields per drug entry (a 40% increase in data ‘depth’). These data field additions include illustrated drug-action pathways, drug transporter data, drug metabolite data, pharmacogenomic data, adverse drug response data, ADMET data, pharmacokinetic data, computed property data and chemical classification data. DrugBank 3.0 also offers expanded database links, improved search tools for drug–drug and food–drug interaction, new resources for querying and viewing drug pathways and hundreds of new drug entries with detailed patent, pricing and manufacturer data. These additions have been complemented by enhancements to the quality and quantity of existing data, particularly with regard to drug target, drug description and drug action data. DrugBank 3.0 represents the result of 2 years of manual annotation work aimed at making the database much more useful for a wide range of ‘omics’ (i.e. pharmacogenomic, pharmacoproteomic, pharmacometabolomic and even pharmacoeconomic) applications.


title: Learning a Family of Detectors via Multiplicative Kernels

abstract: Object detection is challenging when the object class exhibits large within-class variations. In this work, we show that foreground-background classification (detection) and within-class classification of the foreground class (pose estimation) can be jointly learned in a multiplicative form of two kernel functions. Model training is accomplished via standard SVM learning. When the foreground object masks are provided in training, the detectors can also produce object segmentations. A tracking-by-detection framework to recover foreground state in video sequences is also proposed with our model. The advantages of our method are demonstrated on tasks of object detection, view angle estimation, and tracking. Our approach compares favorably to existing methods on hand and vehicle detection tasks. Quantitative tracking results are given on sequences of moving vehicles and human faces.


title: Informal social communication.

abstract: The importance of strict theory in developing and guiding programs of research is becoming more and more recognized today. Yet there is con siderable disagreement about exactly how strict and precise a theoretical formulation must be at various stages in the development of a body of knowledge. Certainly there are many who feel that some "theorizing" is too vague and indefinite to be of much use. It is also argued that such vague and broad "theorizing" may actually hinder the empirical devel opment of an area of knowledge. On the other hand there are many who express dissatisfaction with instances of very precise theories which do exist here and there, for somehow or other a precise and specific theory seems to them to leave out the "real" psychological problem. These persons seem to be more concerned with those aspects of the problem which the precise theory has not yet touched. From this point of view it is argued that too precise and too strict theorizing may also hinder the empirical development of an area of knowledge. It is probably correct that if a theory becomes too precise too early it can have tendencies to become sterile. It is also probably correct that if a theory stays too vague and ambiguous for too long it can be harmful in that nothing can be done to disprove or change it. This probably means that theories, when vague, should at least be stated in a form which makes the adding of precision possible as knowledge increases. It also probably means that theory should run ahead, but not too far ahead, of the data so that the trap of premature precision can be avoided. It


title: MUTAN: Multimodal Tucker Fusion for Visual Question Answering

abstract: Bilinear models provide an appealing framework for mixing and merging information in Visual Question Answering (VQA) tasks. They help to learn high level associations between question meaning and visual concepts in the image, but they suffer from huge dimensionality issues.,,We introduce MUTAN, a multimodal tensor-based Tucker decomposition to efficiently parametrize bilinear interactions between visual and textual representations. Additionally to the Tucker framework, we design a low-rank matrix-based decomposition to explicitly constrain the interaction rank. With MUTAN, we control the complexity of the merging scheme while keeping nice interpretable fusion relations. We show how the Tucker decomposition framework generalizes some of the latest VQA architectures, providing state-of-the-art results.


title: A sensor fusion approach for recognizing continuous human grasping sequences using hidden Markov models

abstract: The Programming by Demonstration (PbD) technique aims at teaching a robot to accomplish a task by learning from a human demonstration. In a manipulation context, recognizing the demonstrator's hand gestures, specifically when and how objects are grasped, plays a significant role. Here, a system is presented that uses both hand shape and contact-point information obtained from a data glove and tactile sensors to recognize continuous human-grasp sequences. The sensor fusion, grasp classification, and task segmentation are made by a hidden Markov model recognizer. Twelve different grasp types from a general, task-independent taxonomy are recognized. An accuracy of up to 95% could be achieved for a multiple-user system.


Network Engagement Behaviors of Three Online Diet and Exercise Programs


Risk-Adjusted Performance of Mutual Funds T


title: Cogging torque minimization of SMC motor with axially tapered stator tooth tip

abstract: In this paper, a novel stator structure with axially tapered stator tooth tip is proposed to mitigate cogging torque and reduce the machine back EMF harmonics. Furthermore, the stator tooth tips are specifically designed to provide balanced axial electromagnetic force. In order to realize continuous variation of stator tooth tip widths, the soft magnetic composite (SMC) material is used for the stator core. It is theoretically demonstrated that the cogging torque of the new structure can be reduced greatly compared with the conventional stator structure. Finally, the theoretical analysis results are confirmed by the finite-element analysis (FEA) results. A prototype has been designed and under manufactured.


title: Interlinking unmanned aerial vehicles with wireless sensor networks for improved large area monitoring

abstract: Wireless Sensor Networks (WSNs) comprising a large number of sensing nodes deployed within the area of interest, are able to measure, process and share specific parameters. Besides enabling effective area coverage, recent research has proven that unmanned aerial vehicles (UAVs) represent a viable addition to large area monitoring through remote sensing and data collecting functions. The proposed interlinking between autonomous UAV and on-ground WSNs overcomes the limitations that prevent sensing nodes from adequately managing large scale applications. This paper presents an overview regarding state of art WSNs and UAVs technologies used for large area monitoring, introduces a novel approach for smart data collecting and further explores the in-network data paradigm. Various simulations have been implemented and analyzed from a comparative standpoint.


title: A Distributed Frank-Wolfe Algorithm for Communication-Efficient Sparse Learning

abstract: Learning sparse combinations is a frequent theme in machine learning. In this paper, we study its associated optimization problem in the distributed setting where the elements to be combined are not centrally located but spread over a network. We address the key challenges of balancing communication costs and optimization errors. To this end, we propose a distributed Frank-Wolfe (dFW) algorithm. We obtain theoretical guarantees on the optimization error $\epsilon$ and communication cost that do not depend on the total number of combining elements. We further show that the communication cost of dFW is optimal by deriving a lower-bound on the communication cost required to construct an $\epsilon$-approximate solution. We validate our theoretical analysis with empirical studies on synthetic and real-world data, which demonstrate that dFW outperforms both baselines and competing methods. We also study the performance of dFW when the conditions of our analysis are relaxed, and show that dFW is fairly robust.


Tocqueville’s Aristocracy in Minnesota


title: HazeEst: Machine Learning Based Metropolitan Air Pollution Estimation From Fixed and Mobile Sensors

abstract: Metropolitan air pollution is a growing concern in both developing and developed countries. Fixed-station monitors, typically operated by governments, offer accurate but sparse data, and are increasingly being augmented by lower fidelity but denser measurements taken by mobile sensors carried by concerned citizens and researchers. In this paper, we introduce HazeEst—a machine learning model that combines sparse fixed-station data with dense mobile sensor data to estimate the air pollution surface for any given hour on any given day in Sydney. We assess our system using seven regression models and tenfold cross validation. The results show that estimation accuracy of support vector regression (SVR) is similar to decision tree regression and random forest regression, and higher than extreme gradient boosting, multi-layer perceptrons, linear regression, and adaptive boosting regression. The air pollution estimates from our models are validated via field trials, and results show that SVR not only yields high spatial resolution estimates that correspond well with the pollution surface obtained from fixed and mobile sensor monitoring systems, but also indicates boundaries of polluted area better than other regression models. Our results can be visualized using a Web-based application customized for metropolitan Sydney. We believe that the continuous estimates provided by our system can better inform air pollution exposure and its impact on human health.


title: Management of hepatocellular carcinoma: An update

abstract: Since the publication of the American Association for the Study of Liver Diseases (AASLD) practice guidelines on the management of hepatocellular carcinoma (HCC) in 2005, new information has emerged that requires that the guidelines be updated. The full version of the new guidelines is available on the AASLD Web site at http://www.aasld.org/practiceguidelines/Documents/Bookmarkedp20Practice%20Guidelines/HCCUpdate2010.pdf. Here, we briefly describe only new or changed recommendations.


title: The design process: properties, paradigms, and structure

abstract: In this paper, we examine the logic and methodology of engineering design from the perspective of the philosophy of science. The fundamental characteristics of design problems and design processes are discussed and analyzed. These characteristics establish the framework within which different design paradigms are examined. Following the discussions on descriptive properties of design, and the prescriptive role of design paradigms, we advocate the plausible hypothesis that there is a direct resemblance between the structure of design processes and the problem solving of scientific communities. The scientific community metaphor has been useful in guiding the development of general purpose highly effective design process meta-tools.


title: Understanding Neural Networks via Rule Extraction

abstract: Although backpropagation neural networks generally predict better than decision trees do for pattern classification problems, they are often regarded as black boxes, i.e., their predictions are not as interpretable as those of decision trees. This paper argues that this is because there has been no proper technique that enables us to do so. With an algorithm that can extract rules, by drawing parallels with those of decision trees, we show that the predictions of a network can be explained via rules extracted from it, thereby, the network can be understood. Experiments demonstrate that rules extracted from neural networks are comparable with those of decision trees in terms of predictive accuracy, number of rules and average number of conditions for a rule; they preserve high predictive accuracy of original networks.


WINDOWS OPERATING SYSTEM VULNERABILITIES


title: Understanding Short Texts

abstract: Many applications handle short texts, and enableing machines to understand short texts is a big challenge. For example, in Ads selection, it is is difficult to evaluate the semantic similarity between a search query and an ad. Clearly, edit distance based string similarity does not work. Moreover, statistical methods that find latent topic models from text also fall short because ads and search queries are insufficient to provide enough statistical signals.