title: Effective Page Recommendation Algorithms Based on Distributed Learning Automata

abstract: Different efforts have been done to address the problem of information overload on the Internet. Recommender systems aim at directing users through this information space, toward the resources that best meet their needs and interests by extracting knowledge from the previous users' interactions. In this paper, we propose three algorithms to solve the web page recommendation problem. In our first algorithm, we use distributed learning automata to learn the behavior of previous users' and recommend pages to the current user based on learned patterns. By introducing a novel weighted association rule mining algorithm, we present our second algorithm for recommendation purpose. Also, a novel method is proposed to pure the current session window. One of the challenging problems in recommendation systems is dealing with unvisited or newly added pages. By considering this problem and improving the efficiency of first two algorithms we present a hybrid algorithm based on distributed learning automata and proposed weighted association rule mining algorithm. In the hybrid algorithm we employ the HITS algorithm to extend the recommendation set. Our experiments on real data set show that the hybrid algorithm performs better than the other algorithms we compared to and, at the same time, it is less complex than other proposed algorithms with respect to memory usage and computational cost too.


title: Design of EtherCAT slave module

abstract: EtherCAT real-time Ethernet technology that proposed by Beckhoff Company, is now widely used in industrial automation and motion control fields. In this paper, slave module design based on EtherCAT fieldbus is carried out from the view of the theoretical study and product application as well.


title: Early immunologic correlates of HIV protection can be identified from computational analysis of complex multivariate T-cell flow cytometry assays*

abstract: Motivation: Polychromatic flow cytometry (PFC), has enormous power as a tool to dissect complex immune responses (such as those observed in HIV disease) at a single cell level. However, analysis tools are severely lacking. Although high-throughput systems allow rapid data collection from large cohorts, manual data analysis can take months. Moreover, identification of cell populations can be subjective and analysts rarely examine the entirety of the multidimensional dataset (focusing instead on a limited number of subsets, the biology of which has usually already been well-described). Thus, the value of PFC as a discovery tool is largely wasted. ::: ::: Results: To address this problem, we developed a computational approach that automatically reveals all possible cell subsets. From tens of thousands of subsets, those that correlate strongly with clinical outcome are selected and grouped. Within each group, markers that have minimal relevance to the biological outcome are removed, thereby distilling the complex dataset into the simplest, most clinically relevant subsets. This allows complex information from PFC studies to be translated into clinical or resource-poor settings, where multiparametric analysis is less feasible. We demonstrate the utility of this approach in a large (n=466), retrospective, 14-parameter PFC study of early HIV infection, where we identify three T-cell subsets that strongly predict progression to AIDS (only one of which was identified by an initial manual analysis). ::: ::: Availability: The ‘flowType: Phenotyping Multivariate PFC Assays’ package is available through Bioconductor. Additional documentation and examples are available at: www.terryfoxlab.ca/flowsite/flowType/ ::: ::: Supplementary information:Supplementary data are available at Bioinformatics online. ::: ::: Contact: rbrinkman@bccrc.ca


title: Re-assessing the usability metric for user experience (UMUX) scale

abstract: Usability Metric for User Experience (UMUX) and its shorter form variant UMUX-LITE are recent additions to standardized usability questionnaires. UMUX aims to measure perceived usability by employing fewer items that are in closer conformance with the ISO 9241 definition of usability, while UMUX-LITE conforms to the technology acceptance model (TAM). UMUX has been criticized regarding its reliability, validity, and sensitivity, but these criticisms are mostly based on reported findings associated with the data collected by the developer of the questionnaire. ::: ::: Our study re-evaluates the UMUX and UMUX-LITE scales using psychometric methods with data sets acquired through two usability evaluation studies: an online word processor evaluation survey (n = 405) and a web-based mind map software evaluation survey for three applications (n = 151). ::: ::: Data sets yielded similar results for indicators of reliability. Both UMUX and UMUX-LITE items were sensitive to the software when the scores for the evaluated software were not very close, but we could not detect a significant difference between the software when the scores were closer. ::: ::: UMUX and UMUX-LITE items were also sensitive to users' level of experience with the software evaluated in this study. Neither of the scales was found to be sensitive to the participants' age, gender, or whether they were native English speakers. The scales significantly correlated with the System Usability Scale (SUS) and the Computer System Usability Questionnaire (CSUQ), indicating their concurrent validity. The parallel analysis of principal components of UMUX pointed out a single latent variable, which was confirmed through a factor analysis, that showed the data fits better to a single-dimension factor structure.


title: The hippocampus as a predictive map

abstract: A cognitive map has long been the dominant metaphor for hippocampal function, embracing the idea that place cells encode a geometric representation of space. However, evidence for predictive coding, reward sensitivity and policy dependence in place cells suggests that the representation is not purely spatial. We approach this puzzle from a reinforcement learning perspective: what kind of spatial representation is most useful for maximizing future reward? We show that the answer takes the form of a predictive representation. This representation captures many aspects of place cell responses that fall outside the traditional view of a cognitive map. Furthermore, we argue that entorhinal grid cells encode a low-dimensionality basis set for the predictive representation, useful for suppressing noise in predictions and extracting multiscale structure for hierarchical planning.


Fertilizer-nitrogen Management in an Onion and Tropical Pumpkin Rotation in Puerto Rico


title: Dermal and subdermal tissue filling with fetal connective tissue and cartilage, collagen, and silicone: Experimental study in the pig compared with clinical results. A new technique of dermis mini-autograft injections

abstract: The early reaction to the injection of silicone, collagen, and lyophilized heterologous fetal connective and cartilage tissues into the limiting zone deep dermis-superficial subcutaneous tissue was histologically examined in the pig and compared with clinical results. The inflammatory reaction to lyophilized heterologous fetal tissue is considerably more intense than that to collagen and silicone and lasts for several weeks. Therefore, it is not recommended for soft tissue filling in the face. Admitting an inferior antigenicity of fetal tissues, the authors suggest that enzymatically denaturalized collagen should be manufactured from heterologous fetal connective tissue, to be then further tested. The reaction of tissue to silicone and collagen is minimal. Silicone is preferred for dermal injections since in clinical experience it remains in the site of injection much longer. For subdermal injections, however, collagen is preferred. Based on experience with over 600 patients since 1958, the first author continues using liquid silicone. The lack of complications is probably a result of the fact that only small amounts (milliliters) of silicone were used in wrinkles or small depressions in the dermal layer and that from the beginning injection into the subcutaneous tissue was avoided. Since 1988 a new technique for the treatment of wrinkles and skin depressions with injections of dermal miniautografts has been used with satisfactory results.


title: Lens on the Endpoint: Hunting for Malicious Software Through Endpoint Data Analysis

abstract: Organizations are facing an increasing number of criminal threats ranging from opportunistic malware to more advanced targeted attacks. While various security technologies are available to protect organizations’ perimeters, still many breaches lead to undesired consequences such as loss of proprietary information, financial burden, and reputation defacing. Recently, endpoint monitoring agents that inspect system-level activities on user machines started to gain traction and be deployed in the industry as an additional defense layer. Their application, though, in most cases is only for forensic investigation to determine the root cause of an incident.


title: Electric field grading of gas insulated switchgears with microvaristor composites

abstract: This paper reports a new field grading method for GIS (gas insulated switchgear) insulators with the non-linear resistive material (NRM) composed of epoxy and microvaristors. NRM should be carefully used for GIS insulators, because the electric field applied to GIS insulators is much higher than that to the air-insulated insulators and there is a possibility that NRM might be damaged due to the excessive stress. The simulation on the field grading effect on a GIS insulator was carried out with the various NRM characteristics based on the measured characteristics of NRM samples. The authors show that NRM has the good field grading effect by both the high dielectric and non-linear resistance and also propose a way of selecting the suitable NRM characteristics.


title: Toward Detecting Collusive Ranking Manipulation Attackers in Mobile App Markets

abstract: Incentivized by monetary gain, some app developers launch fraudulent campaigns to boost their apps' rankings in the mobile app stores. They pay some service providers for boost services, which then organize large groups of collusive attackers to take fraudulent actions such as posting high app ratings or inflating apps' downloads. If not addressed timely, such attacks will increasingly damage the healthiness of app ecosystems. In this work, we propose a novel approach to identify attackers of collusive promotion groups in an app store. Our approach exploits the unusual ranking change patterns of apps to identify promoted apps, measures their pairwise similarity, forms targeted app clusters (TACs), and finally identifies the collusive group members. Our evaluation based on a dataset of Apple's China App store has demonstrated that our approach is able and scalable to report highly suspicious apps and reviewers. App stores may use our techniques to narrow down the suspicious lists for further investigation.


title: Stabilization and nonlinear control for a novel trirotor mini-aircraft

abstract: We present a dynamical model of an original trirotor helicopter obtained using the Euler-Lagrange approach. The obtained nonlinear model is then used for designing a nonlinear controller based on nested saturations. The controller performance is shown in real-time experiments.


title: Supervised strategies for cracks detection in images of road pavement flexible surfaces

abstract: The detection of cracks and other degradations in road pavement surfaces is traditionally done by experts using visual inspection, while driving along the surveyed road. An automatic cracks detection system based on road pavement images, as proposed here, can speed up the process and reduce results' subjectivity. The paper confronts six supervised classification strategies, three parametric and three non-parametric. The analysis is done on data resulting from dividing the image into a set of non-overlapping windows. Dealing with supervised classification strategies, a technique for automatic selection of training images from an image database is proposed as the initial step, after which a human expert should select the image windows containing cracks. The selected classification strategies work with a 2D feature space. Classifiers are evaluated using a set of well-know metrics, indicating that a better performance can be achieved using parametric classification strategies.


title: Stock Market Prediction using Financial News Articles on Ho Chi Minh Stock Exchange

abstract: In this paper, we examined the effects of financial news on Ho Chi Minh Stock Exchange (HoSE) and we tried to predict the direction of VN30 Index after the news articles were published. In order to do this study, we got news articles from three big financial websites and we represented them as feature vectors. Recently, researchers have used machine learning technique to integrate with financial news in their prediction model. Actually, news articles are important factor that influences investors in a quick way so it is worth considering the news impact on predicting the stock market trends. Previous works focused only on market news or on the analysis of the stock quotes in the past to predict the stock market behavior in the future. We aim to build a stock trend prediction model using both stock news and stock prices of VN30 index that will be applied in Vietnam stock market while there has been a little focus on using news articles to predict the stock direction. Experiment results show that our proposed method achieved high accuracy in VN30 index trend prediction.


title: Mooshak: a Web-based multi-site programming contest system

abstract: This paper presents a new Web-based system, Mooshak, to handle programming contests. The system acts as a full contest manager as well as an automatic judge for programming contests. Mooshak innovates in a number of aspects: it has a scalable architecture that can be used from small single server contests to complex multi-site contests with simultaneous public online contests and redundancy; it has a robust data management system favoring simple procedures for storing, replicating, backing up data and failure recovery using persistent objects; it has automatic judging capabilities to assist human judges in the evaluation of programs; it has built-in safety measures to prevent users from interfering with the normal progress of contests. Mooshak is an open system implemented on the Linux operating system using the Apache HTTP server and the Tcl scripting language.This paper starts by describing the main features of the system and its architecture with reference to the automated judging, data management based on the replication of persistent objects over a network. Finally, we describe our experience using this system for managing two official programming contests.


Displacement mapped billboard clouds


title: A Radar-Based Smart Sensor for Unobtrusive Elderly Monitoring in Ambient Assisted Living Applications

abstract: Continuous in-home monitoring of older adults living alone aims to improve their quality of life and independence, by detecting early signs of illness and functional decline or emergency conditions. To meet requirements for technology acceptance by seniors (unobtrusiveness, non-intrusiveness, and privacy-preservation), this study presents and discusses a new smart sensor system for the detection of abnormalities during daily activities, based on ultra-wideband radar providing rich, not privacy-sensitive, information useful for sensing both cardiorespiratory and body movements, regardless of ambient lighting conditions and physical obstructions (through-wall sensing). The radar sensing is a very promising technology, enabling the measurement of vital signs and body movements at a distance, and thus meeting both requirements of unobtrusiveness and accuracy. In particular, impulse-radio ultra-wideband radar has attracted considerable attention in recent years thanks to many properties that make it useful for assisted living purposes. The proposed sensing system, evaluated in meaningful assisted living scenarios by involving 30 participants, exhibited the ability to detect vital signs, to discriminate among dangerous situations and activities of daily living, and to accommodate individual physical characteristics and habits. The reported results show that vital signs can be detected also while carrying out daily activities or after a fall event (post-fall phase), with accuracy varying according to the level of movements, reaching up to 95% and 91% in detecting respiration and heart rates, respectively. Similarly, good results were achieved in fall detection by using the micro-motion signature and unsupervised learning, with sensitivity and specificity greater than 97% and 90%, respectively.


title: Managing technical debt in software-reliant systems

abstract: Delivering increasingly complex software-reliant systems demands better ways to manage the long-term effects of short-term expedients. The technical debt metaphor is gaining significant traction in the agile development community as a way to understand and communicate such issues. The idea is that developers sometimes accept compromises in a system in one dimension (e.g., modularity) to meet an urgent demand in some other dimension (e.g., a deadline), and that such compromises incur a "debt": on which "interest" has to be paid and which the "principal" should be repaid at some point for the long-term health of the project. We argue that the software engineering research community has an opportunity to study and improve this concept. We can offer software engineers a foundation for managing such trade-offs based on models of their economic impacts. Therefore, we propose managing technical debt as a part of the future research agenda for the software engineering field.


title: Taxonomy of malware detection techniques: A systematic literature review

abstract: Malware is an international software disease. Research shows that the effect of malware is becoming chronic. To protect against malware detectors are fundamental to the industry. The effectiveness of such detectors depends on the technology used. Therefore, it is paramount that the advantages and disadvantages of each type of technology are scrutinized analytically. This study's aim is to scrutinize existing publications on this subject and to follow the trend that has taken place in the advancement and development with reference to the amount of information and sources of such literature. Many of the malware programs are huge and complicated and it is not easy to comprehend the details. Dissemination of malware information among users of the Internet and also training them to correctly use anti-malware products are crucial to protecting users from the malware onslaught. This paper will provide an exhaustive bibliography of methods to assist in combating malware.


Effects of Ability and Effort Praise on Children’s Failure Attribution, Self-Handicapping, and Performance


title: Investigations on Clonazepam Loaded Polymeric Micelle-like Nanoparticles for Safe Drug Administration During Pregnancy

abstract: Medication during pregnancy is often a necessity for women to treat their acute or chronic diseases. The goal of this study is to evaluate the potential of micelle-like nanoparticles (MNP) for providing safe drug usage in pregnancy and protect both foetus and mother from medication side effects. Clonazepam-loaded MNP were prepared from copolymers [polystyrene-poly(acrylic acid) (PS-PAA), poly(ethylene glycol)-b-poly(lactic acid) (PEG-PLA) and distearyl-sn-glycero-3-phosphoethanolamine-N-[methoxy-poly(ethylene glycol) (PEG-DSPE)] with varying monomer ratios and their drug-loading efficiency, drug release ratio, particle size, surface charge and morphology were characterised. The cellular transport and cytotoxicity experiments were conducted on clonazepam and MNP formulations using placenta-choriocarcinoma-BeWo and brain-endothelial-bEnd3 cells. Clonazepam-loaded PEG5000-PLA4500 MNP reduced the drug transport through BeWo cells demonstrating that MNP may lower foetal drug exposure, thus reduce the d...


title: Sugar-Sweetened Beverage Consumption Is Adversely Associated with Childhood Attention Deficit/Hyperactivity Disorder

abstract: Attention deficit/hyperactivity disorder (ADHD) is one of the most common childhood neurobehavioral conditions. Evidence of the negative effects of sugar-sweetened beverages (SSBs) on mental health has not been convincing, although a few studies have found an association between high SSB levels and attention problems in children. This study aimed to test the hypothesis that SSB consumption is associated with ADHD among children. Doctor-diagnosed ADHD cases (n = 173) and non-ADHD controls (n = 159) between age 4 to 15 were recruited. SSB consumption, socio-demographic and lifestyle characteristics of the children, as well as of their mothers' characteristics during pregnancy, were collected using a questionnaire. Blood lead levels and polymorphisms of two commonly verified dopaminergic-related genes (the D4 dopamine receptor gene DRD4 and the dopamine transporter gene DAT1) were also analyzed. There was a dose-response relationship between SSB consumption and ADHD. After covariates were adjusted, children who consumed SSBs at moderate levels and high levels had 1.36 and 3.69 odds, respectively, of having ADHD, compared with those who did not consume SSBs (p for trend < 0.05). Similar results were obtained when females were excluded. Our findings highlighted the adverse correlation between SSB consumption and ADHD and indicated a dose-response effect even after covariates were adjusted.


title: Real-world Multi-object, Multi-grasp Detection

abstract: A deep learning architecture is proposed to predict graspable locations for robotic manipulation. It considers situations where no, one, or multiple object(s) are seen. By defining the learning problem to be classification with null hypothesis competition instead of regression, the deep neural network with RGB-D image input predicts multiple grasp candidates for a single object or multiple objects, in a single shot. The method outperforms state-of-the-art approaches on the Cornell dataset with 96.0% and 96.1% accuracy on image-wise and object- wise splits, respectively. Evaluation on a multi-object dataset illustrates the generalization capability of the architecture. Grasping experiments achieve 96.0% grasp localization and 88.0% grasping success rates on a test set of household objects. The real-time process takes less than .25 s from image to plan.


title: AceKG: A Large-scale Knowledge Graph for Academic Data Mining

abstract: Most existing knowledge graphs (KGs) in academic domains suffer from problems of insufficient multi-relational information, name ambiguity and improper data format for large-scale machine processing. In this paper, we present AceKG, a new large-scale KG in academic domain. AceKG not only provides clean academic information, but also offers a large-scale benchmark dataset for researchers to conduct challenging data mining projects including link prediction, community detection and scholar classification. Specifically, AceKG describes 3.13 billion triples of academic facts based on a consistent ontology, including necessary properties of papers, authors, fields of study, venues and institutes, as well as the relations among them. To enrich the proposed knowledge graph, we also perform entity alignment with existing databases and rule-based inference. Based on AceKG, we conduct experiments of three typical academic data mining tasks and evaluate several state-of- the-art knowledge embedding and network representation learning approaches on the benchmark datasets built from AceKG. Finally, we discuss several promising research directions that benefit from AceKG.


title: Stochastic Gradient Descent Algorithm in the Computational Network Toolkit

abstract: We introduce the stochastic gradient descent algorithm used in the computational network toolkit (CNTK) — a general purpose machine learning toolkit written in C++ for training and using models that can be expressed as a computational network. We describe the algorithm used to compute the gradients automatically for a given network. We also propose a low-cost automatic learning rate selection algorithm and demonstrate that it works well in practice. 1 Computational Network Toolkit A computational network (CN) is a directed graph in which each leaf represents an input value or a learnable parameter and each node represents an operator. Figure 1 illustrates an example CN of a log-linear model. Here, each node is identified by a {node name : operator type} pair and takes its ordered children as the operator’s inputs. For example, in the figure, T = Times(W,X) which is different from T = Times(X,W). A CN can have many root nodes which are used under different conditions. For example, one root node may represent a cross-entropy training criterion and another may represent an evaluation criterion. The network in Figure 1 has only one root node {C: Cross Entropy}. Many machine learning models, such as neural networks, that can be described via a series of operations, can be converted into a CN. The computational network toolkit (CNTK) is a general purpose C++ based machine learning toolkit for models that can be described as CNs. Figure 2 illustrates the architecture of CNTK. The core of CNTK is an internal representation of a CN which provides two key methods: Evaluate, which computes the value of a node given its inputs and Compute Gradient, which computes the gradient of a node with respect to its inputs. These methods are executed using an IExecutionEngine such as a CPU, a GPU, or a data flow graph such as pTask [1]. ICNBuilder reads the network description (or language) and creates a CN. IDataReader reads in features and labels stored in different formats.


title: A Comparative Study of Classification Algorithms for Spam Email Data Analysis

abstract: In recent years email has become one of the fastest and most economical means of communication. However increase of email users has resulted in the dramatic increase of spam emails during the past few years. Data mining -classification algorithms are used to categorize the email as spam or non-spam. In this paper, we conducted experiment in the WEKA environment by using four algorithms namely ID3, J48, Simple CART and Alternating Decision Tree on the spam email dataset and later the four algorithms were compared in terms of classification accuracy. According to our simulation results the J48 classifier outperforms the ID3, CART and ADTree in terms of classification accuracy.


Microstructure and Mechanical Properties of SPSed ( Spark Plasma Sintered ) Ti


title: State-of-Charge Estimation of the Lithium-Ion Battery Using an Adaptive Extended Kalman Filter Based on an Improved Thevenin Model

abstract: An adaptive Kalman filter algorithm is adopted to estimate the state of charge (SOC) of a lithium-ion battery for application in electric vehicles (EVs). Generally, the Kalman filter algorithm is selected to dynamically estimate the SOC. However, it easily causes divergence due to the uncertainty of the battery model and system noise. To obtain a better convergent and robust result, an adaptive Kalman filter algorithm that can greatly improve the dependence of the traditional filter algorithm on the battery model is employed. In this paper, the typical characteristics of the lithium-ion battery are analyzed by experiment, such as hysteresis, polarization, Coulomb efficiency, etc. In addition, an improved Thevenin battery model is achieved by adding an extra RC branch to the Thevenin model, and model parameters are identified by using the extended Kalman filter (EKF) algorithm. Further, an adaptive EKF (AEKF) algorithm is adopted to the SOC estimation of the lithium-ion battery. Finally, the proposed method is evaluated by experiments with federal urban driving schedules. The proposed SOC estimation using AEKF is more accurate and reliable than that using EKF. The comparison shows that the maximum SOC estimation error decreases from 14.96% to 2.54% and that the mean SOC estimation error reduces from 3.19% to 1.06%.


Ethnic differences in breast cancer incidence in England are due to differences in known risk factors for the disease: prospective study


title: Image-based clothes animation for virtual fitting

abstract: We propose an image-based approach for virtual clothes fitting, in which a user moves freely in front of a virtual mirror (i.e., video screen) that displays the user wearing a superimposed virtual garment. The motions and deformations of the virtual garment are synthesized by tracking the user's skeleton and utilizing corresponding video clips of the actual garment worn by a model. Our system overcomes challenges in generating a convincing garment animation. With our technique, we developed a real-time system using a Microsoft Kinect camera that demonstrates effective clothes fitting results with a variety of garment types.


title: A Unified Detail-Preserving Liquid Simulation by Two-Phase Lattice Boltzmann Modeling

abstract: Traditional methods in graphics to simulate liquid-air dynamics under different scenarios usually employ separate approaches with sophisticated interface tracking/reconstruction techniques. In this paper, we propose a novel unified approach which is easy and effective to produce a variety of liquid-air interface phenomena. These phenomena, such as complex surface splashes, bubble interactions, as well as surface tension effects, can co-exist in one single simulation, and are created within the same computational framework. Such a framework is unique in that it is free from any complicated interface tracking/reconstruction procedures. Our approach is developed from the two-phase lattice Boltzmann method with the mean field model, which provides a unified framework for interface dynamics but is numerically unstable under turbulent conditions. Considering the drawbacks of the existing approaches, we propose techniques to suppress oscillations for significant stability enhancement, as well as derive a new subgrid-scale model to further improve stability, faithfully preserving liquid-air interface details without excessive diffusion by taking into account the density variation. The whole framework is highly parallel, enabling very efficient implementation. Comparisons with the related approaches show superiority on stable simulations with detail preservation and multiphase phenomena simultaneously involved. A set of animation results demonstrate the effectiveness of our method.


title: Analysis and Verification of Inductor Coupling Effect in Interleaved Multiphase dc–dc Converters

abstract: Interleaved multiphase synchronous buck converters are often used to power computer CPU, GPU, and memory to meet the demands for increasing load current and fast current slew rate of the processors. This paper reports and explains undesired coupling between discrete inductors in typical commercial multiphase applications where space is limited. In this paper, equations of coupling coefficient are derived for single-turn and multiturn Ferrite core inductors commonly used in multiphase converters and are verified by Maxwell static simulation. The influence of the coupling effect on inductor current waveforms is demonstrated by Maxwell transient simulation and confirmed by experiments. The analysis provides a useful tool for mitigating the coupling effect in multiphase converters to avoid early inductor saturation and interference between phases. Design guidelines and recommendations are provided to minimize the unwanted coupling effect in multiphase converters.


title: A Superlative Theory of Amount Relatives

abstract: ion over individuals in a there-relative would produce a variable of type in a there-insertion context, which should result in ungrammaticality, contrary to fact, as illustrated by the grammatical version of (1a). To account for this fact, Heim (1987) proposes that “amount” relative clauses denote sets of degrees, as in (5) for the relative clause in (1a). (5) λd.there were d-many books on the table Her proposal is based on the idea that d-many books is a weak noun phrase despite the fact that the individual variable is strong on its own, by parallelism with the following contrast between a DP with a strong determiner and a DP with a strong determiner embedding a degree expression: (6) a. * There was THAT horse in the pasture. b. There were THAT many horses in the pasture. The central problem with her proposal, however, is that it offers no explanatory account of the absence of the amount reading in (3) or of the limited distribution of the amount reading in (3). 2.1. Two approaches to the problem The existing literature on “amount” relatives contains two general approaches to the problem outlined above. The uniformity approach assumes that there-relatives are a special case of “amount” relatives. For example, Grosu and Landman (1998) postulate a richer notion of degree, which they call DEGREE, that also keeps track of the kind of thing measured and its cardinality. As example (7) below illustrates, the new degree is actually a triple consisting of the cardinality of the plural individual x, the sortal predicate P, and the plural individual x itself. (7) For all plural individuals x: DEGREEP (x)= Assuming that numerals have essentially the semantics of modifiers, we obtain the semantics below for three books: 235


Concept and Dimensions of Web 4.0


title: A framework and testbed for studying manipulation techniques for immersive VR

abstract: Developing virtual realit y (VR) applications which enable actual work over a period of time requires optimization of the most basic interactions, such as object manipulation, so that the immersed participant can concentrate on higher-level tasks rather than on low-level motor activities. This paper presents a framework and experimental testbed for studies of VR object manipulation techniques. The framework provides a systematic task analysis of immersive manipulation and suggests a user-specific nonEuclidean system for the measurement of VR spatial relationships. The Virtual Reality Manipulation Assessment Testbed (VRMAT) is a practical implementation of the framework and is a flexible tool allowing in-depth experimental studies of immersive manipulation. Pilot studies have been conducted to evaluate this framework and testbed and to establish a baseline for further development.


title: Organizing Care for Patients with Chronic Illness

abstract: Usual medical care often fails to meet the needs of chronically ill patients, even in managed, integrated delivery systems. The medical literature suggests strategies to improve outcomes in these patients. Effective interventions tend to fall into one of five areas: the use of evidence-based, planned care; reorganization of practice systems and provider roles; improved patient self-management support; increased access to expertise; and greater availability of clinical information. The challenge is to organize these components into an integrated system of chronic illness care. Whether this can be done most efficiently and effectively in primary care practice rather than requiring specialized systems of care remains unanswered.


title: A novel feature-selection approach based on the cuttlefish optimization algorithm for intrusion detection systems

abstract: A modified version of the cuttlefish algorithm is discussed.The proposed model can be used as a novel feature-selection model.Cuttlefish algorithm is used as a search strategy to find optimal subset of features.Decision tree is used to evaluate the quality of the selected features.Data pre-processing for feature selection is also examined in the paper. This paper presents a new feature-selection approach based on the cuttlefish optimization algorithm which is used for intrusion detection systems (IDSs). Because IDSs deal with a large amount of data, one of the crucial tasks of IDSs is to keep the best quality of features that represent the whole data and remove the redundant and irrelevant features. The proposed model uses the cuttlefish algorithm (CFA) as a search strategy to ascertain the optimal subset of features and the decision tree (DT) classifier as a judgement on the selected features that are produced by the CFA. The KDD Cup 99 dataset is used to evaluate the proposed model. The results show that the feature subset obtained by using CFA gives a higher detection rate and accuracy rate with a lower false alarm rate, when compared with the obtained results using all features.


title: Wavelet-based statistical signal processing using hidden Markov models

abstract: Wavelet-based statistical signal processing techniques such as denoising and detection typically model the wavelet coefficients as independent or jointly Gaussian. These models are unrealistic for many real-world signals. We develop a new framework for statistical signal processing based on wavelet-domain hidden Markov models (HMMs) that concisely models the statistical dependencies and non-Gaussian statistics encountered in real-world signals. Wavelet-domain HMMs are designed with the intrinsic properties of the wavelet transform in mind and provide powerful, yet tractable, probabilistic signal models. Efficient expectation maximization algorithms are developed for fitting the HMMs to observational signal data. The new framework is suitable for a wide range of applications, including signal estimation, detection, classification, prediction, and even synthesis. To demonstrate the utility of wavelet-domain HMMs, we develop novel algorithms for signal denoising, classification, and detection.


title: The sensual evaluation instrument: Developing a trans-cultural self-report measure of affect

abstract: In this paper we describe the development and testing of a tool for self-assessment of affect while interacting with computer systems, meant to be used in many cultures. We discuss our research approach within the context of existing cultural, affective and HCI theory, and describe testing of its effectiveness in the US and Sweden.


title: Semi-Supervised Clustering with Neural Networks

abstract: Clustering using neural networks has recently demonstrated promising performance in machine learning and computer vision applications. However, the performance of current approaches is limited either by unsupervised learning or their dependence on large set of labeled data samples. In this paper, we propose ClusterNet that uses pairwise semantic constraints from very few labeled data samples (<5% of total data) and exploits the abundant unlabeled data to drive the clustering approach. We define a new loss function that uses pairwise semantic similarity between objects combined with constrained k-means clustering to efficiently utilize both labeled and unlabeled data in the same framework. The proposed network uses convolution autoencoder to learn a latent representation that groups data into k specified clusters, while also learning the cluster centers simultaneously. We evaluate and compare the performance of ClusterNet on several datasets and state of the art deep clustering approaches.


title: Mindfulness and Emotion Regulation: Insights from Neurobiological, Psychological, and Clinical Studies

abstract: There is increasing interest in the beneficial clinical effects of mindfulness-based interventions (MBIs). Research has demonstrated their efficacy in a wide range of psychological conditions characterized by emotion dysregulation. Neuroimaging studies have evidenced functional and structural changes in a myriad of brain regions mainly involved in attention systems, emotion regulation and self-referential processing. In this article we review studies on psychological and neurobiological correlates across different empirically derived models of research, including dispositional mindfulness, mindfulness induction, MBIs and expert meditators in relation to emotion regulation. From the perspective of recent findings in the neuroscience of emotion regulation, we discuss the interplay of top-down and bottom-up emotion regulation mechanisms associated with different mindfulness models. From a phenomenological and cognitive perspective, authors have argued that mindfulness elicits a “mindful emotion regulation” strategy; however, from a clinical perspective, this construct has not been properly differentiated from other strategies and interventions within MBIs. In this context we propose the distinction between top-down and bottom-up mindfulness based emotion regulation strategies. Furthermore, we propose an embodied emotion regulation framework as a multilevel approach for understanding psychobiological changes due to mindfulness meditation regarding its effect on emotion regulation. Finally, based on clinical neuroscientific evidence on mindfulness, we open perspectives and dialogues regarding commonalities and differences between MBIs and other psychotherapeutic strategies for emotion regulation.


title: Portable Wind Energy Harvesters for Low-Power Applications: A Survey

abstract: Energy harvesting has become an increasingly important topic thanks to the advantages in renewability and environmental friendliness. In this paper, a comprehensive study on contemporary portable wind energy harvesters has been conducted. The electrical power generation methods of portable wind energy harvesters are surveyed in three major groups, piezoelectric-, electromagnetic-, and electrostatic-based generators. The paper also takes another view of this area by gauging the required mechanisms for trapping wind flow from ambient environment. In this regard, rotational and aeroelastic mechanisms are analyzed for the portable wind energy harvesting devices. The comparison between both mechanisms shows that the aeroelastic mechanism has promising potential in producing an energy harvester in smaller scale although how to maintain the resonator perpendicular to wind flow for collecting the maximum vibration is still a major challenge to overcome for this mechanism. Furthermore, this paper categorizes the previously published portable wind energy harvesters to macro and micro scales in terms of their physical dimensions. The power management systems are also surveyed to explore the possibility of improving energy conversion efficiency. Finally some insights and research trends are pointed out based on an overall analysis of the previously published works along the historical timeline.


title: Low cost ultrasonic smart glasses for blind

abstract: This device includes a pair of glasses and an obstacle detection module fitted in it in the center, a processing unit, an output device i.e. a beeping component, and a power supply. The Obstacle detection module and the output device is connected to the processing unit. The power supply is used to supply power to the central processing unit. The obstacle detection module basically consists of a ultrasonic sensor, processing unit consist of a control module and the output unit consists of a buzzer. The control unit controls the ultrasonic sensors and get the information of the obstacle present in front of the man and processes the information and sends the output through the buzzer accordingly. These Ultrasonic Smart Glasses for Blind people is a portable device, easy to use, light weight, user friendly and cheap in price. These glasses could easily guide the blind people and help them avoid obstacles.


title: A Better Half: The Ethics of Hemicorporectomy Surgery

abstract: This paper discusses the ethical issues related to hemicorporectomy surgery, a radical procedure that removes the lower half of the body in order to prolong life. The literature on hemicorporectomy (HC), also called translumbar amputation, has been nearly silent on the ethical considerations relevant to this rare procedure. We explore five aspects of the complex landscape of hemicorporectomy to illustrate the broader ethical questions related to this extraordinary procedure: benefits, risks, informed consent, resource allocation and justice, and loss and the lived body.


Predicting Cuisine from Ingredients


title: Profiling Smart Contracts Interactions Tensor Decomposition and Graph Mining

abstract: Smart contracts, computer protocols designed for autonomous execution on predefined conditions, arise from the evolution of the Bit-coin's crypto-currency. They provide higher transaction security and allow economy of scale through the automated process. Smart contracts provides inherent benefits for financial institutions such as investment banking, retail banking, and insurance. This technology is widely used within Ethereum, an open source block-chain platform, from which the data has been extracted to conduct the experiments. In this work, we propose an multi-dimensional approach to find and predict smart contracts interactions only based on their crypto-currency exchanges. This approach relies on tensor modeling combined with stochas-tic processes. It underlines actual exchanges between smart contracts and targets the predictions of future interactions among the community. The tensor analysis is also challenged with the latest graph algorithms to assess its strengths and weaknesses in comparison to a more standard approach.


title: Left-Center-Right Separated Neural Network for Aspect-based Sentiment Analysis with Rotatory Attention

abstract: Deep learning techniques have achieved success in aspect-based sentiment analysis in recent years. However, there are two important issues that still remain to be further studied, i.e., 1) how to efficiently represent the target especially when the target contains multiple words; 2) how to utilize the interaction between target and left/right contexts to capture the most important words in them. In this paper, we propose an approach, called left-center-right separated neural network with rotatory attention (LCR-Rot), to better address the two problems. Our approach has two characteristics: 1) it has three separated LSTMs, i.e., left, center and right LSTMs, corresponding to three parts of a review (left context, target phrase and right context); 2) it has a rotatory attention mechanism which models the relation between target and left/right contexts. The target2context attention is used to capture the most indicative sentiment words in left/right contexts. Subsequently, the context2target attention is used to capture the most important word in the target. This leads to a two-side representation of the target: left-aware target and right-aware target. We compare our approach on three benchmark datasets with ten related methods proposed recently. The results show that our approach significantly outperforms the state-of-the-art techniques.


title: Organizational Transformation through Business Models: A Framework for Business Model Design

abstract: Organizations are increasingly inter-connected as they source talent, goods and services from other organizations located in disparate parts of the world. They seek new ways of creating value for themselves, customers and partners. They operate outside and across traditional industry boundaries and definitions. These innovations have lead to a focus on business models as a fundamental statement of direction and identity. This paper highlights what is known about the business model concept and where and why it differs from more established concepts of business strategy. It illustrates how the application of business models has transformed organizations. The contribution of this paper is the guidance that it provides for business model design and the insight it provides into business models and their effects on organizations. Following an analysis of how business models can transform organizations, this paper concludes with practical recommendations for business model design.


title: A decade of agile methodologies: Towards explaining agile software development

abstract: Ever since the agile manifesto was created in 2001, the research community has devoted a great deal of attention to agile software development. This article examines publications and citations to illustrate how the research on agile has progressed in the 10 years following the articulation of the manifesto. Specifically, we delineate the conceptual structure underlying agile scholarship by performing an analysis of authors who have made notable contributions to the field. Further, we summarize prior research and introduce contributions in this special issue on agile software development. We conclude by discussing directions for future research and urging agile researchers to embrace a theory-based approach in their scholarship.


title: Approximation of functions over redundant dictionaries using coherence

abstract: One of the central problems of modern mathematical approximation theory is to approximate functions, or signals, concisely, with elements from a large candidate set called a dictionary. Formally, we are given a signal A ∈ RN and a dictionary D = {φ i } i∈I of unit vectors that span RN. A representation R of B terms for input A ∈ RN is a linear combination of dictionary elements, R = σ i∈A αiφi, for φ i ∈ D and some A, vAv ≥ B. Typically, B ⪡ N, so that R is a concise approximation to signal A. The error of the representation indicates by how well it approximates A, and is given by ∥A - R∥ 2 = √Σ t |A[t - R[t]|2. The problem is to find the best B-term representation, i.e., find a R that minimizes ∥A - R∥ 2 . A dictionary may be redundant in the sense that there is more than one possible exact representation for A, i.e., vDv > N = dim(RN). Redundant dictionaries are used because, both theoretically and in practice, for important classes of signals, as the size of a dictionary increases, the error and the conciseness of the approximations improve.We present the first known efficient algorithm for finding a provably approximate representation for an input signal over redundant dictionaries. We identify and focus on redundant dictionaries with small coherence (ie., vectors are nearly orthogonal). We present an algorithm that preprocesses any such dictionary in time and space polynomial in vDv, and obtains an 1 + e approximate representation of the given signal in time nearly linear in signal size N and polylogarithmic in vDv; by contrast, most algorithms in the literature require Ω(vDv)time, and, yet, provide no provable bounds. The technical crux of our result is our proof that two commonly used local search techniques, when combined appropriately, gives a provably near-optimal signal representation over redundant dictionaries with small coherence. Our result immediately applies to several specific redundant dictionaries considered by the domain experts thus far. In addition, we present new redundant dictionaries which have small coherence (and therefore are amenable to our algorithms) and yet have significantly large sizes, thereby adding to the redundant dictionary construction literature.Work with redundant dictionaries forms the emerging field of highly nonlinear approximation theory. We have presented algorithmic results for some of the most basic problems in this area, but other mathematical and algorithmic questions remain to be explored.


title: Anomaly detection in wide area network mesh using two machine learning anomaly detection algorithms

abstract: Anomaly detection is the practice of identifying items or events that do not conform to an expected behavior or do not correlate with other items in a dataset. It has previously been applied to areas such as intrusion detection, system health monitoring, and fraud detection in credit card transactions. In this paper, we describe a new method for detecting anomalous behavior over network performance data, gathered by perfSONAR, using two machine learning algorithms: Boosted Decision Trees (BDT) and Simple Feedforward Neural Network. The effectiveness of each algorithm was evaluated and compared. Both have shown sufficient performance and sensitivity.


title: Topical valproic acid increases the hair count in male patients with androgenetic alopecia: A randomized, comparative, clinical feasibility study using phototrichogram analysis

abstract: Valproic acid (VPA), a widely used anticonvulsant, inhibits glycogen synthase kinase 3β and activates the Wnt/β-catenin pathway, which is associated with hair growth cycle and anagen induction. To assess the efficacy of topical VPA for treating androgenetic alopecia (AGA), we performed a randomized, double-blind, placebo-controlled clinical trial. Male patients with moderate AGA underwent treatment with either VPA (sodium valproate, 8.3%) or placebo spray for 24 weeks. The primary end-point for efficacy was the change in hair count during treatment, which was assessed by phototrichogram analysis. Of the 40 patients enrolled in the study, 27 (n = 15, VPA group; n = 12, placebo group) completed the entire protocol with good compliance. No statistical differences in age, hair loss duration and total hair count at baseline were found between the groups. The mean change in total hair count was significantly higher in the VPA group than in the placebo group (P = 0.047). Both groups experienced mostly mild and self-limited adverse events, but their differences in prevalence rates were similar between the two groups (P = 0.72). A subject treated with topical VPA developed ventricular tachycardia, but it did not seem to be related to the VPA spray. Topical VPA increased the total hair counts of our patients; therefore, it is a potential treatment option for AGA.


AlphaServer 4100 Performance Characterization


title: Design and simulation of self-tuning PID-type fuzzy adaptive control for an expert HVAC system

abstract: The modelling, numerical simulation and intelligent control of an expert HVAC (heating, ventilating and air-conditioning) system having two different zones with variable flow-rate were performed by considering the ambient temperature in this study. The sub-models of the system were obtained by deriving heat transfer equations of heat loss of two zones by conduction and convection, cooling unit and fan. All models of the variable flow-rate HVAC system were generated by using MATLAB/SIMULINK, and proportional-integral-derivative (PID) parameters were obtained by using Fuzzy sets. For comfortable of people the temperatures of the two different zones were decreased to 5^oC from the ambient temperature. The successful results were obtained by applying self-tuning proportional-integral-derivative (PID)-type fuzzy adaptive controller if comparing with the fuzzy PD-type and the classical PID controller. The obtained results were presented in a graphical form.


title: SSpro/ACCpro 5: almost perfect prediction of protein secondary structure and relative solvent accessibility using profiles, machine learning and structural similarity

abstract: Motivation: Accurately predicting protein secondary structure and relative solvent accessibility is important for the study of protein evolution, structure and function and as a component of protein 3D structure prediction pipelines. Most predictors use a combination of machine learning and profiles, and thus must be retrained and assessed periodically as the number of available protein sequences and structures continues to grow. Results: We present newly trained modular versions of the SSpro and ACCpro predictors of secondary structure and relative solvent accessibility together with their multi-class variants SSpro8 and ACCpro20. We introduce a sharp distinction between the use of sequence similarity alone, typically in the form of sequence profiles at the input level, and the additional use of sequence-based structural similarity, which uses similarity to sequences in the Protein Data Bank to infer annotations at the output level, and study their relative contributions to modern predictors. Using sequence similarity alone, SSpro’s accuracy is between 79 and 80% (79% for ACCpro) and no other predictor seems to exceed 82%. However, when sequence-based structural similarity is added, the accuracy of SSpro rises to 92.9% (90% for ACCpro). Thus, by combining both approaches, these problems appear now to be essentially solved, as an accuracy of 100% cannot be expected for several well-known reasons. These results point also to several open technical challenges, including (i) achieving on the order of 80% accuracy, without using any similarity with known proteins and (ii) achieving on the order of 85% accuracy, using sequence similarity alone. Availability and implementation: SSpro, SSpro8, ACCpro and ACCpro20 programs, data and web servers are available through the SCRATCH suite of protein structure predictors at http://scratch.


title: Detecting opinion leaders and trends in online social networks

abstract: Today, online social networks in the World Wide Web become increasingly interactive and networked. Web 2.0 technologies provide a multitude of platforms, such as blogs, wikis, and forums where for example consumers can disseminate data about products and manufacturers. This data provides an abundance of information on personal experiences and opinions which are extremely relevant for companies and sales organizations. A new approach based on text mining and social network analysis is presented which allows detecting opinion leaders and opinion trends. This allows getting a better understanding of the opinion formation. The overall concept is presented and illustrated by an example.


title: RAPD and SCAR markers linked to resistance to frogeye leaf spot in soybean

abstract: The soybean (Glycine max (L.) Merrill) frogeye leaf spot is caused by the fungus Cercospora sojina Hara and is a widespread disease in Brazil and other countries, causing severe losses in grain yield and also affecting seed quality. The availability of DNA markers linked to genes for resistance to this disease would accelerate breeding programs, particularly when other traits are also being evaluated. Bulked segregant analysis was applied to 3 F2 populations derived from crosses between the resistant cultivars Parana, Cristalina and Uberaba, and the susceptible cultivar Bossier. In the cross 'Parana' x 'Bossier', 2 RAPD markers were identified, CSOPA1800C and CSOPA21,250C, located at 4.4 ± 1.8 centiMorgans (cM) and 3.4 ± 1.7 cM respectively from the resistance locus. DNA fragments of similar molecular weight were observed in the population derived from the cross 'Cristalina' x 'Bossier' at 2.3 ± 1.2 and 4.7 ± 1.5 cM from the resistance locus, respectively. In the offspring of the cross 'Uberaba' x 'Bossier', a DNA fragment corresponding to marker CSOPA1800C was detected at 5.6 ± 2.1 cM from the resistance locus. Although marker CSOPA21,250C was not observed in this population, an additional marker was detected (CSOUB11,100C) at 6.7 ± 2.2 cM from the resistance locus. The 1,250 bp fragment of CSOPA21,250C was cloned and converted into a SCAR marker, which amplified a single fragment whose size corresponded to the cloned segment of the crosses involving cultivars Cristalina and Parana. Markers CSOPA1800C, CSOPA21,250C and CSOUB11,100C were mapped to soybean linkage group J with the aid of known SSRs linked to the Rcs3 locus, indicating that the RAPD and SCAR markers identified in our research also tag this resistance gene.


title: Demand analysis projections for recreational visits to countryside woodlands in Great Britain

abstract: Forests and woodlands have long been associated with tourism and recreation and there is a growing demand for improved planning tools to measure and maximize amenity value from policy intervention. Building on earlier studies, this study develops a trip generation function (TGF) to predict visit rates to public and privately owned forest sites in Great Britain. Using a zonal approach, the models were based on visit counts to 100 countryside woodland sites. A survey of forest managers was made to collect data on facilities and characteristics, and geographical information systems analysis was used to define outset zones and compile data on the population living within the zones and the quality and accessibility of alternative woodlands. A series of stand-alone TGFs were developed. In addition to size of population living within a 2-h travel time and substitute-site accessibility, the results highlighted the importance of the number of site facilities in determining visit rates. The results of a cross-validation suggest that the TGFs are sufficiently robust to predict visitor numbers at an aggregate level. However, the current value of this type of modelling to forest management decision making, and further advancement in modelling to allow transferability of results to unsurveyed sites is currently constrained by a number of data limitations, including the quality of available visit data.


title: Cloud user-centric enhancements of the simulator cloudsim to improve cloud deployment option analysis

abstract: Cloud environments can be simulated using the toolkit CloudSim. By employing concepts such as physical servers in datacenters, virtual machine allocation policies, or coarse-grained models of deployed software, it focuses on a cloud provider perspective. In contrast, a cloud user who wants to migrate complex systems to the cloud typically strives to find a cloud deployment option that is best suited for its sophisticated system architecture, is interested in determining the best trade-off between costs and performance, or wants to compare runtime reconfiguration plans, for instance. We present significant enhancements of CloudSim that allow to follow this cloud user perspective and enable the frictionless integration of fine-grained application models that, to a great extent, can be derived automatically from software systems. Our quantitative evaluation demonstrates the applicability and accuracy of our approach by comparing its simulation results with actual deployments that utilize the cloud environment Amazon EC2.


title: Parsing Excel formulas: A grammar and its application on 4 large datasets

abstract: Spreadsheets are popular end user programming tools, especially in the industrial world. This makes them interesting research targets. However, there does not exist a reliable grammar that is concise enough to facilitate formula parsing and analysis and to support research on spreadsheet codebases. This paper presents a grammar for spreadsheet formulas that can successfully parse 99.99% of more than 8 million unique formulas extracted from 4 spreadsheet datasets. Our grammar is compatible with the spreadsheet formula language, recognizes the spreadsheet formula elements that are required for supporting spreadsheets research, and produces parse trees aimed at further manipulation and analysis. Additionally, we use the grammar to analyze the characteristics of the formulas of the 4 datasets in 3 different dimensions: complexity, functionality, and data utilization. Our results show that (1) most Excel formulas are simple, however formulas with more than 50 functions or operations exist, (2) almost all formulas use data from other cells, which is often not local, and (3) a surprising number of referring mechanisms are used by less than 1% of the formulas.


title: Coupled Ensembles of Neural Networks

abstract: We investigate in this paper architectures of deep convolutional networks. Building on existing state of the art models, we propose a reconfiguration of the model parameters into several parallel branches at the global network level, each branch being a standalone CNN. We show that this arrangement is an efficient way to significantly reduce the number of parameters while at the same time improving the performance. The use of branches brings an additional form of regularization. In addition to splitting the parameters into parallel branches, we propose a tighter coupling of these branches by averaging their log-probabilities. The tighter coupling favours the learning of better representations, even at the level of the individual branches, as compared to when each branch is trained independently. We refer to this branched architecture as “coupled ensembles”. The approach is generic and can be applied to almost any neural network architecture. With coupled ensembles of DenseNet-BC networks and a 25M-parameter budget, we obtain error rates of 2.92%, 15.68% and 1.50% on CIFAR-10, CIFAR-100 and SVHN respectively. For the same parameter budget, DenseNet-Bchas an error rate of 3.46%, 17.18%, and 1.8% respectively. With ensembles of coupled ensembles of DenseNet-BC networks with a 50M-parameter budget, we obtain error rates of 2.72%, 15.13% and 1.42 % respectively on these tasks.


title: Acceptance model of a Hospital Information System

abstract: PURPOSE ::: The purpose of this study is to develop a model of Hospital Information System (HIS) user acceptance focusing on human, technological, and organizational characteristics for supporting government eHealth programs. This model was then tested to see which hospital type in Indonesia would benefit from the model to resolve problems related to HIS user acceptance. ::: ::: ::: METHOD ::: This study used qualitative and quantitative approaches with case studies at four privately owned hospitals and three government-owned hospitals, which are general hospitals in Indonesia. The respondents involved in this study are low-level and mid-level hospital management officers, doctors, nurses, and administrative staff who work at medical record, inpatient, outpatient, emergency, pharmacy, and information technology units. Data was processed using Structural Equation Modeling (SEM) and AMOS 21.0. ::: ::: ::: RESULTS ::: The study concludes that non-technological factors, such as human characteristics (i.e. compatibility, information security expectancy, and self-efficacy), and organizational characteristics (i.e. management support, facilitating conditions, and user involvement) which have level of significance of p<0.05, significantly influenced users' opinions of both the ease of use and the benefits of the HIS. This study found that different factors may affect the acceptance of each user in each type of hospital regarding the use of HIS. Finally, this model is best suited for government-owned hospitals. ::: ::: ::: CONCLUSIONS ::: Based on the results of this study, hospital management and IT developers should have more understanding on the non-technological factors to better plan for HIS implementation. Support from management is critical to the sustainability of HIS implementation to ensure HIS is easy to use and provides benefits to the users as well as hospitals. Finally, this study could assist hospital management and IT developers, as well as researchers, to understand the obstacles faced by hospitals in implementing HIS.


title: Translation UML activity diagram into colored Petri net with inscription

abstract: UML activity diagram is widely used to describe the behavior of the software system. Unfortunately, there is still no practical tool to verify the UML diagrams automatically. This paper proposes an alternative to translate UML activity diagram into a colored petri nets with inscription. The model translation rules are proposed to guide the automatic translation of the activity diagram with atomic action into a CPN model. Moreover, the relevant basic arc inscriptions are generated without manual elaboration. The resulting CPN with inscription is correctly verified as expected.


title: Brain structures and functional connectivity associated with individual differences in Internet tendency in healthy young adults

abstract: Abstract Internet addiction (IA) incurs significant social and financial costs in the form of physical side-effects, academic and occupational impairment, and serious relationship problems. The majority of previous studies on Internet addiction disorders (IAD) have focused on structural and functional abnormalities, while few studies have simultaneously investigated the structural and functional brain alterations underlying individual differences in IA tendencies measured by questionnaires in a healthy sample. Here we combined structural (regional gray matter volume, rGMV) and functional (resting-state functional connectivity, rsFC) information to explore the neural mechanisms underlying IAT in a large sample of 260 healthy young adults. The results showed that IAT scores were significantly and positively correlated with rGMV in the right dorsolateral prefrontal cortex (DLPFC, one key node of the cognitive control network, CCN), which might reflect reduced functioning of inhibitory control. More interestingly, decreased anticorrelations between the right DLPFC and the medial prefrontal cortex/rostral anterior cingulate cortex (mPFC/rACC, one key node of the default mode network, DMN) were associated with higher IAT scores, which might be associated with reduced efficiency of the CCN and DMN (e.g., diminished cognitive control and self-monitoring). Furthermore, the Stroop interference effect was positively associated with the volume of the DLPFC and with the IA scores, as well as with the connectivity between DLPFC and mPFC, which further indicated that rGMV variations in the DLPFC and decreased anticonnections between the DLPFC and mPFC may reflect addiction-related reduced inhibitory control and cognitive efficiency. These findings suggest the combination of structural and functional information can provide a valuable basis for further understanding of the mechanisms and pathogenesis of IA.


Low-resource routing attacks against anonymous systems


title: The Power Grid as a Complex Network: a Survey

abstract: The statistical tools of Complex Network Analysis are of useful to understand salient properties of complex systems, may these be natural or pertaining human engineered infrastructures. One of these that is receiving growing attention for its societal relevance is that of electricity distribution. In this paper, we present a survey of the most relevant scientific studies investigating the properties of different Power Grids infrastructures using Complex Network Analysis techniques and methodologies. We categorize and explore the most relevant literature works considering general topological properties, physical properties, and differences between the various graph-related indicators and reliability aspects. We also trace the evolution in such field of the approach of study during the years to see the improvement achieved in the analysis.


title: Segmentation of malaria parasites in peripheral blood smear images

abstract: Detection of malaria parasites in stained blood smears is critical for treatment of the disease. Automation of this process will help in reducing the time taken for diagnosis and the chance for human errors. However, the variability and artifacts in microscope images of blood samples pose significant challenges for accurate detection. A scheme based on HSV color space that segments Red Blood Cells and parasites by detecting dominant hue range and by calculating optimal saturation thresholds is presented in this paper. Methods that are less computation-intensive than existing approaches are proposed to remove artifacts. The scheme is evaluated using images taken from Leishman-stained blood smears. Sensitivity and specificity of the scheme are found to be 83% and 98% respectively.


title: A Novel Variable Step Size NLMS Algorithm Based on the Power Estimate of the System Noise

abstract: To overcome the tradeoff of the conventional normalized least mean square (NLMS) algorithm between fast convergence rate and low steady-state misalignment, this paper proposes a variable step size (VSS) NLMS algorithm by devising a new strategy to update the step size. In this strategy, the input signal power and the cross-correlation between the input signal and the error signal are used to estimate the true tracking error power, reducing the effect of the system noise on the algorithm performance. Moreover, the steady-state performances of the algorithm are provided for Gaussian white input signal and are verified by simulations. Finally, simulation results in the context of the system identification and acoustic echo cancellation (AEC) have demonstrated that the proposed algorithm has lower steady-state misalignment than other VSS algorithms.


title: Sparsity-Cognizant Total Least-Squares for Perturbed Compressive Sampling

abstract: Solving linear regression problems based on the total least-squares (TLS) criterion has well-documented merits in various applications, where perturbations appear both in the data vector as well as in the regression matrix. However, existing TLS approaches do not account for sparsity possibly present in the unknown vector of regression coefficients. On the other hand, sparsity is the key attribute exploited by modern compressive sampling and variable selection approaches to linear regression, which include noise in the data, but do not account for perturbations in the regression matrix. The present paper fills this gap by formulating and solving TLS optimization problems under sparsity constraints. Near-optimum and reduced-complexity suboptimum sparse (S-) TLS algorithms are developed to address the perturbed compressive sampling (and the related dictionary learning) challenge, when there is a mismatch between the true and adopted bases over which the unknown vector is sparse. The novel S-TLS schemes also allow for perturbations in the regression matrix of the least-absolute selection and shrinkage selection operator (Lasso), and endow TLS approaches with ability to cope with sparse, under-determined"errors-in-variables"models. Interesting generalizations can further exploit prior knowledge on the perturbations to obtain novel weighted and structured S-TLS solvers. Analysis and simulations demonstrate the practical impact of S-TLS in calibrating the mismatch effects of contemporary grid-based approaches to cognitive radio sensing, and robust direction-of-arrival estimation using antenna arrays.


title: Multicomponent behavioral treatment for chronic combat-related posttraumatic stress disorder: a randomized controlled trial.

abstract: This study examined the efficacy of a multicomponent cognitive-behavioral therapy, Trauma Management Therapy, which combines exposure therapy and social emotional rehabilitation, to exposure therapy only in a group of male combat veterans with chronic posttraumatic stress disorder (PTSD). Thirty-five male Vietnam veterans with PTSD were randomly assigned to receive either Trauma Management Therapy (TMT) or Exposure Therapy Only (EXP). Participants were assessed at pre-treatment, mid-treatment, and post-treatment. Primary clinical outcomes were reduction of PTSD symptoms and improved social emotional functioning. Results indicated that veterans in both conditions showed statistically significant and clinically meaningful reductions in PTSD symptoms from pre- to post-treatment, though consistent with a priori hypotheses there were no group differences on PTSD variables. However, compared to the EXP group, participants in the TMT group showed increased frequency in social activities and greater time spent in social activities. These changes occurred from mid-treatment (after completion of exposure therapy) to post-treatment (after completion of the social emotional rehabilitation component); supporting the hypothesis that TMT alone would result in improved social functioning. Although the TMT group also had a significant decrease in episodes of physical rage, that change occurred prior to introduction of the social emotional component of TMT. This study demonstrates efficacy of exposure therapy for treating the core symptoms of PTSD among combat veterans with a severe and chronic form of this disorder. Moreover, multi-component CBT shows promise for improving social functioning beyond that provided by exposure therapy alone, particularly by increasing social engagement/interpersonal functioning in a cohort of veterans with severe and chronic PTSD.


Security vs safety


title: Disinfection and sterilization: An overview

abstract: All invasive procedures involve contact by a medical device or surgical instrument with a patient’s sterile tissue or mucous membranes. The level of disinfection or sterilization is dependent on the intended use of the object: critical (items that contact sterile tissue such as surgical instruments), semicritical (items that contact mucous membrane such as endoscopes), and noncritical (devices that contact only intact skin such as stethoscopes) items require sterilization, high-level disinfection, and low-level disinfection, respectively. Cleaning must always precede high-level disinfection and sterilization.


title: Optimization and Analysis of Probabilistic Caching in $N$ -Tier Heterogeneous Networks

abstract: In this paper, we study the probabilistic caching for an $N$ -tier wireless heterogeneous network (HetNet) using stochastic geometry. A general and tractable expression of the successful delivery probability (SDP) is first derived. We then optimize the caching probabilities for maximizing the SDP in the high signal-to-noise ratio regime. The problem is proved to be convex and solved efficiently. We next establish an interesting connection between $N$ -tier HetNets and single-tier networks. Unlike the single-tier network where the optimal performance only depends on the cache size, the optimal performance of $N$ -tier HetNets depends also on the base station (BS) densities. The performance upper bound is, however, determined by an equivalent single-tier network. We further show that with uniform caching probabilities regardless of content popularities, to achieve a target SDP, the BS density of a tier can be reduced by increasing the cache size of the tier when the cache size is larger than a threshold; otherwise, the BS density and BS cache size can be increased simultaneously. It is also found analytically that the BS density of a tier is inverse to the BS cache size of the same tier and is linear to BS cache sizes of other tiers.


title: Building the model of sustainable trust in e-government

abstract: Although e-government is broadly developed in the world, many citizens are still preferred to use traditional methods. Trust is confirmed to be a critical factor of the success of e-government. From the perspective of the establishment of citizens' sustainable trust in e-government, we construct a conceptual model of sustainable trust in e-government integrated with the updated DeLone and McLean's IS success model.


title: EEG-EMG based Hybrid Brain Computer Interface for Triggering Hand Exoskeleton for Neuro-Rehabilitation

abstract: Traditionally a Brain-Computer Interface (BCI) system uses Electroencephalogram (EEG) signals for communication and control applications. In recent years different biological signals are also combined with EEG signals to produce hybrid BCI devices to overcome the limitation of lower accuracy rates in BCI. This paper presents a new approach of combining EEG and Electromyogram (EMG) signals using the spectral power correlation (SPC) to create a hybrid BCI device for controlling a hand exoskeleton. The proposed method was tested on 10 healthy individuals for measuring its performance level in terms of accuracy. The EEG-EMG SPC based hybrid BCI was trained to classify the grasp attempt and resting states of the user. Upon successful detection of a grasp attempt, the hybrid BCI triggers the hand exoskeleton to perform a finger flexion-extension motion. The proposed EEG-EMG SPC method is also compared with the conventional only EEG based method which uses common spatial pattern (CSP) based spatial filtering. The results have shown that the proposed EEG-EMG SPC method yielded an average accuracy of 90±4.86% while the conventional EEG-CSP method yielded 79.75±5.71%. This significantly (p


title: Story and text generation through computational analogy in the Riu system

abstract: A key challenge in computational narrative is story generation. In this paper we focus on analogy-based story generation, and, specifically, on how to generate both story and text using analogy. We present a dual representation formalism where a human-understandable representation (composed of English sentences) and a computer-understandable representation (consisting in a graph) are linked together in order to generate both story and natural language text by analogy. We have implemented our technique in the Riu interactive narrative system.


title: Age and Gender Identification in Social Media

abstract: This paper describes the submission of the University of Washington's Center for Data Science to the PAN 2014 author profiling task. We examine the predictive quality in terms of age and gender of several sets of features extracted from various genres of online social media. Through comparison, we establish a feature set which maximizes accuracy of gender and age prediction across all genres examined. We report accuracies obtained by two approaches to the multi-label classification problem of predicting both age and gender; a model wherein the multi-label problem is reduced to a single-label problem using powerset transformation, and a chained classifier approach wherein the output of a dedicated classifier for gender is used as input for a classifier for age.


Comparison Between Conventional Buck Converter And Switched Inductor Converter


title: Language Transfer Learning for Supervised Lexical Substitution

abstract: We propose a framework for lexical substitution that is able to perform transfer learning across languages. Datasets for this task are available in at least three languages (English, Italian, and German). Previous work has addressed each of these tasks in isolation. In contrast, we regard the union of three shared tasks as a combined multilingual dataset. We show that a supervised system can be trained effectively, even if training and evaluation data are from different languages. Successful transfer learning between languages suggests that the learned model is in fact independent of the underlying language. We combine state-of-the-art unsupervised features obtained from syntactic word embeddings and distributional thesauri in a supervised delexicalized ranking system. Our system improves over state of the art in the full lexical substitution task in all three languages.


title: Context-Aware Computing : The CyberDesk Project

abstract: The CyberDesk project is aimed at providing a software architecture that dynamically integrates software modules. This integration is driven by a user’s context, where context includes the user’s physical, social, emotional, and mental (focus-of-attention) environments. While a user’s context changes in all settings, it tends to change most frequently in a mobile setting. We have used the CyberDesk system in a desktop setting and are currently using it to build an intelligent home environment.


title: A Target-Centric Ontology for Intrusion Detection

abstract: We present an approach to some security problems in multi-agent systems based on distributed trust and the delegation of permissions, and credibility. We assume an open environment in which agents must interact with other agents with which they are not familiar. In particular, an agent will receive requests and assertions from other agents and must decide how to act on the requests and assess the credibility of the assertions. In a closed environment, agents have well known and familiar transaction partners whose rights and credibility are known. The problem thus reduces to authentication – the reliable identiﬁcation of agents’ true identity. In an open environment, however, agents must transact business even when knowing the true identities is un-informative. Decisions about who to believe and who to serve must be based on an agent’s properties. These properties are established by proving them from an agent’s credentials, delegation assertions, and the appropriate security policy. We begin by describing our approach and the concepts on which it is built. Then we present a design that provides security functions (authorization and credibility assessment) in a typical agent framework (FIPA) and describe initial work in its realization using the semantic web language DAML+OIL.


title: COLORS AND EMOTIONS: PREFERENCES AND COMBINATIONS

abstract: Abstract Within three age groups (7-year-old children, 11-year-old children, and adults), preferences for colors and emotions were established by means of two distinct paired-comparison tasks. In a subsequent task, participants were asked to link colors to emotions by selecting an appropriate color. It was hypothesized that the number of times that each color was tied to a specific emotion would be predictable from the separate preferences for colors and emotions. Within age groups, participants had consistent preferences for colors and emotions, but preferences differed from one age group to another. Especially in the youngest group, the pattern of combinations between colors and emotions appeared to be meaningfully related to the preference order for colors and emotions.


title: Smarter Cities and Their Innovation Challenges

abstract: The transformation to smarter cities will require innovation in planning, management, and operations. Several ongoing projects around the world illustrate the opportunities and challenges of this transformation. Cities must get smarter to address an array of emerging urbanization challenges, and as the projects highlighted in this article show, several distinct paths are available. The number of cities worldwide pursuing smarter transformation is growing rapidly. However, these efforts face many political, socioeconomic, and technical hurdles. Changing the status quo is always difficult for city administrators, and smarter city initiatives often require extensive coordination, sponsorship, and support across multiple functional silos. The need to visibly demonstrate a continuous return on investment also presents a challenge. The technical obstacles will center on achieving system interoperability, ensuring security and privacy, accommodating a proliferation of sensors and devices, and adopting a new closed-loop human-computer interaction paradigm.


title: Context-Dependent Piano Music Transcription With Convolutional Sparse Coding

abstract: This paper presents a novel approach to automatic transcription of piano music in a context-dependent setting. This approach employs convolutional sparse coding to approximate the music waveform as the summation of piano note waveforms (dictionary elements) convolved with their temporal activations (onset transcription). The piano note waveforms are pre-recorded for the specific piano to be transcribed in the specific environment. During transcription, the note waveforms are fixed and their temporal activations are estimated and post-processed to obtain the pitch and onset transcription. This approach works in the time domain, models temporal evolution of piano notes, and estimates pitches and onsets simultaneously in the same framework. Experiments show that it significantly outperforms a state-of-the-art music transcription method trained in the same context-dependent setting, in both transcription accuracy and time precision, in various scenarios including synthetic, anechoic, noisy, and reverberant environments.


title: Why Deep Learning Is Changing the Way to Approach NGS Data Processing: A Review

abstract: Nowadays, big data analytics in genomics is an emerging research topic. In fact, the large amount of genomics data originated by emerging next-generation sequencing (NGS) techniques requires more and more fast and sophisticated algorithms. In this context, deep learning is re-emerging as a possible approach to speed up the DNA sequencing process. In this review, we specifically discuss such a trend. In particular, starting from an analysis of the interest of the Internet community in both NGS and deep learning, we present a taxonomic analysis highlighting the major software solutions based on deep learning algorithms available for each specific NGS application field. We discuss future challenges in the perspective of cloud computing services aimed at deep learning based solutions for NGS.


title: DeepSPINE: Automated Lumbar Vertebral Segmentation, Disc-level Designation, and Spinal Stenosis Grading Using Deep Learning

abstract: The high prevalence of spinal stenosis results in a large volume of MRI imaging, yet interpretation can be time-consuming with high inter-reader variability even among the most specialized radiologists. In this paper, we develop an efficient methodology to leverage the subject-matter-expertise stored in large-scale archival reporting and image data for a deep-learning approach to fully-automated lumbar spinal stenosis grading. Specifically, we introduce three major contributions: (1) a natural-language-processing scheme to extract level-by-level ground-truth labels from free-text radiology reports for the various types and grades of spinal stenosis (2) accurate vertebral segmentation and disc-level localization using a U-Net architecture combined with a spine-curve fitting method, and (3) a multi-input, multi-task, and multi-class convolutional neural network to perform central canal and foraminal stenosis grading on both axial and sagittal imaging series inputs with the extracted report-derived labels applied to corresponding imaging level segments. This study uses a large dataset of 22796 disc-levels extracted from 4075 patients. We achieve state-of-the-art performance on lumbar spinal stenosis classification and expect the technique will increase both radiology workflow efficiency and the perceived value of radiology reports for referring clinicians and patients.


title: Competition and Outsourcing with Scale Economies

abstract: Scale economies are commonplace in operations, yet because of analytical challenges, relatively little is known about how firms should compete in their presence. This paper presents a model of competition between two firms that face scale economies; (i.e., each firm's cost per unit of demand is decreasing in demand). A general framework is used, which incorporates competition between two service providers with price- and time-sensitive demand (a queuing game), and competition between two retailers with fixed-ordering costs and pricesensitive consumers (an Economic Order Quantity game). Reasonably general conditions are provided under which there exists at most one equilibrium, with both firms participating in the market. We demonstrate, in the context of the queuing game, that the lower cost firm in equilibrium may have a higher market share and a higher price, an enviable situation. We also allow each firm to outsource their production process to a supplier. Even if the supplier's technology is no better than the firms' technology and the supplier is required to establish dedicated capacity (so the supplier's scale can be no greater than either firm's scale), we show that the firms strictly prefer to outsource. We conclude that scale economies provide a strong motivation for outsourcing that has not previously been identified in the literature.


title: Detecting spammers and content promoters in online video social networks

abstract: A number of online video social networks, out of which YouTube is the most popular, provides features that allow users to post a video as a response to a discussion topic. These features open opportunities for users to introduce polluted content, or simply pollution, into the system. For instance, spammers may post an unrelated video as response to a popular one aiming at increasing the likelihood of the response being viewed by a larger number of users. Moreover, opportunistic users--promoters--may try to gain visibility to a specific video by posting a large number of (potentially unrelated) responses to boost the rank of the responded video, making it appear in the top lists maintained by the system. Content pollution may jeopardize the trust of users on the system, thus compromising its success in promoting social interactions. In spite of that, the available literature is very limited in providing a deep understanding of this problem. In this paper, we go a step further by addressing the issue of detecting video spammers and promoters. Towards that end, we manually build a test collection of real YouTube users, classifying them as spammers, promoters, and legitimates. Using our test collection, we provide a characterization of social and content attributes that may help distinguish each user class. We also investigate the feasibility of using a state-of-the-art supervised classification algorithm to detect spammers and promoters, and assess its effectiveness in our test collection. We found that our approach is able to correctly identify the majority of the promoters, misclassifying only a small percentage of legitimate users. In contrast, although we are able to detect a significant fraction of spammers, they showed to be much harder to distinguish from legitimate users.


title: The brain–computer interface cycle

abstract: Brain–computer interfaces (BCIs) have attracted much attention recently, triggered by new scientific progress in understanding brain function and by impressive applications. The aim of this review is to give an overview of the various steps in the BCI cycle, i.e., the loop from the measurement of brain activity, classification of data, feedback to the subject and the effect of feedback on brain activity. In this article we will review the critical steps of the BCI cycle, the present issues and state-of-the-art results. Moreover, we will develop a vision on how recently obtained results may contribute to new insights in neurocognition and, in particular, in the neural representation of perceived stimuli, intended actions and emotions. Now is the right time to explore what can be gained by embracing real-time, online BCI and by adding it to the set of experimental tools already available to the cognitive neuroscientist. We close by pointing out some unresolved issues and present our view on how BCI could become an important new tool for probing human cognition.


title: Automatic discovery of botnet communities on large-scale communication networks

abstract: Botnets are networks of compromised computers infected with malicious code that can be controlled remotely under a common command and control (C&C) channel. Recognized as one the most serious security threats on current Internet infrastructure, advanced botnets are hidden not only in existing well known network applications (e.g. IRC, HTTP, or Peer-to-Peer) but also in some unknown or novel (creative) applications, which makes the botnet detection a challenging problem. Most current attempts for detecting botnets are to examine traffic content for bot signatures on selected network links or by setting up honeypots. In this paper, we propose a new hierarchical framework to automatically discover botnets on a large-scale WiFi ISP network, in which we first classify the network traffic into different application communities by using payload signatures and a novel cross-association clustering algorithm, and then on each obtained application community, we analyze the temporal-frequent characteristics of flows that lead to the differentiation of malicious channels created by bots from normal traffic generated by human beings. We evaluate our approach with about 100 million flows collected over three consecutive days on a large-scale WiFi ISP network and results show the proposed approach successfully detects two types of botnet application flows (i.e. Blackenergy HTTP bot and Kaiten IRC bot) from about 100 million flows with a high detection rate and an acceptable low false alarm rate.


title: Dual-band, dual-polarization, interleaved cross-dipole and cavity-backed disc elements phased array antenna

abstract: In ship, submarine, or airborne satellite communication or multi-function radar operations, dual-band phased array antennas with dual-linear or circular polarizations are needed. We present a dual-band/dual-polarization phased array design using a interleaved cross-dipole radiator and a cavity-backed disk radiator in the same lattice structure. The low band radiator is a disk radiator sitting on top of a dielectric puck in a cavity and the high band radiator is the cross dipole element printed on a low-K dielectric layer above the disk-cavity plane. Dual-linear or circular polarizations can be achieved using these two elements to cover two separate bands with about a 2:1 ratio. This is a relatively low profile, compact and rigid array. Good impedance match for both bands can be obtained over a wide scan volume.


title: An Evidence-Based Model of Work Engagement

abstract: Employees who are engaged in their work are fully connected with their work roles. They are bursting with energy, dedicated to their work, and immersed in their work activities. This article presents an overview of the concept of work engagement. I discuss the antecedents and consequences of engagement. The review shows that job and personal resources are the main predictors of engagement. These resources gain their salience in the context of high job demands. Engaged workers are more open to new information, more productive, and more willing to go the extra mile. Moreover, engaged workers proactively change their work environment in order to stay engaged. The findings of previous studies are integrated in an overall model that can be used to develop work engagement and advance job performance in today's workplace.


title: Reinforcement learning with Marr

abstract: To many, the poster child for David Marr's famous three levels of scientific inquiry is reinforcement learning — a computational theory of reward optimization, which readily prescribes algorithmic solutions that evidence striking resemblance to signals found in the brain, suggesting a straightforward neural implementation. Here we review questions that remain open at each level of analysis, concluding that the path forward to their resolution calls for inspiration across levels, rather than a focus on mutual constraints.