title: Challenges in Model-Driven Software Engineering

abstract: After more than a decade of research in Model-Driven Engineering (MDE), the state-of-the-art and the state-of-the-practice in MDE has significantly progressed. Therefore, during this workshop we raised the question of how to proceed next, and we identified a number of future challenges in the field of MDE. The objective of the workshop was to provide a forum for discussing the future of MDE research and practice. Seven presenters shared their vision on the future challenges in the field of MDE. Four breakout groups discussed scalability, consistency and co-evolution, formal foundations, and industrial adoption, respectively. These themes were identified as major categories of challenges by the participants. This report summarises the different presentations, the MDE challenges identified by the workshop participants, and the discussions of the breakout groups.


title: Knowledge Representation with Ontologies: The Present and Future

abstract: Recently, we have seen an explosion of interest in ontologies as artifacts to represent human knowledge and as critical components in knowledge management, the semantic Web, business-to-business applications, and several other application areas. Various research communities commonly assume that ontologies are the appropriate modeling structure for representing knowledge. However, little discussion has occurred regarding the actual range of knowledge an ontology can successfully represent.


title: An Empirical Comparison of PDDL-based and ASP-based Task Planners

abstract: General purpose planners enable AI systems to solve many different types of planning problems. However, many different planners exist, each with different strengths and weaknesses, and there are no general rules for which planner would be best to apply to a given problem. In this paper, we empirically compare the performance of state-of-the-art planners that use either the Planning Domain Description Language (PDDL), or Answer Set Programming (ASP) as the underlying action language. PDDL is designed for automated planning, and PDDL-based planners are widely used for a variety of planning problems. ASP is designed for knowledge-intensive reasoning, but can also be used for solving planning problems. Given domain encodings that are as similar as possible, we find that PDDL-based planners perform better on problems with longer solutions, and ASP-based planners are better on tasks with a large number of objects or in which complex reasoning is required to reason about action preconditions and effects. The resulting analysis can inform selection among general purpose planning systems for a particular domain.


title: Adaptive Critic-Based Event-Triggered Control for HVAC System

abstract: The heating, ventilation, and air conditioning system is an important component for achieving desired thermal condition in rooms or spaces in buildings, office complex, or airports. This paper proposes a real-time event-triggered adaptive critic controller for generating near optimal control actions to achieve desired temperatures. The desired temperatures may have variable or fixed values over time. The real-time controller is designed in two phases. Initially event-triggered control actions are generated by linear quadratic regulator for small period while the actor-critic network of controller is trained. Later, adaptive critic controller takes over for event-based actions. Hence, the event triggering conditions for both general linear and nonlinear discrete time systems using Lyapunov stability analysis are derived in this paper. The event-based actor-critic network weight update formulation and ultimate boundedness of parameters are also presented in this paper. The proposed approach has been validated for different and common temperature sets for four zones, where the control execution events are minimized to 20 $\%$ and 26 $\%$ , respectively.


title: Vortex flow structures and interactions for the optimum thrust efficiency of a heaving airfoil at different mean angles of attack

abstract: The thrust efficiency of a two-dimensional heaving airfoil is studied computationally for a low Reynolds number using a vortex force decomposition. The auxiliary potentials that separate the total vortex force into lift and drag (or thrust) are obtained analytically by using an elliptic airfoil. With these auxiliary potentials, the added-mass components of the lift and drag (or thrust) coefficients are also obtained analytically for any heaving motion of the airfoil and for any value of the mean angle of attack α. The contributions of the leading- and trailing-edge vortices to the thrust during their down- and up-stroke evolutions are computed quantitatively with this formulation for different dimensionless frequencies and heave amplitudes (Stc and Sta) and for several values of α. Very different types of flows, periodic, quasi-periodic, and chaotic described as Stc, Sta, and α, are varied. The optimum values of these parameters for maximum thrust efficiency are obtained and explained in terms of the inte...


title: Biological subtypes of breast cancer: Prognostic and therapeutic implications

abstract: Breast cancer is a heterogeneous complex of diseases, a spectrum of many subtypes with distinct biological features that lead to differences in response patterns to various treatment modalities and clinical outcomes. Traditional classification systems regarding biological characteristics may have limitations for patient-tailored treatment strategies. Tumors with similar clinical and pathological presentations may have different behaviors. Analyses of breast cancer with new molecular techniques now hold promise for the development of more accurate tests for the prediction of recurrence. Gene signatures have been developed as predictors of response to therapy and protein gene products that have direct roles in driving the biology and clinical behavior of cancer cells are potential targets for the development of novel therapeutics. The present review summarizes current knowledge in breast cancer molecular biology, focusing on novel prognostic and predictive factors.


title: A New Low Loss Rotman Lens Design Using a Graded Dielectric Substrate

abstract: In this paper, a new low loss graded dielectric Rotman lens design is presented. The graded dielectric allows for the enhanced focusing of the energy between the beam ports and the array ports of the lens, thereby minimizing the energy spill-over to the Rotman lens sides. New lens design equations are presented, which account for the change in effective wavelength and ray bending within the lens. The graded dielectric within the lens was fabricated by forming a periodic lattice of voids, with a variable density, in the lens substrate. Compared to a conventional Rotman lens, the new graded dielectric Rotman lens shows a measured insertion loss improvement of up to 2.1 dB. In addition, the degradation in peak power at the extreme scan angles is improved from 1.5 to 0.7 dB.


title: Design, Control, and Energetics of an Electrically Actuated Legged Robot

abstract: To study the design, control and energetics of autonomous dynamically stable legged machines we have built a planar one-legged robot, the ARL Monopod. Its top running speed of 4.3 km/h (1.2 m/s) makes it the fastest electrically actuated legged robot to date. We adapted Raibert's control laws for the low power electric actuation necessary for autonomous locomotion and performed a detailed energetic analysis of our experiments. A comparison shows that the ARL Monopod with its 125 W average power consumption is more energy efficient than previously built robots.


title: A Comparative Analysis of Enterprise Architecture Frameworks Based on EA Quality Attributes

abstract: Many Enterprise Architecture Frameworks (EAFs) are in use today to guide or serve as a model for the enterprise architecture development. Researchers offer general comparative information about EAF. However, there has been little work on the quality of Enterprise Architecture (EA). This study provides the characteristics of the five EAFs using comparative analysis based on Enterprise Architecture Quality Attributes (EAQAs). The attribute of EA quality was extracted from the EAFs and the definition of EAQA was defined from EA user viewpoint. The criteria for each quality attribute were developed to compare EAFs using four dimensional concepts. This paper compared several frameworks using the criteria to provide guidance in the selection of an EAF that meets the quality requirement of EA


title: Driver behavior modeling near intersections using Hidden Markov Model based on genetic algorithm

abstract: Driver behavior modeling plays a significant role in the development of Advanced Driver Assistance Systems (ADAS) for assisting drivers in different driving scenarios. One of the scenarios where high numbers of traffic accidents occur is road intersection. It is vital to develop driver behavior models near intersections in order for the ADAS to plan a proper action in avoiding accidents. In this paper, Hidden Markov Models (HMMs) for driver behavior near intersections are trained using Genetic Algorithm combined with Baum-Welch Algorithm based on the hybrid-state system (HSS) framework. HMM is usually trained using Baum-Welch which is easily trapped at local maxima. GA solves this problem by searching the entire solution space. Consequently, the best driver behavior model is trained. In the HSS framework, the vehicle dynamics are represented as a continuous-state system (CSS) and the decisions of the driver are represented as a discrete-state system (DSS). The continuous observations from the vehicle, such as acceleration, velocity and yaw-rate, are used by the proposed technique to estimate the driver's intention at each time step. The models are trained and tested using naturalistic driving data obtained from the Ohio State University, in an experiment with a sensor-equipped vehicle that was driven in the streets of Columbus, OH. The proposed framework improves the HMM accuracy in estimating the driver's intention when approaching an intersection with over 10% higher accuracy.


Exploiting Source-side Monolingual Data in Neural Machine Translation


title: Bluetooth Remote Home Automation System Using Android Application

abstract: This paper presents the overall design of Home Automation System (HAS) with low cost and wireless remote control. This system is designed to assist and provide support in order to fulfill the needs of elderly and disabled in home. Also, the smart home concept in the system improves the standard living at home. The main control system implements wireless Bluetooth technology to provide remote access from PC/laptop or smart phone. The design remains the existing electrical switches and provides more safety control on the switches with low voltage activating method. The switches status is synchronized in all the control system whereby every user interface indicates the real time existing switches status. The system intended to control electrical appliances and devices in house with relatively low cost design, user-friendly interface and ease of installation.


Detecting the Gender of a Tweet Sender


title: Fuzzy Clustered Probabilistic and Multi Layered Feed Forward Neural Networks for Electrocardiogram Arrhythmia Classification

abstract: The role of electrocardiogram (ECG) as a noninvasive technique for detecting and diagnosing cardiac problems cannot be overemphasized. This paper introduces a fuzzy C-mean (FCM) clustered probabilistic neural network (PNN) for the discrimination of eight types of ECG beats. The performance has been compared with FCM clustered multi layered feed forward network (MLFFN) trained with back propagation algorithm. Important parameters are extracted from each ECG beat and feature reduction has been carried out using FCM clustering. The cluster centers form the input of neural network classifiers. The extensive analysis using the MIT-BIH arrhythmia database has shown an average classification accuracy of 97.54% with FCM clustered MLFFN and 99.58% with FCM clustered PNN. Fuzzy clustering improves the classification speed as well. The result reveals the capability of the FCM clustered PNN in the computer-aided diagnosis of ECG abnormalities.


title: Multi-agent framework for social CRM: Extracting and analyzing opinions

abstract: Numerous studies have discussed the benefits of using social networks, even companies started to exploit the usefulness of this valuable information sources. Collecting social data then integrating them into a CRM (Customer Relationship Management) has led companies to understand the customer needs and therefore to improve the development process of their products or their services quality. In this work, we propose a multi-agent framework for analyzing extracted opinions from social media. In the development process, we were brought to consider the huge volumes of data (Big Data) and the response time. To do so, an architecture based on Map/Reduce analysis using Hadoop was made in order to perform the data refinement (classify or remove special words or delete the unvaluable reviews) and sentiment analysis (Sentigem). Finally, a study case using Twitter (Twitter4J API) as a data source, was made to verify the effectiveness of the proposed framework.


Face Recognition-based Lecture Attendance System


title: ScanMed: A Mobile Medicine Adherence Application with Intake Validation Using QR Code

abstract: The impact of medication adherence is proven to affect hospitalization risk and healthcare cost. In Malaysia, failure in taking medication as prescribed is a complicated and common problem. There are many reasons people fail to take their medication as prescribed. Some of the few common issues are remembering to consume their medicine timely, doubts in medication effectiveness, concerns on side effects or having difficulty in taking the medication (especially with injections or inhalers) and rising cost of prescribed medications. Tracking patient medication intake leads to many advantages, one of which is lower medication due to adherence, health-care cost which effects insurance coverage premiums. In this paper, we develop an application to track medication using a smart phone and the use of QR code. Each medication transaction requires a proof of work to signify that the user have taken their medication at the correct date and time. A QR-Code reader will be used to capture medication taken printed on medication labels and each scan is considered as a proof of work. Our contribution is to create a mobile application that can help track medication intake, as well as remind, inform and warn users about the medication that they are taking.


title: Presumptions and actions affecting an e-learning adoption by the educational system - Implementation using virtual private networks

abstract: In this paper we present a model of e-learning suitable for teacher training sessions. The main purpose of our work is to define the components of the educational system which influences the successful adoption of e-learning in the field of education. We also present the factors of the readiness of e-learning mentioned in the literature available and classifies them into the 3 major categories that constitute the components of every organization and consequently that of education. Finally, we present an implementation model of e-learning through the use of virtual private networks, which lends an added value to the realization of e-learning.


title: Dermatological disease detection using image processing and artificial neural network

abstract: Skin diseases are among the most common health problems worldwide. In this article we proposed a method that uses computer vision based techniques to detect various kinds of dermatological skin diseases. We have used different types of image processing algorithms for feature extraction and feed forward artificial neural network for training and testing purpose. The system works on two phases- first pre-process the colour skin images to extract significant features and later identifies the diseases. The system successfully detects 9 different types of dermatological skin diseases with an accuracy rate of 90%.


title: A broadband microstrip-to-CPS transition using composite right/left-handed transmission lines with an antenna application

abstract: A broadband microstrip-to-coplanar strip (CPS) transition employing composite right/left handed (CRLH) transmission line technology is presented. The transition is based on the unique phase slope and offset control properties of the CRLH transmission line. The pairing of a microstrip delay line and the CRLH transmission line has a broadband out-of-phase characteristic, thus enabling the broadband transition between microstrip and CPS. An unbalanced and a balanced type of transition are built and tested. A 3dB insertion loss bandwidth of 85% and 80% can be achieved by connecting the transition back-to- back for the unbalanced and balanced type, respectively. To demonstrate the applicability of the proposed microstrip-to-CPS transition, a modified quasi-Yagi antenna is designed. An impedance bandwidth of 65% can be obtained, showing a 15% improvement compared to the conventional one.


title: Milestones in Software Engineering and Knowledge Engineering History: A Comparative Review

abstract: We present a review of the historical evolution of software engineering, intertwining it with the history of knowledge engineering because “those who cannot remember the past are condemned to repeat it.” This retrospective represents a further step forward to understanding the current state of both types of engineerings; history has also positive experiences; some of them we would like to remember and to repeat. Two types of engineerings had parallel and divergent evolutions but following a similar pattern. We also define a set of milestones that represent a convergence or divergence of the software development methodologies. These milestones do not appear at the same time in software engineering and knowledge engineering, so lessons learned in one discipline can help in the evolution of the other one.


title: Mining and ranking users’ intents behind queries

abstract: How to understand intents behind user queries is crucial towards improving the performance of Web search systems. NTCIR-11 IMine task focuses on this problem. In this paper, we address the NTCIR-11 IMine task with two phases referred to as Query Intent Mining (QIM) and Query Intent Ranking (QIR). (I) QIM is intended to mine users' potential intents by clustering short text fragments related to the given query. (II) QIR focuses on ranking those mined intents in a proper way. Two challenges exist in handling these tasks. (II) How to precisely estimate the intent similarity between user queries which only consist of a few words. (2) How to properly rank intents in terms of multiple factors, e.g. relevance, diversity, intent drift and so on. For the first challenge, we first investigate two interesting phenomena by analyzing query logs and document datasets, namely "Same-Intent-Co-Click" (SICC) and "Same-Intent-Similar-Rank" (SISR). SICC means that when users issue different queries, these queries represent the same intent if they click on the same URL. SISR means that if two queries denote the same intent, we should get similar search results when issuing them to a search engine. Then, we propose similarity functions for QIM based on the two phenomena. For the second challenge, we propose a novel intent ranking model which considers multiple factors as a whole. We perform extensive experiments and an interesting case study on the Chinese dataset of NTCIR-11 IMine task. Experimental results demonstrate the effectiveness of our proposed approaches in terms of both QIM and QIR.


title: Parametric reshaping of human bodies in images

abstract: We present an easy-to-use image retouching technique for realistic reshaping of human bodies in a single image. A model-based approach is taken by integrating a 3D whole-body morphable model into the reshaping process to achieve globally consistent editing effects. A novel body-aware image warping approach is introduced to reliably transfer the reshaping effects from the model to the image, even under moderate fitting errors. Thanks to the parametric nature of the model, our technique parameterizes the degree of reshaping by a small set of semantic attributes, such as weight and height. It allows easy creation of desired reshaping effects by changing the full-body attributes, while producing visually pleasing results even for loosely-dressed humans in casual photographs with a variety of poses and shapes.


title: What do we know about the reliability and validity of physical examination tests used to examine the upper extremity

abstract: Abstract The literature regarding the reliability and validity of commonly used clinical tests for disorders of the upper extremity was reviewed. Formal literature search, standard texts, and experts in the field of upper extremity were consulted to locate relevant articles. Range of motion and strength testing of the upper limb have been shown to be reliable, while various tests used for the diagnosis of conditions in the upper limb, such as carpal tunnel syndrome and rotator cuff tendinopathy, have been shown to have varying degrees of validity. Overall, however, we determined that there is little evidence regarding the reliability and validity of physical examination for the upper extremity and specifically less information available regarding the reliability of diagnostic physical examination tests and the validity of impairment measures used for the upper limb. Further studies in this area are warranted in view of the impact of these findings on the treatment of patients. (J Hand Surg 1999;24A:185–193. Copyright © 1999 by the American Society for Surgery of the Hand.)


Cardiac Arrhythmia Detection By ECG Feature Extraction


title: A Principal Components Approach to Combining Regression Estimates

abstract: The goal of combining the predictions of multiple learned models is to form an improved estimator. A combining strategy must be able to robustly handle the inherent correlation, or multicollinearity, of the learned models while identifying the unique contributions of each. A progression of existing approaches and their limitations with respect to these two issues are discussed. A new approach, PCRa, based on principal components regression is proposed to address these limitations. An evaluation of the new approach on a collection of domains reveals that (1) PCRa was the most robust combining method, (2) correlation could be handled without eliminating any of the learned models, and (3) the principal components of the learned models provided a continuum of “regularized” weights from which PCRa could choose.


title: Principles of sensorimotor learning

abstract: The exploits of Martina Navratilova and Roger Federer represent the pinnacle of motor learning. However, when considering the range and complexity of the processes that are involved in motor learning, even the mere mortals among us exhibit abilities that are impressive. We exercise these abilities when taking up new activities — whether it is snowboarding or ballroom dancing — but also engage in substantial motor learning on a daily basis as we adapt to changes in our environment, manipulate new objects and refine existing skills. Here we review recent research in human motor learning with an emphasis on the computational mechanisms that are involved.


title: Overhead and performance of low latency live streaming using MPEG-DASH

abstract: HTTP Streaming is a recent topic in multimedia communications with on-going standardization activities, especially with the MPEG DASH standard which covers on demand and live services. One of the main issues in live services deployment is the reduction of the overall latency. Low or very low latency streaming is still a challenge. In this paper, we push the use of DASH to its limits with regards to latency, down to fragments being only one frame, and evaluate the overhead introduced by that approach and the combination of: low latency video coding techniques, in particular Gradual Decoding Refresh; low latency HTTP streaming, in particular using chunked-transfer encoding; and associated ISOBMF packaging. We experiment DASH streaming using these techniques in local networks to measure the actual end-to-end latency, as low as 240 milliseconds, for an encoding and packaging overhead in the order of 13% for HD sequences and thus validate the feasibility of very low latency DASH live streaming in local networks.


title: On the ability of neural nets to express distributions

abstract: Deep neural nets have caused a revolution in many classification tasks. A related ongoing revolution---also theoretically not understood---concerns their ability to serve as generative models for complicated types of data such as images and texts. These models are trained using ideas like variational autoencoders and Generative Adversarial Networks. ::: We take a first cut at explaining the expressivity of multilayer nets by giving a sufficient criterion for a function to be approximable by a neural network with $n$ hidden layers. A key ingredient is Barron's Theorem \cite{Barron1993}, which gives a Fourier criterion for approximability of a function by a neural network with 1 hidden layer. We show that a composition of $n$ functions which satisfy certain Fourier conditions ("Barron functions") can be approximated by a $n+1$-layer neural network. ::: For probability distributions, this translates into a criterion for a probability distribution to be approximable in Wasserstein distance---a natural metric on probability distributions---by a neural network applied to a fixed base distribution (e.g., multivariate gaussian). ::: Building up recent lower bound work, we also give an example function that shows that composition of Barron functions is more expressive than Barron functions alone.


title: On the effect of calibration in classifier combination

abstract: A general approach to classifier combination considers each model as a probabilistic classifier which outputs a class membership posterior probability. In this general scenario, it is not only the quality and diversity of the models which are relevant, but the level of calibration of their estimated probabilities as well. In this paper, we study the role of calibration before and after classifier combination, focusing on evaluation measures such as MSE and AUC, which better account for good probability estimation than other evaluation measures. We present a series of findings that allow us to recommend several layouts for the use of calibration in classifier combination. We also empirically analyse a new non-monotonic calibration method that obtains better results for classifier combination than other monotonic calibration methods.


Can we get rid of TREC assessors ? Using Mechanical Turk for relevance assessment


title: Component-Based Cartoon Face Generation

abstract: In this paper, we present a cartoon face generation method that stands on a component-based facial feature extraction approach. Given a frontal face image as an input, our proposed system has the following stages. First, face features are extracted using an extended Active Shape Model. Outlines of the components are locally modified using edge detection, template matching and Hermit interpolation. This modification enhances the diversity of output and accuracy of the component matching required for cartoon generation. Second, to bring cartoon-specific features such as shadows, highlights and, especially, stylish drawing, an array of various face photographs and corresponding hand-drawn cartoon faces are collected. These cartoon templates are automatically decomposed into cartoon components using our proposed method for parameterizing cartoon samples, which is fast and simple. Then, using shape matching methods, the appropriate cartoon component is selected and deformed to fit the input face. Finally, a cartoon face is rendered in a vector format using the rendering rules of the selected template. Experimental results demonstrate effectiveness of our approach in generating life-like cartoon faces.


title: I/O stack optimization for smartphones

abstract: The Android I/O stack consists of elaborate and mature components (SQLite, the EXT4 filesystem, interrupt-driven I/O, and NAND-based storage) whose integrated behavior is not well-orchestrated, which leaves a substantial room for an improvement. We overhauled the block I/O behavior of five filesystems (EXT4, XFS, BTRFS, NILFS, and F2FS) under each of the five different journaling modes of SQLite. We found that the most significant inefficiency originates from the fact that filesystem journals the database journaling activity; we refer to this as the JOJ (Journaling of Journal) anomaly. The JOJ overhead compounds in EXT4 when the bulky EXT4 journaling activity is triggered by an fsync() call from SQLite. We propose (i) the elimination of unnecessary metadata journaling for the filesystem, (ii) external journaling and (iii) polling-based I/O to improve the I/O performance, primarily to improve the efficiency of filesystem journaling in the SQLite environment. We examined the performance trade-offs for each combination of the five database journaling modes, five filesystems and three optimization techniques. When we applied three optimization techniques in existing Android I/O stack, the SQLite performance (inserts/sec) improved by 130%. With the F2FS filesystem, WAL journaling mode (SQLite), and the combination of our optimization efforts, we improved the SQLite performance (inserts/sec) by 300%, from 39 ins/sec to 157 ins/sec, compared to the stock Android I/O stack.


title: A high-performance MEMS transformer for silicon RF ICS

abstract: A new spiral-type suspended transformer for silicon radio frequency integrated circuits (RF ICs) has been fabricated by surface micromachining technology The fabricated transformer on standard silicon substrate has shown a low insertion loss of 1.9 dB at 1 GHz by reducing substrate coupling and ohmic loss using the proposed MEMS technology. Equivalent circuit models for the spiral-type suspended transformer have been extracted and shown that they agree well with measured characteristics.


Free-hand Sketch Recognition Classification


title: An empathetic beginning in education: exploring the prospects of self-regulation skills on pro-social behaviour in the early childhood environment

abstract: One avenue substantially researched and supported in early childhood research is the importance and the cultivation of self-regulation skills in the classroom. Most educational research on self-regulation skills has illustrated the importance between the enhancement of these skills and long-term academic success. Notwithstanding, there is little empirical research in early childhood that links the advancement of self-regulation skills to other contributing educational components, such as the cultivation of children's pro-social skills. This review seeks to establish a link between children's self-regulation and pro-social skills and discusses implications for future research and practice.


title: An Evolutionary Transfer Reinforcement Learning Framework for Multiagent Systems

abstract: In this paper, we present an evolutionary transfer reinforcement learning framework (eTL) for developing intelligent agents capable of adapting to the dynamic environment of multiagent systems (MASs). Specifically, we take inspiration from Darwin’s theory of natural selection and Universal Darwinism as the principal driving forces that govern the evolutionary knowledge transfer process. The essential backbone of our proposed eTL comprises several meme-inspired evolutionary mechanisms, namely meme representation , meme expression , meme assimilation , meme internal evolution , and meme external evolution . Our proposed approach constructs social selection mechanisms that are modeled after the principles of human learning to identify appropriate interacting partners. eTL also models the intrinsic parallelism of natural evolution and errors that are introduced due to the physiological limits of the agents’ ability to perceive differences, so as to generate “growth” and “variation” of knowledge that agents have of the world, thus exhibiting higher adaptivity capabilities on solving complex problems. To verify the efficacy of the proposed paradigm, comprehensive investigations of the proposed eTL against existing state-of-the-art TL methods in MAS, are conducted on the “minefield navigation tasks” platform and the “Unreal Tournament 2004” first person shooter computer game, in which homogeneous and heterogeneous learning machines are considered.


title: A pharmaceutical investigation into exosomes

abstract: During the last 5 years, there has been a marked rise in publications regarding exosomes. Exosomes find potential applications as diagnostic biomarkers, therapeutics, drug delivery vehicles, and functional cosmetics and several exosome-based therapies are under clinical trials. Exosomes are nanosized vesicles that contain cell-derived lipid membranes, nucleic acids, and proteins, and are released from the cells of origin, circulate in eukaryotic fluids, and are taken up by recipient cells. The originating cell can be engineered to express specific genes, which enhance immunity, cross-presentation of antigens, or cell targeting by exosomes. Various methods using centrifugation, size-exclusion chromatography, filtration, precipitation, affinity, and microfluidics have been assessed for the isolation and purification of exosomes from biological samples. In addition, exosomes have advantages as drug delivery systems, with biocompatibility, preservation of cargo drugs during circulation in physiological fluids, uptake into the target cells, and drug release after endosomal escape. In this review, we discuss the various aspects of pharmaceutical investigation into exosomes with special focus on engineering, production, characterization, biological activities, applications of exosomes as drug delivery systems, and regulations for evaluation of exosome products.


title: Problem solving using expert system techniques

abstract: There are basically two approaches to problem solving currently used in expert systems: the derivation approach and the formation approach. The derivation approach involves deriving a solution that is most appropriate for the problem at hand from a list of predefined solutions stored in the knowledge base of the expert system. The formation approach involves forming a solution from the eligible solution components stored in the knowledge base. This paper begins with a description of a subset of the problem solving strategies used in expert systems and how these strategies support the derivation or formation approach. This is followed by the description of a structural design problem and its implementation using first a formation approach and then a derivation approach. Finally, some comments and conclusions are provided.


title: Functions of Social Networking Services

abstract: Abstract. Social Networking Services (SNS) are the fastest growing type of social software – both in the Internet and in company-wide Intranets. Due to the fact that SNS have emerged just recently and the development speed of the services is enormous, there exist large gaps in research about this type of service. For example, so far there has been no attempt to identify and categorize the basic functionalities of SNS. This is the goal of this contribution. Six groups of functionalities for SNS are proposed and their categorization is motivated. The identification of a distinct set of SNS functions will facilitate the modularization and integration of different social network applications. Keywords: Social Networking, Expert Finding, Yellow Pages, Knowledge Management, Web 2.0, Social Software, Enterprise 2.0 1 Introduction While Knowledge Management activities have long been focused on the collection of documents and their storage in anonymous knowledge silos, in the past years companies are becoming more and more aware that employees are the real knowledge repositories and that real knowledge management is supporting communication and networking among the employees. Cohen and Prusak [4] for example highlight the high potential of networking employees to increase productivity and speed of innovation in companies. Therefore, support to find experts and in a broader sense support to human social networks is becoming more and more important in companies. The technological and technical developments of the last years make it possible to digitally reproduce human social networks. Thanks to this technical support users can establish and maintain contact to persons with whom contact would be difficult due to regional and social barriers. Users with alike interests and subject areas can now find each other and can stay connected in communities (of practice) and networks. In the context of the often-quoted Web 2.0 a new form of software to support collaborative work has evolved to cover this: Social Networking Services (SNS). Social Networking Services (SNS) are application systems that offer users functionalities for


SplitNet: Learning to Semantically Split Deep Networks for Parameter Reduction and Model Parallelization


Incorporating Expert Knowledge into Keyphrase Extraction.


title: Focus plus context screens: combining display technology with visualization techniques

abstract: Computer users working with large visual documents, such as large layouts, blueprints, or maps perform tasks that require them to simultaneously access overview information while working on details. To avoid the need for zooming, users currently have to choose between using a sufficiently large screen or applying appropriate visualization techniques. Currently available hi-res "wall-size" screens, however, are cost-intensive, space-intensive, or both. Visualization techniques allow the user to more efficiently use the given screen space, but in exchange they either require the user to switch between multiple views or they introduce distortion.In this paper, we present a novel approach to simultaneously display focus and context information. Focus plus context screens consist of a hi-res display and a larger low-res display. Image content is displayed such that the scaling of the display content is preserved, while its resolution may vary according to which display region it is displayed in. Focus plus context screens are applicable to practically all tasks that currently use overviews or fisheye views, but unlike these visualization techniques, focus plus context screens provide a single, non-distorted view. We present a prototype that seamlessly integrates an LCD with a projection screen and demonstrate four applications that we have adapted so far.


title: An Iterative Global Structure-Assisted Labeled Network Aligner

abstract: Integrating data from heterogeneous sources is often modeled as merging graphs. Given two or more 'compatible', but not-isomorphic graphs, the first step is to identify a graph alignment, where a potentially partial mapping of vertices between two graphs is computed. A significant portion of the literature on this problem only takes the global structure of the input graphs into account. Only more recent ones additionally use vertex and edge attributes to achieve a more accurate alignment. However, these methods are not designed to scale to map large graphs arising in many modern applications. We propose a new iterative graph aligner, gsaNA, that uses the global structure of the graphs to significantly reduce the problem size and align large graphs with a minimal loss of information. Concretely, we show that our proposed technique is highly flexible, can be used to achieve higher recall, and it is orders of magnitudes faster than the current state of the art techniques.


title: Methods to Design Microstrip Antennas for Modern Applications

abstract: The evolution of modern wireless communications systems has increased dramatically the demand for antennas, capable to be embedded in portable, or not, devices which serve a wireless land mobile or terrestrial-satellite network. With time and requirements, these devices become smaller in size and hence the antennas required for transmit and receive signals have also to be smaller and lightweight. As a matter of fact, microstrip antennas can meet these requirements. As they are lightweight and have low profile it is feasible them to be structured conformally to the mounting hosts. Moreover, they are easy fabricated, have low cost and are easy integrated into arrays or into microwave printed circuits. So, they are attractive choices for the above mentioned type of applications. For all that, the design of a microstrip antenna is not always an easy problem and the antenna designer is faced with difficulties coming from a) the inherent disadvantages of a printed resonant antenna element, for example the narrow impedance bandwidth, and b) the various requirements of the specific applications, which concern the operation of the radiating element, and can not be satisfied by a printed scheme with an ordinary configuration. For example, it would be demanded, the microstrip element to have gain characteristics that potentially incommensurate to its size or/and frequency bandwidth greater than the element could give, taking into account that it operates as a resonant cavity. Moreover, the rapid development in the field of Land Mobile Telephony as well as in the field of Wireless Local Area Networks(WLANs) demands devices capable to operate in more than one frequency bands. So the design of a printed antenna with intend to conform to multiple communications protocols, for example the IEEE 802.11b/g, in the band of 2.4GHz, and the IEEE 802.11a at 5.3GHz and 5.8GHz, would be a difficult task but at the same time a challenge for the designer. Counting in the above the possibility the device, and so the antenna, to serve terrestrial and also satellite navigation systems the problem of the antenna design is even more complicated. In this chapter techniques will be analysed, to design microstrip antennas that combine the attributes mentioned above which make them suitable for modern communications applications. Specific examples will be also presented for every case.


Using Neural Networks to Forecast Stock Market Prices


Implementation of FPGA based PID Controller for DC Motor Speed Control System


title: Limitations in improving detection of pancreatic adenocarcinoma

abstract: OBJECTIVE ::: To review the current trends in pancreatic cancer research and propose alternatives for an earlier diagnosis. ::: ::: ::: METHOD ::: A search was conducted using the PubMed and Scielo electronic databases to find statistics related to the incidence of pancreatic cancer. ::: ::: ::: RESULTS ::: Pancreatic cancer is the fourth most common cause of cancer mortality in the USA; in Colombia the incidence of this neoplasia is 4.5 per 100,000 individuals; and in Peru, amongst digestive diseases, it is the fifth most common cause. In Brazil and Chile this cancer has increased in incidence, while in Mexico, it has decreased in terms of the relative percentage of gastrointestinal cancers from 1976 to 2003. Chronic pancreatitis, cigarette smoking, diabetes, obesity and dietary mutagen exposure are the most consistent risk factors implicated in the development of pancreatic cancer; however, the genetic and molecular changes underlying the epidemiological association between these factors and pancreatic cancer remain largely unknown, and only 5-10% are hereditary in nature. ::: ::: ::: CONCLUSION ::: The prognosis for pancreatic cancer has not changed substantially for at least the last 20 years. The most useful tumor marker for pancreatic adenocarcinoma is still the carbohydrate antigen 19-9 (CA19-9). Currently, a multimodal-screening approach of endoscopic ultrasound, computed tomography and endoscopic retrograde cholangiopancreatography are the most effective methods to detect pancreatic cancer in high-risk patients. Future options for early detection of this malignancy are focused on new molecular markers, telomerase enzyme, receptor-targeted imaging using multifunctional nanoparticles, detection of glycan changes and epigenetics.


title: Dueling Network Architectures for Deep Reinforcement Learning

abstract: In recent years there have been many successes of using deep representations in reinforcement learning. Still, many of these applications use conventional architectures, such as convolutional networks, LSTMs, or auto-encoders. In this paper, we present a new neural network architecture for model-free reinforcement learning. Our dueling network represents two separate estimators: one for the state value function and one for the state-dependent action advantage function. The main benefit of this factoring is to generalize learning across actions without imposing any change to the underlying reinforcement learning algorithm. Our results show that this architecture leads to better policy evaluation in the presence of many similar-valued actions. Moreover, the dueling architecture enables our RL agent to outperform the state-of-the-art on the Atari 2600 domain.


title: Facing facts: Neuronal mechanisms of face perception

abstract: The face is one of the most important stimuli carrying social meaning. Thanks to the fast analysis of faces, we are able to judge physical attractiveness and features of their owners' personality, intentions, and mood. From one's facial expression we can gain information about danger present in the environment. It is obvious that the ability to process efficiently one's face is crucial for survival. Therefore, it seems natural that in the human brain there exist structures specialized for face processing. In this article, we present recent findings from studies on the neuronal mechanisms of face perception and recognition in the light of current theoretical models. Results from brain imaging (fMRI, PET) and electrophysiology (ERP, MEG) show that in face perception particular regions (i.e. FFA, STS, IOA, AMTG, prefrontal and orbitofrontal cortex) are involved. These results are confirmed by behavioral data and clinical observations as well as by animal studies. The developmental findings reviewed in this article lead us to suppose that the ability to analyze face-like stimuli is hard-wired and improves during development. Still, experience with faces is not sufficient for an individual to become an expert in face perception. This thesis is supported by the investigation of individuals with developmental disabilities, especially with autistic spectrum disorders (ASD).


title: Batteries and Ultracapacitors for Electric, Hybrid, and Fuel Cell Vehicles

abstract: The application of batteries and ultracapacitors in electric energy storage units for battery powered (EV) and charge sustaining and plug-in hybrid-electric (HEV and PHEV) vehicles have been studied in detail. The use of IC engines and hydrogen fuel cells as the primary energy converters for the hybrid vehicles was considered. The study focused on the use of lithium-ion batteries and carbon/carbon ultracapacitors as the energy storage technologies most likely to be used in future vehicles. The key findings of the study are as follows. 1) The energy density and power density characteristics of both battery and ultracapacitor technologies are sufficient for the design of attractive EVs, HEVs, and PHEVs. 2) Charge sustaining, engine powered hybrid-electric vehicles (HEVs) can be designed using either batteries or ultracapacitors with fuel economy improve- ments of 50% and greater. 3) Plug-in hybrids (PHEVs) can be designed with effective all-electric ranges of 30-60 km using lithium-ion batteries that are relatively small. The effective fuel economy of the PHEVs can be very high (greater than 100 mpg) for long daily driving ranges (80-150 km) resulting in a large fraction (greater than 75%) of the energy to power the vehicle being grid electricity. 4) Mild hybrid-electric vehicles (MHEVs) can be designed using ultracapacitors having an energy storage capacity of 75-150 Wh. The fuel economy improvement with the ultracapacitors is 10%-15% higher than with the same weight of batteries due to the higher efficiency of the ultra- capacitors and more efficient engine operation. 5) Hybrid- electric vehicles powered by hydrogen fuel cells can use either batteries or ultracapacitors for energy storage. Simulation results indicate the equivalent fuel economy of the fuel cell powered vehicles is 2-3 times higher than that of a gasoline fueled IC vehicle of the same weight and road load. Compared to an engine-powered HEV, the equivalent fuel economy of the hydrogen fuel cell vehicle would be 1.66-2.0 times higher.


title: Ultra-Wideband Patch Antenna for K-Band Applications

abstract: An ultra-wideband patch antenna is presented for K-band communication. The antenna is designed by employing stacked geometry and aperture-coupled technique. The rectangular patch shape and coaxial fed configuration is used for particular design. The ultra-wideband characteristics are achieved by applying a specific surface resistance of 75 ohm/square to the upper rectangular patch and it is excited through a rectangular slot made on the lower patch element (made of copper). The proposed patch antenna is able to operate in the frequency range of 12-27.3 GHz which is used in radar and satellite communication, commonly named as K-band. By employing a technique of thicker substrate and by applying a specific surface resistance to the upper patch element, an impedance bandwidth of 77.8\% is achieved having VSWR less than 2. It is noted that the gain of proposed antenna is linearly increased in the frequency range of 12-26 GHz and after that the gain is decreased up to 6 dBi. Simulation results are presented to demonstrate the performance of proposed ultra-wideband microstrip patch antenna.


Asymmetrical barcode adapter-assisted recovery of duplicate reads and error correction strategy to detect rare mutations in circulating tumor DNA


title: K-Means+ID3: A Novel Method for Supervised Anomaly Detection by Cascading K-Means Clustering and ID3 Decision Tree Learning Methods

abstract: In this paper, we present "k-means+ID3", a method to cascade k-means clustering and the ID3 decision tree learning methods for classifying anomalous and normal activities in a computer network, an active electronic circuit, and a mechanical mass-beam system. The k-means clustering method first partitions the training instances into k clusters using Euclidean distance similarity. On each cluster, representing a density region of normal or anomaly instances, we build an ID3 decision tree. The decision tree on each cluster refines the decision boundaries by learning the subgroups within the cluster. To obtain a final decision on classification, the decisions of the k-means and ID3 methods are combined using two rules: 1) the nearest-neighbor rule and 2) the nearest-consensus rule. We perform experiments on three data sets: 1) network anomaly data (NAD), 2) Duffing equation data (DED), and 3) mechanical system data (MSD), which contain measurements from three distinct application domains of computer networks, an electronic circuit implementing a forced Duffing equation, and a mechanical system, respectively. Results show that the detection accuracy of the k-means+ID3 method is as high as 96.24 percent at a false-positive-rate of 0.03 percent on NAD; the total accuracy is as high as 80.01 percent on MSD and 79.9 percent on DED


title: GAD, Metacognition, and Mindfulness: An Information Processing Analysis

abstract: In this commentary I discuss the integration of mindful procedures in cognitive therapy of generalized anxiety disorder (GAD) and attempt to answer questions concerning the effects of mindfulness on information processing and on mechanisms purported to maintain GAD in the metacognitive model of this disorder. Different techniques that promote mindfulness can be identified, including mindfulness meditation and attention training. These techniques are intended to disrupt repetitive styles of dysfunctional thinking. I argue that the effect of mindfulness strategies on information processing in emotional disorder can be conceptualized in metacognitive terms as (a) activating a metacognitive mode of processing; (b) disconnecting the influence of maladaptive beliefs on processing; (c) strengthening flexible responding to threat; and (d) strengthening metacognitive plans for controlling cognition. Although mindfulness meditation may have general treatment applications, the metacognitive model of GAD suggests caution in using this treatment in GAD. It is unclear which dimension of worry should be targeted, and mindfulness meditation does not contain information that can lead to unambiguous disconfirmation of erroneous beliefs about worry.


title: Toward a network of regions: the United States of Europe

abstract: Abstract Starting from the changing role of governments in planning (physical, environmental, economic, etc.), this paper calls attention to the drastic structural changes taking place in the regional scene after the transition towards market orientation and global competitiveness. The main focus is on European developments. In addition to economie forces, two phenomena will be highlighted, namely, the transition towards a network economy and the profound technological changes taking place at the regional and urban level Particular attention will be devoted to European spatial dynamics in a networked society. Various new geographical configurations and maps of Europe will be shown, followed by some exploratory remarks on future trends. The main message of the paper is that transport planning in Europe needs to be integrated with the emerging new economic networks. The paper ends with some policy conclusions.


title: A Three-Layered Mutually Reinforced Model for Personalized Citation Recommendation

abstract: Fast-growing scientific papers pose the problem of rapidly and accurately finding a list of reference papers for a given manuscript. Citation recommendation is an indispensable technique to overcome this obstacle. In this paper, we propose a citation recommendation approach via mutual reinforcement on a three-layered graph, in which each paper, author or venue is represented as a vertex in the paper layer, author layer, and venue layer, respectively. For personalized recommendation, we initiate the random walk separately for each query researcher. However, this has a high computational complexity due to the large graph size. To solve this problem, we apply a three-layered interactive clustering approach to cluster related vertices in the graph. Personalized citation recommendations are then made on the subgraph, generated by the clusters associated with each researcher's needs. When evaluated on the ACL anthology network, DBLP, and CiteSeer ML data sets, the performance of our proposed model-based citation recommendation approach is comparable with that of other state-of-the-art citation recommendation approaches. The results also demonstrate that the personalized recommendation approach is more effective than the nonpersonalized recommendation approach.


title: The effects of loading variables on fatigue crack growth in liquid metal environments

abstract: Liquid-metal-induced embrittlement (LMIE) refers to the loss of ductility in normally ductile metals and alloys when stressed while in contact with a liquid metal. In this study, the fatigue crack growth behaviour of brass in molten gallium is examined using standard LEFM testing procedures. The effects of environment, load ratio, cyclic frequency and load waveform on the rate of crack growth are investigated. At AK > AKth, crack growth rates are approximately an order of magnitude faster in liquid metal than in air. However, at low AK values (low fatigue crack growth rates) and/or where the crack tip is exposed to the liquid metal for long periods of time, crack tip blunting by dissolution of the solid metal into the liquid metal occurs. This markedly decreases the rate of crack growth and results in apparent AKth values in liquid metal that are significantly higher than the unembrittled threshold values.


title: Flow based environmental monitoring for smart cities

abstract: In this work, an IoT based setup, which monitors the surrounding environmental parameters for smart cities, is proposed. The data is sent to a public MQTT broker from where any client, with an appropriate topic, can subscribe for that data. Currently, one of the biggest problems in the world is Air Pollution reaching alarming levels. This work attempts to monitor the air quality of a locale and send this data to people who can use this data to improve the quality of living of citizens, which is one of the primary requirements of a Smart City. This work uses Wio Link board, a ESP8266 based open source Wi-Fi development board and various grove sensors which are connected to the Wio link, viz. temperature and humidity sensor (DHT-11), digital light and air-quality sensors. After this Node-RED comes into play where flows are made to control the publishing of the data over to a MQTT broker. In addition to this, every time data are published an E-mail, an SMS and a Twitter post are generated depicting the values of various sensors.


title: GPOPS-II: A MATLAB Software for Solving Multiple-Phase Optimal Control Problems Using hp-Adaptive Gaussian Quadrature Collocation Methods and Sparse Nonlinear Programming

abstract: A general-purpose MATLAB software program called GPOPS--II is described for solving multiple-phase optimal control problems using variable-order Gaussian quadrature collocation methods. The software employs a Legendre-Gauss-Radau quadrature orthogonal collocation method where the continuous-time optimal control problem is transcribed to a large sparse nonlinear programming problem (NLP). An adaptive mesh refinement method is implemented that determines the number of mesh intervals and the degree of the approximating polynomial within each mesh interval to achieve a specified accuracy. The software can be interfaced with either quasi-Newton (first derivative) or Newton (second derivative) NLP solvers, and all derivatives required by the NLP solver are approximated using sparse finite-differencing of the optimal control problem functions. The key components of the software are described in detail and the utility of the software is demonstrated on five optimal control problems of varying complexity. The software described in this article provides researchers a useful platform upon which to solve a wide variety of complex constrained optimal control problems.


title: Supersparse linear integer models for optimized medical scoring systems

abstract: Scoring systems are linear classification models that only require users to add, subtract and multiply a few small numbers in order to make a prediction. These models are in widespread use by the medical community, but are difficult to learn from data because they need to be accurate and sparse, have coprime integer coefficients, and satisfy multiple operational constraints. We present a new method for creating data-driven scoring systems called a Supersparse Linear Integer Model (SLIM). SLIM scoring systems are built by using an integer programming problem that directly encodes measures of accuracy (the 0–1 loss) and sparsity (the $$\ell _0$$ℓ0-seminorm) while restricting coefficients to coprime integers. SLIM can seamlessly incorporate a wide range of operational constraints related to accuracy and sparsity, and can produce acceptable models without parameter tuning because of the direct control provided over these quantities. We provide bounds on the testing and training accuracy of SLIM scoring systems, and present a new data reduction technique that can improve scalability by eliminating a portion of the training data beforehand. Our paper includes results from a collaboration with the Massachusetts General Hospital Sleep Laboratory, where SLIM is being used to create a highly tailored scoring system for sleep apnea screening.


title: Efficient Patch-Wise Semantic Segmentation for Large-Scale Remote Sensing Images

abstract: Efficient and accurate semantic segmentation is the key technique for automatic remote sensing image analysis. While there have been many segmentation methods based on traditional hand-craft feature extractors, it is still challenging to process high-resolution and large-scale remote sensing images. In this work, a novel patch-wise semantic segmentation method with a new training strategy based on fully convolutional networks is presented to segment common land resources. First, to handle the high-resolution image, the images are split as local patches and then a patch-wise network is built. Second, training data is preprocessed in several ways to meet the specific characteristics of remote sensing images, i.e., color imbalance, object rotation variations and lens distortion. Third, a multi-scale training strategy is developed to solve the severe scale variation problem. In addition, the impact of conditional random field (CRF) is studied to improve the precision. The proposed method was evaluated on a dataset collected from a capital city in West China with the Gaofen-2 satellite. The dataset contains ten common land resources (Grassland, Road, etc.). The experimental results show that the proposed algorithm achieves 54.96% in terms of mean intersection over union (MIoU) and outperforms other state-of-the-art methods in remote sensing image segmentation.


title: Plant Disease Detection Using Image Processing

abstract: Identification of the plant diseases is the key to preventing the losses in the yield and quantity of the agricultural product. The studies of the plant diseases mean the studies of visually observable patterns seen on the plant. Health monitoring and disease detection on plant is very critical for sustainable agriculture. It is very difficult to monitor the plant diseases manually. It requires tremendous amount of work, expertize in the plant diseases, and also require the excessive processing time. Hence, image processing is used for the detection of plant diseases. Disease detection involves the steps like image acquisition, image pre-processing, image segmentation, feature extraction and classification. This paper discussed the methods used for the detection of plant diseases using their leaves images. This paper also discussed some segmentation and feature extraction algorithm used in the plant disease detection.


Preparing for the future of Artificial Intelligence


title: Perception of Average Value in Multiclass Scatterplots

abstract: The visual system can make highly efficient aggregate judgements about a set of objects, with speed roughly independent of the number of objects considered. While there is a rich literature on these mechanisms and their ramifications for visual summarization tasks, this prior work rarely considers more complex tasks requiring multiple judgements over long periods of time, and has not considered certain critical aggregation types, such as the localization of the mean value of a set of points. In this paper, we explore these questions using a common visualization task as a case study: relative mean value judgements within multi-class scatterplots. We describe how the perception literature provides a set of expected constraints on the task, and evaluate these predictions with a large-scale perceptual study with crowd-sourced participants. Judgements are no harder when each set contains more points, redundant and conflicting encodings, as well as additional sets, do not strongly affect performance, and judgements are harder when using less salient encodings. These results have concrete ramifications for the design of scatterplots.


title: Cultural differences in human brain activity: A quantitative meta-analysis

abstract: Psychologists have been trying to understand differences in cognition and behavior between East Asian and Western cultures within a single cognitive framework such as holistic versus analytic or interdependent versus independent processes. However, it remains unclear whether cultural differences in multiple psychological processes correspond to the same or different neural networks. We conducted a quantitative meta-analysis of 35 functional MRI studies to examine cultural differences in brain activity engaged in social and non-social processes. We showed that social cognitive processes are characterized by stronger activity in the dorsal medial prefrontal cortex, lateral frontal cortex and temporoparietal junction in East Asians but stronger activity in the anterior cingulate, ventral medial prefrontal cortex and bilateral insula in Westerners. Social affective processes are associated with stronger activity in the right dorsal lateral frontal cortex in East Asians but greater activity in the left insula and right temporal pole in Westerners. Non-social processes induce stronger activity in the left inferior parietal cortex, left middle occipital and left superior parietal cortex in East Asians but greater activations in the right lingual gyrus, right inferior parietal cortex and precuneus in Westerners. The results suggest that cultural differences in social and non-social processes are mediated by distinct neural networks. Moreover, East Asian cultures are associated with increased neural activity in the brain regions related to inference of others' mind and emotion regulation whereas Western cultures are associated with enhanced neural activity in the brain areas related to self-relevance encoding and emotional responses during social cognitive/affective processes.


title: The effects of infant media usage: what do we know and what should we learn?

abstract: UNLABELLED ::: The rise of infant TV viewing began in the late 1990s and has become an increasingly common occurrence. Today, over 90% of children begin watching TV regularly before the age of 2 years in spite of recommendations to the contrary. This article reviews what is known about the effects of infant TV viewing on multiple domains of child development including language, cognition and attentional capacity as well as directions for future research. ::: ::: ::: CONCLUSION ::: No studies to date have demonstrated benefits associated with early infant TV viewing. The preponderance of existing evidence suggests the potential for harm. Parents should exercise due caution in exposing infants to excessive media.


title: Spatial filters to detect steady-state visual evoked potentials elicited by high frequency stimulation: BCI application

abstract: Brain-computer interfaces (BCIs) based on steady-state visual evoked potentials (SSVEPs) require minimal user training and can offer higher information throughput compared to other BCI modalities. We focused on SSVEPs elicited by high-frequency stimuli (>30 Hz) because they cause minimal fatigue/annoyance and reduce the risk of inducing photoepileptic seizures. This paper presents an approach that analyzes electroencephalographic activity to automatically obtain the optimum spatial filter for detecting the SSVEP at a given stimulation frequency from a short signal where the stimulation is presented at intermittent periods interspersed with breaks. A vector space generated by sinusoidal signals at the stimulation frequency and harmonics is defined. The spatial filter coefficients result from maximizing the ratio between the energy of the spatially filtered signal and that of its orthogonal component with regard to the vector space. The spatial filters are customized for each BCI user through a short calibration procedure taking into account individual specificity. Our experiments on six subjects applying the spatial filters resulted in an average transfer rate ranging from 20.9 to 22.7 bits/min.


Fuzzy gain scheduling of PI controller for dual star induction machine fed by a matrix converter,” Carpathian Journal of Electronic and Computer Engineering 5


title: Character strengths among youth

abstract: Four hundred and fiftynine students from 20 different high school classrooms in Michigan participated in focus group discussions about the character strengths included in the Values in Action Classification. Students were interested in the subject of good character and able to discuss with candor and sophistication instances of each strength. They were especially drawn to the positive traits of leadership, practical intelligence, wisdom, social intelligence, love of learning, spirituality, and the capacity to love and be loved. Students believed that strengths were largely acquired rather than innate and that these strengths developed through ongoing life experience as opposed to formal instruction. They cited an almost complete lack of contemporary role models exemplifying different strengths of character. Implications of these findings for the quantitative assessment of positive traits were discussed, as were implications for designing character education programs for adolescents. We suggest that peers can be an especially important force in encouraging the development and display of good character among youth.


title: Public transit, obesity, and medical costs: Assessing the magnitudes

abstract: OBJECTIVE ::: This paper assesses the potential benefits of increased walking and reduced obesity associated with taking public transit in terms of dollars of medical costs saved and disability avoided. ::: ::: ::: METHODS ::: I conduct a new analysis of a nationally representative U.S. transportation survey to gauge the net increase in walking associated with public transit usage. I translate minutes spent walking into energy expenditures and reductions in obesity prevalence, estimating the present value of costs and disability that may be avoided. ::: ::: ::: RESULTS ::: Taking public transit is associated with walking 8.3 more minutes per day on average, or an additional 25.7-39.0 kcal. Hill et al. [Hill, J.O., Wyatt, H.R., Reed, G.W., Peters, J.C., 2003. Obesity and the environment: Where do we go from here? Science 299 (5608), 853-855] estimate that an increase in net expenditure of 100 kcal/day can stop the increase in obesity in 90% of the population. Additional walking associated with public transit could save $5500 per person in present value by reducing obesity-related medical costs. Savings in quality-adjusted life years could be even higher. ::: ::: ::: CONCLUSIONS ::: While no silver bullet, walking associated with public transit can have a substantial impact on obesity, costs, and well-being. Further research is warranted on the net impact of transit usage on all behaviors, including caloric intake and other types of exercise, and on whether policies can promote transit usage at acceptable cost.


title: A single inductor DIDO DC-DC converter for solar energy harvesting applications using band-band control

abstract: A single inductor dual input dual output (SIDIDO) DC-DC converter is proposed for solar energy harvesting applications. The converter supports hybrid power supplies from both the photovoltaic (PV) cells and the rechargeable battery. The proposed DC-DC converter provides a regulated output voltage to power the load as well as clamps the input voltage from the PV cells to the maximum power point (MPP) value so as to extract the maximum power from the PV cells. At the same time, one of the dual outputs is used to charge up a rechargeable battery when the PV cells output power is larger than the load. A simple bandband control is proposed for controlling the power flow between different sources and destination under different power operation modes. Extensive simulations were carried out to verify the operation and demonstrate the power transfer efficiency of the proposed DC-DC converter.


title: Geometry of Vanishing Points and its Application to External Calibration and Realtime Pose Estimation

abstract: Vanishing points of an image contain important information for camera calibration. Various calibration techniques have been introduced using the properties of vanishing points to find intrinsic and extrinsic calibration parameters. This paper revisits the vanishing points geometry and suggests a simple extrinsic parameter estimation algorithm which uses a single rectangle. The comparison with the Camera Calibration Toolbox for Matlab Â® shows that the proposed algorithm is highly competitive. The suggested technique is also applied to a realtime pose estimation for an unmanned air vehicleâ€™s navigation in an urban environment. We present a realtime vanishing point extraction algorithm and a pose estimation procedure. The experimental result on a real flight video clip is presented.


title: Efficient provenance storage over nested data collections

abstract: Scientific workflow systems are increasingly used to automate complex data analyses, largely due to their benefits over traditional approaches for workflow design, optimization, and provenance recording. Many workflow systems employ a simple dependency model to represent the provenance of data produced by workflow runs. Although commonly adopted, this model does not capture explicit data dependencies introduced by "provenance-aware" processes, and it can lead to inefficient storage when workflow data is complex or structured. We present a provenance model, extending the conventional approach, that supports (i) explicit data dependencies and (ii) nested data collections. Our model adopts techniques from reference-based XML versioning, adding annotations for process and data dependencies. We present strategies and reduction techniques to store immediate and transitive provenance information within our model, and examine trade-offs among update time, storage size, and query response time. We evaluate our approach on real-world and synthetic workflow execution traces, demonstrating significant reductions in storage size, while also reducing the time required to store and query provenance information.


title: Tuning complex event processing rules using the prediction-correction paradigm

abstract: There is a growing need for the use of active systems, systems that act automatically based on events. In many cases, providing such active functionality requires materializing (inferring) the occurrence of relevant events. A widespread paradigm for enabling such materialization is Complex Event Processing (CEP), a rule based paradigm, which currently relies on domain experts to fully define the relevant rules. These experts need to provide the set of basic events which serves as input to the rule, their inter-relationships, and the parameters of the events for determining a new event materialization. While it is reasonable to expect that domain experts will be able to provide a partial rules specification, providing all the required details is a hard task, even for domain experts. Moreover, in many active systems, rules may change over time, due to the dynamic nature of the domain. Such changes complicate even further the specification task, as the expert must constantly update the rules. As a result, we seek additional support to the definition of rules, beyond expert opinion. This work presents a mechanism for automating both the initial definition of rules and the update of rules over time. This mechanism combines partial information provided by the domain expert with machine learning techniques, and is aimed at improving the accuracy of event specification and materialization. The proposed mechanism consists of two main repetitive stages, namely rule parameter prediction and rule parameter correction. The former is performed by updating the parameters using an available expert knowledge regarding the future changes of parameters. The latter stage utilizes expert feedback regarding the actual past occurrence of events and the events materialized by the CEP framework to tune rule parameters. We also include possible implementations for both stages, based on a statistical estimator and evaluate our outcome using a case study from the intrusion detection domain.


title: To FUSE or not to FUSE: performance of user-space file systems

abstract: Traditionally, file systems were implemented as part of OS kernels. However, as complexity of file systems grew, many new file systems began being developed in user space. Nowadays, user-space file systems are often used to prototype and evaluate new approaches to file system design. Low performance is considered the main disadvantage of user-space file systems but the extent of this problem has never been explored systematically. As a result, the topic of user-space file systems remains rather controversial: while some consider user-space file systems a toy not to be used in production, others develop full-fledged production file systems in user space. In this paper we analyze the design and implementation of the most widely known user-space file system framework--FUSE--and characterize its performance for a wide range of workloads. We instrumented FUSE to extract useful statistics and traces, which helped us analyze its performance bottlenecks and present our analysis results. Our experiments indicate that depending on the workload and hardware used, performance degradation caused by FUSE can be completely imperceptible or as high as -83% even when optimized; and relative CPU utilization can increase by 31%.


title: REWIND: Recovery Write-Ahead System for In-Memory Non-Volatile Data-Structures

abstract: Recent non-volatile memory (NVM) technologies, such as PCM, STT-MRAM and ReRAM, can act as both main memory and storage. This has led to research into NVM programming models, where persistent data structures remain in memory and are accessed directly through CPU loads and stores. Existing mechanisms for transactional updates are not appropriate in such a setting as they are optimized for block-based storage. We present REWIND, a user-mode library approach to managing transactional updates directly from user code written in an imperative general-purpose language. REWIND relies on a custom persistent in-memory data structure for the log that supports recoverable operations on itself. The scheme also employs a combination of non-temporal updates, persistent memory fences, and lightweight logging. Experimental results on synthetic transactional workloads and TPC-C show the overhead of REWIND compared to its non-recoverable equivalent to be within a factor of only 1.5 and 1.39 respectively. Moreover, REWIND outperforms state-of-the-art approaches for data structure recoverability as well as general purpose and NVM-aware DBMS-based recovery schemes by up to two orders of magnitude.


title: 3D printing of bacteria into functional complex materials

abstract: Despite recent advances to control the spatial composition and dynamic functionalities of bacteria embedded in materials, bacterial localization into complex three-dimensional (3D) geometries remains a major challenge. We demonstrate a 3D printing approach to create bacteria-derived functional materials by combining the natural diverse metabolism of bacteria with the shape design freedom of additive manufacturing. To achieve this, we embedded bacteria in a biocompatible and functionalized 3D printing ink and printed two types of "living materials" capable of degrading pollutants and of producing medically relevant bacterial cellulose. With this versatile bacteria-printing platform, complex materials displaying spatially specific compositions, geometry, and properties not accessed by standard technologies can be assembled from bottom up for new biotechnological and biomedical applications.


title: The Analysis of the Disease Spectrum in China

abstract: Analysis of the related risks of disease provides a scientific basis for disease prevention and treatment, hospital management, and policy formulation by the changes in disease spectrum of patients in hospital. Retrospective analysis was made to the first diagnosis, age, gender, daily average cost of hospitalized patients, and other factors in the First Affiliated Hospital of Nanjing Medical University during 2006-2013. The top 4 cases were as follows: cardiovascular disease, malignant tumors, lung infections, and noninsulin dependent diabetes mellitus. By the age of disease analysis, we found a younger age trend of cardiovascular disease, and the age of onset of cancer or diabetes was somewhat postponed. The average daily cost of hospitalization and the average daily cost of the main noncommunicable diseases were both on the rise. Noncommunicable diseases occupy an increasingly important position in the constitution of the disease, and they caused an increasing medical burden. People should pay attention to health from the aspects of lifestyle changing. Hospitals should focus on building the appropriate discipline. On the other hand, an integrated government response is required to tackle key risks. Multiple interventions are needed to lower the burden of these diseases and to improve national health.


title: Bootstrap Domain-Specific Sentiment Classifiers from Unlabeled Corpora

abstract: There is often the need to perform sentiment classification in a particular domain where no labeled document is available. Although we could make use of a general-purpose off-the-shelf sentiment classifier or a pre-built one for a different domain, the effectiveness would be inferior. In this paper, we explore the possibility of building domain-specific sentiment classifiers with unlabeled documents only. Our investigation indicates that in the word embeddings learned from the unlabeled corpus of a given domain, the distributed word representations (vectors) for opposite sentiments form distinct clusters, though those clusters are not transferable across domains. Exploiting such a clustering structure, we are able to utilize machine learning algorithms to induce a quality domain-specific sentiment lexicon from just a few typical sentiment words ("seeds"). An important finding is that simple linear model based supervised learning algorithms (such as linear SVM) can actually work better than more sophisticated semi-supervised/transductive learning algorithms which represent the state-of-the-art technique for sentiment lexicon induction. The induced lexicon could be applied directly in a lexicon-based method for sentiment classification, but a higher performance could be achieved through a two-phase bootstrapping method which uses the induced lexicon to assign positive/negative sentiment scores to unlabeled documents first, and then uses those documents found to have clear sentiment signals as pseudo-labeled examples to train a document sentiment classifier via supervised learning algorithms (such as LSTM). On several benchmark datasets for document sentiment classification, our end-to-end pipelined approach which is overall unsupervised (except for a tiny set of seed words) outperforms existing unsupervised approaches and achieves an accuracy comparable to that of fully supervised approaches.


title: Expressing DOACROSS Loop Dependences in OpenMP

abstract: OpenMP is a widely used programming standard for a broad range of parallel systems. In the OpenMP programming model, synchronization points are specified by implicit or explicit barrier operations within a parallel region. However, certain classes of computations, such as stencil algorithms, can be supported with better synchronization efficiency and data locality when using doacross parallelism with point-to-point synchronization than wavefront parallelism with barrier synchronization. In this paper, we propose new synchronization constructs to enable doacross parallelism in the context of the OpenMP programming model. Experimental results on a 32-core IBM Power7 system using four benchmark programs show performance improvements of the proposed doacross approach over OpenMP barriers by factors of 1.4× to 5.2× when using all 32 cores.


title: Tensor decompositions for learning latent variable models

abstract: This work considers a computationally and statistically efficient parameter estimation method for a wide class of latent variable models---including Gaussian mixture models, hidden Markov models, and latent Dirichlet allocation---which exploits a certain tensor structure in their low-order observable moments (typically, of second- and third-order). Specifically, parameter estimation is reduced to the problem of extracting a certain (orthogonal) decomposition of a symmetric tensor derived from the moments; this decomposition can be viewed as a natural generalization of the singular value decomposition for matrices. Although tensor decompositions are generally intractable to compute, the decomposition of these specially structured tensors can be efficiently obtained by a variety of approaches, including power iterations and maximization approaches (similar to the case of matrices). A detailed analysis of a robust tensor power method is provided, establishing an analogue of Wedin's perturbation theorem for the singular vectors of matrices. This implies a robust and computationally tractable estimation approach for several popular latent variable models.


title: Quantum phase transition for the BEC – BCS crossover in condensed matter physics and CPT violation in elementary particle physics

abstract: We discuss the quantum phase transition that separates a vacuum state with fully gapped fermion spectrum from a vacuum state with topologically protected Fermi points (gap nodes). In the context of condensed-matter physics, such a quantum phase transition with Fermi point splitting may occur for a system of ultracold fermionic atoms in the region of BEC-BCS crossover, provided Cooper pairing occurs in the non-s-wave channel. For elementary particle physics, the splitting of Fermi points may lead to CPT violation, neutrino oscillations, and other phenomena.


title: Automation of Attendances in Classrooms using RFID

abstract: This paper automates the design and implementation of students' attendance management system, taken into consideration easy access and time saving. The system will be used in faculty of computing and information system (FCIT) at King Abdulaziz University (KAU). Radio Frequency Identification (RFID) and wireless technology are two technologies which will be applied as infrastructure in the indoor environment. University Based Services (UBS) and tag IDs of the RFID are being used in this paper, in order to determine the attendance list for academic staff. Once the academic staff enters a classroom, he will be able to register student's presence. Therefore, an academic system for identifying and tracking attendance at computing college at KAU will be described, and hybrid RFID and Wireless LAN (WLAN) technologies will be implemented in FCIT academic environment.


title: Off-Axis Performances of Half Maxwell Fish-Eye Lens Antennas at 77 GHz

abstract: The off-axis performances at 77 GHz of multilayered half Maxwell fish-eye (HMFE) lenses fed by an open-ended waveguide is describes. When moving the feed with respect to (w.r.t.) the lens, it is shown that at least 40deg of scanning can be achieved. Several off-axis configurations of the lens antenna are investigated and compared using a full-wave electromagnetic software. The directivity, far field pattern and scan angle of the lens antenna are reported for a rectilinear and angular off-axis displacement of the feed and also for various distances between the feed and the lens. It is also shown that a three-shell lens is good enough to achieve a wide scan angle. Finally, off-axis measurements done with a three-shell 6.15 lambda0-diameter HMFE lens antenna are compared to computed results to validate the simulations


A Neural Network Color Classifier in HSV Color Space


title: SERUM LIPID PROFILES IN GESTATIONAL HYPERTENSION

abstract: Gestational hypertension is multi factorial disorder of pregnancy affecting health of mother and fetus. To know relationship between serum lipids and gestational hypertension serum lipid levels of gestational hypertensive women were measured. Serum levels of triglycerides (TGL), total cholesterol (TC), low density lipoprotein cholesterol (LDLC), very low density lipoprotein cholesterol (VLDLC) and high density lipoprotein cholesterol (HDLC) in 50 gestational hypertensive women and 30 normotensive pregnant women were measured by established methods. The serum lipid profiles of gestational hypertension women were compared with normotensive pregnant women. In gestational hypertension women serum TGL, TC, LDLC, VLDLC were elevated where as HDLC level was more in normotensive pregnant women. The elevated serum TGL.TC, LDLC,VLDLC and low HDLC levels may contribute to pathogenesis of gestational hypertension and lead to development of coronary artery disease.


title: Single Image Super-Resolution via Multiple Mixture Prior Models

abstract: Example learning-based single image super-resolution (SR) is a promising method for reconstructing a high-resolution (HR) image from a single-input low-resolution (LR) image. Lots of popular SR approaches are more likely either time-or space-intensive, which limit their practical applications. Hence, some research has focused on a subspace view and delivered state-of-the-art results. In this paper, we utilize an effective way with mixture prior models to transform the large nonlinear feature space of LR images into a group of linear subspaces in the training phase. In particular, we first partition image patches into several groups by a novel selective patch processing method based on difference curvature of LR patches, and then learning the mixture prior models in each group. Moreover, different prior distributions have various effectiveness in SR, and in this case, we find that student-t prior shows stronger performance than the well-known Gaussian prior. In the testing phase, we adopt the learned multiple mixture prior models to map the input LR features into the appropriate subspace, and finally reconstruct the corresponding HR image in a novel mixed matching way. Experimental results indicate that the proposed approach is both quantitatively and qualitatively superior to some state-of-the-art SR methods.


title: Leveraging Transitive Trust Relations to Improve Cross-Domain Recommendation

abstract: Cross-domain recommendation has become increasingly popular because it can determine dependencies and correlations among different domains. However, this type of recommendation still suffers from data sparsity limitations. Social trust relationship helps alleviate this problem. Existing cross-domain recommendation algorithms focus on modeling user behavior in different domains but disregard the social trust relationship among users. In this paper, we propose a transitive trust-aware cross-domain recommendation model that incorporates the context dependence and transitivity of social trust relations. First, we construct a different transitive trust network for each single domain. Then, we develop a novel probabilistic matrix factorization model for each domain that utilizes the transitive trust-aware model to mine social trust. Finally, we present a nonlinear user–vector mapping algorithm to bridge the feedback of different domains. Experimental results indicate that our method significantly outperforms several state-of-the-art methods, produces higher rating prediction accuracy, and exhibits better recommendation performance on several real-world recommendation tasks.


title: Aircraft Trajectory Prediction Made Easy with Predictive Analytics

abstract: At the heart of Air Traffic Management (ATM) lies the Decision Support Systems (DST) that rely upon accurate trajectory prediction to determine how the airspace will look like in the future to make better decisions and advisories. Dealing with airspace that is prone to congestion due to environmental factors still remains the challenge especially when a deterministic approach is used in the trajectory prediction process. In this paper, we describe a novel stochastic trajectory prediction approach for ATM that can be used for more efficient and realistic flight planning and to assist airspace flow management, potentially resulting in higher safety, capacity, and efficiency commensurate with fuel savings thereby reducing emissions for a better environment. Our approach considers airspace as a 3D grid network, where each grid point is a location of a weather observation. We hypothetically build cubes around these grid points, so the entire airspace can be considered as a set of cubes. Each cube is defined by its centroid, the original grid point, and associated weather parameters that remain homogeneous within the cube during a period of time. Then, we align raw trajectories to a set of cube centroids which are basically fixed 3D positions independent of trajectory data. This creates a new form of trajectories which are 4D joint cubes, where each cube is a segment that is associated with not only spatio-temporal attributes but also with weather parameters. Next, we exploit machine learning techniques to train inference models from historical data and apply a stochastic model, a Hidden Markov Model (HMM), to predict trajectories taking environmental uncertainties into account. During the process, we apply time series clustering to generate input observations from an excessive set of weather parameters to feed into the Viterbi algorithm. Our experiments use a real trajectory dataset with pertaining weather observations and demonstrate the effectiveness of our approach to the trajectory prediction process for ATM.


title: Introduction to the Special Issue on Language in Social Media: Exploiting Discourse and Other Contextual Information

abstract: Social media content is changing the way people interact with each other and share information, personal messages, and opinions about situations, objects, and past experiences. Most social media texts are short online conversational posts or comments that do not contain enough information for natural language processing (NLP) tools, as they are often accompanied by non-linguistic contextual information, including meta-data (e.g., the user’s profile, the social network of the user, and their interactions with other users). Exploiting such different types of context and their interactions makes the automatic processing of social media texts a challenging research task. Indeed, simply applying traditional text mining tools is clearly sub-optimal, as, typically, these tools take into account neither the interactive dimension nor the particular nature of this data, which shares properties with both spoken and written language. This special issue contributes to a deeper understanding of the role of these interactions to process social media data from a new perspective in discourse interpretation. This introduction first provides the necessary background to understand what context is from both the linguistic and computational linguistic perspectives, then presents the most recent context-based approaches to NLP for social media. We conclude with an overview of the papers accepted in this special issue, highlighting what we believe are the future directions in processing social media texts.


title: Crowdsourcing in Software Engineering: Models, Motivations, and Challenges

abstract: Almost surreptitiously, crowdsourcing has entered software engineering practice. In-house development, contracting, and outsourcing still dominate, but many development projects use crowdsourcing-for example, to squash bugs, test software, or gather alternative UI designs. Although the overall impact has been mundane so far, crowdsourcing could lead to fundamental, disruptive changes in how software is developed. Various crowdsourcing models have been applied to software development. Such changes offer exciting opportunities, but several challenges must be met for crowdsourcing software development to reach its potential.


title: Knowledge Graph Embedding with Triple Context

abstract: Knowledge graph embedding, which aims to represent entities and relations in vector spaces, has shown outstanding performance on a few knowledge graph completion tasks. Most existing methods are based on the assumption that a knowledge graph is a set of separate triples, ignoring rich graph features, i.e., structural information in the graph. In this paper, we take advantages of structures in knowledge graphs, especially local structures around a triple, which we refer to as triple context. We then propose a Triple-Context-based knowledge Embedding model (TCE). For each triple, two kinds of structure information are considered as its context in the graph; one is the outgoing relations and neighboring entities of an entity and the other is relation paths between a pair of entities, both of which reflect various aspects of the triple. Triples along with their contexts are represented in a unified framework, in which way structural information in triple contexts can be embodied. The experimental results show that our model outperforms the state-of-the-art methods for link prediction.


title: EmoSenticSpace: a novel framework for affective common-sense reasoning

abstract: Emotions play a key role in natural language understanding and sensemaking. Pure machine learning usually fails to recognize and interpret emotions in text accurately. The need for knowledge bases that give access to semantics and sentics (the conceptual and affective information) associated with natural language is growing exponentially in the context of big social data analysis. To this end, this paper proposes EmoSenticSpace, a new framework for affective common-sense reasoning that extends WordNet-Affect and SenticNet by providing both emotion labels and polarity scores for a large set of natural language concepts. The framework is built by means of fuzzy c-means clustering and support-vector-machine classification, and takes into account a number of similarity measures, including point-wise mutual information and emotional affinity. EmoSenticSpace was tested on three emotion-related natural language processing tasks, namely sentiment analysis, emotion recognition, and personality detection. In all cases, the proposed framework outperforms the state-of-the-art. In particular, the direct evaluation of EmoSenticSpace against psychological features provided in the benchmark ISEAR dataset shows a 92.15% agreement.