title: ORIGINAL ARTICLE Faces of Product Pleasure: 25 Positive Emotions in Human-Product Interactions

abstract: The study of user emotions is hindered by the absence of a clear overview of what positive emotions can be experienced in humanproduct interactions. Existing typologies are either too concise or too comprehensive, including less than five or hundreds of positive emotions, respectively. To overcome this hindrance, this paper introduces a basic set of 25 positive emotion types that represent the general repertoire of positive human emotions. The set was developed with a componential analysis of 150 positive emotion words. A questionnaire study that explored how and when each of the 25 emotions are experienced in human-product interactions resulted in a collection of 729 example cases. On the basis of these cases, six main sources of positive emotions in human-product interactions are proposed. By providing a fine-grained yet concise vocabulary of positive emotions that people can experience in response to product design, the typology aims to facilitate both research and design activities. The implications and limitations of the set are discussed, and some future research steps are proposed.


title: Directional relaying using support vector machine for double circuit transmission lines including cross-country and inter-circuit faults

abstract: Abstract The conventional distance relaying algorithms are unable to detect the inter-circuit faults, cross-country faults, high resistance faults which may occur in a double circuit line. This paper presents combined Discrete Wavelet Transform (DWT) and Support Vector Machine (SVM) based directional relaying and fault classification scheme including inter-circuit faults, cross-country faults and high resistance faults. SVM modules are designed for forward or reverse fault identification and fault classification using single terminal data. The 3rd level approximate discrete wavelet transform coefficients of three phase current signals only have been used. Proposed method is tested with variations in fault type, fault location, fault inception angle, fault resistance, inter-circuit faults, and cross-country faults. The proposed method based on SVM does not need any threshold to operate which is an exceptional attribute for a protective function. As SVMs are not based on comparing with some threshold, rather initially the SVMs are trained with the wide variety of fault patterns which is an offline process and then the trained SVMs are tested online to detect and classify the fault within short time. The test results show that all types of shunt faults can be identified within half cycle time. The proposed scheme offers both primary protection to 95% of the line section and also backup protection to 95% of the adjacent reverse and forward line section also.


title: Facilitating Posttraumatic Growth: A Clinician's Guide

abstract: Contents: Preface. Trauma and Growth: Processes and Outcomes. Case Examples of Posttraumatic Growth. The Process of Encouraging Posttraumatic Growth: An Overview. Helping Clients Develop New Views of Vulnerability and Strength. Helping Clients Make Changes in Relationships. Helping Clients Toward Philosophical and Spiritual Growth. Posttraumatic Growth: Issues for Clinicians. Resources for Clinicians and Clients.


title: Control of the switching transients of IGBTs series strings by high-performance drive units

abstract: In the field of power electronics the use of series connected insulated gate devices, such as IGBTs or power MOSFETs, is interesting in order to obtain fast and efficient power switches in medium range power converters. In this kind of application, the control of the voltage sharing across the series strings of devices is an important aspect worth considering. The proposed technique allows safe commutations of the switches to be obtained by a simple and effective control circuit acting on the gate side of the power devices. In particular, the gate drive units are arranged in order to ensure good performances during the switching transients, while preventing overvoltage peaks on the devices. Both the design criteria and analysis of the control circuit are developed. Several experimental tests are reported in order to demonstrate the validity and correctness of the proposed approach.


title: Improving Execution Concurrency of Large-Scale Matrix Multiplication on Distributed Data-Parallel Platforms

abstract: Matrix multiplication is a dominant but very time-consuming operation in many big data analytic applications. Thus its performance optimization is an important and fundamental research issue. The performance of large-scale matrix multiplication on distributed data-parallel platforms is determined by both computation and IO costs. For existing matrix multiplication execution strategies, when the execution concurrency scales up above a threshold, their execution performance deteriorates quickly because the increase of the IO cost outweighs the decrease of the computation cost. This paper presents a novel parallel execution strategy CRMM (Concurrent Replication-based Matrix Multiplication) along with a parallel algorithm, Marlin, for large-scale matrix multiplication on data-parallel platforms. The CRMM strategy exploits higher execution concurrency for sub-block matrix multiplication with the same IO cost. To further improve the performance of Marlin, we also propose a number of novel system-level optimizations, including increasing the concurrency of local data exchange by calling native library in batch, reducing the overhead of block matrix transformation, and reducing disk heavy shuffle operations by exploiting the semantics of matrix computation. We have implemented Marlin as a library along with a set of related matrix operations on Spark and also contributed Marlin to the open-source community. For large-sized matrix multiplication, Marlin outperforms existing systems including Spark MLlib, SystemML and SciDB, with about $1.29\times$ , $3.53\times$ and $2.21\times$ speedup on average, respectively. The evaluation upon a real-world DNN workload also indicates that Marlin outperforms above systems by about $12.8\times$ , $5.1\times$ and $27.2\times$ speedup, respectively.


title: Latency and player actions in online games

abstract: Latency determines not only how players experience online gameplay but also how to design the games to mitigate its effects and meet player expectations.


title: Decoding ALS: from genes to mechanism

abstract: Amyotrophic lateral sclerosis (ALS) is a progressive and uniformly fatal neurodegenerative disease. A plethora of genetic factors have been identified that drive the degeneration of motor neurons in ALS, increase susceptibility to the disease or influence the rate of its progression. Emerging themes include dysfunction in RNA metabolism and protein homeostasis, with specific defects in nucleocytoplasmic trafficking, the induction of stress at the endoplasmic reticulum and impaired dynamics of ribonucleoprotein bodies such as RNA granules that assemble through liquid-liquid phase separation. Extraordinary progress in understanding the biology of ALS provides new reasons for optimism that meaningful therapies will be identified.


Book Review: The Wisdom of Crowds: Why the Many are Smarter than the Few, and how Collective Wisdom Shapes Business, Economies, Societies, and Nations:


title: BlendCAC: A Smart Contract Enabled Decentralized Capability-Based Access Control Mechanism for the IoT

abstract: While Internet of Things (IoT) technology has been widely recognized as an essential part of Smart Cities, it also brings new challenges in terms of privacy and security. Access control (AC) is among the top security concerns, which is critical in resource and information protection over IoT devices. Traditional access control approaches, like Access Control Lists (ACL), Role-based Access Control (RBAC) and Attribute-based Access Control (ABAC), are not able to provide a scalable, manageable and efficient mechanism to meet the requirements of IoT systems. Another weakness in today’s AC is the centralized authorization server, which can cause a performance bottleneck or be the single point of failure. Inspired by the smart contract on top of a blockchain protocol, this paper proposes BlendCAC, which is a decentralized, federated capability-based AC mechanism to enable effective protection for devices, services and information in large-scale IoT systems. A federated capability-based delegation model (FCDM) is introduced to support hierarchical and multi-hop delegation. The mechanism for delegate authorization and revocation is explored. A robust identity-based capability token management strategy is proposed, which takes advantage of the smart contract for registration, propagation, and revocation of the access authorization. A proof-of-concept prototype has been implemented on both resources-constrained devices (i.e., Raspberry PI nodes) and more powerful computing devices (i.e., laptops) and tested on a local private blockchain network. The experimental results demonstrate the feasibility of the BlendCAC to offer a decentralized, scalable, lightweight and fine-grained AC solution for IoT systems.


title: A circuit-compatible model of ballistic carbon nanotube field-effect transistors

abstract: Carbon nanotube field-effect transistors (CNFETs) are being extensively studied as possible successors to CMOS. Novel device structures have been fabricated and device simulators have been developed to estimate their performance in a sub-10-nm transistor era. This paper presents a novel method of circuit-compatible modeling of single-walled semiconducting CNFETs in their ultimate performance limit. For the first time, both the I-V and the C-V characteristics of the device have been efficiently modeled for circuit simulations. The model so developed has been used to simulate arithmetic and logic blocks using HSPICE.


title: Sports sociology, journals and their editors

abstract: This article discusses the role that journals and their editors have played in the growth of sports sociology as an academic discipline. It is suggested that journals are not neutral spaces in which academics share their research but, rather, they and their editors occupy a powerful position within academic life and are thus worthy of critical examination. Using data from interviews with several previous editors of leading sports sociology journals, examination is made of their motivation to take on the role, their positioning of the journal, their experiences of working with peer reviewers and attracting papers and why they relinquished the role. The article explores the opportunities presented by the recent evolution of scholarly publication, specifically the “open access” platform, and the challenges this presents to sports sociology journals.


title: Reverse Engineering PL/SQL Legacy Code: An Experience Report

abstract: The reengineering of legacy code is a tedious endeavor. Automatic transformation of legacy code from an old technology to a new one preserves potential problems in legacy code with respect to obsolete, changed, and new business cases. On the other hand, manual analysis of legacy code without assistance of original developers is time consuming and error-prone. For the purpose of reengineering PL/SQL legacy code in the steel making domain, we developed tool support for the reverse engineering of PL/SQL code into a more abstract and comprehensive representation. This representation then serves as input for stakeholders to manually analyze legacy code, to identify obsolete and missing business cases, and, finally, to support the re-implementation of a new system. In this paper we briefly introduce the tool and present results of reverse engineering PL/SQL legacy code in the steel making domain. We show how stakeholders are supported in analyzing legacy code by means of general-purpose analysis techniques combined with domain-specific representations and conclude with some of the lessons learned.


title: An overview of design techniques for CMOS phase detectors

abstract: An overview of the recent developments in the design techniques of CMOS phase detectors and an in-depth examination of the advantages and limitations of these techniques are presented. Both linear and nonlinear phase detectors are examined. Critical design issues, such as, sampling mechanism, lock condition, sensitivity to input data pattern, and reliability are investigated in detail.


title: The Impact of Mobile Phone Usage on Student Learning

abstract: In this study, we examined the impact of mobile phone usage, during class lecture, on student learning. Participants in three different study groups (control, low-distraction, and high-distraction) watched a video lecture, took notes on that lecture, and took two learning assessments after watching the lecture. Students who were not using their mobile phones wrote down 62% more information in their notes, took more detailed notes, were able to recall more detailed information from the lecture, and scored a full letter grade and a half higher on a multiple choice test than those students who were actively using their mobile phones. Theoretical and pedagogical implications are discussed.


title: Design of guaranteed safe maneuvers using reachable sets: Autonomous quadrotor aerobatics in theory and practice

abstract: For many applications, the control of a complex nonlinear system can be made easier by modeling the system as a collection of simplified hybrid modes, each representing a particular operating regime. An example of this is the decomposition of complex aerobatic flights into sequences of discrete maneuvers, an approach that has proven very successful for both human piloted and autonomously controlled aircraft. However, a critical step when designing such control systems is to ensure the safety and feasibility of transitions between these maneuvers. This work presents a hybrid dynamics framework for the design of guaranteed safe switching regions and is applied to a quadrotor helicopter performing an autonomous backflip. The regions are constructed using reachable sets calculated via a Hamilton-Jacobi differential game formulation, and experimental results are presented from flight tests on the STARMAC quadrotor platform.


title: Toward Implementation of a Software Defined Cloud on a Supercomputer

abstract: Conventional cloud computing architectures may seriously constrain computational throughput for high performance computing (HPC) and high-performance data (HPD) applications. The traditional approach to circumvent such problems has been to map these applications and problems onto other specialized hardware and coprocessor architectures. This is both time and resource expensive, and poses a challenge for rapidly rising demands for computation and data analytics. In this paper we report on progress to develop an alternative experimental software defined cloud implementation that virtualizes the topology of a standard HPC computational architecture. This software defined system re-arranges access to the nodes and dynamically customizes the features of the HPC hardware architecture so that they map to the specifics of the computation and data analysis application. This allows a cloud computing implementation to utilize the specialized infrastructure capabilities of an HPC system. We have created this type of user reconfigurable architecture on an IBM Blue Gene/P supercomputing environment at the Department of Energy's Argonne Leadership Computing Facility (ALCF). This pilot configuration was implemented using both an open source cloud technology called VCL (Virtual Computing Laboratory) in combination with a provisioning module called Kittyhawk. Cloud security is addressed by configuring and running a root-less version of the VCL cloud system on the ALCF's Blue Gene/P login node.


title: A New Way of Using the Interactive Whiteboard in a High School Physics Classroom: A Case Study

abstract: In recent decades, the interactive whiteboard (IWB) has become a relatively common educational tool in Western schools. The IWB is essentially a large touch screen, that enables the user to interact with digital content in ways that are not possible with an ordinary computer-projector-canvas setup. However, the unique possibilities of IWBs are rarely leveraged to enhance teaching and learning beyond the primary school level. This is particularly noticeable in high school physics. We describe how a high school physics teacher learned to use an IWB in a new way, how she planned and implemented a lesson on the topic of orbital motion of planets, and what tensions arose in the process. We used an ethnographic approach to account for the teacher’s and involved students’ perspectives throughout the process of teacher preparation, lesson planning, and the implementation of the lesson. To interpret the data, we used the conceptual framework of activity theory. We found that an entrenched culture of traditional white/blackboard use in physics instruction interferes with more technologically innovative and more student-centered instructional approaches that leverage the IWB’s unique instructional potential. Furthermore, we found that the teacher’s confidence in the mastery of the IWB plays a crucial role in the teacher’s willingness to transfer agency within the lesson to the students.


title: Performance of Some Image Processing Algorithms in Tensorflow

abstract: Signal, image and Synthetic Aperture Radar imagery algorithms in recent time are used in a daily routine. Due to huge data and complexity, their processing is almost impossible in a real time. Often image processing algorithms are inherently parallel in nature, so they fit nicely into parallel architectures multicore Central Processing Unit (CPU) and Graphics Processing Unit GPUs. In this paper image processing algorithms were evaluated, which are capable to execute in parallel manner on several platforms CPU and GPU. All algorithms were tested in TensorFlow, which is a novel framework for deep learning, but also for image processing. Relative speedups compared to CPU were given for all algorithms. TensorFlow GPU implementation can outperform multi-core CPUs for tested algorithms, obtained speedups range from 3.6 to 15 times.


title: ECGNet: Deep Network for Arrhythmia Classification

abstract: Cardiac arrhythmias are presently diagnosed by manual interpretation of Electrocardiography (ECG) signals. Automated ECG interpretation is required to perform efficient screening of arrhythmia from long term ECG data. Existing automated ECG interpretation tools however require extensive preprocessing and knowledge to determine relevant features. Thus there is a need for a comprehensive feature extractor and classifier to analyze ECG signals. In this paper, we propose three robust deep neural network (DNN) architectures to perform feature extraction and classification of a given two second ECG signal. The first network is a Convolutional Neural Network (CNN) with multiple kernel sizes, the second network is a Long Short Term Memory (LSTM) network and the third network is a combination of CNN and LSTM based feature extractor, CLSTM network. The proposed networks are end to end networks which can be directly trained without any preprocessing. The networks were trained and tested with the MITDB ECG dataset on three classes Normal (N), Premature Ventricular Contraction (PVC) and Premature Atrial Contraction (PAC). The best model CLSTM gave an accuracy of 97.6%. Further, transfer learning is showcased on the best performing network for use with multiple ECG datasets requiring training only on the final three layers. The results showcase the potential of the network as feature extractor for ECG datasets. Our results outperform the state-of-the art works on ECG classification on several metrics.


title: Comparative Assessment of Single Piece and Fir-Tree-Based Spoke Type Rotor Designs for Low-Cost Electric Vehicle Application

abstract: Permanent magnet motors with ferrite magnets may be applicable to demanding electric vehicle applications, provided that designs with high power density and efficiency, as well as high resistance to demagnetization are developed. One rotor topology that allows meeting those challenges is a design with a so-called spoke arrangement of the magnet poles. Although, a spoke type rotor topology is widely known, the influences of the rotor mechanical design on the electromagnetic performance have largely been overlooked, while a comparison of different feasible design solutions has not been provided to date. In this paper, a novel spoke type traction motor using ferrite magnets and based on a single piece rotor topology has been presented. The design is based on a coupled structural-electromagnetic optimization, where the key design features influencing the structural stress and the electromagnetic torque density have been explained. This design is, further, compared against a previously disclosed fir-tree spoke type rotor solution, which has been designed for the same set of geometrical and output requirements. Through the comparisons, and for the first time, it is shown that the two alternative spoke type designs show significant differences in performance, including torque density, efficiency, and demagnetization.


title: Iterative Vessel Segmentation of Fundus Images

abstract: This paper presents a novel unsupervised iterative blood vessel segmentation algorithm using fundus images. First, a vessel enhanced image is generated by tophat reconstruction of the negative green plane image. An initial estimate of the segmented vasculature is extracted by global thresholding the vessel enhanced image. Next, new vessel pixels are identified iteratively by adaptive thresholding of the residual image generated by masking out the existing segmented vessel estimate from the vessel enhanced image. The new vessel pixels are, then, region grown into the existing vessel, thereby resulting in an iterative enhancement of the segmented vessel structure. As the iterations progress, the number of false edge pixels identified as new vessel pixels increases compared to the number of actual vessel pixels. A key contribution of this paper is a novel stopping criterion that terminates the iterative process leading to higher vessel segmentation accuracy. This iterative algorithm is robust to the rate of new vessel pixel addition since it achieves 93.2-95.35% vessel segmentation accuracy with 0.9577-0.9638 area under ROC curve (AUC) on abnormal retinal images from the STARE dataset. The proposed algorithm is computationally efficient and consistent in vessel segmentation performance for retinal images with variations due to pathology, uneven illumination, pigmentation, and fields of view since it achieves a vessel segmentation accuracy of about 95% in an average time of 2.45, 3.95, and 8 s on images from three public datasets DRIVE, STARE, and CHASE_DB1, respectively. Additionally, the proposed algorithm has more than 90% segmentation accuracy for segmenting peripapillary blood vessels in the images from the DRIVE and CHASE_DB1 datasets.


Analysis of ordinal data with cumulative link models — estimation with the R-package ordinal


title: Radar-vision fusion for object classification

abstract: We propose an object classification system that incorporates information from a video camera and an automotive radar. The system implements three processes. The first process is attention selection, in which the radar guides a selection of a small number of candidate images for analysis by the camera and our learning method. In the second process, normalized attention windows are processed by orientation-selective feature detectors, generating a sparse representation for each window. In the final process, a multilayer in-place learning network is used to distinguish sparse representations of different objects. Though it is more flexible in terms of variety of classification tasks, the system currently demonstrates its high accuracy in comparison with others on real-world data of a two-class recognition problem.


title: Combinatorial Network Optimization With Unknown Variables: Multi-Armed Bandits With Linear Rewards and Individual Observations

abstract: We formulate the following combinatorial multi-armed bandit (MAB) problem: There are N random variables with unknown mean that are each instantiated in an i.i.d. fashion over time. At each time multiple random variables can be selected, subject to an arbitrary constraint on weights associated with the selected variables. All of the selected individual random variables are observed at that time, and a linearly weighted combination of these selected variables is yielded as the reward. The goal is to find a policy that minimizes regret, defined as the difference between the reward obtained by a genie that knows the mean of each random variable, and that obtained by the given policy. This formulation is broadly applicable and useful for stochastic online versions of many interesting tasks in networks that can be formulated as tractable combinatorial optimization problems with linear objective functions, such as maximum weighted matching, shortest path, and minimum spanning tree computations. Prior work on multi-armed bandits with multiple plays cannot be applied to this formulation because of the general nature of the constraint. On the other hand, the mapping of all feasible combinations to arms allows for the use of prior work on MAB with single-play, but results in regret, storage, and computation growing exponentially in the number of unknown variables. We present new efficient policies for this problem that are shown to achieve regret that grows logarithmically with time, and polynomially in the number of unknown variables. Furthermore, these policies only require storage that grows linearly in the number of unknown parameters. For problems where the underlying deterministic problem is tractable, these policies further require only polynomial computation. For computationally intractable problems, we also present results on a different notion of regret that is suitable when a polynomial-time approximation algorithm is used.


Dimensionality Reduction : AComparative Review


title: Design a new algorithm to count white blood cells for classification leukemic blood image using machine vision system

abstract: White blood cells protect the immune system against viruses and bacteria. Data extraction from white blood cells may cause problems such as loosing form, dimensions and edges. In this study, a complete and automatic method to identify and classify white blood cells using microscopic images has been presented. In the proposed method, in the first step, white blood cells are identified using color space conversion models. Then leukocytes group are separated using division of watershed conversion. In the next step image cleanup is done and all leukocytes available on the edge of images and abnormal components are removed. This is accomplished by cutting the image with the smallest rectangle that has connected components. The second level of division relates to the detection of the nucleus and cytoplasm. In the last step feature extraction is performed which causes the pathologists can have the best interpretation of them. All the above steps have been performed in MATLAB software. At the end, the proposed method was examined by a database belonging to Imam Reza (AS) hospital in Mashhad, consisting of 29 images of blood cells, and showed the accuracy of 93% in the detection of white blood cells.


title: Research of Improving the Accuracy of License Plate Character Segmentation

abstract: In order to improve the accuracy of license plate character segmentation, this paper strengthens preprocessings before segmentation. In character binarization, use improved Bernsen method to reduce noise interference; use line-shape filter to remove plate frames. Then character segmentation approach combines projection and inherent characteristics of character, can better solve character adhesion and fracture problem. For tilted characters only in horizontal direction, even without tilt correction, this method can achieve better segmentation. Experiment results shows the accuracy of character segmentation reaches 97%.


title: Analysis and Design of High-Frequency Isolated Dual-Bridge Series Resonant DC/DC Converter

abstract: Bidirectional dual-bridge dc/dc converter with high frequency isolation is gaining more attentions in renewable energy system due to small size and high-power density. In this paper, a dual-bridge series resonant dc/dc converter is analyzed with two simple modified ac equivalent circuit analysis methods for both voltage source load and resistive load. In both methods, only fundamental components of voltages and currents are considered. All the switches may work in either zero-voltage-switching or zero-current-switching for a wide variation of voltage gain, which is important in renewable energy generation. It is also shown in the second method that the load side circuit could be represented with an equivalent impedance. The polarity of cosine value of this equivalent impedance angle reveals the power flow direction. The analysis is verified with computer simulation results. Experimental data based on a 200 W prototype circuit is included for validation purpose.


title: Design and study of wide-band patch antenna fed by meandering probe

abstract: A wide-band probe fed patch antenna with low cross polarization and symmetrical broadside radiation pattern is proposed and studied. By employing a novel meandering probe feed and locating a patch about 0.1/spl lambda//sub 0/ above a ground plane, a patch antenna with 30% impedance bandwidth (SWR<2) and 9 dBi gain is designed. The far field radiation pattern of the antenna is stable across the operating bandwidth. Parametric studies and design guidelines of the proposed feeding structure are provided.


title: Integration of network intrusion detection systems and honeypot networks for cloud security

abstract: With an aim of provisioning fast, reliable and low cost services to the users, the cloud-computing technology has progressed leaps and bounds. But, adjacent to its development is ever increasing ability of malicious users to compromise its security from outside as well as inside. The Network Intrusion Detection System (NIDS) techniques has gone a long way in detection of known and unknown attacks. The methods of detection of intrusion and deployment of NIDS in cloud environment are dependent on the type of services being rendered by the cloud. It is also important that the cloud administrator is able to determine the malicious intensions of the attackers and various methods of attack. In this paper, we carry out the integration of NIDS module and Honeypot Networks in Cloud environment with objective to mitigate the known and unknown attacks. We also propose method to generate and update signatures from information derived from the proposed integrated model. Using sandboxing environment, we perform dynamic malware analysis of binaries to derive conclusive evidence of malicious attacks.


title: Temporal construal effects on abstract and concrete thinking: consequences for insight and creative cognition.

abstract: Six studies investigate whether and how distant future time perspective facilitates abstract thinking and impedes concrete thinking by altering the level at which mental representations are construed. In Experiments 1-3, participants who envisioned their lives and imagined themselves engaging in a task 1 year later as opposed to the next day subsequently performed better on a series of insight tasks. In Experiments 4 and 5 a distal perspective was found to improve creative generation of abstract solutions. Moreover, Experiment 5 demonstrated a similar effect with temporal distance manipulated indirectly, by making participants imagine their lives in general a year from now versus tomorrow prior to performance. In Experiment 6, distant time perspective undermined rather than enhanced analytical problem solving.


title: Green and Mobility-Aware Caching in 5G Networks

abstract: With the drastic increase of mobile devices, there are more and more mobile traffic and repeated requests for content. In 5G networks, small cell base stations (SBSs) caching and caching in wireless device-to-device network can effectively decrease the mobile traffic during peak hours. Currently, most of the related work is focused on how to cache content on SBSs and on mobile devices, and it is assumed that the user can download the entire requested content through the connected SBSs and mobile devices. However, few works have taken user mobility and the randomness of contact duration into consideration. How to improve the caching strategy by exploiting user mobility is still a challenging problem. Thus, in this paper, we first investigate the problem of how to conduct caching placement on SBS and on mobile devices leveraging user mobility, aiming to maximize the cache hit ratio. Specifically, the caching placement on SBSs and on mobile devices is formulated as an integer programming problem, and submodular optimization is adopted to solve the formulated problem. Then, we give the optimal transmission power of SBSs and mobile devices to deliver the caching content in order to reduce the energy cost. Simulation results prove that our caching strategy is more efficient than other existing caching strategies in terms of both cache hit ratio and energy efficiency.


title: Simultaneous Microgrid Voltage and Current Harmonics Compensation Using Coordinated Control of Dual-Interfacing Converters

abstract: The growing installation of distributed generation (DG) units in low-voltage distribution systems has popularized the concept of nonlinear load harmonic current compensation using multifunctional DG interfacing converters. It is analyzed in this paper that the compensation of local load harmonic current using a single DG interfacing converter may cause the amplification of supply voltage harmonics to sensitive loads, particularly when the main grid voltage is highly distorted. To address this limitation, unlike the operation of conventional unified power quality conditioners with series converter, a new simultaneous supply voltage and grid current harmonic compensation strategy is proposed using coordinated control of two shunt interfacing converters. Specifically, the first converter is responsible for local load supply voltage harmonic suppression. The second converter is used to mitigate the harmonic current produced by the interaction between the first interfacing converter and the local nonlinear load. To realize a simple control of parallel converters, a modified hybrid voltage and current controller is also developed in the paper. By using this proposed controller, the grid voltage phase-locked loop and the detection of the load current and the supply voltage harmonics are unnecessary for both interfacing converters. Thus, the computational load of interfacing converters can be significantly reduced. Simulated and experimental results are captured to validate the performance of the proposed topology and the control strategy.


title: Comparative assessment for two wheels inverted pendulum mobile robot using robust control

abstract: Over the last decade, numbers of researcher has been increased in the two-wheeled inverted pendulum mobile robots or commonly known as balancing robot. This paper presents the linear modeling of two wheels inverted pendulum mobile robot and the robustness of sliding mode control. Linear modeling plant was obtained from the simplified nonlinear plant that was based on state-space approach. The dynamic model of the system has been established to design and analyze the control system, dynamic of the motors, chassis and wheels of balancing robot. Simulation on MATLAB/SIMULINK application is performed and to determine the performance and reliability of the Sliding Mode Controller.


Original areticle SUPERIOR VENA CAVA THROMBOSIS


title: EMG controlled bionic arm

abstract: The human hand is a complex system, with a large number of degrees of freedom (DoFs), sensors embedded in its structure, actuators and tendons, and a complex hierarchical control. Despite this complexity, the efforts required to the user to carry out the different movements are quite small. On the contrary, prosthetic hands are just a pale replication of the natural hand, with significantly reduced grasping capabilities and no sensory information delivered back to the user. Several attempts have been carried out to develop multifunctional Prosthetic devices controlled by electromyography (EMG) signals (myoelectric hands), harness (kinematic hands), dimensional changes in residual muscles, and so forth, but none of these methods permits the “natural” control of more than two DoFs. This paper presents a review of the traditional methods used to control artificial hands by means of EMG signal, in both the clinical and research contexts, and introduces what could be the future developments in the control strategy of these devices. This paper describes a novel approach to the control of a multifunctional prosthesis based on the classification of myoelectric patterns. It is shown that the myoelectric signal exhibits a deterministic structure during the initial phase of a muscle contraction. Features are extracted from several time segments of the myoelectric signal to preserve pattern structure. These features are then classified using an artificial neural network. The control signals are derived from natural contraction patterns which can be produced reliably with little subject training. The new control scheme increases the number of functions which can be controlled by a single channel of myoelectric signal but does so in a way which does not increase the effort required by the amputee. Results are presented to support this approach.


title: A Review of Several Optimization Problems Related to Security in Networked System

abstract: Security issues are becoming more and more important to activities of individuals, organizations, and the society in our modern networked computerized world. In this chapter we survey a few optimization frameworks for problems related to security of various networked system such as the internet or the power grid system.


title: Parking made easy an embedded design

abstract: The modern world is moving towards the term ‘Automation’ which makes life simpler and easier. The rapid rise in the number of vehicles has increased the necessity of consideration about the parking system in shopping malls, airports, universities etc. Embedded system design for a real time parking is the latest and the emerging trend in the materialized world. A cost effective, reliable and accurate parking system is of great desire. It is believed that a cost effective, reliable and accurate parking system can save the time and minimize human efforts to a great extent. In this paper we present a embedded system design of a real time parking guidance system using PIC 16F877A micro-controllers which employs the incorporation of wireless communication to provide the user with remote control of elements like lights, sensors, and appliances falling within their working range of the designated environment. This system is designed to help the users by assisting them with parking their vehicles in the car lot. The system will automatically provide all the details in the parking areas on the basis of processing all the sensors data. It is designed to be running on a low budget platform and in the meantime, allowing their developers to eventually expand their controls over a variety of electronic devices.


title: Management challenges in creating value from business analytics

abstract: The popularity of big data and business analytics has increased tremendously in the last decade and a key challenge for organizations is in understanding how to leverage them to create business value. However, while the literature acknowledges the importance of these topics little work has addressed them from the organization's point of view. This paper investigates the challenges faced by organizational managers seeking to become more data and information-driven in order to create value. Empirical research comprised a mixed methods approach using (1) a Delphi study with practitioners through various forums and (2) interviews with business analytics managers in three case organizations. The case studies reinforced the Delphi findings and highlighted several challenge focal areas: organizations need a clear data and analytics strategy, the right people to effect a data-driven cultural change, and to consider data and information ethics when using data for competitive advantage. Further, becoming data-driven is not merely a technical issue and demands that organizations firstly organize their business analytics departments to comprise business analysts, data scientists, and IT personnel, and secondly align that business analytics capability with their business strategy in order to tackle the analytics challenge in a systemic and joined-up way. As a result, this paper presents a business analytics ecosystem for organizations that contributes to the body of scholarly knowledge by identifying key business areas and functions to address to achieve this transformation.


title: Evolutionary Delivery versus the "waterfall model"

abstract: The conventional wisdom of planning software engineering projects, using the widely cited "waterfall model" is not the only useful software development process model. In fact, the "waterfall model" may be unrealistic, and dangerous to the primary objectives of any software project.The alternative model, which I choose to call "evolutionary delivery" is not widely taught or practiced yet. But there is already more than a decade of practical experience in using it. In various forms. It is quite clear from these experiences that evolutionary delivery is a powerful general tool for both software development and associated systems development.Almost all experienced software developers do make use of some of the ideas in evolutionary development at one time or another. But, this is often unplanned, informal and it is an incomplete exploitation of this powerful method. This paper will try to expose the theoretical and practical aspects of the method in a fuller perspective. We need to learn the theory fully, so that we can apply and learn it completely.


A 5 GHz Digital Fractional-N PLL Using a 1-bit Delta – Sigma Frequency-to-Digital Converter in 65 nm CMOS


title: Effects of different interval-training programs on cycling time-trial performance

abstract: PURPOSE ::: We have investigated the effect of varying the intensity of interval training on 40-km time-trial performance in 20 male endurance cyclists (peak oxygen uptake 4.8+/-0.6 L x min(-1), mean +/- SD). ::: ::: ::: METHODS ::: Cyclists performed a 25-kJ sprint test, an incremental test to determine peak aerobic power (PP) and a simulated 40-km time-trial on a Kingcycle ergometer. They were then randomly assigned to one of five types of interval-training session: 12x30 s at 175% PP, 12x60 s at 100% PP, 12x2 min at 90% PP, 8x4 min at 85% PP, or 4x8 min at 80% PP. Cyclists completed 6 sessions over 3 wk, in addition to their usual aerobic base training. All laboratory tests were then repeated. ::: ::: ::: RESULTS ::: Performances in the time trial were highly reliable when controlled for training effects (coefficient of variation = 1.1%). The percent improvement in the time trial was modeled as a polynomial function of the rank order of the intensity of the training intervals, a procedure validated by simulation. The cubic trend was strong and statistically significant (overall correlation = 0.70, P = 0.005) and predicted greatest enhancement for the intervals performed at 85% PP (2.8%, 95% CI = 4.3-1.3%) and at 175% PP (2.4%, 95% CI = 4.0-0.7%). Intervals performed at 100% PP and 80% PP did not produce statistically significant enhancements of performance. Quadratic and linear trends were weak or insubstantial. ::: ::: ::: CONCLUSIONS ::: Interval training with work bouts close to race-pace enhance 1-h endurance performance; work bouts at much higher intensity also appear to improve performance, possibly by a different mechanism.


title: Dynamics of dynamics: following the formation of a line attractor

abstract: Trained recurrent neural networks are commonly used both in neuroscience and in machine learning. Recently, several attempts were made to understand the dynamics of such trained networks. Understanding how these dynamics arise through learning, however, is an uncharted territory. Here, we study this process in a simple setting -- learning to memorize analog values, leading to the formation of a line attractor. We show that problems associated with sequential learning of different tasks also occur in this setting. Specifically, catastrophic forgetting dramatically hinders a local learning rule from converging. In contrast, we show that the FORCE algorithm avoids this issue, and analyze the underlying causes. Using these insights, we suggest steps towards a more biologically plausible version of FORCE, as well as an approach to design optimal training protocols.


title: Do Better ImageNet Models Transfer Better?

abstract: Transfer learning is a cornerstone of computer vision, yet little work has been done to evaluate the relationship between architecture and transfer. An implicit hypothesis in modern computer vision research is that models that perform better on ImageNet necessarily perform better on other vision tasks. However, this hypothesis has never been systematically tested. Here, we compare the performance of 16 classification networks on 12 image classification datasets. We find that, when networks are used as fixed feature extractors or fine-tuned, there is a strong correlation between ImageNet accuracy and transfer accuracy (r = 0.99 and 0.96, respectively). In the former setting, we find that this relationship is very sensitive to the way in which networks are trained on ImageNet; many common forms of regularization slightly improve ImageNet accuracy but yield features that are much worse for transfer learning. Additionally, we find that, on two small fine-grained image classification datasets, pretraining on ImageNet provides minimal benefits, indicating the learned features from ImageNet do not transfer well to fine-grained tasks. Together, our results show that ImageNet architectures generalize well across datasets, but ImageNet features are less general than previously suggested.


title: Compact Full Band OMT Based on Dual-Mode Double-Ridge Waveguide

abstract: A nonstandard dual mode, double-ridge waveguide is utilized to design a compact orthomode transducer (OMT). The OMT is terminated with a standard circular waveguide common port and two standard isolated rectangular waveguide ports. The proposed structure exploits a one-fold symmetry to eliminate the coupling to the in-band higher order odd modes within the common port. The proposed concept is utilized to design a full ${K}$ 1-band OMT that is essential for space and mobile applications. Furthermore, the performance of the proposed OMT design is verified through a back-to-back measurement showing an excellent agreement between the full-wave simulations and measurements. Nevertheless, the proposed design achieves a measured matching level of 19 dB for the two modes and isolation level between them beyond 30 dB over the entire band.


title: Effective leadership and decision-making in animal groups on the move

abstract: Moving groups of animals, including fish, ungulates, birds and honeybee swarms seem able to take complex decisions in the absence of signalling mechanisms, and when group members cannot establish who has or has not got information. A numerical simulation shows how such groups can make accurate consensus decisions, and that the larger the group, the smaller the proportion of informed individuals needed to guide the group. A very small proportion of informed individuals is sufficient for near maximal accuracy. This has implications for our understanding of the evolution of information transfer in groups, and also suggests a new design protocol for the guidance of grouping robots. Cover photo, by Phillip Colla Natural History Photography ( ::: http://www.OceanLight.com ::: ), shows schooling jack mackerel. For animals that forage or travel in groups, making movement decisions often depends on social interactions among group members1,2. However, in many cases, few individuals have pertinent information, such as knowledge about the location of a food source3,4, or of a migration route5,6,7,8,9. Using a simple model we show how information can be transferred within groups both without signalling and when group members do not know which individuals, if any, have information. We reveal that the larger the group the smaller the proportion of informed individuals needed to guide the group, and that only a very small proportion of informed individuals is required to achieve great accuracy. We also demonstrate how groups can make consensus decisions, even though informed individuals do not know whether they are in a majority or minority, how the quality of their information compares with that of others, or even whether there are any other informed individuals. Our model provides new insights into the mechanisms of effective leadership and decision-making in biological systems.


title: Acute Salpingitis in a Nonsexually Active Adolescent.

abstract: Acute salpingitis is an uncommon cause of an acute surgical abdomen, especially in an adolescent who is not sexually active. The following is a case of a 12-year-old girl who denied sexual activity, had a remote history of an appendectomy, and a recent diagnosis of a large, right-sided ovarian cyst, who presented with acute abdominal pain, urinary symptoms, and fever. The patient was ill-appearing and progressed to uncompensated septic shock in the emergency department despite aggressive fluid resuscitation and empiric antibiotics. She ultimately underwent an exploratory laparotomy and was diagnosed with acute bilateral salpingitis. This case highlights the diagnostic dilemmas facing those caring for an adolescent girl with abdominal pain and presents an extremely rare etiology for abdominal pain in a nonsexually active adolescent.


title: Spatiotemporal energy models for the perception of motion

abstract: A motion sequence may be represented as a single pattern in x–y–t space; a velocity of motion corresponds to a three-dimensional orientation in this space. Motion sinformation can be extracted by a system that responds to the oriented spatiotemporal energy. We discuss a class of models for human motion mechanisms in which the first stage consists of linear filters that are oriented in space-time and tuned in spatial frequency. The outputs of quadrature pairs of such filters are squared and summed to give a measure of motion energy. These responses are then fed into an opponent stage. Energy models can be built from elements that are consistent with known physiology and psychophysics, and they permit a qualitative understanding of a variety of motion phenomena.


title: Dysmyelination Revealed through MRI as Increased Radial (but Unchanged Axial) Diffusion of Water

abstract: Abstract Myelin loss and axonal damage are both observed in white matter injuries. Each may have significant impact on the long-term disability of patients. Currently, there does not exist a noninvasive biological marker that enables differentiation between myelin and axonal injury. We describe herein the use of magnetic resonance diffusion tensor imaging (DTI) to quantify the effect of dysmyelination on water directional diffusivities in brains of shiverer mice in vivo. The principal diffusion eigenvalues of eight axonal fiber tracts that can be identified with certainty on DTI maps were measured. The water diffusivity perpendicular to axonal fiber tracts, λ⊥, was significantly higher in shiverer mice compared with age-matched controls, reflecting the lack of myelin and the increased freedom of cross-fiber diffusion in white matter. The water diffusivity parallel to axonal fiber tracts, λ∥, was not different, which is consistent with the presence of intact axons. It is clear that dysmyelination alone does not impact λ∥. The presence of intact axons in the setting of incomplete myelination was confirmed by electron microscopy. Although further validation is still needed, our finding suggests that changes in λ⊥ and λ∥ may potentially be used to differentiate myelin loss versus axonal injury.


title: Empathy, mirror neurons and SYNC

abstract: Abstract This article explains how people synchronize their thoughts through empathetic relationships and points out the elementary neuronal mechanisms orchestrating this process. The many dimensions of empathy are discussed, as is the manner by which empathy affects health and disorders. A case study of teaching children empathy, with positive results, is presented. Mirror neurons, the recently discovered mechanism underlying empathy, are characterized, followed by a theory of brain-to-brain coupling. This neuro-tuning, seen as a kind of synchronization (SYNC) between brains and between individuals, takes various forms, including frequency aspects of language use and the understanding that develops regardless of the difference in spoken tongues. Going beyond individual-to-individual empathy and SYNC, the article explores the phenomenon of synchronization in groups and points out how synchronization increases group cooperation and performance.


title: Review of photoplethysmography based non-invasive continuous blood pressure methods

abstract: Blood pressure is an important parameter for early detections of cardiovascular diseases. There have been many relevant technological advances to measure blood pressure, such as in wearable sensing, pervasive computing, and smartphones. The disadvantage of conventional measurement is discomfort for the patients because of painful cuff inflation. The emergence of photoplethysmography for blood pressure estimation is offering a more convenient method. Photoplethysmography can generally be grouped into the following three categories; pulse wave velocity, pulse transit time, and pulse wave analysis. The objective of this review is to study the achievement of photoplethysmography based non-invasive continuous blood pressure methods. The method is quite promising and useful for the future studies and enhancement on cuff-less continuous BP monitoring approach.


title: The Grammar of hitting, breaking, and cutting in Kimaragang Dusun

abstract: The hit, break , and cut classes of verbs are grammatically relevant in Kimaragang, as in English. The relevance of such classes for determining how arguments are expressed suggests that the meaning of a verb is composed of (a) systematic components of meaning (the event template ); and (b) idiosyncratic properties of the individual root. Assuming this approach to be essentially correct, I compare grammatical phenomena in Kimaragang that are sensitive to verb class membership with phenomena that are not class-sensitive. The tendency that emerges is that class-sensitive alternations do not seem to be affix-dependent, and are quite restricted in their ability to introduce new arguments into the argument structure.


title: An Improved Method for Determining Contribution of Utility and Customer Harmonic Distortions In A Power Distribution System

abstract: This paper presents an improved method for determining the share of harmonic distortion generated by utility and customer in a distribution system. The method is based on a Norton equivalent circuit to represent the whole system, modeling the customer side with RLC components by using measured voltage and current at the point of common coupling (PCC). The proposed method evaluates the contribution of harmonic voltage and current distortion, which may appear from utility and customer sides of the system. Several case studies have been made to verify the accuracy of the method in determining the share of utility and customer harmonic distortions. Results showed that the proposed method can accurately determine harmonic contributions of utility and customer sides for measurement made at the PCC.


title: Kernel feature selection to fuse multi-spectral MRI images for brain tumor segmentation

abstract: This paper presents a framework of a medical image analysis system for the brain tumor segmentation and the brain tumor following-up over time using multi-spectral MRI images. Brain tumors have a large diversity in shape and appearance with intensities. Multi-spectral images have the advantage in providing complementary information to resolve some ambiguities. However, they may also bring along a lot of redundant information, increasing the data processing time and segmentation errors. The challenge is how to make use of the multi-spectral images effectively. Our idea of fusing these data is to extract the most useful features to obtain the best segmentation with the least cost in time. The Support Vector Machine (SVM) classification integrated with a selection of the features in a kernel space is proposed. The selection criteria are defined by the kernel class separability. Based on this SVM classification a framework to follow up the brain tumor evolution is proposed, which consists of the following steps: (1) to learn the brain tumor and select the features from the first MRI examination of the patients; (2) to automatically segment the tumor in new data using SVM; (3) to refine the tumor contour by a region growing technique. The system has been tested on real patient images with satisfying results. The quantitative evaluations by comparing with experts' manual traces and with other approaches demonstrate the effectiveness of the proposed method.


title: Full-system analysis and characterization of interactive smartphone applications

abstract: Smartphones have recently overtaken PCs as the primary consumer computing device in terms of annual unit shipments. Given this rapid market growth, it is important that mobile system designers and computer architects analyze the characteristics of the interactive applications users have come to expect on these platforms. With the introduction of high-performance, low-power, general purpose CPUs in the latest smartphone models, users now expect PC-like performance and a rich user experience, including high-definition audio and video, high-quality multimedia, dynamic web content, responsive user interfaces, and 3D graphics. In this paper, we characterize the microarchitectural behavior of representative smartphone applications on a current-generation mobile platform to identify trends that might impact future designs. To this end, we measure a suite of widely available mobile applications for audio, video, and interactive gaming. To complete this suite we developed BBench, a new fully-automated benchmark to assess a web-browser's performance when rendering some of the most popular and complex sites on the web. We contrast these applications' characteristics with those of the SPEC CPU2006 benchmark suite. We demonstrate that real-world interactive smartphone applications differ markedly from the SPEC suite. Specifically the instruction cache, instruction TLB, and branch predictor suffer from poor performance. We conjecture that this is due to the applications' reliance on numerous high level software abstractions (shared libraries and OS services). Similar trends have been observed for UI-intensive interactive applications on the desktop.


title: Object Classification in Thermal Images using Convolutional Neural Networks for Search and Rescue Missions with Unmanned Aerial Systems

abstract: In recent years, the use of Unmanned Aerial Systems (UAS) has become commonplace in a wide variety of tasks due to their relatively low cost and ease of operation. In this paper, we explore the use of UAS in maritime Search And Rescue (SAR) missions by using experimental data to detect and classify objects at the sea surface. The objects are chosen as common objects present in maritime SAR missions: a boat, a pallet, a human, and a buoy. The data consists of thermal images and a Gaussian Mixture Model (GMM) is used to discriminate foreground objects from the background. Then, bounding boxes containing the object are defined and used to train a Convolutional Neural Network (CNN). The CNN achieves the average accuracy of 92.5% when evaluating a testing dataset.


title: Lossless ECG Compression for Event Recorder Based on Burrows-Wheeler Transformation and Move-To-Front Coder

abstract: The field of data compression has developed many algorithms so far and still the process seems to be always increasing in search of a better compression scheme. The Burrows-Wheeler transform (BWT) has been a crucial tool for data compression. Normally, the BWT earns maxi- mum efficiency when the input is in the text format. In the present paper, some real possible difficulties are explored when the input data is in text format. Skewness of all the necessary and possible combinations are calculated and it is found that the combination of move-to-front coder with the Huffman coder gives a better result when the input data is in numbers rather than text. On contrary to the use of a large amount of input data in the existing algorithms which use BWT, in this work all the data used are of small durati- on and finally, a standard compression ratio of 2.7247 is achieved if the quantity of input is compared with that of the output simultaneously. For this reason this technique may be considered quite useful for the data transfer in an ECG event recorder.


Big Mound Key near Charlotte Harbor, Florida


title: An Atari Model Zoo for Analyzing, Visualizing, and Comparing Deep Reinforcement Learning Agents

abstract: Much human and computational effort has aimed to improve how deep reinforcement learning algorithms perform on benchmarks such as the Atari Learning Environment. Comparatively less effort has focused on understanding what has been learned by such methods, and investigating and comparing the representations learned by different families of reinforcement learning (RL) algorithms. Sources of friction include the onerous computational requirements, and general logistical and architectural complications for running Deep RL algorithms at scale. We lessen this friction, by (1) training several algorithms at scale and releasing trained models, (2) integrating with a previous Deep RL model release, and (3) releasing code that makes it easy for anyone to load, visualize, and analyze such models. This paper introduces the Atari Zoo framework, which contains models trained across benchmark Atari games, in an easy-to-use format, as well as code that implements common modes of analysis and connects such models to a popular neural network visualization library. Further, to demonstrate the potential of this dataset and software package, we show initial quantitative and qualitative comparisons between the performance and representations of several deep RL algorithms, highlighting interesting and previously unknown distinctions between them.


title: Smart foot device for women safety

abstract: In this paper, an attempt has been made to develop a smart device that can assist women when they feel unsafe. This smart device will be clipped to the footwear of the user and can be triggered discreetly. On tapping one foot behind the other four times, an alert is sent via Bluetooth Low Energy communication to an application on the victim's phone, programmed to generate a message seeking help with the location of the device attached. The results obtained were analysed using Naive Bayes classifier and this low cost device showed an overall accuracy of 97.5%.


title: Miniaturized broadband Quasi-Yagi antenna for wireless communication and recognition

abstract: This paper presents a miniaturized broadband Quasi-Yagi antenna which adopt new compact layout and modified bowtie driven dipole. In the proposed antenna, the micro strip feed and λ/4 impedance transformer are placed horizontally to reduce the vertical size. Meanwhile, in order to reduce the horizontal size, the vertical metallic strips are loaded to the end of bowtie driven dipole and ground patch. Compared to the conventional Quasi-Yagi antenna, the overall size of the proposed antennas are reduced about approximately 50%. The measured results show that the impedance bandwidth (reflection coefficient< −10dB) of proposed antenna is 1.16GHz (42.6%) for 2.14–3.3GHz and it has a wide bandwidth about 2.23–3.25GHz with reflection coefficient<-15dB. Moreover, the realized gain is around 4dBi in the operating bandwidth. This antenna can be used for wireless communications and recognitions like WLAN, RFID, WiMAX and LTE.


title: MultiCuckoo: Multi-Cloud Service Composition Using a Cuckoo-Inspired Algorithm for the Internet of Things Applications

abstract: Internet of things (IoT) applications aim to provide access to widespread interconnected networks of smart devices, services, and information. This can be achieved by integrating IoT and cloud computing (CC). By using cloud computing service composition (SC), multiple services from various providers can be combined to meet users’ requirements. However, SC is known for its complexity and is classified as an NP-hard problem; such problems are usually approached using heuristics, such as bio-inspired algorithms. This paper aims at developing a bio-inspired algorithm that mimics the behavior of cuckoo birds (which examine the nests of other birds to find eggs similar to their own) to find a composite service that fulfills a user’s request in a multi-cloud environment (MCE). Previous work on cuckoo-inspired algorithms has generally utilized metaheuristics, which try to fit a “good” solution to a general optimization problem. In contrast, we propose a problem-dependent heuristic that considers the SC problem and its particularities in MCE. The proposed algorithm, MultiCuckoo, was thoroughly evaluated based on a well-controlled experimental framework that benchmarks the performance of the new algorithm to other outstanding SC algorithms, including the all clouds combination algorithm, base cloud combination algorithm, and combinatorial optimization algorithm for multiple cloud service Composition. The results show that our algorithm is more efficient in terms of decreasing the number of examined services, the composed clouds, and the running time in comparison to the benchmark algorithms.


title: High Resolution Semantic Change Detection

abstract: Change detection is one of the main problems in remote sensing, and is essential to the accurate processing and understanding of the large scale Earth observation data available through programs such as Sentinel and Landsat. Most of the recently proposed change detection methods bring deep learning to this context, but openly available change detection datasets are still very scarce, which limits the methods that can be proposed and tested. In this paper we present the first large scale high resolution semantic change detection (HRSCD) dataset, which enables the usage of deep learning methods for semantic change detection. The dataset contains coregistered RGB image pairs, pixel-wise change information and land cover information. We then propose several methods using fully convolutional neural networks to perform semantic change detection. Most notably, we present a network architecture that performs change detection and land cover mapping simultaneously, while using the predicted land cover information to help to predict changes. We also describe a sequential training scheme that allows this network to be trained without setting a hyperparameter that balances different loss functions and achieves the best overall results.


title: Clean Technology: An Eagle-Eye Review on the Emerging Development Trends by Application of IOT Devices

abstract: Internet of Things (IoT) is defined as a clustered network, which provides communication amongst the number of physical devices, sensors, actuators and embedded systems. It is often referred as smart devices because of its unique properties to capture, sense, storage and transfer the user required information from the surrounding. The applications of IoT is increased rapidly due to digitization, and a huge amount of data is transferred amongst the number of devices which results in creating a requirement of massive energy. Green IoT is found to be a solution for energy crisis and deployment of green IoT techniques acts as a challenging task because of the larger scale and complexity. In this research, an overview of green IoT along with different techniques deployed by many authors in the field of real-time environment will be ascertained. Initially, more concentration is given to address IoT, challenges faced regarding development and effects of excessive energy usage along with its applications. Further, the concept of green IoT is introduced, and different authors review on the deployment of green IoT in terms of design, data centres, and transmission of data through sensors will be ascertained in this research. The different authors review on several hardware models and strategies developed in terms of green IoT will be explained and the study on result obtained shows that there is an improvement in the field of communication, energy transfer and consumption, transport and society compared to individual IoT systems.


title: Image segmentation techniques

abstract: Abstract There are now a wide variety of image segmentation techniques, some considered general purpose and some designed for specific classes of images. These techniques can be classified as: measurement space guided spatial clustering, single linkage region growing schemes, hybrid linkage region growing schemes, centroid linkage region growing schemes, spatial clustering schemes, and split-and-merge schemes. In this paper, each of the major classes of image segmentation techniques is defined and several specific examples of each class of algorithm are described. The techniques are illustrated with examples of segmentations performed on real images.


title: Efficient stackless hierarchy traversal on GPUs with backtracking in constant time

abstract: The fastest acceleration schemes for ray tracing rely on traversing a bounding volume hierarchy (BVH) for efficient culling and use backtracking, which in the worst case may expose cost proportional to the depth of the hierarchy in either time or state memory. We show that the next node in such a traversal actually can be determined in constant time and state memory. In fact, our newly proposed parallel software implementation requires only a few modifications of existing traversal methods and outperforms the fastest stack-based algorithms on GPUs. In addition, it reduces memory access during traversal, making it a very attractive building block for ray tracing hardware.


title: Gamification in Crowdsourcing: A Review

abstract: This study investigates how different gamification implementations can increase crowdsourcees' motivation and participation in crowdsourcing (CS). To this end, we review empirical literature that has investigated the use of gamification in crowdsourcing settings. Overall, the results of the review indicate that gamification has been an effective approach for increasing crowdsourcing participation. When comparing crowdcreating, -solving, -processing and-rating CS approaches, the results show differences in the use of gamification across CS types. Crowdsourcing initiatives that provide more monotonous tasks most commonly used mere points and other simpler gamification implementations, whereas CS initiatives that seek for diverse and creative contributions have employed gamification in more manifold ways employing a richer set of mechanics. These findings provide insights for designers of gamified systems and further research on the topics of gamification and crowdsourcing.


title: Design Guideline of an AC Hairpin Winding

abstract: This work aims to investigate the design rules and the additional losses in an AC hairpin winding. Such a winding type is more and more used in traction motor of electric and hybrid-electric vehicles. The actual trend is to require higher operating frequency. In fact, the motor power and speed are continuously increasing even though keeping a high number of poles to reduce the end winding length. Hairpin winding allows a higher slot fill factor in comparison with traditional round wire windings. However, the larger cross-area section of such solid wires cause a strong non-uniform current density distribution, which can delete the higher copper area benefit. Hereafter, the main rules design of an AC hairpin winding are described, to satisfy voltage and current constraints. Furthermore, different analysis has been carried out to determine the additional losses amount considering different coil connections. Finite Element analysis has been compared with analytical models.


title: Coarse-to-fine volumetric segmentation of teeth in cone-beam ct

abstract: We consider the problem of localizing and segmenting individual teeth inside 3D Cone-Beam Computed Tomography (CBCT) images. To handle large image sizes we approach this task with a coarse-to-fine framework, where the whole volume is first analyzed as a 33-class semantic segmentation (adults have up to 32 teeth) in coarse resolution, followed by binary semantic segmentation of the cropped region of interest in original resolution. To improve the performance of the challenging 33-class segmentation, we first train the Coarse step model on a large weakly labeled dataset, then fine-tune it on a smaller precisely labeled dataset. The Fine step model is trained with precise labels only. Experiments using our in-house dataset show significant improvement for both weakly-supervised pretraining and for the addition of the Fine step. Empirically, this framework yields precise teeth masks with low localization errors sufficient for many real-world applications.


title: Merging Strategies for Sum-Product Networks: From Trees to Graphs

abstract: Learning the structure of sum-product networks (SPNs) - arithmetic circuits over latent and observed variables - has been the subject of much recent research. These networks admit linear time exact inference, and thus help alleviate one of the chief disadvantages of probabilistic graphical models: accurate probabilistic inference algorithms are often computationally expensive. Although, algorithms for inducing their structure from data have come quite far and often outperform algorithms that induce probabilistic graphical models, a key issue with existing approaches is that they induce tree SPNs, a small, inefficient sub-class of SPNs. In this paper, we address this limitation by developing post-processing approaches that induce graph SPNs from tree SPNs by merging similar sub-structures. The key benefits of graph SPNs over tree SPNs include smaller computational complexity which facilitates faster online inference, and better generalization accuracy because of reduced variance, at the cost of slight increase in the learning time. We demonstrate experimentally that our merging techniques significantly improve the accuracy of tree SPNs, achieving state-of-the-art performance on several real world benchmark datasets.


title: Central Pattern Generators for the control of robotic systems

abstract: Bio-inspired control of motion is an active field of research with many applications in real world tasks. In the case of robotic systems that need to exhibit oscillatory behaviour (i.e. locomotion of snake-type or legged robots), Central Pattern Generators (CPGs) are among the most versatile solutions. These controllers are often based on loosely-coupled oscillators similar to those found in the neural circuits of many animal species, and can be more robust to uncertainty (i.e. external perturbations) than traditional control approaches. This project provides an overview of the state-of-the-art in the field of CPGs, and in particular their applications within robotic systems. The project also tackles the implementation of a CPG-based controller in a small 3D-printed hexapod.


title: Computational lambda-calculus and monads

abstract: The lambda -calculus is considered a useful mathematical tool in the study of programming languages. However, if one uses beta eta -conversion to prove equivalence of programs, then a gross simplification is introduced. The author gives a calculus based on a categorical semantics for computations, which provides a correct basis for proving equivalence of programs, independent from any specific computational model. >


title: Facebook Distributed System Case Study For Distributed System Inside Facebook Datacenters

abstract: Facebook is recognized as the largest online social network system in the last few years, which is come up with billions numbers of users in the last 2013. The system is recognized as distributed system in its design, infrastructure and architecture .The datacenters behind this network system are huge, robust, keeping the system scalable, reliable, secure, and let the Facebook accessible from anywhere with highly availability


title: Assimilation of Remotely-Sensed Leaf Area Index into a Dynamic Vegetation Model for Gross Primary Productivity Estimation

abstract: Quantitative estimation of the magnitude and variability of gross primary productivity (GPP) is required to study the carbon cycle of the terrestrial ecosystem. Using ecosystem models and remotely-sensed data is a practical method for accurately estimating GPP. This study presents a method for assimilating high-quality leaf area index (LAI) products retrieved from satellite data into a process-oriented Lund-Potsdam-Jena dynamic global vegetation model (LPJ-DGVM) to acquire accurate GPP. The assimilation methods, including the Ensemble Kalman Filter (EnKF) and a proper orthogonal decomposition (POD)-based ensemble four-dimensional (4D) variational assimilation method (PODEn4DVar), incorporate information provided by observations into the model to achieve a better agreement between the model-estimated and observed GPP. The LPJ-POD scheme performs better with a correlation coefficient of r = 0.923 and RMSD of 32.676 gC/m2/month compared with the LPJ-EnKF scheme (r = 0.887, RMSD = 38.531 gC/m2/month) and with no data assimilation (r = 0.840, RMSD = 45.410 gC/m2/month). Applying the PODEn4DVar method into LPJ-DGVM for simulating GPP in China shows that the annual amount of GPP in China varied between 5.92 PgC and 6.67 PgC during 2003–2012 with an annual mean of 6.35 PgC/yr. This study demonstrates that integrating remotely-sensed data with dynamic global vegetation models through data assimilation methods has potential in optimizing the simulation and that the LPJ-POD scheme shows better performance in improving GPP estimates, which can provide a favorable way for accurately estimating dynamics of ecosystems.


DEVELOPMENT OF GUAVA LIPOSOME SERUM AND EVALUATION OF FREE RADICAL-SCAVENGING CAPACITY


title: Partial-Resonant Buck–Boost and Flyback DC–DC Converters

abstract: This paper introduces innovative nonisolated and isolated soft-switched dc-dc topologies with the step-up/down ability. The nonisolated topology is constructed by adding a small ac capacitor in parallel with the main inductor of the conventional buck-boost converter and replacing its semiconductor devices with the reverse-blocking switches. By using a novel control scheme, true zero-voltage switching is realized at both turn-on and turn-off of the power switches irrespective of the input voltage, output voltage, or load value. The isolated form of the converter is created by substituting the main inductor with an air-gapped high-frequency transformer, similar to the flyback converter. In this case, two smaller ac capacitors are placed on both sides of the transformer to realize soft switching as well as passive clamping; no extra clamping circuit is required. The basic operation of the proposed partial-resonant converters includes four modes and is described in detail. The comprehensive analysis of the topologies is carried out as well. Various experimental results in different operating conditions are provided to verify the performance of the proposed power converters.


title: UHF RFID localization system based on a phased array antenna

abstract: This paper presents new experimental results of angle of arrival (AoA) measurements for localizing passive RFID tags in the UHF frequency range. The localization system is based on the principle of a phased array with electronic beam steering mechanism. This approach has been successfully applied within a UHF RFID system and it allows the precise determination of the angle and the position of small passive RFID tags. The paper explains the basic principle, the experimental setup with the phased array and shows results of the measurements.


Prominence of Big Data in the Digital Transformation Era


WPI User Experience and Decision Making Research Laboratory Publications User Experience and Decision Making Research Laboratory 2014 Eye Tracking and Web Experience


title: An empirical evaluation of rotation-based ensemble classifiers for customer churn prediction

abstract: Several studies have demonstrated the superior performance of ensemble classification algorithms, whereby multiple member classifiers are combined into one aggregated and powerful classification model, over single models. In this paper, two rotation-based ensemble classifiers are proposed as modeling techniques for customer churn prediction. In Rotation Forests, feature extraction is applied to feature subsets in order to rotate the input data for training base classifiers, while RotBoost combines Rotation Forest with AdaBoost. In an experimental validation based on data sets from four real-life customer churn prediction projects, Rotation Forest and RotBoost are compared to a set of well-known benchmark classifiers. Moreover, variations of Rotation Forest and RotBoost are compared, implementing three alternative feature extraction algorithms: principal component analysis (PCA), independent component analysis (ICA) and sparse random projections (SRP). The performance of rotation-based ensemble classifier is found to depend upon: (i) the performance criterion used to measure classification performance, and (ii) the implemented feature extraction algorithm. In terms of accuracy, RotBoost outperforms Rotation Forest, but none of the considered variations offers a clear advantage over the benchmark algorithms. However, in terms of AUC and top-decile lift, results clearly demonstrate the competitive performance of Rotation Forests compared to the benchmark algorithms. Moreover, ICA-based Rotation Forests outperform all other considered classifiers and are therefore recommended as a well-suited alternative classification technique for the prediction of customer churn that allows for improved marketing decision making.


title: Event Mining over Distributed Text Streams

abstract: This research presents a new set of techniques to deal with event mining from different text sources, a complex set of NLP tasks which aim to extract events of interest and their components including authors, targets, locations, and event categories. Our focus is on distributed text streams, such as tweets from different news agencies, in order to accurately retrieve events and its components by combining such sources in different ways using text stream mining. Therefore this research project aims to fill the gap between batch event mining, text stream mining and distributed data mining which have been used separately to address related learning tasks. We propose a multi-task and multi-stream mining approach to combine information from multiple text streams to accurately extract and categorise events under the assumptions of stream mining. Our approach also combines ontology matching to boost accuracy under imbalanced distributions. In addition, we plan to address two relatively unexplored event mining tasks: event coreference and event synthesis. Preliminary results show the appropriateness of our proposal, which is giving an increase of around 20% on macro prequential metrics for the event classification task.


title: A non-projective greedy dependency parser with bidirectional LSTMs

abstract: The LyS-FASTPARSE team presents BIST-COVINGTON, a neural implementation of the Covington (2001) algorithm for non-projective dependency parsing. The bidirectional LSTM approach by Kipperwasser and Goldberg (2016) is used to train a greedy parser with a dynamic oracle to mitigate error propagation. The model participated in the CoNLL 2017 UD Shared Task. In spite of not using any ensemble methods and using the baseline segmentation and PoS tagging, the parser obtained good results on both macro-average LAS and UAS in the big treebanks category (55 languages), ranking 7th out of 33 teams. In the all treebanks category (LAS and UAS) we ranked 16th and 12th. The gap between the all and big categories is mainly due to the poor performance on four parallel PUD treebanks, suggesting that some `suffixed' treebanks (e.g. Spanish-AnCora) perform poorly on cross-treebank settings, which does not occur with the corresponding `unsuffixed' treebank (e.g. Spanish). By changing that, we obtain the 11th best LAS among all runs (official and unofficial). The code is made available at this https URL


title: An Efficient Hardware Random Number Generator Based on the MT Method

abstract: Mersenne Twister (MT) algorithm is one of the most widely used long-period uniform random number generators. In this paper, we present a novel and efficient hardware architecture for MT method. Our design is implemented on a Xilinx XC6VLX240T-1 FPGA device at 450 MHz. It takes up 0.1% of the device and produces 450 million samples per second, which is 2.25 times faster than a dedicated software version running on a 2.67-GHz Intel core i5 multi-core processor. A dedicated 3R/1W RAM structure is also proposed. It is capable of providing 3 reads and 1 write concurrently in a single clock cycle and is the key component for the entire system to achieve 1 sample-per-cycle throughput. The architecture is also implemented on different FPGA devices. Experimental results show that our generator is superior to those existing architectures reported in the literatures in both performance and hardware complexity. The samples generated by our design are verified via the standard statistics testing suites of Diehard and TestU01.


MULTIVIEW VIDEO DATASETS FOR EVALUATION AND TESTING OF 3D TRACKING AND 3D RECONSTRUCTION ALGORITHMS


title: Taking Perspective in Conversation: The Role of Mutual Knowledge in Comprehension

abstract: When people interpret language, they can reduce the am- biguity of linguistic expressions by using information about perspec- tive: the speaker's, their own, or a shared perspective. In order to investigate the mental processes that underlie such perspective taking, we tracked people's eye movements while they were following instruc- tions to manipulate objects. The eye fixation data in two experiments demonstrate that people do not restrict the search for referents to mutually known objects. Eye movements indicated that addressees considered objects as potential referents even when the speaker could not see those objects, requiring addressees to use mutual knowledge to correct their interpretation. Thus, people occasionally use an ego- centric heuristic when they comprehend. We argue that this egocen- tric heuristic is successful in reducing ambiguity, though it could lead to a systematic error. Language is inherently ambiguous—every linguistic expression can convey more than one intention. Comprehension requires ambi- guity resolution at all levels of linguistic processing, from the percep- tion of phonemes, through syntactic parsing, to the identification of the speaker's intention. Addressees use a variety of sources of infor- mation to reduce the ambiguity of linguistic expressions. For example, people use visual context to restrict the range of possible syntactic structures a sentence might have (Tanenhaus, Spivey-Knowlton, Eb- erhard, & Sedivy, 1995), and they use context to constrain lexical ambiguity (e.g., Glucksberg, Kreuz, & Rho, 1986). One source of information that could be important in resolving ambiguity is knowl- edge regarding who knows what. We investigated the role such knowledge about perspective plays in ambiguity resolution. Traditional theories of language use suggest that addressees rou- tinely use shared or mutual perspective in comprehension (e.g., Clark & Carlson, 1981; Clark & Marshall, 1981; Clark, Schreuder, & But- trick, 1983). These theories assume that comprehension is restricted to mutually known information. Such theories typically take a macro- level approach, describing only the product of comprehension. Our research takes a more fine-grained, micro-level approach to identify the mental processes that underlie perspective taking in comprehen- sion. Specifically, we studied such processes during referential com- munication, in which speakers refer to objects and addressees attempt to identify these objects. Imagine the following situation. Your friend Jill comes into your office, sits down on the other side of your desk, and says, "Can I see this book? It looks very interesting." When she says "this book," she is referring to The Social Life of the Yellow Slug , by H. Dull, a book that is on the desk between the two of you. But how would you know which book Jill is referring to? After all, your shelves are filled with many books, and each one of them is a perfect referent for the de- scription "this book." You might immediately identify the intended book because you rely on common ground with your friend. In this case, the book is in common ground because it is physically or perceptually co-present. You identify it by using what Clark and Marshall (1981) called a co-presence heuristic. Given that you can mutually see the book, you conclude that it is the intended referent. You resolve ambiguity by restricting the set of potential referents to mutual knowledge with your friend. But consider an alternative comprehension strategy that people might use in conversation, an egocentric heuristic. We define an ego- centric heuristic as a tendency to consider as potential referents ob- jects that are not in common ground, but are potential referents from one's own perspective. 1 Suppose that in addition to the book on yellow slugs, you have on your desk a book that you really liked, The Sex Life of Spiders, by D. Excite. But Jill cannot see the book on spiders because your computer monitor is in the way. Clearly, this book is not part of your common ground with Jill, but to the extent that you do use an egocentric comprehension strategy, you might consider it as a referent. If you select the book about the spiders as the referent, you make an error. Indeed, you might pick it up only to be corrected by Jill, who says that she was actually talking about the book about the slugs. But most likely, you would use your knowledge about common ground to correct your interpretation before even reaching for the wrong book. After all, you know that she cannot see the spider book, and therefore she could not have referred to it. We therefore propose that (a) addressees occasionally use an ego- centric strategy, considering potential referents even when they know that these referents are inaccessible to the speaker, and (b) mutual knowledge is used to correct interpretation errors that result from such an egocentric interpretation. We report here two experiments that investigated our proposal in the context of a real conversation. When addressees use an egocentric strategy, and they consider referents that are invisible to the speaker, it should interfere with their ability to detect the shared, intended referent. We show that addressees occa- sionally consider the hidden spider book and that they consequently take longer to identify the shared referent, the slug book, because they need to correct their initial error. In these cases, they use mutual knowledge as error correction. Moreover, we show that sometimes the egocentric interpretation is so compelling that addressees make an error—they pick up the spider book instead of the slug book.


title: Intersectional HCI: Engaging Identity through Gender, Race, and Class

abstract: Understanding users becomes increasingly complicated when we grapple with various overlapping attributes of an individual's identity. In this paper we introduce intersectionality as a framework for engaging with the complexity of users' "and authors" "identities", and situating these identities in relation to their contextual surroundings. We conducted a meta-review of identity representation in the CHI proceedings, collecting a corpus of 140 manuscripts on gender, ethnicity, race, class, and sexuality published between 1982-2016. Drawing on this corpus, we analyze how identity is constructed and represented in CHI research to examine intersectionality in a human-computer interaction (HCI) context. We find that previous identity-focused research tends to analyze one facet of identity at a time. Further, research on ethnicity and race lags behind research on gender and socio-economic class. We conclude this paper with recommendations for incorporating intersectionality in HCI research broadly, encouraging clear reporting of context and demographic information, inclusion of author disclosures, and deeper engagement with identity complexities.


title: Single bit error correction implementation in CRC-16 on FPGA

abstract: Framing protocols employ cyclic redundancy check (CRC) to detect errors incurred during transmission. Generally whole frame is protected using CRC and upon detection of error, retransmission is requested. But certain protocols demand for single bit error correction capabilities for the header part of the frame, which often plays an important role in receiver synchronization. At a speed of 10 Gbps, header error correction implementation in hardware can be a bottleneck. This work presents a hardware efficient way of implementing CRC-16 over 16 bits of data, multiple bit error detection and single bit error correction on FPGA device.


title: Energy-efficient dynamic traffic offloading and reconfiguration of networked data centers for big data stream mobile computing: review, challenges, and a case study

abstract: Big data stream mobile computing is proposed as a paradigm that relies on the convergence of broadband Internet mobile networking and real-time mobile cloud computing. It aims at fostering the rise of novel self-configuring integrated computing-communication platforms for enabling in real time the offloading and processing of big data streams acquired by resource-limited mobile/wireless devices. This position article formalizes this paradigm, discusses its most significant application opportunities, and outlines the major challenges in performing real-time energy-efficient management of the distributed resources available at both mobile devices and Internet-connected data centers. The performance analysis of a small-scale prototype is also included in order to provide insight into the energy vs. performance tradeoff that is achievable through the optimized design of the resource management modules. Performance comparisons with some state-of-the-art resource managers corroborate the discussion. Hints for future research directions conclude the article.


title: Experimental Comparisons of Entity-Relationship and Object Oriented Data Models

abstract: The extended entity-relationship (EER) model is being "threatened" by the object-oriented (OO) approach, which penetrates into the areas of system analysis and data modeling. The issue of which of the two data models is better for data modeling is still an open question. We address the question by conducting experimental comparisons between the models. The results of our experiments reveal that: a) schema comprehension: ternary relationships are significantly easier to comprehend in the EER model than in the OO model; b) the EER model supasses the OO model for designing unary and ternary relationships; c) time: it takes less time to design EER schemas; d) preferences: the EER model is preferred by designers. We conclude that even if the objective is to implement an OO database schema, the following procedure is still recommended: 1) create an EER conceptual schema, 2) map it to an OO schema, and c) augment the OO schema with behavioral constructs that are unique to the OO approach.


title: Mechanical design of a tree gripper for miniature tree-climbing robots

abstract: In this paper, an novel tree gripping mechanism has been proposed for miniature tree-climbing robots. It is capable of attaching on a wide variety of trees with a wide range of gripping curvature. In addition, it is lightweight and simple in control. The gripper is simple in control as it is actuated by one actuator only. In addition, the omni-directional gripping ability also simplifies the use of the gripper as there is no extra actuator needed for orientation control. The special mechanism and optimized settings make the gripper able to attach on different sizes of tree tightly. The mechanism also allows zero energy consumption in static gripping. Numerous on-tree experiments of the proposed mechanism have been conducted and the results are satisfied.