title: Biometrics from Brain Electrical Activity: A Machine Learning Approach

text: The potential of brain electrical activity generated as a response to a visual stimulus is examined in the context of the identification of individuals. Specifically, a framework for the visual evoked potential (VEP)-based biometrics is established, whereby energy features of the gamma band within VEP signals were of particular interest. A rigorous analysis is conducted which unifies and extends results from our previous studies, in particular, with respect to 1) increased bandwidth, 2) spatial averaging, 3) more robust power spectrum features, and 4) improved classification accuracy. Simulation results on a large group of subject support the analysis


title: EARTh: An Environmental Application Reference Thesaurus in the Linked Open Data cloud

text: The paper aims at providing a description of EARTh, the Environmental Application Reference Thesaurus. It represents a general-purpose thesaurus for the environment, which has been published as a SKOS dataset in the Linked Open Data cloud. It promises to become a core tool for indexing and discovery environmental resources by refining and extending GEMET, which is considered the de facto standard when speaking of general-purpose thesaurus for the environment in Europe, besides it has been interlinked to popular LOD datasets as AGROVOC, EUROVOC, DBPEDIA and UMTHES. The paper illustrates the main characteristics of EARTh as a guide to its usage. It clarifies (i) the methodology adopted to define the EARTh content; (ii) the design and technological choices made when publishing EARTh as Linked Data; (iii) the information pertaining to its access and maintenance. Descriptions of EARTh applications and future relevance are highlighted. CNR-IIA-EKOLab Environmental Knowledge Organization Laboratory, Area della Ricerca di Roma 1, Via Salaria Km 29,300 C.P. 10,I-00016 Monterotondo stazione RM, {difranco, vds, plini} @iia.cnr.it


title: Nurse Managers’ Emotional Intelligence and Effective Leadership: A Review of the Current Evidence

text:
Background
Emotional Intelligence has made a significant contribution to effective leadership, becoming one of the key characteristics of leaders.Objective: The aim of the present study was to review qualitative and quantitative studies concerning Emotional Intelligence of nurse leaders and the evidence-based composition of their results.

Method
A search was performed in the electronic databases (Pubmed, Scopus and CINAHL) for articles, which were published in the period 2000-2017 in English or Greek. Eleven studies met the inclusion criteria, of which 10 were quantitative and one was qualitative.

Results
The results suggested that Emotional Intelligence is a useful tool for nurse leaders and contributes decisively to the achievement of effective management in healthcare.

Conclusion
It is necessary for nurses to improve their social and emotional skills because of the particular nature of the nursing profession, which places the healthy or weak person at its center.


title: ProgTest: An environment for the submission and evaluation of programming assignments based on testing activities

text: Programming foundations is not an easy subject to be taught — many students have difficulties understanding the abstract concepts of programming and have a wrong view about the programming activity. In order to address these problems, experiences have suggested the integrated teaching of programming concepts and software testing in introductory CS courses. Shortly, the idea is that testing can contribute to enhance the students' capabilities of understanding and analysis. However, such perspective requires tools to provide an adequate feedback to evaluate the students' performance concerning programming and testing activities. In this paper we describe ProgTest — a web-based tool for the submission and automatic evaluation of practical programming assignments based on testing activities. Results from a preliminary validation of ProgTest are also presented. Such results provide evidences on the practical use of ProgTest as a supporting mechanism for the integrated teaching of programming foundations and software testing.


title: Building a HighLevel Dataflow System on top of MapReduce: The Pig Experience

text: Increasingly, organizations capture, transform and analyze enormous data sets. Prominent examples include internet companies and e-science. The Map-Reduce scalable dataflow paradigm has become popular for these applications. Its simple, explicit dataflow programming model is favored by some over the traditional high-level declarative approach: SQL. On the other hand, the extreme simplicity of MapReduce leads to much low-level hacking to deal with the many-step, branching dataflows that arise in practice. Moreover, users must repeatedly code standard operations such as join by hand. These practices waste time, introduce bugs, harm readability, and impede optimizations. Pig is a high-level dataflow system that aims at a sweet spot between SQL and Map-Reduce. Pig offers SQL-style high-level data manipulation constructs, which can be assembled in an explicit dataflow and interleaved with custom Mapand Reduce-style functions or executables. Pig programs are compiled into sequences of Map-Reduce jobs, and executed in the Hadoop Map-Reduce environment. Both Pig and Hadoop are open-source projects administered by the Apache Software Foundation. This paper describes the challenges we faced in developing Pig, and reports performance comparisons between Pig execution and raw Map-Reduce execution.


title: Matroids and the greedy algorithm

text: (0) Many discrete programming algorithms are being proposed. One thing most of them have in common is that they do not work very well. Solving problems that are a priori finite, astronomically finite, is preferably a matter of finding algorithms that are s o m e h o w better than finite. These considerations prompt looking for good algorithms and trying to understand how and why at least a few combinatorial problems have them. (1) Let H be a finite (for convenience) set of real-valued vectors , x = [xj] , ] ~ E. Often all the members of H will be integer-valued; often they will all be {0, 1}-valued. The index-set, E, is any finite set of elements. Let c = [cj], ] ~ E, be any real vector on E, called the objective or weighting of E. The problem of finding a member o f H which maximizes (or minimizes) cx = iN cjx/, j ~ E, we call a loco problem or loco programming. "Loco" stands for "linear-objective combinatorial". (2) In order for a loco problem to be a completely defined problem, the way H is given must of course be specified. There are various ways to describe implicitly very large sets H so tha t it is relatively easy to determine whether or not any particular vector is a member of H. One well-known:way is in linear programming, where H is the set of extreme points of the solution-set of a given finite system, L, of linear equations and <_ type linear inequalities in the variables xj (briefly, a


title: Bitcoin Network Measurements for Simulation Validation and Parameterization

text: Bitcoin is gaining increasing popularity nowadays, even though the crypto-currencies field has plenty of digital currencies that have emerged before the adoption of Bitcoin idea. Bitcoin is a decentralized digital currency which relies on set of miners to maintain a distributed public ledger and peer-to-peer network to broadcast transactions. In this paper, we analyse how transaction validation is achieved by the transaction propagation round trip and how transaction dissemination throughout the network can lead to inconsistencies in the view of the current transactions ledger by different nodes. We then measure the transaction propagation delay in the real Bitcoin network and how it is affected by the number of nodes and network topology. This measurement enables a precise validation of any simulation model of the Bitcoin network. Large-scale measurements of the real Bitcoin network are performed in this paper. This will provide an opportunity to parameterise any model of the Bitcoin


title: Haptic jamming: A deformable geometry, variable stiffness tactile display using pneumatics and particle jamming

text: Many controllable tactile displays present the user with either variable mechanical properties or adjustable surface geometries, but controlling both simultaneously is challenging due to electromechanical complexity and the size/weight constraints of haptic applications. This paper discusses the design, manufacturing, control, and preliminary evaluation of a novel haptic display that achieves both variable stiffness and deformable geometry via air pressure and a technique called particle jamming. The surface of the device consists of a flat, deformable layer of hollow silicone cells filled with coffee grounds. It selectively solidifies in different regions when the air is vacuumed out of individual cells, jamming the coffee particles together. The silicone layer is clamped over a chamber with regulated air pressure. Different sequences of air pressure and vacuum level adjustment allow regions of the surface to display a small rigid lump, a large soft plane, and various other combinations of lump size and stiffness. Experimental data from individual cells show that surface stiffness increases with vacuum level and the elliptical shape of the cells become increasingly spherical with increased chamber pressure.


title: Design of a Dual-Band High-Gain Antenna Array for WLAN and WiMAX Base Station

text: A dual-band 3 ×2 antenna array with high gain is presented in this letter. A dual-band folded dipole antenna is utilized as the antenna array element. A broadband symmetric feed network is designed to maintain each array element with the identical current magnitude and phase. The antenna array has a low profile of 12 mm (about 0.1λL; λL is the wavelength in free space at the lowest operating frequency). Dual bands 2.3 ~ 2.56 and 3 ~ 4.2 GHz for return loss (RL) being higher than 10 dB can be obtained in simulation, while two 10-dB RL bands 2.31 ~ 2.51 and 3.1 ~ 4.15 GHz in measurement cover the wireless local area network (WLAN, 2.4 ~ 2.48 GHz) and Worldwide Interoperability for Microwave Access (WiMAX, 3.3 ~ 3.8 GHz). The measured results also show that this planar antenna array has a high gain 14.3 ~ 15.1 dBi (2.31 ~ 2.51 GHz), 16.2 ~ 17.3 dBi ( 3.1 ~ 4.15 GHz) in + z-direction and a low cross-polarization level <; - 26 dB.


title: Systematic review of the research evidence examining the effectiveness of interventions using a sensory integrative approach for children.

text: Twenty-seven studies were systematically reviewed to identify, evaluate, and synthesize the research literature on the effectiveness of sensory integration (SI) intervention on the ability of children with difficulty processing and integrating sensory information to engage in desired occupations and to apply these findings to occupational therapy practice. Results suggest the SI approach may result in positive outcomes in sensorimotor skills and motor planning; socialization, attention, and behavioral regulation; reading-related skills; participation in active play; and achievement of individualized goals. Gross motor skills, self-esteem, and reading gains may be sustained from 3 mo to 2 yr. Findings may be limited by Type II error because of small sample sizes, variable intervention dosage, lack of fidelity to intervention, and selection of outcomes that may not be meaningful to clients and families or may not change with amount of treatment provided. Replication of findings with methodologically and theoretically sound studies is needed to support current findings.


title: Speed measurement algorithm for low speed permanent magnet synchronous motor based on overlapped measurement regions

text: In order to improve the speed measurement performance of low speed PMSM drives equipped with absolute optical encoders, this paper proposes a speed measurement method based on overlapped measurement regions. The proposed method replaces the long measurement region of the conventional backward differentiation method with several short ones which are overlapped at most regions. In one speed measurement process, the results obtained from the short regions are averaged to improve precision. Then the relationship between precision, time delay and region length, overlap times, are analyzed using expectation and variance as performance index. Based on the analysis, an optimal design method of the measurement region is derived. Experimental researches are carried out on a low speed permanent magnet synchronous motor equipped with a 13 bit absolute encoder. The results prove the validity and effectiveness of the proposed method.


title: Sensors and systems for fruit detection and localization: A review

text: This paper reviews the research and development of machine vision systems for fruit detection and localization for robotic harvesting and/or crop-load estimation of specialty tree crops including apples, pears, and citrus. Variable lighting condition, occlusions, and clustering are some of the important issues needed to be addressed for accurate detection and localization of fruit in orchard environment. To address these issues, various techniques have been investigated using different types of sensors and their combinations as well as with different image processing techniques. This paper summarizes various techniques and their advantages and disadvantages in detecting fruit in plant or tree canopies. The paper also summarizes the sensors and systems developed and used by researchers to localize fruit as well as the potential and limitations of those systems. Finally, major challenges for the successful application of machine vision system for robotic fruit harvesting and crop-load estimation, and potential future directions for research and development are discussed. 2015 Elsevier B.V. All rights reserved.


title: Change impact analysis for Natural Language requirements: An NLP approach

text: Requirements are subject to frequent changes as a way to ensure that they reflect the current best understanding of a system, and to respond to factors such as new and evolving needs. Changing one requirement in a requirements specification may warrant further changes to the specification, so that the overall correctness and consistency of the specification can be maintained. A manual analysis of how a change to one requirement impacts other requirements is time-consuming and presents a challenge for large requirements specifications. We propose an approach based on Natural Language Processing (NLP) for analyzing the impact of change in Natural Language (NL) requirements. Our focus on NL requirements is motivated by the prevalent use of these requirements, particularly in industry. Our approach automatically detects and takes into account the phrasal structure of requirements statements. We argue about the importance of capturing the conditions under which change should propagate to enable more accurate change impact analysis. We propose a quantitative measure for calculating how likely a requirements statement is to be impacted by a change under given conditions. We conduct an evaluation of our approach by applying it to 14 change scenarios from two industrial case studies.


title: Generalized Haar Filter based Deep Networks for Real-Time Object Detection in Traffic Scene

text: Vision-based object detection is one of the fundamental functions in numerous traffic scene applications such as self-driving vehicle systems and advance driver assistance systems (ADAS). However, it is also a challenging task due to the diversity of traffic scene and the storage, power and computing source limitations of the platforms for traffic scene applications. This paper presents a generalized Haar filter based deep network which is suitable for the object detection tasks in traffic scene. In this approach, we first decompose a object detection task into several easier local regression tasks. Then, we handle the local regression tasks by using several tiny deep networks which simultaneously output the bounding boxes, categories and confidence scores of detected objects. To reduce the consumption of storage and computing resources, the weights of the deep networks are constrained to the form of generalized Haar filter in training phase. Additionally, we introduce the strategy of sparse windows generation to improve the efficiency of the algorithm. Finally, we perform several experiments to validate the performance of our proposed approach. Experimental results demonstrate that the proposed approach is both efficient and effective in traffic scene compared with the state-of-the-art.


title: Classification of Motor Imagery BCI Using Multivariate Empirical Mode Decomposition

text: Brain electrical activity recorded via electroencephalogram (EEG) is the most convenient means for brain-computer interface (BCI), and is notoriously noisy. The information of interest is located in well defined frequency bands, and a number of standard frequency estimation algorithms have been used for feature extraction. To deal with data nonstationarity, low signal-to-noise ratio, and closely spaced frequency bands of interest, we investigate the effectiveness of recently introduced multivariate extensions of empirical mode decomposition (MEMD) in motor imagery BCI. We show that direct multichannel processing via MEMD allows for enhanced localization of the frequency information in EEG, and, in particular, its noise-assisted mode of operation (NA-MEMD) provides a highly localized time-frequency representation. Comparative analysis with other state of the art methods on both synthetic benchmark examples and a well established BCI motor imagery dataset support the analysis.


title: Source Code Metrics for Programmable Logic Controller (PLC) Ladder Diagram (LD) Visual Programming Language

text: The IEC 611131-3, an open international standard for Programmable Logic Controllers (PLC) defines several domain specific languages for industrial and process automation. Domain specific languages have several features, programming constructs and notations which are different than general purpose languages and hence the traditional source code metrics for general purpose programming languages cannot be directly applied to domain specific languages for writing control programs in the area of industrial automation. We propose source code level metrics to measure size, vocabulary, cognitive complexity and testing complexity of a visual Programmable Logic Controller (PLC) programming language. We present metrics for Ladder Diagram (LD) programming language which is one of the five languages defined in the IEC 61131-3 standard. The proposed metric is based on factors like number of distinct operators and operands, total number of operators and operands, number of rungs, weights of different basic control structures, structure of the program and control flow. We apply Weyukur's property to validate the metrics and evaluate the number of properties satisfied by the proposed metric.


title: Accelerometer-based tilt estimation of a rigid body with only rotational degrees of freedom

text: An estimation algorithm is developed for determining pitch and roll angles (tilt) of a rigid body fixed at a pivot point using multiple accelerometers. The estimate is independent of the rigid body dynamics; the method is applicable both in static conditions and for any dynamic motion of the body. No dynamic model is required for the estimator; only the mounting positions of the sensors need to be known. The proposed estimator is the optimal linear estimate in a least-squares sense if knowledge of the system dynamics is not used. The estimate may be used as a basis for further filtering and fusion techniques, such as sensor fusion with rate gyro data. The estimation algorithm is applied to the problem of state estimation for the Balancing Cube, a rigid structure that can actively balance on its corners. Experimental results are provided.


title: Survey on mining subjective data on the web

text: In the past years we have witnessed Sentiment Analysis and Opinion Mining becoming increasingly popular topics in Information Retrieval and Web data analysis. With the rapid growth of the user-generated content represented in blogs, wikis and Web forums, such an analysis became a useful tool for mining the Web, since it allowed us to capture sentiments and opinions at a large scale. Opinion retrieval has established itself as an important part of search engines. Ratings, opinion trends and representative opinions enrich the search experience of users when combined with traditional document retrieval, by revealing more insights about a subject. Opinion aggregation over product reviews can be very useful for product marketing and positioning, exposing the customers’ attitude towards a product and its features along different dimensions, such as time, geographical location, and experience. Tracking how opinions or discussions evolve over time can help us identify interesting trends and patterns and better understand the ways that information is propagated in the Internet. In this study, we review the development of Sentiment Analysis and Opinion Mining during the last years, and also discuss the evolution of a relatively new research direction, namely, Contradiction Analysis. We give an overview of the proposed methods and recent advances in these areas, and we try to layout the future research directions in the field.


title: Estimating Attributes: Analysis and Extensions of RELIEF

text: In the context of machine learning from examples this paper deals with the problem of estimating the quality of attributes with and without dependencies among them. Kira and Rendell (1992a,b) developed an algorithm called RELIEF, which was shown to be very eecient in estimating attributes. Original RELIEF can deal with discrete and continuous attributes and is limited to only two-class problems. In this paper RELIEF is analysed and extended to deal with noisy, incomplete, and multi-class data sets. The extensions are veriied on various artiicial and one well known real-world problem.


title: Automatic Retrieval and Clustering of Similar Words

text: Bootstrapping semantics from text is one of the greatest challenges in natural language learning. We first define a word similarity measure based on the distributional pattern of words. The similarity measure allows us to construct a thesaurus using a parsed corpus. We then present a new evaluation methodology for the automatically constructed thesaurus. The evaluation results show that the thesaurus is significantly closer to WordNet than Roget Thesaurus is.


title: Digital Nudging

text: Digital nudging is the use of user-interface design elements to guide people’s behavior in digital choice environments. Digital choice environments are user interfaces – such as web-based forms and ERP screens – that require people to make judgments or decisions. Humans face choices every day, but the outcome of any choice is influenced not only by rational deliberations of the available options but also by the design of the choice environment in which information is presented, which can exert a subconscious influence on the outcome. In other words, ‘‘what is chosen often depends upon how the choice is presented’’ (Johnson et al. 2012, p. 488) such that the ‘‘choice architecture alters people’s behavior in a predictable way’’ (Thaler and Sunstein 2008, p. 6). Even simple modifications of the choice environment in which options are presented can influence people’s choices and ‘‘nudge’’ them into behaving in particular ways. In fact, there is no neutral way to present choices. For example, Johnson and Goldstein (2003) showed that simply changing default options (from opt-in to opt-out) in the context of organ donation nearly doubled the percentage of people who consent to being organ donors. Many choices are made in online environments. As the design of digital choice environments always (either deliberately or accidentally) influences people’s choices, understanding the effects of digital nudges in these environments can help designers lead users to the most desirable choice. For example, the mobile payment app Square nudges people into giving tips by setting the default to ‘‘tipping’’ so that customers must actively select a ‘‘no tipping’’ option if they choose not to give a tip. Using this simple nudge has raised tip amounts, especially where little or no tipping has been common (Carr 2013). These examples show that simply changing the default option affects the outcome.


title: Perspectives and Limitations of Self-Organizing Maps in Blind Separation of Source Signals

text: |The capabilities of self-organizing maps (SOMs) in parametrizing data manifolds qualify them as candidates for blind separation algorithms. We study the virtues and problems of the SOM-based approach in a simple example. Also numerical simulations of more general cases have been performed. It shows that the performance is unquestionable in the case of a linear mixture only if the observed data are prewhitened and inhomogeneities in the input data are compensated. The algorithm is robust with respect to deviations from linearity, although may fail for complex non-linearly distorted signals. Due to computational restrictions only mixtures from a few sources can be resolved. Under certain conditions it is possible to separate more sources than sensors using a dimension-increasing map.


title: Anomaly Detection in Vessel Tracking Using Support Vector Machines (SVMs)

text: The paper is devoted to supervise method approach to identify the vessel anomaly behaviour in waterways using the Automated Identification System (AIS) vessel reporting data. In this work, we describe the use of SVMs to detect the vessel anomaly behaviour. The SVMs is a supervised method that needs some pre knowledge to extract the maritime movement patterns of AIS raw data into information. This is the basis to remodel information into a meaningful and valuable form. The result of this work shows that the SVMs technique is applicable to be used for the identification of vessel anomaly behaviour. It is proved that the best accuracy result is obtained from dividing raw data into 70% for training and 30% for testing stages.


title: Volley: Automated Data Placement for Geo-Distributed Cloud Services

text: As cloud services grow to span more and more globally distributed datacenters, there is an increasingly urgent need for automated mechanisms to place application data across these datacenters. This placement must deal with business constraints such as WAN bandwidth costs and datacenter capacity limits, while also minimizing user-perceived latency. The task of placement is further complicated by the issues of shared data, data inter-dependencies, application changes and user mobility. We document these challenges by analyzing month-long traces from Microsoft's Live Messenger and Live Mesh, two large-scale commercial cloud services.


title: On Challenges in Evaluating Malware Clustering

text: Malware clustering and classification are important tools that enable analysts to prioritize their malware analysis efforts. The recent emergence of fully automated methods for malware clustering and classification that report high accuracy suggests that this problem may largely be solved. In this paper, we report the results of our attempt to confirm our conjecture that the method of selecting ground-truth data in prior evaluations biases their results toward high accuracy. To examine this conjecture, we apply clustering algorithms from a different domain (plagiarism detection), first to the dataset used in a prior work’s evaluation and then to a wholly new malware dataset, to see if clustering algorithms developed without attention to subtleties of malware obfuscation are nevertheless successful. While these studies provide conflicting signals as to the correctness of our conjecture, our investigation of possible reasons uncovers, we believe, a cautionary note regarding the significance of highly accurate clustering results, as can be impacted by testing on a dataset with a biased cluster-size distribution.


title: Gource: visualizing software version control history

text: This film demonstrates a tool for visualizing the development history of software projects as an interactive animation, showing developers working on the hierarchical file-directory structure of a project over the course of its development.


title: The Quest for Scalable Blockchain Fabric: Proof-of-Work vs. BFT Replication

text: Bitcoin cryptocurrency demonstrated the utility of global consensus across thousands of nodes, changing the world of digital transactions forever. In the early days of Bitcoin, the performance of its probabilistic proof-of-work (PoW) based consensus fabric, also known as blockchain, was not a major issue. Bitcoin became a success story, despite its consensus latencies on the order of an hour and the theoretical peak throughput of only up to 7 transactions per second. The situation today is radically different and the poor performance scalability of early PoW blockchains no longer makes sense. Specifically, the trend of modern cryptocurrency platforms, such as Ethereum, is to support execution of arbitrary distributed applications on blockchain fabric, needing much better performance. This approach, however, makes cryptocurrency platforms step away from their original purpose and enter the domain of database-replication protocols, notably, the classical state-machine replication, and in particular its Byzantine fault-tolerant (BFT) variants. In this paper, we contrast PoW-based blockchains to those based on BFT state machine replication, focusing on their scalability limits. We also discuss recent proposals to overcoming these scalability limits and outline key outstanding open problems in the quest for the “ultimate” blockchain fabric(s).


title: Integrated system of face recognition and sound localization for a smart door phone

text: This paper proposes a smart system using both face recognition and sound localization techniques to identify foreign faces through a door phone in a more efficient and accurate way. In particular, a visitor's voice is used to localize the proper location of the speaker. The location information is then used to adjust the door phone camera position to frame the face in its field of view. In order to accurately locate the facial position of the visitor, 4 microphones are positioned in a cross configuration. Furthermore, face detection and face recognition algorithms as well as a wireless interface are used to recognize the visitor and send the information to the home owner's phone. The entire control system is built using an FPGA chip and tested for actual use in door phone environments1.


title: Deep Convolutional Factor Analyser for Multivariate Time Series Modeling

text: Deep generative models can perform dramatically better than traditional graphical models in a number of machine learning tasks. However, training such models remains challenging because their latent variables typically do not have an analytical posterior distribution, largely due to the nonlinear activation nodes. We present a deep convolutional factor analyser (DCFA) for multivariate time series modeling. Our network is constructed in a way that bottom layer nodes are independent. Through a process of up-sampling and convolution, higher layer nodes gain more temporal dependency. Our model can thus give a time series different representations at different depths. DCFA only consists of linear Gaussian nodes. Therefore, the posterior distributions of latent variables are also Gaussian and can be estimated easily using standard variational Bayes algorithm. We show that even without nonlinearity the proposed deep model can achieve state-of-the-art results in anomaly detection, classification and clustering using both synthetic and real-world datasets.


title: Visual Sentiment Prediction by Merging Hand-Craft and CNN Features

text: Nowadays, more and more people are getting used to social media such as Instagram, Facebook, Twitter, and Flickr to post images and texts to express their sentiment and emotions on almost all events and subjects. In consequence, analyzing sentiment of the huge number of images and texts on social networks has become more indispensable. Most of current research has focused on analyzing sentiment of textual data, while only few research has focused on sentiment analysis of image data. Some of these research has considered handcraft image features, the others has utilized Convolutional Neural Network (CNN) features. However, no research to our knowledge has considered mixing both hand-craft and CNN features. In this paper, we attempt to merge CNN which has shown remarkable achievements in Computer Vision recently, with handcraft features such as Color Histogram (CH) and Bag-of-Visual Words (BoVW) with some local features such as SURF and SIFT to predict sentiment of images. Furthermore, because it is often the case that the large amount of training data may not be easily obtained in the area of visual sentiment, we employ both data augmentation and transfer learning from a pre-trained CNN such as VGG16 trained with ImageNet dataset. With the handshake of hand-craft and End-to-End features from CNN, we attempt to attain the improvement of the performance of the proposed visual sentiment prediction framework. We conducted experiments on an image dataset from Twitter with polarity labels ("positive" and "negative"). The results of experiments demonstrate that our proposed visual sentimental prediction framework outperforms the current state-of-the-art methods.


title: A gender identity interview for children.

text: A 12-item gender identity interview schedule was administered to 85 children referred for concerns regarding their gender identity development and 98 clinical and normal control children. Factor analysis identified two factors, which were labeled Affective Gender Confusion and Cognitive Gender Confusion. The gender-referred group gave significantly more deviant responses than did the controls on both factors. Results were discussed with regard to several diagnostic and assessment issues pertaining to children with gender identity disorder.


title: Factors Affecting Online Search Intention and Online Purchase Intention

text: This research focuses on various factors affecting online search intention which has been found to be a key predictor of online purchase intention. Data were collected from a sample consisting of mostly young adults with familiarity of computer use and online shopping experience. A structural equation model was employed to test hypotheses. According to the findings, utilitarian value of Internet information search, hedonic value of Internet information search, perceived benefits of Internet shopping, perceived risk of Internet shopping, and Internet purchase experience predicted online search intention well. The findings also showed that online search intention positively affects * Professor of Marketing, College of Business Administration, Seoul National University(jaekim@snu.ac.kr) ** Marketing, Telephone Service Team, Hanaro Telecom Co., Inc. (heechun@hanaro.com) *** M.B.A. candidate, College of Business Administration, Seoul National University(haejoo80@yahoo.com) This research was supported by the Institute of Management Research, Seoul National University. Seoul Journal of Business Volume 10, Number 2 (December 2004)


title: Cyber Security Incidents on Critical Infrastructure and Industrial Networks

text: National critical infrastructure and industrial processes are heavily reliant on automation, monitoring and control technologies, including the widely used Supervisory Control and Data Acquisition (SCADA) systems. The growing interconnection of these systems with corporate networks exposes them to cyber attacks, with several security incidents reported over the last few decades. This study provides a classification scheme for categorising security incidents related to critical infrastructure and industrial control systems. The classification scheme is applied to analyse 242 security incidents on critical infrastructure and industrial control networks, which were reported between 1982 and 2014. The results show interesting patterns, with key points highlighted for the purpose of improving the way we plan for and direct efforts toward protecting critical infrastructure and industrial networks.


title: How to evaluate technologies for health behavior change in HCI research

text: New technologies for encouraging physical activity, healthy diet, and other types of health behavior change now frequently appear in the HCI literature. Yet, how such technologies should be evaluated within the context of HCI research remains unclear. In this paper, we argue that the obvious answer to this question - that evaluations should assess whether a technology brought about the intended change in behavior - is too limited. We propose that demonstrating behavior change is often infeasible as well as unnecessary for a meaningful contribution to HCI research, especially when in the early stages of design or when evaluating novel technologies. As an alternative, we suggest that HCI contributions should focus on efficacy evaluations that are tailored to the specific behavior-change intervention strategies (e.g., self-monitoring, conditioning) embodied in the system and studies that help gain a deep understanding of people's experiences with the technology.


title: Millimeter-Wave MIMO Prototype: Measurements and Experimental Results

text: Millimeter-wave MIMO systems are one of the candidate schemes for 5G wireless standardization efforts. In this context, the main contributions of this article are three-fold. First, we describe parallel sets of measurements at identical transmit-receive location pairs with 2.9, 29 and 61 GHz carrier frequencies in indoor office, shopping mall, and outdoor settings. These measurements provide insights on propagation, blockage and material penetration losses, and the key elements necessary in system design to make mm-Wave systems viable in practice. Second, one of these elements is hybrid beamforming necessary for better link margins by reaping the array gain with large antenna dimensions. From the class of fully-flexible hybrid beamformers, we describe a robust class of directional beamformers toward meeting the high data-rate requirements of mm-Wave systems. Third, leveraging these design insights, we then describe an experimental prototype system at 28 GHz that realizes high data rates on both the downlink and uplink and robustly maintains these rates in outdoor and indoor mobility scenarios. In addition to maintaining large signal constellation sizes in spite of radio frequency challenges, this prototype leverages the directional nature of the mm-Wave channel to perform seamless beam switching and handover across mm-Wave base stations, thereby overcoming the path losses in non-line-of-sight links and blockages encountered at mm-Wave frequencies.


title: A unified protocol stack solution for LTE and WLAN in future mobile converged networks

text: The interworking of the LTE system and WLAN technologies has drawn much attention lately, due to the growing demands for various multimedia services and large data traffic in hotspot areas. Existing research studies have mostly investigated the coupling architectures for these two wireless communication standards at the network layer. However, in the current architectures, many important coordination functions and joint optimizations cannot be accomplished efficiently. To tackle this problem, a new CBS solution is proposed, which integrates different RATs at layer 2 in the true sense of convergence. We design a unified protocol stack that includes all the original functions of both LTE and WLAN systems. Then we propose a convergence architecture, the RMC sublayer, for joint management of these two RATs. The proposed CBS solution can support seamless offloading through soft handover, guaranteed QoS, forwarding management by a single IP address, and customized bandwidth aggregation service. Finally, our simulation and initial experiment results demonstrate the feasibility and efficiency of the CBS solution in future mobile converged networks.


X iv :1 40 3. 57 53 v1 [ cs .A I] 2 3 M ar 2 01 4 D-CFPR: D numbers extended consistent fuzzy preference relations Xinyang Deng, Felix T.S. Chan, Rehan Sadiq, Sankaran Mahadevan, Yong Deng School of Computer and Information Science, Southwest University, Chongqing, 400715, China Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, China School of Engineering, University of British Columbia, 1137 Alumni Ave, Kelowna, BC V1V 1V7, Canada School of Engineering, Vanderbilt University, Nashville, TN, 37235, USA


title: Conditional molecular design with deep generative models

text: Although machine learning has been successfully used to propose novel molecules that satisfy desired properties, it is still challenging to explore a large chemical space efficiently. In this paper, we present a conditional molecular design method that facilitates generating new molecules with desired properties. The proposed model, which simultaneously performs both property prediction and molecule generation, is built as a semisupervised variational autoencoder trained on a set of existing molecules with only a partial annotation. We generate new molecules with desired properties by sampling from the generative distribution estimated by the model. We demonstrate the effectiveness of the proposed model by evaluating it on drug-like molecules. The model improves the performance of property prediction by exploiting unlabeled molecules and efficiently generates novel molecules fulfilling various target conditions.


title: Identifying extremism in social media with multi-view context-aware subset optimization

text: Cyber threat intelligence and security informatics play critical roles in the identification of the principal influencers and threats associated with criminal activity and extremism in web-based communities. This paper presents an effective, dynamic learning framework utilizing domain specific context information to design a novel classification model that can robustly identify malicious social media posts (e.g. enrollment propaganda for extremist groups) with expressions of extremism or criminal intent. Research towards automated identification of extreme online posts and their associated key suspects and threats faces numerous challenges, 1) Online data, particularly social media data, originated from numerous independent and heterogeneous sources are largely unstructured. 2) The tactics, techniques, and procedures (TTPs) of criminal activity and extremism are constantly evolving. 3) There are limited ground truth data to support the development of effective classification technologies. In this paper, we present a human-machine collaborative, semi-supervised learning system that can efficiently and effectively identify malicious social media posts in presence of these challenges. Our system and framework develops an initial classifier from limited annotated data and in an interactive manner evolves dynamically into a sophisticated model using shortlisted relevant samples, identified via a graph-based optimization method, solvable by maximum flow algorithm. This same method also may be used to refine the classifier as TTPs evolve. Under this framework, the classifier performance converges faster using roughly 1–2 orders of magnitude fewer annotated samples, as compared to fully supervised solutions, resulting in a reasonably acceptable accuracy of nearly 80%. We validate our framework using a large collection of English and non-English flagged words extracted from three web-based forums and manually verified by multiple independent annotators.


title: Effect of different surface pre-treatments and luting materials on shear bond strength to PEEK.

text:
OBJECTIVES
To assess the bonding potential of a universal composite resin cement and an adhesive/composite system to differently pre-treated PEEK surfaces.

METHODS
One hundred and fifty PEEK disks were embedded in epoxy resin, polished (P4000 grit) and treated as follows (n=30/group): (A) no treatment, (B) acid etching with sulfuric acid (98%) for 1 min, (C) sandblasting for 10s with 50 microm alumina, (D) sandblasting for 10s with 110 microm alumina and (E) silica coating using the Rocatec system (3M ESPE). Polished and sandblasted (50 microm alumina) cp titanium (grade 4) served as a control. Acrylic hollow cylinders were either luted with a universal composite resin cement (RelyX Unicem) or an unfilled resin (Heliobond) and a hybrid composite (Tetric) to the specimens. Bond strength was measured in a shear test and failure modes were assessed. Statistic analysis was performed with one-way ANOVA followed by a post hoc Scheffé test and unpaired t-tests.

RESULTS
With the universal composite resin cement, no bond could be established on any PEEK surfaces, except specimens etched with sulfuric acid (19.0+/-3.4MPa). Shear bond strength to titanium was significantly lower (8.7+/-2.8MPa, p<0.05). Applying the adhesive/composite system, shear bond strength values on pre-treated PEEK ranged from 11.5+/-3.2MPa (silica coating) to 18.2+/-5.4MPa (acid etched) with no statistically significant differences (p>0.05). No bond was obtained on the polished surface.

SIGNIFICANCE
Bonding to PEEK is possible when using a bonding system. No adherence can be achieved with the tested universal composite resin cement except on an etched surface. The results strongly encourage further research in PEEK application in dentistry.


title: The Fastest Pedestrian Detector in the West

text: Figure 1: In many applications, detection speed is as important as accuracy. (a) A standard pipeline for performing modern multiscale detection is to create a densely sampled image pyramid, compute features at each scale, and finally perform sliding window classification (with a fixed scale model). Although effective; the creation of the feature pyramid can dominate the cost of detection, leading to slow multiscale detection. (b) Viola and Jones [6] utilized shift and scale invariant features, allowing a trained detector to be placed at any location and scale without relying on an image pyramid. Constructing such a classifier pyramid results in fast multiscale detection; unfortunately, most features are not scale invariant, including gradient histograms, significantly limiting the generality of this scheme. (c) We propose a fast method for approximating features at multiple scales using a sparsely sampled image pyramid with a step size of an entire octave and within each octave we use a classifier pyramid. The proposed approach achieves nearly the same accuracy as using densely sampled image pyramids, with nearly the same speed as using a classifier pyramid applied to an image at a single scale.


title: A mobile vision system for robust multi-person tracking

text: We present a mobile vision system for multi-person tracking in busy environments. Specifically, the system integrates continuous visual odometry computation with tracking-by-detection in order to track pedestrians in spite of frequent occlusions and egomotion of the camera rig. To achieve reliable performance under real-world conditions, it has long been advocated to extract and combine as much visual information as possible. We propose a way to closely integrate the vision modules for visual odometry, pedestrian detection, depth estimation, and tracking. The integration naturally leads to several cognitive feedback loops between the modules. Among others, we propose a novel feedback connection from the object detector to visual odometry which utilizes the semantic knowledge of detection to stabilize localization. Feedback loops always carry the danger that erroneous feedback from one module is amplified and causes the entire system to become instable. We therefore incorporate automatic failure detection and recovery, allowing the system to continue when a module becomes unreliable. The approach is experimentally evaluated on several long and difficult video sequences from busy inner-city locations. Our results show that the proposed integration makes it possible to deliver stable tracking performance in scenes of previously infeasible complexity.


title: A Boosted Particle Filter: Multitarget Detection and Tracking

text: The problem of tracking a varying number of non-rigid objects has two major difficulties. First, the observation models and target distributions can be highly non-linear and non-Gaussian. Second, the presence of a large, varying number of objects creates complex interactions with overlap and ambiguities. To surmount these difficulties, we introduce a vision system that is capable of learning, detecting and tracking the objects of interest. The system is demonstrated in the context of tracking hockey players using video sequences. Our approach combines the strengths of two successful algorithms: mixture particle filters and Adaboost. The mixture particle filter [17] is ideally suited to multi-target tracking as it assigns a mixture component to each player. The crucial design issues in mixture particle filters are the choice of the proposal distribution and the treatment of objects leaving and entering the scene. Here, we construct the proposal distribution using a mixture model that incorporates information from the dynamic models of each player and the detection hypotheses generated by Adaboost. The learned Adaboost proposal distribution allows us to quickly detect players entering the scene, while the filtering process enables us to keep track of the individual players. The result of interleaving Adaboost with mixture particle filters is a simple, yet powerful and fully automatic multiple object tracking system.


title: Face liveness detection through face structure analysis

text: Liveness detection is a way to detect whether the person is live or not during submission of his/her biometric trait. It is mandatory in order to prevent face spoofing attacks. Therefore, in this paper, we proposed a robust face structure analysis mechanism to detect the liveness by exploiting face shape information. 3D structure/shape of the face is measured on the basis of disparity map between left and right image taken by stereoscopic vision. A gradient-based eight neighbour feature extraction technique has been proposed to extract unique features from these disparity images. It produces minimal computational cost by taking subset of the overall image. We have applied linear discriminant analysis (LDA), C-means algorithms on these features while principal component analysis (PCA) is applied on raw disparity images. We have achieved a recognition rate of 91.6%, 97.5% and 98.3% using PCA, LDA and C-means respectively, which strengthened the confidence of our proposed feature extraction technique.


title: A Hybrid Course Recommendation System by Integrating Collaborative Filtering and Artificial Immune Systems

text: Abstract: This research proposes a two-stage user-based collaborative filtering process using an artificial immune system for the prediction of student grades, along with a filter for professor ratings in the course recommendation for college students. We test for cosine similarity and Karl Pearson (KP) correlation in affinity calculations for clustering and prediction. This research uses student information and professor information datasets of Yuan Ze University from the years 2005–2009 for the purpose of testing and training. The mean average error and confusion matrix analysis form the testing parameters. A minimum professor rating was tested to check the results, and observed that the recommendation systems herein provide highly accurate results for students with higher mean grades.


title: Tactical Language and Culture Training Systems: Using Artificial Intelligence to Teach Foreign Languages and Cultures

text: The Tactical Language and Culture Training System (TLCTS) helps people quickly acquire communicative skills in foreign languages and cultures. More than 20,00 0 learners worldwide have used TLCTS courses. TLCTS utili zes artificial intelligence technologies in multiple wa ys: during the authoring process, and at run time to process l arner speech, interpret learner actions, control the resp on e of non-player characters, and evaluate and assess lear ner performance and proficiency. This paper describes the architecture of TLCTS and the artificial intelligence techn ologies that it employs, and presents results from multiple evaluation studies that demonstrate the benefits of learn ing foreign language and culture using this approach.


title: Neural network model with Monte Carlo algorithm for electricity demand forecasting in Queensland

text: With the rapid growth over the past few decades, people are consuming more and more electrical energies. In order to solve the contradiction between supply and demand to minimize electricity cost, it is necessary and useful to predict the electricity demand. In this paper, we apply an improved neural network algorithm to forecast the electricity, and we test it on a collected electricity demand data set in Queensland to verify its performance. There are two contributions in this paper. Firstly, comparing with backpropagation (BP) neural network, the results show a better performance on this improved neural network. Secondly, the performance on various hidden layers shows that different dimension of hidden layer in this improved neural network has little impact on the Queensland's electricity demand forecasting.


title: Generalized state-space model for an n-phase interleaved buck-boost converter

text: An n-phase interleaved buck-boost converter (IBBC) with generalized steady state model has been presented in this paper. Development in distributed generation system requires low cost, high efficient converters for power conversion. Paralleling of converters is done so as to minimize the ripples at the output and reduce the size of passive components and improve the overall efficiency and cost of the system. An interleaved buck boost converter finds application in fuel cells, electric vehicles and various solar charging applications. The steady state and small signal analysis of an n phase buck-boost converter in CCM mode have been carried out and average state space equations have been developed. In averaging, parasitic elements have been excluded for simplifying the calculations. A simulation model for a three phase interleaved buck boost converter has been presented initially which is then generalized.


title: Attaining human-level performance for anatomical landmark detection in 3D CT data

text: We present an efficient neural network approach for locating anatomical landmarks, using a two-pass, two-resolution cascaded approach which leverages a mechanism we term atlas location autocontext. Location predictions are made by regression to Gaussian heatmaps, one heatmap per landmark. This system allows patchwise application of a shallow network, thus enabling the prediction of multiple volumetric heatmaps in a unified system, without prohibitive GPU memory requirements. Evaluation is performed for 22 landmarks defined on a range of structures in head CT scans and the neural network model is benchmarked against a previously reported decision forest model trained with the same cascaded system. Models are trained and validated on 201 scans. Over the final test set of 20 scans which was independently annotated by 2 observers, we show that the neural network reaches an accuracy which matches the annotator variability.


title: A Regulated Charge Pump for Tunneling Floating-Gate Transistors

text: Flash memory is an important component in many embedded system-on-a-chip applications, which drives the need to generate high write/erase voltages in generic CMOS processes. In this paper, we present a regulated, high-voltage charge pump for erasing Flash memory and floating-gate transistors. This 0.069-mm2 charge pump was fabricated in a 0.35 <inline-formula> <tex-math notation="LaTeX">$\mu \text {m}$ </tex-math></inline-formula> standard CMOS process. While operating from a 2.5 V supply, the charge pump generates regulated voltages up to 16 V with a PSRR of 52 dB and an output impedance of 6.8 <inline-formula> <tex-math notation="LaTeX">$\text{k}\Omega $ </tex-math></inline-formula>. To reduce power consumption for use in battery-powered applications, this charge pump uses a variable-frequency regulation technique and a new circuit for minimizing short-circuit current in the clock-generation circuitry; the resulting charge pump is able to erase the charge on floating-gate transistors using only <inline-formula> <tex-math notation="LaTeX">$1.45 \mu J$ </tex-math></inline-formula>.


title: AMES-Cloud: A Framework of Adaptive Mobile Video Streaming and Efficient Social Video Sharing in the Clouds

text: While demands on video traffic over mobile networks have been souring, the wireless link capacity cannot keep up with the traffic demand. The gap between the traffic demand and the link capacity, along with time-varying link conditions, results in poor service quality of video streaming over mobile networks such as long buffering time and intermittent disruptions. Leveraging the cloud computing technology, we propose a new mobile video streaming framework, dubbed AMES-Cloud, which has two main parts: adaptive mobile video streaming (AMoV) and efficient social video sharing (ESoV). AMoV and ESoV construct a private agent to provide video streaming services efficiently for each mobile user. For a given user, AMoV lets her private agent adaptively adjust her streaming flow with a scalable video coding technique based on the feedback of link quality. Likewise, ESoV monitors the social network interactions among mobile users, and their private agents try to prefetch video content in advance. We implement a prototype of the AMES-Cloud framework to demonstrate its performance. It is shown that the private agents in the clouds can effectively provide the adaptive streaming, and perform video sharing (i.e., prefetching) based on the social network analysis.


title: A Comparative Analysis of Personality-Based Music Recommender Systems

text: This article describes a preliminary study on considering information about the target user’s personality in music recommender systems (MRSs). For this purpose, we devised and implemented four MRSs and evaluated them on a sample of real users and real-world datasets. Experimental results show that MRSs that rely on purely users’ personality information are able to provide performance comparable with those of a state-of-the-art MRS, even better in terms of the diversity of the suggested items.


title: Assembly algorithms for next-generation sequencing data.

text: The emergence of next-generation sequencing platforms led to resurgence of research in whole-genome shotgun assembly algorithms and software. DNA sequencing data from the Roche 454, Illumina/Solexa, and ABI SOLiD platforms typically present shorter read lengths, higher coverage, and different error profiles compared with Sanger sequencing data. Since 2005, several assembly software packages have been created or revised specifically for de novo assembly of next-generation sequencing data. This review summarizes and compares the published descriptions of packages named SSAKE, SHARCGS, VCAKE, Newbler, Celera Assembler, Euler, Velvet, ABySS, AllPaths, and SOAPdenovo. More generally, it compares the two standard methods known as the de Bruijn graph approach and the overlap/layout/consensus approach to assembly.


title: Anomaly Detection in Cyber Physical Systems Using Recurrent Neural Networks

text: This paper presents a novel unsupervised approach to detect cyber attacks in Cyber-Physical Systems (CPS). We describe an unsupervised learning approach using a Recurrent Neural network which is a time series predictor as our model. We then use the Cumulative Sum method to identify anomalies in a replicate of a water treatment plant. The proposed method not only detects anomalies in the CPS but also identifies the sensor that was attacked. The experiments were performed on a complex dataset which is collected through a Secure Water Treatment Testbed (SWaT). Through the experiments, we show that the proposed technique is able to detect majority of the attacks designed by our research team with low false positive rates.


title: Antidepressant effects of sleep deprivation require astrocyte-dependent adenosine mediated signaling

text: Major depressive disorder is a debilitating condition with a lifetime risk of ten percent. Most treatments take several weeks to achieve clinical efficacy, limiting the ability to bring instant relief needed in psychiatric emergencies. One intervention that rapidly alleviates depressive symptoms is sleep deprivation; however, its mechanism of action is unknown. Astrocytes regulate responses to sleep deprivation, raising the possibility that glial signaling mediates antidepressive-like actions of sleep deprivation. Here, we found that astrocytic signaling to adenosine (A1) receptors was required for the robust reduction of depressive-like behaviors following 12 hours of sleep deprivation. As sleep deprivation activates synaptic A1 receptors, we mimicked the effect of sleep deprivation on depression phenotypes by administration of the A1 agonist CCPA. These results provide the first mechanistic insight into how sleep deprivation impacts mood, and provide a novel pathway for rapid antidepressant development by modulation of glial signaling in the brain.


title: Plagiarism Meets Paraphrasing: Insights for the Next Generation in Automatic Plagiarism Detection

text: Although paraphrasing is the linguistic mechanism underlying many plagiarism cases, little attention has been paid to its analysis in the framework of automatic plagiarism detection. Therefore, state-of-the-art plagiarism detectors find it difficult to detect cases of paraphrase plagiarism. In this article, we analyze the relationship between paraphrasing and plagiarism, paying special attention to which paraphrase phenomena underlie acts of plagiarism and which of them are detected by plagiarism detection systems. With this aim in mind, we created the P4P corpus, a new resource that uses a paraphrase typology to annotate a subset of the PAN-PC-10 corpus for automatic plagiarism detection. The results of the Second International Competition on Plagiarism Detection were analyzed in the light of this annotation.The presented experiments show that (i) more complex paraphrase phenomena and a high density of paraphrase mechanisms make plagiarism detection more difficult, (ii) lexical substitutions are the paraphrase mechanisms used the most when plagiarizing, and (iii) paraphrase mechanisms tend to shorten the plagiarized text. For the first time, the paraphrase mechanisms behind plagiarism have been analyzed, providing critical insights for the improvement of automatic plagiarism detection systems.


title: Diode-clamped multilevel converters: a practicable way to balance DC-link voltages

text: The converter topologies identified as diode-clamped multilevel (DCM) or, equivalently, as multipoint clamped (MPC), are rarely used in industrial applications, owing to some serious drawbacks involving mainly the stacked bank of capacitors that constitutes their multilevel dc link. The balance of the capacitor voltages is not possible in all operating conditions when the MPC converter possesses a passive front end. On the other hand, in ac/dc/ac power conversion, the back-to-back connection of a multilevel rectifier with a multilevel inverter allows the balance of the dc-link capacitor voltages and, at the same time, it offers the power-factor-correction capability at the mains ac input. An effective balancing strategy suitable for MPC conversion systems with any number of dc-link capacitors is presented here. The strategy has been carefully studied to optimize the converter efficiency. The simulation results related to a high-power conversion system (up to 10 MW) characterized by four intermediate dc-link capacitors are shown.


title: Anti-falling tree climbing mechanism optimization

text: This paper presents an improved anti-falling tree climbing mechanism. A study and comparison was made on an original tree climbing robotic system published in reference. This optimized robotic system only relies on one stepper to achieve the anti-falling and anti-jamming functionality under either static or dynamic situations. This improved design feature reduces the total robot weight and enhances the control system robustness. Moreover, to achieve the robot spiral climbing morphology and decrease the servomotor rotational frictional forces, a special servomotor module and a bearing supporting mechanism are designed and implemented. The optimized mechanism design of the climbing robot is modelled in SolidWorks and the climbing process is simulated using SimMechanics. The simulation outcome of the initial and improved design shows that the new design has higher climbing speed and lighter weight. Physical prototype experiment matches with the simulation outcome and verifies the feasibility and effectiveness of this redesigned tree climbing mechanism.


title: Convergence Analysis of MAP Based Blur Kernel Estimation

text: One popular approach for blind deconvolution is to formulate a maximum a posteriori (MAP) problem with sparsity priors on the gradients of the latent image, and then alternatingly estimate the blur kernel and the latent image. While several successful MAP based methods have been proposed, there has been much controversy and confusion about their convergence, because sparsity priors have been shown to prefer blurry images to sharp natural images. In this paper, we revisit this problem and provide an analysis on the convergence of MAP based approaches. We first introduce a slight modification to a conventional joint energy function for blind deconvolution. The reformulated energy function yields the same alternating estimation process, but more clearly reveals how blind deconvolution works. We then show the energy function can actually favor the right solution instead of the no-blur solution under certain conditions, which explains the success of previous MAP based approaches. The reformulated energy function and our conditions for the convergence also provide a way to compare the qualities of different blur kernels, and we demonstrate its applicability to automatic blur kernel size selection, blur kernel estimation using light streaks, and defocus estimation.


title: Simulation of Absolute Amplitudes of Ultrasound Signals Using Equivalent Circuits

text: Equivalent circuits for piezoelectric devices and ultrasonic transmission media can be used to cosimulate electronics and ultrasound parts in simulators originally intended for electronics. To achieve efficient system- level optimization, it is important to simulate correct, absolute amplitude of the ultrasound signal in the system, as this determines the requirements on the electronics regarding dynamic range, circuit noise, and power consumption. This paper presents methods to achieve correct, absolute amplitude of an ultrasound signal in a simulation of a pulse-echo system using equivalent circuits. This is achieved by taking into consideration loss due to diffraction and the effect of the cable that connects the electronics and the piezoelectric transducer. The conductive loss in the transmission line that models the propagation media of the ultrasound pulse is used to model the loss due to diffraction. Results show that the simulated amplitude of the echo follows measured values well in both near and far fields, with an offset of about 10%. The use of a coaxial cable introduces inductance and capacitance that affect the amplitude of a received echo. Amplitude variations of 60% were observed when the cable length was varied between 0.07 m and 2.3 m, with simulations predicting similar variations. The high precision in the achieved results show that electronic design and system optimization can rely on system simulations alone. This will simplify the development of integrated electronics aimed at ultrasound systems.


title: PMLAB: An Scripting Environment for Process Mining

text: In a decade of process mining research, several algorithms have been proposed to solve particular process mining tasks. At the same pace, tools have appeared both in the academic and the commercial domains. These tools have enabled the use of process mining practices to a rather limited extent. In this paper we advocate for a change in the mentality: process mining may be an exploratory discipline, and PMLAB – a Python-based scripting environment supporting this – is proposed. This demo presents the main features of the PMLAB environment


title: Metasurface holograms reaching 80% efficiency.

text: Surfaces covered by ultrathin plasmonic structures--so-called metasurfaces--have recently been shown to be capable of completely controlling the phase of light, representing a new paradigm for the design of innovative optical elements such as ultrathin flat lenses, directional couplers for surface plasmon polaritons and wave plate vortex beam generation. Among the various types of metasurfaces, geometric metasurfaces, which consist of an array of plasmonic nanorods with spatially varying orientations, have shown superior phase control due to the geometric nature of their phase profile. Metasurfaces have recently been used to make computer-generated holograms, but the hologram efficiency remained too low at visible wavelengths for practical purposes. Here, we report the design and realization of a geometric metasurface hologram reaching diffraction efficiencies of 80% at 825 nm and a broad bandwidth between 630 nm and 1,050 nm. The 16-level-phase computer-generated hologram demonstrated here combines the advantages of a geometric metasurface for the superior control of the phase profile and of reflectarrays for achieving high polarization conversion efficiency. Specifically, the design of the hologram integrates a ground metal plane with a geometric metasurface that enhances the conversion efficiency between the two circular polarization states, leading to high diffraction efficiency without complicating the fabrication process. Because of these advantages, our strategy could be viable for various practical holographic applications.


title: Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition

text: where fk is the current approximation, and Rk f the current residual (error). Using initial values of Ro f = f , fo = 0, and k = 1 , the M P algorithm is comprised of the following steps, In this paper we describe a recursive algorithm to compute representations of functions with respect to nonorthogonal and possibly overcomplete dictionaries (I) Compute the inner-products {(Rkf, Zn)},. of elementary building blocks e.g. affine (wavelet) frames. We propose a modification to the Matching Pursuit algorithm of Mallat and Zhang (1992) that maintains full backward orthogonality of the residual convergence. We refer to this modified algorithm as Orthogonal Matching Pursuit (OMP). It is shown that all additional computation required for the OMP algorithm may be performed recursively. (11) Find nktl such that (error) at every step and thereby leads to improved I ( R k f , 'nk+I) l 2 asYp I(Rkf, zj)I 1


title: Extensions of compressed sensing

text: We study the notion of Compressed Sensing (CS) as put forward in [14] and related work [20, 3, 4]. The basic idea behind CS is that a signal or image, unknown but supposed to be compressible by a known transform, (eg. wavelet or Fourier), can be subjected to fewer measurements than the nominal number of pixels, and yet be accurately reconstructed. The samples are nonadaptive and measure ‘random’ linear combinations of the transform coefficients. Approximate reconstruction is obtained by solving for the transform coefficients consistent with measured data and having the smallest possible ` norm. We perform a series of numerical experiments which validate in general terms the basic idea proposed in [14, 3, 5], in the favorable case where the transform coefficients are sparse in the strong sense that the vast majority are zero. We then consider a range of less-favorable cases, in which the object has all coefficients nonzero, but the coefficients obey an ` bound, for some p ∈ (0, 1]. These experiments show that the basic inequalities behind the CS method seem to involve reasonable constants. We next consider synthetic examples modelling problems in spectroscopy and image processing, and note that reconstructions from CS are often visually “noisy” . We post-process using translation-invariant de-noising, and find the visual appearance considerably improved. We also consider a multiscale deployment of compressed sensing, in which various scales are segregated and CS applied separately to each; this gives much better quality reconstructions than a literal deployment of the CS methodology. We also report that several workable families of ‘random’ linear combinations all behave equivalently, including random spherical, random signs, partial Fourier and partial Hadamard. These results show that, when appropriately deployed in a favorable setting, the CS framework is able to save significantly over traditional sampling, and there are many useful extensions of the basic idea.


title: Wavelet support vector machine

text: An admissible support vector (SV) kernel (the wavelet kernel), by which we can construct a wavelet support vector machine (SVM), is presented. The wavelet kernel is a kind of multidimensional wavelet function that can approximate arbitrary nonlinear functions. The existence of wavelet kernels is proven by results of theoretic analysis. Computer simulations show the feasibility and validity of wavelet support vector machines (WSVMs) in regression and pattern recognition.


title: A Cognitive Model of Semantic Network Learning

text: Child semantic development includes learning the meaning of words as well as the semantic relations among words. A presumed outcome of semantic development is the formation of a semantic network that reflects this knowledge. We present an algorithm for simultaneously learning word meanings and gradually growing a semantic network, which adheres to the cognitive plausibility requirements of incrementality and limited computations. We demonstrate that the semantic connections among words in addition to their context is necessary in forming a semantic network that resembles an adult’s semantic knowledge.


title: Survey on Fraud Detection Techniques Using Data Mining

text: Now a days, fraud is a million dollar business and every year it is increasing more and more. In a crime survey in 2009, tells that close to 30% of the company’s worldwide reported falling victim to fraud in the past year. Fraud involves individual or in a form of groups who intentionally act secretly to rob another of something of worth, for their own profit. Fraud is as against the humanity and it is unlimited variety of different forms. So in this paper, we introduce different methods and techniques to tackle the fraud and how to prevent the fraud into the organization, businesses, and hospital and insurance companies. Fraud prevention is a continuing battle. Many website owners must continually fight against many kinds of crime ranging from stealing identity theft and credit cards and many other types of fraud related to that. Internet Fraud is also very popular, where fraudster can steal the credit card number and buy thing from the different websites.


title: Effects of Weighted Vests on the Engagement of Children With Developmental Delays and Autism

text: The use of weighted vests for children with autism spectrum disorders and developmental disabilities is a common practice as part of sensory integration therapy programs. The purpose of the current investigation was to extend the research on the use of weighted vests for children with autism and developmental delays in a methodologically rigorous study. The study was conducted using an alternating treatment design. This allowed the comparison of three different conditions: weighted vest, vest with no weight (which served as a placebo), and no vest (which served as a baseline). The results showed no differentiation in engagement between conditions for any of the participants. Implications for practice and future research are provided.


title: The Impact of Information Technology Infrastructure Flexibility on Strategic Alignment and Application Implementations

text: IT infrastructure flexibility is now being viewed as an organizational core competency that is necessary for organizations to survive and prosper in rapidly-changing, competitive, business environments. Using data from 200 U.S. and Canadian companies, this study examines the impact of the four components of IT infrastructure flexibility (compatibility, connectivity, modularity, and IT personnel) on strategic IT-business alignment and the extent to which various applications are implemented within an organization. The “extent” of implementation refers to the the organization’s experience with the particular application and the degree to which the application is implemented and used throughout the organization. The findings from analysis of a structural model provide evidence that connectivity, modularity, and IT personnel (among other considerations that we discuss in the paper) make significant, positive impacts on strategic alignment and that all four components result in significant, positive impacts on the applications implementation. The study reinforces the importance of IT infrastructure flexibility to organizations as one source for sustainable competitive advantage.


title: Investigating Language Universal and Specific Properties in Word Embeddings

text: Recently, many NLP tasks have benefited from distributed word representation. However, it remains unknown whether embedding models are really immune to the typological diversity of languages, despite the language-independent architecture. Here we investigate three representative models on a large set of language samples by mapping dense embedding to sparse linguistic property space. Experiment results reveal the language universal and specific properties encoded in various word representation. Additionally, strong evidence supports the utility of word form, especially for inflectional languages.


title: Color and Uncertainty: It is not always Black and White

text: To fully comprehend the meaning and impact of visualized data it is crucial that users are able to perceive and comprehend the inherent uncertainty of the data in a correct and intuitive way. Data uncertainty is frequently visualized through color mappings. Previous studies argued that color hue is not suitable for communicating uncertainty because most hue scales lack an intrinsic perceptual order. In this paper we examine the use of hue for communicating data uncertainty in more detail. We investigated the potential of distinct color triples (rather than the entire spectrum of colors, as used in previously studies) to represent different levels of uncertainty. We identified several color triples that reliably map to an intuitive ordering of certainty. Bipolar color scales constructed from these color triples can be used to communicate uncertainty in visualizations, particularly to audiences of nonspecialists. A ‘traffic light’ configuration (with red and green at the endpoints and either yellow or orange in the middle) communicates uncertainty most intuitively.


title: A Lender-Based Theory of Collateral∗

text: We consider an imperfectly competitive loan market in which a local relationship lender has an information advantage vis-à-vis distant transaction lenders. Competitive pressure from the transaction lenders prevents the local lender from extracting the full surplus from projects, so that she inefficiently rejects marginally profitable projects. Collateral mitigates the inefficiency by increasing the local lender’s payoff from precisely those marginal projects that she inefficiently rejects. The model predicts that, controlling for observable borrower risk, collateralized loans are more likely to default ex post, which is consistent with the empirical evidence. The model also predicts that borrowers for whom local lenders have a relatively smaller information advantage face higher collateral requirements, and that technological innovations that narrow the information advantage of local lenders, such as small business credit scoring, lead to a greater use of collateral in lending relationships. JEL classification: D82; G21


title: Graph-Level Operations: A High-Level Interface for Graph Visualization Technique Specification

text: Social networks collected by historians or sociologists typically have a large number of actors and edge attributes. Applying social network analysis (SNA) algorithms to these networks produces additional attributes such as degree, centrality, and clustering coefficients. Understanding the effects of this plethora of attributes is one of the main challenges of multivariate SNA. We present the design of GraphDice, a multivariate network visualization system for exploring the attribute space of edges and actors. GraphDice builds upon the ScatterDice system for its main multidimensional navigation paradigm, and extends it with novel mechanisms to support network exploration in general and SNA tasks in particular. Novel mechanisms include visualization of attributes of interval type and projection of numerical edge attributes to node attributes. We show how these extensions to the original ScatterDice system allow to support complex visual analysis tasks on networks with hundreds of actors and up to 30 attributes, while providing a simple and consistent interface for interacting with network data.


title: Table 1 : A Comparison between Lean Construction and Agile Project

text: This paper briefly summarises the evolution of Agile Project Management (APM) and differentiates it from lean and agile production and ‘leagile’ construction. The significant benefits being realized through employment of APM within the information systems industry are stated. The characteristics of APM are explored, including: philosophy, organizational attitudes and practices, planning, execution and control and learning. Finally, APM is subjectively assessed as to its potential contribution to the pre-design, design and construction phases. In conclusion, it is assessed that APM offers considerable potential for application in predesign and design but that there are significant hurdles to its adoption in the actual construction phase. Should these be overcome, APM offers benefits well beyond any individual project.


title: A Bandelet-Based Inpainting Technique for Clouds Removal From Remotely Sensed Images

text: It is well known that removing cloud-contaminated portions of a remotely sensed image and then filling in the missing data represent an important photo editing cumbersome task. In this paper, an efficient inpainting technique for the reconstruction of areas obscured by clouds or cloud shadows in remotely sensed images is presented. This technique is based on the Bandelet transform and the multiscale geometrical grouping. It consists of two steps. In the first step, the curves of geometric flow of different zones of the image are determined by using the Bandelet transform with multiscale grouping. This step allows an efficient representation of the multiscale geometry of the image's structures. Having well represented this geometry, the information inside the cloud-contaminated zone is synthesized by propagating the geometrical flow curves inside that zone. This step is accomplished by minimizing a functional whose role is to reconstruct the missing or cloud contaminated zone independently of the size and topology of the inpainting domain. The proposed technique is illustrated with some examples on processing aerial images. The obtained results are compared with those obtained by other clouds removal techniques.


title: Modeling of Overhead Transmission Lines for Lightning Studies

text: The aim of this paper is to analyze the influence of the models proposed for representing overhead transmission lines in lightning calculations. A full model of a transmission line for lightning overvoltage calculations can be split into several parts: wires (shield wires and phase conductors), towers, footing impedances and insulators. An additional component, the arrester, should be added if the study is aimed at selecting the arrester ratings needed to achieve a given performance of the line. This paper presents a sensitivity study whose main goal is to determine the effect that the model and the parameters selected for representing the main parts of a transmission line can have on the flashover rate.


title: Semantic Structural Alignment of Neural Representational Spaces Enables Translation between English and Chinese Words

text: Two sets of items can share the same underlying conceptual structure, while appearing unrelated at a surface level. Humans excel at recognizing and using alignments between such underlying structures in many domains of cognition, most notably in analogical reasoning. Here we show that structural alignment reveals how different people's neural representations of word meaning are preserved across different languages, such that patterns of brain activation can be used to translate words from one language to another. Groups of Chinese and English speakers underwent fMRI scanning while reading words in their respective native languages. Simply by aligning structures representing the two groups' neural semantic spaces, we successfully infer all seven Chinese–English word translations. Beyond language translation, conceptual structural alignment underlies many aspects of high-level cognition, and this work opens the door to deriving many such alignments directly from neural representational content.


title: AEKF-SLAM: A New Algorithm for Robotic Underwater Navigation

text: In this work, we focus on key topics related to underwater Simultaneous Localization and Mapping (SLAM) applications. Moreover, a detailed review of major studies in the literature and our proposed solutions for addressing the problem are presented. The main goal of this paper is the enhancement of the accuracy and robustness of the SLAM-based navigation problem for underwater robotics with low computational costs. Therefore, we present a new method called AEKF-SLAM that employs an Augmented Extended Kalman Filter (AEKF)-based SLAM algorithm. The AEKF-based SLAM approach stores the robot poses and map landmarks in a single state vector, while estimating the state parameters via a recursive and iterative estimation-update process. Hereby, the prediction and update state (which exist as well in the conventional EKF) are complemented by a newly proposed augmentation stage. Applied to underwater robot navigation, the AEKF-SLAM has been compared with the classic and popular FastSLAM 2.0 algorithm. Concerning the dense loop mapping and line mapping experiments, it shows much better performances in map management with respect to landmark addition and removal, which avoid the long-term accumulation of errors and clutters in the created map. Additionally, the underwater robot achieves more precise and efficient self-localization and a mapping of the surrounding landmarks with much lower processing times. Altogether, the presented AEKF-SLAM method achieves reliably map revisiting, and consistent map upgrading on loop closure.


title: Traffic management: a holistic approach to memory placement on NUMA systems

text: NUMA systems are characterized by Non-Uniform Memory Access times, where accessing data in a remote node takes longer than a local access. NUMA hardware has been built since the late 80's, and the operating systems designed for it were optimized for access locality. They co-located memory pages with the threads that accessed them, so as to avoid the cost of remote accesses. Contrary to older systems, modern NUMA hardware has much smaller remote wire delays, and so remote access costs per se are not the main concern for performance, as we discovered in this work. Instead, congestion on memory controllers and interconnects, caused by memory traffic from data-intensive applications, hurts performance a lot more. Because of that, memory placement algorithms must be redesigned to target traffic congestion. This requires an arsenal of techniques that go beyond optimizing locality. In this paper we describe Carrefour, an algorithm that addresses this goal. We implemented Carrefour in Linux and obtained performance improvements of up to 3.6 relative to the default kernel, as well as significant improvements compared to NUMA-aware patchsets available for Linux. Carrefour never hurts performance by more than 4% when memory placement cannot be improved. We present the design of Carrefour, the challenges of implementing it on modern hardware, and draw insights about hardware support that would help optimize system software on future NUMA systems.


title: Blockchain network slice broker in 5G: Slice leasing in factory of the future use case

text: 5G Network Slice Broker concept aims to enable mobile virtual network operators, over-the-top providers, and industry vertical market players to request and lease resources from infrastructure providers dynamically according to needs. In the future digital factory, the leasing of resources could also happen autonomously by manufacturing equipment. This paper presents Blockchain Slice Leasing Ledger Concept and analysis of its applicability in the Factory of the Future. The novel concept utilizes 5G Network Slice Broker in a Blockchain to reduce service creation time and enable manufacturing equipment autonomously and dynamically acquire the slice needed for more efficient operations.


title: Optimizing neighborhood projection with relaxation factor for inextensible cloth simulation

text: In this paper, we propose a novel method for inextensible cloth simulation. Our method introduces a neighborhood projection optimized with a relaxation factor. The neighborhood projection enforces inextensibility by modifying particle positions with a Jacobi-style iteration, leading to conservation of linear and angular quasi momenta. The relaxation factor is estimated according to the corrections and constraints, and is used to scale the corrections while keeping convergence to a smaller number of iterations. Experimental results demonstrate that our method increases the simulation efficiency, and stably handles inextensible cloth even in overconstrained situations. In addition to the simulation of hanging cloth and draping cloth, the simulated umbrella demonstrates the characters of our method for this type of objects.


title: Early-onset hidradenitis suppurativa.

text: A 9-year-old girl developed hidradenitis suppurativa 3 months after the first signs of adrenarche. Such a close temporal relationship is consistent with the hypothesis that the disease is androgen dependent. Less than 2% of patients have onset of the disease before the age of 11 years. The exceptionally early age of onset in our patient may be partly explained by the fact that she had an early puberty.


title: Prepubertal hidradenitis suppurativa: two case reports and review of the literature.

text: Hidradenitis suppurativa (HS) is a chronic suppurative scarring disease of apocrine sweat gland-bearing skin in the axillary, anogenital, and, rarely, the breast and scalp regions. Females are more commonly affected than males and it is usually seen at puberty or later. We report two girls with prepubertal hidradenitis suppurativa whose initial presentation predated any signs of puberty. This early onset is very rare and its etiology remains unknown. Severe disease can be seen in prepubertal children and surgical intervention is effective in these cases.


title: Finasteride for the treatment of hidradenitis suppurativa in children and adolescents.

text:
IMPORTANCE
Hidradenitis suppurativa (HS) is a chronic debilitating cutaneous disease for which there is no universally effective treatment. Patients typically present at puberty with tender subcutaneous nodules that can progress to dermal abscess formation. Antiandrogens have been used in the treatment of HS, and studies have primarily focused on adult patients.

OBSERVATIONS
We present a case series of 3 pediatric patients with HS who were successfully treated with oral finasteride, resulting in decreased frequency and severity of disease flares with no significant adverse effects.

CONCLUSIONS AND RELEVANCE
Finasteride is a therapeutic option that provides benefit for pediatric patients with HS. Further prospective data and randomized controlled studies will provide helpful information in the management of this disease.


title: Hidradenitis suppurativa in children and adolescents: a review of treatment options.

text: Hidradenitis suppurativa (HS) is a burdensome disease and has the potential to affect the life course of patients. It is a rare disease in children, and the recorded literature is correspondingly scarce. This article reviews the therapeutic options for HS in children and adolescents, and highlights particular differences or challenges with treating patients in this age group compared with adults. The work-up of paediatric patients with HS should include considerations of possible endocrine co-morbidities and obesity. Medical therapy of lesions may include topical clindamycin. Systemic therapy may include analgesics, clindamycin and rifampicin, finasteride, corticosteroids or tumour necrosis factor alpha (TNFα) blockers. Superinfections should be appropriately treated. Scarring lesions generally require surgery.


title: Morbidity in patients with hidradenitis suppurativa.

text:
BACKGROUND
Although skin diseases are often immediately visible to both patients and society, the morbidity they cause is only poorly defined. It has been suggested that quality-of-life measures may be a relevant surrogate measure of skin disease. Hidradenitis suppurativa (HS) leads to painful eruptions and malodorous discharge and is assumed to cause a significant degree of morbidity. The resulting impairment of life quality has not previously been quantitatively assessed, although such an assessment may form a pertinent measure of disease severity in HS.

OBJECTIVES
To measure the impairment of life quality in patients with HS.

METHODS
In total, 160 patients suffering from HS were approached. The following data were gathered: quality-of-life data (Dermatology Life Quality Index, DLQI questionnaire), basic demographic data, age at onset of the condition and the average number of painful lesions per month.

RESULTS
One hundred and fourteen patients participated in the study. The mean +/- SD age of the patients was 40.9 +/- 11.7 years, the mean +/- SD age at onset 21.8 +/- 9.9 years and the mean +/- SD duration of the disease 18.8 +/- 11.4 years. Patients had a mean +/- SD DLQI score of 8.9 +/- 8.3 points. The highest mean score out of the 10 DLQI questions was recorded for question 1, which measures the level of pain, soreness, stinging or itching (mean 1.55 points, median 2 points). Patients experienced a mean of 5.1 lesions per month.

CONCLUSIONS
HS causes a high degree of morbidity, with the highest scores obtained for the level of pain caused by the disease. The mean DLQI score for HS was higher than for previously studied skin diseases, and correlated with disease intensity as expressed by lesions per month. This suggests that the DLQI may be a relevant outcome measure in future therapeutic trials in HS.


title: Real-Time Mental Arithmetic Task Recognition From EEG Signals

text: Electroencephalography (EEG)-based monitoring the state of the user's brain functioning and giving her/him the visual/audio/tactile feedback is called neurofeedback technique, and it could allow the user to train the corresponding brain functions. It could provide an alternative way of treatment for some psychological disorders such as attention deficit hyperactivity disorder (ADHD), where concentration function deficit exists, autism spectrum disorder (ASD), or dyscalculia where the difficulty in learning and comprehending the arithmetic exists. In this paper, a novel method for multifractal analysis of EEG signals named generalized Higuchi fractal dimension spectrum (GHFDS) was proposed and applied in mental arithmetic task recognition from EEG signals. Other features such as power spectrum density (PSD), autoregressive model (AR), and statistical features were analyzed as well. The usage of the proposed fractal dimension spectrum of EEG signal in combination with other features improved the mental arithmetic task recognition accuracy in both multi-channel and one-channel subject-dependent algorithms up to 97.87% and 84.15% correspondingly. Based on the channel ranking, four channels were chosen which gave the accuracy up to 97.11%. Reliable real-time neurofeedback system could be implemented based on the algorithms proposed in this paper.