nils-herrmann/clean_scirepeval huggingface dataset:
Feature Analysis for Fake Review Detection through Supervised Classification

Nowadays, review sites are more and more confronted with the spread of misinformation, i.e., opinion spam, which aims at promoting or damaging some target businesses, by misleading either human readers, or automated opinion mining and sentiment analysis systems. For this reason, in the last years, several data-driven approaches have been proposed to assess the credibility of user-generated content diffused through social media in the form of on-line reviews. Distinct approaches often consider different subsets of characteristics, i.e., features, connected to both reviews and reviewers, as well as to the network structure linking distinct entities on the review-site in exam. This article aims at providing an analysis of the main review- and reviewer-centric features that have been proposed up to now in the literature to detect fake reviews, in particular from those approaches that employ supervised machine learning techniques. These solutions provide in general better results with respect to purely unsupervised approaches, which are often based on graph-based methods that consider relational ties in review sites. Furthermore, this work proposes and evaluates some additional new features that can be suitable to classify genuine and fake reviews. For this purpose, a supervised classifier based on Random Forests have been implemented, by considering both well-known and new features, and a large-scale labeled dataset from which all these features have been extracted. The good results obtained show the effectiveness of new features to detect in particular singleton fake reviews, and in general the utility of this study.
Multiple Text Document Summarization System using hybrid Summarization technique

Text Summarization plays an important role in the area of text mining and natural language processing. As the information resources are increasing tremendously, readers are overloaded with loads of information. Finding out the relevant data and manually summarizing it in short time is much more difficult, challenging and tedious task for a human being. Text Summarization aims to compress the source text into a shorter and concise form with preserving its information content and overall meaning. Summarization can be classified into two main categories i.e. extractive summarization and abstractive summarization. This paper presents a novel approach to generate abstractive summary from extractive summary using WordNet ontology. An experimental result shows the generated summary in well-compressed, grammatically correct and human readable format.
Repeatable Reverse Engineering with the Platform for Architecture-Neutral Dynamic Analysis

Billions of lines of computer code direct the flow of information that drives the world’s activities. This vast amount of code powers software programs that instruct systems to perform tasks as commonplace as word processing and as specialized as analyzing DNA sequence data. However, lurking within this benign software are critical vulnerabilities that cyber criminals exploit to steal or corrupt information. In addition, as new software versions, capabilities, and operating systems are introduced to the marketplace, older software code often becomes incompatible with new technology, rendering the software either ineffective or completely unusable. Although the U.S. government and businesses annually spend millions of dollars to recover from attacks that inject malicious software, or malware, into their computer systems and to keep their software operational, more effective analysis capabilities are still needed to enable rapid, successful diagnosis and resolution of software problems. Lincoln Laboratory researchers have created an open-source tool, the Platform for Architecture-Neutral Dynamic Analysis (PANDA), for analysts to use to quickly develop instrumentation that helps answer complex questions about software and that informs appropriate responses to malware intrusions.
Active defence through deceptive IPS

Modern security mechanisms such as Unified Threat Management (UTM), Next-Generation Firewalls and Security Information and Event Management (SIEM) have become more sophisticated over recent years, promising advanced security features and immediate mitigation of the most advanced threats. While this appears promising, in practice even this cutting-edge technology often fails to protect modern organisations as they are being targeted by attacks that were previously unknown to the security industry. Most security mechanisms are based on a database of previously known attack artefacts (signatures) and they will fail on slightly modified or new attacks. The need for threat intelligence is in complete contrast with the way current security solutions are responding to the threats they identify, as they immediately block them without attempting to acquire any further information. In this report, we present and evaluate a security mechanism that operates as an intrusion prevention system which uses honeypots to deceive an attacker, prevent a security breach and which allows the potential acquisition of intelligence on each intrusion attempt. a aThis article is published online by Computer Weekly as part of the 2017 Royal Holloway information security thesis series http://www.computerweekly.com/ehandbook/Active-defence-through-deceptive-IPS. It is based on an MSc dissertation written as part of the MSc in Information Security at the ISG, Royal Holloway, University of London. The full thesis is published on the ISG’s website at https://www.royalholloway.ac.uk/isg/.
Q-CP: Learning Action Values for Cooperative Planning

Research on multi-robot systems has demonstrated promising results in manifold applications and domains. Still, efficiently learning an effective robot behaviors is very difficult, due to unstructured scenarios, high uncertainties, and large state dimensionality (e.g, hyper-redundant and groups of robot). To alleviate this problem, we present Q-CP a cooperative model-based reinforcement learning algorithm, which exploits action values to both (1) guide the exploration of the state space and (2) generate effective policies. Specifically, we exploit Q-learning to attack the curse-of-dimensionality in the iterations of a Monte-Carlo Tree Search. We implement and evaluate Q-CP on different stochastic cooperative (general-sum) games: (1) a simple cooperative navigation problem among 3 robots, (2) a cooperation scenario between a pair of KUKA YouBots performing hand-overs, and (3) a coordination task between two mobile robots entering a door. The obtained results show the effectiveness of Q- CP in the chosen applications, where action values drive the exploration and reduce the computational demand of the planning process while achieving good performance.
A Simplified Iron Loss Model for Laminated Magnetic Cores

This paper presents a new method for the prediction of iron losses in laminated magnetic cores. The method is based on the evaluation of the magnetization curves and loop shapes by which the iron losses, including hysteresis, excess, and classical eddy-current losses, are calculated from the loop area. The purpose of the method is to omit the numerical solution of the nonlinear diffusion equation (resulting from Maxwell equations) while maintaining reasonable accuracy of the iron losses. Therefore, the proposed method determines, using simple concepts, three magnetic field strength components that are responsible for generating the iron losses. The magnetic field component of the hysteresis loss is calculated by a static hysteresis model. The magnetic field component of the excess loss is calculated using the statistical loss theory. Determining the magnetic field component of the classical eddy-current loss is based upon the assumption that the flux distribution in the steel sheet is uniform, but, here, the eddy-current loss is implicitly enforced to be dependent on the magnetic characteristics of the material. The proposed method is applied to the calculation of iron losses in an electrical steel when the accuracy, stability, and generality of the method are discussed.
CYC: Using Common Sense Knowledge to Overcome Brittleness and Knowledge Acquisition Bottlenecks

MC& CYC project is the building, over the coming decade, of a large knowledge base (or KB) of real world facts and heuristics and-as a part of the KB itself-methods for efficiently reasoning over the KB. As the title of this article suggests, our hypothesis is that the two major limitations to building large intelligent programs might be overcome by using such a system. We briefly illustrate how common sense reasoning and analogy can widen the knowledge acquisition bottleneck The next section (“How CYC Works”) illustrates how those same two abilities can solve problems of the type that stymie current expert systems. We then report how the project is being conducted currently: its strategic philosophy, its tactical methodology, and a case study of how we are currently putting that into practice. We conclude with a discussion of the project’s feasibility and timetable.
Machine Learning, Neural and Statistical Classification

Algorithms which construct classifiers from sample data -such as neural networks, radial basis functions, and decision trees -have attracted growing attention for their wide applicability. Researchers in the fields of Statistics, Artificial Intelligence, Machine Learning, Data Mining, and Pattern Recognition are continually introducing (or rediscovering) induction methods, and often publishing implementing code. It is natural for practitioners and potential users to wonder, "Which classification technique is best?", or more realistically, "What subset of methods tend to work well for a given type of dataset?". This book provides perhaps the best current answer to that question.
Insulators: exploiting transcriptional and epigenetic mechanisms

Insulators are DNA sequence elements that prevent inappropriate interactions between adjacent chromatin domains. One type of insulator establishes domains that separate enhancers and promoters to block their interaction, whereas a second type creates a barrier against the spread of heterochromatin. Recent studies have provided important advances in our understanding of the modes of action of both types of insulator. These new insights also suggest that the mechanisms of action of both enhancer blockers and barriers might not be unique to these types of element, but instead are adaptations of other gene-regulatory mechanisms.
Testing overconfidence bias in Pakistani stock market

Excessive trading phenomenon is contrary to the concept of traditional finance that is based on the rational expectation theorem and efficient market hypothesis. Therefore, this study is aimed at exploring the existence of overconfidence behavior in the stock market. The market-wide panel VAR model is used to investigate the lead–lag relationship between stock returns and turnover. Our results suggest that investors are overconfident in Pakistani stock market because turnover depends directly upon stock returns. The findings have important implications for investors and brokers for developing appropriate trading strategy.  Finance
Applying deep learning on electronic health records in Swedish to predict healthcare-associated infections

Detecting healthcare-associated infections pose a major challenge in healthcare. Using natural language processing and machine learning applied on electronic patient records is one approach that has been shown to work. However the results indicate that there was room for improvement and therefore we have applied deep learning methods. Specifically we implemented a network of stacked sparse auto encoders and a network of stacked restricted Boltzmann machines. Our best results were obtained using the stacked restricted Boltzmann machines with a precision of 0.79 and a recall of 0.88.
Radial-Flux Permanent-Magnet Hub Drives: A Comparison Based on Stator and Rotor Topologies

In this paper, the performances of electric vehicle (EV) in-wheel (hub) nonoverlap-winding permanent-magnet motors with different stator and rotor topologies are compared. The calculation of the frequency losses in the design optimization of the hub motors is specifically considered. Also, the effect of the slot and pole number choice that determines the winding factor of the machine is investigated. It is shown that the torque-per-copper-loss performance of the machine cannot be judged solely on the winding factor of the machine. Wide open stator slot machines with rectangular preformed coils and, hence, low manufacturing costs are found to perform better than expected. The design detail and test results of a prototype 10-kW water-cooled EV hub drive are presented. The test results confirm the finite-element-calculated results, specifically in the high-speed region of the drive where the rotor topology affects the constant power speed range.
An Analysis of Scale Invariance in Object Detection - SNIP

An analysis of different techniques for recognizing and detecting objects under extreme scale variation is presented. Scale specific and scale invariant design of detectors are compared by training them with different configurations of input data. By evaluating the performance of different network architectures for classifying small objects on ImageNet, we show that CNNs are not robust to changes in scale. Based on this analysis, we propose to train and test detectors on the same scales of an image-pyramid. Since small and large objects are difficult to recognize at smaller and larger scales respectively, we present a novel training scheme called Scale Normalization for Image Pyramids (SNIP) which selectively back-propagates the gradients of object instances of different sizes as a function of the image scale. On the COCO dataset, our single model performance is 45.7% and an ensemble of 3 networks obtains an mAP of 48.3%. We use off-the-shelf ImageNet-1000 pre-trained models and only train with bounding box supervision. Our submission won the Best Student Entry in the COCO 2017 challenge. Code will be made available at http://bit.ly/2yXVg4c.
New low-voltage class AB/AB CMOS op amp with rail-to-rail input/output swing

A new low-voltage CMOS Class AB/AB fully differential opamp with rail-to-rail input/output swing and supply voltage lower than two V/sub GS/ drops is presented. The scheme is based on combining floating-gate transistors and Class AB input and output stages. The op amp is characterized by low static power consumption and enhanced slew-rate. Moreover the proposed opamp does not suffer from typical reliability problems related to initial charge trapped in the floating-gate devices. Simulation and experimental results in 0.5-/spl mu/m CMOS technology verify the scheme operating with /spl plusmn/0.9-V supplies and close to rail-to-rail input and output swing.
Boosted Regression Active Shape Models

We present an efficient method of fitting a set of local feature models to an image within the popular Active Shape Model (ASM) framework . We compare two different types of non-linear boosted feature models trained using GentleBoost . The first type is a conventional feature detector classifier, which learns a discrimination function between the appearance of a feature and the local neighbourhood. The second local model type is a boosted regression predictor which learns the relationship between the local neighbourhood appearance and the displacement from the true feature location. At run-time the second regression model is much more efficient as only the current feature patch needs to be processed. We show that within the local iterative search of the ASM the local feature regression provides improved localisation on two publicly available human face test sets as well as increasing the search speed by a factor of eight.
A strategic approach to recharging lithium-sulphur batteries for long cycle life.

The success of rechargeable lithium-ion batteries has brought indisputable convenience to human society for the past two decades. However, unlike commercialized intercalation cathodes, high-energy-density sulphur cathodes are still in the stage of research because of the unsatisfactory capacity retention and long-term cyclability. The capacity degradation over extended cycles originates from the soluble polysulphides gradually diffusing out of the cathode region. Here we report an applicable way to recharge lithium-sulphur cells by a simple charge operation control that offers tremendous improvement with various lithium-sulphur battery systems. Adjusting the charging condition leads to long cycle life (over 500 cycles) with excellent capacity retention (>99%) by inhibiting electrochemical reactions along with severe polysulphide dissolution. This charging strategy and understanding of the reactions in different discharge steps will advance progress in the development of lithium-sulphur batteries.
Extended object tracking using IMM approach for a real-world vehicle sensor fusion system

Autonomous driving poses unique challenges for vehicle environment perception due to the complicated driving environment where the autonomous vehicle connects itself with surrounding objects. Precise tracking of the relevant dynamic traffic participants (e.g., vehicle/byciclist/pedestrian) becomes a key component for the task of comprehensive environmental perception and reliable scene understanding. It is necessary for vehicle trackers to treat the objects as extended (rigid) target, as opposed to traditional point target tracking (say, in aerospace applications). The extended object tracking is an extremely challenging problem in real world, due to high requirements of the object estimation on accuracy of kinematic/shape information, association robustness, model match on various target motion behaviors, and statistical property amicability (e.g., estimation consistency/covariance reliability). We present an extended object tracker — based on an interacting multiple model with unbiased mixing estimator for kinematic information at a specified tracking reference point, a truncated Gaussian scheme for shape (width/length/orientation) estimation, and a hierarchical association method according to both kinematic and shape information — to tackle all of the major challenges. Our special effort is put on handling an intriguing conflict between theory and practice: the so-called likelihood credibility issue. That is, the likelihood is expected to credibly reflect the data statistical probability but is actually distorted/drifting in real world systems, due to mainly artificial physics introduced in multiple-stage data processing. In this study, from systematic point of view, we design an interacting multiple model based extended object tracker with proper likelihood compensation in the statistically-distorted real world. It can be shown that the presented tracker can deliver an effective estimation performance in real road traffic of the imperfect world.
Literature Review: e-Leadership

This article reviews existing literature on e-leadership and the attendant concept of virtual teams. Current scholarship of e-leadership says the goals of leadership have not changed, but the new e-leader needs to implement those goals electronically on computer-mediated virtual teams that are dispersed over space and time. What is very different is that the e-leader may never physically meet one or more of the followers, and that the main communication medium is the computer. The new paradigm provides a range of new opportunities: the ability to instantly communicate one-on-one with employees, customers, and suppliers; the capability to use talent wherever it exists; the opportunity to enhance organizational performance by assembling better multi-functional teams, and to improve better customer satisfaction by using the ―follow the sun‖ methodology; the ability to cut costs; and, scope for better knowledge management. These can positively impact an organization‘s competitive advantage. However, e-leaders also have new challenges: how to bridge the physical distance from the followers; how to communicate effectively with far-flung team members; how to convey enthusiasm and inspire followers electronically; how to build trust with someone who may never see the leader; and so on. The article discusses what new skills the e-leader might require for success. It also examines the concept of the virtual team from various angles: structure, communication, degrees of virtuality, multi-cultural issues, trust-building, ethical issues, and so on. Finally, the technology that supports e-leadership and virtual teams is briefly discussed.
10 An Empirical Approach for the Evaluation of Voice User Interfaces

Nowadays, the convergence of devices, electronic computing, and massive media produces huge volumes of information, which demands the need for faster and more efficient interaction between users and information.  The methodology is scientifically based on principles in linguistics, psychology, and language technology (Cohen et al. 2004; San-Segundo et al., 2005). Given the limited input/output capabilities of mobile devices, speech presents an excellent way to enter and retrieve information either alone or in combination with other modalities. Furthermore, people with disabilities should be provided with a wide range of alternative interaction modalities other than the traditional screen-mouse based desktop computing devices. Whether the disability is temporary or permanent, people with reading difficulty, visual impairment, and/or any difficulty using a keyboard, or mouse can rely on speech as an alternate approach for information access.  Rita Mátrai, ISBN 978-953-307-084-1, pp. 270, May 2010, INTECH, Croatia, downloaded from SCIYO.COM
Invariant Image Recognition by Zernike Moments

This correspondence addresses the problem of rotation, scale, and translation invariant recognition of images. A new set of Manuscript received August 29, 1988; revised October 13, 1989. Recommended for acceptance by C. Y. Suen. This work was supported in part by the Defense Advanced Research Projects Agency under Grant MDAA. Khotanzad is with the Image Processing and Analysis Laboratory, Department of Electrical Engineering, Southern Methodist University, Dallas, TX 75275. Y. H. Hong was with the Image Processing and Analysis Laboratory, Department of Electrical Engineering, Southern Methodist University, Dallas, TX 75275. He is now with Texas Instruments Incorporated, Dallas, TX . 903-86-C-0182.
Development of Docking System for Mobile Robots Using Cheap Infrared Sensors

More and more researches on household mobile robots are being undertaken these days. Since commercial cleaning robots are now available for sale to the public, robots are being used in private homes. As the quality of household robots improves, docking stations, which have a variety of uses, including as automatic recharging ports, have become increasingly important. Generally in household robots, cheap infrared sensors are adopted as part of a homing system that is utilized in conjunction with a docking station, enabling the robot to approach and enter the dock successfully. However, systems that use cheap sensors cannot reach their full potential unless they also have a relatively accurate docking system. In this paper, we propose a homing system that utilizes cheap infrared sensors but that can operate in a broad region, and we suggest a passive docking mechanism that can compensate for docking errors. Experimental results using the proposed system show that the robot is able to home in at a radius of up to 2 m around the docking station. Also, the docking mechanism is able to compensate for an offset error of ±5 cm and an angle error of ±30°, and the probability of docking successfully on the first attempt is over 95%.