nils-herrmann/clean_scirepeval huggingface dataset:
A resource-based perspective on knowledge management capability and competitive advantage: an empirical investigation

The concept of knowledge management (KM) as a powerful competitive weapon has been strongly emphasized in the strategic management literature, yet the sustainability of the competitive advantage provided by KM capability is not well-explained. To fill this gap, this paper develops the concept of KM as an organizational capability and empirically examines the association between KM capabilities and competitive advantage. In order to provide a better presentation of significant relationships, through resource-based view of the firm explicitly recognizes important of KM resources and capabilities. Firm specific KM resources are classified as social KM resources, and technical KM resources. Surveys collected from 177 firms were analyzed and tested. The results confirmed the impact of social KM resource on competitive advantage. Technical KM resource is negatively related with competitive advantage, and KM capability is significantly related with competitive advantage.
SCOPE AND LIMITATION OF ELECTRONIC VOTING SYSTEM

Electronic Voting are now being performed using World Wide Web in many countries of the world due to this advancement a voter need not to visit the polling place. But has to just logging on the computer with an internet connection. Also, thi s voting requires an access code for the e-voting through the advance report of a voter. To reduce these disadvantages, we suggest a process in which a voter, who has the wireless certificate issued in advance, uses its own mobile phone for an e-voting without the unique r gistration for a vote. In this paper, a polling scheme by mean s of mobile technology is resented as most fundamental applicat ion of GSM based Personal Response System, which allows a vote r t cast his vote in simple and convenient way without the limit of time and location by integrating an electronic voting method with the GSM infrastructure.  Voting; Mobile Terminal; Confidentiali ty; Anonymity Full Text: http://www.ijcsmc.com/docs/papers/May2013/V2I5201354.pdf
Convolutional Sparse Coding for Trajectory Reconstruction

Trajectory basis Non-Rigid Structure from Motion (NRSfM) refers to the process of reconstructing the 3D trajectory of each point of a non-rigid object from just their 2D projected trajectories. Reconstruction relies on two factors: (i) the condition of the composed camera & trajectory basis matrix, and (ii) whether the trajectory basis has enough degrees of freedom to model the 3D point trajectory. These two factors are inherently conflicting. Employing a trajectory basis with small capacity has the positive characteristic of reducing the likelihood of an ill-conditioned system (when composed with the camera) during reconstruction. However, this has the negative characteristic of increasing the likelihood that the basis will not be able to fully model the object's “true” 3D point trajectories. In this paper we draw upon a well known result centering around the Reduced Isometry Property (RIP) condition for sparse signal reconstruction. RIP allow us to relax the requirement that the full trajectory basis composed with the camera matrix must be well conditioned. Further, we propose a strategy for learning an over-complete basis using convolutional sparse coding from naturally occurring point trajectory corpora to increase the likelihood that the RIP condition holds for a broad class of point trajectories and camera motions. Finally, we propose an 21 inspired objective for trajectory reconstruction that is able to “adaptively” select the smallest sub-matrix from an over-complete trajectory basis that balances (i) and (ii). We present more practical 3D reconstruction results compared to current state of the art in trajectory basis NRSfM.
pClass: An Effective Classifier for Streaming Examples

In this paper, a novel evolving fuzzy-rule-based classifier, termed parsimonious classifier (pClass), is proposed. pClass can drive its learning engine from scratch with an empty rule base or initially trained fuzzy models. It adopts an open structure and plug and play concept where automatic knowledge building, rule-based simplification, knowledge recall mechanism, and soft feature reduction can be carried out on the fly with limited expert knowledge and without prior assumptions to underlying data distribution. In this paper, three state-of-the-art classifier architectures engaging multi-input-multi-output, multimodel, and round robin architectures are also critically analyzed. The efficacy of the pClass has been numerically validated by means of real-world and synthetic streaming data, possessing various concept drifts, noisy learning environments, and dynamic class attributes. In addition, comparative studies with prominent algorithms using comprehensive statistical tests have confirmed that the pClass delivers more superior performance in terms of classification rate, number of fuzzy rules, and number of rule-base parameters.
Computer Security and Risky Computing Practices: A Rational Choice Perspective

Despite rapid technological advances in computer hardware and software, insecure behavior by individual computer users continues to be a significant source of direct cost and productivity loss. Why do individuals, many of whom are aware of the possible grave consequences of low-level insecure behaviors such as failure to backup work and disclosing passwords, continue to engage in unsafe computing practices? In this article we propose a conceptual model of this behavior as the outcome of a boundedly-rational choice process. We explore this model in a survey of undergraduate students (N = 167) at two large public universities. We asked about the frequency with which they engaged in five commonplace but unsafe computing practices, and probed their decision processes with regard to these practices. Although our respondents saw themselves as knowledgeable, competent users, and were broadly aware that serious consequences were quite likely to result, they reported frequent unsafe computing behaviors. We discuss the implications of these findings both for further research on risky computing practices and for training and enforcement policies that will be needed in the organizations these students will shortly be entering.
DappGuard : Active Monitoring and Defense for Solidity Smart Contracts

Ethereum’s smart contracts present an attractive incentive toward participating in the network. Deploying a smart contract allows a user to run a distributed application (Dapp) that includes storage, payment features, and cryptographic services all within the context of just a contract script and its layout. However, recently exploited vulnerabilities in the Solidity smart contract language have undermined the integrity of Ethereum’s smart contract implementations. After some discussion of previous work, we examine whether known vulnerabilities can be detected as attacks post factum from information available on the Ethereum blockchain. Then, we present findings on what information is available for a few selected contracts. Finally, we propose our design for a live monitoring and protection system based on our research findings, the prototypes we developed to gather data, and documented plans for extension.
TweetCred: Real-Time Credibility Assessment of Content on Twitter

During sudden onset crisis events, the presence of spam, rumors and fake content on Twitter reduces the value of information contained on its messages (or “tweets”). A possible solution to this problem is to use machine learning to automatically evaluate the credibility of a tweet, i.e. whether a person would deem the tweet believable or trustworthy. This has been often framed and studied as a supervised classification problem in an off-line (post-hoc) setting. In this paper, we present a semi-supervised ranking model for scoring tweets according to their credibility. This model is used in TweetCred , a real-time system that assigns a credibility score to tweets in a user’s timeline. TweetCred , available as a browser plug-in, was installed and used by 1,127 Twitter users within a span of three months. During this period, the credibility score for about 5.4 million tweets was computed, allowing us to evaluate TweetCred in terms of response time, effectiveness and usability. To the best of our knowledge, this is the first research work to develop a real-time system for credibility on Twitter, and to evaluate it on a user base of this size.
Change Detection in Satellite Images Using a Genetic Algorithm Approach

In this letter, we propose a novel method for unsupervised change detection in multitemporal satellite images by minimizing a cost function using a genetic algorithm (GA). The difference image computed from the multitemporal satellite images is partitioned into two distinct regions, namely, ¿changed¿ and ¿unchanged,¿ according to the binary change detection mask realization from the GA. For each region, the mean square error (MSE) between its difference image values and the average of its difference image values is calculated. The weighted sum of the MSE of the changed and unchanged regions is used as a cost value for the corresponding change detection mask realization. The GA is employed to find the final change detection mask with the minimum cost by evolving the initial realization of the binary change detection mask through generations. The proposed method is able to produce the change detection result on the difference image without a priori assumptions. Change detection results are shown on multitemporal Advanced Synthetic Aperture Radar images acquired by the ESA/Envisat satellite and on multitemporal optical images acquired by the Landsat multispectral scanner. The comparisons with the state-of-the-art change detection methods are provided.
N³ - A Collection of Datasets for Named Entity Recognition and Disambiguation in the NLP Interchange Format

Extracting Linked Data following the Semantic Web principle from unstructured sources has become a key challenge for scientific research. Named Entity Recognition and Disambiguation are two basic operations in this extraction process. One step towards the realization of the Semantic Web vision and the development of highly accurate tools is the availability of data for validating the quality of processes for Named Entity Recognition and Disambiguation as well as for algorithm tuning. This article presents three novel, manually curated and annotated corpora (N). All of them are based on a free license and stored in the NLP Interchange Format to leverage the Linked Data character of our datasets.
VITAL-The Virtual Environment Interaction Technique Abstraction Layer

Traditionally 3D interaction techniques (3DITs) are implemented in VR applications in a proprietary way on specific target platforms. Mixing 3DIT specific code with application code neither allows for reusability in other applications nor for exchanging 3DITs in a comfortable and flexible way. We propose an additional system software layer called Virtual Environment Interaction Technique Abstraction Layer (VITAL) targeted on platform and application independent (portable) 3DIT implementation. We describe the underlying concepts and provide details on how to integrate VITAL in VR frameworks. Furthermore, development mechanisms targeted on portability and general-purpose interfacing techniques with other system components are outlined and demonstrated in examples.
Vehicle Active Steering Control System Based on Human Mechanical Impedance Properties of the Arms

This paper presents the experimental data of human mechanical impedance properties (HMIPs) of the arms measured in steering operations according to the angle of a steering wheel (limbs posture) and the steering torque (muscle cocontraction). The HMIP data show that human stiffness/viscosity has the minimum/maximum value at the neutral angle of the steering wheel in relax (standard condition) and increases/decreases for the amplitude of the steering angle and the torque, and that the stability of the arms' motion in handling the steering wheel becomes high around the standard condition. Next, a novel methodology for designing an adaptive steering control system based on the HMIPs of the arms is proposed, and the effectiveness was then demonstrated via a set of double-lane-change tests, with several subjects using the originally developed stationary driving simulator and the 4-DOF driving simulator with a movable cockpit.
Using Phishing Experiments and Scenario-based Surveys to Understand Security Behaviours in Practice

Threats from social engineering can cause organisations severe damage if they are not considered and managed. In order to understand how to manage those threats, it is important to examine reasons why organisational employees fall victim to social engineering. In this paper, the objective is to understand security behaviours in practice by investigating factors that may cause an individual to comply with a request posed by a perpetrator. In order to attain this objective, we collect data through a scenario-based survey and conduct phishing experiments in three organisations. The results from the experiment reveal that the degree of target information in an attack increases the likelihood that an organisational employee fall victim to an actual attack. Further, an individual’s trust and risk behaviour significantly affects the actual behaviour during the phishing experiment. Computer experience at work, helpfulness and gender (females tend to be less susceptible to a generic attack than men), has a significant correlation with behaviour reported by respondents in the scenario-based survey. No correlation between the performance in the scenario-based survey and experiment was found. We argue that the result does not imply that one or the other method should be ruled out as they have both advantages and disadvantages which should be considered in the context of collecting data in the critical domain of information security. Discussions of the findings, implications and recommendations for future research are further provided.
VI-Tennis: a vibrotactile/audio exergame for players who are visually impaired

Lack of physical activity is a serious health concern for individuals who are visually impaired as they have fewer opportunities and incentives to engage in physical activities that provide the amounts and kinds of stimulation sufficient to maintain adequate fitness and to support a healthy standard of living. Exergames are video games that use physical activity as input and which have the potential to change sedentary lifestyles and associated health problems such as obesity. We identify that exergames have a number properties that could overcome the barriers to physical activity that individuals with visual impairments face. However, exergames rely upon being able to perceive visual cues that indicate to the player what input to provide. This paper presents VI Tennis, a modified version of a popular motion sensing exergame that explores the use of vibrotactile and audio cues. The effectiveness of providing multimodal (tactile/audio) versus unimodal (audio) cues was evaluated with a user study with 13 children who are blind. Children achieved moderate to vigorous levels of physical activity- the amount required to yield health benefits. No significant difference in active energy expenditure was found between both versions, though children scored significantly better with the tactile/audio version and also enjoyed playing this version more, which emphasizes the potential of tactile/audio feedback for engaging players for longer periods of time.
A moving-target defense strategy for Cloud-based services with heterogeneous and dynamic attack surfaces

Due to deep automation, the configuration of many Cloud infrastructures is static and homogeneous, which, while easing administration, significantly decreases a potential attacker's uncertainty on a deployed Cloud-based service and hence increases the chance of the service being compromised. Moving-target defense (MTD) is a promising solution to the configuration staticity and homogeneity problem. This paper presents our findings on whether and to what extent MTD is effective in protecting a Cloud-based service with heterogeneous and dynamic attack surfaces - these attributes, which match the reality of current Cloud infrastructures, have not been investigated together in previous works on MTD in general network settings. We 1) formulate a Cloud-based service security model that incorporates Cloud-specific features such as VM migration/snapshotting and the diversity/compatibility of migration, 2) consider the accumulative effect of the attacker's intelligence on the target service's attack surface, 3) model the heterogeneity and dynamics of the service's attack surfaces, as defined by the (dynamic) probability of the service being compromised, as an S-shaped generalized logistic function, and 4) propose a probabilistic MTD service deployment strategy that exploits the dynamics and heterogeneity of attack surfaces for protecting the service against attackers. Through simulation, we identify the conditions and extent of the proposed MTD strategy's effectiveness in protecting Cloud-based services. Namely, 1) MTD is more effective when the service deployment is dense in the replacement pool and/or when the attack is strong, and 2) attack-surface heterogeneity-and-dynamics awareness helps in improving MTD's effectiveness.
Deeper Depth Prediction with Fully Convolutional Residual Networks

This paper addresses the problem of estimating the depth map of a scene given a single RGB image. We propose a fully convolutional architecture, encompassing residual learning, to model the ambiguous mapping between monocular images and depth maps. In order to improve the output resolution, we present a novel way to efficiently learn feature map up-sampling within the network. For optimization, we introduce the reverse Huber loss that is particularly suited for the task at hand and driven by the value distributions commonly present in depth maps. Our model is composed of a single architecture that is trained end-to-end and does not rely on post-processing techniques, such as CRFs or other additional refinement steps. As a result, it runs in real-time on images or videos. In the evaluation, we show that the proposed model contains fewer parameters and requires fewer training data than the current state of the art, while outperforming all approaches on depth estimation. Code and models are publicly available.
FACETS: Adaptive Local Exploration of Large Graphs

Visualization is a powerful paradigm for exploratory data analysis. Visualizing large graphs, however, often results in excessive edges crossings and overlapping nodes. We propose a new scalable approach called FACETS that helps users adaptively explore large million-node graphs from a local perspective, guiding them to focus on nodes and neighborhoods that are most subjectively interesting to users. We contribute novel ideas to measure this interestingness in terms of how surprising a neighborhood is given the background distribution, as well as how well it matches what the user has chosen to explore. FACETS uses Jensen-Shannon divergence over information-theoretically optimized histograms to calculate the subjective user interest and surprise scores. Participants in a user study found FACETS easy to use, easy to learn, and exciting to use. Empirical runtime analyses demonstrated FACETS’s practical scalability on large real-world graphs with up to 5 million edges, returning results in fewer than 1.5 seconds.
A cloud based health insurance plan recommendation system: A user centered approach

The recent concept of ‘‘Health Insurance Marketplace’’ introduced to facilitate the purchase of health insurance by comparing different insurance plans in terms of price, coverage benefits, and quality designates a key role to the health insurance providers. Currently, the web based tools available to search for health insurance plans are deficient in offering personalized recommendations based on the coverage benefits and cost. Therefore, anticipating the users’ needs we propose a cloud based framework that offers personalized recommendations about the health insurance plans.We use theMulti-attribute Utility Theory (MAUT) to help users compare different health insurance plans based on coverage and cost criteria, such as: (a) premium, (b) co-pay, (c) deductibles, (d) co-insurance, and (e) maximum benefit offered by a plan. To overcome the issues arising possibly due to the heterogeneous data formats and different plan representations across the providers, we present a standardized representation for the health insurance plans. The plan information of each of the providers is retrieved using the Data as a Service (DaaS). The framework is implemented as Software as a Service (SaaS) to offer customized recommendations by applying a ranking technique for the identified plans according to the user specified criteria.
Analysis of the Bridged T-Coil Circuit Using the Extra-Element Theorem

The bridged T-coil (BTC) circuit is often employed to extend the bandwidth of a wideband amplifier beyond the transition frequency fT of the driver device. Although the optimal design solution is well known, extreme algebraic complexity has caused its derivation to remain arcane. This brief presents an analysis of the BTC circuit using the extra element theorem which breaks the overall problem down into a series of simple network analyses and produces the final result as the product of the transfer function without the "extra element" and a multiplicative correction factor. The design is completed by using pole-zero cancellation to reduce the order of the transfer functions
Joint Learning of Correlated Sequence Labelling Tasks Using Bidirectional Recurrent Neural Networks

The stream of words produced by Automatic Speech Recognition (ASR) systems is devoid of any punctuations and formatting. Most natural language processing applications usually expect segmented and well-formatted texts as input, which is not available in ASR output. This paper proposes a novel technique of jointly modelling multiple correlated tasks such as punctuation and capitalization using bidirectional recurrent neural networks, which leads to improved performance for each of these tasks. This method can be extended for joint modelling of any other correlated multiple sequence labelling tasks.
Blinking During and After Lying

We tested the hypothesis derived from eye blink literature that when liars experience cognitive demand, their lies would be associated with a decrease in eye blinks, directly followed by an increase in eye blinks when the demand has ceased after the lie is told. A total of 13 liars and 13 truth tellers lied or told the truth in a target period; liars and truth tellers both told the truth in two baseline periods. Their eye blinks during the target and baseline periods and directly after the target period (target offset period) were recorded. The predicted pattern (compared to the baseline periods, a decrease in eye blinks during the target period and an increase in eye blinks during the target offset period) was found in liars and was strikingly different from the pattern obtained in truth tellers. They showed an increase in eye blinks during the target period compared to the baseline periods, whereas their pattern of eye blinks in the target offset period did not differ from baseline periods. The implications for lie detection are discussed.