nils-herrmann/clean_scirepeval huggingface dataset:
Beyond concern - a privacy-trust-behavioral intention model of electronic commerce

Despite the recent economic downturn in the Internet and telecommunication sectors, electronic commerce (EC) will continue to grow and corporate Web sites will remain an important communication channel. However, legitimate concerns regarding privacy and trust remain potential obstacles to growth and important issues to both individuals and organizations. This study proposed and tested a theoretical model that considers an individual’s perceptions of privacy and how it relates to his or her behavioral intention to make an online transaction. An experiment that included over 200 subjects was conducted using two EC sites that differed only by the privacy dimensions of their notice, access, choice, and security. The results of this study suggested strong support for the model.
SOME METHODS FOR CLASSIFICATION AND ANALYSIS OF MULTIVARIATE OBSERVATIONS

The main purpose of this paper is to describe a process for partitioning an N-dimensional population into k sets on the basis of a sample. The process, which is called 'k-means,' appears to give partitions which are reasonably efficient in the sense of within-class variance. That is, if p is the probability mass function for the population, S = {S1, S2, * *, Sk} is a partition of EN, and ui, i = 1, 2, * , k, is the conditional mean of p over the set Si, then W2(S) = ff=ISi f z u42 dp(z) tends to be low for the partitions S generated by the method. We say 'tends to be low,' primarily because of intuitive considerations, corroborated to some extent by mathematical analysis and practical computational experience. Also, the k-means procedure is easily programmed and is computationally economical, so that it is feasible to process very large samples on a digital computer. Possible applications include methods for similarity grouping, nonlinear prediction, approximating multivariate distributions, and nonparametric tests for independence among several variables. In addition to suggesting practical classification methods, the study of k-means has proved to be theoretically interesting. The k-means concept represents a generalization of the ordinary sample mean, and one is naturally led to study the pertinent asymptotic behavior, the object being to establish some sort of law of large numbers for the k-means. This problem is sufficiently interesting, in fact, for us to devote a good portion of this paper to it. The k-means are defined in section 2.1, and the main results which have been obtained on the asymptotic behavior are given there. The rest of section 2 is devoted to the proofs of these results. Section 3 describes several specific possible applications, and reports some preliminary results from computer experiments conducted to explore the possibilities inherent in the k-means idea. The extension to general metric spaces is indicated briefly in section 4. The original point of departure for the work described here was a series of problems in optimal classification (MacQueen ) which represented special
Histopathological image classification with bilinear convolutional neural networks

The computer-aided quantitative analysis for histopathological images has attracted considerable attention. The stain decomposition on histopathological images is usually recommended to address the issue of co-localization or aliasing of tissue substances. Although the convolutional neural networks (CNN) is a popular deep learning algorithm for various tasks on histopathological image analysis, it is only directly performed on histopathological images without considering stain decomposition. The bilinear CNN (BCNN) is a new CNN model for fine-grained classification. BCNN consists of two CNNs, whose convolutional-layer outputs are multiplied with outer product at each spatial location. In this work, we propose a novel BCNN-based method for classification of histopathological images, which first decomposes histopathological images into hematoxylin and eosin stain components, and then perform BCNN on the decomposed images to fuse and improve the feature representation performance. The experimental results on the colorectal cancer histopathological image dataset with eight classes indicate that the proposed BCNN-based algorithm is superior to the traditional CNN.
Adolescent sexual risk behavior: a multi-system perspective.

Adolescents are at high risk for a number of negative health consequences associated with early and unsafe sexual activity, including infection with human immunodeficiency virus, other sexually transmitted diseases, and unintended pregnancy. As a result, researchers have attempted to identify those factors that influence adolescent sexual risk behavior so that meaningful prevention and intervention programs may be developed. We propose that research efforts so far have been hampered by the adoption of models and perspectives that are narrow and do not adequately capture the complexity associated with the adolescent sexual experience. In this article, we review the recent literature (i.e., 1990-1999) pertaining to the correlates of adolescent sexual risk-taking, and organize the findings into a multisystemic perspective. Factors from the self, family, and extrafamilial systems of influence are discussed. We also consider several methodological problems that limit the literature's current scope, and consider implications of the adoption of a multisystemic framework for future research endeavors. We conclude with a discussion of the implications of the available research for practitioners working to reduce sexual risk behavior among adolescents.
Psychological Stressors : Overview

Psychological stressors are social and physical environmental circumstances that challenge the adaptive capabilities and resources of an organism. These circumstances represent an extremely wide and varied array of different situations that possess both common and specific psychological and physical attributes. The challenge for theory, research, and practice is to abstract and understand the specific qualities and characteristics of environmental exposures that most strongly elicit noxious psychological and biological responses, which in turn can lead to serious mental and physical health problems over the life course. In the present article, historical perspectives and conceptual considerations are addressed first, which provides the context for the subsequent discussion of key issues for defining and assessing psychological stressors. Susceptibility to psychological stressors is subject to individual differences, which can alter the impact and adverse consequences of such environmental exposures, necessitating a discussion of these moderating influences as well. HISTORICAL AND GENERAL CONSIDERATIONS
Review on Text Mining Algorithms

Nowadays twitter microblog has become very popular in the conversation practice and in spreading awareness about various issues among the people. People share their short messages / tweets among their private / public social network. These messages are valuable for the number of tasks to identify hidden knowledge patterns from the discussions. Many research have been conducted on text classification. Text classification uses terms as features which can be grouped to vote for belongingness of a class. Text classification can be carried on twitter data and various machine learning algorithms can be used for feature based performance evaluation. In this context we have reviewed few papers taken from various sources like IEEE Xplore, ACM, Elsevier etc.
Robust Cognitive Beamforming With Bounded Channel Uncertainties

This paper studies the robust beamforming design for a multi-antenna cognitive radio (CR) network, which transmits to multiple secondary users (SUs) and coexists with a primary network of multiple users. We aim to maximize the minimum of the received signal-to-interference-plus-noise ratios (SINRs) of the SUs, subject to the constraints of the total SU transmit power and the received interference power at the primary users (PUs) by optimizing the beamforming vectors at the SU transmitter based on imperfect channel state information (CSI). To model the uncertainty in CSI, we consider a bounded region for both cases of channel matrices and channel covariance matrices. As such, the optimization is done while satisfying the interference constraints for all possible CSI error realizations. We shall first derive equivalent conditions for the interference constraints and then convert the problems into the form of semi-definite programming (SDP) with the aid of rank relaxation, which leads to iterative algorithms for obtaining the robust optimal beamforming solution. Results demonstrate the achieved robustness and the performance gain over conventional approaches and that the proposed algorithms can obtain the exact robust optimal solution with high probability.
The Impact of Mobile Phone Usage on Student Learning

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Accelerating Open vSwitch with Integrated GPU

With the fast development of Software Defined Networking (SDN) and network virtualization, software-based network virtual switches have emerged as a critical component to provide network services to VMs. Among virtual switches, Open vSwitch (OvS) is an open source virtual switch implementation commonly used and well-studied. Using Data Plane Development Kit (DPDK) with OvS to bypass the OS kernel and process packets in userspace provides tremendous performance benefits on general purpose platforms. Integrated GPUs, residing on the same die with the CPU on general purpose platforms, offering many advanced features such as on-chip interconnect CPU-GPU communication, and sharing physical/virtual memory, become a promising additional compute resource to further accelerate the OvS process. In this paper, we design and implement an inline GPU assisted OvS architecture, via offloading the expensive tuple space search to GPU and balancing switching processing between CPU and GPU. We evaluated the performance on an Intel® Xeon® processor of the E3-1575M v5 product family (code-name Skylake) with an integrated GT4e GPU. The results show that our proposed architecture improved the OvS throughput by 3x, compared to the optimized CPU-only OvS-DPDK implementation.
Adaptive Neuro-Fuzzy Inference System (ANFIS) Based Software Evaluation

Software metric is a measure of some property of a piece of software or its specifications. The goal is to obtain reproducible and quantifiable measurements, which may have several valuable applications in schedule and budget planning, effort and cost evaluation, quality assurance testing, software debugging, software performance optimization, and optimal personnel task assignments. Software effort evaluation is one of the most essential and crucial part of software project planning for which efficient effort metrics is required. Software effort evaluation is followed by software cost evaluation which is helpful for both customers and developers. Thus, efficiency of effort component of software is very essential. The algorithmic models are weak in estimating early effort evaluation with regards to uncertainty and imprecision in software projects. To overcome this problem, there are various machine learning methods. One of the methods is soft computing in which there are various methodologies viz., Artificial Neural Network, Fuzzy Logic, Evolutionary computation based Genetic Algorithm and Metaheuristic based Particle Swarm Optimization. These methods are good at solving real-world ambiguities. This paper highlights the design of an efficient software effort evaluation model using Adaptive Neuro-Fuzzy Inference System (ANFIS) for uncertain datasets and it shows that this technique significantly outperforms with sufficient results.
Smart Car Parking System Solution for the Internet of Things in Smart Cities

The Internet of Things (IoT) is able to connect billions of devices and services at anytime in any place, with various applications. Recently, the IoT became an emerging technology. One of the most significant current research discussion topics on the IoT is about the smart car parking. A modern urban city has over a million of cars on its roads but it does not have enough parking space. Moreover, most of the contemporary researchers propose management of the data on cloud. However, this method may be considered as an issue since the raw data is sent promptly from distributed sensors to the parking area via cloud and then received back after it is processed. This is considered as an expensive technique in terms of the data transmission as well as the energy cost and consumption. While the majority of proposed solutions address the problem of finding unoccupied parking space and ignore some other critical issues such as information about the nearest car parking and the roads traffic congestion, this paper goes beyond and proposes the alternative method. The paper proposes a smart car parking system that will assist users to solve the issue of finding a parking space and to minimise the time spent in searching for the nearest available car park. In addition, it provides users with roads traffic congestion status. Moreover, the proposed system collects the raw data locally and extracts features by applying data filtering and fusion techniques to reduce the transmitted data over the network. After that, the transformed data is sent to the cloud for processing and evaluating by using machine learning algorithms.
Analyzing temporal role based access control models

Today, Role Based Access Control (RBAC) is the de facto model used for advanced access control, and is widely deployed in diverse enterprises of all sizes. Several extensions to the authorization as well as the administrative models for RBAC have been adopted in recent years. In this paper, we consider the temporal extension of RBAC (TRBAC), and develop safety analysis techniques for it. Safety analysis is essential for understanding the implications of security policies both at the stage of specification and modification. Towards this end, in this paper, we first define an administrative model for TRBAC. Our strategy for performing safety analysis is to appropriately decompose the TRBAC analysis problem into multiple subproblems similar to RBAC. Along with making the analysis simpler, this enables us to leverage and adapt existing analysis techniques developed for traditional RBAC. We have adapted and experimented with employing two state of the art analysis approaches developed for RBAC as well as tools developed for software testing. Our results show that our approach is both feasible and flexible.
The peculiarities of knowledge management processes in SMEs: the case of Singapore

The objectives of this study are two-fold. The first is to examine the peculiarities of KM processes that are unique in SMEs from three perspectives, namely knowledge creation, knowledge sharing and knowledge reuse. Secondly, to identify enablers and impediments of these KM processes that influence the competitiveness of SMEs.  The study adopted a case study approach involving 21 participants comprising management staff and front-line employees from four Singaporean SMEs.  The SME owner, rather than the employees, was found to be the key source and creator of knowledge and the sole driver of the KM processes. In SMEs, knowledge creation takes the form of innovative customized solutions to meet customers’ needs; knowledge sharing occurs through cross functionality, overlapping roles, and facilitated by close physical proximity in open workspaces; and knowledge reuse is often made tacitly, where common knowledge is prevalently embedded within the KM processes of SMEs. The enablers of knowledge creation process rested largely on the owner’s innovativeness, creativity and ability to acquire knowledge of the industry. Knowledge sharing processes are enabled by the awareness of roles, mutual respect and the level of trust among employees within the SME while knowledge reuse is fostered by close proximity of employees and the willingness and openness of the owner to impart his knowledge. The lack of the above enablement factors mentioned will hinder these KM processes.  The study is limited by the fact that data was collected from four SMEs in Singapore. Furthermore, only a small sample of staff from these SMEs was interviewed. Hence the findings need to be interpreted in light of such a scope.  For SMEs, this research provides perspectives on the factors influencing KM processes, in particular, the importance of the owners’ knowledge and leadership, the flexibility and adaptability of the organization, and open culture to enable the capitalization of its knowledge assets to survive and stay competitive. For practitioners, this paper reinforces the importance of the management owners’ innovativeness, initiatives and support, and the level of social interaction and high level of trusts among employees in the SMEs to as enablers to effective KM processes in SMEs.  To deepen on-going knowledge management research on SMEs, this paper provides insights and rich context to the distinctness of KM processes in SMEs.
Frequency and Time Domain Design , Analysis and Implementation of a Multi-Gbps UWB Wilkinson Power Divider for 5 G New Spectrum and CAR Applications

5G new spectrum radio access should support data rates exceeding 10 Gbps in most of its applications. An Ultra Wide Band (UWB) Ultra-high data rate Wilkinson power divider up to 6.9 Gbps for 5G new spectrum and CAR applications is presented in this paper. The step by step design procedure, optimization and implementation of this Wilkinson power divider in 20–30 GHz are completely done to achieve the optimum performance. The final fabrication results show the average of −14 dB of input matching, −20 dB of isolation of isolated Ports, −4.2 dB of coupling in output ports (considering 2 SMA connectors and transitions in each path), and linear phase variation of outputs in the whole bandwidth of 20–30 GHz. During the design procedure, a new and very useful coaxial to microstrip transition in K-band is designed, analyzed, developed and fabricated to achieve the best results. Also a complete study of time domain analysis with ultra-high data rate signal is presented to minimize the total reflection coefficient caused by the partial reflections from several discontinuities. To complete and validate the final fabricated Wilkinson power divider in ultra-high data rate application in 5G new spectrum, the extracted results of UWB-IR impulse radio with modulated ultra-high data rate signal up to 7 Gbps and in 20–30 GHz bandwidth is completely done. The measured results show that this fabricated Wilkinson power divider can handle a periodic modulated signal up to 7 Gbps, which are valuable results for many applications in 5G and CAR systems.
Image change detection algorithms: a systematic survey

Detecting regions of change in multiple images of the same scene taken at different times is of widespread interest due to a large number of applications in diverse disciplines, including remote sensing, surveillance, medical diagnosis and treatment, civil infrastructure, and underwater sensing. This paper presents a systematic survey of the common processing steps and core decision rules in modern change detection algorithms, including significance and hypothesis testing, predictive models, the shading model, and background modeling. We also discuss important preprocessing methods, approaches to enforcing the consistency of the change mask, and principles for evaluating and comparing the performance of change detection algorithms. It is hoped that our classification of algorithms into a relatively small number of categories will provide useful guidance to the algorithm designer.
User-Oriented Context Suggestion

Recommender systems have been used in many domains to assist users' decision making by providing item recommendations and thereby reducing information overload. Context-aware recommender systems go further, incorporating the variability of users' preferences across contexts, and suggesting items that are appropriate in different contexts. In this paper, we present a novel recommendation task, "Context Suggestion", whereby the system recommends contexts in which items may be selected. We introduce the motivations behind the notion of context suggestion and discuss several potential solutions. In particular, we focus specifically on user-oriented context suggestion which involves recommending appropriate contexts based on a user's profile. We propose extensions of well-known context-aware recommendation algorithms such as tensor factorization and deviation-based contextual modeling and adapt them as methods to recommend contexts instead of items. In our empirical evaluation, we compare the proposed solutions to several baseline algorithms using four real-world data sets.
Comprehensive evaluation of early retention (fast charge loss within a few seconds) characteristics in tube-type 3-D NAND flash memory

A fast charge loss within a few seconds, which is referred to as early retention, was observed in tube-type 2y word-line stacked 3-D NAND flash memory for the first time, and the origin of the early retention was comprehensively evaluated. Using a fast-response pulse I-V system, the early retention characteristics from microseconds to seconds were thoroughly investigated, and the correlations with various program and erase levels were examined using solid and checkerboard patterns. Our findings indicate that the early retention mainly originates from the lateral charge loss through the shared charge trap layers and suggest that the program and erase levels should be balanced and optimized to reduce the early retention.