nils-herrmann/clean_scirepeval huggingface dataset:
Support-Vector Networks

The support-vector network is a new learning machine for two-group classification problems. The machine conceptually implements the following idea: input vectors are non-linearly mapped to a very high-dimension feature space. In this feature space a linear decision surface is constructed. Special properties of the decision surface ensures high generalization ability of the learning machine. The idea behind the support-vector network was previously implemented for the restricted case where the training data can be separated without errors. We here extend this result to non-separable training data. High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated. We also compare the performance of the support-vector network to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
The curvelet transform for fusion of very-high resolution multi-spectral and panchromatic images

This paper presents a novel image fusion method, suitable for pan-sharpening of multispectral (MS) bands, based on multi-resolution analysis (MRA). The low-resolution MS bands are sharpened by injecting high-pass directional details extracted from the high-resolution panchromatic (Pan) image by means of the curvelet transform, which is a non-separable MRA, whose basis function are directional edges with progressively increasing resolution. The advantage with respect to conventional separable MRA, either decimated or not, is twofold: directional detail coefficients matching image edges may be preliminarily soft-thresholded to achieve denoising better than in the separable wavelet domain; modeling of the relationships between high-resolution detail coefficients of MS bands and of the Pan image is more fitting, being carried out in a directional wavelet domain. Experiments carried out on a very-high resolution MS + Pan QuickBird image show that the proposed curvelet method quantitatively outperforms state-of-the art image fusion methods, in terms of geometric, radiometric, and spectral fidelity.
An extension of Trust and TAM model with TPB in the initial adoption of on-line tax: An empirical study

While on-line tax is considered as a special type of e-service, the adoption rate of this service in Taiwan is still relatively low. The initial adoption of on-line tax is the important driving force to further influence the use and continued use of this service. The model of Trust and technology acceptance model (TAM) in Gefen et al. (2003a, MIS Quarterly 27(1), 51–90) has been well studied in on-line shopping and showed that understanding both the Internet technology and trust issue is important in determining behavioral intention to use. Besides, the diffusion of on-line tax could also be influenced by the potential antecedents such as individuals, organizational members, and social system while the issue for innovative technology is well discussed in Rogers (1995, The Diffusion of Innovation, fourth ed. Free Press, New York). Theory of planned behavior (TPB) is the model widely used to discuss the effect of these antecedents in behavioral intention. An extension of Trust and TAM model with TPB would be in more comprehensive manner to understand behavioral intention to use on-line tax. Furthermore, a large sample survey is used to empirically examine this framework.
Road scene analysis for determination of road traffic density

Road traffic density has always been a concern in large cities around the world, and many approaches were developed to assist in solving congestions related to slow traffic flow. This work proposes a congestion rate estimation approach that relies on real-time video scenes of road traffic, and was implemented and evaluated on eight different hotspots covering 33 different urban roads. The approach relies on road scene morphology for estimation of vehicles average speed along with measuring the overall video scenes randomness acting as a frame texture analysis indicator. Experimental results shows the feasibility of the proposed approach in reliably estimating traffic density and in providing an early warning to drivers on road conditions, thereby mitigating the negative effect of slow traffic flow on their daily lives.
Honey Bee Behavior Based Load Balancing of Tasks in Cloud Computing

Cloud computing has recently emerging technology getting popular day by day having wide scope in future. Cloud computing is defined as a large scale distributed computing paradigm that is driven by economics of scale in which a pool of abstracted virtualized energetically. The number of users in cloud computing is growing exponentially. Large number of user requests tries to designate the resources for many applications which along with to high load not far afield off from cloud server. Whenever certain VMs are overloaded then no more tasks should be send to overloaded virtual machine if under loaded virtual machines are available. For optimize solution and better response time the load has to be balanced among overloaded and under loaded virtual machines. In this paper, an algorithm is proposed named honey bee behavior based load balancing (HBB-LB), which targets to achieve well balanced load across virtual machine. The experimental results show that the algorithm has many advantages over existing algorithms. There is improvement in average execution time and reduction in waiting time of tasks. The paper also describes briefly about other existing load balancing approaches.
SQL Query Optimization Methods of Relational Database System

Oracle provides several options to aid performance, such as partitioning large tables, using materialized views, storing plan outlines, and many others. This chapter examines how DBAs can use these techniques to aid developers’ efforts to increase the efficiency of their application code. This chapter introduces the SQL Tuning Advisor to help you tune SQL statements. You can then use the recommendations of this advisor to rewrite poorly performing SQL code. I begin the chapter with a discussion of how to approach performance tuning. More than the specific performance improvement techniques you use, your approach to performance tuning determines your success in tuning a recalcitrant application system.
Rigor in Information Systems Positivist Case Research: Current Practices

Case research has commanded respect in the information systems (IS) discipline for at least a decade. Notwithstanding the relevance and potential value of case studies, this methodological approach was once considered to be one of the least systematic. Toward the end of the 1980s, the issue of whether IS case research was rigorously conducted was first raised. Researchers from our field (e.g., Benbasat et al. 1987; Lee 1989) and from other disciplines (e.g., Eisenhardt 1989; Yin 1994) called for more rigor in case research and, through theirrecommendations, contributed to the advancement of the case study methodology. Considering these contributions, the present study seeks to determine the extent to which the field of IS has advanced in its operational use of case study method. Precisely, it investigates the level of methodological rigor in positivist IS case research conducted over the past decade. To fulfill this objective, we identified and coded 183 case articles from seven major IS journals. Evaluation attributes or criteria considered in the present review focus on three main areas, namely, design issues, data collection, and data analysis. While the level of methodological rigor has experienced modest progress with respect to some specific attributes, the overall assessed rigor is somewhat equivocal and there are still significant areas for improvement. One of the keys is to include better documentation particularly regarding issues related to the data collection and
Load Balancing in Cloud Computing Environment Using Improved Weighted Round Robin Algorithm for Nonpreemptive Dependent Tasks.

Cloud computing uses the concepts of scheduling and load balancing to migrate tasks to underutilized VMs for effectively sharing the resources. The scheduling of the nonpreemptive tasks in the cloud computing environment is an irrecoverable restraint and hence it has to be assigned to the most appropriate VMs at the initial placement itself. Practically, the arrived jobs consist of multiple interdependent tasks and they may execute the independent tasks in multiple VMs or in the same VM's multiple cores. Also, the jobs arrive during the run time of the server in varying random intervals under various load conditions. The participating heterogeneous resources are managed by allocating the tasks to appropriate resources by static or dynamic scheduling to make the cloud computing more efficient and thus it improves the user satisfaction. Objective of this work is to introduce and evaluate the proposed scheduling and load balancing algorithm by considering the capabilities of each virtual machine (VM), the task length of each requested job, and the interdependency of multiple tasks. Performance of the proposed algorithm is studied by comparing with the existing methods.
Dissecting and Reassembling Color Correction Algorithms for Image Stitching

This paper introduces a new compositional framework for classifying color correction methods according to their two main computational units. The framework was used to dissect fifteen among the best color correction algorithms and the computational units so derived, with the addition of four new units specifically designed for this work, were then reassembled in a combinatorial way to originate about one hundred distinct color correction methods, most of which never considered before. The above color correction methods were tested on three different existing datasets, including both real and artificial color transformations, plus a novel dataset of real image pairs categorized according to the kind of color alterations induced by specific acquisition setups. Differently from previous evaluations, special emphasis was given to effectiveness in real world applications, such as image mosaicing and stitching, where robustness with respect to strong image misalignments and light scattering effects is required. Experimental evidence is provided for the first time in terms of the most recent perceptual image quality metrics, which are known to be the closest to human judgment. Comparative results show that combinations of the new computational units are the most effective for real stitching scenarios, regardless of the specific source of color alteration. On the other hand, in the case of accurate image alignment and artificial color alterations, the best performing methods either use one of the new computational units, or are made up of fresh combinations of existing units.
Text Mining for Documents Annotation and Ontology Support

This paper presents a survey of basic concepts in the area of text data mining and some of the methods used in order to elicit useful knowledge from collections of textual data. Three different text data mining techniques (clustering/visualisation, association rules and classification models) are analysed and its exploitation possibilities within the Webocracy project are showed. Clustering and association rules discovery are well suited as supporting tools for ontology management. Classification models are used for automatic documents annotation.
A real-time tracking system combining template-based and feature-based approaches

In this paper we propose a complete real-time model-based tracking system for piecewise-planar objects which combines template-based and feature-based approaches. Our contributions are an extension to the ESM algorithm (Benhimane and Malis, 2004) used for template-based tracking and the formulation of a feature-based tracking approach, which is specifically tailored for use in a real-time setting. In order to cope with highly dynamic scenarios, such as illumination changes, partial occlusions and fast object movement, the system adaptively switches between the template-based tracking, the feature-based tracking and a global initialization phase. Our tracking system achieves real-time performance by applying a coarse-to-fine optimization approach and includes means to detect a loss of track.
The impact of suicide on the family.

Little research has examined the consequences of a suicide for social or family networks. Because suicide occurs within families, the focus on the aftermath of suicide within families is an important next step to determine exactly how to help survivors. In this article, we review and summarize the research on the impact of suicide on individuals within families and on family and social networks. We begin with a discussion of family changes following suicide. Next, we discuss the effects of suicide on social networks overall and responses of children and the elderly to a suicide in the family. Finally, we identify key issues that remain to be resolved in family survivor research and make recommendations for future studies.
One-Step Time-Dependent Future Video Frame Prediction with a Convolutional Encoder-Decoder Neural Network

There is an inherent need for autonomous cars, drones, and other robots to have a notion of how their environment behaves and to anticipate changes in the near future. In this work, we focus on anticipating future appearance given the current frame of a video. Existing work focuses on either predicting the future appearance as the next frame of a video, or predicting future motion as optical flow or motion trajectories starting from a single video frame. This work stretches the ability of CNNs (Convolutional Neural Networks) to predict an anticipation of appearance at an arbitrarily given future time, not necessarily the next video frame. We condition our predicted future appearance on a continuous time variable that allows us to anticipate future frames at a given temporal distance, directly from the input video frame. We show that CNNs can learn an intrinsic representation of typical appearance changes over time and successfully generate realistic predictions at a deliberate time difference in the near future.
Example-Based Sketch Segmentation and Labeling Using CRFs

We introduce a new approach for segmentation and label transfer in sketches that substantially improves the state of the art. We build on successful techniques to find how likely each segment is to belong to a label, and use a Conditional Random Field to find the most probable global configuration. Our method is trained fully on the sketch domain, such that it can handle abstract sketches that are very far from 3D meshes. It also requires a small quantity of annotated data, which makes it easily adaptable to new datasets. The testing phase is completely automatic, and our performance is comparable to state-of-the-art methods that require manual tuning and a considerable amount of previous annotation [Huang et al. 2014].
A Simple Parallel Algorithm for the Maximal Independent Set Problem

Simple parallel algorithms for the maximal independent set (MIS) problem are presented. The first algorithm is a Monte Carlo algorithm with a very local property. The local property of this algorithm may make it a useful protocol design tool in distributed computing environments and artificial intelligence. One of the main contributions of this paper is the development of powerful and general techniques for converting Monte Carlo algorithms into deterministic algorithms. These techniques are used to convert the Monte Carlo algorithm for the MIS problem into a simple deterministic algorithm with the same parallel running time.
Design considerations and performance evaluation of 1200 V, 100 a SiC MOSFET based converter for high power density application

Silicon Carbide (SiC) MOSFET is capable of achieving better efficiency, power density and reliability of power converters due to its low on-state resistance, high temperature operation capability and lower switching losses compared to silicon (Si) IGBT. Operation of power converters at higher switching frequency using SiC devices allows reduction in filter size and hence improves the power to weight ratio of the converter. This paper presents switching characterization of 1200 V, 100 A SiC MOSFET module and compares efficiency of a Two Level Voltage Source Converter (2L-VSC) using SiC MOSFETs and Si IGBTs. Also, various design considerations of the 1200 V, 100 A SiC MOSFET based 2L-VSC including gate drive design, bus bar packaging and thermal management have been elaborated. The designed and developed 2L-VSC is operated to supply 35 kVA load at 20 kHz switching frequency with DC bus voltage at 800 V and the experimental results are presented.
Toward an integrated framework for innovation in service: A resource-based view and dynamic capabilities approach

Organizations today desire strategies that place them on the frontiers of service innovation. In this research, we provide a novel research framework that incorporates resources and capabilities and how they affect elements of service innovation. This research draws on the resource-based view of the firm to define firm’s internal resources and outside (i.e., relational) capabilities. Because firms compete in a dynamically changing market, we introduce dynamic service capabilities as three distinct processes that allow resources and relational capabilities to affect components of service innovation. Three propositions are introduced in addition to specific examples of promising future empirical work using service-oriented firms. This study aims to contribute to service innovation through a theoretical foundation in which we posit how firms develop their service innovation through a dynamic service capabilities framework. Contributions to theory and practice are discussed in addition to limitations to the study.
Improving Naive Bayes Classifier Using Conditional Probabilities

Naive Bayes classifier is the simplest among Bayesian Network classifiers. It has shown to be very efficient on a variety of data classification problems. However, the strong assumption that all features are conditionally independent given the class is often violated on many real world applications. Therefore, improvement of the Naive Bayes classifier by alleviating the feature independence assumption has attracted much attention. In this paper, we develop a new version of the Naive Bayes classifier without assuming independence of features. The proposed algorithm approximates the interactions between features by using conditional probabilities. We present results of numerical experiments on several real world data sets, where continuous features are discretized by applying two different methods. These results demonstrate that the proposed algorithm significantly improve the performance of the Naive Bayes classifier, yet at the same time maintains its robustness.
Decision support capabilities of enterprise content management systems: An empirical investigation

Enterprise content management (ECM) systems help organizations cope with the increasing complexity and volume of data and information. Despite the growing popularity of ECM, published literature indicates that organizations primarily use ECM for operational benefits, while the strategic decision making capabilities are rarely considered. Thus, the most significant rewards of ECM implementation may be largely forgone. This study investigates the potential of ECM technology for decision support. A research model is proposed and validated via an empirical investigation. The results show that ECM positively influences problem identification and definition, decision making speed and analysis, decision quality, and decision makers’ satisfaction.
Hidradenitis Suppurativa in Children Treated with Finasteride-A Case Series.

Hidradenitis suppurativa (HS) is rare in childhood, with only 2% of cases in patients younger than 11 years. It is a chronic, recurrent, debilitating condition for which no universally effective treatment has been developed. We present five cases of children with HS diagnosed between the ages of 6 and 11 years.



Patients were treated with oral finasteride 1 to 5 mg/day. All had entered puberty at the time of treatment initiation. All had normal laboratory results before starting treatment. The maximum duration of treatment was 24 months.



Four patients were female. Two were overweight. Three had been previously treated with oral antibiotics, and two of these with oral isotretinoin, with partial or no improvement. Overall improvement of the disease was observed in all patients, with a reduction of the frequency and intensity of the flares. No adverse effects were observed or reported during treatment.



Treatment of HS can be challenging. The options available include antimicrobials, immunosuppressants, hormonal therapies, lasers, and surgery. The authors report the largest series of children with HS treated with finasteride. The results support the use of finasteride as monotherapy for the treatment of this disease in children. Further studies are necessary to fully understand the role of this drug in the management of this disease.
Two-stage ELM for phishing Web pages detection using hybrid features

Increasing high volume phishing attacks are being encountered every day due to attackers’ high financial returns. Recently, there has been significant interest in applying machine learning for phishing Web pages detection. Different from literatures, this paper introduces predicted labels of textual contents to be part of the features and proposes a novel framework for phishing Web pages detection using hybrid features consisting of URL-based, Web-based, rule-based and textual content-based features. We achieve this framework by developing an efficient two-stage extreme learning machine (ELM). The first stage is to construct classification models on textual contents of Web pages using ELM. In particular, we take Optical Character Recognition (OCR) as an assistant tool to extract textual contents from image format Web pages in this stage. In the second stage, a classification model on hybrid features is developed by using a linear combination model-based ensemble ELMs (LC-ELMs), with the weights calculated by the generalized inverse. Experimental results indicate the proposed framework is promising for detecting phishing Web pages.
Multiple Kernel Fuzzy Clustering

While fuzzy c-means is a popular soft-clustering method, its effectiveness is largely limited to spherical clusters. By applying kernel tricks, the kernel fuzzy c-means algorithm attempts to address this problem by mapping data with nonlinear relationships to appropriate feature spaces. Kernel combination, or selection, is crucial for effective kernel clustering. Unfortunately, for most applications, it is uneasy to find the right combination. We propose a multiple kernel fuzzy c-means (MKFC) algorithm that extends the fuzzy c-means algorithm with a multiple kernel-learning setting. By incorporating multiple kernels and automatically adjusting the kernel weights, MKFC is more immune to ineffective kernels and irrelevant features. This makes the choice of kernels less crucial. In addition, we show multiple kernel k-means to be a special case of MKFC. Experiments on both synthetic and real-world data demonstrate the effectiveness of the proposed MKFC algorithm.