nils-herrmann/clean_scirepeval huggingface dataset:
Paying a price: culture, trust, and negotiation consequences.

Three studies contrasting Indian and American negotiators tested hypotheses derived from theory proposing why there are cultural differences in trust and how cultural differences in trust influence negotiation strategy. Study 1 (a survey) documented that Indian negotiators trust their counterparts less than American negotiators. Study 2 (a negotiation simulation) linked American and Indian negotiators' self-reported trust and strategy to their insight and joint gains. Study 3 replicated and extended Study 2 using independently coded negotiation strategy data, allowing for stronger causal inference. Overall, the strategy associated with Indian negotiators' reluctance to extend interpersonal (as opposed to institutional) trust produced relatively poor outcomes. Our data support an expanded theoretical model of negotiation, linking culture to trust, strategies, and outcomes.
Human Identification in Information Systems : Management Challenges and Public Policy Issues

Many information systems involve data about people. In order to reliably associate data with particular individuals, it is necessary that an effective and efficient identification scheme be established and maintained. There is remarkably little in the information technology literature concerning human identification. This paper seeks to overcome that deficiency, by undertaking a survey of human identity and human identification. The techniques discussed include names, codes, knowledge-based and token-based id, and biometrics. The key challenge to management is identified as being to devise a scheme which is practicable and economic, and of sufficiently high integrity to address the risks the organisation confronts in its dealings with people. It is proposed that much greater use be made of schemes which are designed to afford people anonymity, or enable them to use multiple identities or pseudonyms, while at the same time protecting the organisation's own interests. Multi-purpose and inhabitant registration schemes are described, and the recurrence of proposals to implement and extent them is noted. Public policy issues are identified. Of especial concern is the threat to personal privacy that the general-purpose use of an inhabitant registrant scheme represents. It is speculated that, where such schemes are pursued energetically, the reaction may be strong enough to threaten the social fabric.
Psychological Predictors of Problem Mobile Phone Use

Mobile phone use is banned or illegal under certain circumstances and in some jurisdictions. Nevertheless, some people still use their mobile phones despite recognized safety concerns, legislation, and informal bans. Drawing potential predictors from the addiction literature, this study sought to predict usage and, specifically, problematic mobile phone use from extraversion, self-esteem, neuroticism, gender, and age. To measure problem use, the Mobile Phone Problem Use Scale was devised and validated as a reliable self-report instrument, against the Addiction Potential Scale and overall mobile phone usage levels. Problem use was a function of age, extraversion, and low self-esteem, but not neuroticism. As extraverts are more likely to take risks, and young drivers feature prominently in automobile accidents, this study supports community concerns about mobile phone use, and identifies groups that should be targeted in any intervention campaigns.
Parallel Local Algorithms for Core, Truss, and Nucleus Decompositions

Finding the dense regions of a graph and relations among them is a fundamental task in network analysis. Nucleus decomposition is a principled framework of algorithms that generalizes the k-core and k-truss decompositions. It can leverage the higher-order structures to locate the dense subgraphs with hierarchical relations. Computation of the nucleus decomposition is performed in multiple steps, known as the peeling process, and it requires global information about the graph at any time. This prevents the scalable parallelization of the computation. Also, it is not possible to compute approximate and fast results by the peeling process, because it does not produce the densest regions until the algorithm is complete. In a previous work, Lu et al. proposed to iteratively compute the h-indices of vertex degrees to obtain the core numbers and prove that the convergence is obtained after a finite number of iterations. In this work, we generalize the iterative h-index computation for any nucleus decomposition and prove convergence bounds. We present a framework of local algorithms to obtain the exact and approximate nucleus decompositions. Our algorithms are pleasingly parallel and can provide approximations to explore time and quality trade-offs. Our shared-memory implementation verifies the efficiency, scalability, and effectiveness of our algorithms on real-world networks. In particular, using 24 threads, we obtain up to 4.04x and 7.98x speedups for k-truss and (3, 4) nucleus decompositions.
MATLAB/PSB based modeling and simulation of 25 kV AC railway traction system - a particular reference to loading and fault conditions

This paper presents the modeling and simulation of a 25 kV 50 Hz AC traction system using power system block set (PSB) / SIMULINK software package. The three-phase system with substations, track section with rectifier-fed DC locomotives and a detailed traction load are included in the model. The model has been used to study the effect of loading and fault conditions in 25 kV AC traction. The relay characteristic proposed is a combination of two quadrilaterals in the X-R plane. A brief summary of the hardware set-up used to implement and test the relay characteristic using a Texas Instruments TMS320C50 digital signal processor (DSP) has also been presented.
Data mining for discrimination discovery

In the context of civil rights law, discrimination refers to unfair or unequal treatment of people based on membership to a category or a minority, without regard to individual merit. Discrimination in credit, mortgage, insurance, labor market, and education has been investigated by researchers in economics and human sciences. With the advent of automatic decision support systems, such as credit scoring systems, the ease of data collection opens several challenges to data analysts for the fight against discrimination. In this article, we introduce the problem of discovering discrimination through data mining in a dataset of historical decision records, taken by humans or by automatic systems. We formalize the processes of direct and indirect discrimination discovery by modelling protected-by-law groups and contexts where discrimination occurs in a classification rule based syntax. Basically, classification rules extracted from the dataset allow for unveiling contexts of unlawful discrimination, where the degree of burden over protected-by-law groups is formalized by an extension of the lift measure of a classification rule. In direct discrimination, the extracted rules can be directly mined in search of discriminatory contexts. In indirect discrimination, the mining process needs some background knowledge as a further input, for example, census data, that combined with the extracted rules might allow for unveiling contexts of discriminatory decisions. A strategy adopted for combining extracted classification rules with background knowledge is called an inference model. In this article, we propose two inference models and provide automatic procedures for their implementation. An empirical assessment of our results is provided on the German credit dataset and on the PKDD Discovery Challenge 1999 financial dataset.
Evaluation of Deep Learning Frameworks Over Different HPC Architectures

Recent advances in deep learning have enabled researchers across many disciplines to uncover new insights about large datasets. Deep neural networks have shown applicability to image, time-series, textual, and other data, all of which are available in a plethora of research fields. However, their computational complexity and large memory overhead requires advanced software and hardware technologies to train neural networks in a reasonable amount of time. To make this possible, there has been an influx in development of deep learning software that aim to leverage advanced hardware resources. In order to better understand the performance implications of deep learning frameworks over these different resources, we analyze the performance of three different frameworks, Caffe, TensorFlow, and Apache SINGA, over several hardware environments. This includes scaling up and out with single-and multi-node setups using different CPU and GPU technologies. Notably, we investigate the performance characteristics of NVIDIA's state-of-the-art hardware technology, NVLink, and also Intel's Knights Landing, the most advanced Intel product for deep learning, with respect to training time and utilization. To our best knowledge, this is the first work concerning deep learning bench-marking with NVLink and Knights Landing. Through these experiments, we provide analysis of the frameworks' performance over different hardware environments in terms of speed and scaling. As a result of this work, better insight is given towards both using and developing deep learning tools that cater to current and upcoming hardware technologies.
Credit scoring using the clustered support vector machine

This work investigates the practice of credit scoring and introduces the use of the clustered support vector machine (CSVM) for credit scorecard development. This recently designed algorithm addresses some of the limitations noted in the literature that is associated with traditional nonlinear support vector machine (SVM) based methods for classification. Specifically, it is well known that as historical credit scoring datasets get large, these nonlinear approaches while highly accurate become computationally expensive. Accordingly, this study compares the CSVM with other nonlinear SVM based techniques and shows that the CSVM can achieve comparable levels of classification performance while remaining relatively cheap computationally.
COBIT and its utilization: a framework from the literature

The control objectives for information and related technology (COBIT) is a "trusted" open standard that is being used increasingly by a diverse range of organizations throughout the world. COBIT is arguably the most appropriate control framework to help an organization ensure alignment between use of information technology (IT) and its business goals, as it places emphasis on the business need that is satisfied by each control objective by J. Colbert, and P. Bowen (1996). This paper reports on the use of a simple classification of the published literature on COBIT, to highlight some of the features of that literature. The appropriate alignment between use of IT and the business goals of a organization is fundamental to efficient and effective IT governance. IT governance "...is the structure of relationships and processes to develop, direct and control IS/IT resources in order to achieve the enterprise's goals". IT governance has been recognized as a critical success factor in the achievement of corporate success by deploying information through the application of technology by N. Korac-Kakabadse and A. Kakabadse (2001). The importance of IT governance can be appreciated in light of the Gartner Group's finding that large organizations spend over 50% of their capital investment on IT by C. Koch (2002). However, research has suggested that the contribution of IT governance varies in its effectiveness. IT control frameworks are designed to promote effective IT governance. Recent pressures, including the failure of organizations such as Enron, have led to an increased focus on corporate accountability. For example, the Sarbanes-Oxley Act of 2002 introduced legislation that imposed new governance requirements by G. Coppin (2003). These and other changes have resulted in a new corporate governance model with an increased emphasis on IT governance, which goes beyond the traditional focus of corporate governance on financial aspects by R. Roussey (2003).
Identifying Important Citations Using Contextual Information from Full Text

In this paper we address the problem of classifying cited work into important and non-important to the developments presented in a research publication. This task is vital for the algorithmic techniques that detect and follow emerging research topics and to qualitatively measure the impact of publications in increasingly growing scholarly big data. We consider cited work as important to a publication if that work is used or extended in some way. If a reference is cited as background work or for the purpose of comparing results, the cited work is considered to be non-important. By employing five classification techniques (Support Vector Machine, Naïve Bayes, Decision Tree, K-Nearest Neighbors and Random Forest) on an annotated dataset of 465 citations, we explore the effectiveness of eight previously published features and six novel features (including context based, cue words based and textual based). Within this set, our new features are among the best performing. Using the Random Forest classifier we achieve an overall classification accuracy of 0.91 AUC.
Learning musical pitch structures with hierarchical hidden Markov models

In this paper we attempt to demonstrate the strengths of Hierarchical Hidden Markov Models (HHMMs) in the representation and modelling of musical structures. We show how relatively simple HHMMs, containing a minimum of expert knowledge, use their advantage of having multiple layers to perform well on tasks where flat Hidden Markov Models (HMMs) struggle. The examples in this paper show a HHMM’s performance at extracting higherlevel musical properties through the construction of simple pitch sequences, correctly representing the data set on which it was trained.
A Comparative Study of White Box, Black Box and Grey Box Testing Techniques

Software testing is the process to uncover requirement, design and coding errors in the program. It is used to identify the correctness, completeness, security and quality of software products against a specification. Software testing is the process used to measure the quality of developed computer software. It exhibits all mistakes, errors and flaws in the developed software. There are many approaches to software testing, but effective testing of complex product is essentially a process of investigation, not merely a matter of creating and following route procedure. It is not possible to find out all the errors in the program. This fundamental problem in testing thus throws an open question, as to what would be the strategy we should adopt for testing. In our paper, we have described and compared the three most prevalent and commonly used software testing techniques for detecting errors, they are: white box testing, black box testing and grey box testing. KeywordsBlack Box; Grey Box; White Box.
A survey on image segmentation

A~tract--For the past decade, many image segmentation techniques have been proposed. These segmentation techniques can be categorized into three classes, (I) characteristic feature thresholding or clustering, (2) edge detection, and (3) region extraction. This survey summarizes some of these techniques, in the area of biomedical image segmentation, most proposed techniques fall into the categories of characteristic feature thresholding or clustering and edge detection.
NOVAMENTE: An Integrative Architecture for Artificial General Intelligence

The Novamente AI Engine, a novel AI software system, is briefly reviewed. Unlike the majority of contemporary AI projects, Novamente is aimed at artificial general intelligence, rather than being restricted by design to one particular application domain, or to a narrow range of cognitive functions. Novamente integrates aspects of many prior AI projects and paradigms, including symbolic, neural-network, evolutionary programming and reinforcement learning approaches; but its overall architecture is unique, drawing on system-theoretic ideas regarding complex mental dynamics and associated emergent patterns.
Rule-based layout solving and its application to procedural interior generation

Due to the recent advancement in procedural generation techniques, games are presenting players with ever growing cities and terrains to explore. However most sandbox-style games situated in cities, do not allow players to wander into buildings. In past research, space planning techniques have already been utilized to generate suitable layouts for both building floor plans and room layouts. We introduce a novel rule-based layout solving approach, especially suited for use in conjunction with procedural generation methods. We show how this solving approach can be used for procedural generation by providing the solver with a userdefined plan. In this plan, users can specify objects to be placed as instances of classes, which in turn contain rules about how instances should be placed. This approach gives us the opportunity to use our generic solver in different procedural generation scenarios. In this paper, we will illustrate mainly with interior generation examples.
Mining Road Traffic Accident Data to Improve Safety on Road-related Factors for Classification and Prediction of Accident Severity

---------------------------------------------------------------------***--------------------------------------------------------------------Abstract Road Traffic Accident is very serious matter of life. The World Health Organization (WHO) reports that about 1.24 million people of the world die annually on the roads. The Institute for Health Metrics and Evaluation (IHME) estimated about 907,900, 1.3 million and 1.4 million deaths from road traffic injuries in 1990, 2010 and 2013, respectively. Uttar Pradesh in particular one of the state of India, experiences the highest rate of such accidents. Thus, methods to reduce accident severity are of great interest to traffic agencies and the public at large. In this paper, we applied data mining technologies to link recorded road characteristics to accident severity and developed a set of rules that could be used by the Indian Traffic Agency to improve safety and could help to save precious life.
Automated assessment of ESOL free text examinations

In this report, we consider the task of automated assessment of English as a Second Language (ESOL) examination scripts written in response to prompts eliciting free text answers. We review and critically evaluate previous work on automated assessment for essays, especially when applied to ESOL text. We formally define the task as discriminative preference ranking and develop a new system trained and tested on a corpus of manually-graded scripts. We show experimentally that our best performing system is very close to the upper bound for the task, as defined by the agreement between human examiners on the same corpus. Finally we argue that our approach, unlike extant solutions, is relatively prompt-insensitive and resistant to subversion, even when its operating principles are in the public domain. These properties make our approach significantly more viable for high-stakes assessment.
Skin Cancer Detection Using Digital Image Processing

Detection of skin cancer in the earlier stage is very Important and critical. In recent days, skin cancer is seen as one of the most Hazardous form of the Cancers found in Humans. Skin cancer is found in various types such as Melanoma, Basal and Squamous cell Carcinoma among which Melanoma is the most unpredictable. The detection of Melanoma cancer in early stage can be helpful to cure it. Computer vision can play important role in Medical Image Diagnosis and it has been proved by many existing systems. In this paper, we present a computer aided method for the detection of Melanoma Skin Cancer using Image processing tools. The input to the system is the skin lesion image and then by applying novel image processing techniques, it analyses it to conclude about the presence of skin cancer. The Lesion Image analysis tools checks for the various Melanoma parameters Like Asymmetry, Border, Colour, Diameter, (ABCD) etc. by texture, size and shape analysis for image segmentation and feature stages. The extracted feature parameters are used to classify the image as Normal skin and Melanoma cancer lesion.
Real Time Speed Estimation of Moving Vehicles from Side View Images from an Uncalibrated Video Camera

In order to estimate the speed of a moving vehicle with side view camera images, velocity vectors of a sufficient number of reference points identified on the vehicle must be found using frame images. This procedure involves two main steps. In the first step, a sufficient number of points from the vehicle is selected, and these points must be accurately tracked on at least two successive video frames. In the second step, by using the displacement vectors of the tracked points and passed time, the velocity vectors of those points are computed. Computed velocity vectors are defined in the video image coordinate system and displacement vectors are measured by the means of pixel units. Then the magnitudes of the computed vectors in image space should be transformed to the object space to find the absolute values of these magnitudes. This transformation requires an image to object space information in a mathematical sense that is achieved by means of the calibration and orientation parameters of the video frame images. This paper presents proposed solutions for the problems of using side view camera images mentioned here.
Dynamic feedback controller of Euler angles and wind parameters estimation for a quadrotor unmanned aerial vehicle

A nonlinear dynamic model for a quadrotor unmanned aerial vehicle is presented with a new vision of state parameter control which is based on Euler angles and open loop positions state observer. This method emphasizes on the control of roll, pitch and yaw angle rather than the translational motions of the UAV. For this reason the system has been presented into two cascade partial parts, the first one relates the rotational motion whose the control law is applied in a closed loop form and the other one reflects the translational motion. A dynamic feedback controller is developed to transform the closed loop part of the system into linear, controllable and decoupled subsystem. The wind parameters estimation of the quadrotor is used to avoid more sensors. Hence an estimator of resulting aerodynamic moments via Lyapunov function is developed. Performance and robustness of the proposed controller are tested in simulation.