nils-herrmann/clean_scirepeval huggingface dataset:
Visualizing RFM Segmentation

Segmentation based on RFM (Recency, Frequency, and Monetary) has been used for over 50 years by direct marketers to target a subset of their customers, save mailing costs, and improve profits. RFM analysis is commonly performed using the Arthur Hughes method, which bins each of the three RFM attributes independently into five equal frequency bins. The resulting 125 cells are depicted in a tabular format or as bar graphs and analyzed by marketers, who determine the best cells (customer segments) to target. We propose an interactive visualization of RFM that helps marketers visualize and quickly identify important customer segments. Additionally, we show an integrated filtering approach that allows marketers to interactively explore the RFM segments in relation to other customer attributes, such as behavioral or demographic, to identify interesting subsegments in the customer base. We depict these RFM visualizations on two large real-world data sets and discuss how customers have used these visualizations in practice to glean interesting insights from their data. Given, the widespread use of RFM as a critical, and many times the only, segmentation tool, we believe that the proposed intuitive and interactive visualization will provide significant business value.
Matrix Profile VII: Time Series Chains: A New Primitive for Time Series Data Mining (Best Student Paper Award)

Since their introduction over a decade ago, time series motifs have become a fundamental tool for time series analytics, finding diverse uses in dozens of domains. In this work we introduce Time Series Chains, which are related to, but distinct from, time series motifs. Informally, time series chains are a temporally ordered set of subsequence patterns, such that each pattern is similar to the pattern that preceded it, but the first and last patterns are arbitrarily dissimilar. In the discrete space, this is similar to extracting the text chain "hit, hot, dot, dog" from a paragraph. The first and last words have nothing in common, yet they are connected by a chain of words with a small mutual difference. Time series chains can capture the evolution of systems, and help predict the future. As such, they potentially have implications for prognostics. In this work, we introduce a robust definition of time series chains, and a scalable algorithm that allows us to discover them in massive datasets.
Analytical Determination of the Phase Inductances of a Brushless DC Motor With Faulhaber Winding

This paper presents a method to analytically model the self and mutual phase inductances of a brushless dc motor with a Faulhaber winding. The method of images is used to “remove” the motor iron parts in the model in order to analyze the winding completely in the air. The analytical model is verified using 3-D finite-element method simulations and measurements.
From IT Addiction to Discontinued Use: A Cognitive Dissonance Perspective

One of the main topics discussed within the realm of the dark side of IT is addiction. IT addiction has been found to bring adverse consequences on users’ lives. In order to overcome the difficulties associated with IT addiction, interrupting and quitting addiction has become an important research agenda. Recent research findings have shown that IT addicts do not always feel guilty about their usage, and in many cases, they do not even perceive their usage as problematic. In this study, we draw on cognitive dissonance theory to theorize and propose a model showing that the degree of users’ cognitive dissonance can make a difference in their willingness to quit their IT addiction. We tested the model using data collected from 226 social network sites users. The analysis provided empirical support for our model and shed light on the mediation and moderation effects of cognitive dissonance in this process.
CodeAttention: translating source code to comments by exploiting the code constructs

Appropriate comments of code snippets provide insight for code functionality, which are helpful for program comprehension. However, due to the great cost of authoring with the comments, many code projects do not contain adequate comments. Automatic comment generation techniques have been proposed to generate comments from pieces of code in order to alleviate the human efforts in annotating the code.Most existing approaches attempt to exploit certain correlations (usually manually given) between code and generated comments, which could be easily violated if coding patterns change and hence the performance of comment generation declines. In addition, recent approaches ignore exploiting the code constructs and leveraging the code snippets like plain text. Furthermore, previous datasets are also too small to validate the methods and show their advantage. In this paper, we propose a new attention mechanism called CodeAttention to translate code to comments, which is able to utilize the code constructs, such as critical statements, symbols and keywords. By focusing on these specific points, CodeAttention could understand the semantic meanings of code better than previous methods. To verify our approach in wider coding patterns, we build a large dataset from open projects in GitHub. Experimental results in this large dataset demonstrate that the proposed method has better performance over existing approaches in both objective and subjective evaluation. We also perform ablation studies to determine effects of different parts in CodeAttention.
Beyond war and PTSD: The crucial role of transition stress in the lives of military veterans.

Although only a relatively small minority of military veterans develop Posttraumatic Stress Disorder (PTSD), mental health theory and research with military veterans has focused primarily on PTSD and its treatment. By contrast, many and by some accounts most veterans experience high levels of stress during the transition to civilian life, however transition stress has received scant attention. In this paper we attempt to address this deficit by reviewing the wider range of challenges, rewards, successes, and failures that transitioning veterans might experience, as well as the factors that might moderate these experiences. To illuminate this argument, we briefly consider what it means to become a soldier (i.e., what is required to transition into military service) and more crucially what kind of stressors veterans might experience when they attempt to shed that identity (i.e., what is required to transition out of military service). We end by suggesting how an expanded research program on veteran transition stress might move forward.
Parametric and nonparametric linkage analysis: a unified multipoint approach.

In complex disease studies, it is crucial to perform multipoint linkage analysis with many markers and to use robust nonparametric methods that take account of all pedigree information. Currently available methods fall short in both regards. In this paper, we describe how to extract complete multipoint inheritance information from general pedigrees of moderate size. This information is captured in the multipoint inheritance distribution, which provides a framework for a unified approach to both parametric and nonparametric methods of linkage analysis. Specifically, the approach includes the following: (1) Rapid exact computation of multipoint LOD scores involving dozens of highly polymorphic markers, even in the presence of loops and missing data. (2) Non-parametric linkage (NPL) analysis, a powerful new approach to pedigree analysis. We show that NPL is robust to uncertainty about mode of inheritance, is much more powerful than commonly used nonparametric methods, and loses little power relative to parametric linkage analysis. NPL thus appears to be the method of choice for pedigree studies of complex traits. (3) Information-content mapping, which measures the fraction of the total inheritance information extracted by the available marker data and points out the regions in which typing additional markers is most useful. (4) Maximum-likelihood reconstruction of many-marker haplotypes, even in pedigrees with missing data. We have implemented NPL analysis, LOD-score computation, information-content mapping, and haplotype reconstruction in a new computer package, GENEHUNTER. The package allows efficient multipoint analysis of pedigree data to be performed rapidly in a single user-friendly environment.
Electrical machine topologies and technologies for electric, hybrid, and fuel cell vehicles

This paper overviews various electrical machine technologies, including brushless permanent magnet, induction, switched reluctance, and brushless reluctance machines, with particular emphasis on the concept of hybrid electrical machine topologies, and compares their relative merits, in terms of torque and power density, operating speed range, over load capability, and efficiency, for application in electric, hybrid and fuel cell vehicles. electric vehicles.
Multi-Scale Patch-Based Image Restoration

Many image restoration algorithms in recent years are based on patch processing. The core idea is to decompose the target image into fully overlapping patches, restore each of them separately, and then merge the results by a plain averaging. This concept has been demonstrated to be highly effective, leading often times to the state-of-the-art results in denoising, inpainting, deblurring, segmentation, and other applications. While the above is indeed effective, this approach has one major flaw: the prior is imposed on intermediate (patch) results, rather than on the final outcome, and this is typically manifested by visual artifacts. The expected patch log likelihood (EPLL) method by Zoran and Weiss was conceived for addressing this very problem. Their algorithm imposes the prior on the patches of the final image, which in turn leads to an iterative restoration of diminishing effect. In this paper, we propose to further extend and improve the EPLL by considering a multi-scale prior. Our algorithm imposes the very same prior on different scale patches extracted from the target image. While all the treated patches are of the same size, their footprint in the destination image varies due to subsampling. Our scheme comes to alleviate another shortcoming existing in patch-based restoration algorithms-the fact that a local (patch-based) prior is serving as a model for a global stochastic phenomenon. We motivate the use of the multi-scale EPLL by restricting ourselves to the simple Gaussian case, comparing the aforementioned algorithms and showing a clear advantage to the proposed method. We then demonstrate our algorithm in the context of image denoising, deblurring, and super-resolution, showing an improvement in performance both visually and quantitatively.
Survey of anti-phishing tools with detection capabilities

Phishers have been continually changed their tricks and emerged novel variants for more security violations and causes of monetary losses in business organizations. The lack of existing anti-phishing solutions considered as an optimum anti-phishing is because of detection incapability specifically against novel phishes. This paper classifies the existing anti-phishing tools, identifies their detection incapability against several kinds of novel phishes and underscores the issues behind this problem. Further it suggests next wave of research to solve it. Targeting academic and industry researchers, this paper provide a valuable source of information to contribute the cyberspace with new products and fulfill the security flaws.
LEADER-MEMBER EXCHANGE AS A MEDIATOR OF THE RELATIONSHIP BETWEEN TRANSFORMATIONAL LEADERSHIP AND FOLLOWERS ’ PERFORMANCE AND ORGANIZATIONAL CITIZENSHIP BEHAVIOR

We developed a model in which leader-member exchange mediated between perceived transformational leadership behaviors and followers’ task performance and organizational citizenship behaviors. Our sample comprised 162 leader-follower dyads within organizations situated throughout the People’s Republic of China. We showed that leader-member exchange fully mediated between transformational leadership and task performance as well as organizational citizenship behaviors. Implications for the theory and practice of leadership are discussed, and future research directions offered.
A Hybrid Ant Colony Optimization Algorithm for Path Planning of Robot in Dynamic Environment

Ant colony optimization and artificial potential field were used respectively as global path planning and local path planning methods in this paper. Some modifications were made to accommodate ant colony optimization to path planning. Pheromone generated by ant colony optimization was also utilized to prevent artificial potential field from getting local minimum. Simulation results showed that the hybrid algorithm could satisfy the real-time demand. The comparison between ant colony optimization and genetic algorithm was also made in this paper.
A Semantic Framework for the Security Analysis of Ethereum smart contracts

Smart contracts are programs running on cryptocurrency (e.g., Ethereum) blockchains, whose popularity stem from the possibility to perform financial transactions, such as payments and auctions, in a distributed environment without need for any trusted third party. Given their financial nature, bugs or vulnerabilities in these programs may lead to catastrophic consequences, as witnessed by recent attacks. Unfortunately, programming smart contracts is a delicate task that requires strong expertise: Ethereum smart contracts are written in Solidity, a dedicated language resembling JavaScript, and shipped over the blockchain in the EVM bytecode format. In order to rigorously verify the security of smart contracts, it is of paramount importance to formalize their semantics as well as the security properties of interest, in particular at the level of the bytecode being executed. In this paper, we present the first complete small-step semantics of EVM bytecode, which we formalize in the F* proof assistant, obtaining executable code that we successfully validate against the official Ethereum test suite. Furthermore, we formally define for the first time a number of central security properties for smart contracts, such as call integrity, atomicity, and independence from miner controlled parameters. This formalization relies on a combination of hyperand safety properties. Along this work, we identified various mistakes and imprecisions in existing semantics and verification tools for Ethereum smart contracts, thereby demonstrating once more the importance of rigorous semantic foundations for the design of security verification techniques.
Automated Essay Scoring: A Literature Review

In recent decades, large-scale English language proficiency testing and testing research have seen an increased interest in constructed-response essay-writing items (Aschbacher, 1991; Powers, Burstein, Chodorow, Fowles, & Kukich, 2001; Weigle, 2002). The TOEFL iBT, for example, includes two constructed-response writing tasks, one of which is an integrative task requiring the test-taker to write in response to information delivered both aurally and in written form (Educational Testing Service, n.d.). Similarly, the IELTS academic test requires test-takers to write in response to a question that relates to a chart or graph that the test-taker must read and interpret (International English Language Testing System, n.d.). Theoretical justification for the use of such integrative, constructed-response tasks (i.e., tasks which require the test-taker to draw upon information received through several modalities in support of a communicative function) date back to at least the early 1960’s. Carroll (1961, 1972) argued that tests which measure linguistic knowledge alone fail to predict the knowledge and abilities that score users are most likely to be interested in, i.e., prediction of actual use of language knowledge for communicative purposes in specific contexts:
Bootstrapping the Linked Data Web

Most knowledge sources on the Data Web were extracted from structured or semi-structured data. Thus, they encompass solely a small fraction of the information available on the document-oriented Web. In this paper, we present BOA, an iterative bootstrapping strategy for extracting RDF from unstructured data. The idea behind BOA is to use the Data Web as background knowledge for the extraction of natural language patterns that represent predicates found on the Data Web. These patterns are used to extract instance knowledge from natural language text. This knowledge is nally fed back into the Data Web, therewith closing the loop. We evaluate our approach on two data sets using DBpedia as background knowledge. Our results show that we can extract several thousand new facts in one iteration with very high accuracy. Moreover, we provide the rst repository of natural language representations of predicates found on the Data Web.
Toward Ubiquitous Blood Pressure Monitoring via Pulse Transit Time: Predictions on Maximum Calibration Period and Acceptable Error Limits

Pulse transit time (PTT) is being widely pursued for ubiquitous blood pressure (BP) monitoring. PTT-based systems may require periodic cuff calibrations but can still be useful for hypertension screening by affording numerous out-of-clinic measurements that can be averaged. The objective was to predict the maximum calibration period that would not compromise accuracy and acceptable error limits in light of measurement averaging for PTT-based systems.  Well-known mathematical models and vast BP data were leveraged. Models relating PTT, age, and gender to BP were employed to determine the maximum time period for the PTT–BP calibration curve to change by <1 mmHg over physiological BP ranges for each age and gender. A model of within-person BP variability was employed to establish the screening accuracy of the conventional cuff-based approach. These models were integrated to investigate the screening accuracy of the average of numerous measurements of a PTT-based system in relation to the accuracy of its individual measurements.  The maximum calibration period was about 1 year for a 30 year old and declined linearly to about 6 months for a 70 year old. A PTT-based system with a precision error of >12 mmHg for systolic BP could achieve the screening accuracy of the cuff-based approach via measurement averaging.  This theoretical study indicates that PTT-based BP monitoring is viable even with periodic calibration and seemingly high measurement errors.   The predictions may help guide the implementation, evaluation, and application of PTT-based BP monitoring systems in practice.
Why people waive their Miranda rights: the power of innocence.

In a laboratory experiment, 72 participants who were guilty or innocent of a mock theft were apprehended for investigation. Motivated to avoid prosecution and trial, they were confronted by a neutral, sympathetic, or hostile male "detective" who sought a waiver of their Miranda rights. Later, 72 other participants watched videotapes of these sessions and answered questions about the detective and suspect. Strikingly, results showed that although the detective's demeanor had no effect, participants who were truly innocent were significantly more likely to sign a waiver than those who were guilty. Naively believing in the power of their innocence to set them free, most waived their rights even in the hostile detective condition, where the risk of interrogation was apparent. The conceptual and policy implications of these results are discussed.
Improving Business Process Quality through Exception Understanding, Prediction, and Prevention

Business process automation technologies are being increasingly used by many companies to improve the efficiency of both internal processes as well as of e-services offered to customers. In order to satisfy customers and employees, business processes need to be executed with a high and predictable quality. In particular, it is crucial for organizations to meet the Service Level Agreements (SLAs) stipulated with the customers and to foresee as early as possible the risk of missing SLAs, in order to set the right expectations and to allow for corrective actions. In this paper we focus on a critical issue in business process quality: that of analyzing, predicting and preventing the occurrence of exceptions, i.e., of deviations from the desired or acceptable behavior. We characterize the problem and propose a solution, based on data warehousing and mining techniques. We then describe the architecture and implementation of a tool suite that enables exception analysis, prediction, and prevention. Finally, we show experimental results obtained by using the tool suite to analyze internal HP processes.
Functional System and Areal Organization of a Highly Sampled Individual Human Brain

Resting state functional MRI (fMRI) has enabled description of group-level functional brain organization at multiple spatial scales. However, cross-subject averaging may obscure patterns of brain organization specific to each individual. Here, we characterized the brain organization of a single individual repeatedly measured over more than a year. We report a reproducible and internally valid subject-specific areal-level parcellation that corresponds with subject-specific task activations. Highly convergent correlation network estimates can be derived from this parcellation if sufficient data are collected-considerably more than typically acquired. Notably, within-subject correlation variability across sessions exhibited a heterogeneous distribution across the cortex concentrated in visual and somato-motor regions, distinct from the pattern of intersubject variability. Further, although the individual's systems-level organization is broadly similar to the group, it demonstrates distinct topological features. These results provide a foundation for studies of individual differences in cortical organization and function, especially for special or rare individuals.
Block-Sparse RPCA for Salient Motion Detection

Recent evaluation , [13] of representative background subtraction techniques demonstrated that there are still considerable challenges facing these methods. Challenges in realistic environment include illumination change causing complex intensity variation, background motions (trees, waves, etc.) whose magnitude can be greater than those of the foreground, poor image quality under low light, camouflage, etc. Existing methods often handle only part of these challenges; we address all these challenges in a unified framework which makes little specific assumption of the background. We regard the observed image sequence as being made up of the sum of a low-rank background matrix and a sparse outlier matrix and solve the decomposition using the Robust Principal Component Analysis method. Our contribution lies in dynamically estimating the support of the foreground regions via a motion saliency estimation step, so as to impose spatial coherence on these regions. Unlike smoothness constraint such as MRF, our method is able to obtain crisply defined foreground regions, and in general, handles large dynamic background motion much better. Furthermore, we also introduce an image alignment step to handle camera jitter. Extensive experiments on benchmark and additional challenging data sets demonstrate that our method works effectively on a wide range of complex scenarios, resulting in best performance that significantly outperforms many state-of-the-art approaches.