nils-herrmann/clean_scirepeval huggingface dataset:
Job Characteristics and Job Satisfaction: Understanding the Role of Enterprise Resource

Little research has examined the impacts of enterprise resource planning (ERP) systems implementation on job satisfaction. Based on a 12-month study of 2,794 employees in a telecommunications firm, we found that ERP system implementation moderated the relationships between three job characteristics (skill variety, autonomy, and feedback) and job satisfaction. Our findings highlight the key role that ERP system implementation can have in altering wellestablished relationships in the context of technology-enabled organizational change situations. This work also extends research on technology diffusion by moving beyond a focus on technology-centric outcomes, such as system use, to understanding broader job outcomes. Carol Saunders was the accepting senior editor for this paper.
Predicting the Analysis of Heart Disease Symptoms Using Medicinal Data Mining Methods

Medicinal data mining methods are used to analyze the medical data information resources. Medical data mining content mining and structure methods are used to analyze the medical data contents. The effort to develop knowledge and experience of frequent specialists and clinical selection data of patients collected in databases to facilitate the diagnosis process is considered a valuable option. Diagnosis of heart disease is a significant and tedious task in medicine. The term Heart disease encompasses the various diseases that affect the heart. The exposure of heart disease from various factors or symptom is an issue which is not complimentary from false presumptions often accompanied by unpredictable effects. Association rule mining procedures are used to extract item set relations. Item set regularities are used in the rule mining process. The data classification is based on MAFIA algorithms which result in accuracy, the data is evaluated using entropy based cross validations and partition techniques and the results are compared. Here using the C4.5 algorithm as the training algorithm to show rank of heart attack with the decision tree. Finally, the heart disease database is clustered using the K-means clustering algorithm, which will remove the data applicable to heart attack from the database. The results showed that the medicinal prescription and designed prediction system is capable of prophesying the heart attack successfully.
SENSOR-BASED OCCUPANCY BEHAVIORAL PATTERN RECOGNITION FOR ENERGY AND COMFORT MANAGEMENT IN INTELLIGENT BUILDINGS

There has been extensive research focusing on developing smart environments by integrating data mining techniques into environments that are equipped with sensors and actuators. The ultimate goal is to reduce the energy consumption in buildings while maintaining a maximum comfort level for occupants. However, there are few studies successfully demonstrating energy savings from occupancy behavioural patterns that have been learned in a smart environment because of a lack of a formal connection to building energy management systems. In this study, the objective is to develop and implement algorithms for sensor-based modelling and prediction of user behaviour in intelligent buildings and connect the behavioural patterns to building energy and comfort management systems through simulation tools. The results are tested on data from a room equipped with a distributed set of sensors, and building simulations through EnergyPlus suggest potential energy savings of 30% while maintaining an indoor comfort level when compared with other basic energy savings HVAC control strategies.
Standards of Care for the Health of Transsexual, Transgender, and Gender-Nonconforming People, Version 7

The Standards of Care (SOC) for the Health of Transsexual, Transgender, and Gender Nonconforming People is a publication of the World Professional Association for Transgender Health (WPATH). The overall goal of the SOC is to provide clinical guidance for health professionals to assist transsexual, transgender, and gender nonconforming people with safe and effective pathways to achieving lasting personal comfort with their gendered selves, in order to maximize their overall health, psychological well-being, and self-fulfillment. This assistance may include primary care, gynecologic and urologic care, reproductive options, voice and communication therapy, mental health services (e.g., assessment, counseling, psychotherapy), and hormonal and surgical treatments. The SOC are based on the best available science and expert professional consensus. Because most of the research and experience in this field comes from a North American and Western European perspective, adaptations of the SOC to other parts of the world are necessary. The SOC articulate standards of care while acknowledging the role of making informed choices and the value of harm reduction approaches. In addition, this version of the SOC recognizes that treatment for gender dysphoria i.e., discomfort or distress that is caused by a discrepancy between persons gender identity and that persons sex assigned at birth (and the associated gender role and/or primary and secondary sex characteristics) has become more individualized. Some individuals who present for care will have made significant self-directed progress towards gender role changes or other resolutions regarding their gender identity or gender dysphoria. Other individuals will require more intensive services. Health professionals can use the SOC to help patients consider the full range of health services open to them, in accordance with their clinical needs and goals for gender expression.
How the brain uses time to represent and process visual information

Information theory provides a theoretical framework for addressing fundamental questions concerning the nature of neural codes. Harnessing its power is not straightforward, because of the differences between mathematical abstractions and laboratory reality. We describe an approach to the analysis of neural codes that seeks to identify the informative features of neural responses, rather than to estimate the information content of neural responses per se. Our analysis, applied to neurons in primary visual cortex (V1), demonstrates that the informative precision of spike times varies with the stimulus modality being represented. Contrast is represented by spike times on the shortest time scale, and different kinds of pattern information are represented on longer time scales. The interspike interval distribution has a structure that is unanticipated from the firing rate. The significance of this structure is not that it contains additional information, but rather, that it may provide a means for simple synaptic mechanisms to decode the information that is multiplexed within a spike train. Extensions of this analysis to the simultaneous responses of pairs of neurons indicate that neighboring neurons convey largely independent information, if the decoding process is sensitive to the neuron of origin and not just the average firing rate. In summary, stimulus-related information is encoded into the precise times of spikes fired by V1 neurons. Much of this information would be obscured if individual spikes were merely taken to be estimators of the firing rate. Additional information would be lost by averaging across the responses of neurons in a local population. We propose that synaptic mechanisms sensitive to interspike intervals and dendritic processing beyond simple summation exist at least in part to enable the brain to take advantage of this extra information.
A Survey of Payload-Based Traffic Classification Approaches

Internet traffic classification has been the subject of intensive study since the birth of the Internet itself. Indeed, the evolution of approaches for traffic classification can be associated with the evolution of the Internet itself and with the adoption of new services and the emergence of novel applications and communication paradigms. Throughout the years many approaches have been proposed for addressing technical issues imposed by such novel services. Deep-Packet Inspection (DPI) has been a very important research topic within the traffic classification field and its concept consists of the analysis of the contents of the captured packets in order to accurately and timely discriminate the traffic generated by different Internet protocols. DPI was devised as a means to address several issues associated with port-based and statistical-based classification approaches in order to achieve an accurate and timely traffic classification. Many research works proposed different DPI schemes while many open-source modules have also become available for deployment. Surveys become then valuable tools for performing an overall analysis, study and comparison between the several proposed methods. In this paper we present a survey in which a complete and thorough analysis of the most important open-source DPI modules is performed. Such analysis comprises an evaluation of the classification accuracy, through a common set of traffic traces with ground truth, and of the computational requirements. In this manner, this survey presents a technical assessment of DPI modules and the analysis of the obtained evaluation results enable the proposal of general guidelines for the design and implementation of more adequate DPI modules.
An Effect Size Primer : A Guide for Clinicians and Researchers

Increasing emphasis has been placed on the use of effect size reporting in the analysis of social science data. Nonetheless, the use of effect size reporting remains inconsistent, and interpretation of effect size estimates continues to be confused. Researchers are presented with numerous effect sizes estimate options, not all of which are appropriate for every research question. Clinicians also may have little guidance in the interpretation of effect sizes relevant for clinical practice. The current article provides a primer of effect size estimates for the social sciences. Common effect sizes estimates, their use, and interpretations are presented as a guide for researchers.
A Hybrid Approach for Automatic Classification of Brain MRI Using Genetic Algorithm and Support Vector Machine

We purpose a hybrid approach for classification of brain tissues in magnetic resonance images (MRI) based on genetic algorithm (GA) and support vector machine (SVM). A wavelet based texture feature set is derived. The optimal texture features are extracted from normal and tumor regions by using spatial gray level dependence method (SGLDM). These features are given as input to the SVM classifier. The choice of features, which constitute a big problem in classification techniques, is solved by using GA. These optimal features are used to classify the brain tissues into normal, benign or malignant tumor. The performance of the algorithm is evaluated on a series of brain tumor images.
Discrepancy and Error in Radiology: Concepts, Causes and Consequences

In all branches of medicine, there is an inevitable element of patient exposure to problems arising from human error, and this is increasingly the subject of bad publicity, often skewed towards an assumption that perfection is achievable, and that any error or discrepancy represents a wrong that must be punished. Radiology involves decision-making under conditions of uncertainty, and therefore cannot always produce infallible interpretations or reports. The interpretation of a radiologic study is not a binary process; the “answer” is not always normal or abnormal, cancer or not. The final report issued by a radiologist is influenced by many variables, not least among them the information available at the time of reporting. In some circumstances, radiologists are asked specific questions (in requests for studies) which they endeavour to answer; in many cases, no obvious specific question arises from the provided clinical details (e.g. “chest pain”, “abdominal pain”), and the reporting radiologist must strive to interpret what may be the concerns of the referring doctor. (A friend of one of the authors, while a resident in a North American radiology department, observed a staff radiologist dictate a chest x-ray reporting stating “No evidence of leprosy”. When subsequently confronted by an irate respiratory physician asking for an explanation of the seemingly-perverse report, he explained that he had no idea what the clinical concerns were, as the clinical details section of the request form had been left blank).
Efficient Gradient Boosted Decision Tree Training on GPUs

In this paper, we present a novel parallel implementation for training Gradient Boosting Decision Trees (GBDTs) on Graphics Processing Units (GPUs). Thanks to the wide use of the open sourced XGBoost library, GBDTs have become very popular in recent years and won many awards in machine learning and data mining competitions. Although GPUs have demonstrated their success in accelerating many machine learning applications, there are a series of key challenges of developing a GPU-based GBDT algorithm, including irregular memory accesses, many small sorting operations and varying data parallel granularities in tree construction. To tackle these challenges on GPUs, we propose various novel techniques (including Run-length Encoding compression and thread/block workload dynamic allocation, and reusing intermediate training results for efficient gradient computation). Our experimental results show that our algorithm named GPU-GBDT is often 10 to 20 times faster than the sequential version of XGBoost, and achieves 1.5 to 2 times speedup over a 40 threaded XGBoost running on a relatively high-end workstation of 20 CPU cores. Moreover, GPU-GBDT outperforms its CPU counterpart by 2 to 3 times in terms of performance-price ratio.
DATA WAREHOUSING AND DATA MINING-A CASE STUDY

This paper shows design and implementation of data warehouse as well as the use of data mining algorithms for the purpose of knowledge discovery as the basic resource of adequate business decision making process. The project is realized for the needs of Student's Service Department of the Faculty of Organizational Sciences (FOS), University of Belgrade, Serbia and Montenegro. This system represents a good base for analysis and predictions in the following time period for the purpose of quality business decision-making by top management. Thus, the first part of the paper shows the steps in designing and development of data warehouse of the mentioned business system. The second part of the paper shows the implementation of data mining algorithms for the purpose of deducting rules, patterns and knowledge as a resource for support in the process of decision making.
Insights and Approaches for Low-Complexity 5G Small-Cell Base-Station Design for Indoor Dense Networks

This paper investigates low-complexity approaches to small-cell base-station (SBS) design, suitable for future 5G millimeter-wave (mmWave) indoor deployments. Using large-scale antenna systems and high-bandwidth spectrum, such SBS can theoretically achieve the anticipated future data bandwidth demand of 10000 fold in the next 20 years. We look to exploit small cell distances to simplify SBS design, particularly considering dense indoor installations. We compare theoretical results, based on a link budget analysis, with the system simulation of a densely deployed indoor network using appropriate mmWave channel propagation conditions. The frequency diverse bands of 28 and 72 GHz of the mmWave spectrum are assumed in the analysis. We investigate the performance of low-complexity approaches using a minimal number of antennas at the base station and the user equipment. Using the appropriate power consumption models and the state-of-the-art sub-component power usage, we determine the total power consumption and the energy efficiency of such systems. With mmWave being typified nonline-of-sight communication, we further investigate and propose the use of direct sequence spread spectrum as a means to overcome this, and discuss the use of multipath detection and combining as a suitable mechanism to maximize link reliability.
Asymptotic Properties of Nearest Neighbor Rules Using Edited Data

The convergence properties of a nearest neighbor rule that uses an editing procedure to reduce the number of preclassified samples and to improve the performance of the rule are developed. Editing of the preclassified samples using the three-nearest neighbor rule followed by classification using the single-nearest neighbor rule with the remaining preclassified samples appears to produce a decision procedure whose risk approaches the Bayes' risk quite closely in many problems with only a few preclassified samples. The asymptotic risk of the nearest neighbor rules and the nearest neighbor rules using edited preclassified samples is calculated for several problems.
The impact of web page text-background colour combinations on readability, retention, aesthetics and behavioural intention

The purpose of this experiment was to examine the effect of web page text/background colour combination on readability, retention, aesthetics, and behavioural intention. One hundred and thirty-six participants studied two Web pages, one with educational content and one with commercial content, in one of four colour-combination conditions. Major findings were: (a) Colours with greater contrast ratio generally lead to greater readability; (b) colour combination did not significantly affect retention; (c) preferred colours (i.e., blues and chromatic colours) led to higher ratings of aesthetic quality and intention to purchase; and (d) ratings of aesthetic quality were significantly related to intention to purchase.
Power Factor Correction Boost Converter Based on the Three-State Switching Cell

The need for solid-state ac-dc converters to improve power quality in terms of power factor correction, reduced total harmonic distortion at input ac mains, and precisely regulated dc output has motivated the investigation of several topologies based on classical converters such as buck, boost, and buck-boost converters. Boost converters operating in continuous-conduction mode have become particularly popular because reduced electromagnetic interference levels result from their utilization. Within this context, this paper introduces a bridgeless boost converter based on a three-state switching cell (3SSC), whose distinct advantages are reduced conduction losses with the use of magnetic elements with minimized size, weight, and volume. The approach also employs the principle of interleaved converters, as it can be extended to a generic number of legs per winding of the autotransformers and high power levels. A literature review of boost converters based on the 3SSC is initially presented so that key aspects are identified. The theoretical analysis of the proposed converter is then developed, while a comparison with a conventional boost converter is also performed. An experimental prototype rated at 1 kW is implemented to validate the proposal, as relevant issues regarding the novel converter are discussed.
Clone region descriptors: Representing and tracking duplication in source code

Source code duplication, commonly known as code cloning, is considered an obstacle to software maintenance because changes to a cloned region often require consistent changes to other regions of the source code. Research has provided evidence that the elimination of clones may not always be practical, feasible, or cost-effective. We present a clone management approach that describes clone regions in a robust way that is independent from the exact text of clone regions or their location in a file, and that provides support for tracking clones in evolving software. Our technique relies on the concept of abstract clone region descriptors (CRDs), which describe clone regions using a combination of their syntactic, structural, and lexical information. We present our definition of CRDs, and describe a clone tracking system capable of producing CRDs from the output of different clone detection tools, notifying developers of modifications to clone regions, and supporting updates to the documented clone relationships. We evaluated the performance and usefulness of our approach across three clone detection tools and five subject systems, and the results indicate that CRDs are a practical and robust representation for tracking code clones in evolving software.
Differential Progressive Path Tracing for High-Quality Previsualization and Relighting in Augmented Reality

In this paper we present a novel method for real-time high quality previsualization and cinematic relighting. The physically based Path Tracing algorithm is used within an Augmented Reality setup to preview high-quality light transport. A novel differential version of progressive path tracing is proposed, which calculates two global light transport solutions that are required for differential rendering. A real-time previsualization framework is presented, which renders the solution with a low number of samples during interaction and allows for progressive quality improvement. If a user requests the high-quality solution of a certain view, the tracking is stopped and the algorithm progressively converges to an accurate solution. The problem of rendering complex light paths is solved by using photon mapping. Specular global illumination effects like caustics can easily be rendered. Our framework utilizes the massive parallel power of modern GPUs to achieve fast rendering with complex global illumination, a depth of field effect, and antialiasing.
Security Issues in Healthcare Applications Using Wireless Medical Sensor Networks: A Survey

Healthcare applications are considered as promising fields for wireless sensor networks, where patients can be monitored using wireless medical sensor networks (WMSNs). Current WMSN healthcare research trends focus on patient reliable communication, patient mobility, and energy-efficient routing, as a few examples. However, deploying new technologies in healthcare applications without considering security makes patient privacy vulnerable. Moreover, the physiological data of an individual are highly sensitive. Therefore, security is a paramount requirement of healthcare applications, especially in the case of patient privacy, if the patient has an embarrassing disease. This paper discusses the security and privacy issues in healthcare application using WMSNs. We highlight some popular healthcare projects using wireless medical sensor networks, and discuss their security. Our aim is to instigate discussion on these critical issues since the success of healthcare application depends directly on patient security and privacy, for ethic as well as legal reasons. In addition, we discuss the issues with existing security mechanisms, and sketch out the important security requirements for such applications. In addition, the paper reviews existing schemes that have been recently proposed to provide security solutions in wireless healthcare scenarios. Finally, the paper ends up with a summary of open security research issues that need to be explored for future healthcare applications using WMSNs.
A Template-based Abstractive Meeting Summarization: Leveraging Summary and Source Text Relationships

In this paper, we present an automatic abstractive summarization system of meeting conversations. Our system extends a novel multi-sentence fusion algorithm in order to generate abstract templates. It also leverages the relationship between summaries and their source meeting transcripts to select the best templates for generating abstractive summaries of meetings. Our manual and automatic evaluation results demonstrate the success of our system in achieving higher scores both in readability and informativeness.