nils-herrmann/clean_scirepeval huggingface dataset:
Applying test-driven code search to the reuse of auxiliary functionality

Software developers spend considerable effort implementing auxiliary functionality used by the main features of a system (e.g. compressing/decompressing files, encryption/decription of data, scaling/rotating images). With the increasing amount of open source code available on the Internet, time and effort can be saved by reusing these utilities through informal practices of code search and reuse. However, when this type of reuse is performed in an ad hoc manner, it can be tedious and error-prone: code results have to be manually inspected and extracted into the workspace. In this paper we introduce the use of test cases as an interface for automating code search and reuse and evaluate its applicability and performance in the reuse of auxiliary functionality. We call our approach Test-Driven Code Search (TDCS). Test cases serve two purposes: (1) they define the behavior of the desired functionality to be searched; and (2) they test the matching results for suitability in the local context. We present CodeGenie, an Eclipse plugin that performs TDCS using a code search engine called Sourcerer. Our evaluation presents evidence of the applicability and good performance of TDCS in the reuse of auxiliary functionality.
Leaky-Wave Antenna With Alternately Loaded Complementary Radiation Elements

In this letter, we compare the scanning beams of two leaky-wave antennas (LWAs), respectively, loaded with capacitive and inductive radiation elements, which have not been fully discussed in previous publications. It is pointed out that an LWA with only one type of radiation element suffers from a significant gain fluctuation over its beam-scanning band. To remedy this problem, we propose an LWA alternately loaded with inductive and capacitive elements along the host transmission line. The proposed LWA is able to steer its beam continuously from backward to forward with constant gain. A microstrip-based LWA is designed on the basis of the proposed method, and the measurement of its fabricated prototype demonstrates and confirms the desired results. This design method can widely be used to obtain LWAs with constant gain based on a variety of TLs.
Verifying Simulink Stateflow model: Timed automata approach

Simulink Stateflow is widely used for the model-driven development of software. However, the increasing demand of rigorous verification for safety critical applications brings new challenge to the Simulink Stateflow because of the lack of formal semantics. In this paper, we present STU, a self-contained toolkit to bridge the Simulink Stateflow and a well-defined rigorous verification. The tool translates the Simulink Stateflow into the Uppaal timed automata for verification. Compared to existing work, more advanced and complex modeling features in Stateflow such as the event stack, conditional action and timer are supported. Then, with the strong verification power of Uppaal, we can not only find design defects that are missed by the Simulink Design Verifier, but also check more important temporal properties. The evaluation on artificial examples and real industrial applications demonstrates the effectiveness.
An Efficient Prediction of Breast Cancer Data using Data Mining Techniques

Breast cancer is one of the major causes of death in women when compared to all other cancers. Breast cancer has become the most hazardous types of cancer among women in the world. Early detection of breast cancer is essential in reducing life losses. This paper presents a comparison among the different Data mining classifiers on the database of breast cancer Wisconsin Breast Cancer (WBC), by using classification accuracy. This paper aims to establish an accurate classification model for Breast cancer prediction, in order to make full use of the invaluable information in clinical data, especially which is usually ignored by most of the existing methods when they aim for high prediction accuracies. We have done experiments on WBC data. The dataset is divided into training set with 499 and test set with 200 patients. In this experiment, we compare six classification techniques in Weka software and comparison results show that Support Vector Machine (SVM) has higher prediction accuracy than those methods. Different methods for breast cancer detection are explored and their accuracies are compared. With these results, we infer that the SVM are more suitable in handling the classification problem of breast cancer prediction, and we recommend the use of these approaches in similar classification problems. breast cancer; classification; Decision tree, Naïve Bayes, MLP, Logistic Regression SVM, KNN and weka;
Design and Implementation of Firing Circuit for Single- Phase Converter

 The theme of the paper is to design and implement the firing circuit for a converter. The necessity of getting synchronized firing pulses for the gate of the thyristor is discussed. Out of many variety of firing circuits available, the ideas behind are the two most popularly used control circuits that are namely using ramp signal and using cosine signal. It shows how a cosine controls scheme work. Detail description and functioning of each block is explained along with the waveforms at the output of the blocks. Experimental results obtained from oscillographic displays at important points of the circuits are included. In this paper, we fabricate a hardware circuit which implements the cosine control technique, test the circuit and also check that desired gate pulses for the thyristors.
An Integrative Implementation Framework for Electronic Customer Relationship Management: Revisiting the General Principles of Usability and Resistance

Electronic Customer Relationship Management (eCRM) has become the latest paradigm in the world of Customer Relationship Management. Recent business surveys suggest that up to 50% of such implementations do not yield measurable returns on investment. A secondary analysis of 13 case studies suggests that many of these limited success implementations can be attributed to usability and resistance factors. The objective of this paper is to review the general usability and resistance principles in order build an integrative framework for analyzing eCRM case studies. The conclusions suggest that if organizations want to get the most from their eCRM implementations they need to revisit the general principles of usability and resistance and apply them.
Modal force and torque control with wire-tension control using series elastic actuator for body weight support system

In this paper, novel mechanical mechanism and control method using SEA are suggested for natural gait pattern and effective gait rehabilitation. Conventional body weight support system (BWS) focuses on static position lifting and has limitation for a subject to constrain natural gait pattern especially in the vertical and medial-lateral movement. To overcome such abnormal gait pattern during BWS rehabilitation, modal force and torque control using wire is applied in this paper. The control makes it possible for a subject to generate natural gait motion which is natural in the vertical and medial-lateral movement during gait rehabilitation. To implement modal force and torque control, reference transformation for control of the tension of two wire is suggested. To control the tension of each wire, SEA which is controlled by the model base is utilized. And experiments are performed to assess the control performance.
Optimizing and Contrasting Recurrent Neural Network Architectures

Recurrent Neural Networks (RNNs) have long been recognized for their potential to model complex time series. However, it remains to be determined what optimization techniques and recurrent architectures can be used to best realize this potential. The experiments presented take a deep look into Hessian free optimization, a powerful second order optimization method that has shown promising results, but still does not enjoy widespread use. This algorithm was used to train to a number of RNN architectures including standard RNNs, long short-term memory, multiplicative RNNs, and stacked RNNs on the task of character prediction. The insights from these experiments led to the creation of a new multiplicative LSTM hybrid architecture that outperformed both LSTM and multiplicative RNNs. When tested on a larger scale, multiplicative LSTM achieved character level modelling results competitive with the state of the art for RNNs using very different methodology.
A Simple NLP-Based Approach to Support Onboarding and Retention in Open Source Communities

Successful open source communities are constantly looking for new members and helping them become active developers. A common approach for developer onboarding in open source projects is to let newcomers focus on relevant yet easy-to-solve issues to familiarize themselves with the code and the community. The goal of this research is twofold. First, we aim at automatically identifying issues that newcomers can resolve by analyzing the history of resolved issues by simply using the title and description of issues. Second, we aim at automatically identifying issues, that can be resolved by newcomers who later become active developers. We mined the issue trackers of three large open source projects and extracted natural language features from the title and description of resolved issues. In a series of experiments, we optimized and compared the accuracy of four supervised classifiers to address our research goals. Random Forest, achieved up to 91% precision (F1-score 72%) towards the first goal while for the second goal, Decision Tree achieved a precision of 92% (F1-score 91%). A qualitative evaluation gave insights on what information in the issue description is helpful for newcomers. Our approach can be used to automatically identify, label, and recommend issues for newcomers in open source software projects based only on the text of the issues.
Epstein-Barr Virus and Its Association with Oral Hairy Leukoplakia: A Short Review

In immunocompromised subjects, Epstein-Barr virus (EBV) infection of terminally differentiated oral keratinocytes may result in subclinical productive infection of the virus in the stratum spinosum and in the stratum granulosum with shedding of infectious virions into the oral fluid in the desquamating cells. In a minority of cases this productive infection with dysregulation of the cell cycle of terminally differentiated epithelial cells may manifest as oral hairy leukoplakia. This is a white, hyperkeratotic, benign lesion of low morbidity, affecting primarily the lateral border of the tongue. Factors that determine whether productive EBV replication within the oral epithelium will cause oral hairy leukoplakia include the fitness of local immune responses, the profile of EBV gene expression, and local environmental factors.
An agile boot camp: Using a LEGO®-based active game to ground agile development principles

Industry-practiced agile methods must become an integral part of a software engineering curriculum. It is essential that graduates of such programs seeking careers in industry understand and have positive attitudes toward agile principles. With this knowledge they can participate in agile teams and apply these methods with minimal additional training. However, learning these methods takes experience and practice, both of which are difficult to achieve in a direct manner within the constraints of an academic program. This paper presents a novel, immersive boot camp approach to learning agile software engineering concepts with LEGO® bricks as the medium. Students construct a physical product while inductively learning the basic principles of agile methods. The LEGO®-based approach allows for multiple iterations in an active learning environment. In each iteration, students inductively learn agile concepts through their experiences and mistakes. Subsequent iterations then ground these concepts, visibly leading to an effective process. We assessed this approach using a combination of quantitative and qualitative methods. Our assessment shows that the students demonstrated positive attitudes toward the boot-camp approach compared to lecture-based instruction. However, the agile boot camp did not have an effect on the students' recall on class tests when compared to their recall of concepts taught in lecture-based instruction.
Happy creativity: Listening to happy music facilitates divergent thinking

Creativity can be considered one of the key competencies for the twenty-first century. It provides us with the capacity to deal with the opportunities and challenges that are part of our complex and fast-changing world. The question as to what facilitates creative cognition-the ability to come up with creative ideas, problem solutions and products-is as old as the human sciences, and various means to enhance creative cognition have been studied. Despite earlier scientific studies demonstrating a beneficial effect of music on cognition, the effect of music listening on creative cognition has remained largely unexplored. The current study experimentally tests whether listening to specific types of music (four classical music excerpts systematically varying on valance and arousal), as compared to a silence control condition, facilitates divergent and convergent creativity. Creativity was higher for participants who listened to 'happy music' (i.e., classical music high on arousal and positive mood) while performing the divergent creativity task, than for participants who performed the task in silence. No effect of music was found for convergent creativity. In addition to the scientific contribution, the current findings may have important practical implications. Music listening can be easily integrated into daily life and may provide an innovative means to facilitate creative cognition in an efficient way in various scientific, educational and organizational settings when creative thinking is needed.
Development of Crack Detection System with Unmanned Aerial Vehicles and Digital Image Processing

Conventional crack detecting inspections of structures have been mainly based on visual investigation methods. Huge and tall structures such as cable bridges, highrising towers, dams and industrial power plants are known to have its inaccessible area and limitation in field inspection due to its geometry. In some cases, inspection of critical structural members is not possible due to its spatial constraints. With rapid technical development of unmanned aerial vehicle (UAV), the limitation of conventional visual inspection could be overcome with advanced digital image processing technique. In this study, the crack detecting system using UAV and digital image processing techniques was developed Structure inspection system to detect cracks in structure.
Separation of Instrument Sounds using Non-negative Matrix Factorization with Spectral Envelope Constraints

Spectral envelope is one of the most important features that characterize the timbre of an instrument sound. However, it is difficult to use spectral information in the framework of conventional spectrogram decomposition methods. We overcome this problem by suggesting a simple way to provide a constraint on the spectral envelope calculated by linear prediction. In the first part of this study, we use a pre-trained spectral envelope of known instruments as the constraint. Then we apply the same idea to a blind scenario in which the instruments are unknown. The experimental results reveal that the proposed method outperforms the conventional methods.
A web-based appointment system to reduce waiting for outpatients: A retrospective study

Long waiting times for registration to see a doctor is problematic in China, especially in tertiary hospitals. To address this issue, a web-based appointment system was developed for the Xijing hospital. The aim of this study was to investigate the efficacy of the web-based appointment system in the registration service for outpatients.



Data from the web-based appointment system in Xijing hospital from January to December 2010 were collected using a stratified random sampling method, from which participants were randomly selected for a telephone interview asking for detailed information on using the system. Patients who registered through registration windows were randomly selected as a comparison group, and completed a questionnaire on-site.



A total of 5641 patients using the online booking service were available for data analysis. Of them, 500 were randomly selected, and 369 (73.8%) completed a telephone interview. Of the 500 patients using the usual queuing method who were randomly selected for inclusion in the study, responses were obtained from 463, a response rate of 92.6%. Between the two registration methods, there were significant differences in age, degree of satisfaction, and total waiting time (P<0.001). However, gender, urban residence, and valid waiting time showed no significant differences (P>0.05). Being ignorant of online registration, not trusting the internet, and a lack of ability to use a computer were three main reasons given for not using the web-based appointment system. The overall proportion of non-attendance was 14.4% for those using the web-based appointment system, and the non-attendance rate was significantly different among different hospital departments, day of the week, and time of the day (P<0.001).



Compared to the usual queuing method, the web-based appointment system could significantly increase patient's satisfaction with registration and reduce total waiting time effectively. However, further improvements are needed for broad use of the system.
Innovation Search Strategy and Predictable Returns : A Bias for Novelty

Because of the intangible and highly uncertain nature of innovation, investors may have difficulty processing information associated with a firm’s innovation and innovation search strategy. Due to cognitive and strategic biases, investors are likely to pay more attention to novel and explorative patents rather than incremental and exploitative patents. We find that firms focusing on exploitation rather than exploration tend to generate superior subsequent operating performance. Analysts do not seem to detect this, as firms currently focused on exploitation tend to outperform the market’s near-term earnings expectations. The market also seems unable to accurately incorporate innovation strategy information. We find that firms with exploitation strategies are undervalued relative to firms with exploration strategies and that this return differential is incremental to standard risk and innovation-based pricing factors examined in the prior literature. This result suggests a more nuanced view on whether stock market pressure hampers innovation.
Cryptanalysis and Improvement of Authentication and Key Agreement Protocols for Telecare Medicine Information Systems

Recently, many authentication protocols have been presented using smartcard for the telecare medicine information system (TMIS). In 2014, Xu et al. put forward a two-factor mutual authentication with key agreement protocol using elliptic curve cryptography (ECC). However, the authors have proved that the protocol is not appropriate for practical use as it has many problems (1) it fails to achieve strong authentication in login and authentication phases; (2) it fails to update the password correctly in the password change phase; (3) it fails to provide the revocation of lost/stolen smartcard; and (4) it fails to protect the strong replay attack. We then devised an anonymous and provably secure two-factor authentication protocol based on ECC. Our protocol is analyzed with the random oracle model and demonstrated to be formally secured against the hardness assumption of computational Diffie-Hellman problem. The performance evaluation demonstrated that our protocol outperforms from the perspective of security, functionality and computation costs over other existing designs.
Margin measurements in optical amplifier system

The margin, or the difference between the received signal-to-noise (SNR) and the SNR required to maintain a given bit error ratio (BER), is important to the design and operation of optical amplifier transmission systems A new tehnique is described for estimating the SNR at the receiver's decision circuit when the BER is too low to be measured in a reasonable time. The SNR is determined from the behavior of the BER as a function of the decision threshold setting in the region where the BER is measurable. The authors obtain good agreement between the BER predicted using the measured SNR value and the actual measured BER.<<ETX>>
A fast algorithm for equitable coloring

A proper vertex coloring of a graph is equitable if the sizes of color classes differ by at most one. The celebrated Hajnal–Szemerédi Theorem states: For every positive integer r, every graph with maximum degree at most r has an equitable coloring with r + 1 colors. We show that this coloring can be obtained in O(rn 2) time, where n is the number of vertices.
Relationships among PTSD symptoms, social support, and support source in veterans with chronic PTSD.

The present study examined the temporal relationship between posttraumatic stress disorder (PTSD) and social support among 128 male veterans treated for chronic PTSD. Level of perceived interpersonal support and stressors were assessed at two time points (6 months apart) for four different potential sources of support: spouse, relatives, nonveteran friends, and veteran peers. Veteran peers provided relatively high perceived support and little interpersonal stress. Spouses were seen as both interpersonal resources and sources of interpersonal stress. More severe PTSD symptoms at Time 1 predicted greater erosion in perceived support from nonveteran friends, but not from relatives. Contrary to expectations, initial levels of perceived support and stressors did not predict the course of chronic PTSD symptoms.
A Survey on Algorithms of Mining Frequent Subgraphs

–Graphs are currently becoming more important in modeling and demonstrating information. In the recent years, graph mining is becoming an interesting field for various processes such as chemical compounds, protein structures, social networks and computer networks. One of the most important concepts in graph mining is to find frequent subgraphs. The major advantage of utilizing subgraphs is speeding up the search for similarities, finding graph specifications and graph classifications. In this article we classify the main algorithms in the graph mining field. Some fundamental algorithms are reviewed and categorized. Some issues for any algorithm are graph representation, search strategy, nature of input and completeness of output that are discussed in this article. Keywords––Frequent subgraph, Graph mining, Graph mining algorithms