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检索条件"任意字段=Proceedings of the 9th Knowledge-Based Software Engineering Conference"

共 20280 条记录，以下是4841-4850 订阅

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Enriching Compiler Testing with Real Program from Bug Report 22

Enriching Compiler Testing with Real Program from Bug Report

引用

proceedings of the 37th IEEE/ACM International conference on Automated software engineering

作者： Hao Zhong Department of Computer Science and Engineering Shanghai Jiao Tong University China

ISBN: (纸本)9781450394758

Researchers have proposed various approaches to generate test programs. the state-of-the-art approaches can be roughly divided into random-based and mutation-based approaches: random-based approaches generate random programs and mutation-based approaches mutate programs to generate more test programs. Both lines of approaches mainly generate random code, but it is more beneficial to use real programs, since it is easier to learn the impacts of compiler bugs and it becomes reasonable to use both valid and invalid code. However, most real programs from code repositories are ineffective to trigger compiler bugs, partially because they are compiled before they are submitted. In this experience paper, we apply two techniques such as differential testing and code snippet extraction to the specific research domain of compiler testing. based on our observations on the practice of testing compilers, we identify bug reports of compilers as a new source for compiler testing. To illustrate the benefits of the new source, we implement a tool, called LeRe, that extracts test programs from bug reports and uses differential testing to detect compiler bugs with extracted programs. After we enriched the test programs, we have found 156 unique bugs in the latest versions of gcc and clang. Among them, 103 bugs are confirmed as valid, and 9 bugs are already fixed. Our found bugs contain 59 accept-invalid bugs and 33 reject-valid bugs. In these bugs, compilers wrongly accept invalid programs or reject valid programs. the new source enables us detecting accept-invalid and reject-valid bugs that were usually missed by the prior approaches. the prior approaches seldom report the two types of bugs. Besides our found bugs, we also present our analysis on our invalid bug reports. the results are useful for programmers, when they are switching from one compiler to another, and can provide insights, when researchers apply differential testing to detect bugs in more types of software.

关键词： compiler testing real program bug report

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Design of a Ka-band Low Sidelobe Slotted Waveguide Array 13

Design of a Ka-band Low Sidelobe Slotted Waveguide Array

引用

13th International conference on Microwave and Millimeter Wave Technology, ICMMT 2021

作者： Wang, Nannan Chen, Huohong Tenigeer Harbin Institute Of Technology School Of Electronics And Information Engineering Harbin China Beijing Xibao Electronic Technology Co. Ltd Beijing100192 China

ISBN: (纸本)9781665434379

A slotted waveguide antenna array working in Ka-band with the size of 80.91 mm×62 mm is proposed in this paper. the proposed antenna is based on Stevenson equivalent circuit analysis method, and the sidelobe level was reduced by Chebyshev distribution amplitude weighing, a 9×12 units array is designed. the antenna array consists of nine 12 slot waveguide slot antennas, which are fed by a waveguide with nine inclined slots. Combined with relevant antenna design theory and software, the slot dimensions and offset are analyzed and optimized. the simulation results show that the sidelobe level of the array is less than - 25dB, the maximum gain is 27.5dBi. © 2021 IEEE.

关键词： Antenna arrays

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A Cross-patient SEEG Epileptic Signal Detection Method based on Adaptive Feature Fusion of Brain Network Features and Single-Channel Features 22

A Cross-patient SEEG Epileptic Signal Detection Method Based...

引用

proceedings of the 2022 9th International conference on Biomedical and Bioinformatics engineering

作者： Ying Wang Zhengfeng Bao Yiping Wang Yanfeng Yang Tao Feng Guixia Kang Key Lab of Ubiquitous Network of Ministry of Education School of Information and Communication Engineering Beijing University of Posts and Telecommunications China Neurosurgery Department Xuanwu Hospital of Capital Medical University China Key Lab of Ubiquitous Network of Ministry of Education Beijing University of Posts and Telecommunications China

ISBN: (纸本)9781450397223

Epilepsy is a common neurological disorder caused by the abnormal discharge of brain neurons. the automatic detection of epileptic signals (ES) is of great significance for the clinical diagnosis of epilepsy. However, there are still problems such as imperfect feature extraction, ignoring channel correlation, and unbalanced data distribution. therefore, in this paper, we propose an ES detection method based on adaptive feature fusion (AFF) of brain network features (BNF) and single-channel features (SCF). First, two types of features, BNF and SCF, are extracted. the in-degree of each channel calculated from brain functional connectivity based on Pearson correlation is used as BNF. A multi-temporal resolution convolutional neural network (MTRCNN) extracts low-frequency and high-frequency features from single-channel stereo electroencephalography (SEEG) data as SCF. In the feature extraction process, cross-norm and self-norm (CNSN) reduce individual differences. Second, AFF automatically adjusts the fused features, aiming to combine BNF with SCF more effectively. Finally, the weighted loss function considers both the proportion and discriminability of positive and negative samples to reduce imbalanced data distribution. In this paper, we investigated interictal SEEG data from 7 patients with refractory focal hippocampal sclerosing epilepsy and performed cross-patient cross-validation. the results show that our method outperforms the state-of-the-art in all 5 metrics. Our ES automatic detection method provides an objective reference for epilepsy diagnosis, thereby reducing the workload of neurologists.

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SinkFinder: Harvesting Hundreds of Unknown Interesting Function Pairs with Just One Seed 2020

SinkFinder: Harvesting Hundreds of Unknown Interesting Funct...

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28th ACM Joint Meeting on European software engineering conference and Symposium on the Foundations of software engineering (ESEC/FSE)

作者： Bian, Pan Liang, Bin Huang, Jianjun Shi, Wenchang Wang, Xidong Zhang, Jian Renmin Univ China Sch Informat Key Lab DEKE Beijing Peoples R China Chinese Acad Sci State Key Lab Comp Sci Inst Software Beijing Peoples R China

ISBN: (纸本)9781450370431

Mastering the knowledge about security-sensitive functions that can potentially result in bugs is valuable to detect them. However, identifying this kind of functions is not a trivial task. Introducing machine learning-based techniques to do the task is a natural choice. Unfortunately, the approach also requires considerable prior knowledge, e.g., sufficient labelled training samples. In practice, the requirement is often hard to meet. In this paper, to solve the problem, we propose a novel and practical method called SinkFinder to automatically discover function pairs that we are interested in, which only requires very limited prior knowledge. SinkFinder first takes just one pair of wellknown interesting functions as the initial seed to infer enough positive and negative training samples by means of sub-word word embedding. By using these samples, a support vector machine classifier is trained to identify more interesting function pairs. Finally, checkers equipped with the obtained knowledge can be easily developed to detect bugs in target systems. the experiments demonstrate that SinkFinder can successfully discover hundreds of interesting functions and detect dozens of previously unknown bugs from large-scale systems, such as Linux, OpenSSL and PostgreSQL.

关键词： Interesting Function Pairs Bug Detection Machine Learning Word Embedding

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Machine learning based short-term load forecasting for smart meter energy consumption data in London households 12

Machine learning based short-term load forecasting for smart...

引用

12th IEEE International conference on Electronics and Information Technologies, ELIT 2021

作者： Bashawyah, Doaa A. Qaisar, Saeed Mian College of Engineering Effat University Jeddah21478 Saudi Arabia Effat University Jeddah Saudi Arabia Communication and Signal Processing Lab. Energy& Technology Research Center Effat University Jeddah Saudi Arabia

ISBN: (纸本)9781665442961

the eolved deployment of smart meters has enabledan extensive authority and monitoring on both electricity operating companies and customers. based on smart meters, it is possible now to establish viable load prediction techniques. these techniques are playing a critical role in modern smart grids by establishing a mechanism of advanced knowledge of energy demand. this information is beneficial for both electricity operating companies and end consumers. the electricity providers can take early decisions to manage an economical and reliable supply of *** can also helps the customers to effectively plan their energy use and thereby reduce their overall consumption and *** paper employs machine learning models for building relationships between historical energy consumption readings to build and test a short-term load forecasting. A publicaly available dataset, about London city households consumption information, is used to examine the performance of suggested *** datasets are organized and preprocessed with the help of Google Colaboratory. the used machine learning models are k-nearest neighbor (KNN) and support vector machine (SVM).the performances of these models are quantified by using metrics such as percentage root mean square error (PRMSE) and mean absolute percent error (MAPE). the lowest MAPE value of 4.13% and PRMSE value of 1.08% are secured. © 2021 IEEE.

关键词： Costs

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9th EAI International conference on Cloud Computing, CloudComp 2019 and the 4th EAI International conference on Smart Grid and Innovative Frontiers in Telecommunications, SmartGIFT 2019

9th EAI International Conference on Cloud Computing, CloudCo...

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9th EAI International conference on Cloud Computing, CloudComp 2019 and the 4th EAI International conference on Smart Grid and Innovative Frontiers in Telecommunications, SmartGIFT 2019

ISBN: (纸本)9783030485122

the proceedings contain 55 papers. the special focus in this conference is on Cloud Computing. the topics include: Rendering of three-Dimensional Cloud based on Cloud Computing;distributed Stochastic Alternating Direction Method of Multipliers for Big Data Classification;personalized Recommendation Algorithm Considering Time Sensitivity;cloud-based Master Data Platform for Smart Manufacturing Process;a Semi-supervised Classification Method for Hyperspectral Images by Triple Classifiers with Data Editing and Deep Learning;A Survey of Image Super Resolution based on CNN;design and Development of an Intelligent Semantic Recommendation System for Websites;a Lightweight Neural Network Combining Dilated Convolution and Depthwise Separable Convolution;resource Allocation Algorithms of Vehicle Networks with Stackelberg Game;research on Coordination Control theory of Greenhouse Cluster based on Cloud Computing;a Multi-objective Computation Offloading Method in Multi-cloudlet Environment;anomalous Taxi Route Detection System based on Cloud Services;collaborative Recommendation Method based on knowledge Graph for Cloud Services;efficient Multi-user Computation Scheduling Strategy based on Clustering for Mobile-Edge Computing;Grazing Trajectory Statistics and Visualization Platform based on Cloud GIS;Cloud-based AGV Control System;a Parallel Drone Image Mosaic Method based on Apache Spark;cycleSafe: Safe Route Planning for Urban Cyclists;prediction of Future Appearances via Convolutional Recurrent Neural Networks based on Image Time Series in Cloud Computing;video knowledge Discovery based on Convolutional Neural Network;time-Varying Water Quality Analysis with Semantical Mining Technology;a Survey of QoS Optimization and Energy Saving in Cloud, Edge and IoT;data-Driven Fast Real-Time Flood Forecasting Model for Processing Concept Drift;intelligent System Security Event Description Method.

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Studying software Developer Expertise and Contributions in Stack Overflow and Github 36

Studying Software Developer Expertise and Contributions in S...

引用

36th IEEE International conference on software Maintenance and Evolution (ICSME)

作者： Vadlamani, Sri Lakshmi Baysal, Olga Carleton Univ Sch Comp Sci Ottawa ON Canada

ISBN: (纸本)9781728156194

knowledge and experience are touted as both the necessary and sufficient conditions to make a person an expert. this paper attempts to investigate this issue in the context of software development by studying software developer's expertise based on their activity and experience on Github and Stack Overflow platforms. We study how developers themselves define the notion of an "expert", as well as why or why not developers contribute to online collaborative platforms. We conducted an exploratory survey with 73 software developers and applied a mixed methods approach to analyze the survey results. the results provided deeper insights into how an expert in the field could be defined. Further, the study provides a better understanding of the underlying factors that drive developers to contribute to Github and Stack Overflow, and the challenges they face when participating on either platform. the quantitative analysis showed that JavaScript remains a popular language, while knowledge and experience are the key factors driving expertise. On the other hand, qualitative analysis showed that soft skills such as effective and clear communication, analytical thinking are key factors defining an expert. We found that both knowledge and experience are only necessary but not sufficient conditions for a developer to become an expert, and an expert would necessarily have to possess adequate soft skills. Lastly, an expert's contribution to Github seems to be driven by personal factors, while contribution to Stack Overflow is motivated more by professional drivers (i.e., skills and expertise). Moreover, developers seem to prefer contributing to Github as they face greater challenges while contributing to Stack Overflow.

关键词： software developer expertise developer contributions developer profiles Github Stack Overflow qualitative study survey developer perception

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Bank loan prediction system using machine learning 9

Bank loan prediction system using machine learning

引用

9th International conference on System Modeling and Advancement in Research Trends, SMART 2020

作者： Gupta, Anshika Pant, Vinay Kumar, Sudhanshu Bansal, Pravesh Kumar Faculty of Engineering and Computing Sciences INurture TMU Moradabad India Government Engineering College Department of Computer Science Bharatpur India

ISBN: (纸本)9781728189086

With the advancement in technology, there are so many enhancements in the banking sector also. the number of applications is increasing every day for loan approval. there are some bank policies that they have to consider while selecting an applicant for loan approval. based on some parameters, the bank has to decide which one is best for approval. It is tough and risky to check out manually every person and then recommended for loan approval. In this work, we use a machine learning technique that will predict the person who is reliable for a loan, based on the previous record of the person whom the loan amount is accredited before. this work's primary objective is to predict whether the loan approval to a specific individual is safe or not. © 2020 IEEE.

关键词： Decision trees

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Retrofitting Soft Rules for knowledge Representation Learning 1

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9th Joint International Semantic Technology conference (JIST)

作者： An, Bo Han, Xianpei Sun, Le Chinese Acad Sci Inst Software State Key Lab Comp Sci Beijing Peoples R China

ISBN: (数字)9783030414078

ISBN: (纸本)9783030414078;9783030414061

Recently, a significant number of studies have focused on knowledge graph completion using rule-enhanced learning techniques, supported by the mined soft rules in addition to the hard logic rules. However, due to the difficulty in determining the confidences of the soft rules without the global semantics of knowledge graph such as the semantic relatedness between relations, the knowledge representation may not be optimal, leading to degraded effectiveness in its application to knowledge graph completion tasks. To address this challenge, this paper proposes a retrofit framework that iteratively enhances the knowledge representation and confidences of soft rules. Specifically, the soft rules guide the learning of knowledge representation, and the representation, in turn, provides global semantic of the knowledge graph to optimize the confidences of soft rules. Extensive evaluation shows that our method achieves new state-of-the-art results on link prediction and triple classification tasks, brought by the fine-tuned confidences of soft rules.

关键词： knowledge representation Soft rules Link prediction

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New Approach on Steganalysis: Reverse-engineering based Steganography SW Analysis 2020

New Approach on Steganalysis: Reverse-Engineering based Steg...

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9th International conference on software and Computer Applications, ICSCA 2020

作者： Lee, Hanseong Lee, Hyung-Woo Div. of Computer Engineering Hanshin University 137 Yangsan-dong Osan Gyeonggi Korea Republic of

ISBN: (纸本)9781450376655

Recently, steganography tools for concealing messages in images have been widely used, and internal mechanisms for hiding messages using steganography tools are mostly unknown to public. therefore, we adopted the reverse engineering for unveiling the information hiding algorithm and operation mechanism applied to 'Steg' steganography software using IDA tool. By reversing the steganography software, we disclosed the internal steganographic mechanism used on 'Steg' software. therefore, we can propose a new approach for efficiently detecting and automatically extracting hidden messages in stego data using the software reversing technique. © 2020 ACM.

关键词： Reverse engineering

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