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Hybrid Collab.rative Filtering-Based API Recommendation

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Hybrid Collaborative Filtering-Based API Recommendation

IEEE International Conference on software Quality, Reliability and Security (QRS)

作者： Yongchao Wang Yu Zhou Taolue Chen Jingxuan Zhang Wenhua Yang Zhiqiu Huang College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China State Key Lab. for Novel Software Technology Nanjing University China Key Laboratory of Safety-Critical Software Ministry of Industry and Information Technology China University of London Birkbeck UK

ISBN: (纸本)9781665458146

Automatic API recommendations can liberate software developers from lab.r-intensive programming tasks. Collab.rative filtering (CF) techniques, which have been proved to be superior to other classic techniques, are widely used in recommendation tasks such as music, book, and goods recommendations, but are rarely used in the recommendation of APIs. In this paper, we employ the hybrid of CF techniques to build an API recommendation system. More precisely, We treat the API recommendation task as an item recommendation problem, where method declarations are regarded as users, API calls are regarded as items. First, we use the memory-based CF technique to find the most similar projects, collect the most similar declarations, and take API calls used by the considered declarations together to generate a rating matrix. Next, we use the model-based CF technique to complete the missing values in the rating matrix, then a ranked list of APIs is generated based on the completed rating matrix and sent to the developers as a recommendation result. Experimental results show that compared with the state-of-the-art work, the proposed approach can achieve better performance in terms of a comprehensive set of metrics, such as Success Rate, Precision, Recall, MRR, and NDCG for the top-1, top-3 and top-5 recommended APIs.

关键词： Measurement Weight measurement Collab.rative filtering software quality Programming Reliability engineering software reliability

EEG based Emotion Recognition: A Tutorial and Review

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arXiv 2022年

作者： Li, Xiang Zhang, Yazhou Tiwari, Prayag Song, Dawei Hu, Bin Yang, Meihong Zhao, Zhigang Kumar, Neeraj Marttinen, Pekka Qilu University of Technology Shandong Academy of Sciences Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China Software Engineering College Zhengzhou University of Light Industry Zhengzhou China State Key Lab. for Novel Software Technology Nanjing University Nanjing China Department of Computer Science Aalto University Finland School of Computer Science and Technology Beijing Institute of Technology Beijing China Institute of Engineering Medicine Beijing Institute of Technology Beijing China Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Deemed University Punjab Patiala India

Emotion recognition technology through analyzing the EEG signal is currently an essential concept in Artificial Intelligence and holds great potential in emotional health care, human-computer interaction, multimedia content recommendation, etc. Though there have been several works devoted to reviewing EEG-based emotion recognition, the content of these reviews needs to be updated. In addition, those works are either fragmented in content or only focus on specific techniques adopted in this area but neglect the holistic perspective of the entire technical routes. Hence, in this paper, we review from the perspective of researchers who try to take the first step on this topic. We review the recent representative works in the EEG-based emotion recognition research and provide a tutorial to guide the researchers to start from the beginning. The scientific basis of EEG-based emotion recognition in the psychological and physiological levels is introduced. Further, we categorize these reviewed works into different technical routes and illustrate the theoretical basis and the research motivation, which will help the readers better understand why those techniques are studied and employed. At last, existing challenges and future investigations are also discussed in this paper, which guides the researchers to decide potential future research directions. Copyright © 2022, The Authors. All rights reserved.

关键词： Speech recognition

A Service-Enhanced Task Offloading Method in MEC-Enabled IoV Networks

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A Service-Enhanced Task Offloading Method in MEC-Enabled IoV...

Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Bohai Zhao Kai Peng Victor C.M. Leung Yunni Xia College of Engineering Huaqiao University Quanzhou P.R.China State Key Lab. for Novel Software Technology Nanjing University Nanjing P.R.China School of Computer Science and Software Engineering Shenzhen University Shenzhen P.R.China Department of Electrical and Computer Engineering The University of British Columbia Vancouver Canada School of computers Chongqing University Chongqing P.R.China

To facilitate the evolution from independent transportation means to vehicle-to-infrastructure communications, traditional vehicle-to-vehicle (V2V) technologies are growing to a more advanced and generalized paradigm, i.e., the Internet of Vehicles (IoV). This paradigm has stimulated ever-increasing calling for more capabilities and intelligence for vehicles to foster the potential of supporting a plethora of emerging applications and ubiquitous automotive network access. Combining caching-aided communication and edge computing has been expected to be an advantageous framework to cope with the surging requirements for timely data processing and storage from intelligent IoV applications. In light of the delay restriction and the heterogeneity of edge service nodes (ESNs), we study the service-enhanced problem in edge-enabled IoV networks by configuring proactive service caching. Specifically, based on task scale and relational restrictions, we design an adaptive non-linear caching probability mapping function and further propose a proactive service-enhanced task offloading method in edge-enabled IoV networks named SEOEV. Performance evaluation demonstrates that SEOEV efficiently reduces task processing overhead in comparison to other comparative computing migration approaches.

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Reach-Avoid Analysis for Delay Differential Equations

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Reach-Avoid Analysis for Delay Differential Equations

IEEE Conference on Decision and Control

作者： Bai Xue Yunjun Bai Naijun Zhan Wenyou Liu Li Jiao State Key Lab. of Computer Science Institute of Software CAS Univ. of Chinese Academy of Sciences Beijing China China Academy of Space Technology Beijing Institute of Control Engineering Beijing China

ISBN: (纸本)9781665436601

Time-delay systems are ubiquitous in nature and occur in connection with various aspects of physical, chemical, biological and economic systems. In this paper we propose a semi-definite programming method to address reach-avoid problems for time-delay systems modeled by polynomial delay differential equations (DDEs). The reach-avoid problem of interest is to compute an inner-approximation (i.e., sub-set) of a reach-avoid set, which is the set of initial functions enabling the time-delay system to eventually enter a desirable target set while remaining inside a specified safe set till the target hit. In our approach we first derive an estimate of discrepancies between current states and delayed ones for the time-delay system, and then propose a semi-definite program for inner-approximating the reach-avoid set via incorporating the discrepancy estimate. The incorporation of discrepancy estimates facilitates the reduction of conservativeness in computing inner-approximations. Finally, three examples with comparisons are presented to demonstrate the performance of our approach.

关键词： Economics Law Computational modeling Conferences Differential equations Programming Mathematical models

A Comprehensive Evaluation of Android ICC Resolution Techniques

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arXiv 2021年

作者： Yan, Jiwei Zhang, Shixin Liu, Yepang Deng, Xi Yan, Jun Zhang, Jian Tech. Center of Software Engineering Institute of Software CAS China Univ. of Chinese Academy of Sciences Beijing China School of Software Engineering Beijing Jiaotong University Beijing China Dept. of Computer Science and Engr. Southern University of Sci. and Tech Shenzhen China State Key Lab. of Computer Science Institute of Software CAS China

Inter-component communication (ICC) is a widely used mechanism in mobile apps, which enables message-based control flow transferring and data passing between Android components. Effective ICC resolution requires precisely identifying entry points, analyzing data values of ICC fields, modeling related framework APIs, etc. Due to various control-flow- and data-flow-related characteristics involved and the lack of oracles for real-world apps, the comprehensive evaluation of ICC resolution techniques is challenging. To fill this gap, we collect multiple-type benchmark suites with 4,104 apps, covering hand-made apps, open-source, and commercial ones. Considering their differences, various evaluation metrics, e.g., number count, graph structure, and reliable oracle based metrics, are adopted on-demand. As the oracle for real-world apps is unavailab.e, we design a dynamic analysis approach to extract the real ICC links triggered during GUI exploration. By auditing the code implementations, we carefully check the extracted ICCs and confirm 1,680 ones to form a reliable oracle set, in which each ICC is lab.led with 25 code characteristic tags. The evaluation performed on six state-of-the-art ICC resolution tools shows that 1) the completeness of static ICC resolution results on real-world apps is not satisfactory, as up to 38%-85% ICCs are missed by tools;2) many wrongly reported ICCs are sent from or received by only a few components and the graph structure information can help the identification;3) the efficiency of fundamental tools, like ICC resolution ones, should be optimized in both engineering and research aspects. By investigating both the missed and wrongly reported ICCs, we discuss the strengths of different tools for users and summarize eight common FN/FP patterns in ICC resolution for tool developers. © 2021, CC BY-NC-SA.

关键词： Graphic methods

Evaluating Code Summarization with Improved Correlation with Human Assessment

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Evaluating Code Summarization with Improved Correlation with...

IEEE International Conference on software Quality, Reliability and Security (QRS)

作者： Juanjuan Shen Yu Zhou Yongchao Wang Xiang Chen Tingting Han Taolue Chen College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China State Key Lab. for Novel Software Technology Nanjing University China School of Information Science and Technology Nantong University China Birkbeck University of London UK

ISBN: (纸本)9781665458146

Code summarization aims to automatically generate functionality descriptions of code snippets. Faithful metrics are needed to measure to which degree the machine generated summaries capture the semantics of the code snippets. Most commonly used metrics in code summarization, such as BLEU -4, METEOR, and ROUGE-L, originate from machine translation and text summarization, and have constantly been found to be inconsistent with human assessment. In this paper, we propose a novel evaluation metric, Consensus-based Code Summarization Evaluation (CCSE), which assigns different semantic weights to the n-grams of the summary. We also provide an algorithm to match the n-gram pairs from the reference and candidate based on the similarities. To validate the effectiveness of our proposed metric, we collect summary pairs from two public Java datasets and calculate the correlation coefficients between CCSE and the human evaluations. The experiment results show that, compared with BLEU-4, METEOR, and ROUGE-L, CCSE is more consistent with the scores assessed by human developers.

关键词： Measurement Java Codes Correlation Semantics software quality Reliability engineering

Asymptotically Unbiased inst.nce-wise Regularized Partial AUC Optimization: Theory and Algorithm

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arXiv 2022年

作者： Shao, Huiyang Xu, Qianqian Yang, Zhiyong Bao, Shilong Huang, Qingming Key Lab of Intell. Info. Process. Inst. of Comput. Tech. CAS China School of Computer Science and Tech. University of Chinese Academy of Sciences China State Key Lab of Info. Security Inst. of Info. Engineering CAS China School of Cyber Security University of Chinese Academy of Sciences China BDKM University of Chinese Academy of Sciences China Peng Cheng Laboratory China

The Partial Area Under the ROC Curve (PAUC), typically including One-way Partial AUC (OPAUC) and Two-way Partial AUC (TPAUC), measures the average performance of a binary classifier within a specific false positive rate and/or true positive rate interval, which is a widely adopted measure when decision constraints must be considered. Consequently, PAUC optimization has naturally attracted increasing attention in the machine learning community within the last few years. Nonetheless, most of the existing methods could only optimize PAUC approximately, leading to inevitable biases that are not controllab.e. Fortunately, a recent work presents an unbiased formulation of the PAUC optimization problem via distributional robust optimization. However, it is based on the pair-wise formulation of AUC, which suffers from the limited scalab.lity w.r.t. sample size and a slow convergence rate, especially for TPAUC. To address this issue, we present a simpler reformulation of the problem in an asymptotically unbiased and inst.nce-wise manner. For both OPAUC and TPAUC, we come to a nonconvex strongly concave minimax regularized problem of inst.nce-wise functions. On top of this, we employ an efficient solver enjoys a linear per-iteration computational complexity w.r.t. the sample size and a time-complexity of O(Ε−1/3) to reach a Ε stationary point. Furthermore, we find that the minimax reformulation also facilitates the theoretical analysis of generalization error as a byproduct. Compared with the existing results, we present new error bounds that are much easier to prove and could deal with hypotheses with real-valued outputs. Finally, extensive experiments on several benchmark datasets demonstrate the effectiveness of our method. Copyright © 2022, The Authors. All rights reserved.

关键词： Optimization

A Quantum Probability Driven Framework for Joint Multi-Modal Sarcasm, Sentiment and Emotion Analysis

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arXiv 2023年

作者： Liu, Yaochen Zhang, Yazhou Song, Dawei School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China School of Computing and Communications The Open University Walton Hall United Kingdom School of Nursing The Hong Kong Polytechnic University Hong Kong Ministry of Education China Artificial Intelligence Laboratory China Mobile Communication Group Tianjin Co. Ltd. China State Key Lab. for Novel Software Technology Nanjing University Nanjing China Zhengzhou University of Light Industry China

Sarcasm, sentiment, and emotion are three typical kinds of spontaneous affective responses of humans to external events and they are tightly intertwined with each other. Such events may be expressed in multiple modalities (e.g., linguistic, visual and acoustic), e.g., multi-modal conversations. Joint analysis of humans’ multi-modal sarcasm, sentiment, and emotion is an important yet challenging topic, as it is a complex cognitive process involving both cross-modality interaction and cross-affection correlation. From the probability theory perspective, cross-affection correlation also means that the judgments on sarcasm, sentiment, and emotion are incompatible. However, this exposed phenomenon cannot be sufficiently modelled by classical probability theory due to its assumption of compatibility. Neither do the existing approaches take it into consideration. In view of the recent success of quantum probability (QP) in modeling human cognition, particularly contextual incompatible decision making, we take the first step towards introducing QP into joint multi-modal sarcasm, sentiment, and emotion analysis. Specifically, we propose a QUantum probabIlity driven multi-modal sarcasm, sEntiment and emoTion analysis framework, termed QUIET. Extensive experiments on two datasets and the results show that the effectiveness and advantages of QUIET in comparison with a wide range of the state-of-the-art baselines. We also show the great potential of QP in multi-affect analysis. Copyright © 2023, The Authors. All rights reserved.

关键词： Emotion Recognition

Object Detection in Aerial Images: A Large-Scale Benchmark and Challenges

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arXiv 2021年

作者： Ding, Jian Xue, Nan Xia, Gui-Song Bai, Xiang Yang, Wen Yang, Michael Ying Belongie, Serge Luo, Jiebo Datcu, Mihai Pelillo, Marcello Zhang, Liangpei The State Key Lab LIESMARS Wuhan University Wuhan430079 China The National Engineering Research Center for Multimedia Software School of Computer Science and Institute of Artificial Intelligence Wuhan University Wuhan430072 China The National Engineering Research Center for Multimedia Software School of Computer Science and Institute of Artificial Intelligence The State Key Lab. LIESMARS Wuhan University Wuhan430072 China The School of Electronic Information Huazhong University of Science and Technology Wuhan430079 China The School of Electronic Information The State Key Lab. LIESMARS Wuhan University Wuhan430072 China University of Twente Netherlands Department of Computer Science Cornell University Cornell Tech United States Department of Computer Science University of Rochester RochesterNY14627 United States 82234 Germany Romania DAIS Ca' Foscari University of Venice Italy

In the past decade, object detection has achieved significant progress in natural images but not in aerial images, due to the massive variations in the scale and orientation of objects caused by the bird’s-eye view of aerial images. More importantly, the lack of large-scale benchmarks has become a major obstacle to the development of object detection in aerial images (ODAI). In this paper, we present a large-scale Dataset of Object deTection in Aerial images (DOTA) and comprehensive baselines for ODAI. The proposed DOTA dataset contains 1,793,658 object inst.nces of 18 categories of oriented-bounding-box annotations collected from 11,268 aerial images. Based on this large-scale and well-annotated dataset, we build baselines covering 10 state-of-the-art algorithms with over 70 configurations, where the speed and accuracy performances of each model have been evaluated. Furthermore, we provide a code library for ODAI and build a website for evaluating different algorithms. Previous challenges run on DOTA have attracted more than 1300 teams worldwide. We believe that the expanded large-scale DOTA dataset, the extensive baselines, the code library and the challenges can facilitate the designs of robust algorithms and reproducible research on the problem of object detection in aerial images. Copyright © 2021, The Authors. All rights reserved.

关键词： Object detection