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Object Oriented BDD and Executable Human-Language Module Specification 26

Object Oriented BDD and Executable Human-Language Module Spe...

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26th ACIS International Winter Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD-Winter 2023

作者： Lee, Eric Gong, Jiayu Cao, Qinghong Intelligent Technology Co. Ltd. Shanghai China Shanghai Key Laboratory of Computer Software Testing & Evaluation Shanghai Development Center of Computer Software Technology Shanghai China System Operation Department Shanghai Stock Exchange Shanghai China

ISBN: (纸本)9798350345865

This paper presents an approach to software development which uses a generative AI Model as compiler to translate human language requirements into high-level programming language. We propose an executable human-language module specification and a tool to support it, which has been used successfully for human-language UI test automation. We anticipate further development of this approach to enable complex software to be programmed in human language, allowing for more intuitive and efficient software development. © 2023 IEEE.

关键词： Specifications

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A Practical Cloud API Complementary Recommendation Service for Mashup Creation

A Practical Cloud API Complementary Recommendation Service f...

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International Conference on computer Supported Cooperative Work in Design

作者： Xiaowei Liu Wenhui Chen Mengmeng Sun Yali Si Zhen Chen School of Information Science and Engineering Yanshan University Qinhuangdao China School of Computer Science and Engineering Changshu Institute of Technology Changshu China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan University Qinhuangdao China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

With the popularity of the service-oriented development paradigm in the cloud era, numerous cloud APIs have been developed. The continuous proliferation of cloud APIs has submerged developers in a sea of options, bringing the need for cloud API recommendation techniques. Most existing cloud API recommendation methods focus on theoretical studies and neglect practical applications. As a result, these methods face several challenges, such as covering recommendation scenarios, generating real-time responses, and handling concurrent requests. In this paper, we propose and implement a practical cloud API complementary recommendation service for mashup creation. To address the above challenges, we first propose a cloud API complementary recommendation framework named FICR, which exploits feature interactions to mine implicit complementarity and models complementarity from the mashup side and the target cloud API side for scenario coverage. Then, we conduct performance optimization via precoding and batch size tuning to optimize FICR for real-time responses. Finally, we design a minimum-connection load balancing algorithm with resource early warning and build a distributed system for concurrent requests. Experiments conducted on real-world datasets show that FICR outperforms state-of-the-art baseline models in complementary recommendation scenarios, and demonstrate that the implemented service meets the real-time and concurrency requirements. The service will serve as a practical tool to help developers during mashup creation.

关键词： Concurrent computing Federated learning Mashups Precoding Load management Real-time systems Tuning

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A Programming Language Learning Service by Linking Stack Overflow with Textbooks

A Programming Language Learning Service by Linking Stack Ove...

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2023 IEEE International Conference on Web Services, ICWS 2023

作者： Wu, Jiawei Sun, Yanchun Zhang, Jiaqi Zhou, Yuqi Huang, Gang Ministry of Education Key Laboratory of High Confidence Software Technologies China Peking University School of Computer Science Beijing China Peking University School of Software and Microelectronics Beijing China National Key Laboratory of Data Space Technology and System China

ISBN: (纸本)9798350304855

Before software developers actually start coding, an essential requirement is to be proficient in a programming language. However, as an important learning resource, programming language textbooks usually contain much about syntax and semantics of a programming language, but little about pragmatics, which is highly relevant with practical skills in solving real-world problems using programming languages. This makes it difficult for learners to transfer knowledge from textbooks to *** solve this problem, this paper proposes a programming language learning service, utilizing abundant development knowledge in Stack Overflow (SO) Q&A posts as the pragmatics knowledge of programming languages to make up for the shortage of pragmatics knowledge in textbooks. To link SO Q&A posts with textbooks, this paper first proposes a deep learning classification model to recognize SO posts containing development knowledge. Second, to align filtered SO posts with programming language learning processes, this paper proposes a weakly supervised link approach to matching SO posts with chapters and sections of textbooks. The feature of weak supervision ensures that the approach can be easily extended to other textbooks. Based on these modules above, this paper implements the programming language learning service in the form of a Chrome plugin. We conduct experiments and user study. Their results demonstrate the effectiveness, scalability and stability of the service. © 2023 IEEE.

关键词： Textbooks

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Machine Learning is All You Need: A Simple Token-Based Approach for Effective Code Clone Detection 24

Machine Learning is All You Need: A Simple Token-Based Appro...

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44th ACM/IEEE International Conference on Software Engineering, ICSE 2024

作者： Feng, Siyue Suo, Wenqi Wu, Yueming Zou, Deqing Liu, Yang Jin, Hai Huazhong University of Science and Technology China Nanyang Technological University Singapore Hubei Engineering Research Center on Big Data Security Cluster and Grid Computing Lab National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security China Jinyinhu Laboratory Wuhan430074 China School of Cyber Science and Engineering Hust Wuhan430074 China School of Computer Science and Technology Hust Wuhan430074 China

ISBN: (纸本)9798400702174

As software engineering advances and the code demand rises, the prevalence of code clones has increased. This phenomenon poses risks like vulnerability propagation, underscoring the growing importance of code clone detection techniques. While numerous code clone detection methods have been proposed, they often fall short in real-world code environments. They either struggle to identify code clones effectively or demand substantial time and computational resources to handle complex clones. This paper introduces a code clone detection method namely Toma using tokens and machine learning. Specifically, we extract token type sequences and employ six similarity calculation methods to generate feature vectors. These vectors are then input into a trained machine learning model for classification. To evaluate the effectiveness and scalability of Toma, we conduct experiments on the widely used BigCloneBench dataset. Results show that our tool outperforms token-based code clone detectors and most tree-based clone detectors, demonstrating high effectiveness and significant time savings. © 2024 ACM.

关键词： Machine learning

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PMM: A Smart Shopping Guider Based on Mobile AR 23

PMM: A Smart Shopping Guider Based on Mobile AR

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16th International Symposium on Visual Information Communication and Interaction, VINCI 2023

作者： Han, Jiahao Ding, Zhifei Chen, Yuxuan Yu, Lingxin Chen, Siru Liu, Richen School of Computer and Electronic Information/School of Artificial Intelligence Nanjing Normal University China School of Computer and Electronic Information/School of Artificial Intelligence Nanjing Normal University China and State Key Laboratory of Virtual Reality Technology and Systems Beihang University China

ISBN: (纸本)9798400707513

Augmented reality (AR) is a burgeoning interaction technology with the ability to provide users with immersive everyday experiences. Shopping is among the most common experiences. When consumers purchase products, they often encounter difficulties in obtaining detailed information relevant to their interests, such as ingredients, origin, and product comparisons, leading to frustration. This situation can be undeniably frustrating. We can use visualization technology [18] to organize it more orderly and friendly. This paper proposes a mobile augmented reality-based application framework, Product Magic Mirror (PMM), which helps to integrate basic visualization design into the application. The augmented information can be rich, e.g. they can be some visualizations and vivid data videos. In the evaluation, we simulated a goods purchase scene, and applied information to the real world by using AR. The real environment and virtual objects were superimposed on the same screen in real time, so as to achieve an experience beyond reality [24]. In our user survey, users rated our tools as a way to make better shopping choices, giving them a positive score for an immersive shopping experience. We predict that displaying information such as instructions, ingredients, and/or user review information next to products could allow consumers to make better consumption choices while reducing decision time and making shopping more immersive. © 2023 ACM.

关键词： Augmented reality

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User Behavior-Based Collaborative Filtering Recommendation Algorithm and Visualization in Second-Hand Trading Platform 2

User Behavior-Based Collaborative Filtering Recommendation A...

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2nd IEEE International Conference on Sensors, Electronics and computer Engineering, ICSECE 2024

作者： Qiao, Rui Wang, Xin Zhong, Xiaoben School of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Hubei Wuhan China Yunnan College of Business Management Anning650301 China

ISBN: (纸本)9798350373646

In the backdrop of the digital era, the demand for second-hand trading within campuses has seen a significant rise. This project aims at designing a second-hand trading platform that merges the latest technological trends, providing a secure and convenient trading environment for students. The system utilizes Spring Boot for backend development and Vue along with ElementUI for constructing the user interface, ensuring efficient operation and enhancing user interaction experience. The server employs Spring Boot's built-in Tomcat, simplifying the deployment process. For data persistence, Mybatis framework is used for flexible and efficient interactions with the MySQL database. The platform introduces a collaborative filtering algorithm based on user behavior for smart product recommendations, further improving the user experience. The system integrates core modules such as user management, product browsing, shopping cart functionalities, and order processing, innovatively introducing an offline transaction confirmation mechanism to ensure transaction security and reliability while avoiding legal risks associated with online payments. With its B/S architecture, the platform operates more smoothly, making it more accessible to users. Experimental results demonstrate that the platform has a user-friendly interface and efficient data processing capabilities, receiving widespread recognition and praise from users, proving its practicality and innovativeness. © 2024 IEEE.

关键词： Collaborative filtering

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Intersecting-Boundary-Sensitive Fingerprinting for Tampering Detection of DNN Models 41

Intersecting-Boundary-Sensitive Fingerprinting for Tampering...

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41st International Conference on Machine Learning, ICML 2024

作者： Bai, Xiaofan He, Chaoxiang Ma, Xiaojing Zhu, Bin Benjamin Jin, Hai School of Cyber Science and Engineering Huazhong University of Science and Technology China National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China Microsoft United States School of Computer Science and Technology Huazhong University of Science and Technology China Cluster and Grid Computing Lab China

Cloud-based AI services offer numerous benefits but also introduce vulnerabilities, allowing for tampering with deployed DNN models, ranging from injecting malicious behaviors to reducing computing resources. Fingerprint samples are generated to query models to detect such tampering. In this paper, we present Intersecting-Boundary-Sensitive Fingerprinting (IBSF), a novel method for black-box integrity verification of DNN models using only top-1 labels. Recognizing that tampering with a model alters its decision boundary, IBSF crafts fingerprint samples from normal samples by maximizing the partial Shannon entropy of a selected subset of categories to position the fingerprint samples near decision boundaries where the categories in the subset intersect. These fingerprint samples are almost indistinguishable from their source samples. We theoretically establish and confirm experimentally that these fingerprint samples' expected sensitivity to tampering increases with the cardinality of the subset. Extensive evaluation demonstrates that IBSF surpasses existing state-of-the-art fingerprinting methods, particularly with larger subset cardinality, establishing its state-of-the-art performance in black-box tampering detection using only top-1 labels. The IBSF code is available at: https://***/CGCL-codes/IBSF. Copyright 2024 by the author(s)

关键词： HTTP

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Feature Decoupled of Deep Mutual Information Maximization 2

Feature Decoupled of Deep Mutual Information Maximization

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2nd International Conference on Automation, Robotics and computer Engineering, ICARCE 2023

作者： He, Xing Peng, Changgen Wang, Lin Tan, Weijie Wang, Zifan State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guizhou Key Laboratory of Pattern Recognition and Intelligent System Guizhou Minzu University Guiyang China Guizhou Big Data Academy Guizhou University Guiyang China Guizhou Minzu University Guizhou Key Laboratory of Pattern Recognition and Intelligent System Guiyang China Institute of Guizhou Aerospace Measuring and Testing Technology Guiyang China

ISBN: (纸本)9798350308341

In deep learning, supervised learning techniques usually require a large amount of expensive labeled data to train the network, and the feature representations extracted by the model usually mix multiple attributes, resulting in feature representations that are difficult to decouple and are non-interpretable, which restricts the application and development of deep learning techniques, and for this reason, it is particularly important to study decoupled feature representation methods for unsupervised learning. Although the Learning deep representations by mutual information estimation and maximization (DIM) method achieves excellent results in unsupervised learning, the feature representations learned by the DIM method still suffer from the problem of difficult decoupling. decoupling problem. To address this problem, we minimize the mutual information between the intermediate layer feature representations learned by the hidden layer of the encoder during the encoder training process, so that the features learned by each filter are as uncorrelated as possible, thus realizing feature decoupling, and our method is called FP-DIM. Finally, the effectiveness of the FP-DIM method is verified on the CIFAR-10, STL-10, and Fashion-MNIST datasets. The experiments show that our proposed FP-DIM method is more significant for the learned decouplable middle layer feature representation. Finally, we also propose a reflection of future research for the FP-DIM method, aiming to provide a research idea and direction for solving unsupervised interpretable machine learning and to lay a solid theoretical and application foundation for machine learning fields such as image classification and migration learning. © 2023 IEEE.

关键词： Unsupervised learning

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An NTT/INTT Accelerator with Ultra-High Throughput and Area Efficiency for FHE 24

An NTT/INTT Accelerator with Ultra-High Throughput and Area ...

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61st ACM/IEEE Design Automation Conference, DAC 2024

作者： Lu, Zhaojun Yu, Weizong Xu, Peng Wang, Wei Zhang, Jiliang Feng, Dengguo Hubei Key Laboratory of Distributed System Security School of Cyber Science and Engineering Hust Wuhan JinYinHu Laboratory Hubei Wuhan China School of Cyber Science and Engineering Hust Hubei Wuhan China School of Computer Science and Technology Hust Hubei Wuhan China Hnu Hunan Changsha China State Key Laboratory of Cryptology Beijing China

ISBN: (纸本)9798400706011

As a core arithmetic operation and security guarantee of Fully Homomorphic Encryption (FHE), Number Theoretic Transform (NTT) of a large degree is the primary source of computational and time overhead. In this paper, we propose a scalable and conflict-free memory mapping algorithm that breaks the memory bound and releases a large amount of on-chip resources. A flexible and no-stall hardware/software pipeline architecture is designed to boost the throughput of NTT/INTT of N = 216 to over 48,543 operations per second with area efficiency, which 4× and 10× speed up the FPGA-based (HPCA'23) and GPU-based (HPCA'23) schemes. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Memory architecture

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Combined Representation Learning for Uncertain Knowledge Graphs 2

Combined Representation Learning for Uncertain Knowledge Gra...

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2nd International Conference on Mechatronics, IoT and Industrial Informatics, ICMIII 2024

作者： Qiao, Rui Zhong, Xiaolei Fu, Yang Wuhan University of Science and Technology School of Computer Science and Technology Wuhan China Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan China Dianchi College Kunming China Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9798350386639

Uncertain knowledge graphs store the relations between entities in the real world in the form of weighted triples, where the weights indicate the confidence scores of the triples to be true. However, there is limited research on the inference of uncertain knowledge graphs, especially in the integration of symbolic and numerical methods. Rules are explicit knowledge. Encoding the rules directly can provide more knowledge graph semantic information and can make full use of the interpretability and accuracy of logical rules. Unfortunately, none of the existing methods directly encode rules for uncertain knowledge graphs, but only use rules to calculate the confidence of unseen triples in training data. In this paper, we propose a new reasoning model CRLUKG for uncertain knowledge graphs, that combines the advantages of symbolic and numerical reasoning. We directly encode rules mined from the knowledge graph as one of the inputs of the CRLUKG model. Compositional representation learning with triple and rule embedding is implemented for optimizing objective function for triples and relations pairs. Experimental results demonstrate that our proposed method enhances the accuracy of reasoning, and furthermore, CRLUKG exhibits superior performance in comparison to baseline models when handling multi-task scenarios. © 2024 IEEE.

关键词： Knowledge graph

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