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International Conference on software engineering (ICSE)

作者： Daniel Oliveira Wesley K. G. Assunção Alessandro Garcia Ana Carla Bibiano Márcie Ribeiro Rohit Gheyi Baldoino Fonseca Informatics Department Pontifical Catholic University of Rio de Janeiro (PUC-Rio) Rio de Janeiro Brazil Institute of Software Systems Engineering - Johannes Kepler University (JKU) Linz Austria Computing Institute - Federal University of Alagoas (UFAL) Maceió Brazil Department of Computing and Systems Federal University of Campina Grande (UFCG) Campina Grande Brazil

Refactoring is a common software maintenance practice. The literature defines standard code modifications for each refactoring type and popular IDEs provide refactoring tools aiming to support these standard modifications. However, previous studies indicated that developers either frequently avoid using these tools or end up modifying and even reversing the code automatically refactored by IDEs. Thus, developers are forced to manually apply refactorings, which is cumbersome and error-prone. This means that refactoring support may not be entirely aligned with practical needs. The improvement of tooling support for refactoring in practice requires understanding in what ways developers tailor refactoring modifications. To address this issue, we conduct an analysis of 1,162 refactorings composed of more than 100k program modifications from 13 software projects. The results reveal that developers recurrently apply patterns of additional modifications along with the standard ones, from here on called patterns of customized refactorings. For instance, we found customized refactorings in 80.77% of the Move Method instances observed in the software projects. We also investigated the features of refactoring tools in popular IDEs and observed that most of the customization patterns are not fully supported by them. Additionally, to understand the relevance of these customizations, we conducted a survey with 40 developers about the most frequent customization patterns we found. Developers confirm the relevance of customization patterns and agree that improvements in IDE's refactoring support are needed. These observations highlight that refactoring guidelines must be updated to reflect typical refactoring customizations. Also, IDE builders can use our results as a basis to enable a more flexible application of automated refactorings. For example, developers should be able to choose which method must handle exceptions when extracting an exception code into a new method.

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Monge-Ampere Regularization for Learning Arbitrary Shapes from Point Clouds

arXiv

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arXiv 2024年

作者： Yang, Chuanxiang Zhou, Yuanfeng Wei, Guangshun Ma, Long Hou, Junhui Liu, Yuan Wang, Wenping The School of Software Shandong University Jinan250100 China The Department of Computer Science City University of Hong Kong Hong Kong The College of Computing and Data Science Nanyang Technological University Singapore The Department of Computer Science and Engineering Texas A&M University College StationTX77843 United States

As commonly used implicit geometry representations, the signed distance function (SDF) is limited to modeling watertight shapes, while the unsigned distance function (UDF) is capable of representing various surfaces. However, its inherent theoretical shortcoming, i.e., the non-differentiability at the zero level set, would result in sub-optimal reconstruction quality. In this paper, we propose the scaled-squared distance function (S2DF), a novel implicit surface representation for modeling arbitrary surface types. S2DF does not distinguish between inside and outside regions while effectively addressing the non-differentiability issue of UDF at the zero level set. We demonstrate that S2DF satisfies a second-order partial differential equation of Monge-Ampere-type, allowing us to develop a learning pipeline that leverages a novel Monge-Ampere regularization to directly learn S2DF from raw unoriented point clouds without supervision from ground-truth S2DF values. Extensive experiments across multiple datasets show that our method significantly outperforms state-of-the-art supervised approaches that require ground-truth surface information as supervision for training. The code will be publicly available at https://***/chuanxiang-yang/S2DF. Copyright © 2024, The Authors. All rights reserved.

关键词： Supervised learning

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Investigating the Use of Natural Language Processing in Electronic Medical Record

Investigating the Use of Natural Language Processing in Elec...

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International Conference on computing and Networking Technology (ICCNT)

作者： Balaram Yadav Kasula Sachin C. Patil Prashamsh Takkalapally Ketan Gupta J. Logeshwaran Dept of Information Technology University Of The Cumberlands Williamsburg KY USA Senior Software Engineer USA Department of Engineering Computing and Mathematical Sciences Lewis University USA Dept. of Information Technology University of The Cumberlands Williamsburg KY USA Department of Computer Science CHRIST (Deemed to be University) Bengaluru Karnataka India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Natural language processing (NLP) implemented in digital scientific records (EMRs) can substantially enhance the nice and efficiency of affected person care. The purpose of NLP implemented in EMRs is to extract applicable facts from affected persons’ notes written in a human language together with English. This information can then be stored in a suitable structured form for further evaluation and records mining. NLP has been carried out in the clinical field for the reason that Fifties as a green approach for retrieving textual content-based data and reading interactions among affected persons and healthcare professionals. With the arrival of electronic facts, NLP has come to be extra extensively applied for the diffusion of purposes, inclusive of automatic coding, scientific choice aid, and medical doctor order access. This summary makes a of exploring the usage of NLP in EMRs. The scope of this research consists of an evaluate of present NLP technologies and their software in EMRs. It additionally outlines a number of the present-day demanding situations inside the use of NLP for clinical information and shows capability answers. Finally, the potential applications of NLP-driven EMRs are discussed, inclusive of making use of in-health practitioner order entry, scientific choice assistance, and population health control.

关键词： Electric potential Accuracy Natural language processing software Encoding Data mining Clinical diagnosis Protection Electronic medical records

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Information Freshness-Oriented Relay Selection in Two-Way Relay Networks: A Multi-Armed Bandit Approach

Information Freshness-Oriented Relay Selection in Two-Way Re...

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Consumer Communications and Networking Conference, CCNC IEEE

作者： Guanyu Chen Tse-Tin Chan Haoyuan Pan Kin-Hon Ho College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Mathematics and Information Technology The Education University of Hong Kong Hong Kong SAR China Department of Computing The Hang Seng University of Hong Kong Hong Kong SAR China

We study the relay selection problem for information freshness-oriented two-way relay networks (TWRNs) operated with physical-layer network coding (PNC). Information freshness is quantified by age of information (AoI), defined as the time elapsed since the generation time of the latest received information update. Since PNC leads to mutual wireless interference in TWRNs, this complicates the relay selection for users. To address this problem, this paper formulates relay selection as a multi-armed bandit (MAB) to dynamically learn the optimal mapping between users and relays. Specifically, the two end users act as agents, interacting with the environment, receiving feedback as rewards in the MAB, and then optimizing the system-level AoI performance through the learning experience. Simulation results demonstrate that the proposed MAB approach significantly outperforms the conventional relay selection scheme.

关键词： Wireless communication Protocols Heuristic algorithms Simulation Relay networks (telecommunication) Interference Network coding

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Cactus Mechanisms: Optimal Differential Privacy Mechanisms in the Large-Composition Regime

arXiv

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arXiv 2022年

作者： Alghamdi, Wael Asoodeh, Shahab Calmon, Flavio P. Kosut, Oliver Sankar, Lalitha Wei, Fei The School of Engineering and Applied Science Harvard University United States The Department of Computing and Software McMaster University Canada The School of Electrical Computer and Energy Engineering Arizona State University United States

Most differential privacy mechanisms are applied (i.e., composed) numerous times on sensitive data. We study the design of optimal differential privacy mechanisms in the limit of a large number of compositions. As a consequence of the law of large numbers, in this regime the best privacy mechanism is the one that minimizes the Kullback-Leibler divergence between the conditional output distributions of the mechanism given two different inputs. We formulate an optimization problem to minimize this divergence subject to a cost constraint on the noise. We first prove that additive mechanisms are optimal. Since the optimization problem is infinite dimensional, it cannot be solved directly;nevertheless, we quantize the problem to derive near-optimal additive mechanisms that we call "cactus mechanisms" due to their shape. We show that our quantization approach can be arbitrarily close to an optimal mechanism. Surprisingly, for quadratic cost, the Gaussian mechanism is strictly sub-optimal compared to this cactus mechanism. Finally, we provide numerical results which indicate that cactus mechanism outperforms the Gaussian mechanism for a finite number of compositions. © 2022, CC BY.

关键词： Additives

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Improving Deep Assertion Generation via Fine-Tuning Retrieval-Augmented Pre-trained Language Models

arXiv

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arXiv 2025年

作者： Zhang, Quanjun Fang, Chunrong Zheng, Yi Zhang, Yaxin Zhao, Yuan Huang, Rubing Zhou, Jianyi Yang, Yun Zheng, Tao Chen, Zhenyu Department of Computing Technologies Swinburne University of Technology Melbourne Australia State Key Laboratory for Novel Software Technology Nanjing University Jiangsu Nanjing China School of Computer Science and Engineering Macau University of Science and Technology Jiangsu Nanjing China Huawei Cloud Computing Technologies Co. Ltd. Beijing China Shenzhen Research Institute of Nanjing University Guangdong Shenzhen China

Unit testing validates the correctness of the units of the software system under test and serves as the cornerstone in improving software quality and reliability. To reduce manual efforts in writing unit tests, some techniques have been proposed to generate test assertions automatically, including deep learning (DL)-based, retrieval-based, and integration-based ones. Among them, recent integration-based approaches inherit from both DL-based and retrieval-based approaches and are considered state-of-the-art. Despite being promising, such integration-based approaches suffer from inherent limitations, such as retrieving assertions with lexical matching while ignoring meaningful code semantics, and generating assertions with a limited training corpus. In this paper, we propose a novel Retrieval-Augmented Deep Assertion Generation approach, namely RetriGen, based on a hybrid assertion retriever and a pre-trained language model (PLM)-based assertion generator. Given a focal-test, RetriGen first builds a hybrid assertion retriever to search for the most relevant test-assert pair from external codebases. The retrieval process takes both lexical similarity and semantical similarity into account via a token-based and an embedding-based retriever, respectively. RetriGen then treats assertion generation as a sequence-to-sequence task and designs a PLM-based assertion generator to predict a correct assertion with historical test-assert pairs and the retrieved external assertion. Although our concept is general and can be adapted to various off-the-shelf encoder-decoder PLMs, we implement RetriGen to facilitate assertion generation based on the recent CodeT5 model. We conduct extensive experiments to evaluate RetriGen against six state-of-the-art approaches across two large-scale datasets and two metrics. The experimental results demonstrate that RetriGen achieves 57.66% and 73.24% in terms of accuracy and CodeBLEU, outperforming all baselines with an average improvement of 50.66

关键词： Semantics

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Instruction Tuning-free Visual Token Complement for Multimodal LLMs

arXiv

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arXiv 2024年

作者： Wang, Dongsheng Cui, Jiequan Li, Miaoge Lin, Wang Chen, Bo Zhang, Hanwang College of Computer Science and Software Engineering Shenzhen University China School of Computer Science and Engineering Nanyang Technological University Singapore Department of Computing Hong Kong Polytechnic University Hong Kong College of Computer Science and Technology Zhejiang University China National Key Lab of Radar Signal Processing Xidian University China

As the open community of large language models (LLMs) matures, multimodal LLMs (MLLMs) have promised an elegant bridge between vision and language. However, current research is inherently constrained by challenges such as the need for high-quality instruction pairs and the loss of visual information in image-to-text training objectives. To this end, we propose a Visual Token Complement framework (VTC) that helps MLLMs regain the missing visual features and thus improve response accuracy. Specifically, our VTC integrates text-to-image generation as a guide to identifying the text-irrelevant features, and a visual selector is then developed to generate complementary visual tokens to enrich the original visual input. Moreover, an iterative strategy is further designed to extract more visual information by iteratively using the visual selector without any additional training. Notably, the training pipeline requires no additional image-text pairs, resulting in a desired instruction tuning-free property. Both qualitative and quantitative experiments demonstrate the superiority and efficiency of our VTC. Copyright © 2024, The Authors. All rights reserved.

关键词： Visual BASIC

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Segmentation and Classification of Stomach Abnormalities Using Deep Learning

引用

Computers, Materials & Continua 2021年第10期69卷 607-625页

作者： Javeria Naz Muhammad Attique Khan Majed Alhaisoni Oh-Young Song Usman Tariq Seifedine Kadry Department of Computer Science HITEC University TaxilaTaxilaPakistanl 2College of Computer Science and EngineeringUniversity of Ha’ilHa’ilSaudi Arabial 3Department of SoftwareSejong UniversitySeoulKoreaGwangjin-guKoreal 4College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAl-KhrajSaudi Arabial 5Faculty of Applied Computing and TechnologyNoroff University CollegeKristiansandNorway

An automated system is proposed for the detection and classification of GI *** proposed method operates under two pipeline procedures:(a)segmentation of the bleeding infection region and(b)classification of GI abnormalities by deep *** first bleeding region is segmented using a hybrid *** threshold is applied to each channel extracted from the original RGB ***,all channels are merged through mutual information and pixel-based *** a result,the image is *** and deep learning features are extracted in the proposed classification *** transfer learning(TL)approach is used for the extraction of deep *** Local Binary Pattern(LBP)method is used for texture ***,an entropy-based feature selection approach is implemented to select the best features of both deep learning and texture *** selected optimal features are combined with a serial-based technique and the resulting vector is fed to the Ensemble Learning *** experimental process is evaluated on the basis of two datasets:Private and *** accuracy achieved is 99.8 per cent for the private data set and 86.4 percent for the KVASIR data *** can be confirmed that the proposed method is effective in detecting and classifying GI abnormalities and exceeds other methods of comparison.

关键词： Gastrointestinal tract contrast stretching segmentation deep learning features selection

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The Saddle-Point Accountant for Differential Privacy

arXiv

引用

arXiv 2022年

作者： Alghamdi, Wael Asoodeh, Shahab Calmon, Flavio P. Gomez, Juan Felipe Kosut, Oliver Sankar, Lalitha Wei, Fei The School of Engineering and Applied Science Harvard University United States The Department of Computing and Software McMaster University Canada The School of Electrical Computer and Energy Engineering Arizona State University United States

We introduce a new differential privacy (DP) accountant called the saddle-point accountant (SPA). SPA approximates privacy guarantees for the composition of DP mechanisms in an accurate and fast manner. Our approach is inspired by the saddle-point method-a ubiquitous numerical technique in statistics. We prove rigorous performance guarantees by deriving upper and lower bounds for the approximation error offered by SPA. The crux of SPA is a combination of large-deviation methods with central limit theorems, which we derive via exponentially tilting the privacy loss random variables corresponding to the DP mechanisms. One key advantage of SPA is that it runs in constant time for the n-fold composition of a privacy mechanism. Numerical experiments demonstrate that SPA achieves comparable accuracy to state-of-the-art accounting methods with a faster runtime. © 2022, CC BY.

关键词： Numerical methods

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Work in Progress: An AI-Assisted Metaverse for Computer Science Education

Work in Progress: An AI-Assisted Metaverse for Computer Scie...

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IEEE World engineering Education Conference (EDUNINE)

作者： Kin-Hon Ho Yun Hou Chun Fai Carlin Chu Chi-Kong Chan Haoyuan Pan Tse-Tin Chan Department of Computing The Hang Seng University of Hong Kong Hong Kong SAR China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Mathematics and Information Technology The Education University of Hong Kong Hong Kong SAR China

This paper proposes the use of metaverse for computer science education by leveraging virtual reality (VR) and artificial intelligence (AI) technologies. VR glasses are used to record videos such as presentation slides and classroom lectures in the metaverse. AI technologies automatically generate customized class notes summarized from the video recordings. This education metaverse can accommodate students' digital avatars in different virtual workspaces, including collaboration spaces, laboratories, and classrooms. It consists of practical features that mimic a physical classroom setup, such as real-time voice chat, avatar movement, and shared screen presentations. In the metaverse journey, the AI notes generator module is used to create personalized class notes using technologies including optical character recognition, automatic speech recognition, natural language processing, and text-to-speech. We discuss current progress and future work to prototype an AI-assisted metaverse for proof-of-concept experiments in computer science education.

关键词： Metaverse Avatars Optical character recognition Prototypes Real-time systems Generators Natural language processing

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