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48th Euromicro Conference on software Engineering and Advanced Applications, SEAA 2022

作者： Zabardast, Ehsan Gonzalez-Huerta, Javier Palma, Francis Blekinge Institute of Technology Software Engineering Research Lab SERL Sweden Karlskrona Sweden Linnaeus University Department of Computer Science and Media Technology Kalmar Sweden

ISBN: (纸本)9781665461528

Background: The COVID-19 outbreak interrupted regular activities for over a year in many countries and resulted in a radical change in ways of working for software development companies, i.e., most software development companies switched to a forced Working-From-Home (WFH) mode. Aim: Although several studies have analysed different aspects of forced WFH mode, it is unknown whether and to what extent WFH impacted the accumulation of technical debt (TD) when developers have different ways to coordinate and communicate with peers. Method: Using the year 2019 as a baseline, we carried out an industrial case study to analyse the evolution of TD in five components that are part of a large project while WFH. As part of the data collection, we carried out a focus group with developers to explain the different patterns observed from the quantitative data analysis. Results: TD accumulated at a slower pace during WFH as compared with the working-from-office period in four components out of five. These differences were found to be statistically significant. Through a focus group, we have identified different factors that might explain the changes in TD accumulation. One of these factors is responsibility diffusion which seems to explain why TD grows faster during the WFH period in one of the components. Conclusion: The results suggest that when the ways of working change, the change between working from office and working from home does not result in an increased accumulation of TD. © 2022 IEEE.

关键词： COVID-19

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The Good, The Bad, and Why: Unveiling Emotions in Generative AI 41

The Good, The Bad, and Why: Unveiling Emotions in Generative...

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41st International Conference on Machine Learning, ICML 2024

作者： Li, Cheng Wang, Jindong Zhang, Yixuan Zhu, Kaijie Wang, Xinyi Hou, Wenxin Lian, Jianxun Luo, Fang Yang, Qiang Xie, Xing Microsoft Research Beijing China CAS Institute of Software Beijing China Department of Computer Science William & Mary WilliamsburgVA United States School of Psychology Beijing Normal University Beijing China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong

Emotion significantly affects our daily behaviors and interactions. Although recent generative AI models, such as large language models, have shown impressive performance in various tasks, it remains unclear whether they truly comprehend emotions and why. This paper aims to address this gap by incorporating psychological theories to gain a holistic understanding of emotions in generative AI models. Specifically, we propose three approaches: 1) EmotionPrompt to enhance the performance of the AI model, 2) EmotionAttack to impair the performance of the AI model, and 3) EmotionDecode to explain the effects of emotional stimuli, both benign and malignant. Through extensive experiments involving language and multi-modal models on semantic understanding, logical reasoning, and generation tasks, we demonstrate that both textual and visual EmotionPrompt can boost the performance of AI models while EmotionAttack can hinder it. More importantly, EmotionDecode reveals that AI models can comprehend emotional stimuli similar to the dopamine mechanism in the human brain. Our work heralds a novel avenue for exploring psychology to enhance our understanding of generative AI models, thus boosting the research and development of human-AI collaboration and mitigating potential risks. Copyright 2024 by the author(s)

关键词： Generative adversarial networks

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The Future of Differentiated Thyroid Cancer Recurrence Prediction Using a Machine Learning Framework Advancements, Challenges, and Prospects 4

The Future of Differentiated Thyroid Cancer Recurrence Predi...

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4th International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Imtiaz, Irsa Rehman, Attique Ur Javaid, Sabeen Ali, Tahir Mohammad Mir, Azka Masud, Mehedi Nirsanametla, Yadaiah University of Sialkot Department of Software Engineering Sialkot Pakistan GULF University for Sciences and Technology Department of Software Engineering Mubarak Al-Abdullah Kuwait Taif University Department of Computer Science Taif Saudi Arabia Lovely Professional University Division of Research and Development Phagwara144411 India KKR and KSR institute of technology and sciences Andhra Pradesh Guntur India

ISBN: (纸本)9798350353266

Differentiated thyroid cancer originates in the thyroid gland, which is positioned in the front of the neck. The thyroid gland generates hormones that control metabolism, heart rate, and other bodily functions. Differentiated thyroid carcinoma (DTC) recurrence is a major challenge in clinical therapy. Early detection and treatment play an important role in reducing the impact of thyroid cancer recurrence. The development of precise prediction algorithms is demanding. The prognosis, diagnosis, and treatment of differentiated thyroid carcinoma have been the subject of extensive investigation. Scholars have investigated diverse methodologies to forecast the likelihood of getting thyroid cancer, refine early identification techniques, and augment therapeutic results. Machine learning (ML) frameworks have emerged as useful tools in this setting, with the potential to improve prediction accuracy and patient outcomes. This paper provides a detailed evaluation of the current state and future directions of DTC recurrence prediction using machine learning. We examine current advances in machine learning techniques, data sources, and feature selection approaches used in DTC recurrence prediction models. Several machine-learning algorithms have been applied. We have suggested the model containing the classifier with the highest accuracy after comparing the accuracy percentage of the various classifiers that were produced. Our suggested model has a 98.17% accuracy rate with Bagging. Finally, we propose solutions to these difficulties and emphasize ML's potential to revolutionize the landscape of DTC recurrence prediction. We hope that this analysis will provide insights into the developing role of machine learning in DTC management and motivate more research in this crucial area. © 2024 IEEE.

关键词： Diseases

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A Design of Fetch Target Buffer Implemented on XiangShan Processor

A Design of Fetch Target Buffer Implemented on XiangShan Pro...

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2022 International Conference on Cloud Computing, Internet of Things, and computer Applications, CICA 2022

作者： Zou, Jiangrui Tang, Dan Cai, Ye Li, Zusong School of Computer and Software Shenzhen University Shenzhen China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China Beijing Vcore Technology Co. Ltd. Beijing China

ISBN: (纸本)9781510656673

In the design of modern high-performance processors, branch prediction is a very important component, and good branch prediction can greatly improve the performance of the processor. And Branch Target Buffer (BTB) is an important part of branch prediction. BTB is used to store information about branch instructions. Different from storing each branch as a unit, Glenn et al. proposed a BTB that stores branch information in a unit of fetch block, which they call Fetch Target Buffer (FTB). This design can optimize the timing by limiting the branch prediction width. But they did not give specific implementation details. In this paper, we propose a specific FTB implementation, and it is used in the second version architecture of XiangShan high-performance processor. First, we give the fields in the FTB entry in this design and their corresponding functions. Most BTB entries need to store the following information: branch tag, branch type, target address. In addition to these fields in this paper, FTB will also store other information for managing entries and optimizing branch prediction performance. Secondly, we proposed the management strategy of FTB entries, when to add, modify, and replace. After all the instructions in a fetch block are committed, we need to decide whether this fetch block needs to be written to FTB, and which way to write, how to avoid the problem of multiple hits. Under the 14nm process, compared to the 1.8GHz of the first version of the architecture, the frequency of the new architecture using FTB can be increased to 2GHz. © 2022 SPIE.

关键词： architecture

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Smartphone-based colorimetric detection of formaldehyde in the air

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Building Simulation 2024年第11期17卷 2007-2015页

作者： Meng Yang Jin Ye Tao Yu Ying Song Hua Qian Tianyi Liu Yang Chen Junqi Wang Shi-jie Cao Cong Liu School of Energy and Environment Southeast UniversityNanjing210096China School of Energy and Power Jiangsu University of Science and TechnologyZhenjiangJiangsu212100China Wuhan Second Ship Design and Research Institute Wuhan430205China Hubei Provincial Engineering and Technology Research Center for Food Quality and Safety Test Hubei Provincial Institute for Food Supervision and TestWuhan430075China Laboratory of Image Science and Technology the School of Computer Science and EngineeringSoutheast UniversityNanjingChina Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing Southeast UniversityNanjingChina School of Cyber Science and Engineering Southeast UniversityNanjingChina Key Laboratory of Computer Network and Information Integration Southeast UniversityMinistry of EducationNanjingChina School of Architecture Southeast UniversityNanjingChina Jiangsu Province Engineering Research Center of Urban Heat and Pollution Control Southeast UniversityNanjingChina Global Centre for Clean Air Research(GCARE) University of SurreyGuildfordUK

Adverse impacts of exposure to formaldehyde on human health significantly increases attention in monitoring formaldehyde concentrations in the *** formaldehyde detection methods typically rely on large and costly instruments and requires high skills of expertise,preventing it from being widely accessible to *** study introduced a novel approach utilizing smartphone-based colorimetric *** of green channel signals of digital images by a smartphone successfully capture variation of purple color of 4-amino-3-hydrazino-5-mercapto-1,2,4-triazol solution,which is proportional to formaldehyde *** is because that green and purple are complimentary color pairs.A calibration curve was established between green channel signals and formaldehyde concentrations,with a correlation coefficient of *** limit of the smartphone-based method is 0.008 mg/m^(3).Measurement errors decrease as formaldehyde concentrations increase,with median relative errors of 34%,17%,and 6%for concentration ranges of 0–0.06 mg/m^(3),0.06–0.12 mg/m^(3),and 0.12–0.35 mg/m^(3),*** method replaced scientific instrumentation with ordinary items,greatly reducing cost and operation *** would provide an opportunity to realize onsite measurements for formaldehyde by occupants themselves and increase awareness of air quality for better health protection.

关键词： indoor air quality VOC RGB color identification machine vision

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Two dimensional DOA estimation of coherent signals based on covariance matrix element transformation 24

Two dimensional DOA estimation of coherent signals based on ...

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8th International Conference on Electronic Information technology and computer Engineering, EITCE 2024

作者： Qu, Zhiyu Wu, Xu Geng, Zhenya College of Information Science and Engineering Hohai University Jiangsu Nanjing China College of Computer Science and Software Engineering Hohai University Jiangsu Nanjing China The 49th Research Institute of China Electronics Technology Group Corporation Heilongjiang Harbin China

ISBN: (纸本)9798400710094

Traditional Direction of Arrival (DOA) estimation algorithms for coherent signals in uniform circular array (UCA), such as mode space transformation, allow for the adaptation of advanced algorithms initially developed for uniform linear arrays to be applied effectively in UCA. However, after mode space transformation, the array will encounter aperture loss and will also be unable to perform two-dimensional DOA estimation effectively. In this paper, we propose a new method that that integrates covariance matrix element transformation with the Multiple Signal Classification (MUSIC) to estimate the DOA of coherent signals in various symmetric antenna arrays, including UCA. The algorithm leverages the phase difference symmetry of the antenna array to decorrelate the incoming signals, effectively restoring the full rank of the covariance matrix. Subsequently, it employs two-dimensional MUSIC for DOA estimation. Simulation results demonstrate the effectiveness of the proposed algorithm. © 2024 Copyright held by the owner/author(s).

关键词： Covariance matrix

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I Can’t Share Code, but I need Translation - An Empirical Study on Code Translation through Federated LLM

arXiv

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arXiv 2025年

作者： Kumar, Jahnavi Janapati, Venkata Lakshmana Sasaank Tanguturi, Mokshith Reddy Chimalakonda, Sridhar Research in Intelligent Software & Human Analytics Lab Department of Computer Science and Engineering Indian Institute of Technology Tirupati India

Owing to the rapid evolution of technologies and project requirements, organizations need to upgrade the code base in their software projects to a new version of the programming language or even translating to an entirely new one. However, code translation is resource-intensive and requires expertise in both the source and target languages. While researchers have made progress in automating translations between legacy and modern languages, recent work has increasingly turned to pre-trained Large Language Models (LLMs) to translate efficiently. Given the proprietary nature of code, organizations prefer finetuning LLMs locally rather than relying on external APIs. This is one of the first empirical studies that proposes a Federated LLM-based approach for code translation. The proposed approach enables clients to jointly train a code translator without sharing sensitive data. This study demonstrates that participants can collaboratively develop a FedLLM for efficient code translation (particularly C# to Java and vice-versa) with superior results (more than 40% improvement in CodeLLaMA’s CodeBLEU score) compared to individual client models. Our findings indicate that FedLLM offers a collaborative approach to code translation and could serve as a promising direction for future research in this field. © 2025, CC BY.

关键词： Program translators

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Path Planning Algorithm for Multiple UAVs Based on Artificial Potential Field 11

Path Planning Algorithm for Multiple UAVs Based on Artificia...

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11th Joint International Information technology and Artificial Intelligence Conference, ITAIC 2023

作者： Ren, Chongde Chen, Jinchao Du, Chenglie Yu, Wenquan School of Software Northwestern Polytechnical University Xi'an China Northwestern Polytechnical University Department of Computer Science Xi'an China North Automatic Control Technology Research Institute Department of Land Unmanned System Research and Development Taiyuan China

ISBN: (纸本)9798350333664

Due to the lower cost and higher maneuverability, unmanned aerial vehicles (UAVs) have found extensive use in both the civilian and military worlds. Path planning, as a crucial problem in the process of UAVs flight, aims to determine the optimal routes for multiple UAVs from various starting points to a single destination. However, because of the involvement of complex conditional constraints, path planning becomes a highly challenging problem. The path planning problem involving numerous UAVs is examined in this research, and a SAAPF-MADDPG algorithm based on Artificial Potential Field (APF) is suggested as a solution. First, a SA-greedy algorithm that can change the probability of random exploration by agents based on the number of steps and successful rounds to prevent UAVs from getting trapped in a local optimum. Then, we design complex reward functions based on APF to guide UAVs to destination faster. Finally, SAAPF-MADDPG is evaluated against the MADDPG, DDPG, and MATD3 methods in simulation scenarios to confirm its efficacy. © 2023 IEEE.

关键词： autonomous navigation deep reinforcement learning multi-agent systems path planning

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An Applied Artificial Intelligence Technique for Early-Stage Alzheimer's Disease Prediction 4

An Applied Artificial Intelligence Technique for Early-Stage...

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4th International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Ali, Hassan Imtiaz, Hussain Rehman, Attique Ur Javaid, Sabeen Ali, Tahir Muhammad Alshammeri, Menwa Mir, Azka Kumar, Himanshu University of Sialkot Department of Software Engineering Sialkot Pakistan Gulf University for Science and Technology Department of Computer Science Kuwait College of CIS Al Jouf University Department of Computer Science Saudi Arabia Lovely Professional University Division of Research and Development Phagwara144411 India KKR and KSR Institute of Technology and Sciences Andhra Pradesh Guntur India

ISBN: (纸本)9798350353266

Alzheimer's disease is a neurodegenerative disorder in which central nervous cells gradually die that poses a significant health challenge globally, especially in older people. As life expectancy increases which suggests a large amount of society will be affected. This increasing number of Alzheimer's cases demands the urgency to develop predictive models for early intervention as the real effect of Alzheimer's shows very late. Although there are no treatments capable of reversing the natural pathological changes, we can delay the development of Alzheimer's. Our goal is to, detect this neurodegenerative disease before it becomes more rooted, we can help the patient identify the disease at an early age so they can adapt to their new condition and perform treatments to help manage symptoms. We used the OASIS dataset which consists of multiple features collection of subjects whose age varies from 18 to 96. This research focuses on employing supervised machine learning, we have used various classifiers such as Decision trees, Naïve Bayes, k-NN, random forest, and logistic regression, and evaluated each classification's effectiveness using several well-known performance indicators to forecast Alzheimer's disease status. We used Rapid Miner to test different models and found that the Naïve Bayes classifier achieves the highest accuracy at 97.5% this model has a superior predictive capability and outperforms alternative classifiers. The insights gained from this research contribute to advancing early diagnosis and intervention for Alzheimer's disease. © 2024 IEEE.

关键词： Alzheimer's disease Classification Health care Machine Learning OASIS Prediction

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DDA: Distillation-Driven Acceleration of the Reverse Diffusion Process for Stochastic Multi-Ship Trajectory Prediction

DDA: Distillation-Driven Acceleration of the Reverse Diffusi...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Ma, Kun Han, Qilong Yao, Jingzheng Wu, Changmao Zhang, Yuntao College of Computer Science and Technology Harbin Engineering University Harbin China Yantai Research Institute Harbin Engineering University Yantai China Institute of Software Chinese Academy of Sciences Beijing China College of Shipbuilding Engineering Harbin Engineering University Harbin China

ISBN: (纸本)9798350368741

Modeling stochastic multi-ship trajectories is vital for maritime safety and interaction efficiency. Recent researches show that diffusion models excel in trajectory prediction, surpassing GANs and VAEs in generation quality, diversity and stability. However, their slow sampling speed remains a major limitation, as producing high-quality trajectories typically requires hundreds of denoising steps. We introduce DDA, a novel method that accelerates multi-ship trajectory generation by distilling the reverse diffusion process, progressively reducing sampling steps by half while minimizing quality loss. We use CVAE-based encoder to map multimodal inputs into state embeddings in the latent space, and use distillation diffusion in the latent space to more quickly and better represent multi-ship trajectories. The diffusion model uses Transformer-based core, and we incorporate SO(2) invariance and equivariance to enhance model representation. Validation on real-world AIS datasets shows that the student model retains high-quality trajectory generation while sampling speed is approximately 30 times faster. © 2025 IEEE.

关键词： Accelerating Sampling AIS Diffusion Model Progressive Distillation VAE

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