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arXiv 2025年

作者： Zuo, Kaiwen Tang, Jing Qin, Hanbing Luo, Binli He, Ligang Tang, Shiyan School of Mechanical Science and Engineering Huazhong University of Science and Technology Hubei Wuhan430074 China School of Computer Science University of Warwick CoventryCV4 7AL United Kingdom School of Software and Microelectronics Peking University Beijing100871 China MRC Weatherall Institute of Molecular Medicine University of Oxford OxfordOX1 2JD United Kingdom

Recent advancements in Large Language Models (LLMs) have marked significant progress in understanding and responding to medical inquiries. However, their performance still falls short of the standards set by professional consultations. This paper introduces a novel framework for medical consultation, comprising two main modules: Terminology-Enhanced Information Retrieval (TEIR) and Emotional In-Context Learning (EICL). TEIR ensures implicit reasoning through the utilization of inductive knowledge and key terminology retrieval, overcoming the limitations of restricted domain knowledge in public databases. Additionally, this module features capabilities for processing long context. The EICL module aids in generating sentences with high attribute relevance by memorizing semantic and attribute information from unlabelled corpora and applying controlled retrieval for the required information. Furthermore, a dataset comprising 803,564 consultation records was compiled in China, significantly enhancing the model’s capability for complex dialogues and proactive inquiry initiation. Comprehensive experiments demonstrate the proposed method’s effectiveness in extending the context window length of existing LLMs. The experimental outcomes and extensive data validate the framework’s superiority over five baseline models in terms of BLEU and ROUGE performance metrics, with substantial leads in certain capabilities. Notably, ablation studies confirm the significance of the TEIR and EICL components. In addition, our new framework has the potential to significantly improve patient satisfaction in real clinical consulting situations. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantics

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Improving students’programming quality with the continuous inspection process:a social coding perspective

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Frontiers of computer science 2020年第5期14卷 43-60页

作者： Yao Lu Xinjun Mao Tao Wang Gang Yin Zude Li College of Computer National University of Defense TechnologyChangsha410073China Key Laboratory of Software Engineering for Complex Systems Changsha410073China School of Information Science and Engineering Central South UniversityChangsha410012China

College students majoring in computer science and software engineering need to master skills for high-quality ***,rich research has shown that both the teaching and learning of high-quality programming are challenging and deficient in most college education ***,the continuous inspection paradigm has been widely used by developers on social coding sites(e.g.,GitHub)as an important method to ensure the internal quality of massive code *** paper presents a case where continuous inspection is introduced into the classroom setting to improve students’programming *** the study,we first designed a specific continuous inspection process for students’collaborative projects and built an execution environment for the *** then conducted a controlled experiment with 48 students from the same course during two school years to evaluate how the process affects their programming *** results show that continuous inspection can help students in identifying their bad coding habits,mastering a set of good coding rules and significantly reducing the density of code quality issues introduced in the ***,we describe the lessons learned during the study and propose ideas to replicate and improve the process and its execution platform.

关键词： continuous inspection programming quality SonarQube

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Impacts and Integration of Remote-First Working Environments

arXiv

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arXiv 2022年

作者： Atti, Christopher Cross, Cliff Dogan, Ahmet Bugra Hubbard, Christopher Page, Cameron Montague, Stephen Rabieinejad, Elnaz College of Computing and Software Engineering Kennesaw State University MariettaGA United States Cyber Science Lab School of Computer Science University of Guelph ON Canada

Due to the Covid-19 pandemic in 2020 or other business decisions, remote work is becoming increasingly popular. "Remote first" working environments exist within companies where most employees work remotely. This paper takes a deep dive into the remote-first mentality. It investigates its effects on employees at varying stages in their careers, day-to-day productivity, and working relationships with team members. We found that the remote-first mentality most impacts seasoned employees and managers, potentially due to trouble adjusting to a new way of working compared to the rest of their careers and the "always on" mentality associated with working from home. Regarding productivity, we found that while software development productivity appears unimpacted, the effectiveness of communication and employee wellbeing saw declines which are generally associated with lowered productivity. Finally, we looked closer at the communication side of things and how remote work impacts relationship building. We found that the most significant impacts on relationship building centered around "trust" and "credibility" being harder to build due to a lack of non-verbal cues during social interactions. © 2022, CC BY.

关键词： Productivity

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Number plate recognition smart parking management system using IoT

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Measurement: Sensors 2025年 37卷

作者： Ditta, Allah Ahmed, Muhammad Maroof Mazhar, Tehseen Shahzad, Tariq Alahmed, Yazan Hamam, Habib Department of Information Sciences Division of Science and Technology University of Education Lahore54000 Pakistan School of Computer Science National College of Business Administration and Economics Lahore54000 Pakistan Department of Computer Science and Information Technology School Education Department Government of Punjab Layyah31200 Pakistan Department of Computer Science COMSATS University Islamabad Sahiwal Campus Sahiwal57000 Pakistan Software Engineering Al Ain University Al Ain University Abu Dhabi United Arab Emirates Faculty of Engineering University of de Moncton MonctonNBE1A3E9 Canada School of Electrical Engineering University of Johannesburg Johannesburg2006 South Africa Av. Grandes Ecoles LibrevilleBP 1989 Gabon Bridges for Academic Excellence - Spectrum Tunis Tunisia

This study aims to address the urban vehicle parking issues by proposing a solution using Automatic Number Plate Recognition (ANPR) through image processing and a sensor-based hardware system. Integrating these technologies forms a Smart Parking Management System (SPMS) to automate parking processes and enhance the parking experience. The study aims to create an efficient system that eliminates manual vehicle registration and optimizes space utilization. ANPR and IoT-based sensors help users identify the available slots and pay only for the actual parking duration, which will help to minimize the fixed billing rates. The proposed ANPR system processes vehicle number plates at entry, ensuring seamless identification and eliminating manual registration. IoT sensors monitor real-time slot occupancy, transmitting data to a web admin panel. This panel provides insights such as entry and exit times, total parking duration, and billing costs, facilitating efficient management and remote monitoring. The ANPR-based SPMS reduces reliance on manual processes, streamlining entry procedures. By dynamically assessing slot availability through IoT sensors, users can locate unoccupied spaces quickly, which enhances user convenience. The web admin panel allows administrators to monitor the system remotely, ensuring smooth operations and maintaining accurate records. This study introduces a comprehensive solution to urban parking challenges by integrating ANPR and IoT technologies. The SPMS improves efficiency, reduces human resource needs, and enhances user experience with flexible billing based on actual duration. The combination of hardware and software provides a foundation for effective urban parking management. © 2024 The Authors

关键词： Digital storage

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A Toolbox of Feminist Wonder: Theories and methods that can make a difference 26

A Toolbox of Feminist Wonder: Theories and methods that can ...

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26th ACM Conference on computer-Supported Cooperative Work and Social Computing, CSCW 2023

作者： Hansson, Karin Bardzell, Shaowen Bhandari, Aparajita Boulicault, Marion Doyle, Dylan Thomas Erete, Sheena Cerratto Pargman, Teresa Lazem, Shaimaa Muller, Michael Normark, Maria Petterson, Adrian Rosén, Anton Poikolainen Taylor, Alex S. Thomas, Jakita O. Watson, Julia Södertörn University The School of Natural Sciences Technology and Environmental Studies Sweden Georgia Institute of Technology The School of Interactive Computing United States Cornell University Department of Communication United States University of Edinburgh Department of Philosophy United States University of Colorado Boulder Department of Information Science United States University of Maryland College of Information United States Stockholm University The Department of Computer and Systems Sciences Sweden City for Scientific Research and Technological Applications The Informatics Research Institute Egypt Ibm Research Human Centered Ai United States Uppsala University Department of Informatics and Media Sweden University of Toronto Faculty of Information Canada Aalto University The School of Arts Design and Architecture Finland City University of London The Centre for Human Computer Interaction Design United Kingdom Auburn University Department of Computer Science and Software Engineering United States University of Toronto Department of Computer Science Canada

ISBN: (纸本)9798400701290

This one-day hybrid workshop builds on previous feminist CSCW workshops to explore feminist theoretical and methodological approaches that have provided us with useful tools to see things differently and make space for change. Since its inception over a decade ago, feminist HCI has progressed from the margins to mainstream HCI, with numerous references in the literature. Feminist HCI has also evolved to incorporate other critical HCI practices such as Queer HCI, participatory design, and speculative design. While feminist approaches have grown in popularity and become mainstream, it is getting more difficult to distinguish the feminist emancipatory core from other attempts of developing and improving society in various ways. In this workshop, we therefore want to revisit our feminist roots, where theory is a liberatory and creative practice, motivated by affect, curiosity, and wonder. From this standpoint, we consider which of our feminist tools can make a significant difference today, in a highly datafied world. The goal of this workshop is to;1) create an inventory of feminist theories and concepts that have had an impact on our work as designers, educators, researchers, and activists;2) develop a feminist toolbox for the CSCW community to strengthen our feminist literacy. © 2023 Owner/Author.

关键词： Computation theory

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A Novel Ensemble Learning Method for Online Learning Scenarios 4

A Novel Ensemble Learning Method for Online Learning Scenari...

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4th IEEE International Conference on Electronics Technology, ICET 2021

作者： Liu, Ye Cao, Weipeng Liu, Yiwen Zou, Weidong Shenzhen University College of Computer Science and Software Engineering Shenzhen China Shenzhen University College of International Exchange Shenzhen China Beijing Institute of Technology School of Automation Beijing China

ISBN: (纸本)9781728176734

Online Sequential Extreme Learning Machine (OS-ELM) is a modeling algorithm for data stream mining that has attracted much attention in recent years. OS-ELM has extremely fast training speed and good generalization ability, however, because it uses random input weights for the model initialization and these parameters remain unchanged throughout the training process, which causes instability in the performance of the model. To alleviate this problem, we propose an improved OS-ELM algorithm based on the ensemble learning mechanism, which we call ELOS-ELM. ELOS-ELM uses multiple initialization methods to construct diverse base online learners, and the prediction results of the model are determined by voting by these base models. In this way, the final model can avoid misjudgments caused by the knowledge blind zone of a single base model. Extensive experimental results on eight benchmarks show that ELOS-ELM can effectively improve the stability and generalization ability of the OS-ELM. © 2021 IEEE.

关键词： E-learning

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KnowPO: Knowledge-Aware Preference Optimization for Controllable Knowledge Selection in Retrieval-Augmented Language Models 39

KnowPO: Knowledge-Aware Preference Optimization for Controll...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zhang, Ruizhe Xu, Yongxin Xiao, Yuzhen Zhu, Runchuan Jiang, Xinke Chu, Xu Zhao, Junfeng Wang, Yasha School of Computer Science Peking University Beijing China Key Laboratory of High Confidence Software Technologies Ministry of Education Beijing China National Engineering Research Center For Software Engineering Peking University Beijing China Center on Frontiers of Computing Studies Peking University Beijing China Peking University Information Technology Institute Binhai Tianjin China Nanhu Laboratory Jiaxing China

ISBN: (纸本)157735897X

By integrating external knowledge, Retrieval-Augmented Generation (RAG) has become an effective strategy for mitigating the hallucination problems that large language models (LLMs) encounter when dealing with knowledge-intensive tasks. However, in the process of integrating external non-parametric supporting evidence with internal parametric knowledge, inevitable knowledge conflicts may arise, leading to confusion in the model’s responses. To enhance the knowledge selection of LLMs in various contexts, some research has focused on refining their behavior patterns through instruction-tuning. Nonetheless, due to the absence of explicit negative signals and comparative objectives, models fine-tuned in this manner may still exhibit undesirable behaviors such as contextual ignorance and contextual overinclusion. To this end, we propose a Knowledge-aware Preference Optimization strategy, dubbed KnowPO, aimed at achieving adaptive knowledge selection based on contextual relevance in real retrieval scenarios. Concretely, we proposed a general paradigm for constructing knowledge conflict datasets, which comprehensively cover various error types and learn how to avoid these negative signals through preference optimization methods. Simultaneously, we proposed a rewriting strategy and data ratio optimization strategy to address preference imbalances. Experimental results show that KnowPO outperforms previous methods for handling knowledge conflicts by over 37%, while also exhibiting robust generalization across various out-of-distribution datasets. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Knowledge acquisition

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A Parallel Evolutionary Strategy for the Large-Scale Dynamic Optimal Reactive Power Flow

A Parallel Evolutionary Strategy for the Large-Scale Dynamic...

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2020 IEEE International Conference on Power Electronics, Smart Grid and Renewable Energy, PESGRE 2020

作者： Xiao, Chixin Sutanto, Danny Muttaqi, Kashem M. Zhang, Minjie University of Wollongong School of Electrical Computer and Telecommunications Engineering Wollongong Australia University of Wollongong School of Computer Science and Software Engineering Wollongong Australia

ISBN: (纸本)9781728142517

This paper proposes a parallel evolutionary strategy to solve a large-scale dynamic optimal reactive power flow (ORPF) problem to minimize the transmission losses while ensuring that the power system constraints are met, by varying the voltage magnitude of generators, the transformer taps and all reactive power support, installed in the power system. The existing heuristic algorithms are time-consuming due to the large number of sequential calculations that need to be carried out. This paper proposes the use of a parallel evolutionary strategy to speed up the optimization of a large-scale ORPF problem based on the orthogonal design and the Nash equilibrium. The paper proposes to apply the orthogonal crossover operator for small but good representative combination samples, and then to apply the Nash Equilibrium to refine the combinations (i.e., to achieve the optima). The proposed design is effective to strengthen both the exploration and the exploitation of the decision space to obtain the global optimum. The simulation results validate the effectiveness of the proposed algorithms for a large-scale ORPF when applied on the IEEE 30-and IEEE 118 bus systems when compared with the results from current state-of-art approaches. © 2020 IEEE.

关键词： Heuristic algorithms

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How Deep is Love in LLMs’ Hearts? Exploring Semantic Size in Human-like Cognition

arXiv

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arXiv 2025年

作者： Yao, Yao Yang, Yifei Ma, Xinbei Yang, Dongjie Zhang, Zhuosheng Li, Zuchao Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Shanghai Key Laboratory of Trusted Data Circulation and Governance in Web3 Shanghai China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan University Wuhan430072 China

How human cognitive abilities are formed has long captivated researchers. However, a significant challenge lies in developing meaningful methods to measure these complex processes. With the advent of large language models (LLMs), which now rival human capabilities in various domains, we are presented with a unique testbed to investigate human cognition through a new lens. Among the many facets of cognition, one particularly crucial aspect is the concept of semantic size—the perceived magnitude of both abstract and concrete words or concepts. This study seeks to investigate whether LLMs exhibit similar tendencies in understanding semantic size, thereby providing insights into the underlying mechanisms of human cognition. We begin by exploring metaphorical reasoning, comparing how LLMs and humans associate abstract words with concrete objects of varying sizes. Next, we examine LLMs’ internal representations to evaluate their alignment with human cognitive processes. Our findings reveal that multi-modal training is crucial for LLMs to achieve more human-like understanding, suggesting that real-world, multi-modal experiences are similarly vital for human cognitive development. Lastly, we examine whether LLMs are influenced by attention-grabbing headlines with larger semantic sizes in a real-world web shopping scenario. The results show that multi-modal LLMs are more emotionally engaged in decision-making, but this also introduces potential biases, such as the risk of manipulation through clickbait headlines. Ultimately, this study offers a novel perspective on how LLMs interpret and internalize language, from the smallest concrete objects to the most profound abstract concepts like ‘love’. The insights gained not only improve our understanding of LLMs but also provide new avenues for exploring the cognitive abilities that define human intelligence. Our code and dataset is publicly available at https://***/Zoeyyao27/LLM-Semantic-Size-Understanding. © 2025, CC BY.

关键词： Semantics

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Analyzing XAI Metrics: Summary of the Literature Review

TechRxiv

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TechRxiv 2022年

作者： Sisk, Marion Majlis, Makeen Page, Cameron Yazdinejad, Abbas College of Computing and Software Engineering Kennesaw State University MariettaGA United States Cyber Science Lab School of Computer Science University of Guelph ON Canada

The widespread adoption of Artificial Intelligence (AI) models by various industries in recent years have made Explainable Artificial Intelligence (XAI) an active field of research. This adoption can cause trust and effectiveness to suffer if the results of these models are not favorable in some way. XAI has advanced to the point where many metrics have been proposed as reasons for the outputs of many AI models. However, there is little consensus about what technical metrics are most important, nor is there a consensus on how best to analyze explainable methods and models. A discussion of varying attempts at this is brought forth, but the paper also goes into the ethics of AI and its societal impact. Given the modern ubiquity with which AI exists and the immensely multidisciplinary approach, AI has evolved into, using only technical metrics cannot fully describe XAI’s effectiveness. This paper explores several approaches to measuring the ethical effects of XAI, whether it has any bearing on modern research, as well as how the impacts of AI and XAI are measured on society. The full attempt at quantifying XAI models’ effectiveness is explored from a technical and non-technical point of view. © 2022, CC BY.

关键词： Artificial intelligence

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