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arXiv 2025年

作者： Chen, Bo-Wei Yen, An-Zi Chen, Chung-Chi Department of Computer Science National Yang Ming Chiao Tung University Taiwan National Institute of Advanced Industrial Science and Technology Japan

Legal writing demands clarity, formality, and domain-specific precision—qualities often lacking in documents authored by individuals without legal training. To bridge this gap, this paper explores the task of legal text refinement that transforms informal, conversational inputs into persuasive legal arguments. We introduce FinDR, a Chinese dataset of financial dispute records, annotated with official judgments on claim reasonableness. Our proposed method, Multi-Scale Model Interaction (MSMI), leverages a lightweight classifier to evaluate outputs and guide iterative refinement by Large Language Models (LLMs). Experimental results demonstrate that MSMI significantly outperforms single-pass prompting strategies. Additionally, we validate the generalizability of MSMI on several short-text benchmarks, showing improved adversarial robustness. Our findings reveal the potential of multi-model collaboration for enhancing legal document generation and broader text refinement tasks. © 2025, CC BY-NC-ND.

关键词： Benchmarking

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What Drives People to Adopt Grocery Apps? The Moderating Role of Household Size

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Business Perspectives and Research 2025年第1期13卷 11-33页

作者： Chakraborty, Debarun Bhatnagar, Shakti Bodh Biswas, Wendrila Khatua, Ajay Kumar Symbiosis Institute of Business Management Nagpur India Symbiosis Centre for Management Studies Constituent of Symbiosis International (Deemed University Maharashtra Nagpur Pune India Department of Management and Social Science Haldia Institute of Technology West Bengal Haldia India Institute of Business and Computer Studies Siksha ‘O’ Anusandhan (Deemed to be University) Odisha Bhubaneswar India Constituent of Symbiosis International Deemed University Maharashtra Pune India

With the advancement of information technology, varied important applications have eventually brought a change in the people’s lifestyle. Moreover, smartphones gave a new face with the emergence of mobile apps. Grocery apps are one of such applications that have created customized and finest shopping experiences for consumers today. The purpose of the paper is to highlight the underlying factors influencing a consumer’s behavioral intention (BI) to adopt and accept the technology (i.e., grocery apps). The unique constructs have been derived from potential models of technology acceptance. The paper investigated the effects and relationships of such factors in adoption of grocery apps and its actual usage. Thus, a general adoption model was evolved that provided a comprehensive understanding of the consumers’ intention to adopt the grocery apps and identify the level of its acceptance. Based on the insights from prior research, a new model has been developed. 440 respondents have provided their valuable responses. The research findings revealed that household size significantly moderates the relationship between BI and actual use (AU). © 2022 K. J. Somaiya institute of Management.

关键词： actual use adoption Grocery apps household size technology acceptance

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A Comparative Analysis of Machine Learning Techniques for Identification and Categorization of Dermatological Disorders

A Comparative Analysis of Machine Learning Techniques for Id...

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International Conference on Signal Processing and Communication (ICSC)

作者： Tanya Shakya Nikita Joshi Department of Computer Science Institute of Management Studies Ghaziabad (University Courses Campus) Ghaziabad Uttar Pradesh India

ISBN: (数字)9798331511890

ISBN: (纸本)9798331511906

The integration of intelligent computing and machine learning (ML) has transformed various fields, leading to significant advancements in automation, data analysis, and decision-making processes. The diagnosis of diseases and access to healthcare services have long been in high demand, yet they face significant challenges. Machine learning offers a wide range of effective solutions for this purpose. While skin disorders may appear harmless, they can become serious if not treated properly. Among the most challenging issues in medical image processing is identifying skin conditions from clinical photos. Furthermore, identifying skin conditions by hand by medical professionals is subjective and time-consuming. Therefore, automatic skin disease prediction is necessary for both patients and dermatologists, which speeds up treatment planning. In this paper a comparitive study has been done on various classification algorithms and performed on various healthcare datasets and the most effective classification algorithm for the dermatological dataset has been identified. Classification algorithms have been compared on the basis of their classification accuracy. The study suggested that Logistic Model Trees (LMT) and Navie Bayes Algorithms can be used for the efficient classification of dermatology disorders.

关键词： Dermatology Signal processing algorithms Machine learning Medical services Signal processing Skin Classification algorithms Planning Medical diagnostic imaging Diseases

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Constructing Rectilinear Steiner Minimum Tree with Conditional Generative Adversarial Network

Constructing Rectilinear Steiner Minimum Tree with Condition...

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International Conference on VLSI Design

作者： Kritanta Saha Pritha Banerjee Susmita Sur-Kolay Advanced Computing and Microelectronics Unit Indian Statistical Institute Kolkata India Department of Computer Science and Engineering Sister Nivedita University Kolkata India Department of Computer Science and Engineering University of Calcutta Kolkata India

ISBN: (数字)9798331522445

ISBN: (纸本)9798331522452

Given n points in the 2D plane which are the pins of a net, a Rectilinear Steiner Minimum Tree (RSMT) which consists only of vertical and horizontal line segments is the shortest network to interconnect the pins along with a few extra (Steiner) points. An RSMT for a net has a minimum total wirelength in Manhattan distance and acts as a backbone to many state-of-the-art global routers. However, finding an RSMT is NPcomplete. In this work, we propose a method for constructing an RSMT with an image-to-image translation using a conditional generative adversarial network (cGAN) based model. Images of RSMTs with optimal wirelength for randomly generated nets form our training dataset. Experimental results show that, after training, our model generates RSMTs with $\leq 0.348\%$ wirelength error on average compared with the optimal solution for nets with degrees up to 50. This is the first successful attempt to solve the RSMT construction problem as an image-to-image translation problem using a cGAN based model.

关键词： Training Steiner trees Translation Computational modeling Machine learning Very large scale integration Generative adversarial networks Generators Pins Testing

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X-ClusterLink: An Efficient Cross-Cluster Communication Framework in Multi-Kubernetes Clusters 25

X-ClusterLink: An Efficient Cross-Cluster Communication Fram...

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Proceedings of the ACM on Web Conference 2025

作者： Pengbo Wang Gongming Zhao Yuantao Wu Hongli Xu Haibo Wang School of Computer Science and Technology University of Science and Technology of China Hefei China and Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou China Department of Computer Science University of Kentucky Lexington USA

ISBN: (纸本)9798400712746

Kubernetes is widely adopted by enterprises to enhance service availability for applications such as web services and large-scale model training, due to its advantages in managing containerized applications. As service demands increase, a single Kubernetes cluster often becomes insufficient, leading to the trend of using multiple clusters to improve service scalability. However, achieving efficient cross-cluster communication poses significant challenges due to the need for low latency, high throughput, and strong robustness. Existing methods for cross-cluster communication either employ a centralized control plane, which becomes a communication bottleneck, or use numerous service-bound proxies, leading to increased management complexity and possibly compromised robustness in cross-cluster *** address the above challenges, we introduce X-ClusterLink, a framework designed for efficient cross-cluster communication in multi-Kubernetes clusters. X-ClusterLink first employs broker clusters to ensure low-latency cross-cluster synchronization. Then, it aggregates multiple containerized gateways to enhance throughput and leverages eXpress Data Path (XDP) for advanced packet processing, thereby accelerating traffic forwarding. Finally, it incorporates Bucket-Based Consistent ECMP to facilitate seamless failover and enhance robustness. Experimental results demonstrate that X-ClusterLink significantly improves cross-cluster communication efficiency, increasing cross-cluster forwarding bandwidth by 3.1 × compared to existing solutions.

关键词： kubernetes

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MIN: Multi-stage Interactive Network for Multimodal Recommendation 25th

MIN: Multi-stage Interactive Network for Multimodal Recomme...

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25th International Conference on Web Information Systems Engineering, WISE 2024

作者： Mo, Minghao Lu, Weihai Xie, Qixiao Lv, Xiang Xiao, Zikai Yang, Hong Zhang, Yanchun Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou China School of Computer Science and Technology Zhejiang Normal University Zhejiang China Peking University Beijing China Department of Industrial and Systems Engineering The Hong Kong Polytechnic University Hung Hom China

ISBN: (纸本)9789819605699

With the surge in multimedia applications and the explosion of multimedia information, multimodal recommendation has garnered increasing attention. The core idea of multimodal recommendation is to effectively utilize the multimodal information of items to enhance the performance of recommendation systems. Previous work has primarily focused on integrating item ID embeddings and multimodal representations. However, there are two main drawbacks: first, a lack of interaction between multimodal features, which neglects the inherent semantic relationships within these features. Second, the distribution gap between ID embeddings and multimodal representations adversely affects recommendation performance. In this paper, we propose a novel Multi-stage Interactive Network for multimodal recommendation, named MIN. Specifically, we first discarded the ID embeddings, retaining only the multimodal features to represent the items. Then, we designed a three-stage interaction network to model the multimodal representations and the latent structural semantics between users and items. Additionally, to obtain more general and robust representations, we applied graph enhancement techniques in the final stage to enhance the representations of users and items. Finally, contrastive learning techniques were employed to address the inconsistency in user and item representations. A series of comprehensive experiments on three publicly available datasets have validated the efficacy of our method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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Beamforming and Reflection Design for Short Packet ISAC With Non-Ideal RIS: An A3C-Based Approach

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IEEE Transactions on Vehicular Technology 2025年

作者： Mahmoudi, Behrad Khonsari, Ahmad Zeinali, Farshad Mili, Mohammad Robat Mashhadi, Mahdi Boloursaz Xiao, Pei University of Tehran Department of Electrical and Computer Engineering Tehran Iran University of Tehran School of Electrical and Computer Engineering College of Engineering Tehran Iran School of Computer Science in the Institute for Research in Fundamental Sciences Tehran Iran United Kingdom Institute for Advanced Innovative Solutions Tehran Iran

Integrated sensing and communication (ISAC) is a promising solution to mitigate the increasing congestion of the wireless spectrum. In this paper, we investigate the short packet communication regime within an ISAC system assisted by a reconfigurable intelligent surface (RIS) to meet the low latency ultra-reliable requirements in the next-generation wireless networks. We consider a non-ideal RIS model that captures effects of the phase-dependent amplitude variations in the reflection coefficients, and we have incorporated the near-field model into the channels between the RIS and the users or targets. In this setup, we jointly design the transmit beamforming and the RIS phase shifts to maximize the sum rate while satisfying the sensing signal-to-noise ratio (SNR) requirement. The system simultaneously carries out multitarget sensing and multi-user short packet communications with the help of the RIS. Considering the non-convex and dynamic nature of the resulting optimization problem, we propose an asynchronous advantage actor-critic (A3C) based method for beamforming and reflection design in this setup. Numerical results demonstrate the superiority of the proposed scheme over the benchmarks. © 1967-2012 IEEE.

关键词： Beamforming

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Social Media Sentiment Analysis for Airline Customer Satisfaction

Social Media Sentiment Analysis for Airline Customer Satisfa...

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Disruptive Technologies (ICDT), International Conference on

作者： Shivani Chaudhary Khushi Garg Purnima Gupta Anshu Kumar Dwivedi Department of Computer Science Institute of Management Studies Ghaziabad (University Courses Campus) Adhyatmik Nagar India School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

Among the most important subfields in natural language processing, one of the fields is sentiment analysis. Sentiment analysis focuses on recognizing and estimating emotions in text. It has gained more importance in aviation in the sense that it can present insights into customer satisfaction and brand reputation as well as operational efficiency. This work investigates the topic of the sentiment analysis of the Twitter US Airline Sentiment Dataset: more than 14,000 tweets regarding the major US airlines labelled as positive, neutral, or negative. By such approaches documented in the literature, challenges consisting of the noise and subtlety of sentiment in the given data largely remain unsolved. To address these deficiencies, this research will use the machine learning techniques in the form of Naive Bayes and Random Forest models, classifying sentiments by methods like class imbalances through SMOTE, and using TF-IDF vectorization in extracting features. Results for the experiment showed that the Random Forest model outperforms Naive Bayes as the former can classify more neutral and positive sentiments correctly. It is, therefore, concluded that sentiment analysis may be useful to airlines for enhancement in customer experience and future research should emphasize on advanced deep learning models with rich data sets for further potential enhancement.

关键词： Sentiment analysis Analytical models Social networking (online) Atmospheric modeling Customer satisfaction Blogs Feature extraction Bayes methods Random forests Airline industry

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A Comprehensive Review of Human-Centric AI, Regulatory Frameworks, and Their Role in Shaping Industry 5.0

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Procedia computer science 2025年 259卷 1672-1681页

作者： Sanjay Misra Kousik Barik Petter Kvalvik Department of Applied Data Science Institute for Energy Technology Halden Norway Department of Computer Sciences Universidad de Alcalá Alcalá de Henares Madrid Spain JIS Institute of Advanced Studies & Research JIS University Kolkata India Digital Systems Institute for Energy Technology Halden Norway

This paper examines the core principles and design approaches that define Human-Centric AI (HCAI) and the enhancement of human-machine collaborations with aligning goals with Industry 5.0. Through a comprehensive analysis of existing studies, this paper systematically determines the key challenges of harmonizing HCAI principles with the regulatory demands of Industry 5.0, using a PRISMA chart to select relevant studies from four databases. The study highlighted the major global regulatory frameworks governing AI and how they address the ethical, legal, and social challenges HCAI poses. This work aims to inspire further exploration and development in assessing the potential benefits and challenges of integrating Human-Centric AI into Industry 5.0 by identifying research gaps and proposing future study areas.

关键词： Human Centric AI Industry 5.0 Regulatory Frameworks Challenges

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Scalable Real-Time Anomaly Detection in Financial Markets Using a Novel Machine Learning Model 3rd

Scalable Real-Time Anomaly Detection in Financial Markets Us...

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3rd International Conference on Intelligent Systems and Sustainable Computing, ICISSC 2023

作者： Andi, Hari Krishnan Bommisetti, Ravi Kumar Subbarao, Buggavarapu V. S. S. Sonthi, Vijaya Krishna Ray, Samrat Ammasai, Viswanathan Sengan, Sudhakar Centre for Postgraduate Studies Asia Metropolitan University Johor Bahru81750 Malaysia Department of Business Administration and Dean-Research and Development Maris Stella College Vijayawada520008 India Department of MBA Kumaraguru College of Technology Tamil Nadu Coimbatore641049 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522502 India Dean Head of International Relations IIMS Pune411033 India School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli627451 India

ISBN: (纸本)9789819783540

Stakeholder objectives can be put at risk if anomalies are not detected sufficiently promptly in the constantly evolving financial systems. This paper recommends an innovative Machine Learning (ML) method, the Hybrid Temporal-Feature Embedding Model (HTEM), dedicated to real-time Anomaly Detection (AD). Fundamental to this framework is the use of a distributed system to store and process real-time data that has been collected through a strong Data Ingestion Layer. The HTEM framework is a vital component of this layout. The temporal flow of data collected from LSTM networks is precisely combined with the significance of individual features that are analyzed by autoencoders in this technique. By applying this dual embedding, a complete model of financial data sequences is accomplished. By applying a fully connected layer to provide this combined representation, the research affects an accurate anomaly value for each data point. If the overall rating is higher than a particular threshold, the data point is classified as an anomaly. Training this model frequently on known anomalies enables it to be fine-tuned to a higher level. The timely, accurate, and efficient finding of anomalies can be ensured through the incorporation of the HTEM within the model being proposed, protecting the interests of stakeholders in the financial market environment. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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