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arXiv 2025年

作者： Wang, Shuo Li, Renhao Chen, Xi Wong, Derek F. Yuan, Yulin Yang, Min NLP2CT Lab Department of Computer and Information Science University of Macau China Shenzhen Key Laboratory for High Performance Data Mining Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China Linguistics and Multilingual Studies Nanyang Technological University Singapore Department of Chinese Language and Literature University of Macau China

With the different roles that AI is expected to play in human life, imbuing large language models (LLMs) with different personalities has attracted increasing research interests. While the "personification" enhances human experiences of interactivity and adaptability of LLMs, it gives rise to critical concerns about content safety, particularly regarding bias, sentiment and toxicity of LLM generation. This study explores how assigning different personality traits to LLMs affects the toxicity and biases of their outputs. Leveraging the widely accepted HEXACO personality framework developed in social psychology, we design experimentally sound prompts to test three LLMs’ performance on three toxic and bias benchmarks. The findings demonstrate the sensitivity of all three models to HEXACO personality traits and, more importantly, a consistent variation in the biases, negative sentiment and toxicity of their output. In particular, adjusting the levels of several personality traits can effectively reduce bias and toxicity in model performance, similar to humans’ correlations between personality traits and toxic behaviors. The findings highlight the additional need to examine content safety besides the efficiency of training or fine-tuning methods for LLM personification. They also suggest a potential for the adjustment of personalities to be a simple and low-cost method to conduct controlled text generation. © 2025, CC BY.

关键词： Social psychology

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Role of Mixup in Topological Persistence Based Knowledge Distillation for Wearable Sensor Data

arXiv

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arXiv 2025年

作者： Jeon, Eun Som Choi, Hongjun Buman, Matthew P. Turaga, Pavan Department of Computer Science and Engineering Seoul National University of Science and Technology Seoul01811 Korea Republic of Lawrence Livermore National Laboratory LivermoreCA94550 United States College of Health Solutions Arizona State University PhoenixAZ85004 United States Geometric Media Lab School of Arts Media and Engineering School of Electrical Computer and Energy Engineering Arizona State University TempeAZ85281 United States

The analysis of wearable sensor data has enabled many successes in several applications. To represent the high-sampling rate time-series with sufficient detail, the use of topological data analysis (TDA) has been considered, and it is found that TDA can complement other time-series features. Nonetheless, due to the large time consumption and high computational resource requirements of extracting topological features through TDA, it is difficult to deploy topological knowledge in machine learning and various applications. In order to tackle this problem, knowledge distillation (KD) can be adopted, which is a technique facilitating model compression and transfer learning to generate a smaller model by transferring knowledge from a larger network. By leveraging multiple teachers in KD, both time-series and topological features can be transferred, and finally, a superior student using only time-series data is distilled. On the other hand, mixup has been popularly used as a robust data augmentation technique to enhance model performance during training. Mixup and KD employ similar learning strategies. In KD, the student model learns from the smoothed distribution generated by the teacher model, while mixup creates smoothed labels by blending two labels. Hence, this common smoothness serves as the connecting link that establishes a connection between these two methods. Even though it has been widely studied to understand the interplay between mixup and KD, most of them are focused on image based analysis only, and it still remains to be understood how mixup behaves in the context of KD for incorporating multimodal data, such as both time-series and topological knowledge using wearable sensor data. In this paper, we analyze the role of mixup in KD with time-series as well as topological persistence, employing multiple teachers. We present a comprehensive analysis of various methods in KD and mixup, supported by empirical results on wearable sensor data. We observe that app

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Evaluating Cyber-Crime Using Machine Learning and AI Approach for Environmental Sustainability 2nd

Evaluating Cyber-Crime Using Machine Learning and AI Approac...

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2nd International Conference on Sustainable Development Using Machine Learning, Artificial Intelligence, and IoT, ICSD 2024

作者： Chauhan, Ritu Mehtar, Khushi Kaur, Harleen Alankar, Bhavya Artificial Intelligence and IOT Lab Center for Computational Biology and Bioinformatics Amity University Uttar Pradesh Noida India Amity Institute of Biotechnology Amity University Uttar Pradesh Noida India Department of Computer Science and Engineering School of Engineering Sciences and Technology Jamia Hamdard New Delhi India

ISBN: (纸本)9783031717284

The online world has become an essential part of everyday life in India due to its fast expansion and technical improvements. But as technology has advanced, cyber-crimes have also increased dramatically, posing serious risks to the economy, security, and privacy. This research examines the intricate terrain of cybercrimes targeting minors in India, scrutinizing more than 16 lakh cases that have been documented during the three years preceding this one. The study takes an extensive approach, applying machine learning modelling such as Gradient Boosting, AdaBoost, Random Forest, Gaussian Naive Bayes, Bernoulli Naive Bayes, Linear SVM, Logistic Regression, and Gradient Boosting in conjunction with exploratory assessment of data. The findings show different accuracy rates;Random Forest, Gradient Boosting, Linear SVM, and Gaussian Naive Bayes all show an impressive 88% accuracy. AdaBoost and Logistic Regression reach 75%, but Bernoulli Naive Bayes comes in lower at 62%. The comprehension of computational interconnections is improved with correlation matrices. This thorough analysis provides important insights for academics, policymakers, and stakeholders in the field of child cybersecurity by exposing common cyber threats targeting minors and highlighting the role that algorithms for learning may play in detecting and averting these risks for environmental sustainability. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Toward Optimal Broadcast Mode in Offline Finding Network

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IEEE Transactions on Mobile Computing 2025年

作者： Li, Tong Ding, Yukuan Liang, Jiaxin Zheng, Kai Zhang, Xu Pan, Tian Wang, Dan Xu, Ke Renmin University DEKE Lab and Information School Beijing100872 China Delft Netherlands 2012 Labs Shenzhen Huawei518129 China Nanjing University School of Electronic Science and Engineering Nanjing210023 China Beijing University of Posts and Telecommunications School of Information and Communication Engineering Beijing100876 China The Hong Kong Polytechnic University Department of Computing Hong Kong Hong Kong Tsinghua University Department of Computer Science and Technology Beijing100084 China

This paper proposes ElastiCast, a novel Bluetooth Low Energy (BLE) broadcast mode that reduces the neighbor discovery latency in offline finding networks (OFNs). ElastiCast adapts the broadcast mode of the lost devices to the scan modes of the finder devices, considering their diversity. We start with an overview of OFNs, followed by a detailed analysis of the issues and challenges of existing solutions, which motivates the design of ElastiCast. Then we provide Blender, a simulator that models the neighbor discovery behavior of different broadcasters and scanners. By adopting Blender, ElastiCast can be implemented with three components: Local Optima Estimation, Common Interest Extraction, and Interval Multiplexing, in which we capture the key features of BLE neighbor discovery and globally optimize the broadcast mode interacting with diverse scan modes. Experimental evaluation results and commercial product deployment experience demonstrate that ElastiCast is effective in achieving stable and bounded neighbor discovery latency within the power budget. © 2002-2012 IEEE.

关键词： Bluetooth

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Fusion Analysis of Resting-State Networks and Its Application to Alzheimer's Disease

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Tsinghua science and technology 2019年第4期24卷 456-467页

作者： Shengbing Pei Jihong Guan Shuigeng Zhou the Department of Computer Science and Technology Tongji University Shanghai Key Lab of Intelligent Information Processing School of Computer ScienceFudan University

Functional networks are extracted from resting-state functional magnetic resonance imaging data to explore the biomarkers for distinguishing brain disorders in disease diagnosis. Previous works have primarily focused on using a single Resting-State Network(RSN) with various techniques. Here, we apply fusion analysis of RSNs to capturing biomarkers that can combine the complementary information among the RSNs. Experiments are carried out on three groups of subjects, i.e., Cognition Normal(CN), Early Mild Cognitive Impairment(EMCI), and Alzheimer's Disease(AD) groups, which correspond to the three progressing stages of AD; each group contains18 subjects. First, we apply group Independent Component Analysis(ICA) to extracting the Default Mode Network(DMN) and Dorsal Attention Network(DAN) for each subject group. Then, by obtaining the common DMN and DAN as templates for each group, we employ the individual ICA to extract the DMN and DAN for each ***, we fuse the DMNs and DANs to explore the biomarkers. The results show that(1) the templates generated by group ICA can extract the RSN for each subject by individual ICA effectively;(2) the RSNs combined with the fusion analysis can obtain more informative biomarkers than without fusion analysis;(3) the most different regions of DMN and DAN are found between CN and EMCI and between EMCI and AD, which show differences. For the DMN, the difference in the medial prefrontal cortex between the EMCI and AD is smaller than that between CN and EMCI, whereas that in the posterior cingulate between EMCI and AD is larger. As for the DAN, the difference in the intraparietal sulcus is smaller than that between CN and EMCI;(4) extracting DMN and DAN for each subject via the back reconstruction of group ICA is invalid.

关键词： Independent Component Analysis(ICA) group analysis fusion analysis Alzheimer’s Disease(AD)

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Deep Unfolding Neural Networks for Fluid Antenna-Enhanced Vehicular Communication

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IEEE Transactions on Vehicular technology 2025年

作者： Feng, Biqian Feng, Chenyuan Wong, Kai-Kit Quek, Tony Q. S. University of Macau State Key Laboratory of Internet of Things for Smart City The Department of Computer and Information Science China EURECOM Sophia Antipolis 06410 France University College London Department of Electronic and Electrical Engineering Torrington Place WC1E 7JE United Kingdom Yonsei University Yonsei Frontier Lab Seoul Korea Republic of Singapore University of Technology and Design Information Systems Technology and Design Pillar Singapore487372 Singapore

Fluid antenna (FA) technology has emerged as a promising technology to achieve higher spectral and energy efficiency by introducing a new dimension. However, the antenna position configuration inevitably increases computational complexity, presenting challenges under real-time configuration requirements, especially in vehicular communication systems characterized by rapidly time-varying channels. To address these issues, this paper investigates the classical weighted sum rate maximization problem and proposes an optimization-empowered neural network framework designed to accelerate convergence without compromising accuracy. Extensive simulations demonstrate that the proposed approach effectively mitigates the computational burdens associated with FAs, delivering superior performance in terms of convergence rate and system performance, thus paving the way for the deployment of next-generation FA-enabled communication systems. © 2025 IEEE.

关键词： Deep neural networks

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Age of Information Analysis of Ber/Geo/1/1 Queue With On-Off Service

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IEEE Internet of Things Journal 2025年

作者： Zeng, Hao Qu, Yi Chen, Zhengchuan Akar, Nail Wang, Min Wu, Dapeng O. Quek, Tony Q. S. Chongqing University School of Microelectronics and Communication Engineering Chongqing China Bilkent University Electrical and Electronics Engineering Department Bilkent Ankara06800 Turkey Chongqing University of Posts and Telecommunications School of Optoelectronics Engineering Chongqing China City University of Hong Kong Department of Computer Science Hong Kong Singapore University of Technology and Design 487372 Singapore Yonsei University Yonsei Frontier Lab Korea Republic of

The Age of Information (AoI), which measures the time since the generation of the latest update, quantifies information freshness in timeliness-critical systems. Minimizing AoI and characterizing it precisely are crucial for system efficiency and decision-making. This work investigates AoI under external interference modeled as an On-Off process, providing a foundation for future research in more complex scenarios. We consider a discrete-time remote status-updating system with a monitor and a sensor, where the sensor observes a physical process, generates timestamped updates, and sends them to the monitor. Both inter-arrival and service times follow geometric distributions, with service interrupted according to a two-state On-Off process. We analyze AoI under two queuing disciplines: 1) non-preemptive, where arriving updates are discarded if the server is occupied, and 2) preemptive, where in-service updates are replaced with new ones during the Off state. For both, we derive closed-form expressions for average AoI and peak AoI (PAoI). We also explore the relationship between discrete-time and continuous-time systems, showing that the latter is the limiting case of the former. Numerical results validate the theoretical analysis, revealing a linear relationship between the relative normalized increase in average PAoI and AoI and the proportion of Off state time. Frequent On-Off switching and higher service rates under the same system load help mitigate freshness deterioration caused by interruptions. The On-Off process is shown to have a large impact on the average AoI (resp. PAoI) of systems with relatively high (resp. low) arrival and service rates. © 2014 IEEE.

关键词： Continuous time systems

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STATIC FILTERING ON STRATIFIED PROGRAMS

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INFORMATION PROCESSING LETTERS 1993年第5期47卷 237-244页

作者： CHANG, BM CHOE, KM HAN, TS Programming Languages Laboratory Department of Computer Science Korea Advanced Institute of Science and Technology 373-1 Kusong-dong Yusung-ku Taejon 305-701 South Korea

We extend static filtering in [8], a query optimization strategy based on seminaive evaluation on system graphs, so that it can efficiently handle stratified programs without extra overhead. The computation of static filters is formalized as a transformation so that the least fixed point of the transformation can be the static filters. The static filtering on stratified programs is shown to be complete with respect to the iterated fixed point semantics.

关键词： APPLICATIVE (LOGIC) programming STRATIFIED PROGRAMS QUERY OPTIMIZATION STATIC FILTERING SYSTEM GRAPHS

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AN EFFICIENT COMPUTATION OF RIGHT CONTEXT FOR LR-BASED ERROR REPAIR

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INFORMATION PROCESSING LETTERS 1994年第2期49卷 63-71页

作者： JUNG, MS CHOE, KM HAN, T Programming Languages Laboratory Department of Computer Science Korea Advanced Institute of Science and Technology 373-1 Kusong-Dong Yusong-Gu Taejon 305-701 South Korea

The left context in LR-based parsing is the sequence of states in the parsing stack. The right context is the vocabulary strings to appear for a given left context. We propose an efficient method of computing right context for LR-based syntax error repair. The efficiency of our method is achieved from removing some redundancies in the method of previous work.

关键词： COMPILERS LR PARSING SYNTAX ERROR REPAIR KERNEL ITEM RIGHT CONTEXT

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Learning 6-DoF Fine-grained Grasp Detection Based on Part Affordance Grounding

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IEEE Transactions on Automation science and Engineering 2025年 22卷 15200-15214页

作者： Song, Yaoxian Sun, Penglei Jin, Piaopiao Ren, Yi Zheng, Yu Li, Zhixu Chu, Xiaowen Zhang, Yue Li, Tiefeng Gu, Jason Zhejiang University Center for X-Mechanics School of Aeronautics and Astronautics Zhejiang Hangzhou China Information Hub Data Science and Analytics Thrust Guangdong China Huawei Technologies Advanced Manufacturing Lab Guangdong Shenzhen China UBTECH Robotics Inc Research Institute Guangdong Shenzhen China Renmin University of China School of Information School of Smart Governance Beijing China Westlake University Institute of Advanced Technology Westlake Institute for Advanced Study School of Engineering Zhejiang Hangzhou China Dalhousie University Department of Electrical and Computer Engineering HalifaxB3H 4R2 Canada

Robotic grasping is a fundamental ability for a robot to interact with the environment. Current methods focus on how to obtain a stable and reliable grasping pose in object level, while little work has been studied on part (shape)-wise grasping which is related to fine-grained grasping and robotic affordance. Parts can be seen as atomic elements to compose an object, which contains rich semantic knowledge and a strong correlation with affordance. However, lacking a large part-wise 3D robotic dataset limits the development of part representation learning and downstream applications. In this paper, we propose a new large Language-guided SHape grAsPing datasEt (named LangSHAPE) to promote 3D part-level affordance and grasping ability learning. From the perspective of robotic cognition, we design a two-stage fine-grained robotic grasping framework (named LangPartGPD), including a novel 3D part language grounding model and a partaware grasp pose detection model, in which explicit language input from human or large language models (LLMs) could guide a robot to generate part-level 6-DoF grasping pose with textual explanation. Our method combines the advantages of humanrobot collaboration and LLMs’ planning ability using explicit language as a symbolic intermediate. To evaluate the effectiveness of our proposed method, we perform 3D part grounding and fine-grained grasp detection experiments on both simulation and physical robot settings, following language instructions across different degrees of textual complexity. Results show our method achieves competitive performance in 3D geometry fine-grained grounding, object affordance inference, and 3D part-aware grasping tasks. © 2004-2012 IEEE.

关键词： Visual languages

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