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31st International Conference on Computational Linguistics, COLING 2025

作者： Fan, Yuchun Mu, Yongyu Wang, Yilin Huang, Lei Ruan, Junhao Li, Bei Xiao, Tong Huang, Shujian Feng, Xiaocheng Zhu, Jingbo NLP Lab School of Computer Science and Engineering Northeastern University Shenyang China NiuTrans Research Shenyang China Harbin Institute of Technology Harbin China Meituan Inc. National Key Laboratory for Novel Software Technology Nanjing University China

ISBN: (纸本)9798891761964

Despite the significant improvements achieved by large language models (LLMs) in English reasoning tasks, these models continue to struggle with multilingual reasoning. Recent studies leverage a full-parameter and two-stage training paradigm to teach models to first understand non-English questions and then reason. However, this method suffers from both substantial computational resource computing and catastrophic forgetting. The fundamental cause is that, with the primary goal of enhancing multilingual comprehension, an excessive number of irrelevant layers and parameters are tuned during the first stage. Given our findings that the representation learning of languages is merely conducted in lower-level layers, we propose an efficient multilingual reasoning alignment approach that precisely identifies and fine-tunes the layers responsible for handling multilingualism. Experimental results show that our method, SLAM, only tunes 6 layers' feed-forward sub-layers including 6.5−8% of all parameters within 7B and 13B LLMs, achieving superior average performance than all strong baselines across 10 languages. Meanwhile, SLAM only involves one training stage, reducing training time by 4.1 − 11.9× compared to the two-stage method. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Model LEGO: Creating Models Like Disassembling and Assembling Building Blocks 38

Model LEGO: Creating Models Like Disassembling and Assemblin...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Hu, Jiacong Gao, Jing Ye, Jingwen Gao, Yang Wang, Xingen Feng, Zunlei Song, Mingli College of Computer Science and Technology Zhejiang University China Robotics Institute School of Computer Science Carnegie Mellon University United States Electrical and Computer Engineering National University of Singapore Singapore School of Software Technology Zhejiang University China State Key Laboratory of Blockchain and Data Security Zhejiang University China Institute of Blockchain and Data Security China Bangsheng Technology Co. Ltd. China

With the rapid development of deep learning, the increasing complexity and scale of parameters make training a new model increasingly resource-intensive. In this paper, we start from the classic convolutional neural network (CNN) and explore a paradigm that does not require training to obtain new models. Similar to the birth of CNN inspired by receptive fields in the biological visual system, we draw inspiration from the information subsystem pathways in the biological visual system and propose Model Disassembling and Assembling (MDA). During model disassembling, we introduce the concept of relative contribution and propose a component locating technique to extract task-aware components from trained CNN classifiers. For model assembling, we present the alignment padding strategy and parameter scaling strategy to construct a new model tailored for a specific task, utilizing the disassembled task-aware components. The entire process is akin to playing with LEGO bricks, enabling arbitrary assembly of new models, and providing a novel perspective for model creation and reuse. Extensive experiments showcase that task-aware components disassembled from CNN classifiers or new models assembled using these components closely match or even surpass the performance of the baseline, demonstrating its promising results for model reuse. Furthermore, MDA exhibits diverse potential applications, with comprehensive experiments exploring model decision route analysis, model compression, knowledge distillation, and more. For more information, please visit https://***/. © 2024 Neural information processing systems foundation. All rights reserved.

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Designing Data Permissions in the Enterprise Application Environment 4

Designing Data Permissions in the Enterprise Application Env...

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4th International Seminar on Artificial Intelligence, Networking and Information technology, AINIT 2023

作者： Yuan, Ying Yi, Xin Shangguan, Junbin School of Computer and Information Engineering Xiamen University of Technology Xiamen China Institute of Space Information Technology Xiamen University of Technology Xiamen China Information Technology Co. Ltd Software Department Xiamen China

ISBN: (纸本)9798350314526

With the continuous improvement of social informationization, the digitalization of corporate construction is also developing comprehensively. Data, as a crucial source for creating wealth and competitive advantage, is undoubtedly the most significant asset for all enterprises. However, many companies face the dilemma of insufficient data granularity and configuration flexibility, which undermines the security and availability of their data assets. Therefore, this paper proposes an Api-based data permission control model (ADPC) based on the theoretical foundation of RBAC in the background of data application in the enterprise environment. Firstly, the model maps data objects to different levels of environmental variables in the corporate department structure, then flexibly authorizes and controls permissions through API configuration, and finally verifies its security with SQL AST. The ADPC model was implemented using the Spring Boot, Spring Security and MyBatis framework and was applied in a real enterprise environment. Through this approach, the ADPC model effectively addresses the limitations of traditional data permission control mechanisms in the enterprise application environment. © 2023 IEEE.

关键词： Competition

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FewFine: Few-shot Malware Traffic Classification Via Transfer Learning based on Fine-tuning Strategy 19

FewFine: Few-shot Malware Traffic Classification Via Transfe...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Liu, Xingtong Shen, Meng Cui, Laizhong Ye, Ke Jia, Jizhe Yue, Guangchun School of Cyberspace Science and Technology Beijing Institute of Technology Beijing China Shenzhen University College of Computer Science and Software Engineering Shenzhen China School of Computer Science Beijing Institute of Technology Beijing China

ISBN: (纸本)9798350346558

Malware traffic is constantly evolving and remains destructive. The detection and classification of malware traffic is crucial for maintaining cyberspace security. Only by swiftly and accurately detecting and classifying malware traffic can user privacy and cyberspace security be effectively *** this paper, we propose FewFine, an approach for few-shot malware traffic classification based on transfer learning. We initially pre-train a detection model and two classification models with substantial quantity of malware and application traffic samples. For classifying new types of malware traffic accurately and promptly, we utilize transfer learning based on fine-tuning strategy and freeze several blocks in the pre-trained model. Utilizing prior knowledge from the pre-trained models, we leverage few samples of novel classes to perform accurate malware detection and classification. We execute extensive experiments on publicly available datasets to evaluate the effectiveness of FewFine. In model pre-training, with considerable number of samples, the accuracy of malware detection and classification can reach 0.99. The pre-trained models are saved for fine-tuning. When detecting and classifying novel malware traffic, FewFine can achieve the accuracy of 0.95 leveraging only 10 samples per class through fine-tuning the pre-trained model. It outperforms methods under comparison in terms of accuracy and efficiency. © 2022 IEEE.

关键词： Knowledge management

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DRRS-BC:Decentralized Routing Registration System Based on Blockchain

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IEEE/CAA Journal of Automatica Sinica 2021年第12期8卷 1868-1876页

作者： Huimin Lu Yu Tang Yi Sun College of Computer Science and Technology Qingdao UniversityQingdao 266071China School of Engineering Kyushu Institute of TechnologyKitakyushu 804-8550Japan School of Computer Science(National Pilot Software Engineering School) Beijing University of Posts and TelecommunicationsBeijing 100876China

The border gateway protocol(BGP)has become the indispensible infrastructure of the Internet as a typical inter-domain routing ***,it is vulnerable to misconfigurations and malicious attacks since BGP does not provide enough authentication mechanism to the route *** a result,it has brought about many security incidents with huge economic *** solutions to the routing security problem such as S-BGP,So-BGP,Ps-BGP,and RPKI,are based on the Public Key Infrastructure and face a high security risk from the centralized *** this paper,we propose the decentralized blockchain-based route registration framework-decentralized route registration system based on blockchain(DRRS-BC).In DRRS-BC,we produce a global transaction ledge by the information of address prefixes and autonomous system numbers between multiple organizations and ASs,which is maintained by all blockchain nodes and further used for *** applying blockchain,DRRS-BC perfectly solves the problems of identity authentication,behavior authentication as well as the promotion and deployment problem rather than depending on the authentication ***,it resists to prefix and subprefix hijacking attacks and meets the performance and security requirements of route registration.

关键词： Blockchain decentralized routing protocol routing registration

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SWPFOPLD: A Profiling and Optimizing Loader for SW26010Pro Processors 8

SWPFOPLD: A Profiling and Optimizing Loader for SW26010Pro P...

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8th International Conference on computer and Communication Systems, ICCCS 2023

作者： Qian, Hong Wu, Wei Zhu, Qi Wang, Fei Zhao, Jinwei Zheng, Yan School of Computer Science and Technology University of Science and Technology of China Hefei China Jiangnan Institute of Computing Technology System Software Research Department Wuxi China Tsinghua University Department of Computer Science and Technology Beijing China National Research Center of Parallel Computer Engineering and Technology Beijing China

ISBN: (纸本)9781665456128

The Sunway family supercomputers have achieved a series of remarkable achievements. However, the toolchains provided by them are not perfect, which has brought great challenges to the development of high-performance application software. In this paper, a profiling and optimizing tool is proposed to assist people to analyze and optimize the performance of their programs. SWPFOPLD is independent of the application program and gathers the runtime performance data through the PMUs, an automatically hot functions rearrange optimization based on the performance data is furtherly accomplished. The evaluation shows that SWPFOPLD can be easily and effectively used to analyze and optimize the performance of the application programs. © 2023 IEEE.

关键词： Supercomputers

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An Approach for Performance Calculation of AVS/RS in Multi-floor Workshop 3rd

An Approach for Performance Calculation of AVS/RS in Multi-f...

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3rd International Conference on Smart Manufacturing, Industrial and Logistics engineering, SMILE 2023 and the 7th Asian Conference of Management Science and Applications, ACMSA 2023

作者： Liu, Xiaopeng Wu, Cuiying Zhang, Baolin Xi, An Chen, Xuanrui Ministry of Industry and Information Technology Key Laboratory of Industrial Software Engineering Application Technology China Electronic Product Reliability and Environmental Testing Research Institute Guangzhou510619 China School of Computer and Information Engineering Hanshan Normal University Guangdong Chaozhou521041 China

ISBN: (纸本)9789819701933

Warehouse management is an important part of the production process. As a result of the development of technology, more and more companies tend to use automated logistics technologies such as AVS/RS to handle the internal logistics of the production system. Especially in multi-floor workshops, the production process on each floor are connected with each other through AVS/RS. It is becoming more and more important to study the integration process of AVS/RS and multi-floor workshop production process. In this paper, an integrated queuing network model of multi-floor workshop and AVS/RS was established, and a calculation method of system performance index was proposed. The accuracy and efficiency of the proposed method are demonstrated by comparing the results with simulations from numerical experiments. The work of this article can provide a basis for the design of autonomous vehicle storage and retrieve systems in multi-floor workshop, and the calculation method can be used as the support of WMS technology base. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Floors

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A Privacy Preserving Method for IoT Forensics

A Privacy Preserving Method for IoT Forensics

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Zhang, Wenzheng Chen, Boxi Fu, Xiao Gu, Qing Shi, Jin Du, Xiaojiang Zhou, Xiaoyang Nanjing University State Key Laboratory for Novel Software Technology Nanjing China Nanjing University School of Information Management Nanjing China Stevens Institute of Technology Department of Electrical and Computer Engineering HobokenNJ United States Innovation Research Institute Nanjing China

ISBN: (纸本)9798350351255

The diversity of the Internet of Things (IoT) poses challenges to privacy protection, especially in the field of digital forensics. How to ensure that only the private information of the suspect is provided, and not the irrelevant information of other users is disclosed is crucial, especially when obtaining evidence in the complex IoT environment. To the best of our knowledge, there are few studies on protecting the privacy of irrelevant users in the IoT forensics. However, it is very important to ensure that the evidence does not violate the privacy of other users when collecting evidence, because it directly determines whether the evidence is legal and whether it can be admissible in court. In this paper, a new method based on data provenance graph is designed to solve the privacy protection problem of IoT forensics. The key idea of this method is to protect privacy by dividing multi-user information and protecting it from an encryption perspective. The method consists of three main phrases: distinguishing different users' data provenance graphs using traversal search, node abstraction, and hiding techniques, utilizing pseudo-random dual-key negotiation methods tailored for the scenario to enhance privacy protection for unrelated users, and employing identity authentication technology to facilitate better investigation and extraction of data provenance graph information of criminal accomplices in specific scenarios. Example proves that this method has practical significance and promising application prospects in protecting the privacy of unrelated users in IoT forensics while ensuring evidence accessibility in special criminal scenarios. © 2024 IEEE.

关键词： Differential privacy

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3D Gaze Estimation via Binocular Disparity and Feature Refinement 10

3D Gaze Estimation via Binocular Disparity and Feature Refin...

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10th International Conference on Big Data Computing and Communications, BIGCOM 2024

作者： Wang, Chengxin Chen, Qiuxia Tian, Ying Yang, Jinfeng School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan China Institute of Applied Artificial Intelligence of the Guangdong-Hong Kong-Macao Greater Bay Area Shenzhen Polytechnic Shenzhen China

ISBN: (纸本)9798331509538

The eyes are the windows to the soul and are crucial for studying human behavior. Therefore, gaze estimation has attracted much attention in the field of computer vision. In recent years, convolutional neural networks have driven significant progress in gaze estimation research. Despite the remarkable achievements of gaze technology based on full-face images, the inability to obtain full-face images in specific scenarios makes research based on eye images particularly important. This paper proposes a three-dimensional gaze estimation model that takes binocular images as input, using VGG16 as the backbone network for feature extraction. To enhance the feature extraction capability, we have embedded channel attention and spatial attention mechanisms. Meanwhile, to improve the stability of gaze estimation, we classify real gaze angles. First, we perform a preliminary screening of gaze direction through a classification task, and then perform linear regression with the true angle. Finally, we optimize gaze features using binocular vision differences to finetune the gaze estimator. Experiments have shown that our method performs well on three public datasets. © 2024 IEEE.

关键词： Convolutional neural networks

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IPL Data Analysis by Web Scraping and Using PowerBI

IPL Data Analysis by Web Scraping and Using PowerBI

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IT Innovation and Knowledge Discovery (ITIKD), International Conference on

作者： Mogith P Mothiswar T B G Mohammad Abubak J. Jagannathan N. Balaganesh Software Engineering Vellore Institute of Technology Vellore India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (数字)9798350355468

ISBN: (纸本)9798350355475

Cricket fans throughout the world avidly follow the Indian Premier League (IPL), a competition of great fame. The IPL data is examined in this using Exploratory Data Analysis (EDA) approaches to find hidden trends, patterns, and insights. Key findings are summarized in EDA, a critical stage of data processing that is frequently complemented with visual aids. Through EDA, the goal is to investigate several factors, including team performance, player data, match results, and venue effects. Data transformation, cleaning, and visualization will all be part of producing insightful findings. Win percentages, player averages, and run distributions are examples of key performance indicators (KPIs) that will be looked at. The analysis's findings will give teams, athletes, and supporters important new information.

关键词： Visualization Technological innovation Fans Data analysis Key performance indicator Data visualization Market research Knowledge discovery Cleaning

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