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International Symposium on Parallel and Distributed Processing with Applications, ISPA

作者： Zheng Wang Weiwei Xing Yue Cheng Weibin Liu Zhiyuan Zou Zhi Wang Genxiang Chen Wei Lu School of Software Engineering Beijing Jiaotong University Beijing China Institute of Information Science Beijing Jiaotong University Beijing China Computer School Beijing Information Science and Technology University Beijing China School of Physical Sciences and Engineering Beijing Jiaotong University Beijing China College of Science Minzu University of China Beijing China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Optical Communication Simulation Task Allocation (OCSTA) constitutes an interdisciplinary quandary. This paper proposes a swarm intelligence approach harnessing a Discrete Dung Beetle Optimizer (DDBO) algorithm with a globally equilibrated strategy to offer a resolution avenue for this intricate conundrum. Firstly, we provide a comprehensive exposition of the mathematical model underpinning the OCSTA, which employs the spatial positioning of the population to articulate diverse allocation solutions. Then, a weighted random selection technique is used to generate initial solutions. Thirdly, a predator avoidance strategy is introduced to facilitate updates in the dung beetle position. Finally, the global equilibrium mechanism with the swap operator is exploited to harmonize the exploratory and exploitative capabilities, thereby further augmenting the quality of the solutions. We conducted several simulation experiments 1 across various distinct task load scenarios, and statistical tests are employed to evaluate the significant differences between the proposed algorithm and other state-of-the-art methods. The outcomes revealed that the DDBO solution yielded an improvement of approximately 15.8%, which underscores the competitiveness and robustness of solving the OCSTA.

关键词： Distributed processing Heuristic algorithms Dynamic scheduling Optical fiber communication Robustness Mathematical models Resource management Spatial resolution Particle swarm optimization Optimization

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A Multi-Feature Learning Model with Enhanced Local Attention for Vehicle Re-Identification

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computers, Materials & Continua 2021年第12期69卷 3549-3561页

作者： Wei Sun Xuan Chen Xiaorui Zhang Guangzhao Dai Pengshuai Chang Xiaozheng He Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Nanjing University of Information Science&TechnologyNanjing210044China School of Automation Nanjing University of Information Science&TechnologyNanjing210044China Engineering Research Center of Digital Forensics Ministry of EducationJiangsu Engineering Center of Network MonitoringSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China Rensselaer Polytechnic Institute TroyNY12180USA

Vehicle re-identification(ReID)aims to retrieve the target vehicle in an extensive image gallery through its appearances from various views in the cross-camera *** has gradually become a core technology of intelligent transportation *** existing vehicle re-identification models adopt the joint learning of global and local ***,they directly use the extracted global features,resulting in insufficient feature ***,local features are primarily obtained through advanced annotation and complex attention mechanisms,which require additional *** solve this issue,a multi-feature learning model with enhanced local attention for vehicle re-identification(MFELA)is proposed in this *** model consists of global and local *** global branch utilizes both middle and highlevel semantic features of ResNet50 to enhance the global representation *** addition,multi-scale pooling operations are used to obtain multiscale *** the local branch utilizes the proposed Region Batch Dropblock(RBD),which encourages the model to learn discriminative features for different local regions and simultaneously drops corresponding same areas randomly in a batch during training to enhance the attention to local *** features from both branches are combined to provide a more comprehensive and distinctive feature *** experiments on VeRi-776 and VehicleID datasets prove that our method has excellent performance.

关键词： Vehicle re-identification region batch dropblock multi-feature learning local attention

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Learning from Mistakes: A Comprehensive Review of Knowledge Editing for Large Language Models

Learning from Mistakes: A Comprehensive Review of Knowledge ...

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IEEE International Conference on Smart Internet of Things (SmartIoT)

作者： Yanhong Li Chunling Fan Mingqing Huang Chengming Li Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen China School of Science Harbin Institute of Technology (Shenzhen) Shenzhen China School of Electronic and Communication Engineering Shenzhen Polytechnic University Shenzhen China School of Computer and Software Shenzhen Institute of Information Technology Shenzhen China

ISBN: (数字)9798350366440

ISBN: (纸本)9798350366457

In recent years, there has been a growing recognition that large language models like GPT-4 have the capability to store vast amounts of knowledge and possess extremely powerful abili-ties. However, these models are not immune to making mistakes. The knowledge stored within these models may be erroneous or outdated, leading to incorrect outputs. Consequently, it is imperative to implement methods to edit the knowledge within the models. In this paper, we primarily summarize and organize several key knowledge editing methods that have emerged in recent years. By drawing an analogy to the human process of correcting mistakes, we categorize these methods into three types: rote memorization, learning from examples, and thoroughly understanding. In addition, we briefly discuss the potential positive impact of researches on the knowledge mechanisms of large models on knowledge editing. By summarizing the existing researches on the knowledge of large models, we also briefly explore the role of fully utilizing knowledge mechanisms and resolving knowledge conflicts in enhancing the effectiveness of knowledge editing.

关键词： Reviews Large language models Face recognition Internet of Things Faces Immune system

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Federated Causally Invariant Feature Learning 39

Federated Causally Invariant Feature Learning

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Guo, Xianjie Yu, Kui Cui, Lizhen Yu, Han Li, Xiaoxiao School of Computer Science and Information Engineering Hefei University of Technology China Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education China School of Software Shandong University China College of Computing and Data Science Nanyang Technological University Singapore Department of Electrical and Computer Engineering The University of British Columbia Canada Vector Institute Canada

ISBN: (纸本)157735897X

Federated feature selection (FFS) is a promising field for selecting informative features while preserving data privacy in federated learning (FL) settings. Existing FFS methods focus on capturing the correlations between features and labels. They struggle to achieve satisfactory performance in the face of data distribution heterogeneity among FL clients, and cannot address the out-of-distribution (OOD) problem that arises when a significant portion of clients do not actively participate in FL training. To address these limitations, we propose Federated Causally Invariant Feature Learning (FedCIFL), a novel approach for learning causally invariant features in a privacy-preserving manner. We design a sample reweighting strategy to eliminate spurious correlations introduced by selection bias and iteratively estimate the federated causal effect between each feature and the labels (with the remaining features initially treated as confounders). By iteratively refining the confounding feature set to identify the true confounders, FedCIFL mitigates the impact of limited local data on the accuracy of federated causal effect estimation. Theoretical analysis proves the correctness of FedCIFL under reasonable assumptions. Extensive experiments on synthetic and real-world datasets demonstrate the superiority of FedCIFL against eight state-of-the-art baselines, beating the best-performing approach by 3.19%, 9.07% and 2.65% in terms of average test Accuracy, RMSE and F1 score, respectively. It is a first-of-its-kind FFS approach capable of handling Non-IID and OOD data simultaneously. The source code is available at https://***/Xianjie-Guo/FedCIFL. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Federated learning

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Design of Concurrent engineering Systems for Global Product Development Using Artificial Intelligence 10th

Design of Concurrent Engineering Systems for Global Product ...

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10th International Conference on Innovations in computer science and engineering, ICICSE 2022

作者： Karn, Arodh Lal Mehbodniya, Abolfazl Webber, Julian L. Jayagopal, Vellingiri Stalin David, D. Rangasamy, Rajasekar Sengan, Sudhakar Department of Financial and Actuarial Mathematics School of Mathematics and Physics Xi’an Jiaotong-Liverpool University Jiangsu Suzhou215123 China Kuwait City Kuwait Department of Software and System Engineering School of Information Technology and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Information Technology Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Tamil Nadu Chennai600072 India Department of Computer Science and Engineering GITAM School of Technology GITAM University Karnataka Bengaluru561203 India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli627152 India

ISBN: (纸本)9789811974540

Competition and concurrent engineering (CE) businesses are growing. Faster product creation, quality improvement, manufacturing process adoption, and reduced client demand cost are vital for corporate success. Implement concurrent engineering to make high-quality goods. CE is a parallel, systematic method to design. The chief executive officer (CEO’s) approach impacted how many firms build products. You may use the fundamental ideas of concurrent engineering conversation and the failure mode and effects analysis (FMEA) tool. To provide a smooth and efficient CE environment, conduct the product development process from market research to detailed design. Concurrent engineering is a novel product development (NPD) approach. Concurrency and cross-functional integration separate CE from traditional product development. NPD continues through developing ideas, validation, proof-of-concept validation, Minimum Viable Product development, pre-launch, and post-launch. In other words, product discovery should continue throughout its life span. Each organization's growth operations operate simultaneously. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Product development

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The Parables of the Mustard Seed and the Yeast: Extremely Low-Budget, High-Performance Nighttime Semantic Segmentation 39

The Parables of the Mustard Seed and the Yeast: Extremely Lo...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Wang, Shiqin Xu, Xin Chen, Haoyang Jiang, Kui Wang, Zheng School of Computer Science and Technology Wuhan University of Science and Technology Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System China National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University China School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China

ISBN: (纸本)157735897X

Nighttime Semantic Segmentation (NSS) is essential to many cutting-edge vision applications. However, existing technologies overly rely on massive labeled data, whose annotation is time-consuming and laborious. In this paper, we pioneer a new task focusing on exploring the potential of training strategy and framework design with limited annotation to achieve high-performance NSS. Insufficient information at very low labeling budgets can easily lead to under-optimization or overfitting of the model. Our solution comprises two main components: i) a novel region-based active sampling strategy called Contextual-Aware Region Query (CARQ), which identifies highly informative target nighttime regions for labeling;and ii) an innovative Fragmentation Synergy Active Domain Adaptation framework (FS-ADA), which progressively broadcasts the limited annotation to the unlabeled regions, achieving high performance with a minimal annotation budget. Extensive experiments demonstrate that our method outperforms state-of-the-art UDA-NSS & ADA-SS methods across four day-to-nighttime benchmarks, and generalizes well to foggy, rainy, & snowy scenes. In particular only with 1% target nighttime data annotation, our method is on par with the mainstream fully-supervised methods on the BDD100K-Night val dataset. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Budget control

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Dynamic Prompting of Frozen Text-to-Image Diffusion Models for Panoptic Narrative Grounding

arXiv

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arXiv 2024年

作者： Li, Hongyu Zhang, Jing Hui, Tianrui Ma, Bin Ding, Zihan Wei, Xiaoming Han, Jizhong Liu, Si School of Artificial Intelligence Beihang University Beijing China School of Software Beihang University Beijing China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Meituan Beijing China Institute of Information Engineering Chinese Academy of Sciences Beijing China

Panoptic narrative grounding (PNG), whose core target is fine-grained image-text alignment, requires a panoptic segmentation of referred objects given a narrative caption. Previous discriminative methods achieve only weak or coarse-grained alignment by panoptic segmentation pretraining or CLIP model adaptation. Given the recent progress of text-to-image Diffusion models, several works have shown their capability to achieve fine-grained image-text alignment through cross-attention maps and improved general segmentation performance. However, the direct use of phrase features as static prompts to apply frozen Diffusion models to the PNG task still suffers from a large task gap and insufficient vision-language interaction, yielding inferior performance. Therefore, we propose an Extractive-Injective Phrase Adapter (EIPA) bypass within the Diffusion UNet to dynamically update phrase prompts with image features and inject the multimodal cues back, which leverages the fine-grained image-text alignment capability of Diffusion models more sufficiently. In addition, we also design a Multi-Level Mutual Aggregation (MLMA) module to reciprocally fuse multi-level image and phrase features for segmentation refinement. Extensive experiments on the PNG benchmark show that our method achieves new state-of-the-art performance. © 2024, CC BY-NC-ND.

关键词： Image segmentation

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N-Gram-Based Machine Learning Approach for Bot or Human Detection from Text Messages 22

N-Gram-Based Machine Learning Approach for Bot or Human Dete...

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6th International Conference on Intelligent Systems, Metaheuristics and Swarm Intelligence, ISMSI 2022

作者： Kavadi, Durga Prasad Sanaboina, Chandra Sekhar Patan, Rizwan Gandomi, Amir Department Of Information Technology B V Raju Institute Of Technology Narsapur Telangana India Department Of Computer Science And Engineering University College Of Engineering Kakinada Jawaharlal Nehru Technological University Kakinada India Decentralized Science Lab College Of Computing And Software Engineering Kennesaw State University MariettaGA30060 United States Faculty Of Engineering And Information Systems University Of Technology Sydney UltimoNSW Australia

ISBN: (纸本)9781450396288

Social bots are computer programs created for automating general human activities like the generation of messages. The rise of bots in social network platforms has led to malicious activities such as content pollution like spammers or malware dissemination of misinformation. Most of the researchers focused on detecting bot accounts in social media platforms to avoid the damages done to the opinions of users. In this work, n-gram based approach is proposed for a bot or human detection. The content-based features of character n-grams and word n-grams are used. The character and word n-grams are successfully proved in various authorship analysis tasks to improve accuracy. A huge number of n-grams is identified after applying different pre-processing techniques. The high dimensionality of features is reduced by using a feature selection technique of the Relevant Discrimination Criterion. The text is represented as vectors by using a reduced set of features. Different term weight measures are used in the experiment to compute the weight of n-grams features in the document vector representation. Two classification algorithms, Support Vector Machine, and Random Forest are used to train the model using document vectors. The proposed approach was applied to the dataset provided in PAN 2019 competition bot detection task. The Random Forest classifier obtained the best accuracy of 0.9456 for bot/human detection. © 2022 ACM.

关键词： Feature Selection

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Comprehensive Evaluation of ChatGPT Reliability Through Multilingual Inquiries

arXiv

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arXiv 2023年

作者： Puttaparthi, Poorna Chander Reddy Deo, Soham Sanjay Gul, Hakan Tang, Yiming Shang, Weiyi Yu, Zhe Department of Software Engineering Rochester Institute of Technology RochesterNY United States School of Information Rochester Institute of Technology RochesterNY United States Department of Computer Science Rochester Institute of Technology RochesterNY United States Department of Electrical and Computer Engineering University of Waterloo WaterlooON Canada

ChatGPT is currently the most popular large language model (LLM), with over 100 million users, making a significant impact on people's lives. However, due to the presence of jailbreak vulnerabilities, ChatGPT might have negative effects on people's lives, potentially even facilitating criminal activities. Testing whether ChatGPT can cause jailbreak is crucial because it can enhance ChatGPT's security, reliability, and social responsibility. Inspired by previous research revealing the varied performance of LLMs in different language translations, we suspected that wrapping prompts in multiple languages might lead to ChatGPT jailbreak. To investigate this, we designed a study with a fuzzing testing approach to analyzing ChatGPT's cross-linguistic proficiency. Our study includes three strategies by automatically posing different formats of malicious questions to ChatGPT: (1) each malicious question involving only one language, (2) multilingual malicious questions, (3) specifying that ChatGPT responds in a language different from the questions. In addition, we also combine our strategies by utilizing prompt injection templates to wrap the three aforementioned types of questions. We examined a total of 7,892 Q&A data points, discovering that multilingual wrapping can indeed lead to ChatGPT's jailbreak, with different wrapping strategies having varying effects on jailbreak probability. Prompt injection can amplify the probability of jailbreak caused by multilingual wrapping. This study provides insights for OpenAI developers to enhance ChatGPT's support for language diversity and inclusion. © 2023, CC BY.

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VS-Net: A Voxel Encoding and Sparse Convolution Embedded Network for LiDAR 3D Object Detection

VS-Net: A Voxel Encoding and Sparse Convolution Embedded Net...

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2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022

作者： Liu, Meng Niu, Jianwei Liu, Yu The China North Artificial Intelligence and Innovation Research Institute Beijing100072 China The China North Vehicle Research Institute Beijing100072 China The State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing100191 China The State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing100191 China The Hangzhou Innovation Institute Beihang University Hangzhou310051 China The Zhengzhou University Research Institute of Industrial Technology School of Information Engineering Zhengzhou University Zhengzhou450001 China

ISBN: (纸本)9781665481090

Accurate and efficient 3D object detection in LiDAR point cloud is critical for autonomous driving. In this paper, we provide a solution for LiDAR-only detection which employs voxel features and a sparse convolution network. Specifically, we utilized voxel centers to encode point clouds' voxel features. Then, combining the sparse convolution method, we leveraged residual learning to design the backbone network. The detector output predicted results for multi-category and multi-object detection. Our method was evaluated on three popular and challenging datasets (KITTI, nuScence and Waymo). Experimental results demonstrated that the accuracy and speed (27.8 FPS) of our model could effectively support object detection for autonomous driving in various scenarios. © 2022 IEEE.

关键词： Object detection

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