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arXiv 2025年

作者： Tang, Anke Yang, Enneng Shen, Li Luo, Yong Hu, Han Du, Bo Tao, Dacheng School of Computer Science Wuhan University China Northeastern University China School of Cyber Science and Technology Shenzhen Campus of Sun Yat-sen University China School of Information and Electronics Beijing Institute of Technology China College of Computing & Data Science Nanyang Technological University Singapore

Deep model merging represents an emerging research direction that combines multiple fine-tuned models to harness their specialized capabilities across different tasks and domains. Current model merging techniques focus on merging all available models simultaneously, with weight interpolation-based methods being the predominant approaches. However, these conventional approaches are not well-suited for scenarios where models become available sequentially, and they often suffer from high memory requirements and potential interference between tasks. In this study, we propose a training-free projection-based continual merging method that processes models sequentially through orthogonal projections of weight matrices and adaptive scaling mechanisms. Our method operates by projecting new parameter updates onto subspaces orthogonal to existing merged parameter updates while using an adaptive scaling mechanism to maintain stable parameter distances, enabling efficient sequential integration of task-specific knowledge. Our approach maintains constant memory complexity to the number of models, minimizes interference between tasks through orthogonal projections, and retains the performance of previously merged models through adaptive task vector scaling. Extensive experiments on CLIP-ViT models demonstrate that our method achieves a 5-8% average accuracy improvement while maintaining robust performance in different task orderings. © 2025, CC BY.

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Rumor Detection with Hierarchical Representation on Bipartite Adhoc Event Trees

arXiv

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arXiv 2023年

作者： Zhang, Qi Yang, Yayi Shi, Chongyang Lao, An Hu, Liang Wang, Shoujin Naseem, Usman Data Science Lab University of Technology Sydney UltimoNSW2007 Australia Beijing Institute of Technology Beijing100081 China School of Computer Science Beijing Institute of Technology Beijing100081 China Tongji University Shanghai200092 China DeepBlue Academy of Sciences Shanghai200336 China Data Science Institute University of Technology Sydney SydneyNSW2007 Australia University of Sydney Sydney CamperdownNSW2006 Australia

The rapid growth of social media has caused tremendous effects on information propagation, raising extreme challenges in detecting rumors. Existing rumor detection methods typically exploit the reposting propagation of a rumor candidate for detection by regarding all reposts to a rumor candidate as a temporal sequence and learning semantics representations of the repost sequence. However, extracting informative support from the topological structure of propagation and the influence of reposting authors for debunking rumors is crucial, which generally has not been well addressed by existing methods. In this paper, we organize a claim post in circulation as an adhoc event tree, extract event elements, and convert it to bipartite adhoc event trees in terms of both posts and authors, i.e., author tree and post tree. Accordingly, we propose a novel rumor detection model with hierarchical representation on the bipartite adhoc event trees called BAET. Specifically, we introduce word embedding and feature encoder for the author and post tree, respectively, and design a root-aware attention module to perform node representation. Then we adopt the tree-like RNN model to capture the structural correlations and propose a tree-aware attention module to learn tree representation for the author tree and post tree, respectively. Extensive experimental results on two public Twitter datasets demonstrate the effectiveness of BAET in exploring and exploiting the rumor propagation structure and the superior detection performance of BAET over state-of-the-art baseline methods. © 2023, CC BY.

关键词： Social networking (online)

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Smart Warehouse Solutions for Efficient Onion Buffer Stock Management System 7

Smart Warehouse Solutions for Efficient Onion Buffer Stock M...

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7th International Conference on Electronics, Communication and Aerospace technology, ICECA 2023

作者： Raman, Ramakrishnan Ramalingam, L. Kishorbhai Sutaria, Kamal Kamthan, Manika Sangeetha, S. Murugan, S. Maharashtra Pune India Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Parul Institute of Engineering and Technology Faculty of Engineering and Technology Parul University Gujarat Vadodara India Symbiosis Law School Maharashtra Pune India Karpagam College of Engineering Department of Computer Science and Engineering Tamil Nadu Coimbatore India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350340600

This research study presents an innovative smart warehouse system for onion buffer stock management. The system includes an RFID reader, a central controlling unit, a displaying unit, a temperature sensor, a moisture sensor, a relay, a cooling fan, storage nodes, and a cloud server. The RFID reader automatically tracks onion crates or pallets in the warehouse, enabling real-time visibility and traceability. The central controlling unit coordinates activities and collects data from sensors. Onions are stored at appropriate temperatures and moisture levels via sensors. Warehouse workers can monitor this data in real-time on the displaying unit once the central controlling unit processes it. Maintaining temperature and humidity need the relay and cooling fan. Based on sensor data, the main controlling unit activates the cooling fan to avoid onion spoiling. Storage nodes in the warehouse store onion bags and pallets. These nodes have RFID tags or sensors for convenient tracking of stock. data analysis, storage, and integration depend on the cloud server. It allows advanced data analytics, forecasting, and decision-making. It also helps suppliers, distributors, and retailers communicate and distribute products efficiently. Real-time visibility, smart monitoring, and proactive management of storage conditions advance efficient operations, prevent spoiling, and improve onion supply chain efficiency. © 2023 IEEE.

关键词： Decision making

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Transfer Function Based Feature Selection Using Harris Hawk Optimization for Biomedical Applications

Transfer Function Based Feature Selection Using Harris Hawk ...

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Smart Electronics and Communication Systems (ISENSE), International Conference on

作者： S. Jayachitra A. Prasanth Prasannakiruba G S Srichandana Abbineni Department of Electronics and Communication Engineering PSNA College of Engineering and Technology Tamilnadu India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India Department of Computer Technology Bannari Amman Institute of Technology Sathyamangalam Department of ET-CSE(Data Science) CVR College of Engineering Hyderabad

ISBN: (数字)9798331528126

ISBN: (纸本)9798331528133

Feature Selection plays key role to truncate the high dimensionality in the datasets through eradicating the unwanted and noisy attributes to improve the performance of classification. The Meta-heuristic technique is one of the most optimistic search techniques to travel the feature space to locate finest feature subset. The Harris Hawk Optimization (HHO) excited from hunting approach of hawk in nature. The cost effectiveness and simplicity formulates the algorithm prominent and superior in solving optimization problems. The binary version of the optimization is accountable for delineating continuous searching space to discrete feature space. Here, 6 transfer functions with S-Shaped and V-Shaped families were interposed and analyzed. Twelve benchmark datasets from UCI repository has been introduced to examine the transfer functions and choose the best feature pertaining to mitigate local minima and high speed of convergence. To analyze the efficacy of transfer function, the proposed method was compared over exiting algorithms and the proposed results attain higher classification accuracy in many of the datasets.

关键词： Costs Communication systems Metaheuristics Transfer functions Benchmark testing Feature extraction Space exploration Classification algorithms Noise measurement Convergence

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Robotic Control Optimization Through Kernel Selection in Safe Bayesian Optimization

Robotic Control Optimization Through Kernel Selection in Saf...

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IEEE International Conference on Robotics and Biomimetics

作者： Lihao Zheng Hongxuan Wang Xiaocong Li Jun Ma Prahlad Vadakkepat School of Data Science The Chinese University of Hong Kong Research Intern at the Singapore Institute of Manufacturing Technology A*STAR Shenzhen China Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore Institute of Manufacturing Technology Agency for Science Technology and Research (A*STAR) Singapore Robotics and Autonomous Systems Thrust The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

Control system optimization has long been a fundamental challenge in robotics. While recent advancements have led to the development of control algorithms that leverage learning-based approaches, such as SafeOpt, to optimize single feedback controllers, scaling these methods to high-dimensional complex systems with multiple controllers remains an open problem. In this paper, we propose a novel learning-based control optimization method, which enhances the additive Gaussian process-based Safe Bayesian Optimization algorithm to efficiently tackle high-dimensional problems through kernel selection. We use PID controller optimization in drones as a representative example and test the method on Safe Control Gym, a benchmark designed for evaluating safe control techniques. We show that the proposed method provides a more efficient and optimal solution for high-dimensional control optimization problems, demonstrating significant improvements over existing techniques.

关键词： Additives Trajectory tracking Optimization methods Control systems Bayes methods Kernel Robots Tuning Standards Quadrotors

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IoT-Enabled Table Turnover for Dining Experiences with AdaBoost Algorithm

IoT-Enabled Table Turnover for Dining Experiences with AdaBo...

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2024 Asia Pacific Conference on Innovation in technology, APCIT 2024

作者： Ranganathan, Chitra Sabapathy Mohankumar, N. Sumathi, G. Nithya, A. Juliet, P. Sudha Muthulekshmi, M. Mphasis Corporation Tamil Nadu Chennai India Maharashtra Nagpur India Department of Artificial Intelligence & Data Science Tamil Nadu Trichy India St. Joseph's Institute of Technology Department of Information Technology Tamil Nadu Chennai India Meenakshi College of Engineering Department of Electronics and Instrumentation Engineering Tamil Nadu Chennai India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350361537

Traditional dining experiences are undergoing a period of transition because of the growing use of Internet of Things (IoT) technologies and machine learning in the restaurant business. This article presents revolutionary IoT-driven technology, intended to maximize the table turnover rate and improve client happiness. The system can gather real-time data on the occupancy status of each table since it places occupancy sensors at each table. This allows for the effective distribution of tables as they become available. model is software that automates table cleaning alerts, staff coordination, and predictive analytics for resource planning. It is designed to connect seamlessly with queue management and point-of-sale (POS) systems. This novel strategy improves the restaurant's efficiency, reduces customers' wait times, and maximizes earning potential during peak hours. The suggested solution offers a chance to change dining venues by combining the capabilities of the IoT with dynamic table management. This will result in a better customer dining experience and improved operational results for companies. © 2024 IEEE.

关键词： Sales

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Neural Machine Translation with Contrastive Translation Memories

Neural Machine Translation with Contrastive Translation Memo...

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2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Cheng, Xin Gao, Shen Liu, Lemao Zhao, Dongyan Yan, Rui Wangxuan Institute of Computer Technology Peking University China Center for Data Science Peking University China Tencent AI Lab School of Computer Science and Technology Shandong University China State Key Laboratory of Media Convergence Production Technology and Systems China China Gaoling School of Artificial Intelligence Renmin University of China China

Retrieval-augmented Neural Machine Translation models have been successful in many translation scenarios. Different from previous works that make use of mutually similar but redundant translation memories (TMs), we propose a new retrieval-augmented NMT to model contrastively retrieved translation memories that are holistically similar to the source sentence while individually contrastive to each other providing maximal information gains in three phases. First, in TM retrieval phase, we adopt a contrastive retrieval algorithm to avoid redundancy and uninformativeness of similar translation pieces. Second, in memory encoding stage, given a set of TMs we propose a novel Hierarchical Group Attention module to gather both local context of each TM and global context of the whole TM set. Finally, in training phase, a Multi-TM contrastive learning objective is introduced to learn salient feature of each TM with respect to target sentence. Experimental results show that our framework obtains improvements over strong baselines on the benchmark datasets. © 2022 Association for Computational Linguistics.

关键词： Neural machine translation

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An Innovation of Multichip Dynamic Industrial Network Development with The Flooding Network Resources

An Innovation of Multichip Dynamic Industrial Network Develo...

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Distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE International Conference on

作者： Anupam Lakhanpal Garima Garima Department of Computer Science and Engineering Galgotias University Greater Noida India Department of Computer Science and Engineering (Data Science) Noida Institute of Engineering and Technology Greater Noida India

The multichip dynamic industrial network is a powerful tool for industrial applications. It enables communication between multiple devices, such as sensors, actuators, controllers, and other industrial components. It is an efficient means of connecting and controlling a variety of industrial systems in a single network. The multichip dynamic industrial network is a distributed control system consisting of several interconnected nodes. Each node contains a processor, memory, and other components that are essential for communication. The nodes are connected to one another through a network of cables and connectors. The nodes can be located anywhere in the world and can communicate with each other in real-time. The multichip dynamic industrial network is used in many industrial applications such as manufacturing, process control, and machine automation. It can be used to monitor, control, and modify the machines in a factory. It can also be used to monitor and control the supply chain from the raw material supplier to the finished product.

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Security Enhancing for IRS-Aided NOMA Networks via Constructive Interference Precoding 10

Security Enhancing for IRS-Aided NOMA Networks via Construct...

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10th IEEE International Symposium on Microwave, Antenna, Propagation and EMC Technologies for Wireless Communications, MAPE 2024

作者： Bao, Jingying Cao, Yang Qin, Xiaoqi Xu, Lexi Zhao, Nan Niyato, Dusit School of Information and Communication Engineering Dalian University of Technology Dalian116024 China Singapore University of Technology and Design Engineering Systems and Design Pillar Singapore487372 Singapore Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Research Institute China United Network Communications Corporation Beijing100048 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore

ISBN: (纸本)9798331533922

In this paper, we propose a constructive interference precoding (CIP) enabled secure intelligent reflecting surface (IRS)-non-orthogonal multiple access (NOMA) scheme, lever-aging both the inter-user interference and artificial noise to boost the legitimate transmission of far user, while inducing the eavesdropper to decode the deceptive information. Based on this CIP-NOMA scheme, we minimize the transmit power under the perfect channel state information (CSI), subject to the CIP constraint for the far user and eavesdropper, while guaranteeing the quality of service and successive interference cancellation for the near user and the IRS unit modulus constraint. To handle this non-convex problem, an alternating optimization algorithm is proposed. Specifically, by alternately optimizing the precoding vectors at the base station and the IRS reflecting matrix via the successive convex approximation and the penalty-based algorithm, respectively, a reliable solution can be obtained. Simulation results demonstrate that the proposed scheme can achieve better security performance with less energy consumption compared to the conventional NOMA. © 2024 IEEE.

关键词： Multiple access interference

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Multi-video Moment Ranking with Multimodal Clue

arXiv

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arXiv 2023年

作者： Hou, Danyang Pang, Liang Lan, Yanyan Shen, Huawei Cheng, Xueqi Data Intelligence System Research Center Institute of Computing Technology CAS Beijing China CAS Key Lab of Network Data Science and Technology Institute of Computing Technology CAS Beijing China University of Chinese Academy of Sciences Beijing China Institute for AI Industry Research Tsinghua University Beijing China

Video corpus moment retrieval (VCMR) is the task of retrieving a relevant video moment from a large corpus of untrimmed videos via a natural language query. State-of-the-art work for VCMR is based on two-stage method. In this paper, we focus on improving two problems of two-stage method: (1) Moment prediction bias: The predicted moments for most queries come from the top retrieved videos, ignoring the possibility that the target moment is in the bottom retrieved videos, which is caused by the inconsistency of Shared Normalization during training and inference. (2) Latent key content: Different modalities of video have different key information for moment localization. To this end, we propose a two-stage model MultI-video raNking with mUlTimodal cluE (MINUTE). MINUTE uses Shared Normalization during both training and inference to rank candidate moments from multiple videos to solve moment predict bias, making it more efficient to predict target moment. In addition, Mutilmdaol Clue Mining (MCM) of MINUTE can discover key content of different modalities in video to localize moment more accurately. MINUTE outperforms the baselines on TVR and DiDeMo datasets, achieving a new state-of-the-art of VCMR. Our code will be available at GitHub. Copyright © 2023, The Authors. All rights reserved.

关键词： Forecasting

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