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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Zhihui Wang Xubing Dou Shuyun Li Huan Ma Xinchang Zhang Maoli Wang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Shandong Normal University Jinan China QuFu normal university Jining China

ISBN: (数字)9798331515966

ISBN: (纸本)9798331515973

In addressing the multi-path problem of cloud-edge data transmission, this paper introduces a multi-path orchestration method based on deep reinforcement learning (DRL) within the context of software-defined networks (SDN), termed Deep Q-Routing (DQR). This method establishes optimal data transmission paths between the cloud and multiple edges, based on available bandwidth and transmission delay, while avoiding path congestion to improve bandwidth utilization and reduce transmission delay. The optimal routing in SDN is defined through the design of state space sets, reward functions, and neural networks. Simulation experiments were conducted to evaluate the DQR method. Compared to existing routing methods, the proposed DQR method, which includes a reward function with weighted parameters, demonstrates superior overall network performance.

关键词： Cloud computing Scalability Neural networks Distributed databases Bandwidth Routing Deep reinforcement learning Delays Data communication Software defined networking

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Prediction of piRNA-mRNA interactions based on an interactive inference network

Prediction of piRNA-mRNA interactions based on an interactiv...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yajun Liu Ru Li Aimin Li Rong Fei Xie Guo Fang-Xiang Wu Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an University of Technology Xi'an China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an China Department of Computer Science Department of Mechanical Engineering Division of Biomedical Engineering University of Saskatchewan Saskatoon Canada

As the largest class of small non-coding RNAs, piRNAs primarily present in the reproductive cells of mammals, which influence post-transcriptional processes of mRNAs in multiple ways. Effective methods for predicting piRNA and mRNA target relationships can help identify piRNA functions, investigate the possibility of piRNAs as biomarkers and therapeutic targets. In this study, we propose a computational approach for classifying the relationships of piRNA-mRNA pairs based on an interactive inference network (IIN). First, we gather piRNA-mRNA target data, collect sequence data by position alignment, and construct a benchmark dataset. Furthermore, a reliable negative set is constructed by positive-unlabeled learning. Finally, we view a piRNA and a mRNA sequence as a premise and hypothesis sentence, respectively, and IIN model is used to predict the relationship between them. The experiments demonstrate that our method effectively characterizes piRNA-mRNA interaction and could be beneficial for researchers to investigate piRNA functions.

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Learning Fine-Grained information Alignment for Calibrated Cross-Modal Retrieval

Learning Fine-Grained Information Alignment for Calibrated C...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jianhua Dong Shengrong Zhao Hu Liang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China

Masked Language Modeling (MLM) and Image-Text Matching (ITM) are always used in fusion encoder to learn the joint representation of images and text. In existing methods, the masking strategy of MLM leads to the neglect of image details during the modeling process. Meanwhile, the sampling strategy of ITM struggles to consistently select high-difficulty hard negative instances, reducing the effectiveness of constraints. This leads to challenges in aligning fine-grained information in cross-modal retrieval. In response to this challenge, a fine-grained information alignment-based visual language model (FAM) is proposed in this paper. On one hand, the attribute-based masking strategy is employed in MLM, helping the model focus on the details of objects in images during modeling. On the other hand, the robust hard negative sample generation strategy provides challenging negative samples for ITM by altering the relationships between objects. This enables the model to align relationships between objects in different modalities and thus calibrates cross-modal retrieval. Extensive experiments demonstrate the effectiveness of the model in cross-modal retrieval tasks.

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Dual Prototypes Contrastive Learning for Semi-Supervised Medical Image Segmentation

SSRN

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SSRN 2024年

作者： Yue, Tianai Xu, Rongtao Wu, Jingqian Yang, Wenjie Du, Shide Wang, Changwei Johns Hopkins University Baltimore21218 United States State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The University of Hong Kong Hong Kong999077 Hong Kong College of Computer and Big Data Fuzhou University Fuzhou350108 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Jinan250014 China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan250014 China

Semi-supervised techniques for medical image segmentation have demonstrated potential, effectively training models using scarce labeled data alongside a wealth of unlabeled data. Therefore, semi-supervised medical image segmentation is one of the key issues in engineering applications of medical artificial intelligence. Consistency constraints based on prototype alignment provide an intuitively sensible way to discover valuable insights from unlabeled data that can motivate segmentation performance. In this work, we propose a dual prototypes contrastive network (DPC-Net) to motivate semi-supervised medical segmentation accuracy by imposing image-level global prototype constraints and pixel-level local prototype constraints. First, we introduce a Background-Separation Global Prototype Contrastive Learning technique that utilizes the natural mutual exclusivity of foreground and background to separate the inter-class distances and encourage the segmentation network to obtain segmentation results that are more complete and do not contain background regions. Second, we design a Cross-Consistent Local Prototype Contrastive Learning techniques to extend the perturbation consistency of the two networks to the prototype's localized response to the feature map, thereby shaping a more stable intra-class prototype space and producing accurate and robust pixel-level predictions. Finally, we comprehensively evaluate our method on mainstream semi-supervised medical image segmentation benchmarks and settings, and experimental results show that our DPC-Net outperforms current state-of-the-art methods. © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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External Knowledge Is Not Always Needed: An Adaptive Retrieval Augmented Generation Method 4

External Knowledge Is Not Always Needed: An Adaptive Retriev...

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4th International Symposium on Artificial Intelligence and Intelligent Manufacturing, AIIM 2024

作者： Guan, Wenbo Lu, Jiyu Feng, Qinyu Li, Xiaoqian Zhou, Jun Key Laboratory of Speech Acoustics and Content Understanding Institute of Acoustics Chinese Academy of Sciences Beijing100190 China Institute of Information Engineering Chinese Academy of Sciences Beijing100084 China School of Cyber Security University of Chinese Academy of Sciences Beijing100049 China Artificial Intelligence Technology Co. Ltd China Southern Power Grid Guangzhou510663 China School of Computing Science and Engineering South China University of Technology Guangzhou510006 China

ISBN: (纸本)9798331541729

Retrieval augmented generation (RAG), by integrating external knowledge with large language models (LLMs), has become a common practice to alleviate LLMs’ hallucination problem. The performance of RAG, however, depends on the capability of the adopted information retriever to a large extent. Specifically, a good information retriever can manage to obtain the most useful information from the external knowledge, which will then enhance the quality of generated content from LLMs and vice versa. Since the vanilla RAG will always leverage the retrieved information to assist with LLMs’ content generation despite of its usefulness, when combined with a retriever of limited capacity, therefore, the vanilla RAG will not benefit LLMs’ content generation sometimes but introduce additional noise, having a negative impact on the final performance. To address this problem, this paper proposes a novel adaptive RAG method which first uses LLMs to determine the usefulness of the retrieved information and then let LLMs to generate content on their own without the retrieved information if it is useless. Experimental results on four datasets demonstrate the effectiveness of the proposed adaptive RAG method with information retrievers of various capabilities, improving the performance of the vanilla RAG by an obvious margin. © 2024 IEEE.

关键词： Modeling languages

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Fair and autonomous sharing of federate learning models in mobile Internet of Things

arXiv

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arXiv 2020年

作者： Hao, Xiaohan Ren, Wei Xiong, Ruoting Zheng, Xianghan Zhu, Tianqing Xiong, Neal N. School of Computer Science China University of Geoscience Wuhan China Guangxi Key Laboratory of Cryptography and Information Security Guilin541004 China Key Laboratory of Network Assessment Technology CAS Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Mingbyte Technology QingDao China School of Software University of Technology Sydney UltimoNSW2007 Australia Dept. of Mathematics and Computer Science Northeastern State University United States

Federate learning can conduct machine learning as well as protect the privacy of self-owned training data on corresponding ends, instead of having to upload to a central trusted data aggregation server. In mobile scenarios, a centralized trusted server may not be existing, and even though it exists, the delay will not be manageable, e.g., smart driving cars. Thus, mobile federate learning at the edge with privacy-awareness is attracted more and more attentions. It then imposes a problem - after data are trained on a mobile terminal to obtain a learned model, how to share the model parameters among others to create more accurate and robust accumulative final model. This kind of model sharing confronts several challenges, e.g., the sharing must be conducted without a third trusted party (autonomously), and the sharing must be fair as model training (by training data)is valuable. To tackle the above challenges, we propose a smart contract and IPFS (Inter-Planetary File System) based model sharing protocol and algorithms to address the challenges. The proposed protocol does not rely on a trusted third party, where individual-learned models are shared/stored in corresponding ends. Conducted through extensive experiments, three main steps of the proposed protocol are evaluated. The average executive time of the three steps are 0.059s, 0.060s and 0.032s, demonstrating its efficiency. Copyright © 2020, The Authors. All rights reserved.

关键词： Smart contract

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The Utility Model Relates to a Real-Time Interaction Method of Mobile Terminal information Based on power Emergency Scene

The Utility Model Relates to a Real-Time Interaction Method ...

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International Conference on Data Science and Business Analytics (ICDSBA)

作者： Zhansheng Hou Shijun Sun Lin Peng Zhen Yu Jinyin Song He Wang Min Xu Gang Wang Xingchuan Bao Hai Yu Zhimin He Liang Zhu Xiyuan Xu Zehao Zhang Shiyang Tang State Grid Key Laboratory of Information & Network Security Global Energy Interconnection Research Institute Nanjing China State Grid ZheJiang Electric Power Co. Ltd Hangzhou China Global Energy Interconnection Research Institute Nanjing China

At present domestic emergency information interaction lack of flexibility, after a natural disaster, on-site rescue workers with the rescue headquarters, emergency command center, sharing of information between were studied based on the scene of the electric power emergency mobile information real-time interactive methods, design a kind of power at the scene of the emergency mobile friendly each other, are at the scene of the emergency and emergency command center cluster information real-time interactive technology, gives the power emergency field information real-time interactive system prototype and power at the scene of the emergency information real-time interactive software and hardware integration solutions, to build the efficient method of multi-type information submitted by emergency disposal across departments and information interaction ability.

关键词： Interactive systems Prototypes Streaming media Data science Real-time systems Software Hardware

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Adaptive Fine-grained Access Control Method in Social Internet of Things

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International Journal of network security 2021年第1期23卷 42-48页

作者： Zhang, Hongbin Ma, Pengcheng Liu, Bin School of Information Science and Engineering Hebei University of Science and Technology Shijiazhuang China Hebei Key Laboratory of Network and Information Security Hebei Normal China School of Economics and Management Hebei University of Science China Technology Research Center of Big Data and Social Computing Hebei University of Science China

Social Internet of Things (SloT), as a new carrier of integration of social and Internet of Things, applies the research results of social networks from different aspects of the Internet of Things. Different types of connected intelligent objects interact socially, compared with random data access between them, access control technology is more stringent. This paper integrates social attributes into attributebased access control of Internet of Things, initializes relational attribute tags, and labels social interest attributes for different objects, then quantifes tag similarity and implements initial access control authorization, integrates social attributes into game theory to dynamically adjust access control policies, so the adaptive finegrained division of access control under the Social Internet of Things is effectively realized. The experimental results show that our method can not only effectively carry out initial authorization according to tag similarity, but also further adaptively adjust the permission policy according to social attributes, and further meet the finegrained partition requirements of access control, which is ensure the effective implementation of access control under the Social Internet of things. © 2021. All Rights Reserved.

关键词： Game theory

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An Emergency Drill Command System Facing the power Grid Emergency Operation Site

An Emergency Drill Command System Facing the Power Grid Emer...

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International Conference on Data Science and Business Analytics (ICDSBA)

作者： Zhan Sheng Hou Qin Yi Zhang Bin Geng Xi Yuan Xu Shi Jun Sun Global Energy Interconnection Research Institute Nanjing China State Grid Key Laboratory of Information & Network Security Nanjing China State Grid JiLin Electric Power Co. Ltd JiLin China State Grid ZheJiang Electric Power Co. Ltd Hangzhou China

Emergency command system is now working at an emergency exercise special technical support on the strength is limited, play a role in the various emergency drills support is not enough, difficult to meet the urgent need of the current company to carry out all kinds of drill in promoting drill streamline, scientific, intelligent, etc, there is still a large gap in this paper, based on the typical scenario of drill as the carrier, guided by the demands of practical application, can achieve the company at all levels, all kinds of emergency drills "together, sharing, practical" information effect, promote the emergency exercise procedural, scientific, intelligent, can exercise emergency team, Improving emergency personnel's ability to deal with accidents in emergency response, and improving the coordination and handling ability among various business departments and emergency organizations can improve the efficiency of emergency repair, shorten the time of emergency response, and minimize the loss caused by emergency incidents.

关键词： Companies Maintenance engineering Data science Emergency services power grids Communications technology Personnel

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Strong security Enabled Certificateless Aggregate Signatures Applicable to Mobile Computation

Strong Security Enabled Certificateless Aggregate Signatures...

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International Conference on Intelligent networking and Collaborative Systems (INCOS)

作者： Hu Xiong Qianhong Wu Zhong Chen Key Laboratory of Network and Software Security Assurance of the Ministry of Education Institute of Software School of Electronics Engineering and Computer Science Peking University Beijing China Department of Computer Engineering and Mathematics UNESCO Chair in Data Pr Universitat Rovira i Virgili Tarragona Spain Key Laboratory of AeroSpace Information Security and Trusted Computing Ministry of Education School of Computer Wuhan University of China China

An aggregate signature scheme allows a public algorithm to aggregate n signatures of n distinct messages from n signers into a single signature. By validating the single resulting signature, one can be convinced that the messages have been endorsed by all the signers. Certificateless aggregate signatures allow the signers to authenticate messages without suffering from the complex certificate management in the traditional public key cryptography or the key escrow problem in identity-based cryptography. In this paper, we present a new efficient certificate less aggregate signature scheme. Compared with up-to-date certificate less aggregate signatures, our scheme is equipped with a number of attracting features: (1) it is shown to be secure under the standard computational Diffie-Hellman assumption in the random oracle model, (2) the security is proven in the strongest security model so far, (3) the signers do not need to be synchronized, and (4) its performance is comparable to the most efficient up-to-date schemes. These features are desirable in a mobile networking and computing environment where the storage/computation capacity of the end devices are limited, and due to the wireless connection and distributed feature, the computing devices are easy to be attacked and hard to be synchronized.

关键词： Aggregates Games Generators Synchronization Public key cryptography

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