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Design and implementation of a kind of EUPS for mobile service

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Journal of Information and Computational Science 2010年第11期7卷 2248-2257页

作者： Zhang, Xiaodan Zhang, Degan Qiao, Xiaodong Liang, Bing Peng, Hongtao Institute of Scientific and Technical Information of China Beijing 100038 China Tianjin Key Lab. of Intelligent Computing and Novel software Technology Tianjin University of Technology Tianjin 300191 China Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology Tianjin 300191 China Beijing Petroleum Managers Training Institute Beijing 100096 China

In order to support mobile service of long-distance monitoring and controlling UPS based on Web, a kind of design and implementation solution of embedded UPS (EUPS) system is brought forward in this paper. The design and implementation of system is based on ATmega161 and RTL8019AS with TCP/IP protocol suite for communication. In the EUPS system, an embedded file system is designed and implemented which can store the data and index information on a serial EEPROM chip in structured way and communicate with MCU through I2C bus. By embedding the file system into UPS system or other information appliances, users can browse and manipulate local data on the web client side. Based on our experimental tests, the performance of EUPS can meet the requirements of Web-based mobile services. © 2010 Binary Information Press.

关键词： Websites

MULTIPLE HEADS ARE BETTER THAN ONE: MIXTURE OF MODALITY KNOWLEDGE EXPERTS FOR ENTITY REPRESENTATION LEARNING

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arXiv 2024年

作者： Zhang, Yichi Chen, Zhuo Guo, Lingbing Xu, Yajing Hu, Binbin Liu, Ziqi Zhang, Wen Chen, Huajun College of Computer Science and Technology Zhejiang University China ZJU-Ant Group Joint Lab of Knowledge Graph China Ant Group China School of Software Technology Zhejiang University China Zhejiang Key Laboratory of Big Data Intelligent Computing China

Learning high-quality multi-modal entity representations is an important goal of multi-modal knowledge graph (MMKG) representation learning, which can enhance reasoning tasks within the MMKGs, such as MMKG completion (MMKGC). The main challenge is to collaboratively model the structural information concealed in massive triples and the multi-modal features of the entities. Existing methods focus on crafting elegant entity-wise multi-modal fusion strategies, yet they overlook the utilization of multi-perspective features concealed within the modalities under diverse relational contexts. To address this issue, we introduce a novel framework with Mixture of Modality Knowledge experts (MOMOK for short) to learn adaptive multi-modal entity representations for better MMKGC. We design relation-guided modality knowledge experts to acquire relation-aware modality embeddings and integrate the predictions from multi-modalities to achieve joint decisions. Additionally, we disentangle the experts by minimizing their mutual information. Experiments on four public MMKG benchmarks demonstrate the outstanding performance of MOMOK under complex scenarios. Our code and data are available at https://***/zjukg/MoMoK. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

Tokenization, Fusion, and Augmentation: Towards Fine-grained Multi-modal Entity Representation

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arXiv 2024年

Multi-modal knowledge graph completion (MMKGC) aims to discover unobserved knowledge from given knowledge graphs, collaboratively leveraging structural information from the triples and multi-modal information of the entities to overcome the inherent incompleteness. Existing MMKGC methods usually extract multi-modal features with pre-trained models, resulting in coarse handling of multi-modal entity information, overlooking the nuanced, fine-grained semantic details and their complex interactions. To tackle this shortfall, we introduce a novel framework MYGO to tokenize, fuse, and augment the fine-grained multi-modal representations of entities and enhance the MMKGC performance. Motivated by the tokenization technology, MYGO tokenizes multi-modal entity information as fine-grained discrete tokens and learns entity representations with a cross-modal entity encoder. To further augment the multi-modal representations, MYGO incorporates fine-grained contrastive learning to highlight the specificity of the entity representations. Experiments on standard MMKGC benchmarks reveal that our method surpasses 19 of the latest models, underlining its superior performance. Code and data can be found in https://***/zjukg/MyGO. Copyright © 2024, The Authors. All rights reserved.

关键词： Knowledge graph

HERO: Heterogeneity-aware erasure coded redundancy optimal allocation for reliable storage in distributed networks

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HERO: Heterogeneity-aware erasure coded redundancy optimal a...

IEEE International Conference on Performance, computing and Communications (IPCCC)

作者： Guangping Xu Sheng Lin Gang Wang Xiaoguang Liu Kai Shi Hua Zhang Tianjin Key Lab. of Intelligence Computing and New Software Technology Tianjin China College of Information Tech. and Science Nankai University Tianjin China School of Computer and Communication Tianjin University of Technology Tianjin China

Heterogeneity is the natural feature in distributed networks. Different from the traditional disk array, the amount of data allocated on heterogenous peers may be not the same. To maximize the reliability of stored data objects in heterogeneous networks, the optimal allocation of erasure-coded fragments is a challenging problem constrained with heterogeneous peer availabilities and redundancy overhead. This paper examines this optimal problem considered MDS erasure codes applied into distributed storage networks. First, we model the reliability of an allocation with the weighted-k-out-of-s model and extend its properties to efficiently calculate the reliability of an allocation; then we reduce the reliability computation of a given allocation to linear computation cost based on the weighted k-out-of-s model. Then, we deduce the problem to integer partition problem and propose two order-based search algorithms. Our experiments show that our proposed algorithms can be applied to find the optimal allocations efficiently in various practical coding cases. Furthermore, we evaluate the performance of our proposed search algorithms with some practical storage settings, and then present experimental results including the reliability, redundancy overheads and allocation pattern for the optimal allocation driven by practical network traces.

关键词： Resource management Reliability theory Availability Computational modeling Redundancy Computer network reliability

Prompt-based Unifying Inference Attack on Graph Neural Networks

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arXiv 2024年

作者： Wei, Yuecen Fu, Xingcheng Liu, Lingyun Sun, Qingyun Peng, Hao Hu, Chunming School of Software Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University China

Graph neural networks (GNNs) provide important prospective insights in applications such as social behavior analysis and financial risk analysis based on their powerful learning capabilities on graph data. Nevertheless, GNNs’ predictive performance relies on the quality of task-specific node labels, so it is common practice to improve the model’s generalization ability in the downstream execution of decision-making tasks through pre-training. Graph prompting is a prudent choice but risky without taking measures to prevent data leakage. In other words, in high-risk decision scenarios, prompt learning can infer private information by accessing model parameters trained on private data (publishing model parameters in pre-training, i.e., without directly leaking the raw data, is a tacitly accepted trend). However, myriad graph inference attacks necessitate tailored module design and processing to enhance inference capabilities due to variations in supervision signals. In this paper, we propose a novel Prompt-based unifying Inference Attack framework on GNNs, named ProIA. Specifically, ProIA retains the crucial topological information of the graph during pre-training, enhancing the background knowledge of the inference attack model. It then utilizes a unified prompt and introduces additional disentanglement factors in downstream attacks to adapt to task-relevant knowledge. Finally, extensive experiments show that ProIA enhances attack capabilities and demonstrates remarkable adaptability to various inference attacks. Copyright © 2024, The Authors. All rights reserved.

关键词： Information leakage

Poincaré Differential Privacy for Hierarchy-Aware Graph Embedding

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arXiv 2023年

作者： Wei, Yuecen Yuan, Haonan Fu, Xingcheng Sun, Qingyun Peng, Hao Li, Xianxian Hu, Chunming Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China School of Software Beihang University Beijing China Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University China

Hierarchy is an important and commonly observed topological property in real-world graphs that indicate the relationships between supervisors and subordinates or the organizational behavior of human groups. As hierarchy is introduced as a new inductive bias into the Graph Neural Networks (GNNs) in various tasks, it implies latent topological relations for attackers to improve their inference attack performance, leading to serious privacy leakage issues. In addition, existing privacy-preserving frameworks suffer from reduced protection ability in hierarchical propagation due to the deficiency of adaptive upper-bound estimation of the hierarchical perturbation boundary. It is of great urgency to effectively leverage the hierarchical property of data while satisfying privacy guarantees. To solve the problem, we propose the Poincaré Differential Privacy framework, named PoinDP, to protect the hierarchy-aware graph embedding based on hyperbolic geometry. Specifically, PoinDP first learns the hierarchy weights for each entity based on the Poincaré model in hyperbolic space. Then, the Personalized Hierarchy-aware Sensitivity is designed to measure the sensitivity of the hierarchical structure and adaptively allocate the privacy protection strength. Besides, the Hyperbolic Gaussian Mechanism (HGM) is proposed to extend the Gaussian mechanism in Euclidean space to hyperbolic space to realize random perturbations that satisfy differential privacy under the hyperbolic space metric. Extensive experiment results on five real-world datasets demonstrate the proposed PoinDP's advantages of effective privacy protection while maintaining good performance on the node classification task. Copyright © 2023, The Authors. All rights reserved.

关键词： Backpropagation

Emergent Communication for Rules Reasoning

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arXiv 2023年

作者： Guo, Yuxuan Hao, Yifan Zhang, Rui Zhou, Enshuai Du, Zidong Zhang, Xishan Song, Xinkai Wen, Yuanbo Zhao, Yongwei Zhou, Xuehai Guo, Jiaming Yi, Qi Peng, Shaohui Huang, Di Chen, Ruizhi Guo, Qi Chen, Yunji University of Science and Technology of China China State Key Lab of Processors Institute of Computing Technolgy CAS China Cambricon Technologies China University of Chinese Academy of Sciences China Shanghai Innovation Center for Processor Technologies China Intelligent Software Research Center Institute of Software CAS China

Research on emergent communication between deep-learning-based agents has received extensive attention due to its inspiration for linguistics and artificial intelligence. However, previous attempts have hovered around emerging communication under perception-oriented environmental settings, that forces agents to describe low-level perceptual features intra image or symbol contexts. In this work, inspired by the classic human reasoning test (namely Raven’s Progressive Matrix), we propose the Reasoning Game, a cognition-oriented environment that encourages agents to reason and communicate high-level rules, rather than perceived low-level contexts. Moreover, we propose 1) an unbiased dataset (namely rule-RAVEN) as a benchmark to avoid overfitting, 2) and a two-stage curriculum agent training method as a baseline for more stable convergence in the Reasoning Game, where contexts and semantics are bilaterally drifting. Experimental results show that, in the Reasoning Game, a semantically stable and compositional language emerges to solve reasoning problems. The emerged language helps agents apply the extracted rules to the generalization of unseen context attributes, and to the transfer between different context attributes or even tasks. © 2023, CC BY.

关键词： Deep learning

A Novel Offloading Approach of Iot User Perception Task Based on Quantum Behavior Particle Swarm Optimization

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SSRN

SSRN 2022年

作者： Chen, Lu Zhang, De-Gan Zhang, Jie Zhang, Ting Wang, Wen-Jing Cao, Ya-Hui Tianjin Key Lab of Intelligent Computing and Novel Software Technology Tianjin University of Technology Tianjin300384 China School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China School of Sports Economics and Management Tianjin University of Sport Tianjin301617 China

To solve the problem of high time consumption and energy loss of edge computing perceptual task offloading in the mobile IoT (Internet of things), it is necessary to increase the particle population diversity and the performance of escaping the local optimum solution in the intelligent optimization algorithm. For the offloading scenario of multi edge central servers and smart devices of multi IoT users, the NQPSOOM method (A new method of user perceptual task offloading in the IoT based on optimizing strategy of quantum behavior particle population) was proposed. The adaptability variance of the quantum behavior particle population was designed to measure the convergence of the particle population. The logistic chaos perturbation strategy was used to improve the performance of the intelligent optimization algorithm of escaping the local optimum solution. The exchange and mutation operations of fast elite non-dominated sorting genetic algorithm based on crowding coefficient were designed to select the particle population with a high diversity of quantum behavior particle population. Compared with the other five perceptual task computing offloading methods in recent three years, the NQPSOOM method reduces the adaptability of the intelligent optimization algorithm, decreases the time consumption and energy loss of the algorithm. © 2022, The Authors. All rights reserved.

关键词： Internet of things

new Method of Vehicle Cooperative Communication Based on Fuzzy Logic and Signal Game Strategy

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SSRN

SSRN 2022年

作者： Zhang, De-Gan Ni, Chen-Hao Zhang, Jie Zhang, Ting Zhang, Zhi-Hao Su, Hao Tianjin Key Lab of Intelligent Computing and Novel Software Technology Tianjin University of Technology Tianjin300384 China School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China School of Sports Economics and Management Tianjin University of Sport Tianjin301617 China

With the rapid development of the Internet of Vehicles, at the same time, due to the limited storage space of vehicles and more and more data requirements, vehicles need more and more vehicle-to-vehicle collaboration. At this time, a vehicular ad-hoc network (VANET) is required for inter-vehicle communication cooperation, and link breaks often occur due to the rapid movement of vehicles and the uncertainty of vehicle communication quality, which affects the communication quality. Because of these situations, this paper proposes a vehicle cooperative communication method based on fuzzy logic and signal game strategy, obtains more real vehicle position and speed information on the Kalman filter, and then selects relay nodes to assist in requesting information message distribution of fuzzy logic. Each time the vehicle receives a request, the first data information is returned to the signal game. If all the information requests are not received the first time, the information requesting vehicle will send the second information request according to the signal strength and the beliefs observed on the previous vehicle. The experimental verification shows that this method can realize the function of vehicle cooperative communication, solve the problems of low VANET link stability and broadcast storm, and has a good supporting role in establishing the practicality of vehicle cooperation in VANET. © 2022, The Authors. All rights reserved.

关键词： Fuzzy logic