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International Conference on Computer Supported Cooperative Work in Design

作者： Yuejia Wu Jian-Tao Zhou College of Computer Science Inner Mongolia University National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Engineering Research Center of Ecological Big Data Ministry of Education Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Key Laboratory of Discipline Inspection and Supervision Big Data Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Hohhot China

Knowledge Graph (KG) is an essential research direction that involves storing and managing knowledge data, but its incompleteness and sparsity hinder its development in various applications. Knowledge Graph Reasoning (KGR) is an effective method to solve this limitation via reasoning missing knowledge based on existing knowledge. The graph Convolution Network (GCN) based method is one of the state-of-the-art approaches to this work. However, there are still some problems, such as the insufficient ability to perceive graph structure and the poor effect of learning data features which may limit the reasoning accuracy. This paper proposes a KGR architecture based on a graph sequence generator and multi-head self-attention mechanism, named GaM-KGR, to improve the above problems and enhance prediction accuracy. Specifically, the GaM-KGR first introduces the graph generation model into the field of KGR for graph representation learning to obtain the hidden features in the data so that enhancing the reasoning effect and then embeds the generated graph sequence into the multi-head self-attention mechanism for subsequent processing to improve the graph structure perception ability of the proposed architecture, so that it can process the graph structure data more appropriately. Extensive experimental results show that the GaM-KGR architecture can achieve the state-of-the-art prediction results of current GCN-based models.

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An Adaptive Partition-Based Approach for Adaptive Random Testing on Real Programs

An Adaptive Partition-Based Approach for Adaptive Random Tes...

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IEEE International Conference on software Analysis, Evolution and Reengineering (SANER)

作者： Yisheng Xia Weifeng Sun Meng Yan Lei Xu Dan Yang School of Big Data & Software Engineering Chongqing University Chongqing China Qingdao Haier Smart Technology R & D Co. Ltd Qingdao China

Adaptive random testing (ART) is a family of algorithms to enhance random testing (RT) by generating test cases extensively and evenly. For this purpose, many ART algorithms have been proposed, the most well-known and the first approach is the Fixed-Size-Candidate-Set ART (FSCS-ART). In recent years, researchers have also proposed many ART methods to continuously improve the performance of FSCS-ART, but the focus has been more on reducing the time overhead of FSCSART while retaining its failure detection effectiveness as much as possible due to the boundary effect. To alleviate the boundary effect and improve the effectiveness of FSCS-ART, this paper proposes an algorithm AP-FSCS-ART, an Adaptive Partition-based method on top of FSCS-ART. First, AP-FSCS-ART divides the entire input domain into external and internal sub-domains. Then, two different algorithms are adaptively applied to the two sub-domains to find the next test case from the randomly generated candidate test cases. During the selecting process, APFSCS-ART takes into account not only the most recently executed test case of a candidate test case but also its position relative to the input domain. Experiments using the 12 most common real programs and comparisons with other algorithms in this paper show that the AP-FSCS-ART algorithm has significantly better failure detection capability, with improvements from 8.8% to 11.4% compared to three state-of-the-art ART algorithms, including the FSCS-ART, FSCS-ctsr, and NNDC-ART.

关键词： Subspace constraints software algorithms software Power capacitors Partitioning algorithms Testing

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DST: Personalized Charging Station Recommendation for Electric Vehicles Based on Deep Reinforcement Learning and Spatio-Temporal Preference Analysis

DST: Personalized Charging Station Recommendation for Electr...

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IEEE International Conference on Web Services (ICWS)

作者： Xiaoying Fan Yongqiang Gao Fujun Zhang College of Computer Science Inner Mongolia University Hohhot China Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Hohhot China Engineering Research Center of Ecological Big Data Ministry of Education Hohhot China

ISBN: (数字)9798350368550

ISBN: (纸本)9798350368567

With the rapid development and popularization of electric vehicles (EVs), the increased demand for charging stations (CSs) has made configuring charging facilities and optimizing charging station recommendations crucial. Consequently, personalized recommendation services for charging stations have emerged as a crucial strategy to mitigate the "mileage anxiety" experienced by drivers. Previous work has typically focused on optimizing CSs’ resource allocation or drivers’ costs, with little consideration of the complexity of the charging behavior of individuals, such as the distribution of Point Of Interests (POIs) surrounding the CS, the similarity of driver selection in different temporal scales, and the tradeoff between different users’ inherent preferences and external cost (e.g., travel times and electric prices), which may result in lower satisfaction, decreased accuracy of recommendations, and sub-optimal decision-making. To address this problem, we propose a charging station recommendation framework based on Actor-Critic Deep Reinforcement Learning, called DST, to assist electric vehicle drivers in finding the proper spots for charging. In the DST, both the actor and critic networks are implemented by Deep Neural Networks (DNNs). The Actor networks, utilizing a Semantic-based Bidirectional Long Short-Term Memory Network and a Stacked Convolutional Neural Network with Multiple Channels, extract the geographical semantic features of CSs and specific preference patterns from different temporal scales as hidden state representations. The critic networks employ a Twin-Critic architecture for joint optimization of inherent preference rewards and external environmental rewards. Extensive experiments on two real-world datasets demonstrate that the DST achieves the best comprehensive performance compared with seven baseline approaches.

关键词： Costs Web services Semantics Computer architecture Charging stations Feature extraction Electric vehicles Deep reinforcement learning Spatiotemporal phenomena Vehicles

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High-Available Cloud Platform Based on OpenStack 2022

High-Available Cloud Platform Based on OpenStack

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8th International Conference on Computing and data engineering, ICCDE 2022

作者： Li, Jie Zhang, Tian-Xiang Li, Leixiao Kang, Zefeng Niu, Tieming Gao, Jing College of Data Science and Application Inner Mongolia University of Technology China Inner Mongolia Autonomous Reg. Software Serv. Eng. Technology Research Center Based on Big Data Hohhot China College of Computer and Information Engineering Inner Mongolia Agricultural University Hohhot China

ISBN: (纸本)9781450395717

The availability of cloud services is an important performance index of cloud platform. It is the premise and basis for a cloud platform to provide services. Aiming at the problem of insufficient reliability of cloud services in university data centers, this paper proposes a high available and decentralized cloud platform scheme based on OpenStack for university micro data center. Firstly, the scheme sets the super fusion of the network layer and detects the state through URL location, which effectively avoids the single points of failure. And then, the database layer adopts multi-master design to realize the super redundant architecture and ensure the high availability. Finally, the reliability evaluation model and experiments show that the service availability of this scheme is very high. © 2022 ACM.

关键词： Platform as a Service (PaaS)

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Thermal-Aware Approximate Real-Time Task Scheduling for Multicore Embedded Systems

Thermal-Aware Approximate Real-Time Task Scheduling for Mult...

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Chinese Automation Congress (CAC)

作者： Chujun Peng Lei Mo Ji Liu Dan Niu School of Software Engineering Southeast University Suzhou China School of Automation Southeast University Nanjing China Big Data Laboratory Baidu Research Beijing China

High temperature affects the processing performance of chips and accelerates electronic component aging, leading to reliability issues. This paper proposes a two-stage temperature-aware scheduling scheme for approximate tasks based on homoge-neous multicore platforms, solving approximate task scheduling problem under time, energy, reliability, and temperature constraints. In the first stage, we design a heuristic scheduling algorithm to perform task-to-processor assignment and preliminarily task frequency selection to minimize energy and makespan. In the second stage, we design a temperature optimization algorithm that combines DVFS and slack distribution to ensure that processor temperature is lower than the temperature threshold. Compared with the existing methods, the experimental simulations show that our method achieves 97.6% runtime reduction and 14.9% peak temperature reduction.

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A High-capacity Information Hiding Scheme Based on Blakley Secret Sharing

A High-capacity Information Hiding Scheme Based on Blakley S...

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Artificial Intelligence and Intelligent Manufacturing (AIIM), International Symposium on

作者： Jincheng Zhou Yue Li Yujie Shen Dan Tang Hongliang Cai Jing Gong Ningchao Bai School of Software Engineering Chengdu University of Information Technology Chengdu China Sichuan Province Engineering Technology Research Center of Support Software of lnformatization Application Chengdu China School of Computer Science and Engineering Sichuan University of Science & Engineering Yibin China Sichuan Provincial Big Data Technical Service Center Chengdu China

ISBN: (数字)9798331541729

ISBN: (纸本)9798331541736

A high-capacity reversible information hiding scheme based on Blakley secret sharing is proposed to address the issues of low embedding rate and weak disaster recovery performance of images when using reversible information hiding to protect data in scenarios with large data transmission. The scheme employs Euclidean division and a two-dimensional modulus function to establish a robust mapping relationship between the secret information and the pixels of the cover image. Subsequently, the values derived from the preprocessing of the secret information are incorporated into the secret sharing method proposed by Blakley as the elements of the secret vector. The proposed scheme exploits the disaster-tolerance property of the secret sharing method and enhances the redundancy of the secret-carrying encrypted image, thereby improving the embedding rate and security of the algorithm while maintaining the security of the secret sharing.

关键词： Correlation Disasters Redundancy Reconstruction algorithms Vectors Manufacturing Cryptography data mining data communication Computational complexity

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Joint Task Offloading and Resource Allocation for NOMA-Based Vehicular Networks

Joint Task Offloading and Resource Allocation for NOMA-Based...

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International Conference on Computer Supported Cooperative Work in Design

作者： Yunfei Song Yongqiang Gao Yipei He Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Hohhot China College of Computer Science Inner Mongolia University Hohhot China Engineering Research Center of Ecological Big Data Ministry of Education Hohhot China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Vehicle Edge Computing (VEC) is a critical technology that can achieve low latency and energy consumption for Telematics. However, with the high-speed mobility of new energy-electric vehicles and their cross-regional nature, performing high-quality service of vehicle tasks on the VEC model is still challenging. Considering the electric vehicle range problem, this paper plans to jointly optimize task offloading, task result forwarding and computational resource allocation (OOFR) within the maximum tolerable delay of vehicle tasks to minimize vehicle tasks' delay and energy consumption. The non-orthogonal multiple access (NOMA) technology is used with roadside units (RSUs) to achieve multiplexing of limited spectrum resources. This allows multiple vehicle users to perform task transmission simultaneously, thus reducing vehicle task transmission delay. In addition, we propose a cooperative game approach based on NOMA for task grouping to reduce the signal interference of vehicle task transmission. Finally, a deep reinforcement learning method is proposed for task offloading decision selection. A simulation platform is built to compare with MEC, COMO and MADDPG methods, combined with simulation results, showing that the superiority of our proposed scheme is verified.

关键词： NOMA Energy consumption Simulation Games Electric vehicles Deep reinforcement learning Telematics

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multiPI-TransBTS: A Multi-Path Learning Framework for Brain Tumor Image Segmentation Based on Multi-Physical Information

arXiv

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arXiv 2024年

作者： Zhu, Hongjun Huang, Jiaohang Chen, Kuo Ying, Xuehui Qian, Ying School of Software Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China Chongqing Engineering Research Center of Software Quality Assurance Testing and Assessment Chongqing400065 China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing400065 China

Brain Tumor Segmentation (BraTS) plays a critical role in clinical diagnosis, treatment planning, and monitoring the progression of brain tumors. However, due to the variability in tumor appearance, size, and intensity across different MRI modalities, automated segmentation remains a challenging task. In this study, we propose a novel Transformer-based framework, multiPI-TransBTS, which integrates multi-physical information to enhance segmentation accuracy. The model leverages spatial information, semantic information, and multi-modal imaging data, addressing the inherent heterogeneity in brain tumor characteristics. The multiPI-TransBTS framework consists of an encoder, an Adaptive Feature Fusion (AFF) module, and a multi-source, multi-scale feature decoder. The encoder incorporates a multi-branch architecture to separately extract modality-specific features from different MRI sequences. The AFF module fuses information from multiple sources using channel-wise and element-wise attention, ensuring effective feature recalibration. The decoder combines both common and task-specific features through a Task-Specific Feature Introduction (TSFI) strategy, producing accurate segmentation outputs for Whole Tumor (WT), Tumor Core (TC), and Enhancing Tumor (ET) regions. Comprehensive evaluations on the BraTS2019 and BraTS2020 datasets demonstrate the superiority of multiPI-TransBTS over the state-of-the-art methods. The model consistently achieves better Dice coefficients, Hausdorff distances, and Sensitivity scores, highlighting its effectiveness in addressing the BraTS challenges. Our results also indicate the need for further exploration of the balance between precision and recall in the ET segmentation task. The proposed framework represents a significant advancement in BraTS, with potential implications for improving clinical outcomes for brain tumor patients. Copyright © 2024, The Authors. All rights reserved.

关键词： Decoding

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Local Information Guided Global Integration for Infrared Small Target Detection

Local Information Guided Global Integration for Infrared Sma...

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

作者： Qiang Li Qianchen Mao Wenjie Liu Jinbao Wang Wenmin Wang Bingshu Wang School of Software Northwestern Polytechnical University Xi’an China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Faculty of Innovation Engineering Macau University of Science and Technology Macau China

Infrared small targets often exhibit small scale and weak semantic features, which makes it a great challenge to their detection. To address this situation, we propose a novel network for infrared small target detection that combines local details information and global contextual information. To preserve the local and high-frequency details present in infrared images, we introduce a High-frequency Aware Encoder. To extract contextual information from multi-scale feature maps, we propose a Multi-scale Context Learning Bottleneck that incorporates contextual information repeatedly and performs cross-level fusion, which enables the recognition of small targets based on their surroundings. Finally, a lightweight Transformer Decoder is employed to restore the feature map, while placing attention on the target pixels. Experimental results on the IRSTD-1k dataset demonstrate that our method outperforms other state-of-the-art approaches.

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Weakness of Enhanced DNA and ElGamal Cryptosystem for Secure data Storage and Retrieval in Cloud

Weakness of Enhanced DNA and ElGamal Cryptosystem for Secure...

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IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Yibiao Fan Chenglian Liu Yuxia Yang Jie Fang Department of Networking Technology Software Engineering Institute of Guangzhou Guangzhou China School of Big Data and Artificial Intelligence Fujian Polytechnic Normal University Fuqing China

DNA cryptography has been developed for nearly thirty years, and there is a lot of research on DNA based encryption and decryption. In 2018, Thangavel and Varlakshmi proposed an article “Enhanced DNA and ElGamal cryptosystem for secure data storage and retrieval in cloud”, they provide two cryptosystem schemes DNA and ElGamal. In this paper the authors would like to analyze security to DNA scheme, this is our research goal. We also prove it may occur insecure when the XOR operation using in special environment. The mathematical Proof and experimental evidence be demonstrated in this article, this is our method. The results point out it is not secure to Thangavel and Varlakshmi's DNA protocol.

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