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10th IEEE International Conference on Cyber Security and Cloud computing and 9th IEEE International Conference on Edge computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Shuhan, Mirza K. B. Islam, Tariqul Shuvo, Enam A. Bappy, Faisal H. Hasan, Kamrul Caicedo, Carlos Software Research and Engineering BKash Limited Dhaka Bangladesh SyracuseNY United States Asia Pacific University of Technology and Innovation School of Computing Kuala Lumpur Malaysia Tennessee State University NashvilleTN United States

ISBN: (纸本)9798350312461

Over the past two decades, the popularity of messaging systems has increased both in enterprise and consumer level. Many of these systems used secure protocols like end-to-end encryption to ensure strong security features such as 'future secrecy' for one-to-one communication. However, the majority of them rely on centralized servers owned by big IT companies, which allows them to use their users' personal data. Also it allows the government to track and regulate their citizens' activities, which poses significant threats to 'digital freedom'. Also, these systems have failed to achieve security attributes like confidentiality, integrity, privacy, and future secrecy for group communications. In this paper, we present a novel blockchain-based secure messaging system named Quarks that overcomes the security pitfalls of the existing systems and eliminates the centralized control. We have analyzed our design of the system with security models and definitions from existing literature to demonstrate the system's reliability and usability. We have developed a Proof of Concept (PoC) of the Quarks system leveraging Distributed Ledger Technology (DLT), and conducted load testing on that. We noticed that our PoC system achieves all the desired attributes that are prevalent in a traditional centralized messaging scheme despite the limited capacity of the development and testing environment. Therefore, this assures us the applicability of such systems in near future if scaled up properly. © 2023 IEEE.

关键词： Blockchain

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CESFusion: Cross-Frequency Enhanced Spatial—Spectral Fusion Network for Hyperspectral and Multispectral Image Fusion

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IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Zhang, Haozheng Yang, Yanhong Li, Chaoyang Lu, Yanjie Zhang, Guodao Chen, Shengyong Tianjin University of Technology School of Computer Science and Engineering Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin300384 China School of Computer Science and Engineering Tianjin300384 China Hangzhou Dianzi University Institute of Intelligent Media Computing Hangzhou310018 China

The fusion of hyperspectral and multispectral images (MSIs) involves integrating a high spectral resolution hyperspectral image (HSI) and a high spatial resolution MSI to generate an HSI with high-resolution HSI (HR-HSI). Existing HSI-MSI fusion methods primarily focus on information fusion within the spatial domain;however, few solutions have explored the employment of frequency analysis to enhance spatial resolution, limiting their capability for global perception. In this article, we propose an efficient and novel paradigm for HSI-MSI fusion through the cross-frequency enhanced spatial-spectral fusion network, named CESFusion, exploring the complementary fusion of information between the spatial and frequency domains. Specifically, we first present the cross-frequency fusion module (CFFM) to perform global analysis through the Fourier transform (FT) and effectively integrate and enhance the frequency domain information from both HSI and MSI. Subsequently, we propose the spectral modeling module (SpeMM) based on a state space model (SMM) to capture long-range spectral dependencies with linear complexity, and integrate it with the spatial residual block-based module (SRM) for joint spatial-spectral feature extraction. Finally, to enable sufficient interaction between the spatial and frequency domains, we adopt the cross-domain interaction module (CDIM), capturing and integrating complementary information from both domains. Moreover, a frequency-based loss function is purposely designed to further improve the restoration of global information. Extensive experiments conducted on both synthetic and real datasets demonstrate the superiority of our CESFusion, as evidenced by both quantitative and qualitative evaluation results. © 1980-2012 IEEE.

关键词： Image fusion

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Ensuring IoT Controller Reliability with Colored Generalized Stochastic Petri Net

Ensuring IoT Controller Reliability with Colored Generalized...

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International Computer Science and engineering Conference (ICSEC)

作者： Kruntarat Samngamnoi Sutinun Pomsiri Apipath Kamput Chanon Dechsupa Department of Computer Science Intelligent Software Engineering Research Group College of Computing Khon Kaen University Khon Kaen Thailand

ISBN: (数字)9798350366860

ISBN: (纸本)9798350366877

In IoT systems managing multiple devices simultaneously, errors in system controllers often undermine intended operations. Formal verification offers a method to assess system reliability. Colored Generalized Stochastic Petri Net (CGSPN), a formal language, facilitate correctness checks of such systems. This study proposes a verification approach by translating a C++-based system controller of a self-service machine into a CGSPN models and validating it using the Snoopy Tool. Mapping techniques employed to transform components in the controller into CGSPN models are provided. Results demonstrate the method’s efficacy in verifying system safety properties, simulating system events, and enabling quantitative verification.

关键词： Codes Petri nets Stochastic processes C++ languages Transforms Control systems Internet of Things Reliability Washing machines Self-service

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TrajTrace: Tracing Moving Objects over Social Media 28th

TrajTrace: Tracing Moving Objects over Social Media

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28th International Conference on Database Systems for Advanced Applications, DASFAA 2023

作者： Yang, Zhihao Zhang, Yunqi Li, Songda Chen, Qinhui Zhao, Hui Cai, Wei Lin, Xi Software Engineering Institute East China Normal University Shanghai China Shanghai Key Laboratory of Trustworthy Computing Shanghai China Electronics Technology Group Corporation The 51st Research Institue of China Beijing China

ISBN: (纸本)9783031306778

Online social media has lots of moving object information. Extracting movement information is a challenging work. It suffers from spatio-temporal information extraction, vague time, and vague location. Previous information extraction methods merely focus on the trajectory extraction. In this demonstration, we develop a web-based application, TrajTrace, to track moving objects. TrajTrace extracts triple from social media text employing our proposed span-level joint entity and relation extraction model, OMLer. OMLer casts joint extraction as a token pair multi-categories classification task. It predicts the triple list corresponding to the input sequence. We employ BERT to encode the input sentence word by word. The self-attention mechanism and BiLSTM are applied to learn sequence features. Then, an order-first time matching algorithm is designed to solve the lacking temporal information problem in the extracted triples. Utilizing the proposed TF-IDF based clustering algorithm, we make the vague time accurate. The vague geographic location is converted to accurate latitude and longitude using the Bezier geodetic coordinate conversion algorithm. Toward aircraft and ships, besides the keyword search and the trajectories visualization, TrajTrace provides the historical activity area search of a specific object and the spatio-temporal distribution of moving objects at a given time or location. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Trajectories

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Humanoid Robot Collaborative Lifting Integrating Executable Judgment

Humanoid Robot Collaborative Lifting Integrating Executable ...

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2022 IEEE International Symposium on Product Compliance engineering - Asia, ISPCE-ASIA 2022

作者： Chang, Hua Yi, Pengfei Liu, Rui Dong, Jing Hou, Yaqing Zhou, Dongsheng The Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian University Dalian China Dalian University of Technology Dalian China

ISBN: (数字)9798350332483

ISBN: (纸本)9798350332483

Humanoid robot collaborative lifting can be used in a variety of scenarios that require repetitive lifting tasks. Most existing studies of humanoid robot collaboration often assume that objects can always be lifted, which may result in damage to both the robot and the object if objects are too heavy to lift. To avoid such situations as much as possible, a collaborative lifting approach integrating executable judgment is proposed. First, a target search and localization method is constructed using monocular vision and marker points to identify the task object. Then, an executable judgment strategy is designed to determine whether the object is overweight or not according to robot force analysis. Finally, a multi-robot joint control model is proposed based on collaborative communication to perform collaborative tasks with different loads based on the judgment results. Experiments on two humanoid robots for different types and weights of targets show the effectiveness of the proposed approach. © 2022 IEEE.

关键词： Anthropomorphic robots

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Target Permutation for Feature Significance and Applications in Neural Networks

Target Permutation for Feature Significance and Applications...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Sanad Biswas Nina Grundlingh Jonathan Boardman Joseph White Linh Le College of Computing and Software Engineering Kennesaw State University Marietta GA USA Equifax Inc. Atlanta GA USA

ISBN: (数字)9798350374889

ISBN: (纸本)9798350374896

Statistical techniques like generalized linear models have always been the indispensable tools for understanding relationships in data. However, over the last two decades, growth in data size and complexity has fueled the rise of powerful “black box” machine learning models (i.e., Deep Learning). Such models lack transparent mechanisms to evaluate the importance and contributions of individual features. This may be unacceptable ethically and/or legally. Moreover, the inability to precisely assess contributions from features complicates model validation and understanding. In this paper, we propose a permutation test for feature importance in differentiable models with a focus on neural networks. Unlike existing permutation-based methods, ours shuffles the target instead of the inputs. This change allows for simultaneous testing of all features and does not require the assumption of independence among inputs that is prevalent in current work in this area. Through extensive experiments, we empirically demonstrate that this permutation test can reveal highly nonlinear associations, is robust to multicollinearity among features, and can be used to filter unnecessary inputs, while preserving or improving models' predictive performance in both classification and regression.

关键词： Training Deep learning Ethics Explainable AI Computational modeling Neural networks Redundancy Noise Predictive models Data models

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Sinhala Named Entity Recognition Model: Domain-Specific Classes in Sports 4

Sinhala Named Entity Recognition Model: Domain-Specific Clas...

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4th International Conference on Advancements in computing, ICAC 2022

作者： Wijesinghe, W.M.S.K. Tissera, Muditha Department of Statistics and Computer Science Faculty of Science University of Kelaniya Sri Lanka Department of Software Engineering Faculty of Computing & Technology University of Kelaniya Sri Lanka

ISBN: (纸本)9798350398090

Named Entity Recognition (NER) is one of the crucial and vital subtasks that must be solved in most Natural Language Processing (NLP) tasks. However, constructing a NER system for the Sinhala Language is challenging. Because it comes under the category of low-resource languages. Therefore, the proposed approach attempted designing a mechanism to identify specific named entities in the sports domain. Firstly, a domain-specific corpus was built using Sinhala sport e-News articles. Then a semi-automated, rule-based component named as "Class_Label_Suggester" was built to annotate pre-defined named entities. After auto annotation, the outcome was further validated manually with a little effort. Finally, it was trained using the annotated data. Linear Perceptron, Stochastic Gradient Descent (SGD), Multinomial Naive Bayes (MNB), and Passive Aggressive classifiers were used to train the NER model. Though, the above Machine Learning (ML) algorithms showed approximately 98% accuracy, the MNB model demonstrated highest accuracy for the identified class labels of which, 99.76% for 'Ground', 99.53% for 'School', 98.55% for 'Tournament', and 97.87% for 'Other' classes. Additionally, high precision values of the above classes were 81%, 72%, 62%, and 98% respectively. An accurately annotated Sinhala dataset and the trained Sinhala NER model are main contributions of the study. © 2022 IEEE.

关键词： Sports

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DRL-Based Time-Varying Workload Scheduling With Priority and Resource Awareness

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IEEE Transactions on Network and Service Management 2025年第3期22卷 2838-2852页

作者： Liu, Qifeng Fan, Qilin Zhang, Xu Li, Xiuhua Wang, Kai Xiong, Qingyu Chongqing University School of Big Data and Software Engineering Chongqing400044 China Chongqing University Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education Chongqing400044 China Nanjing University School of Electronic Science and Engineering Nanjing210023 China Haihe Laboratory of Information Technology Application Innovation Tianjin300072 China Harbin Institute of Technology School of Computer Science and Technology Weihai264209 China Shandong Key Laboratory of Industrial Network Security Weihai264209 China

With the proliferation of cloud services and the continuous growth in enterprises' demand for dynamic multi-dimensional resources, the implementation of effective strategy for time-varying workload scheduling has become increasingly significant. In this paper, we propose a deep reinforcement learning (DRL)-based method for time-varying workload scheduling, aiming to allocate resources efficiently across servers in the cluster. Specifically, we integrate a classifier and queue scorer to construct a priority queue that exploits temporal resource utilization patterns across different workload classes. Then, we design parallel graph attention layers to capture the dimensional features and temporal dynamics of cloud server cluster. Moreover, we propose a DRL algorithm to generate scheduling strategies that can adapt to dynamic environments. Validation on real-world traces from Google cluster demonstrates that our method outperforms existing approaches in key metrics of cloud server cluster management. © 2004-2012 IEEE.

关键词： Cloud platforms

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Beyond Data Collection: Safeguarding User Privacy in Social Robotics

Beyond Data Collection: Safeguarding User Privacy in Social ...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Mahboobeh Dorafshanian Muhammad Aitsam Mohamed Mejri Alessandro Di Nuovo Department of Computer Science & Software Engineering Laval University QC Canada Department of Computing Sheffield Hallam University Sheffield UK

ISBN: (数字)9798350340266

ISBN: (纸本)9798350340273

In an era marked by the advanced capabilities of social robots in personal and public spaces, the issue of pervasive data collection by these entities becomes increasingly pertinent. Social robots, deployed by government entities, hospitals, and corporations, are at the forefront of gathering sensitive personal data, necessitating careful consideration of privacy concerns. The vast amounts of data collected by these robots, while beneficial for decision-making and fostering research, also pose significant privacy risks. In particular, the challenge intensifies when robots collect and potentially share data that includes sensitive personal information. This paper presents a user-friendly Differential Privacy (DP) library that addresses this challenge. The library incorporates a risk threshold and evaluates the potential impact of data disclosure to accurately quantify privacy levels. Designed for nontechnical users, it enables the secure release of statistical data without the risk of privacy breaches. With privacy breaches and re-identification becoming increasingly common, this library offers a robust solution for safeguarding individuals' privacy while facilitating the sharing of valuable insights.

关键词： Differential privacy Service robots Hospitals Social robots Information sharing Data collection Robot sensing systems

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A Web Service Recommendation Method Based on Adaptive Gate Network and xDeepFM 1

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22nd International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2022

作者： Tao, Zhi Cao, Buqing Ye, Hongfan Kang, Guosheng Peng, Zhenlian Wen, Yiping School of Computer Science and Engineering and Hunan Key Laboratory of Service Computing and New Software Service Technology Hunan University of Science and Technology Hunan Xiangtan China School of Computer and Artificial Intelligence Huaihua University Huaihua China

ISBN: (数字)9783031226779

ISBN: (纸本)9783031226762

With the rapid development of service computing technology, more and more companies and organizations are encapsulating and publishing their operational data or resources to the Internet in the form of Web services, resulting in an exponential increase in the number of Web services. To automatically generate or recommend a group of Web services according to the user's natural language requirements description, to build a Mashup application to meet the user's requirement is a hot topic in service computing. Some researchers enhance the quality of Web service recommendation by using auxiliary information into the recommendation system. However, they mainly focus on adding external information (e.g., pre-training of external corpora) to enhance semantic features, while some internal statistical features of the corpus such as word distribution on labels and frequency are not fully exploited. Compared to other exterior knowledge, statistical features are naturally compatible with Web service recommendation tasks. To fully exploit the statistical features, this paper proposes a Web service recommendation method based on adaptive gate network and xDeepFM model. In this method, firstly, the description document of Web services is taken as the basic corpus, the semantic and statistical information in the corpus are mined by utilizing the adaptive gate network, and the statistical features are encoded by a variational encoder. Secondly, the similarity between Web services is derived from the semantic features, at the same time the popularity and co-occurrence of Web services are calculated. Thirdly, the xDeepFM model is used to mine the explicit and implicit higher-order interactions in the sparse matrix which consists of the above information to recommend Web services for Mashup application. Finally, a multiple sets of experiments based on the dataset crawled from the ProgrammableWeb have been conducted to evaluate the proposed method and the experimental result shows that t

关键词： Web services

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