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CRFs for Digital Signature and NIZK Proof System in Web Services 1

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

作者： Kang, Burong Zhang, Lei Yang, Yafang Meng, Xinyu Engineering Research Center of Software/Hardware Co-design Technology and Application Ministry of Education East China Normal University Shanghai200062 China Science and Technology on Communication Security Laboratory Sichuan610041 China Research Institute of China Telecom Corporation Limited Shanghai200062 China School of Computer Science Fudan University Shanghai200433 China

ISBN: (数字)9783031226779

ISBN: (纸本)9783031226762

Web services are service-oriented computing technology which allows computers running different operating domains to access and share each other’s databases. Each web service is an application (like online business) which may require the private information of users. Thus, it will be important to preserve these web users’ individual privacy. The traditional approaches to achieve this goal in web security is to use the cryptographic technologies, such as digital signature, NIZK proof system. Whereas, some recent research results indicate that these cryptographic technologies may suffer from the algorithm substitution attack (ASA). ASA means that the cryptographic technology would be embedded some backdoor in the process of its implementation by the attacker, and with the backdoor information the attacker can steal the user’s private information. To address this problem, the concept of cryptographic reverse firewall (CRF) has been introduced, which could sanitize the messages inputting and outputting the user’s computer. In this paper, we construct the CRFs for the efficient Pointcheval-Sanders (PS) signature as well as the NIZK proof system. © 2023, Springer Nature Switzerland AG.

关键词： Web services

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Location-Based Service Recommendation for Cold-Start in Mobile Edge Computing 17th

Location-Based Service Recommendation for Cold-Start in Mobi...

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17th IFIP WG 10.3 International Conference on Network and Parallel Computing, NPC 2020

作者： Yu, Mengshan Fan, Guisheng Yu, Huiqun Chen, Liang Department of Computer Science and Engineering East China University of Science and Technology Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China

ISBN: (纸本)9783030794774

With the rapid development of the 5G and Internet of Things (IoT), mobile edge computing has gained considerable popularity in academic and industrial field, which provides physical resources closer to end users. Service recommendation in such a distributed environment is a hot issue. However, the cold-start problem for service recommendation in mobile edge computing is still urgent to be solved. In this paper, we propose a service recommendation method based on collaborative filtering (CF) and user location, by comprehensively considering the characteristic of services at the edge and mobility of users. In detail, we first synthesize the service characteristics of each dimension through multidimensional weighting method. We further introduce the idea of Inverse_CF_Rec to the traditional CF and then predict the lost QoS value to solve the problem of sparse data. Finally, a recommendation algorithm based on predicted QoS value and user geographic location is proposed to recommend appropriate services to users. The experimental results show that our multidimensional inverse similarity recommendation algorithm based on collaborative filtering (MDICF) outperforms Inverse_CF_Rec in terms of the accuracy of recommendation. © 2021, IFIP International Federation for Information Processing.

关键词： Location based services

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LGMRec: Local and Global Graph Learning for Multimodal Recommendation

arXiv

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arXiv 2023年

作者： Guo, Zhiqiang Li, Jianjun Li, Guohui Wang, Chaoyang Shi, Si Ruan, Bin School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China School of Software Engineering Huazhong University of Science and Technology Wuhan China Wuhan Digital Engineering Institute Wuhan China Shenzhen China

The multimodal recommendation has gradually become the infrastructure of online media platforms, enabling them to provide personalized service to users through a joint modeling of user historical behaviors (e.g., purchases, clicks) and item various modalities (e.g., visual and textual). The majority of existing studies typically focus on utilizing modal features or modal-related graph structure to learn user local interests. Nevertheless, these approaches encounter two limitations: (1) Shared updates of user ID embeddings result in the consequential coupling between collaboration and multimodal signals;(2) Lack of exploration into robust global user interests to alleviate the sparse interaction problems faced by local interest modeling. To address these issues, we propose a novel Local and Global Graph Learning-guided Multimodal Recommender (LGMRec), which jointly models local and global user interests. Specifically, we present a local graph embedding module to independently learn collaborative-related and modality-related embeddings of users and items with local topological relations. Moreover, a global hypergraph embedding module is designed to capture global user and item embeddings by modeling insightful global dependency relations. The global embeddings acquired within the hypergraph embedding space can then be combined with two decoupled local embeddings to improve the accuracy and robustness of recommendations. Extensive experiments conducted on three benchmark datasets demonstrate the superiority of our LGMRec over various state-of-the-art recommendation baselines, showcasing its effectiveness in modeling both local and global user interests. Copyright © 2023, The Authors. All rights reserved.

关键词： Behavioral research

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Beyond Subspace Isolation: Many-to-Many Transformer for Light Field Image Super-resolution

arXiv

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arXiv 2024年

作者： Hu, Zeke Zexi Chen, Xiaoming Chung, Vera Yuk Ying Shen, Yiran School of Computer Science University of Sydney DarlingtonNSW2008 Australia School of Computer Science and Engineering Beijing Technology and Business University Beijing102488 China School of Software Shandong University Jinan250100 China

The effective extraction of spatial-angular features plays a crucial role in light field image super-resolution (LFSR) tasks, and the introduction of convolution and Transformers leads to significant improvement in this area. Nevertheless, due to the large 4D data volume of light field images, many existing methods opted to decompose the data into a number of lower-dimensional subspaces and perform Transformers in each subspace individually. As a side effect, these methods inadvertently restrict the self-attention mechanisms to a One-to-One scheme accessing only a limited subset of LF data, explicitly preventing comprehensive optimization on all spatial and angular cues. In this paper, we identify this limitation as subspace isolation and introduce a novel Many-to-Many Transformer (M2MT) to address it. M2MT aggregates angular information in the spatial subspace before performing the self-attention mechanism. It enables complete access to all information across all sub-aperture images (SAIs) in a light field image. Consequently, M2MT is enabled to comprehensively capture long-range correlation dependencies. With M2MT as the foundational component, we develop a simple yet effective M2MT network for LFSR. Our experimental results demonstrate that M2MT achieves state-of-the-art performance across various public datasets, and it offers a favorable balance between model performance and efficiency, yielding higher-quality LFSR results with substantially lower demand for memory and computation. We further conduct in-depth analysis using local attribution maps (LAM) to obtain visual interpretability, and the results validate that M2MT is empowered with a truly non-local context in both spatial and angular subspaces to mitigate subspace isolation and acquire effective spatial-angular representation. Copyright © 2024, The Authors. All rights reserved.

关键词： Image enhancement

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The Importance of Performance Feedback in Virtual Reality (VR) Training: A Table Tennis Coach Perspective

The Importance of Performance Feedback in Virtual Reality (V...

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2025 CHI Conference on Human Factors in Computing Systems, CHI EA 2025

作者： Sunday, Kissinger Anvari, Soraya S. Li, Yiwei Sun, Junwei Wehbe, Rina R. Neyedli, Heather Batmaz, Anil Ufuk Machuca, Mayra Donaji Barrera Faculty of Computer Science VR Lab. Dalhousie University HalifaxNS Canada HCI4Good Dalhousie University HalifaxNS Canada University of Western Ontario TorontoON Canada School of Interactive Arts and Technology Simon Fraser University VancouverBC Canada HCI4Good Faculty of Computer Science Dalhousie University HalifaxNS Canada School Health & Human Performance Dalhousie University HalifaxNS Canada Department of Computer Science & Software Engineering Concordia University MontrealQC Canada Faculty of Computer Science Dalhousie University HalifaxNS Canada

ISBN: (纸本)9798400713958

Advances in Virtual Reality (VR) technology have redefined sports training, offering a new modality for athletes to prepare for competitions. This paper explores the coaching strategies used to train novice table tennis players in VR. Through interviews with the coach and participant feedback, we identified key strategies such as personalized feedback, table division, and a structured training regimen for skill development in VR. Challenges include limited facial awareness and the absence of virtual cheering, which impacted the coach’s ability to engage fully with participants. These findings offer valuable insights into coaching strategies in VR environments, contributing to developing more effective and engaging VR training systems. © 2025 Copyright held by the owner/author(s).

关键词： Virtualization

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Retyping of triple-negative breast cancer based on clustering method

Retyping of triple-negative breast cancer based on clusterin...

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作者： Liu, Bo Li, Xingrui Wang, Huina Zhao, Shuangtao Li, Jianqiang Qu, Guangzhi Wang, Fei School of Software Engineering Beijing University of Technology Beijing China Department of Genomic Medicine The University of Texas MD Anderson Cancer Center HoustonTX United States Computer Science and Engineering Department Oakland University RochesterMI United States Healthcare Policy and Research Weill Cornell Medical College New YorkNY United States

Triple-negative breast cancer is the worst prognosis in breast cancer, accounting for 10.0–20.8% of all breast cancers. Considering that triple-negative breast cancer has great heterogeneity and very poor prognosis, clinical medication guidance is in urgent need of a more detailed classification of breast cancer itself. Although many researchers have been dedicated to the clustering of triple-negative breast cancer and have found possible targets based on typing, their results are not closely related to the prognosis. This paper utilizes three clustering methods to retype the patient data with triple-negative breast cancer, and the results show that the triple-negative breast cancer data could be classified into two categories. Eight important genes and three important clinical factors related to the prognosis of two types of triple-negative breast cancer have been obtained. These genes have the following three characteristics: co-expression, differential expression and interaction. In terms of breast cancer control, the prognosis can be controlled as much as possible by regulating gene levels, which provides new directions and ideas for related research on breast cancer prognosis. © 2020 John Wiley & Sons, Ltd.

关键词： Cluster analysis

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Integrated Product-Process Design: Conceptual Framework for Data Driven Manufacturing Resource Selection 2

Integrated Product-Process Design: Conceptual Framework for ...

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2nd International Conference on Digital Futures and Transformative Technologies, ICoDT2 2022

作者： Uz Zaman, Uzair Khaleeq Ghoto, Mujtaba Rafique Siadat, Ali Baqai, Aamer Ahmed Aqeel, Anas Bin Qamar, Usman College of Electrical & Mechanical Engineering National University of Science and Technology Department of Mechatronics Engineering Islamabad44000 Pakistan Arts et Métiers ParisTech - Campus de Metz Laboratoire Conception Fabrication Commande Metz57070 France College of Electrical & Mechanical Engineering National University of Science and Technology Department of Computer & Software Engineering Islamabad44000 Pakistan

ISBN: (纸本)9781665498197

With the extensive development of new technologies and the application of information in the manufacturing industry, immense volumes of distinct data are being generated and collected daily. However, this data is largely unusable as its not meticulously cleaned and processed. The effective utilization of such complex data is the cornerstone of data analytics, as successful analysis leads to useful, relevant, and actionable knowledge, which in the long run can prove to be revolutionary for any field and open new avenues. Although the application of data analytics in areas such as sales & marketing, healthcare, cybersecurity, and climate change is largely prevalent, the implementation of data analysis and its tools for efficient product & process design is an unexplored opportunity with large volumes of data generated by major stakeholders throughout the manufacturing and product-process design activity remaining underutilized. This paper, therefore, defines a novel conceptual framework that applies data analysis to integrated product-process design (IPPD) for weighted data-driven IPPD that amalgamates data generated from multiple streams. Primarily from the user perspective, supply chain network, current & upcoming technological processes, and competitor process and product designs, will be utilized. The proposed framework can be further used to create new products better aligned with customer requirements, enhance the overall quality of the product, improve production efficiency through new technological advancements, support the supply chain network, and give the applicant industry a competitive advantage against its competitors. © 2022 IEEE.

关键词： Big data

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Early Fault Detection in Rolling Element Bearings Using Signal Processing Techniques

Early Fault Detection in Rolling Element Bearings Using Sign...

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International Symposium on High Capacity Optical Networks and Enabling Technologies, HONET

作者： Ali Saeed M. Usman Akram Muazzam A. Khan M. Belal Afsar Department of Computer Science ICESCO Chair Big Data Analytics and Edge Computing Quaid-i-Azam University Islamabad Pakistan Department of Computer and Software Engineering National University of Sciences & Technology Islamabad Pakistan Faculty of Electrical Engineering and Information Technology Otto-von-Guericke University Maddeburg DE

ISBN: (数字)9798350378078

ISBN: (纸本)9798350378085

Many studies show that bearings are the most vulnerable components in low-voltage motors. While advanced bearing diagnostic systems exist, their cost can be a barrier for non-critical machinery due to the potential wait time to see a return on investment. This research explores the feasibility of using basic signal processing techniques on vibration data for bearing condition monitoring. The primary goal is to analyze benchmark data from the Case Western Reserve University (CWRU) dataset and establish a baseline performance for this data-driven approach. The results suggest that, with appropriate signal processing, it's possible to achieve early detection of bearing faults, leading to more efficient time-to-fault identification with higher detection accuracies ranging from 83 % to 100 % for each method.

关键词： Vibrations Fault diagnosis Low voltage Smart cities Rolling bearings Signal processing Motors Robots Monitoring Investment

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An Efficient Ambient Noise Cross-Correlation Algorithm on Heterogeneous CPU-GPU Cluster 13

An Efficient Ambient Noise Cross-Correlation Algorithm on He...

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13th IEEE International Symposium on Parallel Architectures, Algorithms and Programming, PAAP 2022

作者： Wu, Chao Tan, Xing Li, Huikun Sun, Guangzhong University of Science and Technology of China Network and Information Center Supercomputing Center Hefei China University of Science and Technology of China School of Earth and Space Sciences Hefei China University of Science and Technology of China School of Software Engineering Hefei China University of Science and Technology of China School of Computer Science and Technology Hefei China

ISBN: (纸本)9781665452182

Calculation of noise cross-correlation functions (NCF) plays a vital role in ambient noise seismology. However high computation and storage requirements of NCF become huge obstacles in development of ambient noise seismology. Nowadays heterogeneous clusters show great impact in scientific computing. In this paper, we propose a parallel NCF algorithm based on heterogeneous cluster. Firstly, input SAC files are partitioned by dimension of dates and distributed to different nodes of heterogeneous cluster. Secondly, during NCF computation in each node, workloads are divided by station pairs, and calculated in different graphics processing units (GPUs) embedded in computing node. Finally, all NCF results of different dates are gathered to one computing node and stacked using all central processing units (CPUs) of that node. Experimental results demonstrate that the parallel NCF computing can be accomplished within much less time than that of the CPU counterpart, and the speedup remains almost linear when increasing number of computing nodes. © 2022 IEEE.

关键词： Program processors

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Accurate and Complete Captions for Question-controlled Text-aware Image Captioning

Accurate and Complete Captions for Question-controlled Text-...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Yehuan Wang Jian Hu Lin Shang Department of Computer Science and Technology State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Mechanical Engineering Nanjing University of Science and Technology Nanjing China

Question-controlled Text-aware Image Captioning (Qc-TextCap), is the task of generating a distinctive scene text aware caption according to several personalized questions when given an image. However, due to the diversity of visual scene, it is hard for current Optical Character Recognition (OCR) systems to extract complete scene text sentences from the images. Besides, existing works are limited in their use of question features and visual features. In this paper, we propose a Multimodal Transformer plus Scene text clustering and Cross modal attention (MTSC) to tackle the above challenges. We devise scene text clustering to group relevant scene text pieces which are detected as separate results by current OCR systems. To better utilize the information in questions and images, we design cross modal attention to enrich the features of both modalities. We extensively evaluate our model on the two Qc-TextCap datasets and superior results are achieved when comparing to state-of-the-art approaches.

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