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3rd International Conference on Frontiers in Cyber Security, FCS 2020

作者： Jin, Chunhua Kan, Ge Chen, Guanhua Yu, Changhui Xu, Chengjie Faculty of Computer and Software Engineering Huaiyin Institute of Technology Huai’an233003 China

ISBN: (纸本)9789811597381

Deniable authenticated encryption can achieve confidentiality and deniable authentication in a logical single step. Such a cryptographic primitive simplifies the design of cryptographic scheme and reduces the cost of computation and communication. In this paper, we propose a heterogeneous deniable authenticated encryption scheme called HDAE. The proposed scheme permits a sender in a public key infrastructure environment to transmit a message to a receiver in an identity-based environment. Our design utilizes tag-key encapsulation mechanism (tag-KEM) and data encapsulation mechanism (DEM) hybrid encryption methods, which is especially applicable in some privacy protection occasions. In addition, we give how to design an HDAE scheme utilizing a heterogeneous deniable authenticated tag-KEM (HDATK) and a DEM. We also construct an HDATK scheme and provide security proof in the random oracle model. Comprehensive analysis shows that our scheme is efficient and secure. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Public key cryptography

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Knowledge-Infused Spatiotemporal Graph Convolution Network for Megacity Origin-Destination Taxi Demand Forecasting

SSRN

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SSRN 2024年

作者： Tong, Ning Yu, Xuehui Li, Xiaorong Li, Fengqi Dalian Key Laboratory of Blockchain Technology and Application Liaoning Dalian116028 China School of Software Dalian Jiaotong University Dalian116028 China School of Computer and Communication Engineering Dalian Jiaotong University Dalian116028 China

In the management of urban transportation within major cities, the precise prediction of traffic demand is fundamental to the implementation of intelligent transport systems. Although existing studies have endeavoured to describe the spatiotemporal characteristics of taxi demand and the influence of external factors, megacities such as Beijing still face two primary challenges: 1. The pronounced spatiotemporal variations in metropolitan areas can lead to severe imbalances between taxi demand and supply, exacerbating the difficulty passengers face in successfully hailing cabs;2. Taxi demand is influenced by dynamically changing, complex interactions of multidimensional external factors. Without accurate identification and integration of these factors, prediction models may fail to capture demand patterns accurately, leading to imprecise demand forecasts and subsequently affecting the efficiency of taxi dispatch and management. To address these issues, this paper proposes a knowledge-Infused spatiotemporal graph convolution network for megacity origin-destination taxi demand forecasting (ODT-GCN). Initially, the TransR knowledge graph model is employed to map the complex relationships between external factors in the form of entity triplets. Subsequently, a dynamic fusion unit integrates the semantic information between traffic demand and external factors and it is also capable of capturing dynamic changes in the external factors and updating the traffic demand information. Finally, to capture the spatiotemporal characteristics of Beijing’s traffic demand, the updated traffic demand information, combined with a regional adjacency matrix, is input into a spatiotemporal prediction model, thereby forecasting taxi demand. Tests on the Shenzhou Premium Taxi order dataset in Beijing demonstrate that the ODT-GCN outperforms other baseline models in terms of prediction accuracy, interpretability and robustness. © 2024, The Authors. All rights reserved.

关键词： Convolution

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Automatic Detection of Multiple Disease Signals Based on Novel Biomolecular computer 3

Automatic Detection of Multiple Disease Signals Based on Nov...

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3rd IEEE International Conference on Information Systems and computer Aided Education, ICISCAE 2020

作者： Chen, Qiguo Wan, Biao Wuhan Polytechnic College of Computer Technology and Software Engineering Wuhan430074 China

ISBN: (纸本)9781728183039

A biomolecular computer is a nano-scale computer assembled with biomolecular elements. When it is implanted in the human body, it can automatically scan body signals, detect physiological indicators, diagnose diseases, and control drug release. Nano-scale computers can roam the body and detect diseases early. This paper proposes an automatic detection method for a variety of disease signals based on a novel biomolecular computer, which can detect disease indicators, diagnose diseases, and activate drug molecules to implement treatment. They put into a blood vessel and implant an organ or tissue cell inside for preventive care. However, biomolecular computers still have many challenges in biological living technology and program-controlled drug release. The biggest challenge is how to operate these devices in the living environment, such as blood vessels or cytoplasm. Researchers are currently developing simpler machines that do not require restriction enzymes or can rely on the cell to operate itself. © 2020 IEEE.

关键词： Blood vessels

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In-Network Accumulation: Extending the Role of NoC for DNN Acceleration

arXiv

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arXiv 2022年

作者： Tiwari, Binayak Yang, Mei Wang, Xiaohang Jiang, Yingtao Department of Electrical and Computer Engineering University of Nevada Las Vegas United States School of Software Engineering South China University of Technology Guangzhou China

Network-on-Chip (NoC) plays a significant role in the performance of a DNN accelerator. The scalability and modular design property of the NoC help in improving the performance of a DNN execution by providing flexibility in running different kinds of workloads. Data movement in a DNN workload is still a challenging task for DNN accelerators and hence a novel approach is required. In this paper, we propose the In-Network Accumulation (INA) method to further accelerate a DNN workload execution on a many-core spatial DNN accelerator for the Weight Stationary (WS) dataflow model. The INA method expands the router's function to support partial sum accumulation. This method avoids the overhead of injecting and ejecting an incoming partial sum to the local processing element. The simulation results on AlexNet, ResNet-50, and VGG-16 workloads show that the proposed INA method achieves 1.22× improvement in latency and 2.16× improvement in power consumption on the WS dataflow model. © 2022, CC BY.

关键词： Network-on-chip

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The STIRData Approach to Interoperability of European Company High-Value Datasets

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SN computer Science 2024年第4期5卷 387页

作者： Klímek, Jakub Chortaras, Alexandros Míšek, Jakub Yang, Jim J. Skagemo, Steinar Tzouvaras, Vassilis Department of Software Engineering Faculty of Mathematics and Physics Charles University Malostranské nám. 25 Prague 118 00 Czech Republic School of Electrical and Computer Engineering National Technical University of Athens 9 Iroon Polytechniou St Athens 157 80 Greece Institute of Law and Technology Masaryk University Faculty of Law Veveří 70 Brno 60200 Czech Republic Norwegian Digitalisation Agency Postboks 1382 Vika Oslo 0114 Norway Brønnøysund Register Centre Postboks 900 Brønnøysund 8910 Norway

The European Commission has published a list of high-value datasets (HVDs) that public sector bodies must make available as open data as part of the Open Data Directive. One of the HVD topics is company data. Although the HVD description contains items that must be included in these datasets, it does not prescribe any technical means of how the data should be published. This is a major obstacle to the interoperability of the datasets once they are published. In this extended paper, we elaborate on the results of STIRData, a project co-financed by the Connecting Europe Facility Programme of the European Union, focusing on various aspects of data interoperability of open data from business registries, covering the company data HVDs topic. These aspects include the semantic, technical, and legal interoperability of this data. The results include a data architecture and a data specification to make the published data technically and semantically interoperable. In addition, we present basic legal interoperability guidelines to ensure legal interoperability of the published data, which is a topic often neglected by technically focused data experts. The project results include proof-of-concept transformations of data from selected European business registries using open source tools and in accordance with the data specification. Moreover, a user-orientated platform for browsing and analysing the data is presented as an example of the possibilities of using the data published in an interoperable way. Finally, we present an example of how compliant data can be processed by data experts for further analysis. © The Author(s) 2024.

关键词： Company data Data spaces High-value datasets (HVDs) Interoperability Linked data Open data

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RITFIS: Robust input testing framework for LLMs-based intelligent software

arXiv

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arXiv 2024年

作者： Xiao, Mingxuan Xiao, Yan Dong, Hai Ji, Shunhui Zhang, Pengcheng College of Computer Science and Software Engineering Hohai University Nanjing China School of Cyber Science and Technology Shenzhen Campus of Sun Yat-sen University Shenzhen China School of Computing Technologies RMIT University Melbourne Australia

The dependence of Natural Language Processing (NLP) intelligent software on Large Language Models (LLMs) is increasingly prominent, underscoring the necessity for robustness testing. Current testing methods focus solely on the robustness of LLM-based software to prompts. Given the complexity and diversity of real-world inputs, studying the robustness of LLM-based software in handling comprehensive inputs (including prompts and examples) is crucial for a thorough understanding of its performance. To this end, this paper introduces RITFIS, a Robust Input Testing Framework for LLM-based Intelligent software. To our knowledge, RITFIS is the first framework designed to assess the robustness of LLM-based intelligent software against natural language inputs. This framework, based on given threat models and prompts, primarily defines the testing process as a combinatorial optimization problem. Successful test cases are determined by a goal function, creating a transformation space for the original examples through perturbation means, and employing a series of search methods to filter cases that meet both the testing objectives and language constraints. RITFIS, with its modular design, offers a comprehensive method for evaluating the robustness of LLM-based intelligent software. RITFIS adapts 17 automated testing methods, originally designed for Deep Neural Network (DNN)-based intelligent software, to the LLM-based software testing scenario. It demonstrates the effectiveness of RITFIS in evaluating LLM-based intelligent software through empirical validation. However, existing methods generally have limitations, especially when dealing with lengthy texts and structurally complex threat models. Therefore, we conducted a comprehensive analysis based on five metrics and provided insightful testing method optimization strategies, benefiting both researchers and everyday users. © 2024, CC BY-NC-ND.

关键词： software testing

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A Unified Transformer Framework for Group-based Segmentation: Co-Segmentation, Co-Saliency Detection and Video Salient Object Detection

arXiv

引用

arXiv 2022年

作者： Su, Yukun Deng, Jingliang Sun, Ruizhou Lin, Guosheng Wu, Qingyao The School of Software Engineering South China University of Technology Guangzhou510640 China The School of Computer Science and Engineering Nanyang Technological University Singapore Singapore

Humans tend to mine objects by learning from a group of images or several frames of video since we live in a dynamic world. In the computer vision area, many researches focus on co-segmentation (CoS), co-saliency detection (CoSD) and video salient object detection (VSOD) to discover the co-occurrent objects. However, previous approaches design different networks on these similar tasks separately, and they are difficult to apply to each other, which lowers the upper bound of the transferability of deep learning frameworks. Besides, they fail to take full advantage of the cues among inter- and intra-feature within a group of images. In this paper, we introduce a unified framework to tackle these issues, term as UFO (Unified Framework for Co-Object Segmentation). Specifically, we first introduce a transformer block, which views the image feature as a patch token and then captures their long-range dependencies through the self-attention mechanism. This can help the network to excavate the patch structured similarities among the relevant objects. Furthermore, we propose an intra-MLP learning module to produce self-mask to enhance the network to avoid partial activation. Extensive experiments on four CoS benchmarks (PASCAL, iCoseg, Internet and MSRC), three CoSD benchmarks (Cosal2015, CoSOD3k, and CocA) and four VSOD benchmarks (DAVIS16, FBMS, ViSal and SegV2) show that our method outperforms other state-of-the-arts on three different tasks in both accuracy and speed by using the same network architecture, which can reach 140 FPS in real-time. Code is available at https://***/suyukun666/UFO © 2022, CC0.

关键词： Chemical activation

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On Invariance of Concept Stability for Attribute Reduction in Concept Lattice 1

引用

11th International Conference on computer Science and its Applications, CSA 2019 and 14th KIPS International Conference on Ubiquitous Information Technologies and Applications, CUTE 2019

作者： Hao, Fei Yang, Erhe Guo, Lantian Nasridinov, Aziz Park, Doo-Soon School of Computer Science Shaanxi Normal University Xi’an China School of Automation and Electronic Engineering Qingdao University of Science and Technology Qingdao China Department of Computer Science Chungbuk National University Cheongju Korea Republic of Department of Computer Software Engineering Soonchunhyang University Asan Korea Republic of

ISBN: (数字)9789811593437

ISBN: (纸本)9789811593420

Formal Concept Analysis (FCA) methodology, as an efficient knowledge representation and knowledge discovery tool and has been widely used in various fields, such as data mining, expert systems, and others. Knowledge reduction is an essential issue for knowledge discovery. This paper focuses on attribute reduction in FCA and explores the internal relation between concept stability and attribute reduction. By observing the concept stability of concepts in original concept lattice and reduced concept lattice, a theorem about the invariance of concept stability for attribute reduction in concept lattice is presented and proved mathematically. It is believed that the proposed theorem provides a novel solution for quick attribute reduction and benefit for other social system applications. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Data mining

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AI-Driven Strategic Foresight: Anticipating Future Trends and Modelling Business Strategies

AI-Driven Strategic Foresight: Anticipating Future Trends an...

引用

Decision Aid Sciences and Application (DASA), International Conference on

作者： Ahmed Abdel Qader Ismail Alnajem Amer Ibrahim Mayasa M. Abdulrahman Abdulrazak A. Abdulrazak Al-Kubaisi Jamal Fadhil Tawfeq Mohammad Khishe Ali Saeed Business Administration Department Baghdad College of Economic Sciences Department of Computer Science and Software Engineering College of Information Technology Computer Engineering Department College of Engineering University of Baghdad Baghdad Iraq Department of Medical Instrumentation Technical Engineering Medical Technical College Al-Farahidi University Baghdad Iraq Innovation Center for Artificial Intelligence Applications Yuan Ze University Taiwan Jadara research center Jadara University Jordan Computer Center Northern Technical University Ninevah Iraq

ISBN: (数字)9798350369106

ISBN: (纸本)9798350369113

In a generation characterized with the aid of speedy technological advancements, socio-monetary shifts, and environmental challenges, groups need to undertake proactive strategies to stay aggressive and resilient. Strategic foresight, which entails expecting destiny tendencies and modeling enterprise techniques, accordingly, emerges as a vital device in navigating this uncertainty. The trouble this have a look at addresses is the growing trouble groups face in predicting and making ready for destiny disruptions the use of conventional forecasting techniques. The number one goals of this have a look at are to discover the important thing methodologies and gear of strategic foresight, display how those techniques may be implemented to assume destiny tendencies, and offer insights on integrating foresight into enterprise approach formulation. This has a look at contributes to the present frame of information with the aid of using providing a complete evaluation of strategic foresight techniques, which includes environmental scanning, situation planning, the Delphi method, fashion evaluation, SWOT and PESTLE evaluation, and era street mapping. Research findings show that forward-looking strategies provide an organization with the ability to identify potential risks and opportunities, develop adaptive strategies, and achieve long-term growth Specifically, strategic organizations using anticipation reports a 30% increase in the ability to anticipate market fluctuations, a 25% improvement in strategic haste and a 20% reduction in unexpected disruptions, of consumer goods etc. Case studies of various industries demonstrate the practical application and usefulness of forecasting in real-world situations The study concludes that incorporating forward-looking strategies into business planning does not that it not only prepares organizations for future challenges but empowers them to shape their future and ensure sustainable competitive advantage.

关键词： Industries Uncertainty Fluctuations Shape Navigation Organizations Market research Planning Forecasting Sustainable development

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Heterogeneous Hadoop Cluster-Based Image Processing Workload Distribution Framework between CPU and GPU

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Scientific Programming 2023年第1期2023卷

作者： Naz, Najia Zada, Islam Malik, Abdul Haseeb Nadeem, Muhammad Ali, Sikandar Department of Computer Science Bacha Khan University Charsadda Khyber Pakhtunkhwa Peshawar24420 Pakistan Department of Computer Science and Software Engineering Faculty of Computing and Information Technology International Islamic University Islamabad44000 Pakistan Department of Computer Science University of Peshawar Khyber Pakhtunkhwa Peshawar25120 Pakistan Department of Information Technology The University of Haripur Khyber Pakhtunkhwa Haripur22620 Pakistan

Due to the rapid development of image data and the necessity to analyze it to extract meaningful information, heterogeneous systems have gained prominence. One of the most critical aspects of distributed systems is load balancing. When it comes to the distribution of workload in a balanced manner in a cluster, some heterogeneous systems are used for image processing. When workloads are allocated in these systems, the computational power of the processors is not considered. As a result, in these heterogeneous systems for image processing applications, an uneven workload distribution issue is found. A workload distribution programming framework is presented and discussed in this paper. The proposed strategy consists of two parts. As a first step, image data is split into optimal split sizes and distributed across nodes, then the image data is distributed across CPU and GPU in a second step for processing. A heterogeneous environment is created by combining the CPU and GPU. The OpenCL Java bindings are used to set up both the CPU and GPU to run the program. To assess the performance of the suggested technique, certain tests are carried out and compared to current platforms. For image processing applications in heterogeneous clusters, the proposed workload distribution approach distributed image data efficiently. The results of the proposed solution (Hadoop + GPU) show that using an effective workload allocation mechanism in heterogeneous systems reduces average execution time while improving overall application performance. © 2023 Najia Naz et al.

关键词： Graphics processing unit

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