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2nd Eclipse Research International Conference on Security, Artificial Intelligence, Architecture and Modelling for Next Generation Mobility, SAAM Mobility 2021

作者： Hirvonsalo, Harri Seppänen, Pertti Faculty of Information Technology and Electrical Engineering Empirical Software Engineering in Software Systems and Services M3S Group University of Oulu Oulu Finland

After an era of huge propagation within the field of mobile communications, digitalization has been spreading during the latest years at an accelerating speed to automotive technology and business. Following the developments experienced earlier in the mobile communications branch, open solutions and platforms are emerging in the automotive industries, challenging the traditional proprietary systems. Like in mobile communications, open platforms enable development of a wide variety of novel applications and systems that speed up the digitalization of the automotive and traffic ecosystems and offer the developers new attractive business opportunities. One of such open approaches is the Eclipse Kuksa framework developed in a consortium of European research institutions and automotive industries. In this study, we explored how the Kuksa framework could be used as a technology basis for an automotive data system combining in-vehicle functionality to cloud-based services. The study was carried out as a part of the SMAD research project of the University of Oulu aiming at an intelligent moving test platform for research of autonomous vehicles built on top of a Toyota Rav4 hybrid car. Our study covered subsystems of the Kuksa framework, the in-vehicle subsystem, cloud subsystems and the data communications between them. Our results indicate that the Kuksa framework is a feasible basis for the development of open, intelligent automotive data systems, though with a considerable learning needs. The results and experiences of our study provide besides additional knowledge for our continuing research on automotive software, also new, valuable contribution to the Eclipse Kuksa community and the practitioners planning to deploy open approaches in their automotive software development. Copyright © 2021 for this paper by its authors.

关键词： Open systems

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Adaptive Scheme for Crowd Counting Using off-the-Shelf Wireless Routers

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Computer Systems Science & engineering 2022年第4期41卷 255-269页

作者： Wei Zhuang Yixian Shen Chunming Gao Lu Li Haoran Sang Fei Qian School of Computer and Software Nanjing University of Information Science&TechnologyNanjing210044China Engineering Research Center of Digital Forensics Ministry of EducationNanjing University of Information Science and TechnologyNanjing210044China School of Engineering&Technology University of WashingtonTacomaWA 98402USA China General Nuclear Power Group Nanjing210028China Jiangsu Province Hospital of Chinese Medicine Affiliated Hospital of Nanjing University of Chinese MedicineNanjing210029China

Since the outbreak of the world-wide novel coronavirus pandemic,crowd counting in public areas,such as in shopping centers and in commercial streets,has gained popularity among public health administrations for preventing the crowds from *** this paper,we propose a novel adaptive method for crowd counting based on Wi-Fi channel state information(CSI)by using common commercial wireless *** with previous researches on device-free crowd counting,our proposed method is more adaptive to the change of environ-ment and can achieve high accuracy of crowd count *** the dis-tance between access point(AP)and monitor point(MP)is typically non-ﬁxed in real-world applications,the strength of received signals varies and makes the tra-ditional amplitude-related models to perform poorly in different *** order to achieve adaptivity of the crowd count estimation model,we used convo-lutional neural network(ConvNet)to extract features from correlation coefﬁcient matrix of subcarriers which are insensitive to the change of received signal *** conducted experiments in university classroom settings and our model achieved an overall accuracy of 97.79%in estimating a variable number of participants.

关键词： CSI device-free deep learning crowd counting Wi-Fi wireless sensing

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Liquifying Quantum-Classical software-Intensive System of Systems

Liquifying Quantum-Classical Software-Intensive System of Sy...

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

作者： Álvaro M. Aparicio-Morales Majid Haghparast Niko Mäkitalo Jose Garcia-Alonso Javier Berrocal Vlad Stirbu Tommi Mikkonen Juan Manuel Murillo Quercus Software Engineering Group University of Extremadura Cáceres Spain Faculty of Information Technology University of Jyväskylä Jyväskylä Finland

ISBN: (数字)9798350351576

ISBN: (纸本)9798350351583

software-Intensive Systems are applications that use a high amount of computational resources for the execution of complex tasks in which a constant flow of information is needed. In the current conception of the computing continuum, such computational resources are spread in the infrastructure from the Cloud to the Edge and the tasks are mapped on resources following placing and replacing strategies. In this scope, this paper puts together two emergent paradigms. On the one hand, the emergence of the Quantum Computation paradigm will allow these intensive systems to incorporate specific tasks that are unaffordable by classic computers. On the other hand, the liquid software model proposes a fluid movement of the software and information from one device to another. Therefore, in this paper, we present a broad overview of software-intensive systems from a liquid software perspective and support quantum functionalities in their processes. To provide a better understanding of how these systems can take form, we explore a case study based on weather forecasts.

关键词： Computers Quantum computing Liquids Computational modeling Clouds Weather forecasting software

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A Hybrid Genetic Algorithm with Integer Coding for Task Offloading in Edge-Cloud Cooperative Computing

IAENG International Journal of Computer Science

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IAENG International Journal of Computer Science 2022年第2期49卷 503页

作者： Wang, Bo Lv, Bin Song, Ying Software Engineering College Zhengzhou University of Light Industry China Linyi Branch of China Mobile Communications Group Shandong Co. Ltd China Beijing Advanced Innovation Center for Materials Genome Engineering and Beijing Key Laboratory of Internet Culture and Digital Dissemination Beijing Information Science and Technology University China

Edge-Cloud Cooperative Computing (E3C) is an efficient way to address the resource scarcity issue of mobile devices by edge servers with low network delays and the cloud with abundant computing resources. To improve the user satisfaction which directly affects the profit and the reputation of service providers, we focus on the task offloading problem for E3C in this paper. We design an integer genetic algorithm incorporating earliest deadline first (EDF) to map each chromosome as a task offloading solution. Our method uses each gene of a chromosome to represent the computing core that the corresponding task assigned to, and decides the task execution order by EDF in each computing core. Experiment results show that our method achieves better accepted ratio and resource efficiency than several offloading methods based on heuristics, the particle swarm optimization, and the binary genetic algorithm © 2022. IAENG International Journal of Computer *** Rights Reserved.

关键词： Genetic algorithms

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Towards Fully Decoupled End-to-End Person Search

Towards Fully Decoupled End-to-End Person Search

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2023 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2023

作者： Zhang, Pengcheng Bai, Xiao Zheng, Jin Ning, Xin Jiangxi Research Institute Beihang University School of Computer Science and Engineering State Key Laboratory of Software Development Environment Beijing China Institute of Semiconductors Chinese Academy of Sciences Cognitive Computing Technology Joint Laboratory Wave Group Beijing China

ISBN: (纸本)9798350382204

End-to-end person search aims to jointly detect and re-identify a target person in raw scene images with a unified model. The detection task unifies all persons while the re-id task discriminates different identities, resulting in conflict optimal objectives. Existing works proposed to decouple end-to-end person search to alleviate such conflict. Yet these methods are still sub-optimal on one or two of the sub-tasks due to their partially decoupled models, which limits the overall person search performance. In this paper, we propose to fully decouple person search towards optimal person search. A task-incremental person search network is proposed to incrementally construct an end-to-end model for the detection and re-id sub-task, which decouples the model architecture for the two sub-tasks. The proposed task-incremental network allows task-incremental training for the two conflicting tasks. This enables independent learning for different objectives thus fully decoupled the model for personsearch. Comprehensive experimental evaluations demonstrate the effectiveness of the proposed fully decoupled models for end-to-end person search. © 2023 IEEE.

关键词： Computer vision

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Deep IoU Network for Dense Rebar Object Detection 18

Deep IoU Network for Dense Rebar Object Detection

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18th IEEE International Conference on e-Business engineering, ICEBE 2022

作者： Zhong, Xiaojing Hu, Hao Li, Li Cen, Junhua Wu, Qingyao School of Software Engineering South China University of Technology Guangzhou China Zhongnan Building Materials Group Co. Ltd. Guangzhou China Internet Technology Co. Ltd. Guangzhou China

ISBN: (纸本)9781665492447

Typically, dense rebar detection scenes comprise cross-sections of hundreds or even thousands of rebars. We demonstrate that most commonly used object detectors still have trouble detecting objects accurately in such settings. We present a novel deep-learning-based approach for tackling this problem, which is combined with a useful soft-Iou layer to predict the Iou of a detected bounding box and its ground truth and an efficient EM-Merger unit to resolve a single detection per object, enabling the accurate detection of the bounding box of tiny objects such as rebars. Experiments show that the proposed method can achieve excellent results in our collected rebar images while our network is trained on the RebarDSC dataset. © 2022 IEEE.

关键词： Object detection

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BFSA: Blockchain-Enhanced Flexible Storage Architecture for Smart Building IoT Applications

BFSA: Blockchain-Enhanced Flexible Storage Architecture for ...

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IEEE Annual International Computer software and Applications Conference (COMPSAC)

作者： Jinqian Jiang Xiaohua Wu Zirui Wang Jun Cheng Tao Meng School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Midea building technology Midea Group (Shanghai) Co. Ltd Shanghai China

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

The development of blockchain technology has promoted the advancement of information security, making it a top choice for addressing data management challenges in IoT-enabled smart buildings. However, traditional IoT blockchain systems face scalability issues and high storage costs due to their data storage strategies, such as full replication. Recently proposed encoded blockchain technology offers a solution, yet this approach often comes at the expense of query efficiency. This paper introduces an efficient distributed storage architecture for smart buildings that integrates hierarchical data aggregation and Reed-Solomon coding for classification storage. Our approach significantly reduces storage overhead and query latency while maintaining query performance. Theoretical analysis and simulations demonstrate the effectiveness of this architecture in optimizing storage efficiency, and query response time for IoT applications in smart buildings.

关键词： Smart buildings Costs Architecture Heuristic algorithms software algorithms Computer architecture Encoding

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DR-DRL: Distributed Redundant Task Offloading Based on Reinforcement Learning

DR-DRL: Distributed Redundant Task Offloading Based on Reinf...

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Information Science, Computer technology and Transportation (ISCTT), International Conference on

作者： Shizhan Lan Hongcai Lin Huaqian Liang Shan Chen Zhenyu Wang School of Software Engineering South China University of Technology Guangzhou China China Mobile Group Guangxi branch Co. Ltd Nanning China

ISBN: (数字)9798350388435

ISBN: (纸本)9798350388442

Edge computing enhances task reliability by employing redundant task executions across edge nodes. Conventional decentralized task offloading strategies, based on heuristics and game theory, either focus on optimization or are based on unrealistic assumptions. Moreover, existing Deep Reinforcement Learning (DRL) task offloading approaches underperform due to discrepancies between simulated environments and real systems, and overlook task redundancy, thus failing to meet reliability requirements. This paper proposes a DRL-based approach to offload distributed redundant tasks, namely DR-DRL (Decentralized and Redundant DRL), to solve the redundancy problem of distributed task offloading under edge computing. Experimental results show that DR-DRL has about 8% higher task success rate than other benchmark methods.

关键词： Learning systems Markov decision processes Termination of employment Transportation Spread spectrum communication Reliability theory Benchmark testing Routing Optimization Edge computing

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Non-IT Program Students Learn Basic Web Language;The Preliminary Study of Affective and Cognitive Response on IVML Prototype 17th

Non-IT Program Students Learn Basic Web Language;The Prelimi...

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17th International Conference on Information technology and Applications, ICITA 2023

作者： Naim, Nur Faraha Mohd Ibrahim, Ag. Asri Ag. Baharum, Aslina Moung, Ervin Gubin Salimun, Carolyn Multimedia Technology Programme Faculty of Computing and Informatics Universiti Malaysia Sabah Kampus Antarabangsa Labuan Federal Territory of Labuan Malaysia Software Engineering Programme Faculty of Computing and Informatics Universiti Malaysia Sabah Kota Kinabalu Sabah Malaysia User Experience Research Group Faculty of Computing and Informatics Universiti Malaysia Sabah Kota Kinabalu Sabah Malaysia School of Engineering and Technology Sunway University Selangor Petaling Jaya Malaysia

ISBN: (纸本)9789819983230

This paper examines the response of affective and cognitive processes under human–computer interaction (HCI) toward interactive video mobile learning known as the IVML prototype. The idea is to determine the potential of the interactive elements in the educational video as the key factor that can influence learners’ engagement and comprehension of the presented topic. This paper aims to measure the level of affective and cognitive response of learners toward the subject learning that are not from their field, not exposed to, or not familiar with. The data analyze based on user experience, user interface, and human–computer interaction. The statistical result may produce a significant outcome that could contribute to the hyper video platform. The findings may confirm the significant role of technical aspects and psychological aspects included in video production style, hence heightening the hyper video platform in teaching and learning activities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Human computer interaction

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RepCFT: A Fusion Model of Reparameterized Convolutional Neural Network and Visual Transformer for Wood Species Recognition 4

RepCFT: A Fusion Model of Reparameterized Convolutional Neur...

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4th International Conference on Artificial Intelligence and Computer engineering, ICAICE 2023

作者： Wang, Junyan Lian, Ziyan Kang, Feiyang Chen, Jinkai School of Internet Anhui University Hefei230039 China School of Software Engineering University of Science and Technology of China Hefei230026 China School of Advanced Technology University of Science and Technology of China Hefei230026 China Department of Network Management Center China Mobile Communications Group Anhui Co. LTD. Hefei230088 China

ISBN: (纸本)9798400708831

Wood is an extremely important foundational building material. Rapid and accurate wood species recognition is crucial for the rational application of wood and for preventing economic losses caused by the misidentification of wood species. Current research primarily focuses on image preprocessing and the use of Convolutional Neural Networks (CNNs) for wood species recognition. Most of the existing methods employ non-lightweight CNN models, making it difficult for the models to achieve rapid recognition. Addressing the above issues, this paper introduces a hybrid model that combines Reparameterized CNN and Visual Transformer, which we refer to as RepCFT. We have designed a new module, RepLG, with strong local and global information extraction capabilities. RepCFT, constructed from RepLG, achieves a recognition accuracy of 98.7% without any data augmentation or training strategies, while using only 4.3 million parameters and 0.6 billion Multiply-Accumulate operations (MACs). Its performance is on par with ResNet50 and surpasses some existing lightweight models. At the same time, it achieves a recognition speed of up to 601.46 frames per second, effectively meeting the need for rapid and accurate recognition in wood species recognition tasks. All code and models have been uploaded to https://***/OnlyForWW/RepCFT. © 2023 ACM.

关键词： Wood

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