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13th Mining Humanistic Data Workshop, MHDW 2024, 9th Workshop on 5G-Putting Intelligence to the Network Edge, 5G-PINE 2024 and 1st Workshop on AI in Applications for Achieving the Green Deal Targets, AI4GD 2024 held as parallel events of the IFIP WG 12.5 International Workshops on Artificial Intelligence Applications and Innovations, AIAI 2024

作者： Gatzianas, M. Kalfas, G. Vagionas, C. Vargemidou, M. Papadakis, A. Saliou, F. Theodoropoulou, E. Lyberopoulos, G. Piscione, P. Abdulwahed, A. Giardina, P. Gavrilut, V. Alsted, C. Gardes, F. Zeimpekis, I. Domínguez, T. Yu, X. Chakraborty, I. Atwi, H. Dris, S. Argyris, N. Bakopoulos, P. Petridou, S. Miliou, A. Pleros, N. Department of Informatics Aristotle University of Thessaloniki Thessaloniki Greece Center for Interdisciplinary Research and Innovation Thessaloniki Greece Department of Applied Informatics University of Macedonia Thessaloniki Greece Orange 2 Avenue Pierre Marzin Lannion22307 France Athens Greece Nextworks Srl Via Livornese 1027 Pisa56122 Italy Comcores ApS Haldor Topsøes Allé 1 Lyngby Denmark Optoelectronics Research Centre University of Southampton SouthamptonSO17 1BJ United Kingdom Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom Teem Photonics Meylan France NVIDIA Photonic Systems Lab Ermou 56 Athens10563 Greece

ISBN: (纸本)9783031632266

Considering the challenges faced by the current Central Offices, this paper presents a novel architecture and related technological aspects of the Next Generation Central Offices (NGCOs), as envisioned within the OCTAPUS research project, towards the flexible support of the emerging capacity and latency demanding services. First, the main functional blocks of the OCTAPUS NGCO Data and Control planes are presented, with emphasis on the dynamicity of the optical paths within the NGCO and its reconfiguration capabilities. Then, focusing on the Control plane, we describe in detail its hierarchical structure, the role of the associated entities and the interactions among them. An example of network provisioning to support multiple concurrent next-generation services of realistic use cases in a real urban area is used to stress the need for NGCOs to adapt to different service requirements and temporal fluctuations in traffic demands, while catering for time-sensitive services with stringent delay requirements. © IFIP International Federation for Information Processing 2024.

关键词： Next generation networks

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Predicting Bitcoin Market Trends with Enhanced Technical Indicator Integration and Classification Models

arXiv

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arXiv 2024年

作者： Hafid, Abdelatif Rahouti, Mohamed Kong, Linglong Ebrahim, Maad Serhani, Mohamed Adel ESISA Analytica Laboratory Higher School of Engineering in Applied Sciences Fez24000 Morocco The Department of Computer & Information Science Fordham University New YorkNY10023 United States The Department of Mathematical and Statistical Sciences Alberta Machine Intelligence Institute University of Alberta EdmontonABT6G 2G1 Canada GAIA Ericsson Montreal Canada The Department of Information Systems University of Sharjah United Arab Emirates

Thanks to the high potential for profit, trading has become increasingly attractive to investors as the cryptocurrency and stock markets rapidly expand. However, because financial markets are intricate and dynamic, accurately predicting prices remains a significant challenge. The volatile nature of the cryptocurrency market makes it even harder for traders and investors to make decisions. This study presents a machine learning model based on classification to forecast the direction of the cryptocurrency market, i.e., whether prices will increase or decrease. The model is trained using historical data and important technical indicators such as the Moving Average Convergence Divergence, the Relative Strength Index, and Bollinger Bands. We illustrate our approach with an empirical study of the closing price of Bitcoin. Several simulations, including a confusion matrix and Receiver Operating Characteristic curve, are used to assess the model’s performance, and the results show a buy/sell signal accuracy of over 92%. These findings demonstrate how machine learning models can assist investors and traders of cryptocurrencies in making wise/informed decisions in a very volatile market. Copyright © 2024, The Authors. All rights reserved.

关键词： Prediction models

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The Development Results of the Current SSPS Project and the Progress Status of the Follow-on OHISAMA Project for the Realization of the Operational SSPS

The Development Results of the Current SSPS Project and the ...

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2024 IAF Space Power Symposium at the 75th International Astronautical Congress, IAC 2024

作者： Ijichi, Koichi Yanagawa, Hiroki Yanagi, Kouki Kitabatake, Hidetoslii Iiiada, Hitoini Kasliimura, Osamu Tanaka, Koji Ishikawa, Ryo Honjo, Kazuhiko Iskiimira, Kosei Ogawa, Akihiko Yamaguchi, Koji Japan Space Systems 3-5-8 Shibakoen Minato-ku Tokyo105-0011 Japan Institute of Space and Astronautical Science Japan Aerospace Exploration Agency 3-1-1 Yoshinodai Chuo-ku Kanagawa Sagamihara252-5210 Japan Department of Computer and Network Engineering The University of Electro-Communications 1-5-1 Chofugaoka C-hofu Tokyo1S2-S5S5 Japan Dept. of Modern Mechanical Engineering School of Creative Science and Engineering Waseda University 30401 Okubo Shinjuku-ku Tokyo169-S555 Japan Technologies Development Department IHI Aerospace Co. Ltd. 900 Fujiki Tomioka-shi Gunma-ken370-2398 Japan 1-7-8 Nishi-Kanagawa Kanagawa-ku Kanagawa-ken Yokohama-City221-0822 Japan

ISBN: (纸本)9798331312213

In the year of 2006, the METI of Japan established the SSPS '"USEF model"as the target SSPS configuration to be realized and also established the road map to achieve by the year 2050 based on the conclusion of the discussion based on the concept development, the related FS and also the basic technology development started from the end of 1980th by the committee members consisted of the space related universities and the organizations. Since then, the step-by-step technical development activities started and steadily proceeded according the roadmap for the realization of the ultimate carbon-free and Net-Zero power generation means. The horizontal power beaming and transmission technology development was the first step, and then proceed to the vertical transmission as the next steps. And then more, stepped into the development of the Power Generation and Transmission Panel (PGTP) which is the basic element of the USEF model, improvement of the efficiency of the DC to RP conversion efficiency and also to extend the distance of the WPT to the range of several kilo-meters. This phase stepped into the final year with full success. In parallel with current development phase but according to the road, the demonstration by 180kg class small satellite project called OHISAMA (On-orbit experiment of High-precision beam control using small SAtellite for MicrowAve power transmission) project was started to demonstrate and develop the precise power beaming technology from the LEO to the ground in the JFY of 2025. Our step-by-step approach towards the realization of USEF model according to the roadmap will be introduced with the results of the development activities, and also the progress of the OHISAMA project will be introduced and discussed. © 2024 by the International Astronautical Federation (IAF). All rights reserved.

关键词： Microwave power transmission

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Reduct-based ranking of attributes 24

Reduct-based ranking of attributes

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24th KES International Conference on Knowledge-Based and Intelligent Information and Engineering systems, KES 2020

作者： Zielosko, Beata Stanczyk, Urszula Institute of Computer Science University of Silesia in Katowice Bedzi nska 39 Sosnowiec41-200 Poland Department of Graphics Computer Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A Gliwice44-100 Poland

The paper is dedicated to the area of feature selection, in particular a notion of attribute rankings that allow to estimate importance of variables. In the research presented for ranking construction a new weighting factor was defined, based on relative reducts. A reduct constitutes an embedded mechanism of feature selection, specific to rough set theory. The proposed factor takes into account the number of reducts in which a given attribute exists, as well as lengths of reducts. Two approaches for reduct generation were employed and compared, with search executed by a genetic algorithm. To validate the usefulness of the reduct-based rankings in the process of feature reduction, for gradually decreasing subsets of attributes, selected through rankings, sets of decision rules were induced in classical rough set approach. The performance of all rule classifiers was evaluated, and experimental results showed that the proposed rankings led to at least the same, or even increased classification accuracy for reduced sets of features than in the case of operating on the entire set of condition attributes. The experiments were performed on datasets from stylometry domain, with treating authorship attribution as a classification task, and stylometric descriptors as characteristic features defining writing styles. © 2020 The Authors. Published by Elsevier B.V.

关键词： Classification (of information)

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Automatic localization of supraorbital and infraorbital foramina region on CBCT images 1

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2nd International Conference on Advanced Intelligent systems for Sustainable Development, AI2SD 2019

作者： Ed-Dhahraouy, Mohammed Riri, Hicham Ezzahmouly, Manal El Moutaouakkil, Abdelmajid Bourzgui, Farid Aghoutan, Hakima Belaqziz, Salwa Laboratory of Research Optimization Emerging System Networks and Imaging Computer Science Department Chouaïb Doukkali University El Jadida Morocco Dentofacial Orthopedics Department Faculty of Dental Medicine University Hassan 2 Ain Chok Casablanca Morocco Laboratory of Computer Systems and Vision Computer Science Department Ibn Zohr University Agadir Morocco

ISBN: (数字)9783030366643

ISBN: (纸本)9783030366636

CBCT (Cone Beam Computed Tomography) has fast become a key to produce 3D images in orthodontic and maxilla-facial surgery. Our purpose in this study is to develop a new approach for automatic localization of the region of supraorbital and infraorbital foramina using CBCT images. 19 CBCT images of Moroccan patients were used to validate the proposed algorithm. The automatic algorithm is based on geometric and anatomic information of maxillo-facial structures. The local priori information of supraorbital and infraorbital foramina was used to design the automatic algorithm. The detection rate for supraorbital foramina was 90% and 85% for right and left sides respectively, while the detection rate for infraorbital foramina was 79% and 74% for right and left sides respectively. This study has shown the relevance of local criteria such as geometric and anatomic knowledge in the automation of localization of supraorbital and infraorbital region. This work has gone some way towards the automation of morphometric and anthropometric measurements using CBCT images. © Springer Nature Switzerland AG 2020.

关键词： Medical imaging

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Towards Mutual Trust-Based Matching For Federated Learning Client Selection

Towards Mutual Trust-Based Matching For Federated Learning C...

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International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Osama Wehbi Omar Abdel Wahab Azzam Mourad Hadi Otrok Hoda Alkhzaimi Mohsen Guizani Mohammad Bin Zayed University of Artificial Intelligence Abu Dhabi UAE Department of CSM Cyber Security Systems and Applied AI Research Center Lebanese American University Lebanon Department of Computer and Software Engineering Polytechnique Montréal Montreal Quebec Canada Division of Science New York University Abu Dhabi UAE Department of EECS Center of Cyber-Physical Systems (C2PS) Khalifa University Abu Dhabi UAE Division of Engineering New York University Abu Dhabi UAE

Federated Learning (FL) is a revolutionary privacy-preserving distributed learning framework that allows a small group of users to cooperatively build a machine-learning model using their own data locally. Smart cities are areas that can generate high volume and critical data, which has the potential to revolutionize federated learning. Nevertheless, it is highly challenging to select a trustworthy group of clients to collaborate in model training. The utilization of a random selection technique would pose many threats due to malicious clients’ targeted and untargeted attacks. Such vulnerability may cause attacks and poisoning in the produced model. To address this problem, we present a mutual trust client-server selection approach based on matching game theory and bootstrapping mechanisms for federated learning in smart cities. Our solution entails the creation of: (1) preference functions for federated servers and smart devices (i.e., IoT/IoV) that enables them to sort each other based on trust score, (2) light feedback-base technique that leverages the cooperation of multiple client devices to assign trust value to the newly connected federated server, and (3) intelligent matching algorithms consider trust preferences of both parties in their design. According to our simulation results, our technique outperforms the baseline selection approach VanillaFL in terms of increasing the trust level and hence the global accuracy of the federated learning model and optimizing the number of untrusted selected clients.

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Vulnerability of Machine Learning Approaches Applied in IoT-based Smart Grid: A Review

arXiv

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arXiv 2023年

作者： Zhang, Zhenyong Liu, Mengxiang Sun, Mingyang Deng, Ruilong Cheng, Peng Niyato, Dusit Chow, Mo-Yuen Chen, Jiming The State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550000 China The Department of Automatic Control and Systems Engineering University of Sheffield SheffieldS10 2TN United Kingdom The State Key Laboratory of Industrial Control Technology the College of Control Science and Engineering Zhejiang University Hangzhou310027 China The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore The UM SJTU Joint Institute Shanghai Jiao Tong University Shanghai200240 China

Machine learning (ML) sees an increasing prevalence of being used in the internet-of-things (IoT)-based smart grid. However, the trustworthiness of ML is a severe issue that must be addressed to accommodate the trend of ML-based smart grid applications (MLsgAPPs). The adversarial distortion injected into the power signal will greatly affect the system’s normal control and operation. Therefore, it is imperative to conduct vulnerability assessment for MLsgAPPs applied in the context of safety-critical power systems. In this paper, we provide a comprehensive review of the recent progress in designing attack and defense methods for MLsgAPPs. Unlike the traditional survey about ML security, this is the first review work about the security of MLsgAPPs that focuses on the characteristics of power systems. We first highlight the specifics for constructing the adversarial attacks on MLsgAPPs. Then, the vulnerability of MLsgAPP is analyzed from both the aspects of the power system and ML model. Afterward, a comprehensive survey is conducted to review and compare existing studies about the adversarial attacks on MLsgAPPs in scenarios of generation, transmission, distribution, and consumption, and the countermeasures are reviewed according to the attacks that they defend against. Finally, the future research directions are discussed on the attacker’s and defender’s side, respectively. We also analyze the potential vulnerability of large language model-based (e.g., ChatGPT) power system applications. Overall, we encourage more researchers to contribute to investigating the adversarial issues of MLsgAPPs. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Designing a Trajectory Control System for Wheelchairs with a Combination of Automatic and Manual Steering Methods

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Periodica Polytechnica Transportation Engineering 2024年第4期52卷 412-422页

作者： Vu, Van Tan Nguyen, Quang Thinh Nakagawa, Chihiro Nguyen, Trung Nguyen Le, Thanh Long Nguyen, Van Vinh Hoang, Thi Lan Anh Do, Trong Tu Mihály, András Gáspár, Péter Department of Automotive Mechanical Engineering Faculty of Mechanical Engineering University of Transport and Communications Building A6 no. 3 Cau Giay Street Lang Thuong Ward Dong Da District Hanoi100000 Viet Nam Japan Technology Solution Joint Stock Company 9 Dau Ma Street Section 2 Thi Cau Ward Bac Ninh Bac Ninh222000 Viet Nam School of Engineering Osaka Metropolitan University 1-1 Gakuen-cho Nakaku Sakai Osaka599-8531 Japan Emergency abdominal surgery Department Viet Duc University Hospital no. 40 Trang Thi Street Hanoi100000 Viet Nam Mechanical and Power Engineering Faculty Electric Power University no. 235 Hoang Quoc Viet Street Co Nhue Ward Bac Tu Liem District Hanoi100000 Viet Nam Systems and Control Laboratory Institute for Computer Science and Control Hungarian Research Network Kende u. 13-17 Budapest1111 Hungary

A wheelchair is a specialized form of Personal Mobility Vehicle (PMV) designed to help people with disabilities move safely to their desired locations. Unlike conventional PMVs, wheelchairs for people with disabilities are challenging to manually control in environments with heavy traffic. This study focuses on designing and feasibility testing of control systems for PMVs to meet safety and efficiency standards, even in complex environments. First, a control structure model for PMVs is proposed. This is followed by the application of a combination of First In First Out (FIFO) and Dijkstra traffic control methods to determine effective trajectories for PMVs. The proposed control system is then tested on the campus of the University of Transport and Communications. Simulation results show that when comparing the travel time of 10 PMVs, the order execution time for each PMV remains stable (within 10%) for up to 20 PMVs. However, when the number of PMVs increases to 25, 30, 35, and 40 vehicles, the order execution time increases by 35%, 55%, 103%, and 119%, respectively. These results demonstrate that the system can control a large number of PMVs, ensuring safety, flexibility, and efficiency. © 2024 Budapest University of Technology and Economics. All rights reserved.

关键词： Travel time

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A novel three-phase expansion algorithm for uncovering communities in social networks using local influence and similarity in embedding space

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Decision Analytics Journal 2024年 11卷

作者： Adraoui, Meriem Akachar, Elyazid Bougteb, Yahya Ouhbi, Brahim Frikh, Bouchra Retbi, Asmaa Bennani, Samir Center of Urban Systems (CUS) Mohammed VI Polytechnic University (UM6P) Benguerir Morocco Department of Computer Science Faculty of Sciences Moulay Ismail University Meknes Morocco LM2I Laboratory National Higher School of Arts and Crafts (ENSAM) Moulay Ismaïl University Meknes Morocco LIASSE Laboratory Computer Science Department National School of Applied Sciences (ENSA) Sidi Mohamed Ben Abdellah University Fez Morocco RIME TEAM-Networking Modeling and e-Learning Team- LRIE Laboratory- Research in Computer Science and Education Laboratory- Mohammadia School of Engineers (EMI). Mohammed V University in Rabat Morocco

Community detection can help uncover and understand complex networks’ underlying patterns and structures. It involves identifying cohesive groups with similar entities while being separated from other groups. Social networks are a prime example of an area where community detection is particularly relevant, as it can provide insight into the behaviors of individuals. Several community detection methods have been proposed, each addressing the problem from different perspectives. However, the rise of vast and intricate networks from diverse domains has necessitated the development of community detection methods that can effectively handle large-scale graphs. This study introduces a novel three-phase expansion algorithm for community discovery based on nodes’ local information and similarity in embedding space. The proposed model consists of three steps. In the first stage, we generate an embedding space in which nodes are represented as vectors, and we extract nodes that greatly influence others and have an extraordinary ability to create communities using degree centrality measures. Then, based on cosine similarity in the embedding space, we group the most similar nodes to the influential ones in the same community and create an initial community structure. In the last phase, we extract the weak communities from the initial community structure generated in the second phase and merge them with the strong ones. We conduct extensive experiments on both real-world and synthetic networks to demonstrate the effectiveness of our proposal. The experimental results show that the proposed algorithm performs better than other widely used algorithms and is highly reliable and efficient in large-scale graphs. © 2024 The Author(s)

关键词： Centrality measures Community detection Cosine similarity Embedding space Influential nodes Social networks

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Optimized Deformed Residual Neural Network Accompanied by a Featured Discriminator for Wet and Fine-Grained Fingerprint Sensor Image

SSRN

引用

SSRN 2024年

作者： Chen, Pin-Hsuan Chiu, Ching-Te Chen, Sung-Che Hsu, Mao-Hsiu Institute of Communications Engineering National Tsing Hua University 101 Section 2 Guangfu Road East Distric Hsinchu300 Taiwan Information Systems and Applications National Tsing Hua University 101 Section 2 Guangfu Road East Distric Hsinchu300 Taiwan Department of Computer Science National Tsing Hua University 101 Section 2 Guangfu Road East Distric Hsinchu300 Taiwan Nation Formosa University Electro-Optical Engineering No. 64 Wenhua Road Yunlin County Huwei Township632 Taiwan

Fingerprint recognition is crucial for device and data security, especially with the widespread use of capacitive sensors in mobile devices. However, denoising wet fingerprints from these sensors poses challenges due to small fingerprint areas, limited features, and significant moisture-induced dark regions. Our "DRB-FD" method combines a Featured Discriminator (FD) and a Deformed Residual Block (DRB) with attention mechanisms, drop-out layers, and pre-activation. In experiments using the Nasic9395 0606 aug dataset, DRB-FD achieved a remarkable 73.1% improvement, with the False Rejection Rate (FRR) decreasing by 25% compared to the baseline PGT-Net. Both FD and DRB significantly contributed, showing improvements of 47.4% (FRR decreased by 16.2%) and 48.9% (FRR decreased by 8.8%), respectively. Besides achieving better FRR results, there is a significant increase in the number of fingerprint points successfully matched after repair by the DRB-FD model. In conclusion, our innovative approach effectively denoises wet fingerprints from capacitive sensors in mobile systems, substantially enhancing the accuracy and reliability of fingerprint recognition systems. This advancement represents a significant step forward in biometric technology, offering promising applications in mobile security and user authentication. © 2024, The Authors. All rights reserved.

关键词： Capacitive sensors

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