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arXiv 2023年

作者： Syu, Jia-Hao Fojcik, Marcin Lin, Jerry Chun-Wei Cupek, Rafal Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen Norway Department of Distributed Systems and Informatic Devices Silesian University of Technology Gliwice Poland

Medical internet of things leads to revolutionary improvements in medical services, also known as smart healthcare. With the big healthcare data, data mining and machine learning can assist wellness management and intelligent diagnosis, and achieve the P4-medicine. However, healthcare data has high sparsity and heterogeneity. In this paper, we propose a Heterogeneous Transferring Prediction System (HTPS). Feature engineering mechanism transforms the dataset into sparse and dense feature matrices, and autoencoders in the embedding networks not only embed features but also transfer knowledge from heterogeneous datasets. Experimental results show that the proposed HTPS outperforms the benchmark systems on various prediction tasks and datasets, and ablation studies present the effectiveness of each designed mechanism. Experimental results demonstrate the negative impact of heterogeneous data on benchmark systems and the high transferability of the proposed HTPS. Copyright © 2023, The Authors. All rights reserved.

关键词： Diagnosis

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Data poisoning: issues, challenges, and needs

Data poisoning: issues, challenges, and needs

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7th IET Smart Cities Symposium (SCS 2023)

作者： Mohammad Aljanabi Alaa Hamza Omran Maad M Mijwil Mostafa Abotaleb El-Sayed M. El-kenawy Sahar Yousif Mohammed Abdelhameed Ibrahim Department of computer College of Education Aliarqai university Baghdad Iraq University of information technology and communications Baghdad Iraq Computer Techniques Engineering Department Baghdad College of Economic Sciences University Baghdad Iraq Department of System Programming South Ural State University Chelyabinsk Russia Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Egypt Computer Science Department Computer Science & Information Technology College Anbar University Anbar Iraq Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura Egypt

Data poisoning attacks, where adversaries manipulate training data to degrade model performance, are an emerging threat as machine learning becomes widely deployed in sensitive applications. This paper provides a comprehensive overview of data poisoning including attack techniques, adversary incentives, impacts on security and reliability, detection methods, defenses, and key research gaps. We examine label flipping, instance injection, backdoors, and other attack categories that enable malicious outcomes ranging from IP theft to accidents in autonomous systems. Promising detection approaches include statistical tests, robust learning, and forensics. However, significant challenges remain in translating academic defenses like adversarial training and sanitization into practical tools ready for operational use. With safety and trustworthiness at stake, more research on benchmarking evaluations, adaptive attacks, fundamental tradeoffs, and real-world deployment of defenses is urgently needed. Understanding vulnerabilities and developing resilient machine learning pipelines will only grow in importance as data integrity is fundamental to developing safe artificial intelligence.

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FedAccess: Federated Learning-Based Built Surface Recognition for Wheelchair Routing

FedAccess: Federated Learning-Based Built Surface Recognitio...

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IEEE Annual International computer Software and Applications Conference (COMPSAC)

作者： Rochishnu Banerjee Ethan Han Longze Li Haoxiang Yu Md Osman Gani Vaskar Raychoudhury Roger O. Smith Department of Computer Science & Software Engineering Miami University Oxford OH USA Department of Electrical and Computer Engineering University of Texas Austin Austin TX USA Department of Information Systems University of Maryland Baltimore County Baltimore MD USA Department of Rehabilitation Sciences & Technology University of Wisconsin Milwaukee WI USA

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

Wheelchair and mobility aid users often face challenges in navigating the built environment due to uneven sidewalks, temporary barriers, steep inclines, and narrow lanes. To assist these users, accessible routing systems have been introduced that generate wheelchair-accessible paths to facilitate navigation in unfamiliar environments. In general, accessible routing systems rely on surface and path characteristics like surface type, incline, width, etc., and crowd-sourced information about barriers to provide the optimal route. Emerging routing systems even provide personalized routing to users that are catered to the user's specific needs and requirements. However, these types of systems collect crowd-sourced personal/identifiable information which introduces privacy and data heterogeneity concerns that are not addressed by them or elsewhere in the concerned domain. To address these two issues specifically, we propose the novel FedAccess system for accessible routing that utilizes the federated learning paradigm for surface recognition using vibration data. The surface-induced vibrations are captured through smartphone-embedded motion sensors (accelerometers and gyroscopes) from 23 manual wheelchair users during their regular navigation. We have covered 10 distinct surfaces from the USA. As a result, the distribution of the data is naturally non-IID. Empirical evaluation shows that the FedAccess system can protect user data and identity while dealing with non-IID data and still recognize heterogeneous surfaces with higher accuracy than the state-of-the-art.

关键词： Vibrations Navigation Federated learning Wheelchairs Face recognition Manuals Routing

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Deep Reinforcement Learning based Joint Spectrum Allocation and Configuration Design for STAR-RIS-Assisted V2X Communications

arXiv

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arXiv 2023年

作者： Aung, Pyae Sone Nguyen, Loc X. Tun, Yan Kyaw Han, Zhu Hong, Choong Seon The Department of Computer Science and Engineering Kyung Hee University Gyeonggi-do Yongin-si17104 Korea Republic of Division of Network and Systems Engineering School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Brinellvägen 8 Stockholm114 28 Sweden The Electrical and Computer Engineering Department University of Houston HoustonTX77004 United States

Vehicle-to-Everything (V2X) communications play a crucial role in ensuring safe and efficient modern transportation systems. However, challenges arise in scenarios with buildings, leading to signal obstruction and coverage limitations. To alleviate these challenges, reconfigurable intelligent surface (RIS) is regarded as an effective solution for communication performance by tuning passive signal reflection. RIS has acquired prominence in 6G networks due to its improved spectral efficiency, simple deployment, and cost-effectiveness. Nevertheless, conventional RIS solutions have coverage limitations. Therefore, researchers have started focusing on the promising concept of simultaneously transmitting and reflecting RIS (STAR-RIS), which provides 360°coverage while utilizing the advantages of RIS technology. In this paper, a STAR-RIS-assisted V2X communication system is investigated. An optimization problem is formulated to maximize the achievable data rate for vehicle-to-infrastructure (V2I) users while satisfying the latency and reliability requirements of vehicle-to-vehicle (V2V) pairs by jointly optimizing the spectrum allocation, amplitudes, and phase shifts of STAR-RIS elements, digital beamforming vectors for V2I links, and transmit power for V2V pairs. Since it is challenging to solve in polynomial time, we decompose our problem into two sub-problems. For the first sub-problem, we model the control variables as a Markov Decision Process (MDP) and propose a combined double deep Q-network (DDQN) with an attention mechanism so that the model can potentially focus on relevant inputs. For the latter, a standard optimization-based approach is implemented to provide a real-time solution, reducing computational costs. Extensive numerical analysis is developed to demonstrate the superiority of our proposed algorithm compared to benchmark schemes. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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Historical Study of the Development of the Misuse of Biological Weapons Technology 8

Historical Study of the Development of the Misuse of Biologi...

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8th IEEE International Conference on Computing, engineering and Design, ICCED 2022

作者： Agustin, Dian Mantoro, Teddy Yulivan, Ivan State Intelligence College Medical Intelligence Post-graduate Program Bogor Indonesia Sampoerna University and State Intelligence College Media-Tech Lab Department of Computer Science Jakarta Indonesia Indonesia Defence University Department of Information Systems Jakarta Indonesia

ISBN: (纸本)9781665453899

Technology will continue to be an important thing in life, technological developments can be found in everyday life. In medical intelligence technology, to identify bio-Terrorism actions, and aspects that can threaten national security, such as targets that may be the object of attack, identify indicators early, build biosecurity, and analyze pathogen communities. The threat that hit the world is when resources are used by irresponsible people or groups and are used as biological weapons (bioweapons), bioterrorism, and others. These biological weapons are not only used for war but can also be used for terrorism purposes. The involvement of biological weapons in the history of war has been used, various weapons were created before the 20th century biological weapons in the form of biological toxins or bacteria from decaying animals and plants, after the 20th-century weapons switched to bacteria (anthrax), viruses (smallpox), and poison (botulinum). Regarding what is used as a biological weapon, this is regulated by various policies, both national policies, and defense policies. Anticipatory steps and efforts to overcome the threat of bioterrorism need to involve all parties, including the community, security forces, health workers, and scientists. Legal reforms can be carried out to perfect and complement existing legal loopholes as a result of controlling bioterrorism which widens the opportunities or loopholes for bioterrorism attacks in Indonesia that should receive state attention and formulate bioterrorism regulations. © 2022 IEEE.

关键词： Bioterrorism

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Haptic Communication in a Redundant Sensorimotor Interactive Task

Haptic Communication in a Redundant Sensorimotor Interactive...

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IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob)

作者： Cecilia De Vicariis Ekaterina Ivanova Vittorio Sanguineti Etienne Burdet Department of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Genoa Italy Imperial College of Science Technology and Medicine London UK School of Electronic Engineering and Computer Science Queen Mary University of London UK

ISBN: (数字)9798350386523

ISBN: (纸本)9798350386530

We refer to joint action as any social interaction where two or more people coordinate their actions in space and time to bring about a change in the environment. We are constantly involved in joint actions in our daily life - lifting a piece of furniture, playing team sport, and during a rehabilitation session. Here, we aim at characterizing emergent coordination in pairs of participants mechanically coupled through a virtual elastic band while moving the hand through multiple via-points. We assessed the level of coordination in terms of temporal synchronization and trajectory similarity metrics. Overall, we found that participants changed their behaviour when mechani-cally coupled toward coordination, both in space and time, compared to their solo performances. However, coordination rapidly disappeared when the mechanical coupling was removed. These results provide insights into the understanding of the mecha-nisms underlying the development of joint coordination while haptically connected.

关键词： Measurement Couplings Robot kinematics Biomechatronics Robot sensing systems Trajectory Haptic interfaces Synchronization Sports

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Quantum Graph Optimization Algorithm

arXiv

引用

arXiv 2024年

作者： Huang, Yuhan Nugraha, Ferris Prima Jin, Siyuan Zhang, Yichi Zeng, Bei Shao, Qiming Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong Department of Information Systems The Hong Kong University of Science and Technology Hong Kong Department of Physics The Hong Kong University of Science and Technology Hong Kong

Quadratic unconstrained binary optimization (QUBO) tasks are very important in chemistry, finance, job scheduling, and so on, which can be represented using graph structures, with the variables as nodes and the interaction between them as edges. Variational quantum algorithms, especially the Quantum Approximate Optimization Algorithm (QAOA) and its variants, present a promising way, potentially exceeding the capabilities of classical algorithms, for addressing QUBO tasks. However, the possibility of using messagepassing machines, inspired by classical graph neural networks, to enhance the power and performance of these quantum algorithms for QUBO tasks was not investigated. This study introduces a novel variational quantum graph optimization algorithm that integrates the message-passing mechanism, which demonstrates significant improvements in performance for solving QUBO problems in terms of resource efficiency and solution precision, compared to QAOA, its variants, and other quantum graph neural networks. Furthermore, in terms of scalability on QUBO tasks, our algorithm shows superior performance compared to QAOA, presenting a substantial advancement in the field of quantum approximate optimization. Copyright © 2024, The Authors. All rights reserved.

关键词： Graph neural networks

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Multi-Object RANSAC: Efficient Plane Clustering Method in a Clutter

Multi-Object RANSAC: Efficient Plane Clustering Method in a ...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Seunghyeon Lim Youngjae Yoo Jun Ki Lee Byoung-Tak Zhang Interdisciplinary Program in Cognitive Science Seoul National University Seoul Korea Department of Computer Science and Engineering Seoul National University Seoul Korea AIIS Seoul National University Seoul Korea

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

In this paper, we propose a novel method for plane clustering specialized in cluttered scenes using an RGB-D camera and validate its effectiveness through robot grasping experiments. Unlike existing methods, which focus on large- scale indoor structures, our approach—Multi-Object RANSAC emphasizes cluttered environments that contain a wide range of objects with different scales. It enhances plane segmentation by generating subplanes in Deep Plane Clustering (DPC) module, which are then merged with the final planes by postprocessing. DPC rearranges the point cloud by voting layers to make subplane clusters, trained in a self-supervised manner using pseudo-labels generated from RANSAC. Multi-Object RANSAC demonstrates superior plane instance segmentation performances over other recent RANSAC applications. We conducted an experiment on robot suction-based grasping, comparing our method with vision-based grasping network and RANSAC applications. The results from this real-world scenario showed its remarkable performance surpassing the baseline methods, highlighting its potential for advanced scene understanding and manipulation.

关键词： Point cloud compression Instance segmentation Clustering methods Robot vision systems Grasping Cameras Clutter

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Application of Artificial Intelligence in Malignant Tumors: A science Mapping Analysis

Application of Artificial Intelligence in Malignant Tumors: ...

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IEEE Control and System Graduate Research Colloquium (ICSGRC)

作者： Samuel-Soma M. Ajibade Anwar P.P. Abdul Majeed Muhammed Basheer Jasser Ghassan Saleh ALDharhani Ismail Ahmed Al-Qasem Al-Hadi Ganesh Kumar Department of Computing and Information Systems School of Engineering & Technology Sunway University Petaling Jaja Selangor Malaysia Department of Computer Engineering Istanbul Ticaret Universitesi Istanbul Turkiye Institute of Computer Science and Digital Innovation UCSI University Kuala Lumpur Malaysia Faculty of Information Science and Technology UKM University Bangi Selangor Malaysia

ISBN: (数字)9798350386554

ISBN: (纸本)9798350386561

A science mapping analysis was performed on Scopus papers spanning from 2003 to 2022 to investigate the utilization of artificial intelligence techniques in malignant tumors research. The analysis unveiled a progressive rise in publications, amounting to a total of 206, with articles accounting for 55.34% and conference papers making up 44.66%. Zuherman Rustam and Universitas Indonesia made substantial contributions. India's exceptional performance might be due to the availability of financial support from prestigious organizations such as Universitas Indonesia, the National Natural science Foundation of China, and Brazil's Conselho Nacional de Desenvolvimento Científico e Tecnológico. An investigation of social networks, specifically looking at collaborations between authors, revealed robust international cooperation, which improved the availability of resources and infrastructure. The hotspot research revealed the prominent terms to be genetic algorithms, diseases, and feature extraction. The cluster analysis identified three main areas of focus: Precision Health Analytics, Genomic Cancer Profiling, and Integrated AI Diagnosis. In summary, AIMTR research is actively tackling cancer and oncological concerns through the use of computational approaches, making substantial contributions to both societal and scientific progress.

关键词： Productivity Social networking (online) Genomics Organizations Market research Paper making Artificial intelligence Bioinformatics Cancer Genetic algorithms

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Disentangled Motion Modeling for Video Frame Interpolation

arXiv

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arXiv 2024年

作者： Lew, Jaihyun Choi, Jooyoung Shin, Chaehun Jung, Dahuin Yoon, Sungroh Interdisciplinary Program in AI Seoul National University Korea Republic of Department of Electrical and Computer Engineering Seoul National University Korea Republic of School of Computer Science and Engineering Soongsil University Korea Republic of AIIS ASRI and INMC Seoul National University Korea Republic of

Video Frame Interpolation (VFI) aims to synthesize intermediate frames between existing frames to enhance visual smoothness and quality. Beyond the conventional methods based on the reconstruction loss, recent works have employed generative models for improved perceptual quality. However, they require complex training and large computational costs for pixel space modeling. In this paper, we introduce disentangled Motion Modeling (MoMo), a diffusion-based approach for VFI that enhances visual quality by focusing on intermediate motion modeling. We propose a disentangled two-stage training process. In the initial stage, frame synthesis and flow models are trained to generate accurate frames and flows optimal for synthesis. In the subsequent stage, we introduce a motion diffusion model, which incorporates our novel U-Net architecture specifically designed for optical flow, to generate bi-directional flows between frames. By learning the simpler low-frequency representation of motions, MoMo achieves superior perceptual quality with reduced computational demands compared to the generative modeling methods on the pixel space. MoMo surpasses state-of-the-art methods in perceptual metrics across various benchmarks, demonstrating its efficacy and efficiency in VFI. © 2024, CC BY-NC-SA.

关键词： Pixels

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