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6th International Conference on Communications, information System and computer engineering, CISCE 2024

作者： Moshayedi, Ata Jahangir Lskar, Izhar Yang, Shuxin Shinde, Shashikant Kisanrao Razi, Abolfazl Andani, Mehran Emadi School of Information Engineering Jiangxi University of Science and Technology Jiangxi Ganzhou China Yashavantrao Chavan Institute of Science Maharashtra Satara India School of Computer Science Clemson University ClemsonSC United States University of Verona Department of Neurosciences Biomedicine and Movement Sciences Verona Italy

ISBN: (纸本)9798350353174

Toe-in and toe-out angles show difficulties in assessing and addressing gait abnormalities, posture issues, and joint stress, requiring sophisticated monitoring and treatment strategies in musculoskeletal health. Diagnostic and Detecting toe-in/out angles is important because of its rules and index into gait abnormalities, posture imbalances, and potential joint stress, enabling early intervention and personalized treatment to improve musculoskeletal health and prevent long-term complications. While various diagnostic methods have been developed, the diagnosis of this disease continues to rely heavily on the expertise of healthcare professionals. Researchers are actively seeking more dependable and convenient approaches to assist patients in achieving earlier diagnoses and monitoring treatment progress effectively. With the importance of this measurement, the proposed design system named IOTToe based on the Internet of Things (IOT) aims to create an effective and user-friendly tool for measuring toe-in and toe-out angles accurately and easily. A web-based interface enables real-time remote tracking of foot alignment, reducing the need for physical tests and making reports. The telemetric capabilities facilitate efficient data transmission, empowering medical professionals with valuable insights for personalized treatment and enhancing healthcare efficiency. Integration with hospital systems supports advanced research in musculoskeletal health. The obtained results show the ability of the system to help the diagnosis and treatment process. © 2024 IEEE.

关键词： Musculoskeletal system

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A GTFS data acquisition and processing framework and its application to train delay prediction

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International Journal of Transportation science and technology 2023年第1期12卷 201-216页

作者： Jianqing Wu Bo Du Zengyang Gong Qiang Wu Jun Shen Luping Zhou Chen Cai School of Computing and Information Technology University of WollongongNSW 2522Australia SMART Infrastructure Facility University of WollongongNSW 2522Australia School of Civil Mining and EnvironmentalUniversity of WollongongNSW 2522Australia Department of Computer Science and Engineering Hong Kong University of Science and TechnologyHong Kong 999077China Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of ChinaChengdu 611731China School of Electrical and Information Engineering University of SydneyNSW 2006Australia CSIRO’s Data61 NSW 2015Australia

With advanced artificial intelligence and deep learning techniques, a growing number of data sources are playing more and more critical roles in planning and operating transportation services. The General Transit Feed Specification (GTFS), with standard open-source data in both static and real-time formats, is being widely used in public transport planning and operation management. However, compared to other extensively studied data sources such as smart card data and GPS trajectory data, the GTFS data lacks proper investigation yet. Utilization of the GTFS data is challenging for both transport planners and researchers due to its difficulty and complexity of understanding, processing, and leveraging the raw data. In this paper, a GTFS data acquisition and processing framework is proposed to offer an efficient and effective benchmark tool for converting and fusing the GTFS data to a ready-to-use format. To validate and test the proposed framework, a multivariate multistep Long Short-Term Memory is developed to predict train delay with minor anomaly in Sydney as a case study. The contribution of this new framework will render great potential for broader applications and deeper research.

关键词： General transit feed specification Delay prediction Train delay Long short-term memory Data fusion

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Probabilistic Model Building Evolutionary Algorithm Using the Same Type and Size of Model for Any Binary Optimization Problems

Probabilistic Model Building Evolutionary Algorithm Using th...

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International Symposium on Soft Computing and Intelligent systems (SCIS)

作者： Atushi Kishimoto Kei Ohnishi Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Fukuoka Japan

ISBN: (数字)9798350373332

ISBN: (纸本)9798350373349

We propose $a$ new probabilistic model building evolutionary algorithm that uses the same type and size of probabilistic model for any binary optimization problems. The proposed method represents a population as a binary matrix for any binary optimization problems and solves a given problem by transforming the population in the form of matrix to better one with 512 production rules of cellular automaton. Concretely, the proposed method builds a probabilistic model for generating new solution candidates while applying the 512 production rules to the matrix representing the population. The probabilistic model consists of the 512 inverse production rules and their application probabilities. The strong point of the proposed method is to be able to transform any binary optimization problems to the same size of 512-bit production rule optimization problem. Since the proposed method treats any binary optimization problems in a unified manner, we can easily explore a robust method for various binary optimization problems as the proposed method with particular configurations. As the first evaluation of the proposed method, we apply the proposed method to the same kind but different sizes of problems. The results show that depending strongly on the bit patterns of the global optima, the proposed method with the same configurations realizes a good scalability of the number of function evaluations required for obtaining near optimum to the problem size growth.

关键词： Scalability Buildings Automata Production Evolutionary computation Transforms Probabilistic logic Optimization

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A Verifiable Secret Sharing Scheme using Elliptic Curve Cryptography

A Verifiable Secret Sharing Scheme using Elliptic Curve Cryp...

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International Conference on Computing and Networking technology (ICCNT)

作者： Haradhan Ghosh Pramod Kumar Maurya Satya Bagchi Department of Mathematics National Institute of Technology Durgapur Durgapur India School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (数字)9798350335095

ISBN: (纸本)9798350335101

One of the significant problems with communication technology is data privacy, which impacts our everyday activities. Sharing a secret is crucial for safe contact and has many uses in information security. Secret sharing allows a select group of authorised users to view private information. Few authorised members are given access to the secret key in various organisations, with a certain number of them being expected to figure out the secret. In secret sharing, all individuals are assumed to be trustworthy, but in reality, there is the chance of cheating. This study suggests a threshold method called verifiable secret sharing to detect a cheater and verify each individual’s honesty. The elliptic curve cryptosystem, hash function, XOR, and Lagrange interpolation technique do the sharing process. Only shadow shares, not the actual shares, are given to the participants, and before sending information, participants perform combiner authentication. Analysis of security and performance reveals that the proposed method is more effective.

关键词： Hash functions Interpolation Data privacy information security Elliptic curve cryptography Computational efficiency Cryptography

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On Implementation of Brownian Circuits by Swarm Agents 11

On Implementation of Brownian Circuits by Swarm Agents

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11th International Symposium on Computing and Networking Workshops, CANDARW 2023

作者： Kimura, Masahiro Isokawa, Teijiro Peper, Ferdinand Nakade, Sho Lee, Jia Kamiura, Naotake University of Hyogo Graduate School of Engineering Himeji Japan National Institute of Information and Communications Technology Center for Information and Neural Networks Kobe Japan Chongqing University College of Computer Science Chongqing China

ISBN: (纸本)9798350306941

Brownian circuit is a type of delay-insensitive asynchronous circuits in which circuit modules are connected by bi-directional signal lines and computational signals can be fluctuated on signal lines. It is expected to achieve low power dissipation for conducting computation, and there are several types of implementations such as by Cellular Automata and Swarm Networks. In this paper, we propose an implementation of Brownian circuit based on swarm agents for possible applications on biomolecular computers. Swarm agents consist of several types of agents each of which has only a few functions with a fixed internal states. Operations on signals for the circuit elements are conducted by mechanical interactions between groups of agents, rather than by changing states in cells or agents through their communication. We show how to construct each of elements in Brownian circuit, such as signal, signal line, Hub, C-Join, and Ratchet, by collections of agents, thus computational universality is established by swarm agents. © 2023 IEEE.

关键词： Timing circuits

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Multi-StudentNet: a Semi-supervised Deep-Learning Framework for Endometrial Segmentation in Transvaginal Ultrasound Images

Multi-StudentNet: a Semi-supervised Deep-Learning Framework ...

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2024 IEEE International Conference on E-Health Networking, Application and Services, HealthCom 2024

作者： Peng, Boyuan Liu, Yiyang Zhu, Xin Huang, Jie Wang, Wenwen Fang, Li Zhou, Qin Graduate Department of Computer Science and Engineering The University of Aizu Fukushima Aizu-Wakamatsu Japan Department of AI Technology Development M&D Data Science Center Institute of Science Tokyo Chiyodaku Tokyo Japan Graduate Department of Computer Science and Engineering Division of Information Systems The University of Aizu Fukushima Aizu-Wakamatsu Japan Department of Obstetrics and Gynecology Tongji Hospital Huazhong University of Science and Technology Hubei Wuhan China

ISBN: (纸本)9798350350548

Semi-supervised learning (SSL) is a promising solution for the problem of insufficient medical labeled data. However, it is still a challenging task to segment transvaginal ultrasound (TVUS) images because of their poor contrast and speckle noise. In this study, we propose Multi-StudentNet, a semi-supervised deep-learning framework for endometrial segmentation in TVUS images. This method employs multiple student models to effectively integrate labeled and unlabeled data, improving the reliability and performance of segmentation while significantly reducing the need for extensive manual labeling. By leveraging the weights of multiple models, our framework facilitates feature sharing, which enhances model robustness. Specifically, Multi-StudentNet achieves a Dice coefficient (DSC) of 0.81 and a specificity of 0.99. Our multi-student models consistently outperform single-student models across various conditions, including normal, polyp, and cancer cases. Extensive experiments demonstrate that Multi-StudentNet achieves state-of-the-art performance in both accuracy and robustness. © 2024 IEEE.

关键词： Self-supervised learning

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ChatAN: Interval Adaptive Normalization based on ChatGPT Knowledge Augmentation 8

ChatAN: Interval Adaptive Normalization based on ChatGPT Kno...

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8th International Conference on Intelligent Computing and Signal Processing, ICSP 2023

作者： Yang, Hanyu Zhao, Xiaoyong Wang, Lei Zhang, Jingle Li, Jianhua Beijing Information Science & Technology University School of Information & Management Beijing China Beijing Information Science & Technology University Information Systems Institute Beijing Advanced Innovation Center for Materials Genome Engineering Beijing China City Brain Zhongguancun Science City City Brain Co. Ltd. Beijing China

ISBN: (纸本)9798350302455

Big data streams have massive and complex characteristics. Due to the lack of global static information, the normalization of large data streams is susceptible to outliers and conceptual drift, which leads to incorrect modeling results. In addition, in online anomaly detection applications, it is significant to ensure real-time data processing to prevent severe consequences from missing critical points due to data flow backlogs. In this paper, we propose a method for interval adaptive normalization based on ChatGPT knowledge augmentation. The approach combines domain knowledge obtained by ChatGPT knowledge augmentation with interval adaptive sliding windows. Experimental results on three datasets in the cybersecurity domain show that the anomaly detection algorithm incorporating our proposed normalization algorithm achieves the maximum improvement of 32.46% and 32.68% in AUCROC and AUCPR compared to the traditional normalization method. © 2023 IEEE.

关键词： Anomaly detection

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Botnet Attack Intrusion Detection In IoT Enabled Automated Guided Vehicles

Botnet Attack Intrusion Detection In IoT Enabled Automated G...

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Shaikh, Sumaiya Rupa, Ch. Srivastava, Gautam Reddy Gadekallu, Thippa Lords Institute of Engineering and Technology Department of Cse Hyderabad India Vr Siddhartha Engineering College Department of Cse Vijayawada India Brandon University Department of Math and Computer Science Canada Vellore Institute of Technology School of Information Technology and Engineering India Lebanese American University Department of Electrical and Computer Engineering Byblos Lebanon

ISBN: (纸本)9781665480451

The Internet of Things (IoT), with the ease of access of Automated Vehicles, is the most reliable technology in the 21st century making every possible thing within seconds. The people in this era are blessed with all the new technology, and new gadgets, due to which many things which used to take longer are done within nanoseconds. IoT combines computing devices, mechanical devices, digital machines, objects, and people which possess the ability to transfer data over the network with Unique identifiers (UIDs) without human intervention. Such IoT-enabled Automated Guided Vehicles (AGV) are more reliable on networks for every action. Deep learning and machine learning techniques are pivotal for the successful implementation of IoT-based applications including AGVs. A botnet refers to the attacks which come from robot Network attacks. Recently, many organizations have been compromised using this attack. Most affected devices are connected to IoT as it uses automatically generated data. The ideology of this study is to propose an intrusion prediction system which can predict botnet attacks in AGVs. In this study, N - Balo dataset is used for classification, clustering and prediction. The technique implemented in this paper may provide some roots to develop the most reliable and highly secured AGV network. © 2022 IEEE.

关键词： Internet of things

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Optimal Bandwidth Utilization Techniques for IoT Device Networks using Edge Computing

Optimal Bandwidth Utilization Techniques for IoT Device Netw...

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Innovation in technology (ASIANCON), Asian Conference on

作者： Dankan Gowda V G. Vijayakumar J. Chandrasekar Shekhar R KDV Prasad Senthil Kumar C Department of Electronics and Communication Engineering BMS Institute of Technology and Management Bangalore Karnataka Department of Information Technology V.S.B. Engineering College Karur Tamilnadu India Department of Social Work ( Aided) Madras School of Social Work Chennai Tamilnadu India Department of Computer Science and Engineering Alliance University Bangalore Karnataka India Symbiosis Institute of Business Management Hyderabad Symbiosis International (Deemed University) Pune India Department of Information Technology School of Computer Science Engineering & Information Systems Vellore Institute of Technology Vellore India

ISBN: (数字)9798350354218

ISBN: (纸本)9798350354225

The IoT devices have persisted on growing at a very fast pace and this has proven very challenging in managing the network bandwidth. This paper spins the new concept of increasing the IoT networks bandwidth by adopting the edge computing strategies. It also offers data processing near the sources which reduces the load on the central servers and cuts down data transfer requirements. The usage of the Dynamic Bandwidth Allocation algorithm in the proposed method also enhances the priority of the imperative data and scales up the transmission rates according to the network status. The number of data units achieve higher values, the latency is less and overall, better results have been obtained for the total network performance. Consequently, the results obtained presented pathways through which the bandwidth challenge in IoT could be addressed by means of edge computing to enhance IoT capacity in the corresponding surroundings.

关键词： Performance evaluation Technological innovation Costs Heuristic algorithms Bandwidth Channel allocation Dynamic scheduling Internet of Things Time factors Edge computing

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RoboMP2: a robotic multimodal perception-planning framework with multimodal large language models 24

RoboMP2: a robotic multimodal perception-planning framework ...

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Proceedings of the 41st International Conference on Machine Learning

作者： Qi Lv Hao Li Xiang Deng Rui Shao Michael Yu Wang Liqiang Nie School of Computer Science and Technology Harbin Institute of Technology (Shenzhen) and School of Engineering and School of Computing and Information Technology Great Bay University School of Computer Science and Technology Harbin Institute of Technology (Shenzhen) School of Engineering Great Bay University

Multimodal Large Language Models (MLLMs) have shown impressive reasoning abilities and general intelligence in various domains. It inspires researchers to train end-to-end MLLMs or utilize large models to generate policies with human-selected prompts for embodied agents. However, these methods exhibit limited generalization capabilities on unseen tasks or scenarios, and overlook the multimodal environment information which is critical for robots to make decisions. In this paper, we introduce a novel Robotic Multimodal Perception-Planning (RoboMP2) framework for robotic manipulation which consists of a Goal-Conditioned Multimodal Preceptor (GCMP) and a Retrieval-Augmented Multimodal Planner (RAMP). Specially, GCMP captures environment states by employing a tailored MLLMs for embodied agents with the abilities of semantic reasoning and localization. RAMP utilizes coarse-to-fine retrieval method to find the k most-relevant policies as in-context demonstrations to enhance the planner. Extensive experiments demonstrate the superiority of RoboMP2 on both VIMA benchmark and real-world tasks, with around 10% improvement over the baselines.

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