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10th International Conference on ICT for Smart Society, ICISS 2023

作者： Sunindyo, Wikan Danar Alfrojems School of Electrical Engineering and Informatics Bandung Institute of Technology Knowledge and Software Engineering Research Group Bandung Indonesia Indonesia Business Coalition for Women Empowerment South Jakarta Indonesia

ISBN: (纸本)9798350339543

Accelerating regional economic growth is needed to be able to catch up with a region, especially if the region has economic potential, especially in terms of natural capital, such as a beautiful environment. However, in practice, a development process will experience obstacles as an inseparable part. This condition certainly requires appropriate treatment efforts based on empirical evidence data. This also happened to West Lombok. Where in fact the regions that get the opportunity to become Smart City pilots certainly have a positive impact, but the challenges that must be carried out also go hand in hand with the process of implementing these efforts. Referring to this background, the purpose of this research is to be able to explore and analyze the challenges experienced by West Lombok in implementing the Smart City program in its region. The method used is qualitative with data collection techniques in the form of documentation studies, observations, interviews, and focus group discussions. This study involved 50 informants with various backgrounds but still in accordance with the characteristics of the informants determined by the researcher through the use of purposive sampling technique. The results of this study are that there are four challenges that need to get more attention from the local government of West Lombok and its stakeholders as an effort to realize West Lombok as a smart city. The four challenges include policy, infrastructure, human capital, and environment. The need for strategic efforts carried out by West Lombok to be able to encourage the achievement of the target of becoming a smart city in Indonesia, especially in West Nusa Tenggara Province. © 2023 IEEE.

关键词： Smart city

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MRI Joint Super-Resolution and Denoising based on Conditional Stochastic Normalizing Flow

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期6卷 1472-1487页

作者： Liu, Zhenhong Wang, Xingce Wu, Zhongke Zhu, Yi-Cheng Frangi, Alejandro F. Beijing Normal University School of Artificial Intelligence Beijing China Peking Union Medical College Hospital Department of Neurology Beijing China The University of Manchester Christabel Pankhurst Institute Division of Informatics Imaging and Data Sciences School of Health Sciences Department of Computer Science School of Engineering United Kingdom Department of Cardiovascular Sciences Department of Electrical Engineering Leuven Belgium Alan Turing Institute London United Kingdom

Magnetic resonance imaging (MRI) is often limited by noise and low-resolution (LR), which can impact the precision of the diagnosis and treatment of patients. LR images and mixed noise (e.g., Gaussian noise, Rician noise, and Impulse noise) are inherent in MR images, and current approaches typically address image super-resolution (SR) reconstruction and denoising separately, resulting in a discrepancy between the actual MRI data distribution and the reconstructed images. This research introduces a new algorithm SRDSNF, the stochastic normalizing flow-based MR image SR and denoising model, which tackles SR and denoising simultaneously through a stochastic normalizing flow. Our method integrates the encoded information of the input image as a conditional variable in each reverse step of the stochastic normalizing flow, ensuring a consistent representation of the spatial distribution between the reconstructed image and the original data. Additionally, we incorporate range-null space decomposition and subsequence sampling techniques to increase the consistency between the original and constructed data and accelerate model generation. To assess the efficacy of our approach, we conducted experiments on the BrainWeb and NFBS datasets, which include simultaneous SR and denoising, standalone denoising, and standalone SR tasks. The results of the experiments illustrate that our method achieves superior SR and denoising performance with fewer sampling steps, closely approximating the ground truths. Furthermore, our results surpass those of existing methods in various tasks, showing improvement of up to 4.64 dB in PSNR and 13.8% in SSIM achieved by our SRDSNF model was contextualized against state-of-the-art approaches such as AMIR, SwinIR, and InstructIR. These methods typically report PSNR improvements ranging from 0.5 to 2 dB and SSIM increases of 3-6%, underscoring the potential clinical value of our methodology. © 2020 IEEE.

关键词： Stochastic systems

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Expeditious detection and segmentation of bone mass variation in DEXA images using the hybrid GLCM-AlexNet approach

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Soft Computing 2024年第19期28卷 11633-11646页

作者： Amiya, Gautam Murugan, Pallikonda Rajasekaran Ramaraj, Kottaimalai Govindaraj, Vishnuvarthanan Vasudevan, Muneeswaran Thirumurugan, M. Zhang, Yu-Dong Abdullah, S. Sheik Thiyagarajan, Arunprasath Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu India Data Science Division School of Computer Science and Engineering VIT Bhopal University Madhya Pradesh Bhopal India Consultant Orthopaedic Surgeon MGR Medical University Tamilnadu Chennai India School of Informatics University of Leicester LeicesterLE1 7RH United Kingdom

Osteoporosis (OP) is an osteometabolic disorder characterized by a lesser bone mineral density (BMD) and the disruption of bone tissue micro—architecture, resulting in a greater bone fragility and higher likelihoods of fractures. OP emerges once the bone mass declines faster than the body's capacity to replenish it, leading to a significant reduction in the strength of bone. OP impacts each and every bone throughout the body and provides no clinical signs until a fracture happens. Aging leads to the reduction in BMD, and the rate of percentage of fractures rises over time, usually causing mortality and morbidity. Numerous BMD evaluation methods are available, and they are used in a variety of settings by considering the location of the fracture. Dual-energy x-ray absorptiometry (DEXA/DXA) is recognized as the gold standard for predicting the fracture, since it is the most advanced, technologically affirmed, and also has excellent performance. According to the findings, most researchers do not endeavour the identification and the segmentation of low bone masses from DEXA images. Medical image segmentation supports in analyzing and visualizing the bone's low bone mass. The envisaged hybrid approach, that integrates GLCM for feature extraction and AlexNet for a low bone mass variation classification, provides segmented images that assist in categorizing bone health as normal, osteopenia, or osteoporosis. The developed algorithm's performance metrics, including Dice Co-efficient, Sensitivity, and Specificity, were 92.35, 90.26, and 92.42%, respectively. The Orthopedicians ascertained the efficacy of the outcomes rendered by the proposed algorithm. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Fracture

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Sensing-Assisted Channel Estimation in ISAC Systems 14th

Sensing-Assisted Channel Estimation in ISAC Systems

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14th EAI International Conference on Wireless and Satellite Systems, WiSATS 2024

作者： Yin, Rui Shi, Yexin Qi, Wei Chen, Xianfu Wu, Celimuge Ji, Yusheng School of Information and Electrical Engineering Hangzhou City University Hangzhou310018 China College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China Shenzhen CyberAray Network Technology Company Ltd Shenzhen China Graduate School of Informatics and Engineering The University of Electro-Communications Tokyo182-8585 Japan Information Systems Architecture Research Division National Institute of Informatics Tokyo101-8430 Japan

ISBN: (纸本)9783031862021

In future Sixth-Generation (6G) mobile communication, satellite communication is a crucial means of extending mobile networks worldwide. However, in satellite communication systems, the transmission capacity between ground stations and users has become a bottleneck for satellite communication systems. For this issue, Integrated Sensing and Communication (ISAC) technology and Artificial Intelligence (AI), which has been widely studied, has the potential to offer excellent solutions. This paper explores a Uformer-based sensing-assisted communication system that enhances channel estimation accuracy through the design of a Uformer-based channel estimation enhancement algorithm. By formulating a global optimization problem, we seek to improve resource efficiency through finding the optimal resource allocation schemes. Initially, the system utilizes radar data to obtain reconstructed channels and employs communication data for channel estimation. The proposed neural network model then fuses these data into a new and more accurate channel state information. Finally, we present a resource allocation algorithm and validate the performance of all proposed algorithms through simulations. © ICST Institute for Computer Sciences, Social informatics and Telecommunications engineering 2025.

关键词： Resource allocation

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Differential-Trust-Mechanism Based Trade-off Method Between Privacy and Accuracy in Recommender Systems

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IEEE Transactions on Information Forensics and Security 2025年 20卷 5054-5068页

作者： Xu, Guangquan Feng, Shicheng Xi, Hao Yan, Qingyang Li, Wenshan Wang, Cong Wang, Wei Liu, Shaoying Tian, Zhihong Zheng, Xi Qingdao Huanghai University School of Big Data Qingdao China Tianjin University College of Intelligence and Computing Tianjin300350 China KLISS and School of Software Beijing100084 China Sichuan University School of Cyber Science and Engineering Chengdu610207 China Xi’an Jiaotong University School of Cyber Science and Engineering Xi’an710049 China East China Normal University Shanghai200062 China Hiroshima University School of Informatics and Data Science Higashihiroshima739-8511 Japan Guangzhou University Cyberspace Institute of Advanced Technology Guangdong Key Laboratory of Industrial Control System Security Huangpu Research School of Guangzhou University China Macquarie University School of Computing SydneyNSW2109 Australia

In the era where Web3.0 values data security and privacy, adopting groundbreaking methods to enhance privacy in recommender systems is crucial. Recommender systems need to balance privacy and accuracy, while also having the ability to overcome cold start problems. The Differential Trust Mechanism (DTM) introduced in this paper is such an approach. The DTM provides a unique use of Gaussian distributions in modeling trust relationships within data, offering a novel way to balance recommendation accuracy with user privacy. This mechanism innovatively applies differential privacy principles, using Gaussian noise addition to protect individual user data from inference attacks, while maintaining the integrity and utility of the overall dataset. Unlike traditional anonymization techniques that often compromise data utility or vulnerability to reverse engineering, DTM provides a robust solution by dynamically adjusting privacy levels based on the trustworthiness of data requests. By combining DTM with existing mainstream recommendation algorithms, the prediction accuracy of MAE and RMSE increases by at least 6.60% and 2.69%, respectively. This dual benefit positions DTM as a significant advancement in secure data processing, especially relevant for online businesses and platforms where personalized recommendations are crucial yet privacy concerns are paramount. © 2005-2012 IEEE.

关键词： Sensitive data

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Detecting Model Poisoning Attacks Via Dummy Symbol Insertion for Secure Over-the-Air Federated Learning 22

Detecting Model Poisoning Attacks Via Dummy Symbol Insertion...

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22nd IEEE Consumer Communications and Networking Conference, CCNC 2025

作者： Zhou, Hang Chiang, Yi-Han Chen, Caijuan Wang, Xiaoyan Ji, Yusheng National Institute of Informatics Information Systems Architecture Science Research Division Tokyo Japan Osaka Metropolitan University Department of Electrical and Electronic Systems Engineering Osaka Japan Graduate School of Science and Engineering Ibaraki University Hitachi Japan

ISBN: (纸本)9798331508050

With the rapid advancement of intelligent IoT services, the need for high-quality and efficient wireless communication has become increasingly critical for information exchanges and collaborations among intelligent edge devices such as autonomous mobile robots (AMRs), robots in smart factories, and surveillance cameras, etc., Federated Learning (FL) is a promising distributed learning framework by exploring the computation capability on edge devices while protecting data privacy of users. On the other hand, Over-the-Air (OTA) computation techniques is a new paradigm of integrated computation and communication. OTA based FL (OTA-FL) can enhance spectrum efficiency by leveraging the superposition properties of wireless channels for model aggregations. The implementation of OTA-FL on edge devices can further protect user data privacy for clients, improve the throughput of inter-device communications, and enhance the distributed learning performance. However, despite these advantages, the convergence of OTA-FL systems remain vulnerable to model poisoning attacks, presenting significant challenges. In this study, we address these vulnerabilities by proposing a two-phase detection mechanism that secures OTA-FL system while preserving high spectral efficiency. Our simulation results demonstrate that under varying conditions, our approach can make distributed learning more secure and communication-efficient. © 2025 IEEE.

关键词： Secure communication

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Adaptive Activation Steering: A Tuning-Free LLM Truthfulness Improvement Method for Diverse Hallucinations Categories 25

Adaptive Activation Steering: A Tuning-Free LLM Truthfulness...

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34th ACM Web Conference, WWW 2025

作者： Wang, Tianlong Jiao, Xianfeng Zhu, Yinghao Chen, Zhongzhi He, Yifan Chu, Xu Gao, Junyi Wang, Yasha Ma, Liantao School of Software and Microelectronics Peking University Beijing China Key Laboratory of High Confidence Software Technologies Ministry of Education Beijing China National Engineering Research Center for Software Engineering Peking University Beijing China Beihang University Beijing China Center on Frontiers of Computing Studies Peking University Beijing China Centre for Medical Informatics University of Edinburgh Edinburgh United Kingdom Health Data Research UK London United Kingdom Key Laboratory of High Confidence Software Technologies Ministry of Education National Engineering Research Center for Software Engineering Peking University Beijing China

ISBN: (纸本)9798400712746

Recent studies have indicated that Large Language Models (LLMs) harbor an inherent understanding of truthfulness, yet often fail to consistently express it and generate false statements. This gap between "knowing" and "telling" poses a challenge for ensuring the truthfulness of generated content. Inspired by recent work on the practice of encoding human-interpretable concepts linearly within large language models, we treat truthfulness as a specially linearly encoded concept within LLMs, and introduce Adaptive Activation Steering (ACT), a tuning-free method that adaptively shifts LLM’s activations in the "truthful" direction during inference. ACT addresses diverse categories of hallucinations by utilizing diverse truthfulness-related steering vectors and adjusting the steering intensity adaptively. Applied as an add-on across various models, ACT significantly improves truthfulness in LLaMA (↑ 142%), LLaMA2 (↑ 24%), Alpaca (↑ 36%), Vicuna (↑ 28%), LLaMA2-Chat (↑ 19%), and LLaMA3(↑ 34%). Furthermore, we verify ACT’s scalability across larger models (13B, 33B, 65B), underscoring the adaptability of ACT to large-scale language models. © 2025 Copyright held by the owner/author(s).

关键词： hallucination

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Classification of Real and Deepfakes Visual Samples with Pre-trained Deep Learning Models 7th

Classification of Real and Deepfakes Visual Samples with Pre...

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Proceedings of the 7th International Conference on Advances in Computing and data Sciences, ICACDS 2023

作者： Nawaz, Marriam Javed, Ali Nazir, Tahira Khan, Muhammad Attique Rajinikanth, Venkatesan Kadry, Seifedine Department of Software Engineering UET Taxila Taxila47050 Pakistan Department of Computing Riphah International University Islamabad Pakistan Department of Computer Science HITEC University Taxila Pakistan Department of Computer Science and Engineering Division of Research and Innovation Saveetha School of Engineering SIMATS Chennai602105 India Department of Applied Data Science Noroff University College Kristiansand4612 Norway Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon

ISBN: (纸本)9783031379390

Serious security and privacy problems have arisen as a result of significant advancements in the creation of deepfakes. Attackers can easily replace a person’s face with the target person’s face in an image using sophisticated Deep learning (DL) algorithms to spoof their identity. Deepfakes detection algorithms have been proposed in response to the growing concerns about the potential harm caused by deepfakes. However, a reliable deepfakes detector that can keep up with contemporary deepfakes creation techniques is required. In this work, we have proposed an end-to-end methodology for detecting manipulated visual content. We used multiple CNN models i.e., ResNet18, ResNet50, DenseNet65, DenseNet77, and DenseNet100 along with the SVM classifier to compute effective cues from the input facial faces to distinguish between actual and altered content. We have also applied the concept of transfer learning to solve the issue of model over-fitting and improve generalizability against different manipulation algorithms. A comparison study is carried out to evaluate the performance of several feature extractors. Through thorough experiments performed using the Deepfakes Detection Challenge dataset, our results demonstrated that DenseNet100 surpasses the other CNN models by better recognizing deepfakes. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Learning algorithms

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3D Skull Completion via Two-stage Conditional Diffusion-Based Signed Distance Fields

3D Skull Completion via Two-stage Conditional Diffusion-Base...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Liu, Zhenhong Ru, Xudong Wang, Xingce Wu, Zhongke Zhu, Yi-Cheng Zhang, Chong Frangi, Alejandro F. Beijing Normal University School of Artificial Intelligence Beijing China Peking Union Medical College Hospital Department of Neurology Beijing China Bioimage Analysis Barcelona Spain School of Engineering University of Manchester Christabel Pankhurst Institute Division of Informatics Imaging and Data Sciences School of Health Sciences Department of Computer Science United Kingdom Department of Cardiovascular Sciences Department of Electrical Engineering Leuven Belgium Alan Turing Institute London United Kingdom

ISBN: (纸本)9798350386226

A fast and fully automatic design of 3D cranial implants is highly desired in cranioplasty, and is key to the treatment of skull trauma. We have defined the repair of skull defects as a 3D shape completion task by proposing a two-stage diffusion model based on the representation of 3D shapes using signed distance function (SDF). Specifically, we design a diffusion model conditioned on partial shapes, we compress the 3D shape into a compact latent representation using the encoder in the vector quantized variational autoencoder (VQ-VAE) and learn the diffusion model based on this compressed discrete representation. Encoding the latent space with the autoencoder can achieve high-quality 3D cranial shape completion. In order to accurately capture local and fine-grained shape details, the training data is geometrically encoded from a compactly learned code-book. The two-stage diffusion generator with a coarse-to-fine approach possesses precise and expressive structural modeling capabilities to ensure the supplementation of detailed geometric information. Experimental results verified sufficient expressiveness of our model with generating high-fidelity results with fine-grained local details, outperforming the state-of-the-art methods. © 2024 IEEE.

关键词： Signal encoding

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Automatic Plane Pose Estimation for Cardiac Left Ventricle Coverage Estimation via Deep Adversarial Regression Network

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第9期5卷 4738-4752页

作者： Zhang, Le Bronik, Kevin Piechnik, Stefan K. Lima, Joao A. C. Neubauer, Stefan Petersen, Steffen E. Frangi, Alejandro F. University of Birmingham School of Engineering College of Engineering and Physical Sciences BirminghamB15 2TT United Kingdom University of Oxford Department of Engineering Science OxfordOX1 3PJ United Kingdom Division of Cardiovascular Medicine OxfordOX3 9DU United Kingdom Johns Hopkins University School of Medicine Division of Cardiology BaltimoreMD21287 United States Queen Mary University of London Centre for Cardiovascular Medicine The William Harvey Research Institute LondonE1 1BB United Kingdom Barts Health Nhs Trust Barts Heart Center LondonE1 1BB United Kingdom The University of Manchester Division of Informatics Imaging and Data Science School of Health Sciences The Department of Computer Science School of Engineering ManchesterM13 9PL United Kingdom

Accurate segmentation of the ventricles plays a crucial role in determining cardiac functional parameters such as ventricular volume, ventricular mass, and ejection fraction. However, poor image quality, such as inadequate coverage of the left ventricle (LV) and right ventricle (RV) in cardiac magnetic resonance (CMR) image sequences, can significantly affect the assessment of cardiac function. This study investigates issues related to missing or corrupted imaging planes, which often lead to incomplete ventricle coverage. To address the challenge of estimating ventricle coverage in CMR images regardless of variations in imaging parameters such as device type, magnetic field strength, and protocol execution, we introduce a novel convolutional neural network (CNN) based on adversarial learning. Additionally, we integrate supplementary information (e.g., cross-view image data) as privileged information to enhance the interpretability of our model's predictions and identify potential biases or inaccuracies. This research represents the first attempt to automatically estimate ventricular coverage by identifying missing slices and plane orientations in CMR images using a dataset-agnostic approach. The effectiveness of the proposed model is demonstrated through the evaluation of datasets from three diverse and sizable image acquisition cohorts, demonstrating superior performance compared to existing methods. © 2020 IEEE.

关键词： Image quality

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