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IEEE International Conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Muhammad Adil Mian Ahmad Jan Safayat Bin Hakim Houbing Herbert Song Zhanpeng Jin Deptartment of Computer Science University at Buffalo Buffalo USA College of Computing and Informatics University of Sharjah Sharjah UAE School of Information Systems University of Maryland Baltimore County USA School of Future Technology South China University of Technology Guangdong China And University at Buffalo Buffalo USA

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

In this paper, we focus on addressing the challenges of detecting malicious attacks in networks by designing an advanced Explainable Intrusion Detection System (xIDS). The existing machine learning and deep learning approaches have invisible limitations, such as potential biases in predictions, a lack of interpretability, and the risk of overfitting to training data. These issues can create doubt about their usefulness, and transparency, and decrease the trust of involved stakeholders. To overcome these challenges, we propose an ensemble learning technique called the "EnsembleGuard". This approach uses the predicted outputs of multiple models, including tree-based (LightGBM, GBM, Bagging, XGBoost, CatBoost) and deep learning models such as neural network (LSTM (long short-term memory networks) and GRU (gated recurrent unit), to maintain a balance and achieve trustworthy results. Our work is unique because it combines both tree-based and deep learning models to design an interpretable and explainable meta-model through model distillation. By considering the predictions of all individual models, our neta-model effectively addresses key challenges, and ensures both explainable and reliable results. We evaluate our model using well-known datasets, including UNSW-NB15, NSL-KDD, and CIC-IDS-2017, to assess its reliability against various types of attacks. During analysis, we found that our model outperforms both tree-based models and other comparative approaches when it comes to different kinds of attack scenarios.

关键词： Deep learning Analytical models Accuracy Intrusion detection Training data Predictive models NSL-KDD Reliability Ensemble learning Long short term memory

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PortraitBooth: A Versatile Portrait Model for Fast Identity-Preserved Personalization

PortraitBooth: A Versatile Portrait Model for Fast Identity-...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Xu Peng Junwei Zhu Boyuan Jiang Ying Tai Donghao Luo Jiangning Zhang Wei Lin Taisong Jin Chengjie Wang Rongrong Ji Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University China School of Informatics Xiamen University China Tencent Youtu Lab. School of Intelligence Science and Technology Nanjing University Suzhou China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Recent advancements in personalized image generation using diffusion models have been noteworthy. However, existing methods suffer from inefficiencies due to the requirement for subject-specific fine-tuning. This computationally intensive process hinders efficient deployment, limiting practical usability. Moreover, these methods often grapple with identity distortion and limited expression diversity. In light of these challenges, we propose Portrait-Booth, an innovative approach designed for high efficiency, robust identity preservation, and expression-editable text-to-image generation, without the need for fine-tuning. Por-traitBooth leverages subject embeddingsfrom aface recognition model for personalized image generation without fine-tuning. It eliminates computational overhead and mitigates identity distortion. The introduced dynamic identity preservation strategy further ensures close resemblance to the original image identity. Moreover, PortraitBooth incorporates emotion-aware cross-attention control for diverse facial expressions in generated images, supporting text-driven expression editing. Its scalability enables efficient and high-quality image creation, including multi-subject generation. Extensive results demonstrate superior performance over other state-of-the-art methods in both single and multiple image generation scenarios. Our project page is at https://***.

关键词： Training Limiting Image recognition Image synthesis Scalability Face recognition Text to image

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Challenges in Eye-Gaze Interaction Systems: A Survey 10

Challenges in Eye-Gaze Interaction Systems: A Survey

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10th International Conference on Platform technology and Service, PlatCon 2024

作者： Baharum, Aslina Ismail, Rozita Turumogan, Punitha Ismail, Ismassabah Noor, Noorsidi Aizuddin Mat Deris, Farhana Diana School of Engineering and Technology Sunway University Bandar Sunway Selangor Malaysia College of Computing and Informatics Universiti Tenaga Nasional Putrajaya Campus Selangor Kajang Malaysia Department of Computing and Information Technology Tunku Abdul Rahman University of Management and Technology Penang Malaysia Centre of Foundation Studies Universiti Teknologi MARA Cawangan Selangor Kampus Dengkil Selangor Dengkil Malaysia Mass Appraisal Housing and Planning Research Group Centre for Real Estate Studies Real Estate Department Faculty of Built Environment and Surveying Universiti Teknologi Malaysia Johor Johor Bahru Malaysia Faculty of Social Sciences and Humanities Universiti Teknologi Malaysia Johor Johor Bahru Malaysia

ISBN: (纸本)9798350367874

Eye-gaze, also referred to as eye tracking, enables users to control their computers or communication devices using their eyes as a mouse. Achieving accurate and precise eye-gaze positioning with low-cost devices is a crucial advancement in this field. This paper presents a survey of eye-gaze-based interaction systems, aiming to identify current techniques employed in this domain. Key issues related to eye-gaze-based interaction systems are discussed, along with the challenges they pose and potential future developments. Recent eye-gaze system review methods are also presented. Additionally, the paper provides a summary of research findings on eye-gaze methods, comparing the main phases of eye-gaze-based interaction. Over the past few years, researchers have proposed various methods and solutions to enhance the accuracy and performance of eye-gaze-based systems. These techniques find applications in diverse areas, such as virtual keyboard typing, immersive video technologies, virtual communication, assistive devices, screen control, effortless scrolling, and enriched reading experiences. Overall, eye-gaze-based interaction systems have the potential to enhance user-human experiences by identifying eye-gaze positions or points, thereby improving users' lifestyles and understanding of their machine. © 2024 IEEE.

关键词： Eye movements

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NeRF-Texture: Synthesizing Neural Radiance Field Textures

arXiv

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arXiv 2024年

作者： Huang, Yi-Hua Cao, Yan-Pei Lai, Yu-Kun Shan, Ying Gao, Lin Beijing Key Laboratory of Mobile Computing and Pervasive Device Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences Beijing China VAST ARC Lab Tencent PCG China School of Computer Science & Informatics Cardiff University United Kingdom

Texture synthesis is a fundamental problem in computer graphics that would benefit various applications. Existing methods are effective in handling 2D image textures. In contrast, many real-world textures contain meso-structure in the 3D geometry space, such as grass, leaves, and fabrics, which cannot be effectively modeled using only 2D image textures. We propose a novel texture synthesis method with Neural Radiance Fields (NeRF) to capture and synthesize textures from given multi-view images. In the proposed NeRF texture representation, a scene with fine geometric details is disentangled into the meso-structure textures and the underlying base shape. This allows textures with meso-structure to be effectively learned as latent features situated on the base shape, which are fed into a NeRF decoder trained simultaneously to represent the rich view-dependent appearance. Using this implicit representation, we can synthesize NeRF-based textures through patch matching of latent features. However, inconsistencies between the metrics of the reconstructed content space and the latent feature space may compromise the synthesis quality. To enhance matching performance, we further regularize the distribution of latent features by incorporating a clustering constraint. In addition to generating NeRF textures over a planar domain, our method can also synthesize NeRF textures over curved surfaces, which are practically useful. Experimental results and evaluations demonstrate the effectiveness of our approach. Copyright © 2024, The Authors. All rights reserved.

关键词： Image texture

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Ensuring Zero Trust IoT Data Privacy: Differential Privacy in Blockchain using Federated Learning

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IEEE Transactions on Consumer Electronics 2024年 1-1页

作者： Hussain, Altaf Akbar, Wajahat Hussain, Tariq Bashir, Ali Kashif Dabel, Maryam M. Al Ali, Farman Yang, Bailin School of Electronic and Control Engineering Chang’an University Xi’an China School of Computer Science and Technology and School of Mathematics and Statistics Zhejiang Gongshang University Hangzhou China Department of Computing and Mathematics Manchester Metropolitan University UK Department of Computer Science and Engineering College of Computer Science and Engineering University of Hafr Al Batin Saudi Arabia Department of Applied AI School of Convergence College of Computing and Informatics Sungkyunkwan University Seoul South Korea

In the increasingly digitized world, the privacy and security of sensitive data shared via IoT devices are paramount. Traditional privacy-preserving methods like k-anonymity and ldiversity are becoming outdated due to technological advancements. In addition, data owners often worry about misuse and unauthorized access to their personal information. To address this, we propose a secure data-sharing framework that uses local differential privacy (LDP) within a permissioned blockchain, enhanced by federated learning (FL) in a zero-trust environment. To further protect sensitive data shared by IoT devices, we use the Interplanetary File System (IPFS) and cryptographic hash functions to create unique digital fingerprints for files. We mainly evaluate our system based on latency, throughput, privacy accuracy, and transaction efficiency, comparing the performance to a benchmark model. The experimental results show that the proposed system outperforms its counterpart in terms of latency, throughput, and transaction efficiency. The proposed model achieved a lower average latency of 4.0 seconds compared to the benchmark model’s 5.3 seconds. In terms of throughput, the proposed model achieved a higher throughput of 10.53 TPS (transactions per second) compared to the benchmark model’s 8 TPS. Furthermore, the proposed system achieves 85% accuracy, whereas the counterpart achieves only 49%. IEEE

关键词： Anonymity

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Security System for Digital Land Certificate Based on Blockchain and QR Code Validation in Indonesia

Security System for Digital Land Certificate Based on Blockc...

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Advanced Creative Networks and Intelligent Systems (ICACNIS), International Conference on

作者： Mega Adi Kusuma Parman Sukarno Aulia Arif Wardana Informatics Department School of Computing Telkom University Bandung Indonesia Information and Communication Technology Wroclaw University of Science and Technology Wroclaw Poland

In this research, we developed a land certificate security system based on blockchain technology and a qr code validation scheme to prevent counterfeiting of land certificates and illegal levies in the process of making land certificates in Indonesia. The process of making land certificates is currently still being carried out by involving various related parties so that it requires a complex and long process. This triggers cases of illegal levies carried out by related parties on the grounds of speeding up the process of obtaining land certificates, thereby reducing the transparency aspect. Conventional paper certificates are prone to forgery and easy to lose, besides that electronic certificates designed by the government still use the concept of a centralized system, so they are vulnerable to being hacked. Another problem is the falsification of ownership of land certificates by the land mafia. Therefore, a land certificate security system is needed for the online land certificate data storage process. From the test results, it was found that the system built with the implementation of blockchain technology guarantees data validity, based on verification by smart contracts and consensus mechanisms, and ensures that processes that occur in blockchain-based systems are guaranteed transparency, data is stored globally in a peer-topeer and immutable.

关键词： Hash functions Smart contracts Government Memory QR codes Forgery Recording

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An Investigation on the Impact of Energy Harvesting in Green IoT with Netsims 1

An Investigation on the Impact of Energy Harvesting in Green...

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1st International Conference on Information System and Information technology, ICISIT 2022

作者： Ahmad, Mohd Nabil Iqbal Bin Sajak, Aznida Binti Abu Bakar Ismail, Mohd Nazri Bin Allias, Noormadinah Universiti Kuala Lumpur Miit Computer Engineering Technology Kuala Lumpur Malaysia Universiti Pertahanan Nasional Malaysia Department of Computer Science Kuala Lumpur Malaysia Albukhary International University School of Computing and Informatics Alor Setar Kedah05200 Malaysia

ISBN: (数字)9781665402002

ISBN: (纸本)9781665402002

Real-world applications such as habitat monitoring, air quality, natural disaster prevention, smart agriculture, and smart building are possible due to the utilization of Wireless Sensor Networks (WSNs). However, in most cases, classic WSN nodes are fueled by non-rechargeable batteries with a limited capacity for energy storage. In this paper, a solar energy harvesting implementation is proposed in a wireless sensor network to increase the lifetime of each sensor node. The implementation may also ideally achieve an infinite lifetime. Nevertheless, there are many other obstacles to overcome and consider in solar energy harvestings, such as temperature fluctuations, solar panel conversion efficiency, solar energy forecasting difficulties, power interruptions, and other environmental or natural phenomenon issues. Therefore, the objective of this research is to review the recent literature on energy harvesting for wireless sensor networks focusing on solar ambient light energy. Using NETSIM simulations, the goal of this investigation would be to achieve reliability and energy efficiency in wireless sensor networks through solar energy harvesting. © 2022 IEEE.

关键词： Energy harvesting

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Adaptive Human-Swarm Interaction based on Workload Measurement using Functional Near-Infrared Spectroscopy

arXiv

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arXiv 2024年

作者： Abioye, Ayodeji O. Landowska, Aleksandra Hunt, William Maior, Horia Ramchurn, Sarvapali D. Naiseh, Mohammad Banks, Alec Soorati, Mohammad D. Electronics and Computer Science University of Southampton United Kingdom School of Computer Science University of Nottingham United Kingdom Computing and Informatics Bournemouth University United Kingdom Defence Science and Technology Laboratory United Kingdom

One of the challenges of human-swarm interaction (HSI) is how to manage the operator's workload. In order to do this, we propose a novel neurofeedback technique for the realtime measurement of workload using functional near-infrared spectroscopy (fNIRS). The objective is to develop a baseline for workload measurement in human-swarm interaction using fNIRS and to develop an interface that dynamically adapts to the operator's workload. The proposed method consists of using fNIRS device to measure brain activity, process this through a machine learning algorithm, and pass it on to the HSI interface. By dynamically adapting the HSI interface, the swarm operator's workload could be reduced and the performance improved. © 2024, CC BY-NC-ND.

关键词： Near infrared spectroscopy

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End-to-end dilated convolution network for document image semantic segmentation

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Journal of Central South University 2021年第6期28卷 1765-1774页

作者： XU Can-hui SHI Cao CHEN Yi-nong School of Information Sciences and Technology Qingdao University of Science and TechnologyQingdao 266061China School of Computing Informatics and Decision Systems EngineeringArizona State UniversityTempeAZ 85287-8809USA

Semantic segmentation is a crucial step for document *** this paper,an NVIDIA Jetson Nano-based platform is applied for implementing semantic segmentation for teaching artificial intelligence concepts and *** extract semantic structures from document images,we present an end-to-end dilated convolution network *** convolutions have well-known advantages for extracting multi-scale context information without losing spatial *** model utilizes dilated convolutions with residual network to represent the image features and predicting pixel *** convolution part works as feature extractor to obtain multidimensional and hierarchical image *** consecutive deconvolution is used for producing full resolution segmentation *** probability of each pixel decides its predefined semantic class *** understand segmentation granularity,we compare performances at three different *** fine grained class to coarse class levels,the proposed dilated convolution network architecture is evaluated on three document *** experimental results have shown that both semantic data distribution imbalance and network depth are import factors that influence the document’s semantic segmentation *** research is aimed at offering an education resource for teaching artificial intelligence concepts and techniques.

关键词： semantic segmentation document images deep learning NVIDIA jetson nano

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A Framework for Tele-rehabilitation Gaming System

A Framework for Tele-rehabilitation Gaming System

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Applied Computational Intelligence in Information Systems (ACIIS), International Conference on

作者： Ahmed M. S. Elaklouk Ratna Zuarni Ramli Ibrahim Edris Nena Padilla-Valdez Adel Al Jumaily School of Computing and Informatics Universiti Teknologi Brunei Bandar Seri Begawan Brunei Darussalam College of Computing Informatics and Mathematics Universiti Teknologi MARA Negeri Seremban Malaysia Centre for Communication Universiti Teknologi Brunei Bandar Seri Begawan Brunei Darussalam School of Computing University of Technology Brunei Department of Computer Science & Software Engineering University of Western Australia and ENSTA Bretagne University France

Brain damage resulting from stroke and traumatic brain injury is the primary cause of complicated disability and one of the major causes of mortality in most countries, burdening healthcare systems owing to the growth in the number of brain injury victims. Rehabilitation is required to deal with the brain damage. With fewer therapists available, it is very challenging to manage patients and even motivate them to participate actively in their rehabilitation. Patients have frequently complained that many traditional rehabilitation systems are monotonous and uninteresting. The study’s objective is to propose a telerehabilitation gaming system framework that can serve as guidance for developing an affordable rehabilitation gaming system that can motivate and engage patients and increase rehabilitation effectiveness. The research methodology is based on synthesizing related research and currently available technologies. The proposed framework includes the therapist and patient attached with vital constructs: tailoring tools, instructional content, game characteristics, tailored games, and performance. It is an internet-based communication structure that connects the Rehabilitation Gaming System to the hospital and other care provider networks. The proposed framework is evaluated by a panel of five experts. The results reveal that all experts believe that the proposed framework can serve as a useful guide for creating gaming interventions that can boost the patient’s motivation and engagement and increase the effectiveness of rehabilitation.

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