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International Conference on Wireless and Telematics (ICWT)

作者： Helmi Ahmad Khaderani Ishlah Muhammadi Lutfi Razan Muhammad Adi Sucipto Luthfi Restu Kusumastuti Ade Nurhayati Syaiful Ahdan Nana Rachmana Syambas Dept. of Telecommunication Engineering Bandung Institute of Technology Bandung Indonesia School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia Faculty of Engineering & Computer Science Universitas Teknokrat Indonesia Lampung Indonesia Faculty of Engineering Telkom University Jakarta Barat Indonesia

Pending interest table (PIT) is one of the data structures on each named data network router. The speed of delivery of interest as well as the interest served at PIT are several important parameters for measuring PIT performance. This research was conducted to analyze the performance of PIT by measuring the round trip time (RTT) of sending data and sending interests from consumers to producers on the Palapa Ring Sumatra topology. Testing is carried out using miniNDN by varying the length of the prefix and the number of interests sent. From the simulation and results of the analysis that has been carried out, it is known that the correlation between the length of the prefix and RTT is directly proportional. The longer the prefix of an interest packet, the higher the RTT value. Apart from the simulation results, the number of interest packets sent is the same as those received at each node.

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Video Streaming QoS Analysis with TIPHON Standard NDNts

Video Streaming QoS Analysis with TIPHON Standard NDNts

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International Conference on Wireless and Telematics (ICWT)

作者： I Made Aditya Putra J.D Lisdatul Alifa Muhammad Hilmi Fawwaz Nissha Kinaya Mauren Sibayak Ratna Mayasari Hasan Nur Arifin Syaiful Ahdan Ridha Muldina Negara Nana Rachmana Syambas Dept. of Telecommunication Engineering Bandung Institute of Technology Bandung Indonesia School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia School of Electrical Engineering Telkom University Bandung Indonesia Faculty of Engineering & Computer Science Universitas Teknokrat Indonesia Lampung Indonesia

Named data networks are a new concept in network architecture that can process data transmission by name. This concept can be utilized in the case of video streaming. TIPHON is a standard used to support streaming multimedia activities. According to TIPHON, three factors-delay, packet loss, and throughput-can influence how a stream behaves. This study's goal is to evaluate the quality of service (QoS) of video streaming services using an NDN network that adheres to the TIPHON NDN standard. Several things can affect throughput, such as bottlenecks on the computer or network device used.

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Performance evaluation of TRNG based on Ring Oscillators implemented on FPGA

Performance evaluation of TRNG based on Ring Oscillators imp...

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International Young Engineers Forum (YEF-ECE)

作者： Guilherme Matos Pedro Correia José Rocha João Casaleiro Luis Oliveira Dept. of Eletrical Telecomunication and Computers Engineering ISEL – Inst. Sup. Eng. Lisboa Lisbon Portugal Dept. of Eletrical Telecomunication and Computers Engineering ISEL. Dept. Eletrical and Computer Engineering NOVA School of Science and Tecnology Center for Technologies and System(CTS) -UNINOVA Portugal

Nowadays cryptography is increasingly more important, not only to keep communications safe but also to authenticate users and devices alike. A few years ago, most of the algorithms and cryptographic systems available to the public were implemented in software. However currently one of the best ways to tackle this problem is using specific hardware to produce a random output. In this paper we present an investigation to different True Random Number Generator (TRNG) architectures, implemented in a Field Programmable Gate Array (FPGA), based on ring oscillators and the results of a specific set of tests developed to verify the resilience and randomness characteristics of random number generators, NIST SP800-22R. Our objective is to create a dynamic entropy generator based on standard Ring Oscillators using the lowest number of resources possible. The tests ran using a 10 Mbytes sample stored in a .txt file received from the FPGA with the size of 1 kbytes and 7000 bitstreams (number of times the 1 kbytes will be tested). The best performing configuration only uses 160 gates. But it is possible to reduce by almost a third the number of gates and still get on the 5th best performing configuration with a higher efficiency.

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Hierarchical Context Transformer for Multi-level Semantic Scene Understanding

arXiv

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arXiv 2025年

作者： Hao, Luoying Hu, Yan Yue, Yang Wu, Li Fu, Huazhu Duan, Jinming Liu, Jiang Research Institute of Trustworthy Autonomous Systems Dept. of Computer Science and Engineering Southern University of Science and Technology China School of Computer Science University of Birmingham United Kingdom Division of Informatics Imaging and Data Sciences School of Health Sciences University of Manchester United Kingdom MGI Tech Co. Ltd. China Singapore

A comprehensive and explicit understanding of surgical scenes plays a vital role in developing context-aware computer-assisted systems in the operating theatre. However, few works provide systematical analysis to enable hierarchical surgical scene understanding. In this work, we propose to represent the tasks set [phase recognition → step recognition → action and inst.ument detection] as multi-level semantic scene understanding (MSSU). For this target, we propose a novel hierarchical context transformer (HCT) network and thoroughly explore the relations across the different level tasks. Specifically, a hierarchical relation aggregation module (HRAM) is designed to concurrently relate entries inside multi-level interaction information and then augment task-specific features. To further boost the representation learning of the different tasks, inter-task contrastive learning (ICL) is presented to guide the model to learn task-wise features via absorbing complementary information from other tasks. Furthermore, considering the computational costs of the transformer, we propose HCT+ to integrate the spatial and temporal adapter to access competitive performance on substantially fewer tunable parameters. Extensive experiments on our cataract dataset and a publicly available endoscopic PSI-AVA dataset demonstrate the outstanding performance of our method, consistently exceeding the state-of-the-art methods by a large margin. The code is available at https://***/Aurorahao/HCT. Copyright © 2025, The Authors. All rights reserved.

关键词： Transplantation (surgical)

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Enhanced Outsourced and Secure Inference for Tall Sparse Decision Trees

Enhanced Outsourced and Secure Inference for Tall Sparse Dec...

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IEEE International Conference on Performance, Computing and Communications (IPCCC)

作者： Andrew Quijano Spyros T. Halkidis Kevin Gallagher Kemal Akkaya Nikolaos Samaras Dept. of Computer Science and Engineering New York University New York NY USA Dept. of Applied Informatics University of Macedonia Thessaloniki Greece Nova Lincs Nova School of Science and Technology Lisbon Portugal Advanced Wireless and Security Lab Florida International University Miami FL USA

ISBN: (数字)9798350367942

ISBN: (纸本)9798350367959

A decision tree is an easy-to-understand tool that has been widely used for classification tasks. On the one hand, due to privacy concerns, there has been an urgent need to create privacy-preserving classifiers that conceal the user’s input from the classifier. On the other hand, with the rise of cloud computing, data owners are keen to reduce risk by outsourcing their model, but want security guarantees that third parties cannot steal their decision tree model. To address these issues, Joye and Salehi introduced a theoretical protocol that efficiently evaluates decision trees while maintaining privacy by leveraging their comparison protocol that is resistant to timing attacks. However, their approach was not only inefficient but also prone to side-channel attacks. Therefore, in this paper, we propose a new decision tree inference protocol in which the model is shared and evaluated among multiple entities. We partition our decision tree model by each level to be stored in a new entity we refer to as a "level-site." Utilizing this approach, we were able to gain improved average run time for classifier evaluation for a non-complete tree, while also having strong mitigations against.side-channel attacks.

关键词： Resistance Hands Privacy Protocols Prevention and mitigation Computational modeling Side-channel attacks Data models Timing Decision trees

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Advancing Fetal Surveillance with Physiological Sensing: Detecting Hypoxia in Fetal Sheep

Advancing Fetal Surveillance with Physiological Sensing: Det...

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IEEE SENSORS

作者： Weitao Tang Nhi Tran Nasim Katebi Reza Sameni Gari D. Clifford David Walker Vaishnavi Horlali Callum Taylor Robert Galinsky Faezeh Marzbanrad Electrical and Computer System Engineering Monash University Melbourne Australia The Ritchie Centre Hudson Institute of Medical Research Melbourne Australia Dept. of Biomedical Informatics Emory University Atlanta USA Dept. of Biomedical Engineering Georgia Institute of Technology Emory University Atlanta USA Department of Obstetrics and Gynaecology Monash University Melbourne Australia

ISBN: (数字)9798350363517

ISBN: (纸本)9798350363524

Accurate detection of hypoxia during fetal monitoring is critical for timely intervention and prevention of brain injury. This study investigated hypoxia detection in fetal sheep by developing a classifier to differentiate between baseline, during acute hypoxia (umbilical cord occlusion; UCO), and post-hypoxia recovery (post-UCO), using physiological signals and an XGBoost machine learning model. A multi-modal approach integrating electrocardiogram (ECG), electroencephalogram (EEG), and electromyogram (EMG) signals was compared to a model based on the ECG signal only. We included recordings from 14 fetal sheep, each of which underwent acute hypoxia via a single UCO procedure. Performance of hypoxia detection was measured by Marco F1 score (F1 m ), Accuracy (Acc), and standard deviation of validation error (SDE). Our multi-modal model achieved F1 m = 0.88, Acc = 0.90, while the ECG-based approach achieved F1 m = 0.82, Acc = 0.85. These results indicate that the multi-modal approach was superior to the ECG-only model for overall classification. While ECG and multimodal models correctly classified over 92% and 97% of acute hypoxia intervals, respectively, the classification of post- UCO intervals proved more challenging. The complexity of interpreting physiological signals underscores the need to consider temporal dynamics and physiological context in fetal monitoring.

关键词： Accuracy Machine learning Electrocardiography Hypoxia Brain modeling Feature extraction Electromyography Electroencephalography Complexity theory Biomedical monitoring

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Machine Vision Approach Using Multi Features for Detection of Oil Palm Stem Disease

Machine Vision Approach Using Multi Features for Detection o...

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Advanced engineering and Technologies (ICONNIC), International Conference on

作者： Anindita Septiarini Hamdani Hamdani Encik Akhmad Syaifudin Emy Setyaningsih Damar Nurcahyono Noor Alam Hadiwijaya Dept. of Informatics Mulawarman University Samarinda Indonesia Faculty of Agriculture Mulawarman University Samarinda Indonesia Department of Computer System Engineering Institut Sains Teknologi AKPRIND Yogyakarta Indonesia Dept. of Information Technology Politeknik Negeri Samarinda Samarinda Indonesia

Indonesia is a significant palm oil producer, widely cultivated due to its status as a prominent vegetable oil-producing plant. Palm oil is a highly sought-after crop in agriculture due to its profitability and the demand for substantial quantities of high-quality oil. Early diagnosis of oil palm diseases is crucial for prompt prevention and eradication measures essential for maintaining high-quality palm oil production. Hence, there is a requirement for a machine vision-based method to classify diseases on plant stems. The proposed method consists of several main processes: pre-processing, feature extraction, and classification. Several machine learning algorithms and feature extraction using color and texture features were performed to develop a machine vision for detecting oil palm stem disease. It applied cross-validation with a K-fold value of 10. Performance evaluation was carried out using three parameters: precision, recall, and accuracy. The Linear Discriminant Analysis (LDA) method achieves the optimum results in testing scenarios combining color features based on HSV color space and textures features, achieving an accuracy value of 90.50%.

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Mental Health Professionals' Insights on Digital Health Solutions for Anxiety in the UAE

Mental Health Professionals' Insights on Digital Health Solu...

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IEEE International Requirements engineering Conference Workshops (REW)

作者： Mariam Al Meqbaali Sofia Ouhbi Reem K Jan Leena Amiri Fadwa Al Mugaddam Mohamed Adel Serhani Nazar Zaki Dept. Computer Science & Software Engineering CIT UAEU Al Ain UAE Dept. Information Technology Uppsala University Uppsala Sweden Mohammed Bin Rashid University of Medicine and Health Sciences Dubai UAE Dept. Psychiatry and Behavioral Science CMHS UAEU Al Ain UAE College of Computing and Informatics University of Sharjah Sharjah UAE

ISBN: (数字)9798350395518

ISBN: (纸本)9798350395525

In recent years, anxiety disorders have become more prevalent worldwide, leading to an increased demand for mental healthcare services. Barriers such as stigma and cost, limit access to these services, prompting the exploration of alternative solutions. Digital technology shows significant promise in improving access to mental healthcare services. This study aims to explore the preferences, and concerns of Mental Health Professionals (MHPs) regarding digital health solutions for self-management of anxiety disorders in the UAE via an online survey. Participation was voluntary and anonymous, with 18 MHPs taking part. The survey included 12 questions covering demographics and key design elements. Most respondents displayed positive attitudes toward integrating digital anxiety solutions in the UAE, providing feedback and highlighting considerations. 61% endorsed the use of digital technology in anxiety disorder treatment, while 94% favored mindfulness exercises for anxiety management. 72% expressed interest in utilizing technology for therapy. Notable concerns raised included privacy and dependency. Guided by MHPs insights, we propose a conceptual framework for the design of digital anxiety solutions in the UAE. This framework prioritizes cultural appropriateness and language accessibility, offering solutions in Arabic and English. It encompasses diverse features, including a blended approach, treatment approaches, and relaxation techniques, to address anxiety disorders and ensures user safety through risk assessment and management. This framework can assist developers of digital anxiety solutions by highlighting the features and elements that MHPs deem most important.

关键词： Surveys Privacy Anxiety disorders Medical treatment Mental health Safety Electronic healthcare

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Multi-Person Key Points Detection for Abnormal Human Behavior Analysis Using The ConvLSTM-AE Method

Multi-Person Key Points Detection for Abnormal Human Behavio...

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International Conference on computer engineering, Network and Intelligent Multimedia (CENIM)

作者： Sofia Ariyani Eko Mulyanto Yuniarno Mauridhi Hery Purnomo Dept. Of Electrical Engineering Dept. of Electrical Engineering Faculty of Intelligent Electrical and Informatics Technology (F-Electics) Faculty of engineering Institut Teknologi Sepuluh Nopember Universitas Muhammadiyah Jember Surabaya Indonesia Dept. Of Electrical Engineering Dept. of Computer Engineering Faculty of Intelligent Electrical and Informatics Technology (F-Electics) Institut Teknologi Sepuluh Nopember Surabaya Indonesia Department of Electrical Engineering Dept. of Computer Engineering Faculty of Intelligent Electrical and Informatics Technology (F-Electics) Institut Teknologi Sepuluh Nopember University Center of Excellence on Artificial Intelligence for Healthcare and Society Surabaya Indonesia

ISBN: (纸本)9781665476515

The detection of abnormal human behavior is an interesting issue to consider in computer vision. The problem of detecting abnormal activities carried out in this study is a problem formulated in the process of monitoring human activities. In understanding the nature of human activities, a system is needed that is applied to data training which is specifically proposed based on ConvLSTM-AE for the detection of moving objects in motion-based events. Detection of anomalies and localization that arise due to camera jitter and movement of objects in the area. With the unusual motion of the video frame, connected component analysis is exploited to provide bounding boxing to find out whether human movements whose activities are based on human poses will be classified as normal or abnormal in a set of activity data. The learning and training process so that the proposed model can capture different duration of time and more optimal processes.

关键词： Training Location awareness computer vision Surveillance Multimedia systems Computational modeling Estimation

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A Critical Study of Challenges and Risk in Healthcare sector based on Cloud Computing

A Critical Study of Challenges and Risk in Healthcare sector...

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International Conference on Computing and Networking technology (ICCNT)

作者： Somesh Srivastava Manish Kumar Soni Ajay Pratap Dept. of Computer Sci. & Engineering Bansal Inst of Eng. & Technology Lucknow India Amity Institute of Info Technology Amity University Uttar Pradesh Lucknow India

Cloud computing has been critical to the world's modern technological growth. Cloud computing is one of the most advantageous solutions to digital challenges. Security is a major concern in cloud computing technology, and this study tries to investigate cloud computing security concerns and potential solutions. A systematic review was conducted utilizing the databases Scopus, PubMed, Science Direct, and Web of Science. Firstly, this paper discussed the basics of cloud computing environment and mapped that how this technology can be helpful for health care sector. Technical, managerial, security and legal aspects while implementing cloud-based health care has also been observed and discussed. We have also discussed various cloud-based healthcare solutions offered by various companies such as Apple Health Kit, Google Health, AWS e-Health. Paper also presents the workflow of using cloud-based solution in health care sector.

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