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共 25770 条记录，以下是101-110 订阅

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Quantum Resistance Blockchain Algorithm using QHF and Novel Consensus Mechanism

Quantum Resistance Blockchain Algorithm using QHF and Novel ...

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2025 IEEE International Students' Conference on Electrical, Electronics and computer Science, SCEECS 2025

作者： Paul, Soumyadip Mazumdar, Mausumi Maitra Chakraborty, Sarit Government College of Engineering & Ceramic Technology Dept. of Information Technology Kolkata700010 India Government College of Engineering and Leather Technology Department of Computer Science and Engineering Kolkata700106 India

ISBN: (纸本)9798331529833

The emergence of quantum computing poses a significant threat to the security of traditional blockchain systems, which rely heavily on classical cryptographic algorithms. To safeguard the integrity and reliability of blockchain technology in the upcoming era, this work proposes a novel quantum-resistant blockchain architecture. This approach integrates a robust consensus mechanism, with advanced quantum cryptographic primitives, including quantum digital signatures based on a Quantum Hash Function (QHF). Incorporating a quantum hash function enhances the blockchain resistance, providing an additional layer of security against quantum attacks. By combining these elements, our method creates a blockchain system fortified against the potential vulnerabilities introduced by quantum computers. The proposed solution leverages the power of QHFs to safeguard transaction integrity, while the consensus mechanism ensures the reliability and decentralization of the network. This paper also delves into the theoretical underpinnings of quantum blockchains, exploring their design, feasibility, and potential applications, emphasizing their promising future, particularly in areas such as decentralized identity authentication. Finally, it contributes to the development of a secure and resilient blockchain ecosystem capable of thriving in the coming era. © 2025 IEEE.

关键词： Quantum computers

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Live Audio Recognition and Text Conversion System 2

Live Audio Recognition and Text Conversion System

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2nd International Conference on Computational Intelligence, Communication technology and Networking, CICTN 2025

作者： Sharma, Pratyaksha Gupta, Sumita Bhavya, Boddu Asmitha Sharma, Khushi Dhir, Saru Gambhir, Sapna Department of Computer Science & Engineering Amity School of Engineering & Technology Amity University UP India Department of Information Technology Amity School of Engineering & Technology Amity University UP India Dept. of Computer Science College of Engineering and Computing George Mason University United States

ISBN: (纸本)9798331530389

With the advancement of technologies, different methods are currently being used for converting spoken language into text. These systems offer a hands-free alternative to traditional input methods, especially for individuals with physical disabilities or those requiring efficient documentation of spoken content. The goal of this study is to review recent advancements in this field and highlight gaps in current research, with a focus on accuracy, language adaptability, ease of use, and the integration of various technologies. We also suggest future directions for enhancing and developing this technology further. © 2025 IEEE.

关键词： Hidden Markov models

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Towards speed-of-sound imaging with conventional ultrasound transducers using laser diode photoacoustic

Towards speed-of-sound imaging with conventional ultrasound ...

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Medical Imaging 2025: Ultrasonic Imaging and Tomography

作者： Bezek, Can Deniz Moradi, Hamid Rohling, Robert Salcudean, Septimiu Goksel, Orcun Department of Information Technology Uppsala University Uppsala Sweden Dept. of Electrical & Computer Eng. University of British Columbia VancouverBC Canada

ISBN: (纸本)9781510686021

Most imaging solutions that utilize ultrasound wave propagation require the projection of temporal signals received by transducer elements into spatial maps. This process, known as beamforming, requires knowledge of the speed-of-sound (SoS) in the medium. Incorrect SoS assumptions lead to aberration artifacts, reducing image quality and limiting clinical usability. SoS is also a novel imaging biomarker for assessing tissue characteristics. In this study, we propose a spatial SoS distribution estimation method using conventional hand-held ultrasound transducers and simple laser-diode based photoacoustics. By identifying the time of flight between photoacoustic point sources and transducer elements and relating these to SoS values along corresponding propagation paths, the underlying SoS maps are reconstructed by solving an inverse problem. We validate our method through numerical simulations and ex vivo experiments. Numerical phantoms are successfully reconstructed under various noise levels and numbers of photoacoustic sources. In ex vivo experiments with chicken breast, the estimated SoS value is consistent with results reported in the literature. The proposed approach offers a low-cost, compact solution for photoacoustics-based SoS estimation in various clinical applications, such as breast and intra-operative prostate imaging, both for diagnosis and for improving image quality of acoustic-based modalities, including photoacoustic imaging. © 2025 SPIE.

关键词： Ultrasonic propagation

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Data Generation in Healthcare Using Constraint Satisfaction Parameters (CSP) 2

Data Generation in Healthcare Using Constraint Satisfaction ...

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2nd International Conference on Advanced Innovations in Smart Cities, ICAISC 2025

作者： Al-Barhamtoshy, Hassanin M. Himdi, Tarik King Abdulaziz University Information Technology Dept. Faculty of Computing Jeddah Saudi Arabia Jeddah International College Computer Science Dept. Jeddah Saudi Arabia

ISBN: (纸本)9798331506995

In this research, we examine the synthetic creation, alteration, and enhancement of data through Generative Artificial Intelligence (GAI). This inaugural study focused on generating healthy data via GAI for analysis and detection of chronic diseases (Depression, Stress, and Anxiety). Following the generation process using this deep learning approach, we analyze and visualize synthetic data through various metrics. The outcomes of this methodology are expected to yield novel insights into data generation concerning pressure-related diseases (depression, stress, and anxiety). Integrating high-quality synthetic data is anticipated to improve the production of financial transaction data. Finally, the proposed model is evaluated, and the results of the initial GAI model are generated. In this paper, we introduce a specialized generative Transformer language model that has been pre-trained on an extensive collection of medical constraint satisfaction parameters. We assess generation across medical natural language processing tasks and show that it surpasses existing models in most evaluations. Additionally, we offer a comprehensive analysis of the model's limitations by detailing numerous errors and challenges it faces. The mean scores for the produced descriptive data and shape pair trends in continuous columns were around 0.99 and 0.92, respectively. Conversely, the average scores for the categorical text generated in the initial region were notably high. This highlights the GAI model utilized in implementing each method. Our evaluation revealed that a majority of studies employ synthetic data generators to (i) reduce the costs and duration linked to clinical trials for rare diseases, (ii) enhance the predictive performance of AI models in personalized medicine, and (iii) provide researchers with access to high-quality, representative multimodal datasets while protecting sensitive patient information, among other advantages. Synthetic data generators based on deep learn

关键词： Theranostics

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HLSPilot: LLM-based High-Level Synthesis 24

HLSPilot: LLM-based High-Level Synthesis

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43rd International Conference on computer-Aided Design, ICCAD 2024

作者： Xiong, Chenwei Liu, Cheng Li, Huawei Li, Xiaowei SKLP Institute of Computing Technology Chinese Academy of Sciences Beijing China Dept. of Computer Science University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798400710773

Large language models (LLMs) have catalyzed an upsurge in automatic code generation, garnering significant attention for register transfer level (RTL) code generation. Despite the potential of RTL code generation with natural language, it remains error-prone and limited to relatively small modules because of the substantial semantic gap between natural language expressions and hardware design intent. In response to the limitations, we propose a methodology that reduces the semantic gaps by utilizing C/C++ for generating hardware designs via High-Level Synthesis (HLS) tools. Basically, we build a set of C-to-HLS optimization strategies catering to various code patterns, such as nested loops and local arrays. Then, we apply these strategies to sequential C/C++ code through in-context learning, which provides the LLMs with exemplary C/C++ to HLS prompts. With this approach, HLS designs can be generated effectively. Since LLMs still face problems in determining the optimized pragma parameters precisely, we have a design space exploration (DSE) tool integrated for pragma parameter tuning. Furthermore, we also employ profiling tools to pinpoint the performance bottlenecks within a program and selectively convert bottleneck components to HLS code for hardware acceleration. By combining the LLM-based profiling, C/C++ to HLS translation, and DSE, we have established HLSPilot—the first LLM-enabled high-level synthesis framework, which can fully automate the high-level application acceleration on hybrid CPU-FPGA architectures. According to our experiments on real-world application benchmarks, HLSPilot achieve comparable performance in general and can even outperform manually crafted counterparts, thereby underscoring the substantial promise of LLM-assisted hardware designs. © 2024 Copyright is held by the owner/author(s).

关键词： High level synthesis

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Advancing Environmental Sustainability: Renewable Energy Transition, Carbon Footprint Reduction, and Emission Control Innovations 22

Advancing Environmental Sustainability: Renewable Energy Tra...

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22nd International Learning and technology Conference, L and T 2025

作者： Hussein, Aziza I. Alghamdi, Roba W. Al-Amoudi, Amal Abdullah Altahawi, Omar Hisham Kabbaja, Narjisse Mabrook, Mohamed Mourad Lotfy, Mostafa Wageh Effat University Electrical & Computer Eng. Dept. Jeddah Saudi Arabia Beni-Suef University Faculty of Navigation Science & Space Technology Space Communication Dept. Beni-Suef Egypt Beni-Suef University Faculty of Technology and Education Dept. of Process Control Technology Beni-Suef Egypt

ISBN: (纸本)9798331532215

Combating climate change and promoting environmental sustainability necessitate a dedicated emphasis on renewable energy sources and lowering carbon footprints. Shifting from fossil fuels to renewable resources, such as solar, wind, biomass, and geothermal energy, is imperative. Equally important is enhancing energy efficiency across industries, transportation, buildings, and household appliances to reduce overall energy consumption and greenhouse gas emissions. Technological advancements, such as improved energy storage and smart grid systems, are essential for boosting the efficiency and dependability of renewable energy sources. Strategies to lower carbon footprints include transitioning to clean transportation, optimizing land use, reducing waste, recycling, promoting sustainable agriculture, and implementing carbon offset initiatives. Public education, corporate responsibility, and personal environmental awareness play vital roles in supporting carbon reduction. By combining sustainable energy practices with carbon reduction measures, we can collectively work towards curbing climate change and fostering a sustainable future. Additionally, utilized techniques to address the problem of high air pollution during vehicle cold starts. We modeled catalytic converter dynamics in MATLAB Simulink, using simplifying assumptions. One significant assumption involved treating the catalytic converter as a Continuous Stirred-Tank Reactor (CSTR), which allowed us to model its temperature uniformly, without accounting for temperature changes along its length. This approach is aimed at saving lives and reducing costs by effectively minimizing air pollution. © 2025 IEEE.

关键词： Clean energy

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Dual-Domain Feature-Guided Task Alignment for Enhanced Small Object Detection

Dual-Domain Feature-Guided Task Alignment for Enhanced Small...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Guo, Fangrui Wu, Junwei Zhang, Quan Dept. Master of Engineering University of California Los Angeles Los Angles United States Dept. Computer Science University of Liverpool Liverpool United Kingdom Dept. School of advanced technology Xi'an Jiaotong-Liverpool University Suzhou China

ISBN: (纸本)9798350368741

Small object detection is a critical challenge in Unmanned Aerial Vehicles (UAVs) due to the limited pixel representation of small objects and the impact of successive pooling operations, which frequently results in the disappearance of small objects within intricate backgrounds. To tackle this issue, we propose the Small Object Enhancement Pyramid (SOEP) module, which first transforms feature representations (i.e., in the spatial domain) into the frequency domain to better capture small objects typically characterized by high-frequency components. These feature representations are then fused in the spatial domain using a frequency-based attention map, enhancing small object representations by integrating information from both complementary domains. Furthermore, we introduce a Task Aligned Head (TAH) that integrates classification and localization tasks interactively, reducing the misalignment that occurs when these tasks are learned independently, particularly in the context of small objects. Experimental results on the Visdrone dataset verify that our proposed method (D2FTA) outperforms the baseline method by 12.7%, 14.19% on mAP0.5 and mAP0.5:0.95. © 2025 IEEE.

关键词： frequency domain small object detection spatial domain

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Decomposition Algorithm for Security-Constrained Unit Commitment with AC Optimal Power Flows

Decomposition Algorithm for Security-Constrained Unit Commit...

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2025 IEEE Texas Power and Energy Conference, TPEC 2025

作者： Renl, Weihang Li, Mingze Fan, Lei Guan, Yongpei University of Florida Dept. of Industrial and Systems Engineering FL United States University of Houston Dept. of Electrical and Computer Engineering TX United States University of Houston Engineering Technology TX United States

ISBN: (纸本)9798331541125

This paper presents a comprehensive approach to solving the security-constrained unit commitment with alternating current power flows (SCUC-ACPF) problem in contemporary power systems. We introduce algorithms that decompose the problem into subproblems suitable for specialized solvers, so that the large-scale mixed-integer nonlinear program-ming SCUC-ACPF problem can be solved. In case studies, we validate the efficiency and effectiveness of our algorithms on synthetic and industry-scale power system networks. © 2025 IEEE.

关键词： Lagrange multipliers

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Advancing Wi-Fi Intrusion Detection Systems with Machine Learning Techniques for Enhanced Classification of Wireless Attacks

Advancing Wi-Fi Intrusion Detection Systems with Machine Lea...

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International Conference on Advancements in Computational Sciences (ICACS)

作者： Hasseeb Ahmad Bhutta Muhammad Talha Jahangir Areeb Ahmad Bhutta Computer Science Dept. National University of Sciences and Technology Islamabad Pakistan Computer Science Dept. MNS University of Engineering and Technology Multan Pakistan Computer Science Dept. Information Technology University Lahore Pakistan

ISBN: (数字)9798331533038

ISBN: (纸本)9798331533045

WiFi usability and scalability have contributed to an improvement in human living standards. However, this has also led to increased vulnerabilities and attack vectors within WiFi networks. Authentic users can face network failures and denial of service caused by attacks on wireless networks by some third-party. Wireless Intrusion Detection System (IDS) is needed to detect wireless attacks. Main of objective of wireless IDS is to monitor network traffic and classify if it is an attack or normal traffic. Modern anomaly based wireless intrusion detection systems use machine learning (ML) to learn from previous attacks in the dataset to learn patterns of the attack. Although this method is effective, but there is also downside to this approach. The time required to train and test these ML models are exceedingly high along with high computational costs. Big data is emerging technologies that is getting advanced day by day, due to which, network technology is increasing rapidly. For that reason, we need to discuss the issue, that is high computational costs. In our proposed solution, we used our custom oversampling technique BBOT (Balanced Boost Oversampling Technique) to rectify class imbalance, generating artificial samples for the classes, enhancing model performance. The results of the experiments show that the combination of feature selection, BBOT, and Decision Tree outperformed all other classifiers with reduced computational overhead, providing a practical and efficient solution for real-time intrusion detection in WiFi networks. When compared to XGBoost, the provided solution achieves a similar level of accuracy, but with significantly reduced training time.

关键词： Wireless communication Computational modeling Wireless networks Intrusion detection Machine learning Big Data Feature extraction Vectors Computational efficiency Wireless fidelity

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Benefits of Machine Learning Explanations: Improved Learning in an AI-assisted Sequence Prediction Task 25

Benefits of Machine Learning Explanations: Improved Learning...

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30th International Conference on Intelligent User Interfaces, IUI 2025

作者： Liang, Yu Collaris, Dennis Willemsen, Martijn C. Van Wijk, Jarke J. Human-Technology Interaction Eindhoven University of Technology Eindhoven Netherlands Dept. Mathematics and Computer Science Eindhoven University of Technology Eindhoven Netherlands Jheronimus Academy of Data Science Den Bosch Netherlands

ISBN: (纸本)9798400713064

Research in Explainable AI (XAI) has shown that explanations can improve users' understanding of AI models, improve user performance and potentially reduce overreliance on AI predictions. However, this is mostly evaluated by static rather than dynamic measures, and the role of XAI on learning over trials is rarely studied. In this study, we use a context-free sequence prediction task, in which 458 participants predict the next symbol in a fixed sequence (with some noise) over 80 trials. We compare performance with AI and XAI advice against no AI support, and subsequently we test for learning by taking away the AI support after 40 trials (i.e., a reversal study design). Our results show that users learn faster with XAI than with AI without explanations or no AI and are better able to recover in performance from the removal of AI. However, the benefits of XAI on learning are much smaller for more difficult tasks. This work demonstrates the benefits of repeated measures user studies and multilevel modeling to better understand learning processes in XAI. It also shows the potential of AI explanations to help users to learn and poses XAI design suggestions to support learning in human-AI collaboration. © 2025 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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