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2024 IEEE International Conference on Big Data, BigData 2024

作者： Hossain Faruk, Md Jobair Raya, Pratusha Siam, Md Kamrul Cheng, Jerry Q. Shahriar, Hossain Cuzzocrea, Alfredo Bringas, Pablo Garcia New York Institute of Technology Department of Computer Science United States Kennesaw State University Department of Software Engineering United States University of West Florida Center for Cybersecurity United States University of Deusto D4K Group Bilbao Spain University of Calabria IDEA Lab Rende Italy University of Paris City Department of Computer Science Paris France

ISBN: (纸本)9798350362480

The Internet has opened the floor to stakeholders by redefining the way of organizing, communicating, and collaborating that was initiated by the Web's development. The advancement of the World Wide Web is an outright phenomenon and significant and we witnessed the evolution of the Web. As decentralized technologies continue to gain traction, Web3, or the decentralized internet, has emerged as a promising approach to enable a more secure, transparent, and privacy-preserved digital landscape. In this paper, we thoroughly conduct a systematic study to explore the challenges and opportunities encountered by blockchain developers in the context of decentralized applications (dApps) in Web3. We analyze a set of peer-reviewed research articles, whitepapers, and technical reports and present an in-depth understanding of the current state of Web3 development and its implications. Our finding indicates the opportunities that Web3 can facilitate, such as expanded use cases, enhanced security and privacy, decentralized infrastructure, and the potential for enabling inclusive development resources for blockchain developers. Additionally, we highlight various challenges that blockchain developers deal with including scalability, security, privacy, interoperability, and the need for standardized tools and frameworks along with various challenges in the software development lifecycle (SDLC). While there are significant challenges to overcome, the potential benefits of Web3 are substantial and could lead to a more inclusive, secure, and transparent digital ecosystem. Furthermore, we emphasize the importance of continued research, collaboration, and innovation among stakeholders to address the identified challenges and capitalize on Web3's opportunities. © 2024 IEEE.

关键词： Decentralized finance

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LEARNING CONTINUOUS NORMALIZING FLOWS FOR FASTER CONVERGENCE TO TARGET DISTRIBUTION VIA ASCENT REGULARIZATIONS 11

LEARNING CONTINUOUS NORMALIZING FLOWS FOR FASTER CONVERGENCE...

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11th International Conference on Learning Representations, ICLR 2023

作者： Chen, Shuangshuang Ding, Sihao Karayiannidis, Yiannis Björkman, Mårten School of Electrical Engineering and Computer Science Royal Institute of Technology Sweden AI Lab Volvo Car Corporation Sweden Department of Automatic Control Lund University Sweden

Normalizing flows (NFs) have been shown to be advantageous in modeling complex distributions and improving sampling efficiency for unbiased *** this work, we propose a new class of continuous NFs, ascent continuous normalizing flows (ACNFs), that makes a base distribution converge faster to a target *** solving such a flow is non-trivial and barely possible, we propose a practical implementation to learn flexibly parametric ACNFs via ascent regularization and apply it in two learning cases: maximum likelihood learning for density estimation and minimizing reverse KL divergence for unbiased sampling and variational *** learned ACNFs demonstrate faster convergence towards the target distributions, therefore, achieving better density estimations, unbiased sampling and variational approximation at lower computational ***, the flows show to stabilize themselves to mitigate performance deterioration and are less sensitive to the choice of training flow length T. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Maximum likelihood estimation

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An Iterative Optimizing Framework for Radiology Report Summarization With ChatGPT

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第8期5卷 4163-4175页

作者： Ma, Chong Wu, Zihao Wang, Jiaqi Xu, Shaochen Wei, Yaonai Liu, Zhengliang Zeng, Fang Jiang, Xi Guo, Lei Cai, Xiaoyan Zhang, Shu Zhang, Tuo Zhu, Dajiang Shen, Dinggang Liu, Tianming Li, Xiang Northwestern Polytechnical University School of Automation Xi'an710072 China University of Georgia School of Computing AthensGA30602 United States Northwestern Polytechnical University School of Computer Science Xi'an710072 China Massachusetts General Hospital Department of Radiology BostonMA02114 United States University of Electronic Science and Technology of China Clinical Hospital of Chengdu Brain Science Institute MOE Key Lab for Neuroinformation School of Life Science and Technology Chengdu611731 China The University of Texas at Arlington Department of Computer Science and Engineering ArlingtonVA76019 United States ShanghaiTech University School of Biomedical Engineering Shanghai201210 China Shanghai United Imaging Intelligence Company Ltd. Department of Research and Development Shanghai200030 China Shanghai Clinical Research and Trial Center Shanghai201210 China

The 'Impression' section of a radiology report is a critical basis for communication between radiologists and other physicians. Typically written by radiologists, this part is derived from the 'Findings' section, which can be laborious and error-prone. Although deep-learning-based models, such as bidirectional encoder representation from transformers (BERT), have achieved promising results in automatic impression generation (AIG), such models often require substantial amounts of medical data and have poor generalization performance. Recently, large language models (LLMs) like Chat Generative Pre-trained Transformer (ChatGPT) have shown strong generalization capabilities and performance, but their performance in specific domains, such as radiology, remains under-investigated and potentially limited. To address this limitation, we propose ImpressionGPT, leveraging the contextual learning capabilities of LLMs through our dynamic prompt and iterative optimization algorithm to accomplish the AIG task. ImpressionGPT initially employs a small amount of domain-specific data to create a dynamic prompt, extracting contextual semantic information closely related to the test data. Subsequently, the iterative optimization algorithm automatically evaluates the output of LLMs and provides optimization suggestions, continuously refining the output results. The proposed ImpressionGPT model achieves superior performance of AIG task on both the Medical Information Mart for Intensive Care - Chest X-ray database (MIMIC-CXR) and Open Access Biomedical Image Search Engine (OpenI) datasets without requiring additional training data or fine-tuning the LLMs. This work presents a paradigm for localizing LLMs that can be applied in a wide range of similar application scenarios, bridging the gap between general-purpose LLMs and the specific language processing needs of various domains. © 2020 IEEE.

关键词： Semantics

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Giving AI Personalities Leads to More Human-Like Reasoning

arXiv

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arXiv 2025年

作者： Nighojkar, Animesh Moydinboyev, Bekhzodbek Duong, My Licato, John Lab Department of Computer Science and Engineering University of South Florida TampaFL United States

In computational cognitive modeling, capturing the full spectrum of human judgment and decision-making processes, beyond just optimal behaviors, is a significant challenge. This study explores whether Large Language Models (LLMs) can emulate the breadth of human reasoning by predicting both intuitive, fast System 1 and deliberate, slow System 2 processes. Unlike traditional AI research focused on optimizing accuracy, this paper investigates the potential of AI to mimic diverse reasoning behaviors across a human population, addressing what we call the full reasoning spectrum problem. We designed reasoning tasks using a novel generalization of the Natural Language Inference (NLI) format to evaluate LLMs’ ability to replicate human reasoning. The questions were crafted to elicit both System 1 and System 2 responses. Human responses were collected through crowd-sourcing and the entire distribution was analyzed and modeled, rather than just the majority of the answers. We used personality-based prompting inspired by the Big Five personality model to elicit AI responses reflecting specific personality traits, capturing the diversity of human reasoning, and exploring how personality traits influence LLM outputs. Combined with genetic algorithms to optimize the weighting of these prompts, this method was tested alongside traditional machine learning models. The results show that LLMs can mimic human response distributions, with open-source models like Llama and Mistral unexpectedly outperforming proprietary GPT models. Personality-based prompting, especially when optimized with genetic algorithms, significantly enhanced LLMs’ ability to predict human response distributions, suggesting that capturing suboptimal, naturalistic reasoning may require modeling techniques incorporating diverse reasoning styles and psychological profiles. The study concludes that personality-based prompting combined with genetic algorithms is promising for enhancing AI’s human-ness in reasoning, pr

关键词： Genetic algorithms

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Improving Method of Anomaly Detection Performance for Industrial IoT Environment

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computers, Materials & Continua 2022年第9期72卷 5377-5394页

作者： Junwon Kim Jiho Shin Ki-Woong Park Jung Taek Seo Department of Information Security Engineering Gachon UniversitySeongnam13120Korea Police Science Institute Korea National Police UniversityAsan31539Korea SysCore Lab. Sejong UniversitySeoul05006Korea Department of Computer Engineering Gachon UniversitySeongnam13120Korea

Industrial Control System(ICS),which is based on Industrial IoT(IIoT),has an intelligent mobile environment that supports various mobility,but there is a limit to relying only on the physical security of the ICS *** to various threat factors that can disrupt the workflow of the IIoT,machine learning-based anomaly detection technologies are being presented;it is also essential to study for increasing detection performance to minimize model errors for promoting stable ICS *** this paper,we established the requirements for improving the anomaly detection performance in the IIoT-based ICS environment by analyzing the related *** that,we presented an improving method of the performance of a machine learning model specialized for IIoT-based ICS,which increases the detection rate by applying correlation coefficients and clustering;it provides a mechanism to predict thresholds on a ***,we adopted the HAI dataset environment that actively reflected the characteristics of IIoT-based ICS and demonstrated that performance could be improved through comparative experiments with the traditional method and our proposed *** presented method can further improve the performance of commonly applied error-based detection techniques and includes a primary method that can be enhanced over existing detection techniques by analyzing correlation coefficients between features to consider feedback between ICS *** can contribute to improving the performance of several detection models applied in ICS and other areas.

关键词： Industrial IoT industrial control system anomaly detection clustering algorithm correlation coefficient

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A Class of Hierarchical Sliding Mode Control based on Extended Kalman filter for Quadrotor UAVs

arXiv

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arXiv 2025年

作者： Nguyen, Van Chung La, Hung Manh Lab Department of Computer Science and Engineering University of Nevada RenoNV89557 United States

This study introduces a novel methodology for controlling Quadrotor Unmanned Aerial Vehicles (UAVs), focusing on Hierarchical Sliding Mode Control (HSMC) strategies and an Extended Kalman Filter (EKF). Initially, an EKF is proposed to enhance robustness in estimating UAV states, thereby reducing the impact of measured noises and external disturbances. By locally linearizing UAV systems, the EKF can mitigate the disadvantages of the Kalman filter and reduce the computational cost of other nonlinear observers. Subsequently, in comparison to other related work in terms of stability and computational cost, the HSMC framework shows its outperformance in allowing the quadrotor UAVs to track the references. Three types of HSMC—Aggregated HSMC (AHSMC), Incremental HSMC (IHSMC), and Combining HSMC (CHSMC)—are investigated for their effectiveness in tracking reference trajectories. Moreover, the stability of the quadrotor UAVs is rigorously analyzed using the Lyapunov stability principle. Finally, experimental results and comparative analyses demonstrate the efficacy and feasibility of the proposed methodologies. © 2025, CC BY.

关键词： Extended Kalman filters

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On-Chip Quantum Dot Lasers Driven High-Speed Optical Neural Networks

On-Chip Quantum Dot Lasers Driven High-Speed Optical Neural ...

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CLEO: science and Innovations in CLEO 2024, CLEO: S and I 2024 - Part of Conference on Lasers and Electro-Optics

作者： Zhou, Zhican Fang, Yuetong Ou, Xiangpeng He, William Wu, Xuhao Xu, Renjing Pan, David Z. Wan, Yating Integrated Photonics Lab King Abdullah University of Science and Technology Thuwal Makkah Saudi Arabia Function Hub The Hong Kong University of Science and Technology Guangzhou China Department of Electrical and Computer Engineering The University of Texas Austin78703 United States

We present an optical neural network architecture driven by on-chip quantum dot lasers, achieving 3.5 TOPS/mm2 theoretical computational density. With an optimized model (reducing programming operations by over 75%), ... 详细信息

关键词： Quantum dot lasers

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Anywhere Projected AR for Robot Communication: A Mid-Air Fog Screen-Robot System 25

Anywhere Projected AR for Robot Communication: A Mid-Air Fog...

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ACM/IEEE International Conference on Human-Robot Interaction (HRI)

作者： Adrian Lozada Uthman Tijani Villa Keth Hong Wang Zhao Han Department of Computer Science and Engineering RARE Lab University of South Florida Tampa Florida USA

ISBN: (数字)9798350378931

ISBN: (纸本)9798350378948

Augmented reality (AR) allows visualizations to be situated where they are relevant, e.g., in a robot's operating environment or task space. Yet, headset-based AR suffers a scalability issue because every viewer must wear a headset. Projector-based spatial AR solves this problem by projecting augmentations onto the scene, e.g., recognized objects or navigation paths, viewable to crowds. However, this solution mostly requires vertical flat surfaces that may not exist in large open areas like auditoriums, warehouses, construction sites, or search and rescue scenes. Moreover, when humans are not co-located with the robot or situated at a distance, the projection may not be legible to humans. Thus, there is a need to create a projectable, viewable surface for humans in such scenarios. In this HRI systems paper, we introduce a fog screen-robot system that integrates a mid-air fog screen device into a robot to create such a projectable surface and presents two evaluations in a construction site and a search and rescue scenario for high-stakes communication needs. Specifically, we implemented an existing fog screen device, which can only project one-third of a meter (33cm). We improved it to achieve a fog screen length of half a meter (53cm). In the noisy construction site scenario, the robot inspected the site and projected icons for missing wall sockets and plumbing fixtures. In the unstructured search and rescue scenario, the robot was able to project a person icon for a first responder to save life. All 3D models and code are available at https://***/TheRARElab/fog-screen-robot-system. Videos are available at https://***/b4efu/.

关键词： Meters Visualization Solid modeling Three-dimensional displays Sockets Scalability Human-robot interaction Robots Augmented reality Videos

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EveMRC: Two-Stage Bidirectional Evidence Modeling for Multi-Choice Machine Reading Comprehension

IEEE Transactions on Audio, Speech and Language Processing

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IEEE Transactions on Audio, Speech and Language Processing 2025年 33卷 1011-1022页

作者： Hongshen Xu Lu Chen Liangtai Sun Ruisheng Cao Da Ma Kai Yu X-LANCE Lab Department of Computer Science and Engineering MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai China

Machine Reading Comprehension (MRC) requires a machine to answer questions after reading and comprehending the given documents. Multi-choice MRC is one of the most studied MRC tasks due to the convenience of evaluation and the diversity of question types. However, the interpretability of multi-choice MRC, especially in evidence extraction, remains underexplored because a correct answer may be derived by eliminating incorrect options rather than being supported by positive evidence. In this work, we propose a bidirectional evidence modeling framework, EveMRC, to enhance the explainability of Multi-choice MRC systems. Compared to previous works, our framework exclusively addresses the problem of bidirectional evidence selection, which not only selects positive evidence for the right answer but also selects negative evidence for wrong answers. The bidirectional evidence can also facilitate model decisions by incorporating it into a competition process. To avoid the high annotation cost of bidirectional evidence, our framework utilizes a novel weakly-supervised pipeline to train the evidence selector. Experimental results on four multi-choice MRC datasets demonstrate the effectiveness of our framework, which not only enhances the explainability of MRC systems but also improves their overall performance.

关键词： Training Hands Speech processing Question answering (information retrieval) Pipelines Numerical models Cognitive processes Benchmark testing Annotations Predictive models

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Pareto Data Framework: Steps Towards Resource-Efficient Decision Making Using Minimum Viable Data (MVD)

arXiv

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arXiv 2024年

作者： Ahmed, Tashfain Siegel, Josh DeepTech Lab Department of Computer Science and Engineering Michigan State University United States

This paper introduces the Pareto Data Framework, an approach for identifying and selecting the Minimum Viable Data (MVD) required for enabling machine learning applications on constrained platforms such as embedded systems, mobile devices, and Internet of Things (IoT) devices. We demonstrate that strategic data reduction can maintain high performance while significantly reducing bandwidth, energy, computation, and storage costs. The framework identifies Minimum Viable Data (MVD) to optimize efficiency across resource-constrained environments without sacrificing performance. It addresses common inefficient practices in an IoT application such as overprovisioning of sensors and overprecision, and oversampling of signals, proposing scalable solutions for optimal sensor selection, signal extraction and transmission, and data representation. An experimental methodology demonstrates effective acoustic data characterization after downsampling, quantization, and truncation to simulate reduced-fidelity sensors and network and storage constraints;results shows that performance can be maintained up to 95% with sample rates reduced by 75% and bit depths and clip length reduced by 50% which translates into substantial cost and resource reduction. These findings have implications on the design and development of constrained systems. The paper also discusses broader implications of the framework, including the potential to democratize advanced AI technologies across IoT applications and sectors such as agriculture, transportation, and manufacturing to improve access and multiply the benefits of data-driven insights. Copyright © 2024, The Authors. All rights reserved.

关键词： Cost reduction

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