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arXiv 2024年

作者： Ifargan, Tal Hafner, Lukas Kern, Maor Alcalay, Ori Kishony, Roy Faculty of Data and Decision Sciences Technion–Israel Institute of Technology Haifa Israel Faculty of Biology Technion–Israel Institute of Technology Haifa Israel Epsio Tel Aviv Israel Faculty of Computer Science Technion–Israel Institute of Technology Haifa Israel Faculty of Biomedical Engineering Technion–Israel Institute of Technology Haifa Israel

As AI promises to accelerate scientific discovery, it remains unclear whether fully AI-driven research is possible and whether it can adhere to key scientific values, such as transparency, traceability and verifiability. Mimicking human scientific practices, we built data-to-paper, an automation platform that guides interacting LLM agents through a complete stepwise research process, while programmatically back-tracing information flow and allowing human oversight and interactions. In autopilot mode, provided with annotated data alone, data-to-paper raised hypotheses, designed research plans, wrote and debugged analysis codes, generated and interpreted results, and created complete and information-traceable research papers. Even though research novelty was relatively limited, the process demonstrated autonomous generation of de novo quantitative insights from data. For simple research goals, a fully-autonomous cycle can create manuscripts which recapitulate peer-reviewed publications without major errors in about 80-90%, yet as goal complexity increases, human co-piloting becomes critical for assuring accuracy. Beyond the process itself, created manuscripts too are inherently verifiable, as information-tracing allows to programmatically chain results, methods and data. Our work thereby demonstrates a potential for AI-driven acceleration of scientific discovery while enhancing, rather than jeopardizing, traceability, transparency and verifiability. Copyright © 2024, The Authors. All rights reserved.

关键词： Transparency

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Cloud-Based Oxygen Management System with Seamless HTTP Protocol Integration

Cloud-Based Oxygen Management System with Seamless HTTP Prot...

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Innovative Practices in Technology and Management (ICIPTM), International Conference on

作者： S. Hemalatha Jayabharathi Ramasamy K. Selvi P. Anandan R. Meenakshi S. P. Maniraj Department of Artificial Intelligence and Data Science Tagore Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering RMK College of Engineering and Technology Chennai Tamil Nadu India Faculty of Management Studies S. A. Engineering College Chennai Tamil Nadu India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India Department of Computer Science and Engineering SRM Institute of Science and Technology Chennai Tamil Nadu India

ISBN: (数字)9798350307757

ISBN: (纸本)9798350307764

In this study, an efficient system to improve the oxygen supply in hospitals and other healthcare institutions is proposed. It uses Internet of things (IoT) devices and cloud based data transmission and processing system. The controller unit collects the oxygen sensor data for the analysis and uses HTTP protocols to send and receive data with cloud computing in HTTP website server. The cloud is data storage, processes and real-time monitoring, analysis and control system. It has a user interface with viewed administrators and medical professionals. The interface provides real-time data regarding oxygen supply, consumption patterns, and possible concerns. Authorized workers can access and manage the system via the display, allowing for timely adjustments to oxygen supply settings, optimizations to refill schedules, and responses to emergency circumstances. It's adaptable, flexible, and resilient, which are benefits. There will be less need to worry about oxygen shortages and interruptions, and healthcare institutions of all sizes can benefit from this. The cloud's remote access and control capabilities provide continuous functioning and rapid reaction times. It's an effective solution for hospitals and other healthcare facilities that need to control their oxygen supplies. With its cloud-based design, the solution increases productivity, improves patient safety, and simplifies oxygen management.

关键词： Cloud computing Hospitals Process control User interfaces Control systems HTTP Real-time systems

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AI-Driven Solutions for Social Engineering Attacks: Detection, Prevention, and Response

AI-Driven Solutions for Social Engineering Attacks: Detectio...

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Cyber Resilience (ICCR), International Conference on

作者： Hussam N. Fakhouri Basim Alhadidi Khalil Omar Sharif Naser Makhadmeh Faten Hamad Niveen Z. Halalsheh Data Science and Artificial Intelligence Department Faculty of Information Technology University of Petra Amman Jordan Department of Computer Information Systems Al-Balqa Applied University Salt Jordan Computer Science Department University of Petra Amman Jordan Sultan Qaboos University Sultanate of Oman. University of Jordan Amman Amman Jordan The University of Jordan Amman Amman Jordan

ISBN: (数字)9798350394962

ISBN: (纸本)9798350394979

With the rapid evolution of cyber threats, social engineering attacks have become increasingly sophisticated, leveraging human vulnerabilities to bypass traditional security measures. While many conventional defense mechanisms have been overwhelmed, Artificial Intelligence (AI) offers a promising avenue to detect, prevent, and respond to these emerging threats. This research analyzes the intricacies of contemporary social engineering attacks, from their methods of deployment to their recent adaptations, such as leveraging social media and mobile apps. By contrasting prior solutions with the potential of AI-based defenses, we highlight the key role of machine learning in behavioral pattern recognition, Natural Language Processing's (NLP) efficacy in identifying phishing attempts, and predictive analytics' power to anticipate future attack vectors. Through detailed case studies, we showcase real-world scenarios where AI mechanisms have successfully countered social engineering ploys. The findings reveal that AI-enhanced mechanisms significantly improve the identification and mitigation of social engineering threats. Specifically, AI-driven behavioral analytics effectively detect subtle, manipulative cues indicative of phishing and other deceitful tactics, considerably reducing the incidence of successful attacks. Furthermore, predictive analytics has shown great promise in forecasting and preemptively countering potential cyber threats, In addition, while effective, AI tools must evolve with the changing tactics of cyber threats, Continuous learning and updating are necessary to maintain and improve accuracy and effectiveness.

关键词： Technological innovation Phishing Weapons Natural language processing Vectors Threat assessment Trajectory

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Assessing the Ability of AI-Driven Natural Language Processing to Accurately Analyze Unstructured Text data

Assessing the Ability of AI-Driven Natural Language Processi...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： M. Vigenesh RaviKumar V Vivek D. Patil Sharma Sonu Kumar Nyarik Geyi Subir Chattopadhyay Department of Computer Science Engineering Karpagam Academy of Higher Education Coimbatore Department of Computer Science and Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune India Department of Computer Science Mangalayatan University Aligarh India Department of Bussiness & Management Himalayan University Itanagar India Faculty of Business Mangagement & Commerce Usha Martin University Ranchi India

This technical abstract examines the ability of herbal language processing (NLP)-driven synthetic Intelligence (AI) to analyze unstructured textual content facts. Unstructured textual content records include information from assets with webpages, social media, emails, and consumer comments. They are widely used in sentiment evaluation, hazard detection, and entity extraction programs. NLP-pushed AI answers along with natural language knowledge (NLU), herbal language knowledge (NLG), and natural language understanding (NLP) are used to manner and examine unstructured textual content information. The analysis technique starts evolving with tokenizing the facts, breaking the textual content into smaller factors, including phrases and phrases. After tokenizing the statistics, the AI device discovers styles inside the records. Those styles can then discover the emotions and entities inside the data. NLU and NLG models are used to expand the facts' expertise by assigning sentiment and issue-level scores. The accuracy of NLP-driven AI is similarly progressed through more excellent complex fashions consisting of deep studying and reinforcement.

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VERTEX-REMOVAL STABILITY AND THE LEAST POSITIVE VALUE OF HARMONIC MEASURES

arXiv

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arXiv 2023年

作者： Cai, Zhenhao Kozma, Gady Procaccia, Eviatar B. Zhang, Yuan School of Mathematical Sciences Peking University Beijing China Faculty of Mathematics and Computer Science Weizmann Institute of Science Rehovot Israel Faculty of Data and Decision Sciences Technion Israel Institute of Technology Haifa Israel Center for Applied Statistics School of Statistics Renmin University of China Beijing China

We prove that for $\\mathbb{Z}^d$ ($d\\ge 2$), the vertex-removal stability ofharmonic measures (i.e. it is feasible to remove some vertex while changing theharmonic measure by a bounded factor) holds if and only if $d=2$. The proofmainly relies on geometric arguments, with a surprising use of the discreteKlein bottle. Moreover, a direct application of this stability verifies aconjecture of Calvert, Ganguly and Hammond [9] for the exponential decay of theleast positive value of harmonic measures on $\\mathbb{Z}^2$. Furthermore, theanalogue of this conjecture for $\\mathbb{Z}^d$ with $d\\ge 3$ is also proved inthis paper, despite vertex-removal stability no longer holding. © 2023, CC BY.

关键词： Bottles

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Regression models for the prediction of the influence of magnesium ions on primary endothelial cell (HUVEC) proliferation and migration

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Computational and Structural Biotechnology Journal 2025年 27卷 2711-2718页

作者： Heike Helmholz Redon Resuli Marius Tacke Jalil Nourisa Sven Tomforde Roland Aydin Regine Willumeit-Römer Berit Zeller-Plumhoff Institute of Metallic Biomaterials Helmholz-Zentrum Hereon Geesthacht Germany Institute of Material Systems Modeling Helmholz-Zentrum Hereon Geesthacht Germany Department of Computer Science Faculty of Engineering Kiel University Kiel Germany Institute for Continuum and Material Mechanics Hamburg University of Technology Hamburg Germany Kiel Nano Surface and Interface Science - KiNSIS Kiel University Germany Data-driven Analysis and Design of Materials Faculty of Mechanical Engineering and Marine Technologies University of Rostock Germany Department Life Light & Matter Interdisciplinary Faculty University of Rostock Germany

Angiogenesis is one of the first stages in fracture healing and bone repair. Therefore, numerous studies evaluating the effect of Mg as a promising degradable, metallic biomaterial on the proliferation and function of endothelial cells have been performed. However, these studies lack methodological homogeneity and therefore differ in fundamental conclusions. Here, Mg-concentration-, donor- and cell age- dependent relations to primary human umbilical cord vein endothelial cells (HUVEC) proliferation and migration were investigated systematically. The generated data were utilized to develop regression models in order to assess and predict the cell response on Mg exposition in a concentration range of 2–20 mM Mg in cell culture medium extract. A concentration of > 2 mM already induced a detrimental effect in the sensitive primary HUVECs. Molecular data quantifying angiogenesis markers supported this finding. An increased migration capacity has been observed at a concentration of 10 mM Mg. We compared linear regression, random forests, support vector machines, neural networks and large language models for the prediction of HUVEC proliferation for a number of scenarios. Using these machine learning methods, we were able to predict the proliferation of HUVECs for missing Mg concentrations and for missing passages with mean absolute errors below 10 % and as low as 8.5 %, respectively. Due to strong differences between the cell behaviour of different donors, information for missing donors can be predicted with mean absolute errors of 15.7 % only. Support vector machines with linear kernel performed best on the tested data, but large language models also showed promising results.

关键词： Regression models HUVEC Cell proliferation Large Language Model

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data Freshness Performance Analysis in NOMA-Enabled Green Mobile Crowdsensing

Data Freshness Performance Analysis in NOMA-Enabled Green Mo...

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IEEE International Conference on Communications (ICC)

作者： Yaoqi Yang Bangning Zhang Daoxing Guo Renhui Xu Lin Ge Weizheng Wang Xiaokang Zhou Communication Engineering School Army Engineering University Nanjing China Department of Computer Science City University of Hong Kong China Faculty of Data Science Shiga University Hikone Japan The RIKEN Center for Advanced Intelligence Project RIKEN Tokyo Japan

Green communication has attracted lots of attention recently, where NOMA (Non-Orthogonal Multiple Access) is one of the most promising technologies to realize energy-efficient communication. Specifically, by making massive wireless devices connect to the same time-frequency resource, NOMA can enhance the spectrum efficiency. In this paper, to investigate the freshness of the sensing data, we analyze the Age of Information (AoI) performance in NOMA-enabled Mobile Crowdsensing (MCS) circumstance, where the stochastic geometry theory is adopted. Firstly, we focus on the data submission process between mobile workers (MWs) and service provides (SPs), which drives to establish a model of the NOMA-enabled MCS. Then, given the transmission schemes of NOMA and OMA (Orthogonal Multiple Access), the mathematical expressions of the AoI metric are derived in the closed form respectively. Furthermore, simulation experiments are conducted to obtain AoI numerical results under various parameter settings (e.g., power strategies, queue models, and transmission protocols). Finally, the evaluation results not only prove the validness of the established models, but also provide some efficient solutions to achieve the optimal AoI value under the considered NOMA-enabled MCS scenario.

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When Large Language Models Meet Citation: A Survey

arXiv

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arXiv 2023年

作者： Zhang, Yang Wang, Yufei Wang, Kai Sheng, Quan Z. Yao, Lina Mahmood, Adnan Zhang, Wei Emma Zhao, Rongying School of Computing Faculty of Science and Engineering Macquarie University Sydney Australia School of Information Management Wuhan University Wuhan China School of Computer Science and Engineering Nanyang Technological University Singapore CSIRO’s Data61 Sydney Australia Computer Science and Engineering University of New South Wales Sydney Australia School of Computer and Mathematical Sciences The University of Adelaide Adelaide Australia

Citations in scholarly work serve the essential purpose of acknowledging and crediting the original sources of knowledge that have been incorporated or referenced. Depending on their surrounding textual context, these citations are used for different motivations and purposes. Large Language Models (LLMs) could be helpful in capturing these fine-grained citation information via the corresponding textual context, thereby enabling a better understanding towards the literature. Furthermore, these citations also establish connections among scientific papers, providing high-quality inter-document relationships and human-constructed knowledge. Such information could be incorporated into LLMs pre-training and improve the text representation in LLMs. Therefore, in this paper, we offer a preliminary review of the mutually beneficial relationship between LLMs and citation analysis. Specifically, we review the application of LLMs for in-text citation analysis tasks, including citation classification, citation-based summarization, and citation recommendation. We then summarize the research pertinent to leveraging citation linkage knowledge to improve text representations of LLMs via citation prediction, network structure information, and inter-document relationship. We finally provide an overview of these contemporary methods and put forth potential promising avenues in combining LLMs and citation analysis for further investigation. Copyright © 2023, The Authors. All rights reserved.

关键词： Text processing

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Applications of AI to Age-Related Macular Degeneration: A case study and a brief review

Applications of AI to Age-Related Macular Degeneration: A ca...

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2022 International Conference on computer Engineering and Artificial Intelligence, ICCEAI 2022

作者： Wang, Han Lung Chong, Kelvin Kam Li, Zefeng City University of Macau Faculty of Data Science Macau999078 China Zhuhai Institute of Advanced Technology Chinese Academy of Sciences Department of Artificial Intelligence and Big Data Applications Zhuhai519000 China School of Computer College of Beijing University of Technology Zhuhai Zhuhai519000 China Chinese University of Hong Kong Faculty of Medicine Department of Ophthalmology and Visual Sciences Hong Kong999077 Hong Kong

ISBN: (数字)9781665468039

ISBN: (纸本)9781665468039

Age-Related Macular Degeneration (AMD) is one of the major causes of blindness. The artificial intelligence (AI) based method is a novel solution computer-Aided diagnosis approach. This study implemented a brief review by utilizing a case study on AI-based AMD diagnosis studies. Challenges and solutions are discussed., which makes a reference value for future research. © 2022 IEEE.

关键词： computer aided diagnosis

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Robust and digital hyper-polarization protocol of nuclear spins via magic sequential sequence

arXiv

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arXiv 2025年

作者： Li, Haiyang Liao, Hao Wang, Ping Faculty of Arts and Sciences Beijing Normal University Zhuhai519087 China National Engineering Laboratory on Big Data System Computing Technology Guangdong Province Engineering Center of China-made High Performance Data Computing System College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

Hyper-polarization of nuclear spins is crucial for advancing nuclear magnetic resonance (NMR) and quantum information technologies, as nuclear spins typically exhibit extremely low polarization at room temperature due to their small gyro-magnetic ratios. A promising approach to achieving high nuclear spin polarization is transferring the polarization of electron to nuclear spin. The nitrogen-vacancy (NV) center in diamond has emerged as a highly effective medium for this purpose, and various hyper-polarization protocols have been developed. Among these, the pulsed polarization (PulsePol) method has been extensively studied due to its robustness against static energy shifts of the electron spin. In this study, we introduce a sequential polarization protocol and identify a series of magic and digital sequences for hyper-polarizing nuclear spins. Notably, we demonstrate that some of these magic sequences exhibit significantly greater robustness compared to the PulsePol protocol in the presence of finite half π pulse duration of the protocol. This enhanced robustness positions our protocol as a more suitable candidate for hyper-polarizing nuclear spins species with large gyromagnetic ratios and also ensures better compatibility with high-efficiency readout techniques at high magnetic fields. Additionally, the generality of our protocol allows for its direct application to other solid-state quantum systems beyond the NV center. © 2025, CC BY.

关键词： Chemical shift

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