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arXiv 2023年

作者： Rodriguez-Fernandez, Victor Montalvo, David Piccialli, Francesco Nalepa, Grzegorz J. Camacho, David School of Computer Systems Engineering Universidad Politécnica de Madrid Calle de Alan Turing Madrid28038 Spain Valencian International University Calle Pintor Sorolla 21 Valencia46002 Spain Department of Mathematics and Application ‘R. Caccioppoli’ University of Naples Federico II Italy Institute of Applied Computer Science Jagiellonian University Krakow30-348 Poland

The field of Deep Visual Analytics (DVA) has recently arisen from the idea of developing Visual Interactive Systems supported by deep learning, in order to provide them with large-scale data processing capabilities and to unify their implementation across different data and domains. In this paper we present DeepVATS, an open-source tool that brings the field of DVA into time series data. DeepVATS trains, in a self-supervised way, a masked time series autoencoder that reconstructs patches of a time series, and projects the knowledge contained in the embeddings of that model in an interactive plot, from which time series patterns and anomalies emerge and can be easily spotted. The tool includes a back-end for data processing pipeline and model training, as well as a front-end with a interactive user interface. We report on results that validate the utility of DeepVATS, running experiments on both synthetic and real datasets. The code is publicly available on https://***/vrodriguezf/deepvats © 2023, CC BY-NC-ND.

关键词： Time series

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The Unconstrained Ear Recognition Challenge 2023: Maximizing Performance and Minimizing Bias*

The Unconstrained Ear Recognition Challenge 2023: Maximizing...

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IEEE International Joint Conference on Biometrics (IJCB)

作者： Ž. Emeršić T. Ohki M. Akasaka T. Arakawa S. Maeda M. Okano Y. Sato A. George S. Marcel I. I. Ganapathi S. S. Ali S. Javed N. Werghi S. G. Işık E. Sarıtaş H. K. Ekenel V. Hudovernik J. N. Kolf F. Boutros N. Damer G. Sharma A. Kamboj A. Nigam D. K. Jain G. Cámara-Chávez P. Peer V. Štruc University of Ljubljana (UL SI) Shizuoka University (SU JP) Idiap Research Institute (IDIAP CH) Electrical Engineering and Computer Science Department Khalifa University (KU UAE) Department of Computer Engineering (ITU TR) Istanbul Technical University Microelectronic Guidance and Electro-Optical Group ASELSAN Fraunhofer Institute for Computer Graphics Research IGD (IGD DE) Technische Universität Darmstadt (TUD DE) Indian Institute of Technology Mandi (IIT IN) HCL Imaging and Robotics R&D Lab (HCL IN) School of Artificial Intelligence Dalian University of Technology (DLUT CN) Federal University of Ouro Preto (UFOP BR)

The paper provides a summary of the 2023 Unconstrained Ear Recognition Challenge (UERC), a benchmarking effort focused on ear recognition from images acquired in uncontrolled environments. The objective of the challenge was to evaluate the effectiveness of current ear recognition techniques on a challenging ear dataset while analyzing the techniques from two distinct aspects, i.e., verification performance and bias with respect to specific demographic factors, i.e., gender and ethnicity. Seven research groups participated in the challenge and submitted a seven distinct recognition approaches that ranged from descriptor-based methods and deep-learning models to ensemble techniques that relied on multiple data representations to maximize performance and minimize bias. A comprehensive investigation into the performance of the submitted models is presented, as well as an in-depth analysis of bias and associated performance differentials due to differences in gender and ethnicity. The results of the challenge suggest that a wide variety of models (e.g., transformers, convolutional neural networks, ensemble models) is capable of achieving competitive recognition results, but also that all of the models still exhibit considerable performance differentials with respect to both gender and ethnicity. To promote further development of unbiased and effective ear recognition models, the starter kit of UERC 2023 together with the baseline model, and training and test data is made available from: http://***/

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Multi-Layered Ontology Models for Dynamic Knowledge Representation in Social Network Analysis

Multi-Layered Ontology Models for Dynamic Knowledge Represen...

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Trends in Quantum Computing and Emerging Business Technologies (TQCEBT), International Conference on

作者： Y Manohar Reddy Vijilius Helena Raj Saloni Bansal H Pal Thethi Ravi Kalra Mohammed Khalid Institute of Aeronautical Engineering Hyderabad Department of Applied Sciences New Horizon College of Engineering Bangalore Department of Computer Engineering and Applications GLA University Mathura Lovely Professional University Phagwara Lloyd Institute of Engineering & Technology Knowledge Park II Greater Noida Uttar Pradesh College of Technical Engineering National University of Science and Technology Dhi Qar Iraq

ISBN: (数字)9798350384277

ISBN: (纸本)9798350384284

In the rapidly evolving landscape of social networks, understanding the multifaceted nature of human interactions and the dissemination of information is pivotal. This paper introduces a novel approach to dynamic knowledge representation through the development of Multi-Layered Ontology Models tailored for Social Network Analysis (SNA). The core of this research lies in architecting a robust, scalable, and semantically rich framework that leverages the principles of data mining and web technologies to dissect and interpret the complex structure of social networks. The ontology layers are intricately designed to represent various dimensions of social interactions, including temporal dynamics, user behavior, and network evolution. Each layer is interlinked yet distinct, providing a granular view of the social fabric and enabling precise inference and prediction of patterns. The methodology adopts an innovative blend of semantic web technologies for ontology construction, coupled with advanced data mining techniques to efficiently process and analyze large volumes of social data. The result is a dynamic model that adapts to the ever-changing social context, offering valuable insights into trends, community structures, and influence dissemination. The implications of this research are vast, impacting areas ranging from targeted marketing to public opinion analysis. This paper sets a new precedent for interdisciplinary research in SNA, opening avenues for further exploration and refinement.

关键词： Semantic Web Adaptation models Analytical models Social networking (online) Simulation Ontologies Predictive models

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UGV-CBRN: An Unmanned Ground Vehicle for Chemical, Biological, Radiological, and Nuclear Disaster Response

arXiv

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arXiv 2024年

作者： Schwaiger, Simon Muster, Lucas Novotny, Georg Schebek, Michael Wöber, Wilfried Thalhammer, Stefan Böhm, Christoph University of Applied Sciences Technikum Wien Faculty of Industrial Engineering Vienna1200 Austria Graz University of Technology Faculty of Computer Science and Biomedical Engineering Institute of Software Technology Inffeldgasse 16b/II Graz8010 Austria University of Natural Resources and Life Sciences Department of Biotechnology Institute for Computational Biology Muthgasse 18 Vienna1190 Austria

Robotic search and rescue (SAR) supports response teams by accelerating disaster assessment and by keeping operators away from hazardous environments. In the event of a chemical, biological, radiological, and nuclear (CBRN) disaster, robots are deployed to identify and locate radiation sources. Human responders then assess the situation and neutralize the danger. The presented system takes a step toward enhanced integration of robots into SAR teams. Integrating autonomous radiation mapping with semi-autonomous substance sampling and online analysis of the CBRN threat lets the human operator localize and assess the threat from a safe distance. Two LiDARs, an IMU, and a Geiger counter are used for mapping the surrounding area and localizing potential radiation sources. A mobile manipulator with six Degrees of Freedom manipulates valves and samples substances that are analyzed by an onboard Raman spectrometer. The human operator monitors the mission’s progression from a remote location defining target locations and directing the semi-autonomous manipulation processes. Diverse recovery behaviours aid robot deployment, system state monitoring, as well as recovery of hard- and software. Field tests showcase the capabilities of the presented system during trials at the CBRN disaster response challenge European Robotics Hackathon (EnRicH). We provide recorded sensor data and implemented software through a GitHub repository: https://***/TW-Robotics/ search-and-rescue-robot-2024. © 2024, CC BY.

关键词： Disasters

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Cloud Forensics Analysis Framework for Secure and Efficient Data Retrieval

Cloud Forensics Analysis Framework for Secure and Efficient ...

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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： Revathi V Bk Aishwarya Manju Tripathi Navdeep Singh Haider Alabdely Ashwani Kumar Department of Applied Sciences New Horizon College of Engineering Bangalore Institute of Aeronautical Engineering Dundigal Hyderabad Department of Computer Science & Engineering IES College of Technology Bhopal M. P. India Lovely Professional University Phagwara The Islamic university Najaf Iraq Lloyd Institute of Engineering & Technology Knowledge Park II Greater Noida Uttar Pradesh

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

This study’s Cloud Forensics Analysis Framework seeks secure and effective data recovery methods. The framework’s five strategies make digital evidence retrieval methodical and versatile. These include adaptive evidence collecting, secure data retrieval, machine learning-based anomaly detection, intelligent automation, and context-aware metadata analysis. Each approach targets a distinct forensic process step. This makes cloud forensics investigations seem difficult. The customizable Evidence gathering Algorithm adapts to cloud characteristics to collect evidence. This allows contextually aware and flexible digital evidence retrieval. The secure Data Retrieval Techniques Algorithm protects data privacy and chain of custody with advanced encryption and validation. The Machine Learning-Based Anomaly Detection Algorithm checks protected data for anomalies before sending it, making the system safer. Investigators may focus on more complex cases by simplifying and sorting basic forensic activities with the Intelligent Automation Algorithm. The Context-Aware Metadata study Algorithm weights and prioritizes cloud metadata. This completes metadata analysis. Compared to other approaches, the framework has greater Precision, Recall, F1 Score, Processing Time, Resource Utilization, and Compatibility Score. Charts convey system operation in a simple manner. This powerful and adaptable Cloud Forensics Analysis Framework improves cloud forensics by providing a thorough and effective solution to recover and safeguard data. The Cloud Forensics, This paper discusses context-aware metadata analysis, data retrieval, intelligent automation, machine learning-based anomaly detection, performance metrics, secure data retrieval techniques, support vector machines, and visual representation.

关键词： Intelligent automation Support vector machines Visualization Technological innovation Machine learning algorithms Forensics Systems operation

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Speculative Vulnerability: Uncovering the Temporalities of Vulnerability in People s Experiences of the Pandemic

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Proceedings of the ACM on Human-computer Interaction 2022年第CSCW2期6卷 1-27页

作者： Seberger, John S. Obi, Ike Loukil, Mariem Liao, William Wild, David J. Patil, Sameer Department of Information Science College of Computing and Informatics Drexel University 3675 Market Street PhiladelphiaPA19104 United States Department of Computer Graphics Technology Purdue Polytechnic Institute Purdue University 401 N. Grant St. West LafayetteIN47907 United States Mediterranean Institute of Technology Les Jardins du Lac 2 Tunis1053 Tunisia Department of Informatics Luddy School of Informatics Computing and Engineering Indiana University Bloomington 901 E. Tenth Street BloomingtonIN48104 United States School of Computing University of Utah 50 Central Campus Dr. Salt Lake CityUT84112 United States

Pandemic-tracking apps may form a future infrastructure for public health surveillance. Yet, there has been relatively little exploration of the potential societal implications of such an infrastructure. In semi-structured interviews with 23 participants from India, the Middle East and North Africa (MENA), and the United States, we discussed attitudes and preferences regarding the deployment of apps that support contact tracing to contain the spread of COVID-19. Through interpretive analysis, we examined the relationship between persistent discomfort and vulnerability when using such apps. Such an examination yielded three temporal forms of vulnerability: real, anticipatory, and speculative. By identifying and defining the temporalities of vulnerability through an analysis of people's pandemic-related thoughts and experiences, we develop the overlapping discourses of humanistic infrastructure studies and infrastructural speculation. In doing so, we explore the concept of vulnerability itself and present implications for the study of vulnerability in Human-computer Interaction (HCI) and for the oversight of app-based public health surveillance. © 2022 Owner/Author.

关键词： Public health

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Machine Learning-Driven Blockchain for Enhanced Drug Discovery and Development in Pharmaceutical Research

Machine Learning-Driven Blockchain for Enhanced Drug Discove...

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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： V Asha Shaik Anjimoon Meenu Rani Verma Atul Singla Irfan Khan Mohammed Ayad Alkhafaji Department of Computer Applications New Horizon College of Engineering Bangalore Institute of Aeronautical Engineering Dundigal Hyderabad DoCSBS GyanGanga Institute of Technology and Sciences Jabalpur M.P India Lovely Professional University Phagwara Lloyd Institute of Engineering & Technology Knowledge Park II Greater Noida Uttar Pradesh College of Technical Engineering National University of Science and Technology Dhi Qar Iraq

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

Machine learning (ML) and blockchain technologies have made huge steps forward in the pharmacy business by being used in the process of creating and discovering new medicines. Both breakthroughs were made possible by using both tools together. With the help of a blockchain design powered by machine learning, the study’s main goal is to come up with a new way to make drug research more effective and efficient. This method makes it possible to make molecules with a wide range of structure features that have the right pharmacological qualities. You can also make these molecules, improve the plans of clinical trials, and guess how drugs will interact with their targets. Technologies like generative adversarial networks, reinforcement learning methods, and deep learning algorithms have made it possible for these things to be used. If blockchain technology is used throughout the whole process of making medicines, it improves global openness, traceability, and data security. This is because it makes sure that managing data is safe. The tests show that the suggested method works better than the others when it comes to the process of finding and developing new drugs. This is better because the creation process is more precise, accurate, and efficient.

关键词： Drugs Deep learning Technological innovation Data security Reinforcement learning Production Clinical trials

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Two-in-One Refinement for Interactive Segmentation 31

Two-in-One Refinement for Interactive Segmentation

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31st British Machine Vision Conference, BMVC 2020

作者： Majumder, Soumajit Rai, Abhinav Khurana, Ansh Yao, Angela Institute of Computer Science II University of Bonn Germany School of Computing National University of Singapore Singapore Indian Institute of Technology Bombay Mumbai India

Deep convolutional neural networks are now mainstream for click-based interactive image segmentation. Most frameworks refine false negatives and false positive regions via a succession of positive and negative clicks placed centrally in these regions. We propose a simple yet intuitive two-in-one refinement strategy placing clicks on the boundary of the object of interest. As boundary clicks are a strong cue for extracting the object of interest and we find that they are much more effective in correcting wrong segmentation masks. In addition, we propose a boundary-aware loss that encourages segmentation masks to respect instance boundaries. We place our new refinement scheme and loss formulation within a task-specialized segmentation framework and achieve state-of-the-art performance on the standard datasets - Berkeley, Pascal VOC 2012, DAVIS, and MS COCO. We exceed competing methods by 6.5%, 9.4%, 10.5% and 2.5%, respectively. © 2020. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms.

关键词： Image segmentation

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Discrete part of the second Lagrange spectrum

arXiv

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arXiv 2023年

作者： Gayfulin, Dmitry Graz University of Technology Institute of Analysis and Number Theory Steyrergasse 30/II Graz8010 Austria Big Data and Information Retrieval School Faculty of Computer Science National Research University Higher School of Economics 11 Pokrovsky boulevard Moscow109028 Russia

Given an irrational number α consider its irrationality measure function ψα(t) = 16qmin 6t,q∈Z kqαk. The set of all values of λ(α) = (lim sup tψα(t))−1 where α runs through the set R \ Q is t→∞ called the Lagrange spectrum L. In the paper [6] an irrationality measure function ψα[2](t) = min kqαk was introduced. In other words, we consider the best approximations by 16q6t,q∈Z,q6=qi fractions, whose denominators are not the denominators of the convergents to α. Replacing the function ψα in the definition of L by ψα[2], one can get a set L2 which is called the"second" Lagrange spectrum. In this paper we give the complete structure of discrete part of L2MSC Codes 11J06 © 2023, CC BY.

关键词： Lagrange multipliers

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Navigating the Artificial Intelligence Revolution in NeuroOncology: A Multidisciplinary Viewpoint

SSRN

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SSRN 2024年

作者： Saxena, Sanjay Panda, Soumyaranjan Tiwari, Ekta Singh, Rajesh Fouda, Mostafa M. Kalra, Mannudeep K. Kotecha, Ketan Saba, Luca Suri, Jasjit S. Department of Computer Science & Engineering International Institute of Information Technology Odisha Bhubaneswar India Department of Computer Science & Information Technology Mahatma Gandhi Central University Bihar India Department of Computer & Engineering Visvesvaraya National Institute of Technology Maharashtra Nagpur India Department of Research and Innovation Uttaranchal Institute of Technology Uttaranchal University Dehradun248007 India Department of Electrical and Computer Engineering Idaho State University PocatelloID83209 United States Department of Radiology Massachusetts General Hospital BostonMA United States Symbiosis centre for Applied Artificial Intelligence Symbiosis Institute of Technology Symbiosis International University Pune India Cagliari Italy Department of ECE Idaho State University PocatelloID83209 United States Department of CE Graphics Era Deemed to be University Dehradun248002 India University Center for Research & Development Chandigarh University Mohali India Pune India Stroke Diagnostic and Monitoring Division AtheroPoint RosevilleCA95661 United States Harvard Medical School United States University Hospital of Cagliari Italy

This comprehensive review presents the role of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in neuro-oncology, focussing their groundbreaking impact on the field. It gives a historical overview of AI's evolution in area of neuro-oncology, representing its progress from basic data management to high-level predictive analytics that now significantly makes personalized treatment ***, This study examines various ML based methods that emphasize brain tumor classification, genetic profiling, and individualized care plans proposals, which basically reflects the shift towards precision medicine. This paper also highlights, developments in DL particularly in the development of neuro-imaging analysis, which has significantly guided surgical planning and radiation therapy for improved patient outcomes. Through various case studies, the paper presents recent breakthroughs where AI interventions have noticeably enhanced the treatment of neuro-oncological conditions. Moreover, It also addresses the ethical considerations encompassing the use of automated decision-making tools, emphasizing the need for ethical governance in AI deployment. By integrating multidisciplinary perspectives, the study underscores the collaborative effort between clinicians, data scientists, and ethicists, which is very crucial for the ethical and effective implementation of AI in current clinical practice. The main aim of discussion is to equip neurooncologists/sergeons and doctors with understandings into sustainable AI adoption which ensures that AI's path in neuro-oncology aligns with both clinical needs for personalized medicine and ethical *** paper concludes by outlining considered methods and frameworks for the future integration of AI in neuro-oncology, predicting trends, and preparing to meet the challenges ahead. © 2024, The Authors. All rights reserved.

关键词： Personalized medicine

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