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Any Bluetooth device can be hacked. Know how?

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Cyber Security and Applications

Cyber Security and Applications 2024年 2卷

作者： Muraleedhara, Prathibha Christo, Mary Subaja J, Jaya Yuvasini  D College of Technology – Information Security and Program Management University of Houston 77204 TX United States Department of Networking and Communication School of Computing SRM Institute of Science and Technology Kantankulathur Chennai India Department of Electronics and Communication Engineering Hindustan College of Engineering and Technology India Assistant Professor Department of Computer Science and Business Systems Thiagarajar College of Engineering Madurai 625015 India

In today's world, Bluetooth technology is integrated into almost every device we use, from wireless headsets, mice, and keyboards to cars and smart home devices. But with the convenience of this technology comes the risk of privacy and security breaches. Each Bluetooth device has potential vulnerabilities that cybercriminals can exploit and take advantage of. It is important to create awareness about various Bluetooth vulnerabilities, exploits, and ways to prevent them. This article examines the dark side of Bluetooth technology by explaining how hackers can find ways to bypass the advanced security features of laptops, phones, cars, or smart home devices to compromise the devices and steal personal sensitive data. Whether it is a casual user of Bluetooth technology or a business owner with multiple devices, understanding these risks is crucial to protecting yourself and your information. This article begins by explaining the basics of Bluetooth technology and how it works. Followed by a list of security risks involved while using Bluetooth and finally, it highlights the best practices to protect the devices and the data they contain. © 2024

关键词： Ad hoc networks and security risk Bluetooth Exploit Threat Vulnerability

ERP-Based CTU Splitting Early Termination for Intra Prediction of 360 videos

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ERP-Based CTU Splitting Early Termination for Intra Predicti...

IEEE Visual Communications and Image Processing (VCIP)

作者： Bernardo Beling Iago Storch Luciano Agostini Bruno Zatt Sergio Bampi Daniel Palomino Video Technology Research Group (ViTech) Graduate Program in Computing (PPGC) Federal University of Pelotas (UFPel) Brazil Graduate Program in Computing (PPGC) Federal University of Rio Grande do Sul (UFRGS) Brazil

ISBN: (数字)9781728180687

ISBN: (纸本)9781728180694

This work presents an Equirectangular projection (ERP) based Coding Tree Unit (CTU) splitting early termination algorithm for the High Efficiency Video Coding (HEVC) intra prediction of 360-degree videos. The proposed algorithm adaptively employs early termination in the HEVC CTU splitting based on distortion properties of the ERP projection, that generate homogeneous regions at the top and bottom portion of a video frame. Experimental results show an average of 24% time saving with 0.11% coding efficiency loss, significantly reducing the encoding complexity with minor impacts in the encoding efficiency. Besides, solution presents the best results considering the relation between time saving and coding efficiency when compared with all related works.

关键词： Videos Encoding Complexity theory Distortion Two dimensional displays Prediction algorithms Standards

Towards Safe AI Clinicians: A Comprehensive Study on Large Language Model Jailbreaking in Healthcare

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arXiv 2025年

作者： Zhang, Hang Lou, Qian Wang, Yanshan Intelligent Systems Program School of Computing and Information University of Pittsburgh PA United States Department of Computer Science University of Central Florida OrlandoFL United States Department of Health Information Management University of Pittsburgh PA United States Department of Biomedical Informatics University of Pittsburgh PA United States Clinical and Translational Science Institute University of Pittsburgh PittsburghPA United States University of Pittsburgh Medical Center PittsburghPA United States

Large language models (LLMs) are increasingly utilized in healthcare applications. However, their deployment in clinical practice raises significant safety concerns, including the potential spread of harmful information. This study systematically assesses the vulnerabilities of seven LLMs to three advanced black-box jailbreaking techniques within medical contexts. To quantify the effectiveness of these techniques, we propose an automated and domain-adapted agentic evaluation pipeline. Experiment results indicate that leading commercial and open-source LLMs are highly vulnerable to medical jailbreaking attacks. To bolster model safety and reliability, we further investigate the effectiveness of Continual Fine-Tuning (CFT) in defending against medical adversarial attacks. Our findings underscore the necessity for evolving attack methods evaluation, domain-specific safety alignment, and LLM safety-utility balancing. This research offers actionable insights for advancing the safety and reliability of AI clinicians, contributing to ethical and effective AI deployment in healthcare. © 2025, CC BY-NC-SA.

关键词： Patient treatment

The Arquive of Tatuoca Magnetic Observatory Brazil: from paper to intelligent bytes

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The Arquive of Tatuoca Magnetic Observatory Brazil: from pap...

IEEE International Conference on Big Data

作者： Cristian Berrío-Zapata Ester Ferreira da Silva Mayara Costa Pinheiro Vinicius Augusto Carvalho de Abreu Cristiano Mendel Martins Mario Augusto Góngora Kelso Dunman Faculty of Archival Science Information Science Post Graduation Program (PPGCI) Universidade Federal do Pará (UFPA) Belém Brazil Archives and Libraries in the Digital Environment Universidad Carlos III (UC3M) Madrid Spain Post Graduation Program (PPGCI) Universidade Federal do Pará (UFPA) Belém Brazil Faculty of Computer Science Information Science Post graduation Program (PPGCI) Universidade Federal do Pará (UFPA) Belém Brazil Faculty of Geology Geoscience Post graduation Program (PPGGF) Universidade Federal do Pará (UFPA) Belém Brazil Faculty of Computing Engineering and Media De Montfort University Leicester UK School of Information MAS/LIS program University of British Columbia Canada

ISBN: (纸本)9781665480468

The Magnetic Observatory of Tatuoca (TTB) was installed by Observatório Nacional (ON) in 1957, near Belém city in the state of Pará, Brazilian Amazon. Its history goes back to 1933, when a Danish mission used this location to collect data, due to its privileged position near the terrestrial equator. Between 1957 and 2007, TTB produced 18,000 magnetograms on paper using photographic variometers, and other associated documents like absolute value forms and yearbooks. Data was obtained manually from these graphs with rulers and grids, taking 24 average readings per day, that is, one per hour. In 2017, the Federal University of Pará (UFPA in the Portuguese acronym) and ON collaborated to rescue this physical archive. In 2022 UFPA took a step forward and proposed not only digitizing the documents but also developing an intelligent agent capable of reading and extracting the information of the curves with a resolution better than an hour, being this the central goal of the project. If the project succeeds, it will rescue 50 years of data imprisoned in paper, increasing measurement sensitivity far beyond what these sources used to give. This will also open the possibility of applying the same AI to similar documents in other observatories or disciplines like seismography. This article recaps the project, and the complex challenges faced in articulating Archival Science principles with AI and Geoscience.

关键词： Deep learning Observatories Magnetic resonance imaging Urban areas Collaboration Big Data Data mining

An Analysis of a Gamification Application for Teaching and Learning Knowledge Management under the Qualitative and Affective computing Contexts

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An Analysis of a Gamification Application for Teaching and L...

Frontiers in Education (FIE) Conference

作者： Igor Ernesto Ferreira Costa Antonilson da Silva Alcantara Elisiane Monteiro Soares Raimundo Viégas Junior Elziane Monteiro Soares Emanuel Amoras Rodrigues Sandro Ronaldo Bezerra Oliveira Graduate Program in Computer Sciense (PPGCC) Institute of Exact and Natural Sciences (ICEN) Federal University of Pará (UFPA) Belém PA Brazil Departament of Teaching Federal Institute of Education Science and Technology of Maranhão (IFMA) Viana MA Brazil Faculty of Computing Institute of Exact and Natural Sciences (ICEN) Federal University of Pará (UFPA) Belém PA Brazil

Knowledge as an organizational asset is of great importance in the decision-making process, which needs to be managed properly in order to achieve, from this, a competitive differential in the respective segment of the labor market where the organization is inserted. Thus, managing knowledge and making it an exploitable asset within the organization is of great importance. Therefore, generating new ideas, solutions and innovations and making them available to members of the organization, whether from an organizational database or even from the process of socialization among employees, is necessary for the maintenance of knowledge. Based on this, this work presents the evaluation of an experience report that consisted of the application of a gamification to support the teaching and learning of the knowledge management assets and process in a research laboratory context. The data collected, both during the application of gamification, and at the end of the experiment in the feedback and evaluation meeting, were analyzed qualitatively from the application of Affective computing and also through the SWOT (Strengths, Weaknesses, Opportunities, Threats) matrix. After analyzing these data, the effectiveness of this gamified proposal was proven in order to stimulate the teaching and learning process of knowledge management.

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Moving Mesh Simulations of Pitting Corrosion

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arXiv 2023年

作者： Sarker, Abu Naser Haynes, Ronald D. Robertson, Michael Scientific Computing Program Memorial University St. John’sNLA1C 5S7 Canada Department of Mathematics and Statistics Memorial University NLA1C 5S7 Canada Department of Physics Acadia University WolfvilleNSB4P 2R6 Canada

Damages due to pitting corrosion of metals cost industry billions of dollars per year and can put human lives at risk. The design and implementation of an adaptive moving mesh method is provided for a moving boundary problem related to pitting corrosion. The adaptive mesh is generated automatically by solving a mesh PDE coupled to the nonlinear potential problem. The moving mesh approach is shown to enable initial mesh generation, provide mesh recovery and is able to smoothly tackle changing pit geometry. Materials with varying crystallography are considered. Changing mesh topology due to the merging of pits is also handled. The evolution of the pit shape, the pit depth and the pit width are computed and compared to existing results in the literature. Copyright © 2023, The Authors. All rights reserved.

关键词： Finite element method

Modular boundaries in recurrent neural networks

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arXiv 2023年

作者： Tanner, Jacob C. Mansour, Sina L. Coletta, Ludovico Gozzi, Alessandro Betzel, Richard F. Cognitive Science Program 2 School of Informatics Computing and Engineering Indiana University BloomingtonIN47405 United States National University of Singapore Singapore Fondazione Bruno Kessler Trento Italy Functional Neuroimaging Lab Istituto Italiano di Tecnologia Center for Neuroscience and Cognitive Systems Rovereto Italy Department of Psychological and Brain Sciences Indiana University BloomingtonIN47405 United States

Recent theoretical and experimental work in neuroscience has focused on the representational and dynamical character of neural manifolds –subspaces in neural activity space wherein many neurons coactivate. Importantly, neural populations studied under this "neural manifold hypothesis" are continuous and not cleanly divided into separate neural populations. This perspective clashes with the "modular hypothesis" of brain organization, wherein neural elements maintain an "all- or-nothing" affiliation with modules. In line with this modular hypothesis, recent research on recurrent neural networks suggests that multi-task networks become modular across training, such that different modules specialize for task-general dynamical motifs [1, 2]. If the modular hypothesis is true, then it would be important to use a dimensionality reduction technique that captures modular structure. Here, we investigate the features of such a method. We leverage RNNs as a model system to study the character of modular neural populations, using a community detection method from network science known as modularity maximization to partition neurons into distinct modules. These partitions allow us to ask the following question: do these modular boundaries matter to the system? We find evidence that they do. First, we find that these boundaries neatly divide the representational content and role of neurons. Next, we find that these boundaries can be directly inferred from features of the weight matrix in feed-forward neural networks, and are related to clustering of the Jacobian matrix in recurrent neural networks. We also find that the weights of input neurons to recurrent neurons partially inform their modular structure, an observation that we corroborated using structural and functional imaging data from mice and humans. Finally, we find that the dynamics of these RNNs reflected the boundaries between modules. Collectively, our results suggest that neural populations in RNNs sometimes form modu

关键词： Recurrent neural networks

Deep STI: Deep Stochastic Time-series Imputation on Electronic Health Records

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Deep STI: Deep Stochastic Time-series Imputation on Electron...

Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Ming-Che Cheng Yi-Hsien Hsieh Te-Cheng Hsu Tung-Hung Su Che Lin Institute of Communications Engineering National Tsing Hua University Hsinchu Taiwan Graduate Institute of Communication Engineering National Taiwan University (NTU) Taipei Taiwan Dept. of Internal Medicine Division of Gastroenterolgy and Hepatology National Taiwan University Hospital Taipei Taiwan Department of Electrical Engineering NTU Taipei Taiwan Center for Advanced Computing and Imaging in Biomedicine NTU Taipei Taiwan Smart Medicine and Health Informatics Program NTU Taipei Taiwan

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Electronic Health Records (EHRs) are a cornerstone of modern healthcare analytics, offering rich datasets for various disease analyses through advanced deep learning algorithms. However, the pervasive issue of missing values in EHRs significantly hampers the development and performance of these models. Addressing this challenge is crucial for enhancing clinical decision-making and patient care. Existing methods for handling missing data, ranging from simple imputation to more sophisticated approaches, often fall short of capturing the temporal dynamics inherent in EHRs. To bridge this gap, we introduce the Deep Stochastic Time-series Imputation (Deep STI) algorithm, an innovative end-to-end deep learning model that seamlessly integrates a sequence-to-sequence generative network with a prediction network. Deep STI is designed to leverage the observed time-series data in EHRs, learning to infer missing values from the temporal context with high accuracy. We evaluated Deep STI on the liver cancer data from the National Taiwan University Hospital (NTUH), Taiwan. Our results showed that Deep STI achieved better 5-year hepatocellular carcinoma predictions (19.21% in the area under the precision-recall curve) than extreme gradient boosting (18.15%) and Transformer (18.09%). The ablation study also illustrates the efficacy of our generative architecture design compared to regular imputations. This approach not only promises to improve the reliability of disease analysis in the presence of incomplete data but also sets a new standard for utilizing EHRs in predictive healthcare. Our work aims to advance the field of healthcare analytics and open new avenues for research in deep learning applications to EHRs.

关键词： Deep learning Time series analysis Stochastic processes Predictive models Transformers Imputation Reliability Electronic medical records Standards Diseases

Effect of electron- and hole-doping on properties of kagomé-lattice ferromagnet Fe3Sn2

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arXiv 2023年

作者： Adams, Milo Huang, Chen Shatruk, Michael Department of Chemistry and Biochemistry Florida State University TallahasseeFL32306-4390 United States Department of Scientific Computing Florida State University TallahasseeFL32306-4120 United States Materials Science and Engineering Program Florida State University TallahasseeFL32310 United States National High Magnetic Field Laboratory TallahasseeFL32310 United States

We report a theoretical investigation of effects of Mn and Co substitution in the transition metal sites of the kagomé-lattice ferromagnet, Fe3Sn2. Herein, hole- and electron-doping effects of Fe3Sn2 have been studied by density-functional theory calculations on the parent phase and on the substituted structural models of Fe3–xMxSn2 (M = Mn, Co;x = 0.5, 1.0). All optimized structures favor the ferromagnetic ground state. Analysis of the electronic density of states (DOS) and band structure plots reveals that the hole (electron) doping leads to a progressive decrease (increase) in the magnetic moment per Fe atom and per unit cell overall. The high DOS is retained nearby the Fermi level in the case of both Mn and Co substitutions. The electron doping with Co results in the loss of nodal band degeneracies, while in the case of hole doping with Mn emergent nodal band degeneracies and flatbands initially are suppressed in Fe2.5Mn0.5Sn2 but re-emerge in Fe2MnSn2. These results provide key insights into potential modifications of intriguing coupling between electronic and spin degrees of freedom observed in Fe3Sn2. Copyright © 2023, The Authors. All rights reserved.

关键词： Ferromagnetism