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Distributed Trust Management for Secured Localization systems in Mobile Cloud Services

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Distributed Trust Management for Secured Localization System...

IEEE International Conference on Utility and Cloud Computing (UCC)

作者： Farouq Aliyu Mustafa Ghaleb Saud Mohammad Mostafa Sani Umar AbdalRahman Aburakhia Center of Excellence in Dev of NPO King Fahd University of Petr. & Min. Dhahran Saudi Arabia IRC for Intelligent Secure Systems King Fahd University of Petr. & Min. Dhahran Saudi Arabia Computer Engineering Department King Fahd University of Petr. & Min. Dhahran Saudi Arabia Dept. of Electrical Engr. and Computer Sci. Khalifa University Abu Dhabi UAE Information & Computer Science King Fahd University of Petr. & Min. Dhahran Saudi Arabia

ISBN: (数字)9798350367201

ISBN: (纸本)9798350367218

The market for the Internet of Things (IoT) is expanding at an exponential rate. As IoT devices increasingly rely on cloud computing for data processing, storage, and management, the security of cloud-based IoT services becomes paramount. Many of these services depend on the IoT device’s location. Therefore, secured localization systems are necessary. However, traditional security techniques are resource-consuming. An alternative method for detecting localization attacks in IoT is through trust models. This paper proposes a distributed trust management technique for localization in Mobile IoT, specifically designed to enhance the security and trustworthiness of cloud-based IoT services. The trust of a node is computed based on its availability and service quality. Nodes use this information to calculate their neighbors’ and anchor nodes’ trust values. We simulated the proposed system using the Cooja simulator. The results show that the proposed trust-based localization technique can help unknown nodes mobilize and reach a target location, reducing the control group’s error by 186.4% at the cost of 4.8 μJ only. The system finds application in indoor localization for location-based cloud services, such as locating shops of items in shopping malls.

关键词： Location awareness Cloud computing Costs Computational modeling Data processing Internet of Things Security Trust management

Spectroscopic detection of pigments in tissues: correlation with tissue aging and cancer development

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Spectroscopic detection of pigments in tissues: correlation ...

2022 International Conference Laser Optics, ICLO 2022

作者： Oliveira, L.M. Goncalves, T.M. Botelho, A.R. Martins, I.S. Silva, H.F. Carneiro, I. Carvalho, S. Henrique, R. Tuchin, V.V. Porto Portugal Center of Innovation in Engineering and Industrial Technology Isep Porto Portugal Department of Electrical and Computer Engineering Porto University-School of Engineering Porto Portugal Department of Pathology and Cancer Biology and Epigenetics Group Portuguese Oncology Institute of Porto Porto Portugal Porto Portugal Viana do Castelo Portugal Dept. of Pathol. and Molecular Immunology Porto University-Institute of Biomedical Sciences Abel Salazar Porto Portugal Science Medical Center Saratov State University Saratov Russia Laboratory of Laser Molecular Imaging and Machine Learning National Research Tomsk State University Tomsk Russia Laboratory of Laser Diagnostics of Technical and Living Systems Institute of Precision Mechanics and Control Frc-Saratov Scientific Centre-of the Russian Academy of Sciences Saratov Russia

ISBN: (纸本)9781665466646

The direct calculation of the absorption coefficient spectra of various tissues from spectral measurements allowed to retrieve the contents of melanin and lipofuscin. In the rabbit brain cortex, 1.8 times higher melanin content is explained by the neuron degeneration process. Similar melanin and lipofuscin contents were found in the rabbit pancreas as a result of the tissue aging process. The conversion of 83 % of the melanin in the human normal kidney into lipofuscin in the cancer kidney indicates that lipofuscin can be considered a kidney cancer marker in humans. © 2022 IEEE.

关键词： Tissue

Combining imitation and deep reinforcement learning to accomplish human-level performance on a virtual foraging task

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arXiv 2022年

作者： Giammarino, Vittorio Dunne, Matthew F. Moore, Kylie N. Hasselmo, Michael E. Stern, Chantal E. Paschalidis, Ioannis Ch. Division of Systems Engineering Boston University BostonMA02446 United States Dept. of Electrical and Computer Engineering Boston University BostonMA02446 United States Dept. of Biomedical Engineering Boston University BostonMA02215 United States Cognitive Neuroimaging Center Boston University BostonMA02215 United States Graduate Program for Neuroscience Boston University BostonMA02215 United States Center for Systems Neuroscience Boston University BostonMA02215 United States

We develop a simple framework to learn bio-inspired foraging policies using human data. We conduct an experiment where humans are virtually immersed in an open field foraging environment and are trained to collect the highest amount of rewards. A Markov Decision Process (MDP) framework is introduced to model the human decision dynamics. Then, Imitation Learning (IL) based on maximum likelihood estimation is used to train Neural Networks (NN) that map human decisions to observed states. The results show that passive imitation substantially underperforms humans. We further refine the human-inspired policies via Reinforcement Learning (RL) using the on-policy Proximal Policy Optimization (PPO) algorithm which shows better stability than other algorithms and can steadily improve the policies pretrained with IL. We show that the combination of IL and RL can match human results and that good performance strongly depends on combining the allocentric information with an egocentric representation of the environment. © 2022, CC BY.

关键词： Reinforcement learning

Twenty Years of Personality Computing: Threats, Challenges and Future Directions

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arXiv 2025年

作者： Celli, Fabio Kartelj, Aleksandar Ðorðević, Miljan Suhartono, Derwin Filipović, Vladimir Milutinović, Veljko Spathoulas, Georgios Vinciarelli, Alessandro Kosinski, Michal Lepri, Bruno Maggioli SpA Italy Faculty of Mathematics University of Belgrade Serbia School of Electrical Engineering University of Belgrade Serbia School of Computer Science Bina Nusantara University Indonesia Dept. of Information Security Norwegian University of Science and Technology Norway School of Computing Science University of Glasgow United Kingdom Graduate School of Business Stanford University United States Center for Augmented Intelligence Fondazione Bruno Kessler Italy

Personality Computing is a field at the intersection of Personality Psychology and computer Science. Started in 2005, research in the field utilizes computational methods to understand and predict human personality traits. The expansion of the field has been very rapid and, by analyzing digital footprints (text, images, social media, etc.), it helped to develop systems that recognize and even replicate human personality. While offering promising applications in talent recruiting, marketing and healthcare, the ethical implications of Personality Computing are significant. Concerns include data privacy, algorithmic bias, and the potential for manipulation by personality-aware Artificial Intelligence. This paper provides an overview of the field, explores key methodologies, discusses the challenges and threats, and outlines potential future directions for responsible development and deployment of Personality Computing technologies. © 2025, CC BY-NC-ND.

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Non-Debye impedance and relaxation models for dissipative electrochemical capacitors

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arXiv 2022年

作者： Allagui, Anis Benaoum, Hachemi Elwakil, Ahmed S. Alshabi, Mohammad Dept. of Sustainable and Renewable Energy Engineering University of Sharjah P.O. Box Sharjah27272 United Arab Emirates Dept. of Mechanical and Materials Engineering Florida International University MiamiFL33174 United States Dept. of Applied Physics and Astronomy University of Sharjah PO Box 27272 Sharjah United Arab Emirates Dept. of Electrical and Computer Engineering University of Sharjah P.O. Box Sharjah27272 United Arab Emirates Nanoelectronics Integrated Systems Center Nile University Cairo12588 Egypt Dept. of Electrical and Computer Engineering University of Calgary CalgaryABT2N 1N4 Canada Dept. of Mechanical and Nuclear Engineering University of Sharjah PO Box 27272 Sharjah United Arab Emirates

Electrochemical capacitors are a class of energy devices in which complex mechanisms of accumulation and dissipation of electric energy take place when connected to a charging or discharging power system. Reliably modeling their frequency-domain and time-domain behaviors is very important for their proper design and integration in engineering applications, knowing that electrochemical capacitors in general exhibit anomalous tendency that cannot be adequately captured with traditional integer-order-based models. In this study we first review some of the widely used fractional-oder models for the description of impedance and relaxation functions of dissipative resistive-capacitive system, namely the Cole-Cole, Davidson-Cole, and Havriliak-Negami models. We then propose and derive new q-deformed models based on modified evolution equations for the charge or voltage when the device is discharged into a parallel resistive load. We verify our results on anomalous spectral impedance response and time-domain relaxation data for voltage and charge obtained from a commercial supercapacitor. © 2022, CC BY.

关键词： Frequency domain analysis

A Survey on Explainable Artificial Intelligence for Cybersecurity

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arXiv 2023年

作者： Rjoub, Gaith Bentahar, Jamal Wahab, Omar Abdel Mizouni, Rabeb Song, Alyssa Cohen, Robin Otrok, Hadi Mourad, Azzam King Hussein School of Computing Sciences Princess Sumaya University for Technology Jordan Concordia Institute for Information Systems Engineering Concordia University Montreal Canada Department of Computer and Software Engineering Polytechnique Montréal Montréal Canada Electrical Engineering and Computer Science Department Khalifa University Abu Dhabi United Arab Emirates Department of EECS Khalifa University Abu Dhabi United Arab Emirates David R. Cheriton School of Computer Science University of Waterloo Canada Cyber Security Systems and Applied AI Research Center Dept. of CSM Lebanese American University Lebanon Division of Science New York University Abu Dhabi United Arab Emirates

The "black-box" nature of artificial intelligence (AI) models has been the source of many concerns in their use for critical applications. Explainable Artificial Intelligence (XAI) is a rapidly growing research field that aims to create machine learning models that can provide clear and interpretable explanations for their decisions and actions. In the field of cybersecurity, XAI has the potential to revolutionize the way we approach network and system security by enabling us to better understand the behavior of cyber threats and to design more effective defenses. In this survey, we review the state of the art in XAI for cybersecurity and explore the various approaches that have been proposed to address this important problem. The review follows a systematic classification of cybersecurity threats and issues in networks and digital systems. We discuss the challenges and limitations of current XAI methods in the context of cybersecurity and outline promising directions for future research. © 2023, CC BY.

关键词： Cybersecurity

Radio-Frequency Pseudo-Null Induced by Light in an Ion Trap

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arXiv 2025年

作者： Chung, Daun Cha, Yonghwan Shon, Hosung Park, Jeonghyun Lee, Woojun Lee, Kyungmin Cho, Beomgeun Choi, Kwangyeul Kim, Chiyoon Yoo, Seungwoo Kim, Suhan Jeong, Uihwan Kang, Jiyong You, Jaehun Kim, Taehyun Dept. of Computer Science and Engineering Seoul National University Seoul08826 Korea Republic of Automation and Systems Research Institute Seoul National University Seoul08826 Korea Republic of NextQuantum Seoul National University Seoul08826 Korea Republic of Institute of Computer Technology Seoul National University Seoul08826 Korea Republic of Inter-University Semiconductor Research Center Seoul National University Seoul08826 Korea Republic of Institute of Applied Physics Seoul National University Seoul08826 Korea Republic of

In a linear radio-frequency (rf) ion trap, the rf null is the point of zero electric field in the dynamic trapping potential where the ion motion is approximately harmonic. When displaced from the rf null, the ion is superimposed by fast oscillations known as micromotion, which can be probed through motion-sensitive light-atom interactions. In this work, we report on the emergence of the rf pseudo-null, a locus of points where the ion responds to light as if it were at the true rf null, despite being displaced from it. The phenomenon is fully explained by accounting for the general two-dimensional structure of micromotion and is experimentally verified under various potential configurations, with observations in great agreement with numerical simulations. The rf pseudo-null manifests as a line in a two-dimensional parameter space, determined by the geometry of the incident light and its overlap with the motional structure of the ion. The true rf null occurs uniquely at the concurrent point of the pseudo-null lines induced by different light sources. © 2025, CC BY.

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Photonic microwave and RF spectrum channelizers based on optical Kerr micro-combs

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TechRxiv

TechRxiv 2021年

作者： Moss, David J. Tan, M. Xu, X. Optical Sciences Centre Swinburne University of Technology HawthornVIC3122 Australia Dept. of Electrical and Computer Systems Engineering Monash University ClaytonVIC3800 Australia

We review recent work on broadband RF channelizers based on integrated optical frequency Kerr microcombs combined with passive micro-ring resonator filters, with microcombs having channel spacings of 200GHz and 49GHz. This approach to realizing RF channelizers offers reduced complexity, size, and potential cost for a wide range of applications to microwave signal detection. © 2021, CC BY.

关键词： Natural frequencies

Photonic single perceptron at Giga-OP/s speeds with Kerr microcombs for scalable optical neural networks

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arXiv 2021年

作者： Tan, Mengxi Xu, Xingyuan Moss, David J. Optical Sciences Centre Swinburne University of Technology HawthornVIC3122 Australia Dept. of Electrical and Computer Systems Engineering Monash University ClaytonVIC3800 Australia

Optical artificial neural networks (ONNs) have significant potential for ultra-high computing speed and energy efficiency. We report a novel approach to ONNs that uses integrated Kerr optical micro-combs. This approach is programmable and scalable and is capable of reaching ultra-high speeds. We demonstrate the basic building block ONNs — a single neuron perceptron — by mapping synapses onto 49 wavelengths to achieve an operating speed of 11.9 x 109 operations per second, or Giga-OPS, at 8 bits per operation, which equates to 95.2 gigabits/s (Gbps). We test the perceptron on handwritten-digit recognition and cancer-cell detection — achieving over 90% and 85% accuracy, respectively. By scaling the perceptron to a deep learning network using off-the-shelf telecom technology we can achieve high throughput operation for matrix multiplication for real-time massive data processing. © 2021, CC BY.

关键词： Energy efficiency