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2020 International Conference on Computational science and Computational Intelligence, CSCI 2020

作者： Mulahuwaish, Aos Towne, Wyatt Ghafoor, Kayhan Zrar Maghdid, Halgurd S. Saginaw Valley State University Dept. of Computer Science and Information Systems Michigan United States Salahaddin University-Erbil Dep. of Software Engineering and Informatics Iraq Koya University Faculty of Engineering Dept. of Software Engineering Kurdistan Region-F.R. Iraq

ISBN: (纸本)9781728176246

The positioning accuracy of various kinds of devices is often calculated by using Wi-Fi fingerprints as a data source of Received Signal Strength (RSS). Through different matching algorithms, these RSS values are related to corresponding positioning coordinates for positioning estimation. In this paper, we compare two well known Machine Learning (ML) structures for calculating positioning accuracy: Recurrent Neural Network-Long Short-Term Memory (RNN-LSTM) and a Decision Tree (DT). Both matching algorithms run through a series of datasets in two parts. In the first part, the datasets contain raw, non-normalized RSS values. In the second part, the RSS values are all normalized to remove noise in the datasets. After extensive testing and simulations, the tests showed that as long as RSS values are first normalized, a Decision Tree offers better average positioning accuracy. © 2020 IEEE.

关键词： Decision trees

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Characterization of Glioblastoma Organoid Bio-Responses Upon 30.5 GHz Continuous Wave Exposure

Characterization of Glioblastoma Organoid Bio-Responses Upon...

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IEEE MTT-S International Microwave Bio Conference ((IMBIOC)

作者： Elena Rampazzo Nissar Karim Luca Persano Rosanna Pinto Arianna Casciati Mirella Tanori George Hodgkins Ilan W. Davies Alessandro Zambotti Cristopher Hancock Cristiano Palego Gimapietro Viola Mariateresa Mancuso Caterina Merla Dep. of Women's and Children's Health (SDB) University of Padova Padova Italy School of Computer Science and Electronic Engineering Bangor University Bangor UK Div. Health Protection Technologies ENEA Rome Italy Group R&D CREO Medical Bath UK Group R& School of Computer Science and Electronic Engineering D CREO Medical Bath UK

This paper reports on the effects of 30.5 GHz continuous wave exposure on cancer stem cells of glioblastoma organoids. The exposure setup was completely developed and the dosimetry carried on based on both numerical and experimental activities. Our results showed modification in cell death and proliferation, suggesting that presented exposure at a power level of 0.1 W (RMS) seems useful for targeting glioblastoma cells, and improving future treatment of this complex pathology.

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Development of a Low-Cost Interrogation System Using a MEMS Fabry-Pérot Tunable Filter 5

Development of a Low-Cost Interrogation System Using a MEMS ...

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5th International Conference on Applications of Optics and Photonics, AOP 2022

作者： Araújo, João C. C. Dias, Bernardo Dos Santos, Paulo S. S. De Almeida, J.M.M.M. Coelho, Luís C. C. INESC TEC - Institute for Systems and Computer Engineering Technology and Science FCUP Univ. of Porto R. Do Campo Alegre Porto4169-007 Portugal FCUP University of Porto Rua do Campo Alegre Porto4169-007 Portugal Dep. of Physics Univ. of Trás-os-Montes e Alto Douro Vila Real5001-801 Portugal

The interrogation of optic fiber sensors usually relies in complex and costly equipment with low portability due to their size such as Optical Spectrum Analyzers (OSA) or high-resolution spectrometers. Because of this, micro spectrometer devices, such as Micro-Electromechanical Systems (MEMS) with Fabry-Pérot tunable filters, are emerging as simpler and compact alternatives capable of being used to acquire spectral information in a wide wavelength band. In this work it is described the development of an interrogation system capable of infrared spectroscopy using a MEMS Fabry-Pérot Interferometer (MEMS-FPI) with a spectral response in the 1350nm to 1650nm range. Its performance is tested with the interrogation of long period fiber gratings both as a refractive index sensor and as a temperature sensor. Deconvolution techniques such as Wiener filtering are used to reduce the impact of the tunable filter's impulse response in the measured signal. Results are comparable to those obtained using a typical OSA which shows the system's potential as a cheaper and more transportable alternative. © Published under licence by IOP Publishing Ltd.

关键词： Fiber optic sensors

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Deep Learning-based Road Object Detection for Collision Avoidance in Autonomous Driving

Deep Learning-based Road Object Detection for Collision Avoi...

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Public Safety Technology (WFPST), IEEE World Forum on

作者： Teena Sharma Abdellah Chehri Issouf Fofana Benoit Debaque Nicolas Duclos Siddhartha Khare Department of Applied Sciences (DSA) University of Quebec in Chicoutimi Saguenay QC Canada Dep. of Mathem & Computer Science Royal Military College of Canada Kingston ON Canada Department of Applied Sciences University of Quebec in Chicoutimi Saguenay QC Canada Thales Communications and Security SAS Quebec Canada Department of Civil Engineering IIT Roorkee India

ISBN: (数字)9798350329148

ISBN: (纸本)9798350329155

With the population in metro cities rising, traffic congestion has become a significant issue, resulting in an alarming number of traffic collisions and road accidents. Given the advancements in automated vehicles, it is crucial to have a vehicle detection system that is equipped to handle the various challenges that arise on the road, particularly when it comes to traffic collisions. Although many datasets are available to support object detection for traffic monitoring and management, the suitability of these datasets for specific weather conditions across the globe needs to be analyzed. We present Canadian vehicle datasets (CVD) and analyze the YOLOv8-based deep learning model using CVD. Thales, Canada, captured street-level videos in Quebec City, Canada, with a vehicle equipped with a high-quality RGB camera. The videos are taken day and night in all four seasons: rain, snow, hazy, and bright sunlight. We created 27378 labels for 11 classes for 8000 images. The YOLOv8 model trained on the publicly available roboFlow dataset was compared to the model trained on the combined roboflow and our weather-specific CVD.

关键词： Road accidents Roads Snow Urban areas Sociology Transportation Object detection

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Cybersecurity Threats in the Era of AI: Detection of Phishing Domains Through Classification Rules

Cybersecurity Threats in the Era of AI: Detection of Phishin...

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Electrical, Energy, and Artificial Intelligence (EICEEAI), International engineering Conference on

作者： Mohammad Aljaidi Ayoub Alsarhan Dimah Al-Fraihat Ahmed Al-Arjan Bashar Igried Subhieh M. El-Salhi Muhammad Khalid Qais Al-Na'amneh Department of Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan Department of Information Technology The Hashemite University Jordan Department of Software Engineering Faculty of Information Technology Isra University Amman Jordan Department of Information Technology Faculty of Prince AL-Hussein bin Abdullah II of Information Technology The Hashemite University Jordan Department of Computer Science and Applications The Hashemite University Zarqa Jordan The Hashemite University Zarqa Jordan Department of Computer Sciences and Technology University of Hull Kingston upon Hull UK Dep. of Cyber Security Applied Science Private University Amman Jordan

ISBN: (数字)9798350373363

ISBN: (纸本)9798350373370

Phishing is a cyber threat where attackers create deceptive websites or emails to exploit individuals for fraudulent purposes such as providing sensitive information like usernames, passwords, or financial details. Hence, detecting phishing emails is of paramount importance to protect sensitive information, preventing financial losses, protecting against identity theft, and maintaining trust. This study employs machine learning techniques to classify and categorize websites that are used for phishing attacks. To achieve this, we proposed a classification model for the detection of phishing domains based on classification rules. These rules were formulated based on analyzing and distributing them into three classifiers: domain name features, search engine opinion (SEO) information of the domain, and security scans by security vendors. A total of eleven classification rules were formulated which were distributed as follows: six rules for the first classifier, four rules for the second classifier, and one rule for the third classifier. Each classifier feeds its results to the main classifier, which then evaluates the received results based on its rules. Domain names from 222 confirmed phishing URLs were used to test the suggested model. The model demonstrated exceptional performance, achieving an impressive accuracy rate of 99.1% during testing.

关键词： Adaptation models Accuracy Phishing Identity theft Refining Machine learning Passwords

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Automating Public Announcement Logic with Relativized Common Knowledge as a Fragment of HOL in LogiKEy

arXiv

引用

arXiv 2021年

作者： Benzmüller, Christoph Reiche, Sebastian University of Bamberg AI Systems Engineering Bamberg Germany FU Berlin Dep. of Mathematics and Computer Science Berlin Germany

A shallow semantical embedding for public announcement logic with relativized common knowledge is presented. This embedding enables the first-time automation of this logic with off-the-shelf theorem provers for classical higher-order logic. It is demonstrated (i) how meta-theoretical studies can be automated this way, and (ii) how non-trivial reasoning in the target logic (public announcement logic), required e.g. to obtain a convincing encoding and automation of the wise men puzzle, can be realized. Key to the presented semantical embedding is that evaluation domains are modeled explicitly and treated as an additional parameter in the encodings of the constituents of the embedded target logic;in previous related works, e.g. on the embedding of normal modal logics, evaluation domains were implicitly shared between meta-logic and target logic. The work presented in this article constitutes an important addition to the pluralist LogiKEy knowledge engineering methodology, which enables experimentation with logics and their combinations, with general and domain knowledge, and with concrete use cases - all at the same *** Codes 03B42, 03B60, 03B62, 68T27, 68T30 Copyright © 2021, The Authors. All rights reserved.

关键词： Formal logic

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LLM Acceleration on FPGAs: A Comparative Study of Layer and Spatial Accelerators

LLM Acceleration on FPGAs: A Comparative Study of Layer and ...

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IEEE Central America and Panama Convention (CONCAPAN)

作者： Luis D. Prieto-Sibaja Luis G. Leόn-Vega Indra Leόn-Vega Jorge Castro-Godínez Stefano Cozzini Computer Engineering School Costa Rica Institute of Technology Cartago Costa Rica Math. Inf. and Geoscience Dep. University of Trieste Trieste Italy Industrial Design Engineering School Instituto Tecnolόgico de Costa Rica Cartago Costa Rica Electronics Engineering School Costa Rica Institute of Technology Cartago Costa Rica Institute of Research and Technology AREA Science Park Trieste Italy

ISBN: (数字)9798350366723

ISBN: (纸本)9798350366730

Large Language Models (LLMs) have emerged as the leading technique in Natural Language Processing due to their remarkable capabilities. These models are exceptionally complex, often utilising billions of parameters and consuming tens of gigabytes of memory unless optimised. Typically, LLMs are trained and accelerated using general-purpose Graphics Processing Units (GPUs), with a single inference potentially occupying an entire GPU. Consequently, cloud services may require hundreds of GPUs to deliver high-quality AI assistant services. This demand for extensive hardware acceleration allows exploring alternative architectures that offer improved execution time and energy *** study evaluates Field-Programmable Gate Arrays (FP-GAs), renowned for their flexibility and efficiency, to accelerate LLMs. We specifically compare the resource consumption and latency of two distinct architectures: layer and spatial accelerators. Analysing the Llama 2-7B model, we identified potential optimisation opportunities within its composition and operational graphs. Our most successful implementations demonstrate a performance improvement ranging from 1.37× to 10.98× over two AMD EPYC processors with 64 cores each. Moreover, our results indicate that, with further refinement, FPGAs have the potential to surpass GPU performance for LLM inference, showcasing their feasibility as a viable alternative for this demanding application.

关键词： Large language models High performance computing Graphics processing units computer architecture Logic gates Distance measurement Natural language processing Field programmable gate arrays Hardware acceleration Optimization

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Preventing unauthorized use of proprietary data: Poisoning for secure dataset release

arXiv

引用

arXiv 2021年

作者： Fowl, Liam Chiang, Ping-Yeh Goldblum, Micah Geping, Jonas Bansal, Arpit Czaja, Wojtek Goldstein, Tom Department of Mathematics University of Maryland United States Department of Computer Science University of Maryland United States Dep. of Electrical Engineering and Computer Science University of Siegen Germany

Large organizations such as social media companies continually release data, for example user images. At the same time, these organizations leverage their massive corpora of released data to train proprietary models that give them an edge over their competitors. These two behaviors can be in conflict as an organization wants to prevent competitors from using their own data to replicate the performance of their proprietary models. We solve this problem by developing a data poisoning method by which publicly released data can be minimally modified to prevent others from training models on it. Moreover, our method can be used in an online fashion so that companies can protect their data in real time as they release it. We demonstrate the success of our approach on ImageNet classification and on facial recognition. © 2021, CC BY.

关键词： Face recognition

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Gait-Based User Identification for Smart Home: When Machine Learning Meets FBG Accelerometers

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IEEE Transactions on Consumer Electronics 2024年

作者： Alhussein, Ghada Chi, Hao Ran Alberto, Nelia Antunes, Paulo Hadjileontiadis, Leontios Radwan, Ayman Domingues, M. Fatima Instituto de Telecomunicações-University of Aveiro Aveiro Portugal Instituto de Telecomunicações I3N and Dep. of Physics-University of Aveiro Aveiro Portugal Khalifa University of Science and Technology Department of Biomedical Engineering and Biotechnology Abu Dhabi United Arab Emirates Aristotle University of Thessaloniki Department of Electrical and Computer Engineering Thessaloniki Greece Instituto de Telecomunicações Instituto Superior Tècnico -University of Lisbon Lisbon Portugal

Advancements in fiber optic sensor technology and telecommunications have paved the way for innovative health monitoring systems. However, previous works in this area have often been limited by the lack of comprehensive datasets, hindering the development of accurate and robust solutions. This paper addresses this gap by presenting a novel dataset and approach to gait-based user identification using a set of four optical Fiber Bragg Grating (FBG) sensor-based accelerometers, integrated into smart home environments. By computing features such as entropy, mean, standard deviation, kurtosis, and skewness from raw signals, and employing three unsupervised machine learning models-K-means, DB-scan, and Gaussian Mixture Model (GMM)-we achieve high accuracy in distinguishing individuals. Our dataset results for each sensor are as follows: for Sensor 1, K-means with a Silhouette Score of 0.3038 and Davies-Bouldin Index of 1.1238;for Sensor 2, K-means with a Silhouette Score of 0.3543 and Davies-Bouldin Index of 0.9659;for Sensor 3, DB-scan with a Silhouette Score of 0.2650 and Davies-Bouldin Index of 0.6855;and for Sensor 4, K-means with a Silhouette Score of 0.3949 and Davies-Bouldin Index of 1.0438. This approach not only enhances user identification but also facilitates personalized healthcare applications and unobtrusive monitoring. © 1975-2011 IEEE.

关键词： Smart homes

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Vegetation and Drought Trends in Sweden's Mälardalen Region-Year-on-Year Comparison by Gaussian Process Regression

Vegetation and Drought Trends in Sweden's Mälardalen Region...

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2020 Swedish Workshop on Data science, SweDS 2020

作者： Paszkowsky, Nuria Agues Brannvall, Rickard Carlstedt, Johan Milz, Mathias Kovacs, Gyorgy Liwicki, Marcus Unit for Data Center Systems and Applied Data Science Research Institutes of Sweden Sweden Luleå University of Technology Dep. of Computer Science Electrical and Space Engineering Sweden

ISBN: (纸本)9781728192048

This article describes analytical work carried out in a pilot project for the Swedish Space Data Lab (SSDL), which focused on monitoring drought in the Mälardalen region in central Sweden. Normalized Difference Vegetation Index (NDVI) and the Moisture Stress Index (MSI)-commonly used to analyse drought- A re estimated from Sentinel 2 satellite data and averaged over a selection of seven grassland areas of interest. To derive a complete time-series over a season that interpolates over days with missing data, we use Gaussian Process Regression, a technique from multivariate Bayesian analysis. The analysis show significant differences at 95% confidence for five out of seven areas when comparing the peak drought period in the dry year 2018 compared to the corresponding period in 2019. A cross-validation analysis indicates that the model parameter estimates are robust for temporal covariance structure (while inconclusive for the spatial dimensions). There were no signs of over-fitting when comparing in-sample and out-of-sample RMSE. © 2020 IEEE.

关键词： Remote sensing

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