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Integrated Circuits and Communication systems (ICICACS), IEEE International Conference on

作者： Fateh Bahadur Kunwar Satyajee Srivastava R Dhivya Monika Gulati Taruna Anand D Antony Arul Raj Department of Computer Science and Engineering Geetanjali Institute of Technical Studies Udaipur India Department of Computer Science and Engineering School of Engineering and Technology Manav Rachna International Institute of Research and Studies Faridabad India Department of Information Technology M.Kumarasamy College of Engineering Karur India MMIM Maharishi Markandeshwar (Deemed to be University) Ambala India Department of Humanities and Social Sciences Graphic Era (Deemed to be) University Dehradun India Department of Software Systems PSG College of Arts & Science Coimbatore India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

AI's revolutionary potential in higher education is examined in this proposal, including how it could revolutionize teaching, learning, administration, and research. Adaptive learning platforms, intelligent tutoring systems, and virtual learning assistants may change how students learn by improving engagement, providing quick feedback, and enabling tailored learning. The suggested process includes preprocessing, feature extraction, and model training. Preprocessing involves de-anonymizing, tokenizing, and removing punctuation, spaces, and numbers before analysis. The linear transformation method PCA extracts features. Training the systems uses the RF -SVM approach, which combines the advantages of RF and SVM. The suggested RF -SVM model beats two RF and SVM models with an average accuracy of 90.39 *** improves resource use, strategic decision-making, and research capacity in university management. The RF-SVM model's success in academia highlights the practicality of AI-driven solutions.

关键词： Support vector machines Radio frequency Training Accuracy Education Symbols Feature extraction Tokenization Integrated circuit modeling Principal component analysis

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Discovering Complex Correlations Among Multiple IoT Devices in Smart Environments

Discovering Complex Correlations Among Multiple IoT Devices ...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Andrew D'Angelo Chenglong Fu Xiaojiang Du Paul Ratazzi Department of Electrical and Computer Engineering Stevens Institute of Technology Hoboken NJ USA Department of Software and Information Systems University of North Carolina at Charlotte Charlotte NC USA Air Force Research Laboratory Rome NY USA

The ubiquity of the Internet of Things (IoT) in a vast range of consumer applications is unparalleled. Unfortunately, despite the benefits of IoT, its widespread integration comes with significant security challenges. Considering IoT devices' capability to interact with the physical environment, there is an urgent need for effective anomaly detection. The state-of-the-art anomaly detection method, HAWatcher, models the normal behaviors of smart homes with inter-device correlations and demonstrates great results. Nonetheless, it is limited to capturing only simple one-to-one correlations between two events or states, which undermines its capability to detect anomalies in more complicated environments. To address this issue, we present a novel correlation discovering method to mine complex two-to-one correlations in such complicated IoT-enabled environments. We conduct experiments over two weeks on four smart home testbeds and obtain 70 two-to-one correlations. The correlations are applied to 9 anomaly scenarios, which show significant improvements in detecting anomalies over one-to-one correlations.

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Benchmarking Cryptographic Protocols for (IoT) Malware Defence

Benchmarking Cryptographic Protocols for (IoT) Malware Defen...

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International Symposium on Applied Machine Intelligence and Informatics (SAMI)

作者： Khalil Ben Khaled Dönìz Borsos Jozsef Katona Kandó Kalmán Faculty of Electrical Engineering Institute of Microelectronics and Technology Obuda University Budapest Hungary Kándó Kálmán Faculty of Electrical Engineering Institute of Electronics and Communication Systems Obuda University Budapest Hungary Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary GAMF Faculty of Engineering and Computer Science John Von Neumann University Kecskemet Hungary Department of Applied Quantitative Methods Faculty of Finance and Accountancy Budapest Business University Budapest Hungary

ISBN: (数字)9798350379365

ISBN: (纸本)9798350379372

This study presents a comprehensive benchmarking analysis of cryptographic protocols for Internet of Things (IoT) malware defense. The framework was specifically tailored to evaluate cryptographic protocols such as AES-128, AES-256, ChaCha20, RSA (1024-bit, 2048-bit, and 4096-bit), SHA256, SHA512, and HMAC-SHA256 were tested in both standalone and emulated environments. The primary objective was to evaluate the performance and resource consumption of these protocols, focusing on their encryption and hashing efficiency. Key innovations include the comparative analysis of resource consumption and performance efficiency across diverse cryptographic operations, under both real-world and emulated conditions. By identifying protocols like ChaCha20 for high efficiency and minimal resource usage, and RSA 4096-bit for enhanced security at higher computational costs, this study provides actionable insights into the trade-offs between security and performance. These findings offer a foundational reference for selecting optimized cryptographic protocols, advancing IoT malware defense strategies through informed decision-making.

关键词： Performance evaluation Technological innovation Benchmark testing Malware Encryption Internet of Things Reliability Informatics Machine intelligence Cryptographic protocols

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A Melting Pot of Evolution and Learning

arXiv

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arXiv 2023年

作者： Sipper, Moshe Elyasaf, Achiya Halperin, Tomer Haramaty, Zvika Lapid, Raz Segal, Eyal Tzruia, Itai Tamam, Snir Vitrack Department of Computer Science Ben-Gurion University of the Negev Beer-Sheva8410501 Israel Department of Software and Information Systems Engineering Ben-Gurion University of the Negev Beer-Sheva8410501 Israel DeepKeep Tel-Aviv Israel

We survey eight recent works by our group, involving the successful blending of evolutionary algorithms with machine learning and deep learning: 1. Binary and Multinomial Classification through Evolutionary Symbolic Regression, 2. Classy Ensemble: A Novel Ensemble Algorithm for Classification, 3. EC-KitY: Evolutionary Computation Tool Kit in Python, 4. Evolution of Activation Functions for Deep Learning-Based Image Classification, 5. Adaptive Combination of a Genetic Algorithm and Novelty Search for Deep Neuroevolution, 6. An Evolutionary, Gradient-Free, Query-Efficient, Black-Box Algorithm for Generating Adversarial Instances in Deep Networks, 7. Foiling Explanations in Deep Neural Networks, 8. Patch of Invisibility: Naturalistic Black-Box Adversarial Attacks on Object Detectors. Copyright © 2023, The Authors. All rights reserved.

关键词： Genetic programming

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VoiceGuard: An Effective and Practical Approach for Detecting and Blocking Unauthorized Voice Commands to Smart Speakers

VoiceGuard: An Effective and Practical Approach for Detectin...

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International Conference on Dependable systems and Networks (DSN)

作者： Xuening Xu Chenglong Fu Xiaojiang Du E. Paul Ratazzi Department of Electrical and Computer Engineering Stevens Institute of Technology Hoboken NJ USA Department of Software and Information Systems UNC Charlotte Charlotte NC USA Information Directorate Air Force Research Laboratory Rome NY USA

Smart speakers bring convenience to people's daily lives. However, various attacks can be launched against smart speakers to execute malicious commands, which may cause serious safety or security issues. The existing solutions against sophisticated attacks such as voice replay attacks and voice synthesis attacks require intrusive modifications of the smart speaker hardware and/or software, which are impractical for general users. In this work, we present a novel security scheme- VoiceGuard that can effectively detect and block unauthorized voice commands to smart speakers. VoiceGuard does not require any modification to smart speakers' hardware or software. We implement a prototype of VoiceGuard on two popular smart speakers: Amazon Echo Dot and Google Home Mini, and evaluate the scheme in three real-world testbeds, which include both single-user and multi-user scenarios. The experimental results show that VoiceGuard achieves an accuracy of 97% in blocking malicious voice commands issued by illegitimate sources while having a negligible impact on the user experience.

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Signal Processing Techniques from Accelerometers on Railway Transport Based on the Wavelet Transform

Signal Processing Techniques from Accelerometers on Railway ...

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IEEE NW Russia Young Researchers in Electrical and Electronic engineering Conference (EIConRusNW)

作者： Alexander M. Boronakhin Alexandra V. Bolshakova Dmitry M. Klionskiy Daniil Yu. Larionov Roman V. Shalymov dept. of Laser Measurement and Navigational Systems faculty of Information Measurement and Biotechnical Systems Saint Petersburg Electrotechnical University “LETI” Saint Petersburg Russian Federation dept. of Laser Measurement and Navigational Systems Saint Petersburg Electrotechnical University “LETI” Saint Petersburg Russian Federation dept. of Information Systems dept. of Software Engineering and Computer Applications Saint Petersburg Electrotechnical University “LETI” Saint Petersburg Russian Federation

Railway transport safety is directly connected with the condition of railway tracks and also the condition of railway wheels’ surface. One of the main reasons for abnormal situations on railways are various defects such as irregularities of a railway track. Thus, we need to estimate the length of short and impulse irregularities. It is also important to carry out joint analysis of vibration acceleration signals recorded by accelerometers in order to study types and sizes of railway track irregularities.

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Investigating Online Searching Behavior Based on Google Trends in MENA Region Before and After COVID-19

Investigating Online Searching Behavior Based on Google Tren...

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Frontiers in Education (FIE) Conference

作者： Abdelkader Nasreddine Belkacem Nuraini Jamil Saed Alrabaee Dept. of Computer and Network Engineering College of IT UAE University Al Ain United Arab Emirates Dept. of Computer Science and Software Engineering College of IT UAE University Al Ain United Arab Emirates Dept. of Information Systems and Security College of IT UAE University Al Ain United Arab Emirates

The outbreak of the coronavirus disease (COVID-19) has had a profound impact on education worldwide. The rise of remote learning is one of the most significant changes in this regard, as many schools and universities were forced to close down by regional health authorities. This has also caused people to become more conservative in trade-offs between healthcare and education. Google Trends is the most common tool for analyzing online search behaviors. It is a free resource that provides information on the trends and changes in users' online interests over time based on certain terms and subjects. The online search queries on Google can be used to assess users' behaviors concerning online learning to forecast their choices regarding online education. This paper examines the frequency of users' web searches for online communication tools, courses, and learning terms. We statistically compared users in the Middle East and North Africa regions by using the volumes of searches recorded on Google Trends from January 2016 to August 2022. Moreover, we used machine learning techniques to identify differences among the keywords used. The findings statistically show that COVID-19 has led to an increase in the extent of students' attention to and interest in online learning.

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Street Light Cum Garbage System Optimal Design Based on IOT

Street Light Cum Garbage System Optimal Design Based on IOT

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Emerging Trends in Information Technology and engineering (ic-ETITE), Conference on

作者： Rajesh G Deepa J Ezilarasan M R Syed Fiaz A S Siva Rama Lingham N J. Jagannathan Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Information Technology Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

Street lights currently use more energy than other types of lighting because of an inefficient mechanism that makes the bulbs use a lot of electricity. The suggested approach uses various sensors on intelligent streetlights to keep an eye on and manage the lights. It has sensors for measuring temperature, brightness, and power that control dimming levels and keep track of status. One Zigbee network serves as the connection point for these lights. In the modification, a cloud- and IoT-based installation was made. Through Zigbee -based data transmission, sensor values are recorded on a distant server known as the Cloud. Additionally, it lowers electricity theft. We include a trash can notice in addition to this street light idea. When the garbage can is whole, a machine automatically notifies the business as people approach the sun, and the brightness of the street lights changes or is increased.

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Open Knowledge Base Canonicalization with Multi-task Unlearning

arXiv

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arXiv 2023年

作者： Liu, Bingchen Hou, Shihao Zeng, Weixin Zhao, Xiang Liu, Shijun Pan, Li The School of Software Shandong University Jinan250098 China The School of Computer Science and Technology Shandong University of Finance and Economics Jinan250000 China The College of Systems Engineering National University of Defense Technology Changsha410073 China

The construction of large open knowledge bases (OKBs) is integral to many applications in the field of mobile computing. Noun phrases and relational phrases in OKBs often suffer from redundancy and ambiguity, which calls for the investigation on OKB canonicalization. However, in order to meet the requirements of some privacy protection regulations and to ensure the timeliness of the data, the canonicalized OKB often needs to remove some sensitive information or outdated data. The machine unlearning in OKB canonicalization is an excellent solution to the above problem. Current solutions address OKB canonicalization by devising advanced clustering algorithms and using knowledge graph embedding (KGE) to further facilitate the canonicalization process. Effective schemes are urgently needed to fully synergise machine unlearning with clustering and KGE learning. To this end, we put forward a multi-task unlearning framework, namely MulCanon, to tackle machine unlearning problem in OKB canonicalization. Specifically, the noise characteristics in the diffusion model are utilized to achieve the effect of machine unlearning for data in OKB. MulCanon unifies the learning objectives of diffusion model, KGE and clustering algorithms, and adopts a two-step multi-task learning paradigm for training. A thorough experimental study on popular OKB canonicalization datasets validates that MulCanon achieves advanced machine unlearning effects. Copyright © 2023, The Authors. All rights reserved.

关键词： Diffusion

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Exploring the Potential of 3d Printing Technology for Sustainable Plastic Roads: A Preliminary Investigation

SSRN

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SSRN 2023年

作者： Al-Atroush, Mohamed Ezzat Almushcab, Jumana Alhudaif, Duha Meskinyar, Yosra Civil and Environmental Engineering Program College of Engineering Prince Sultan University Riyadh11543 Saudi Arabia Software Engineering College of Computer and Information Systems Prince Sultan University Riyadh11543 Saudi Arabia Architectural Engineering Department College of Architecture and Design Prince Sultan University Riyadh11543 Saudi Arabia

The urgency of climate change has highlighted the need for sustainable road construction materials, replacing the conventional asphalt that significantly contributes to global warming and the urban heat island effect. With that in mind, the construction of the world's first 30-meter plastic road in Zwolle, Netherlands, in 2018 opened the door for novel plastic applications as paving materials. However, the current application is still limited to sidewalks and light-loaded cycling lanes. This preliminary study investigated the feasibility of using 3D printing technology to provide the required structural design flexibility for manufacturing plastic pavement modules able to sustain heavy traffic and extreme weather conditions. The suitability of six plastic materials for 3D printing was evaluated. Polylactic Acid (PLA), and Polyethylene Terephthalate Glycol (PETG) were identified as the most suitable materials for this study. Three small-scale structural systems, namely hollow modular with plastic columns, hollow modular with solid plastic cones, and hollow modular with X-bracing, were designed and successfully printed using the adopted materials and a 3D printer. The developed systems were then tested under compression to evaluate their structural performance under heavy traffic loads and prove the concept. The results showed that the PLA conic structural system was effective and exhibited the highest compression strength, while PETG conic system exhibited ductile behavior with superior thermal stability. The study suggested that the hybrid system of PLA and PETG materials may improve overall performance, combine flexibility and strength, and potentially improve resistance to extreme weather and heavy traffic. These findings proved that 3D printing technology has the potential to revolutionize the road construction industry and provide more sustainable solutions for infrastructure development. © 2023, The Authors. All rights reserved.

关键词： Plastic bottles

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