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IEEE International Conference on Performance, Computing and Communications (IPCCC)

作者： Amin Sarihi Ahmad Patooghy Abdel-Hameed A. Badawy Peter Jamieson Klipsch School of Electrical and Computer Engineering New Mexico State University Las Cruces NM MIPS Architecture and Performance Austin TX Department of Computer Systems Technology North Carolina A&T State University Greensboro NC Department of Electrical and Computer Engineering Miami University Oxford OH

ISBN: (数字)9798350367942

ISBN: (纸本)9798350367959

This work focuses on advancing the security field in the hardware design space by formally defining the problem of Hardware Trojan (HT) detection. The goal is to model HT detection more closely to the real world, i.e., describing the problem as "The Seeker’s Dilemma" (an extension of Hide&Seek on a graph), where a detecting agent is unaware of whether HTs infect circuits or not. Using this problem formulation, we create a benchmark that consists of a mixture of HT-free and HT-infected restructured circuits while preserving their original functionalities. The restructured circuits are randomly infected by HTs, causing a situation where the defender is uncertain if a circuit is infected. Our innovative dataset will help the community better judge the detection quality of different methods by comparing their success rates in circuit classification. We use our benchmark to evaluate three state-of-the-art HT detection tools to show baseline results for this approach. We use Principal Component Analysis to assess the strength of our benchmark, where we observe that some restructured HT-infected circuits are mapped closely to HT-free circuits, leading to significant label misclassification by detectors.

关键词： Accuracy Circuits Detectors Benchmark testing Hardware Quality assessment Trojan horses Security Integrated circuit modeling Principal component analysis

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Unsupervised Deep Learning Approaches for Anomaly Detection in IoT Data Streams 4

Unsupervised Deep Learning Approaches for Anomaly Detection ...

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4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Suresh, Helina Rajini Neelaveni, R. Kumar, Bonda Kiran Arunadevi, R. Indhumathi, G. Varun, M. Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Department of ECE Tamilnadu Chennai India Srm Institute of Science and Technology Department of Cse Tamilnadu Chennai India Kl School of Architecture Kl Deemed to Be University Andhrapradesh India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Tamilnadu Chennai India Rajalakshmi Engineering College Department of ECE Tamilnadu Chennai India Saveetha Engineering College Department of ECE Tamilnadu Chennai India

ISBN: (纸本)9798350300888

The proliferation of IoT devices is primarily responsible for the data deluge that has engulfed multiple industries. Nevertheless, due to the high dimensionality and complexity of IoT data streams, anomaly detection remains a significant challenge. This suggests that an unsupervised deep learning strategy could be used to identify anomalies in IoT data streams. Instead of relying on labeled data, this method employs deep neural networks, which can be trained to recognize anomalies and comprehend complex patterns. Experiments are conducted on a real-world IoT dataset to evaluate the efficacy and accuracy of the proposed method. The results demonstrate its proficiency in detecting anomalies in IoT data streams, paving the way for a safer, more reliable Internet of Things. Using unsupervised deep learning techniques, the method provides a practicable solution to the age-old problem of IoT anomaly identification. © 2023 IEEE.

关键词： Internet of things

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Speech to Reality: On-Demand Production using Natural Language, 3D Generative AI, and Discrete Robotic Assembly

arXiv

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arXiv 2024年

作者： Kyaw, Alexander Htet Jeon, Se Hwan Smith, Miana Gershenfeld, Neil Department of Architecture The Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Department of Mechanical Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Center for Bits and Atoms Massachusetts Institute of Technology CambridgeMA02139 United States

We present a system that transforms speech into physical objects by combining 3D generative Artificial Intelligence (AI) with robotic assembly. The system leverages natural language input to make design and manufacturing more accessible, enabling individuals without expertise in 3D modeling or robotic programming to create physical objects. We propose utilizing discrete robotic assembly of lattice-based voxel components to address the challenges of using generative AI outputs in physical production—such as design variability, fabrication speed, structural integrity, and material waste. The system interprets speech to generate 3D objects, discretizes them into voxel components, computes an optimized assembly sequence, and generates a robotic toolpath. The results are demonstrated through the assembly of various objects, ranging from chairs to shelves, which are prompted via speech and realized within 5 minutes using a 6-axis robotic arm. Copyright © 2024, The Authors. All rights reserved.

关键词： Robotic assembly

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Unsupervised Fouling Reconstruction in the Pipe Bend

Unsupervised Fouling Reconstruction in the Pipe Bend

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IEEE Symposium (IUS) Ultrasonics

作者： Denys Iablonskyi Carlos-Omar Rasgado-Moreno Madis Ratassepp Arto Klami Edward Hæggström Ari Salmi Department of Computer Science Department of Physics University of Helsinki Helsinki Finland Department of Civil Engineering and Architecture Tallinn University of Technology Tallinn Estonia Department of Computer Science University of Helsinki Helsinki Finland Department of Physics University of Helsinki Helsinki Finland

Guided wave tomography allows the investigation of extended areas with a limited number of measurements, typically using transducer arrays. Most imaging methods rely heavily on the material properties such as dispersion curves, that in the case of fouling deposition are not always known. In the case of complex shaped structures, geometrical anisotropy can bring additional complexity. Here we present an unsupervised machine learning approach based on the Gaussian process to detect and characterize the fouling in a pipe bend that relies only on the difference between clean/healthy and fouled/damaged measured signals.

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SOCIO-SPATIAL SEGREGATION AND HUMAN MOBILITY: A REVIEW OF EMPIRICAL EVIDENCE

arXiv

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arXiv 2024年

作者： Liao, Yuan Gil, Jorge Yeh, Sonia Pereira, Rafael H.M. Alessandretti, Laura Department of Space Earth and Environment Chalmers University of Technology Gothenburg Sweden Department of Architecture and Civil Engineering Chalmers University of Technology Gothenburg Sweden - Brazil Data Science Division Brazil Department of Applied Mathematics and Computer Science Technical University of Denmark Lyngby Denmark

Socio-spatial segregation is the physical separation of different social, economic, or demographic groups within a geographic space, often resulting in unequal access to resources, services, and opportunities. The literature has traditionally focused on residential segregation, examining how individuals' residential locations are distributed differently across neighborhoods based on various social attributes, e.g., race, ethnicity, and income. However, this approach overlooks the complexity of spatial segregation in people's daily activities, which often extend far beyond residential areas. Since the 2010s, emerging mobility data sources have enabled a new understanding of socio-spatial segregation by considering daily activities such as work, school, shopping, and leisure visits. From traditional surveys to GPS trajectories, diverse data sources reveal that daily mobility can result in spatial segregation levels that differ from those observed in residential segregation. This literature review focuses on three critical questions: (a) What are the strengths and limitations of segregation research incorporating extensive mobility data? (b) How do human mobility patterns relate to individuals' residential vs. experienced segregation levels? and (c) What key factors explain the relationship between one's mobility patterns and experienced segregation? Our literature review enhances the understanding of socio-spatial segregation at the individual level and clarifies core concepts and methodological challenges in the field. Our review explores studies of key themes: segregation, activity space, co-presence, and the built environment. By synthesizing their findings, we aim to offer actionable insights for reducing segregation. © 2024, CC BY-SA.

关键词： Leisure

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Exploring the Use of WebAssembly in HPC

arXiv

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arXiv 2023年

作者： Chadha, Mohak Krueger, Nils John, Jophin Jindal, Anshul Gerndt, Michael Benedict, Shajulin Chair of Computer Architecture and Parallel Systems Technische Universität München Germany Department of Computer Science and Engg. Indian Institute of Information Technology Kerala Kottayam India

Containerization approaches based on namespaces offered by the Linux kernel have seen an increasing popularity in the HPC community both as a means to isolate applications and as a format to package and distribute them. However, their adoption and usage in HPC systems faces several challenges. These include difficulties in unprivileged running and building of scientific application container images directly on HPC resources, increasing heterogeneity of HPC architectures, and access to specialized networking libraries available only on HPC systems. These challenges of container-based HPC application development closely align with the several advantages that a new universal intermediate binary format called WebAssembly (Wasm) has to offer. These include a lightweight userspace isolation mechanism and portability across operating systems and processor architectures. In this paper, we explore the usage of Wasm as a distribution format for MPI-based HPC applications. To this end, we present MPIWasm, a novel Wasm embedder for MPI-based HPC applications that enables high-performance execution of Wasm code, has low-overhead for MPI calls, and supports high-performance networking interconnects present on HPC systems. We evaluate the performance and overhead of MPIWasm on a production HPC system and AWS Graviton2 nodes using standardized HPC benchmarks. Results from our experiments demonstrate that MPIWasm delivers competitive native application performance across all scenarios. Moreover, we observe that Wasm binaries are 139.5x smaller on average as compared to the statically-linked binaries for the different standardized benchmarks. Copyright © 2023, The Authors. All rights reserved.

关键词： Containers

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Countering DoS Threats in WSN: Inventive Machine Learning Strategies with a Minimalistic Twist

Countering DoS Threats in WSN: Inventive Machine Learning St...

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Smart Power Control and Renewable Energy (ICSPCRE), IEEE International Conference on

作者： Senthilnathan Chidambaranathan Vanitha C.S. Sasireka Naduvathezhath Nessariose Jose K. Madhusudhana Rao T. Lakshmi Devi Architecture and Design Virtusa Department of Computer Science and Engineering Sri Sairam Institute of Technolgoy Department of Computer Science and Engineering SRM Institute of Science and Technology Regent Strategic Solutions Rochester USA KKR&KSR Institute Of Technology & Science Guntur A.P Department of Data Science Siddhartha Institute of Engineering and Technology (SIET) (A UGC Autonomous Institute) Hyderabad Telangana India

ISBN: (数字)9798350377002

ISBN: (纸本)9798350377019

In the era of expanding wireless sensor network (WSN) applications, safeguarding these networks against a spectrum of threats, including Denial-of-Service (DoS) attacks, is paramount. The innate resource limitations of sensor nodes often render conventional security measures ineffective. This study introduces a pioneering approach integrating minimalist machine learning techniques to combat DoS attacks within WSNs. Our methodology adeptly leverages machine learning capabilities while strategically curbing computational and resource demands. By distilling essential features from network traffic patterns, our model adeptly identifies aberrations signaling DoS attacks, all while preserving the efficiency of resource-limited sensor nodes. Its minimalist framework seamlessly integrates with existing WSN architectures, negating the need for substantial hardware upgrades and maintaining network performance. Results underscore the superior detection accuracy and efficiency of our minimalist machine learning strategy compared to traditional methods. Moreover, its scalability and adaptability render it ideal for deployment across varied WSN setups, fortifying them against DoS threats while safeguarding core network *** mitigating DoS attack impacts without burdening computational resources, our approach marks a significant stride towards bolstering the security and dependability of wireless sensor networks amid evolving threat landscapes.

关键词： Wireless sensor networks Renewable energy sources Scalability Machine learning Telecommunication traffic Network security Denial-of-service attack

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IoT-enabled smart cities: a hybrid systematic analysis of key research areas, challenges, and recommendations for future direction

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Discover Cities 2024年第1期1卷 1-34页

作者： Omrany, Hossein Al-Obaidi, Karam M. Hossain, Mohataz Alduais, Nayef A. M. Al-Duais, Husam S. Ghaffarianhoseini, Amirhosein School of Architecture and Built Environment The University of Adelaide Adelaide Australia Department of the Natural and Built Environment College of Social Sciences and Arts Sheffield Hallam University Sheffield UK Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Parit Raja Malaysia Department of Architecture Faculty of Built Environment Universiti Malaya Kuala Lumpur Malaysia School of Future Environments Auckland University of Technology Auckland New Zealand

Cities are expected to face daunting challenges due to the increasing population in the near future, putting immense strain on urban resources and infrastructures. In recent years, numerous studies have been developed to investigate different aspects of implementing IoT in the context of smart cities. This has led the current body of literature to become fairly fragmented. Correspondingly, this study adopts a hybrid literature review technique consisting of bibliometric analysis, text-mining analysis, and content analysis to systematically analyse the literature connected to IoT-enabled smart cities (IESCs). As a result, 843 publications were selected for detailed examination between 2010 to 2022. The findings identified four research areas in IESCs that received the highest attention and constituted the conceptual structure of the field. These include (i) data analysis, (ii) network and communication management and technologies, (iii) security and privacy management, and (iv) data collection. Further, the current body of knowledge related to these areas was critically analysed. The review singled out seven major challenges associated with the implementation of IESCs that should be addressed by future studies, including energy consumption and environmental issues, data analysis, issues of privacy and security, interoperability, ethical issues, scalability and adaptability as well as the incorporation of IoT systems into future development plans of cities. Finally, the study revealed some recommendations for those interconnected challenges in implementing IESCs and effective integrations within policies to support net-zero futures.

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Application of Digital Twin in Smart Battery Electric Vehicle: Industry 4.0

Application of Digital Twin in Smart Battery Electric Vehicl...

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2022 International Conference on IT and Industrial Technologies, ICIT 2022

作者： Shoukat, Muhammad Usman Yan, Lirong Du, Changqing Mair Raza, Muhammad U Adeel, Muhammad Khan, Tauqeer Wuhan University of Technology School of Automotive Engineering Wuhan430070 China Shenzhen University College of CS and SE Shenzhen518060 China Punjab Tianjin Univ. of Technology Department of Architecture Lahore53720 Pakistan Islamia College University Peshawar Department of Computer Science Peshawar25120 Pakistan

ISBN: (数字)9781665489454

ISBN: (纸本)9781665489454

It is believed that using electric vehicles (EVs) for transportation is essential for addressing environmental and sustainable development challenges. Current ecofriendly concerns, such as the fast depletion of fossil fuels, rising air pollution, rising energy demand, global warming, and climate change, have made it possible to electrify the transportation industry. EVs can address all of the aforementioned issues. For electric vehicles (EVs), and particularly those powered by lithium-ion (Li-ion) batteries, portable power supplies have become indispensable. Although Li- ion batteries have been the focus of EV research for a long time, issues like battery aging and safety have yet to be fully understood. According to our current understanding of smart technology, we have the ability to use the digital twin (DT) to overcome a problem that has been holding back battery development, as well as the preliminary DT applications in complicated systems such as industry 4.0. This research focuses on the characteristics of batteries and how they relate to their modeling, state estimation, remaining usable life prediction, safety, and control. We put together an analysis of some of the most current achievements in battery prognostics and health monitoring. Finally, we provide prospects for the development of DTs in the field of EV batteries. © 2022 IEEE.

关键词： Lithium-ion batteries

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URL Spam Detection Using Machine Learning Classifiers

URL Spam Detection Using Machine Learning Classifiers

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Omar Almomani Adeeb Alsaaidah Mosleh M. Abualhaj Mohammed Amin Almaiah Ammar Almomani Shahzad Memon Department of Networks and Cybersecurity Al-Ahliyya Amman University Amman Jordan Department of Computer Science King Abdullah the II IT School The University of Jordan Amman Jordan Department of Information Technology Al-Huson University College Al-Balqa Applied University Irbid Jordan Department of Computer Information Science Higher Colleges of Technology Sharjah United Arab Emirates Department of Computer Science and Digital Technologies Faculty of Architecture Computing and Engineering University of East London UK

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Cybersecurity has emerged as one of the most prevalent and significant challenges in recent years due to the advancement of technology. Among the most frequent and hazardous cybersecurity threats are spam URLs (Uniform Resource Locators), which are also one of the most popular methods for user fraud. Users are the victims of this attack, which also steals their data and infects their devices with harmful software. The detection of spam URLs has become very important in protecting the user. Therefore, this study aims to investigate the efficiency of machine learning classifiers in detecting spam URLs. The following machine learning classifiers were chosen: Random Forest, Decision Tree, and SVM. The evaluation was based on the ISCXURL2016 dataset, which is divided into three groups: All Features, BestFirst Features, and Infogain Features and evaluation matrices were the Accuracy, Precision, Sensitivity, and F-measure. The results obtained showed that Random Forest with All Features is superior to others with an accuracy of 99.75%, Precision of 99.74%, and Sensitivity of 99. 79%, and F-measure 99.76 %.

关键词： Uniform resource locators Support vector machines Sensitivity Accuracy Phishing Metaheuristics Feature extraction Malware Decision trees Random forests

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