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IEEE European Test Symposium (ETS)

作者： Athanasios Xynos Vasileios Tenentes Yiorgos Tsiatouhas Dept. of Computer Science & Engineering University of Ioannina Greece

Secure computing necessitates hardware root of trust (RoT) integrated in Systems-on-Chips (SoCs) for cryptographic keys generation, authentication and identification. In this paper, we observe that bitflips in SRAM cells that appear while accessing multiple cells from the same bitline, are not stochastic, as previously considered, but systematic. Based on this observation, a novel strong in-memory Physical Unclonable Function (PUF) computation is proposed for harvesting static entropy from SRAM arrays. The proposed design is compatible with existing in-SRAM computing architectures. To verify our PUF operation, we implement a 6T SRAM array model that performs in-memory computing using a 32 nm CMOS Technology, and, through SPICE simulation, we evaluate the proposed PUF performance. The proposed PUF operation achieves uniqueness and uniformity of 49.99%, and 49.74%, respectively, and reliability higher than 97.4% when the temperature is varied from 0°C to 100°C, and higher than 95.2% when the nominal voltage supply is varied by 10%. Furthermore, we explore the scaling of the number of Challenge Response Pairs (CRPs) of the proposed PUF, and we compare it against the state-of-the-art. Our PUF offers orders of magnitude higher number of CRPs, therefore it is suitable for integrated mechanisms that assure secure computing in SoCs.

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Autonomous Drone Control for Wireless Charging Using Power Lines

Autonomous Drone Control for Wireless Charging Using Power L...

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International Conference on Ubiquitous Information Management and Communication (IMCOM)

作者： Pengkai Wang Jonghoek Kim Husna Mutahira Mannan Saeed Muhammad Dept. of Electrical and Computer Engineering Sungkyunkwan University Suwon South Korea Dept. of Defense System Engineering Sejong University Seoul South Korea Dept. of Computer Science and Engineering Sogang University Seoul South Korea

The limited autonomy of flight has long been considered a significant constraint in drone systems. In the context of drone inspections of power lines, this study focuses on a drone equipped with a coil designed for autonomous battery charging. Positioned atop the drone is a charging coil, and on the upper surface, there is an upward-facing camera with a restricted field-of-view (FOV), serving the crucial role of aligning the drone with power lines. This research introduces a wireless power transfer strategy to facilitate the self-charging process of the drone while maintaining its position beneath power lines. To enable the drone's autonomous approach to power lines, a method is devised to guide the drone from below. As the drone approaches the power lines, it monitors the current induced by these lines and gradually adjusts its altitude until a sufficiently high induced current is achieved. The proposed approach ensures safety, as the drone avoids direct contact with high-voltage power lines, setting it apart from other existing methods that require such contact. To the best of our knowledge, this study offers a novel approach to precisely controlling drone movements, and establishing reliable wireless power transfer from power lines. The effectiveness of the developed wireless transfer strategy has been verified through MATLAB simulations.

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Switching Activity Reduction in Graphene PN Junction Circuits using Circuit Re-structuring

Switching Activity Reduction in Graphene PN Junction Circuit...

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2023 International Symposium on Devices, Circuits and Systems, ISDCS 2023

作者： Das, Subrata Deb, Arighna Fiser, Petr Sister Nivedita University Dept. of Computer Science & Engineering Kolkata India School of Electronics Engineering Kiit Bhubaneswar India Czech Technical University Faculty of Information Technology Prague Czech Republic

ISBN: (纸本)9798350315042

Graphene PN Junction (GPNJ) logic circuits received significant attention from the researchers thanks to the availability of electrostatically doped graphene PN Junction (GPNJ) device- a promising one for designing low-power, high-speed circuits. Several design approaches for GPNJ logic circuits realizing important arithmetic and arbitrary Boolean functions exist in literature. However, no detailed evaluations of the resulting circuit complexities with respect to relevant cost metrics are reported. It remains open, how do we synthesize GPNJ logic circuits with minimal switching activity, an essential cost metric influencing the dynamic power dissipation in the resulting circuits. In this paper, we introduce a synthesis approach for GPNJ logic circuits that interconnects a set of GPNJ devices in parallel. Such parallel circuit structures ensure the reduction of switching activity in the resulting GPNJ logic circuits. To the best of our knowledge, so far only one research work discussed the computation of switching activity of GPNJ logic circuits. Experimental evaluations confirm that an average 67.35% reduction in switching activity can be achieved using the proposed approach over the existing one. © 2023 IEEE.

关键词： Graphene

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Pseudo slicer on three dimensional brain tumor segmentation

Pseudo slicer on three dimensional brain tumor segmentation

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Iqbal, Mohammad Urbach, Fitria Pao, Hsing-Kuo Sensusiati, Anggraini Dwi Hidayat, Nurul Mukhlash, Imam Institut Teknologi Sepuluh Nopember Dept. of Mathematics Surabaya Indonesia National Taiwan University of Science and Technology Dept. of Computer Science and Information Engineering Taipei Taiwan Airlangga University Dept. of Radiology Surabaya Indonesia

ISBN: (纸本)9781665480451

A constructive way to assist surgeons before performing brain tumor surgery is by visualizing a three-dimensional (3D) MRI image to determine the brain tumor volume when a pre-operative examination. However, the availability of 3D MRI brain tumors is more limited than 2D, especially for meningioma type. Therefore, we attempt to propose a model to construct a pseudo-3D volume of a meningioma tumor from a few 2D MRI slices. In the proposed model, we first generate pseudo slices for each given 2D MRI slice until satisfying 3D MRI construction from interpolation techniques, called pseudoslicer. Next, we segment the pseudo-3D MRIs to capture the 3D meningioma tumor using 3D volume-to-volume generative adversarial networks (3D-V2V-GAN). That is, we call the proposed model 3D-slicer-V2V-GAN or 3DS-V2V-GAN. In this work, the proposed model was evaluated from 27 meningioma patients at a private university hospital in Indonesia. The proposed model outperformed the SOTA model in terms of the Jaccard Index and dice coefficient. Additionally, we have radiologists to affirm the segmentation results in good quality. © 2022 IEEE.

关键词： Image segmentation

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Crafting Connections: Empowering Kondapalli Artisans through a Mobile Application

Crafting Connections: Empowering Kondapalli Artisans through...

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Intelligent Systems and Machine Learning Conference (ISML)

作者： Chinta Vara Prasad J V D Prasad Vallakati Navyasri Dept. of Artificial Intelligence and Data Science V R Siddhartha Engineering College Vijayawada India Dept. of Computer Science and Engineering VR Siddhartha Engineering College Vijayawada India

ISBN: (数字)9798350343878

ISBN: (纸本)9798350343885

Handicrafts hold historical significance in representing the essence of our nation's native culture. However, these handcrafted goods still need to be explored globally, and the manufacturers frequently need help connecting directly with larger markets and potential customers. The scope of this project is bound to Kondapalli Toys, also known as Kondapalli Bommalu, renowned wooden handicrafts originating from Andhra Pradesh, India. The current resources for resolving this issue are not easily available on the market, and there is a lack of a user-friendly application. A dedicated Android application is needed to strengthen the handicraft business. This application enables artisans and potential customers to communicate effortlessly, guaranteeing secure transactions, offering valuable data insights, and providing resources for skill and capacity development. The user interface for this application was developed using the Flutter framework, which prioritizes data security and is scalable and region-adaptive. Through this application, manufacturers can conveniently reach markets while customers have the opportunity to explore and connect with their businesses, allowing them to display their craft items to a global audience.

关键词： Technological innovation Sentiment analysis Toy manufacturing industry Globalization User interfaces Real-time systems Mobile applications Registers Monitoring Business

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Review on Brain Tumor Detection using Custom CNN Layers and Transfer Learning on MRI images

Review on Brain Tumor Detection using Custom CNN Layers and ...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Akshit Saxena Jaspreet Singh Dept. of Computer Science and Engineering Chandigarh University India

Early diagnosis and treatment planning are greatly aided by the early identification of brain tumors. Because of its high resolution, low radiation, and low risk of patient discomfort, magnetic resonance imaging (MRI) is frequently used to diagnose brain tumors. Brain tumor identification is only one area where recent developments in convolutional neural networks (CNNs) have shown exceptional effectiveness. This article summarizes recent progress in detecting brain tumors by utilizing MRI images and bespoke CNN layers with transfer learning. The review kicks off with a discussion of the difficulties of detecting brain cancers, such as the tumors' complexity and heterogeneity and the scarcity of available annotated data. The article proceeds to go into the foundations of CNNs and their applicability to MRI image processing. To improve detection accuracy, we incorporate custom CNN layers that are tailored to capture salient tumor-specific information. The concept of transfer learning, in which CNN models trained on large-scale datasets are repurposed for brain tumor detection, is also discussed at length in the review. Using transfer learning, we can take advantage of what we've learned about general image identification to better train models to spot brain tumors. Fine-tuning, feature extraction, and other transfer learning methods are addressed at length. Recent research using custom CNN layers and transfer learning approaches to detect brain cancers in MRI images is thoroughly analyzed in this study. Among the benefits and drawbacks discussed are the methods' adaptability to small datasets, enhanced detection accuracy, and decreased training time. Also, the significance of using metrics for measuring performance and benchmark datasets for comparing methods fairly is discussed. The analysis concludes with suggestions for future study, such as the combination of functional and diffusion tensor imaging with conventional MRI scans to better detect brain tumors. Further

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Efficient Read Disturb Management Schemes in Resource-Constrained Flash Memory Controller

Efficient Read Disturb Management Schemes in Resource-Constr...

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IEEE Non-Volatile Memory Systems and Applications Symposium (NVMSA)

作者： Ikjoon Son Jin-Soo Kim Dept. Computer Science and Engineering Seoul National University

Whenever a read operation is performed in a flash memory storage device, the surrounding cells in the same block are affected and their reliability gradually decreases. This phenomenon is known as read disturb problem. A remedy to this read disturb is to count the number of read operations for each block and perform read reclaim, the process of moving the existing data to a new block when the count reaches a predefined threshold. However, as the number of blocks in the flash memory device increases, maintaining the per-block read count requires a great amount of memory space in the controller. Therefore, system designers are forced to maintain read counts for a group of blocks (i.e. superblock) which results in the loss of accuracy. This paper proposes novel read disturb management schemes that can reduce the number of read reclaims significantly even if the read count is maintained for each superblock. In the proposed Pointer-based scheme, we keep track of the last block read so as to avoid excessive increase in the read count when the data is read sequentially. We also propose the Bitmap-based scheme that can successfully approximate the actual read count in the presence of random reads, with a negligible space overhead. Our experiments with real-world traces show that the Pointer-based scheme and the Bitmap-based scheme reduce the number of read reclaims by 65.5% and 90.5% on average, respectively, compared to the conventional scheme.

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Slang Word Detection in the Context of User Profiling in the Social Media Platformes

Slang Word Detection in the Context of User Profiling in the...

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Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), International Conference on

作者： Bikram Samanta Ritika Shil Alok Ranjan Pal Antara Pal Dept. of Computer Science & Engg. College of Engg. & Management Kolaghat India Dept. of Computer Science Engg. Atharva College of Engineering Mumbai India

On social media platforms, people express their views in various ways, sometimes using formal words and sometimes in informal form. The informal conversations may contain jargon words i.e. slang words which might be improper for all kinds of audience. In this work, an attempt has been made to identify slang words in a text, posted on the social media platform. The overall experiment has been carried out in 4 modules. In module-1, the suspicious words from the posted text have been identified using minimum edit distance. This experiment has been implemented by the help of a dataset of printable slang words collected from Kaggle. In module-2, the topic of discussion has been analyzed by using a knowledge based approach. This part of the experiment has been carried out by creating a newspaper archive from the online repository of the "Times of India" newspaper. But, this knowledge based strategy could not produce an appreciable accuracy at its baseline. Therefore, as a modification of this technique, in module-3, the context analysis task has been implemented combining knowledge based approach and machine learning based approach. In module-4, the module-1 and module-3 are merged to tag a suspicious word with its level of abusiveness.

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Blockchain-based Reputation System For IoT Data Ecosystem: A Utility Maximization Approach 19

Blockchain-based Reputation System For IoT Data Ecosystem: A...

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19th IEEE International Conference on Smart Communities: Improving Quality of Life Using ICT, IoT and AI, HONET 2022

作者： Khezr, Seyednima Yassine, Abdulsalam Benlamri, Rachid Lakehead University Dept. of Electrical and Computer Engineering Thunder BayON Canada Lakehead University Dept. of Software Engineering Thunder BayON Canada University of Doha for Science and Technology Dept. of Information Technology Doha Qatar

ISBN: (纸本)9781665461979

Recently, blockchain technology has been used to build private and secure internet of things (IoT) data markets and trading systems. Reputation of the trading parties is an important attribute that affect their profitability and trading prosperity. However, current reputation systems are prone to malicious manipulation. Such an issue can be avoided by registering all reputation scores in the blockchain which guarantees trans-parency, encryption, privacy, and security. To this end, this paper proposes a blockchain-based reputation system where market players can maximize their utility while engaged in the IoT data ecosystem. We also propose an adaptive learning mechanism that allows the data providers and consumers to enhance their reputation and review credibility scores. We provide extensive experiments to ensure our system satisfies the feasibility, usability, and availability properties. These properties are essential for functional deployment in real IoT data markets. © 2022 IEEE.

关键词： Blockchain

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RETHINKING MACHINE UNLEARNING FOR LARGE LANGUAGE MODELS

arXiv

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arXiv 2024年

作者： Liu, Sijia Yao, Yuanshun Jia, Jinghan Casper, Stephen Baracaldo, Nathalie Hase, Peter Yao, Yuguang Liu, Chris Yuhao Xu, Xiaojun Li, Hang Varshney, Kush R. Bansal, Mohit Koyejo, Sanmi Liu, Yang Computer Science & Engineering Dept. Michigan State University United States IBM Research United States ByteDance Research United States Computer Science and Artificial Intelligence Laboratory MIT United States Computer Science Dept. University of North Carolina Chapel Hill United States Computer Science & Engineering Dept. University of California Santa Cruz United States Computer Science Dept. Stanford University United States

We explore machine unlearning (MU) in the domain of large language models (LLMs), referred to as LLM unlearning. This initiative aims to eliminate undesirable data influence (e.g., sensitive or illegal information) and the associated model capabilities, while maintaining the integrity of essential knowledge generation and not affecting causally unrelated information. We envision LLM unlearning becoming a pivotal element in the life-cycle management of LLMs, potentially standing as an essential foundation for developing generative AI that is not only safe, secure, and trustworthy, but also resource-efficient without the need of full retraining. We navigate the unlearning landscape in LLMs from conceptual formulation, methodologies, metrics, and applications. In particular, we highlight the often-overlooked aspects of existing LLM unlearning research, e.g., unlearning scope, data-model interaction, and multifaceted efficacy assessment. We also draw connections between LLM unlearning and related areas such as model editing, influence functions, model explanation, adversarial training, and reinforcement learning. Furthermore, we outline an effective assessment framework for LLM unlearning and explore its applications in copyright and privacy safeguards and sociotechnical harm reduction. © 2024, CC BY.

关键词： Adversarial machine learning

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