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Open Innovations Association (FRUCT), Conference of

作者： Vinay Kumar Kasula Sarath Babu Rakki Rajkumar Banoth Dept. Information Technology University of the Cumberlands Williamsburg KY USA Dept. of Computer Science and Engineering JNTUH University College of Engineering Hyderabad TG India Dept. of Computer Science University of Texas at San Antonio San Antonio TX USA

ISBN: (数字)9789526524634

ISBN: (纸本)9798331595425

Hyperledger Fabric is a scalable and modular consortium blockchain platform designed for enterprise applications, where cryptographic algorithms play a fundamental role in ensuring data security and integrity. However, the native Fabric framework lacks support for national cryptographic standards, necessitating the integration of secure cryptographic mechanisms. This study proposes an enhanced approach to embedding national cryptographic algorithms into the Fabric platform. First, Graph-based Dependency Analysis is employed to investigate the interaction logic among Fabric components and the invocation of cryptographic functions, facilitating an efficient integration strategy. Next, the Lightweight Post-Quantum Cryptography (L-PQC) framework, which enhances resistance to quantum threats while maintaining computational efficiency, is utilized to integrate SM2, SM3, and SM4 cryptographic algorithms into Fabric’s Blockchain Cryptographic Service Provider (BCCSP) module. Subsequently, a Microservices-Based Cryptography Integration mechanism is designed to establish seamless mapping between Fabric’s cryptographic function calls and the national cryptographic algorithm interfaces, ensuring compatibility and interoperability. Finally, the implementation is evaluated using Blockchain Simulation Environments, where a fabric-gm consortium blockchain instance is deployed to validate the correctness and efficiency of the embedded cryptographic modules. Comparative analysis with the original Fabric platform reveals that the enhanced system introduces a 2.5% increase in network startup time, a 1.8× rise in transaction latency, and a 7.5% increase in dynamic certificate generation time, while maintaining operational performance within acceptable limits. The proposed integration strategy ensures secure and efficient cryptographic support within Hyperledger Fabric, making it resilient to emerging cryptographic threats.

关键词： Resistance Technological innovation Quantum computing Distributed ledger Heuristic algorithms System performance Fabrics Blockchains Cryptography Standards

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Ink Detection from Carbonized Herculaneum Papyri using Deep Learning

Ink Detection from Carbonized Herculaneum Papyri using Deep ...

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International Conference on computer and Information Technology (ICCIT)

作者： Farzana Sharmin Mou Tanvir Ahmed Dept. of Computer Science University of Birmingham Birmingham UK Dept. of Computer Science and Engineering Dhaka University of Engineering & Technology Gazipur Dhaka Bangladesh

A Roman villa in the town of Herculaneum, next to Pompeii, housed a library with thousands of ancient Greek and Roman scrolls. The eruption of Mount Vesuvius buried this villa approximately 2000 years old ago which has been discovered. The scrolls can no longer be opened without breaking them because they were carbonized by the heat of the volcano. Since Herculaneum ink is carbon-based, it affords no X-ray contrast against the underlying carbon-based papyrus. Some detached papyrus fragments that can be read under infrared light can be used as ground truth data for a machine learning model that could identify otherwise undetectable ink from X-rays. The process of ink detection involves locating the inked portions of the papyrus using data from a 3D X-ray scan of the papyrus surface. The model has been trained by selecting a pixel from the binary label picture and sampling a subvolume from the surface volume at the same coordinates. In order to update the model weights, then backpropagate the known label data. Once trained, the model can be used to predict the appearance of a label picture from diverse input data. Three distinct models, namely U-Net, Attention U-Net, and a Pretrained Autoencoder with Attention U-Net, have been applied. Then, utilizing those three models, ensemble approaches have been employed (average, voting, and weighted average) to choose the optimum outcome. The best dice score of 0.766 has been achieved by the average ensembling technique. This project shows how machine learning can detect ink in 3D scans and decipher carbonized parchment contents.

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Design and Implementation of Low Power Circuits Based on 5nm Double Gate MOSFET Technology Using Different Materials

Design and Implementation of Low Power Circuits Based on 5nm...

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IEEE Electron Devices Kolkata Conference (EDKCON)

作者： Shrabanti Kundu Jyotsna Kumar. Mandal Dept. of Computer Science and Engineering University of Kalyani Kalyani Nadia WB India

ISBN: (数字)9798350374643

ISBN: (纸本)9798350374650

This paper presents a comprehensive investigation of 5nm double gate MOSFETs (DG MOSFETs) circuits using different substrate semiconductor materials for low-power devices. A comparative analysis of characteristics of SiGe, InGaAs and GaN as substrate materials is performed. The study reveals that InGaAs offers superior performance due to its higher electron mobility, lower threshold voltage and thermal stability than other materials studied here for low-power applications. The electrical properties of DG MOSFETs on these substrates have been investigated by calculating key parameters such as threshold voltages (Vth), Ion and Ioff currents. The results show that InGaAs-based DG MOSFETs exhibit superior performance with threshold voltage, improved leakage current and Ion/Ioff ratio compared to other substrate materials. Additionally, a lower threshold voltage has been observed for InGaAs-based devices, indicating reduced power consumption. These findings demonstrate the potential of InGaAs as a preferred substrate material for low-power, high-performance DG MOSFET devices. Double gate MOSFET circuits are designed using InGaAs and power dissipation is calculated. The results show a significant reduction in power consumption and improved circuit performance compared to traditional Si-based devices. This research demonstrates the potential of InGaAs-based DG MOSFETs for low-power electronic devices, enabling the development of energy-efficient circuits for various applications.

关键词： Performance evaluation MOSFET Electric potential Power demand Logic gates MOSFET circuits Threshold voltage Indium gallium arsenide Substrates Thermal stability

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Simple Lightweight Cryptographic Algorithm to Secure Imbedded IoT Devices

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2022年第20期16卷 98-113页

作者： Mhaibes, Hakeem Imad Abood, May Hattim Farhan, Alaa Kadhim Technical Institute of Kut Middle Technical University Baghdad Iraq College of Engineering Al Iraqia University Baghdad Iraq Computer Science Dept. University of Technology Baghdad Iraq

Improving the voting system has become a widely discussed issue. Paper-based elections are not safe because of the possibility of changing and adding ballots. Consequently, many countries use e-voting systems to ensure security, authenticity and time efficiency. Blockchain e-voting systems can be adopted to reduce fraud and increase voting access from home, especially in pandemics. This paper suggests a blockchain e-voting system that tackles two security and authentication issues. The security has been ensured using hybrid publickey cryptography;the voter information is encrypted using the regional election office elliptic public key, while the homomorphic public supreme election authority encrypts the vote. Using homomorphic encryption for voice enables the calculations of results as the authority encrypts it without revealing the vote itself. Authentication has been improved for home voting by a robust login system. This login system consists of two steps. In the first step, the voter enters the site using his unique QR code number scanned by webcam;in the second step, the system checks the voter's face using a face recognition system by web camera to be routed to the voting page. Voting public keys are also authenticated using a digital certificate schema. The system has been tested to show its efficiency and suitability in block establishment time and the encryption and key generator randomness using NIST tests. The internet of things (IoT) revolution has been sparked by the exponential increase in connected devices caused by recent advances in wireless technology. These embedded devices gather, analyze, and send vast data via the network. Data transmission security is a primary problem in IoT networks. Several lightweight encryption/decryption algorithms have been developed for such resource-constraint devices. One of most effective and fast lightweight encryption algorithms for IoT applications is the Tiny Encryption Algorithm (TEA). TEA has few lines source

关键词： Internet of things

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Multisport Dynamics Explored Through Advanced Activity Recognition Techniques in Athletic Performance

Multisport Dynamics Explored Through Advanced Activity Recog...

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IEEE Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)

作者： Supriya Salian Preethi Salian K Ria D'Souza Rencita Monterio Rinette Veronica Fernandes Riya D Almeida Dept. of Computer Science and Engineering St. Joseph Engineering College Vamanjoor Mangaluru India Dept. of Information Science and Engineering NMAM Institute of Technology Nitte (Deemed to be University) Udupi India

ISBN: (数字)9798350350593

ISBN: (纸本)9798350350609

Recognition of human activities has drawn a lot of interest lately in the field of computer vision and machine learning. Group activity recognition is a significant subcategory in which several people participate in a common activity. The primary obstacle in these tasks is learning the relationships between individuals in a scene and understanding their evolution over time. The suggested study offers a new taxonomy to classify state-of-the-art (SOTA) group activity recognition approaches and subcategorizes the current literature. It also critically analyzes these techniques. To understand scenes involving multiple people, models must describe individual actions in context and infer collective activities. Accurately capturing relationships between actors and performing relational reasoning is crucial for comprehending group activities. However, modelling these relationships is challenging due to the limited availability of interaction information, relying only on individual action labels and collective activity labels. Inferring relationships from other aspects is thus essential. Group activity recognition has garnered increased research attention recently due to its importance in video understanding. The difficulties lie not only in recognizing individual actions but also in exploring scene information and collaborative relations among people. This research addresses these challenges by providing a comprehensive review of existing techniques, offering a structured approach to categorize them, and highlighting the importance of relational reasoning in understanding group activities.

关键词： computer vision Reviews Taxonomy Collaboration Machine learning Activity recognition Very large scale integration Distributed computing Context modeling

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Real-Time Network Monitoring: A Big Data Approach

Real-Time Network Monitoring: A Big Data Approach

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International Conference on Computing and Networking Technology (ICCNT)

作者： Kunisetty Jaswanth S Sruthi Pranav Ramachandrula S. Saravanan Dept. of Computer Science Engineering Amrita School of Computing Bengaluru India Dept. of Computer Science Engineering Amrita School of Computing Chennai India

Real-time network monitoring is a critical requirement for tracking user activities and ensuring optimal network performance. In this paper, we propose a big data approach to real-time network monitoring that leverages the capabilities of Apache Spark, MongoDB, and Apache Kafka with Python. Our approach utilizes distributed stream processing of network data, enabling real-time analytics for user activity monitoring and network performance optimization. Our approach demonstrates the effectiveness of our system through experiments with real network traffic data, showing how it can identify and track user activities in real-time. Our system can be used by network administrators and IT professionals to optimize network performance, improve user experience, and enhance security through real-time monitoring and analysis of user activity data. This approach can also be opted as a feature in SDNs.

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Image-Driven Face Presentation Attack Detection in Biometric Systems

Image-Driven Face Presentation Attack Detection in Biometric...

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Intelligent Systems (ETIS), Emerging Technologies for

作者： Rini Vijayan Salim A Dept. of Computer Science and Engineering College of Engineering Trivandrum Affliated to APJ Abdul Kalam Technological University Kerala India

ISBN: (数字)9798331507541

ISBN: (纸本)9798331507558

As biometric systems become increasingly relied on secure authentication, concerns about their susceptibility to Face Presentation Attacks (FPAs) have intensified. This paper introduces a framework for detection of image-driven FPAs in biometric systems, focusing on enhancing the security of face recognition technologies. We propose a methodology that integrates Histogram of Oriented Gradients (HOG) with deep learning techniques. This approach utilizes pretrained models and custom layers to effectively differentiate between legitimate user images and various forms of presentation attacks, including those generated by GANs, morphing techniques, and photoshop manipulations. Through extensive experimentation, we analyzed the performance of each model, offering detailed comparisons concerning accuracy, precision, and F1 score across various categories as well as overall performance of each model. Our findings imply that fusion of HOG with a fine-tuned VGG16 model can achieve significantly higher overall accuracy.

关键词： Support vector machines Analytical models Histograms Accuracy Biological system modeling Face recognition Computational modeling Security Resource management Reliability

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Construction Research Congress 2022

Construction Research Congress 2022

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Construction Research Congress 2022: Project Management and Delivery, Controls, and Design and Materials, CRC 2022

作者： Naguib, Mina Eldamnhoury, Ebrahim S. Hanna, Awad S. Lotfallah, Wafik VDC Engineer Swinerton Buildersformerly Dept. of Civil and Environmental Engineering MadisonWI United States Technology Business Analyst DPR Constructionformerly Dept. of Civil and Environmental Engineering MadisonWI United States Boldt Company Professor of Construction Engineering and Management Dept. of Civil and Environmental Engineering Univ. of Wisconsin-Madison MadisonWI United States Dept. of Mathematics and Actuarial Science American Univ. in Cairo Egypt

ISBN: (纸本)9780784483978

The scope of this research is to investigate project competencies as leading indicators of project performance using two robust data-driven weighting mathematical frameworks. The objectives of this research are to (1) create a comprehensive performance score named the Project Performance Assessment (PPA) score by combining eight performance metrics spanning over six performance areas: cost, schedule, productivity, safety, quality, and communication;(2) benchmark successful projects from less-than-successful projects by performing K-means clustering;(3) compute a customized competency category-specific score from various competency factors spanning nine categories: alignment and team integration, planning and design, procurement management, contract and risk management, health, safety and environmental, quality management, labor productivity, execution and commissioning, and change management and project control;(4) quantitatively assess the relative contribution of the different competency categories to the PPA score and provide one unique comprehensive score named the Competency Category Assessment (CCA) Score;and (5) utilize a developed regression model to examine the degree of association between the CCA score and the PPA score. The results of this study can assist the construction industry in accurately identifying areas that require improvements based on an individualized assessment of different competency factors, ultimately ensuring more effective and efficient execution of projects. © 2022 ASCE.

关键词： Risk management

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Modeling the effect of hatched grounds on the radio-frequency performance of microstrip lines on flexible substrate

Modeling the effect of hatched grounds on the radio-frequenc...

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作者： Abdelatty, Mohamed Y. Umar, Ashraf Obeidat, Abdullah S. Enakerakpo, Emuobosan Alhendi, Mohammed Poliks, Mark D. Dept. of Systems Science and Industrial Engineering State University of New York Binghamton United States Dept. of Electrical and Computer Engineering State University of New York Binghamton United States

Cross-hatching of PCB grounds refers to a process in which certain ground planes appear as copper lattices;regular openings are placed at regular intervals. Nowadays, the efficacy of ground hatching on rigid PCBs is minimal. However, it is becoming essential for flexible hybrid electronics. Utilizing hatched grounds for flexible applications could offer some benefits. Hatched grounds can perform dual roles, act as ground surfaces, and offer structural support. Hatched grounds are more durable for bending and stretching applications. Also, hatched grounds are desirable in terms of material conservation. In this paper, we focus on studying how hatched grounds affect the RF performance of a straight microstrip line. Simulations show that cross-hatched grounds with more than 50% filling produces good radio-frequency performance. We will study the effect hatching pattern as well as the density of the hatching on the RF performance of the microstrips. The theoretical modeling is considered in this work and fabricating aerosol jet silver ink microstrip lines on PET substrates will be considered as an extension of this work. © 2022 IMAPS-International Microelectronics and Packaging Society. All rights reserved.

关键词： Microstrip lines

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SMOTE and Hyperparameter Optimization: A Dual Machine Learning Strategy for Enhancing Coupon Recommendation in Vehicular Contexts

SMOTE and Hyperparameter Optimization: A Dual Machine Learni...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Ravikumar R N Sanjay Jain Manash Sarkar Dept. of Computer Science and Engineering Amity University Rajasthan Jaipur India Dept. of Computer Science and Engineering Atria Institute of Technology Banglore India

This study centers on enhancing the accurateness of coupon recommendation algorithms within the framework of in-vehicle settings. The In-Vehicle Coupon Recommendation dataset from the UCI Repository was utilized in this study. A range of machine learning algorithms, such as Random Forest, Gradient Boosting, Extra Trees, LightGBM, XGBoost, AdaBoost, Stochastic Gradient Descent (SGD), and CatBoost, were employed for analysis. Among the several algorithms considered, CatBoost exhibited notable performance, with a baseline accuracy of 74%. In order to improve the efficacy of the model, we utilized the “Synthetic Minority Over-sampling Technique” (SMOTE) to mitigate the issue of class imbalance. This was subsequently followed by a thorough hyperparameter adjustment of the CatBoost algorithm. The improved model demonstrated a significant upgrade, with an accuracy rate of 77%. Additionally, there were noteworthy improvements in precision, recall, and F1 score. The AUC, which represents the Area Under the Receiver Operating Characteristic Curve, saw a notable improvement to 0.82. This signifies a noteworthy enhancement in the capacity to differentiate between positive and negative cases. The results emphasize the efficacy of CatBoost in the context of recommending coupons within vehicles, while also drawing attention to the influence of preprocessing methods and hyperparameter adjustment in enhancing the model's overall performance. This study is a valuable contribution to the domain of personalized advertising in the context of vehicles, providing valuable insights for the advancement of coupon recommendation systems that are more precise and dependable.

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