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5th International Conference on Blockchain Computing and Applications, BCCA 2023

作者： Dwivedi, Ras Singla, Tushar Shukla, Sandeep Indian Institute of Technology Computer Science and Engineering Kanpur India

ISBN: (纸本)9798350339239

Since the introduction of the blockchain technology in the form of the Bitcoin [1] cryptocurrency, numerous blockchains have been introduced with applications beyond cryptocurrency. Introduction of smart contracts and tokenization of assets has made blockchain technology a decentralized, replicated and transparent program execution engine with the capability to represent real-world assets as tokens. However, with the diversity of blockchain platforms, and diversity of applications, interoperability between applications has become an important problem. Especially with the increasing use of asset tokenizations, cross-chain token transfer which guarantees safety and live-ness properties became necessary. Atomic cross chain transactions are building blocks for the cross chain execution of the smart contract. Atomic cross-chain swap is the simplest kind of the cross chain transaction. In this paper we present a novel atomic cross chain swap protocol. Our protocol does not use time locks unlike previously proposed solutions. Time-locking leaves the funds locked at the whim of the proposer, and hash-time locking protocol is probabilistic, in the sense that there is a possibility that proposer might not complete the transaction and cheat the other party. Our protocol is deterministic and ends in a finite number of steps. We also provide formal proofs of safety and liveness properties of our protocol. © 2023 IEEE.

关键词： Smart contract

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An Effective CNN-Based Method for Camera Model Identification in Privacy Preserving Settings

An Effective CNN-Based Method for Camera Model Identificatio...

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2023 IEEE International Conference on Image Processing Challenges and Workshops, ICIPCW 2023

作者： Rana, Kapil Rathi, Vishwas Goyal, Puneet Indian Institute of Technology Ropar Computer Science and Engineering Punjab India Thapar Institute of Engineering and Technology Computer Science and Engineering Punjab India

ISBN: (纸本)9798350302585

Recognizing information about the camera model of digital images has been identified as a crucial task in the field of image forensics. Numerous methods have been proposed for camera model identification (CMI), with recent emphasis on convolutional neural networks (CNNs) based methods for their better efficacy. However, current approaches generally consider original images without consideration to privacy preserving aspects. This paper introduces a novel CNN-based approach for CMI even in privacy preserving settings, where unlike conventional approaches, the training and evaluation is performed on encrypted images. For encryption, we consider position scrambling at pixel-level and we also present block-level position scrambling for better efficacy. Experimental results demonstrate the feasibility of our approach in terms of accuracy and privacy preservation. Furthermore, the experiments highlight the effectiveness of our CNN-based approach, having around 10% better accuracy in comparison to traditional statistical models for CMI of encrypted images. © 2023 IEEE.

关键词： Convolution

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Evaluation of Reliability in Request-segregated Clouds through Remiadiations Predicting Hardware Failure Scheme

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Recent Patents on engineering 2025年第1期19卷 112-127页

作者： Sharma, Rohit Yadav, Vibhash Singh, Raghuraj Department of Computer Science and Engineering Dr. Ambedkar Institute of Technology for Handicapped U.P. Kanpur208024 India Department of Information Technology Rajkiya Engineering College Banda210201 India Department of Computer Science and Engineering Harcourt Butler Technical University Kanpur208002 India

Background: Cloud services have become a popular approach for offering efficient services for a wide range of activities. Predicting hardware failures in a cloud data center can minimize downtime and make the system more reliable and fault-tolerant. Objective: This research aims to analyze a predictive hardware failure model based on machine learning that anticipates the required remediations for undiagnosed failures in a cloud computing system serving multiclass requests. Methods: The model is tested on a carefully designed cloud data center that categorizes incoming requests as web, compute, storage, and dedicated server requests. To demonstrate improved reliability, a carefully designed test case is run on ReliaCloud-NS, which is a simulator for creating a CCS and computing its reliability. Results: The work found that using this model considerably enhanced the reliability of cloud computing systems when compared to not using the model. Conclusion: Although various estimation methods are patented to evaluate the system reliability of a cloud computing network, the emphasis of this study was mostly on improving the reliability of request-segregated clouds upon failing hardware resources like CPU, memory, bandwidth, and hard disc. Moreover, the prediction model might potentially be expanded to other system resources such as GPUs, software, and database packages. © 2025 Bentham science Publishers.

关键词： Cloud platforms

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Thin film ferroelectric photonic-electronic memory

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Light(science & Applications) 2024年第10期13卷 2251-2262页

作者： Gong Zhang Yue Chen Zijie Zheng Rui Shao Jiuren Zhou Zuopu Zhou Leming Jiao Jishen Zhang Haibo Wang Qiwen Kong Chen Sun Kai Ni Jixuan Wu Jiezhi Chen Xiao Gong Department of Electrical and Computer Engineering National University of SingaporeSingapore 119077Singapore Department of Microelectronic Engineering Rochester Institute of TechnologyRochesterNY 14623USA School of Information Science and Engineering Shandong UniversityJinan 250100China

To reduce system complexity and bridge the interface between electronic and photonic circuits,there is a high demand for a non-volatile memory that can be accessed both electrically and ***,practical solutions are still lacking when considering the potential for large-scale complementary metal-oxide semiconductor compatible ***,we present an experimental demonstration of a non-volatile photonic-electronic memory based on a 3-dimensional monolithic integrated ferroelectric-silicon ring *** successfully demonstrate programming and erasing the memory using both electrical and optical methods,assisted by optical-to-electrical-to-optical *** memory cell exhibits a high optical extinction ratio of 6.6 dB at a low working voltage of 5 V and an endurance of 4×10^(4) ***,the multi-level storage capability is analyzed in detail,revealing stable performance with a raw bit-error-rate smaller than 5.9×10^(−2).This ground-breaking work could be a key technology enabler for future hybrid electronic-photonic systems,targeting a wide range of applications such as photonic interconnect,high-speed data communication,and neuromorphic computing.

关键词： resonator electronic ferroelectric

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Scheduling of Flexible Manufacturing System with Penalty Cost Using Simulated Genetic Algorithm 15

Scheduling of Flexible Manufacturing System with Penalty Cos...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Singh, Vijai Singh, Varsha Inderprastha Engineering College dept. of Computer Science and Engineering Uttar Pradesh Ghaziabad India Indian Institute of Information Technology Allahabad dept. of Information Technology Uttar Pradesh Prayagraj India

ISBN: (纸本)9798350370249

This paper discuss about the scheduling problem for flexible manufacturing systems (FMSs) using the genetic algorithm. In FMSs, the operation of a job is performed on more than one machine. Therefore, FMS scheduling is considered to be a computationally hard problem. In this paper, various approaches are reviewed that are applied to the FMS scheduling problem. This paper also attempts to find a possible solution to the scheduling problem of FMS. To identify the order in which the various jobs would be assigned to a number of machines, this paper uses a genetic algorithm with Simulated Annealing to generate a machine sequence. In this, the developed tool requires tabular input. To obtain a schedule with minimum make span, many iterations of the genetic algorithm are applied. In this system, an optimal schedule is given for FMS by taking the makespan time and minimum penalty cost, and therefore calculating the minimum combined objective function. © 2024 IEEE.

关键词： Scheduling algorithms

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On the Parameterized Complexity of Diverse SAT 35

On the Parameterized Complexity of Diverse SAT

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35th International Symposium on Algorithms and Computation, ISAAC 2024

作者： Misra, Neeldhara Mittal, Harshil Rai, Ashutosh Department of Computer Science and Engineering Indian Institute of Technology Gandhinagar India Department of Mathematics Indian Institute of Technology Delhi New Delhi India

ISBN: (纸本)9783959773546

We study the Boolean Satisfiability problem (SAT) in the framework of diversity, where one asks for multiple solutions that are mutually far apart (i.e., sufficiently dissimilar from each other) for a suitable notion of distance/dissimilarity between solutions. Interpreting assignments as bit vectors, we take their Hamming distance to quantify dissimilarity, and we focus on the problem of finding two solutions. Specifically, we define the problem Max Differ SAT (resp. Exact Differ SAT) as follows: Given a Boolean formula on n variables, decide whether has two satisfying assignments that differ on at least (resp. exactly) d variables. We study the classical and parameterized (in parameters d and n − d) complexities of Max Differ SAT and Exact Differ SAT, when restricted to some classes of formulas on which SAT is known to be polynomial-time solvable. In particular, we consider affine formulas, Krom formulas (i.e., 2-CNF formulas) and hitting formulas. For affine formulas, we show the following: Both problems are polynomial-time solvable when each equation has at most two variables. Exact Differ SAT is NP-hard, even when each equation has at most three variables and each variable appears in at most four equations. Also, Max Differ SAT is NP-hard, even when each equation has at most four variables. Both problems are W[1]-hard in the parameter n − d. In contrast, when parameterized by d, Exact Differ SAT is W[1]-hard, but Max Differ SAT admits a single-exponential FPT algorithm and a polynomial-kernel. For Krom formulas, we show the following: Both problems are polynomial-time solvable when each variable appears in at most two clauses. Also, both problems are W[1]-hard in the parameter d (and therefore, it turns out, also NP-hard), even on monotone inputs (i.e., formulas with no negative literals). Finally, for hitting formulas, we show that both problems can be solved in polynomial-time. © Neeldhara Misra, Harshil Mittal, and Ashutosh Rai.

关键词： Hamming distance

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A Comprehensive Analysis of Fast-Food Classification Using You Only Look Once (YOLO) Model 15

A Comprehensive Analysis of Fast-Food Classification Using Y...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Bandyopadhyay, Shaon Dutta, Dipankar Pratihar, Sanjoy Indian Institute of Information Technology Computer Science and Engineering Department West Bengal Kalyani KalyaniPIN 741235 India University Institute of Technology The University of Burdwan Computer Science and Engineering Department West Bengal BurdwanPIN 713104 India

ISBN: (纸本)9798350370249

In modern fast-paced society, the prevalence of fast food has spurred a surge in its consumption, emphasizing the need for accurate food identification systems. This study explores the efficacy of using the 'You Only Look Once' (YOLO) principles for classifying fast foods using the Food-101 dataset, which includes 1 0 1, 0 0 0 images across 101 food categories. Traditional machine learning (ML) methods, i.e., methods not using deep learning (DL), have struggled with the significant variation within classes and minimal distinction between classes characteristic of food images. In contrast, DL models, particularly convolutional neural networks (CNNs), have shown significant promise. This research leverages YOLO version 5 (YOLOv5), a cutting-edge object detection model, to achieve a classification accuracy of 8 0. 2 0 %. It outperforms other models such as Inceptionv3 and GoogLeNet. The study employs advanced techniques such as data augmentation, batch normalization, and sophisticated loss functions to optimize performance. The results underscore YOLOv5's superior capability in handling the complexities of fast-food classification. In the future, this can be useful for developing a robust solution for automated dietary monitoring and caloric intake assessment. © 2024 IEEE.

关键词： Convolutional Neural Networks Deep Learning Fast-Food Classification Food-101 Dataset YOLO

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Centralized Platform for Sensor Integration and AI Models in Precision Agriculture 2

Centralized Platform for Sensor Integration and AI Models in...

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2nd IEEE International Conference on Networks, Multimedia and information technology, NMITCON 2024

作者： Chowdary, Ch Srinivasa Akshith Likith, S. Maya, B.S. Lokesh, S.V. Manish, Y. Asha, T. Bangalore Institute of Technology Department of Computer Science and Engineering Bengaluru India Bangalore Institute of Technology Department of Information Science and Engineering Bengaluru India

ISBN: (纸本)9798350372892

A centralized platform for integrating various sensors and AI models in precision agriculture. The platform connects soil moisture sensors with AI models to generate crop recommendations and irrigation alerts, aiming to enhance resource utilization and crop yield. Key outcomes include real-time monitoring, actionable insights, and data-driven decision support for farmers. The practical implications for farmers and the agricultural industry are profound, as this system enables them to adopt precision agriculture techniques, reducing waste, increasing efficiency, and ultimately improving profitability © 2024 IEEE.

关键词： Smart agriculture

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A Self-Labeling Method for Adaptive Machine Learning by Interactive Causality

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第5期5卷 2093-2102页

作者： Ren, Yutian Yen, Aaron Haohua Li, Guann-Pyng University of California Irvine Department of Electrical Engineering and Computer Science The California Institute for Telecommunications and Information Technology IrvineCA92697 United States

Learning from unlabeled data or self-learning, can substantially reduce the complexity of machine learning (ML) utilization in real-time deployment. While the development of un/semisupervised algorithms shows promising results in learning with reduced labels, the fundamental assumption of data smoothness restricts its scope of application, especially with nonstationary data distributions in different domains. Leveraging transdomain invariant causal relationships, causality has recently been employed to foster robust ML. In this article, we have developed a generic method for self-labeling data that relies on known causality among interactive objects and learned temporal relations among causal events for identifying and associating labels and input data. The causal time-interval between asynchronous cause and effect events is studied to achieve self-labeling. We utilize dynamical system theory in a 1-d setting to demonstrate that our proposed method outperforms traditional feature similarity-based semisupervised learning. A computer simulation experiment was conducted to generate high-dimensional data, and the comprehensive results reveal the potential of learning adaptation in dynamic environments to improve ML robustness against shifts in data distribution. © 2023 IEEE.

关键词： Timing circuits

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Labor Safety Analysis Using Object Recognition and Deep Learning 1

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2nd International Conference on Human-Centric Smart Computing, ICHCSC 2023

作者： Evangeline, D. Jatin, B. Manoj, S. Pannaga, N. Sanjeev, G. Parkavi, A. Department of Information Science and Engineering Ramaiah Institute of Technology Bangalore India Department of Computer Science and Engineering Ramaiah Institute of Technology Bangalore India

ISBN: (数字)9789819977116

ISBN: (纸本)9789819977109

Occupational safety is a major concern in any industry or workplace, and the use of advanced technologies such as object recognition and deep learning can greatly enhance safety measures. This paper proposes a labor safety analysis system that utilizes object recognition and deep learning algorithms to detect potential hazards and ensure safe working conditions. The proposed system can recognize various types of safety gear and analyze their positions and movements to detect potential safety in real time. The system can also provide alerts and warnings to workers and supervisors in case of the absence of safety gear. Experimental results demonstrate the effectiveness and efficiency of the proposed work. This can significantly improve the safety and productivity of the workplace. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Object recognition

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