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11th International Conference on Intelligent Computing and Applications, ICRTC 2023

作者： Yadav, Poonam Satheesh Kumar, S. Muthukumaran, V. Sandeep Kumar, Mathivanan Radhika, E.K. Department of Computer Science School of Applied Sciences Reva University Bangalore India Department of Mathematics College of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur 603203 India School of Computer Science and Engineering Galgotias University Uttar Pradesh Greater Noida203201 India Department of Computer Science and Applications Sindhi College Bangalore560024 India

ISBN: (纸本)9789819717231

In the polygram substitution, one set of characters is replaced by another set based on algorithm. The model is proposed for calculating and estimating secure online secret shares, where dynamic modification in any of the shares will be identified if number of the shares are more than specified threshold value. The security is based on the intractability of factoring as it uses Lagrange polynomial function. This is further enhanced by a graphical masking and unmasking technique;it includes two phases: encryption of the individual secrets and sharing of the data. The generation of the shares is done by masking using Polygram substitution, and the shares are regenerated and unmasking of the qualified shares is done to find original secret. Masking is done on original secret, then shares are generated. Each encrypted share contains information which is different than original secret. Masking by Polygram substitution gives additional protection of secret data on the Internet. Moreover, we achieve efficient recovery of the original secret data. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Polynomials

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Rethinking attribute localization for zero-shot learning

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science China(Information sciences) 2024年第7期67卷 184-196页

作者： Shuhuang CHEN Shiming CHEN Guo-Sen XIE Xiangbo SHU Xinge YOU Xuelong LI School of Electronic Information and Communication Huazhong University of Science and Technology School of Computer Science and Engineering Nanjing University of Science and Technology School of Artificial Intelligence Optics and Electronics Northwestern Polytechnical University

Recent advancements in attribute localization have showcased its potential in discovering the intrinsic semantic knowledge for visual feature representations, thereby facilitating significant visual-semantic interactions essential for zero-shot learning(ZSL). However, the majority of existing attribute localization methods heavily rely on classification constraints, resulting in accurate localization of only a few attributes while neglecting the rest important attributes associated with other classes. This limitation hinders the discovery of the intrinsic semantic relationships between attributes and visual features across all *** address this problem, we propose a novel attribute localization refinement(ALR) module designed to enhance the model's ability to accurately localize all attributes. Essentially, we enhance weak discriminant attributes by grouping them and introduce weighted attribute regression to standardize the mapping values of semantic attributes. This module can be flexibly combined with existing attribute localization *** experiments show that when combined with the ALR module, the localization errors in existing methods are corrected, and state-of-the-art classification performance is achieved.

关键词： zero-shot learning attention mechanism attribute localization image classification

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An Energy-Aware Nanoscale Design of Reversible Atomic Silicon Based on Miller Algorithm

IEEE Design and Test

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IEEE Design and Test 2023年第5期40卷 62-69页

作者： Ahmadpour, Seyed-Sajad Jafari Navimipour, Nima Bahar, Ali Nawaz Mosleh, Mohammad Yalcin, Senay Kadir Has University Department of Computer Engineering Faculty of Engineering and Natural Sciences Istanbul34083 Turkey National Yunlin University of Science and Technology Future Technology Research Center Douliou64002 Taiwan SaskatoonS7N 5A9 Canada Islamic Azad University Dezful Branch Department of Computer Engineering Dezful98210 Iran Nisantasi University Department of Computer Engineering Istanbul34398 Turkey

Area overhead and energy consumption continue to dominate the scalability issues of modern digital circuits. In this context, atomic silicon and reversible logic have emerged as suitable alternatives to address both issues. In this article, the authors propose novel nano-scale circuit design with low area and energy overheads using the same. In particular, the authors propose a reversible gate with Miller algorithm and atomic silicon technology. This article is extremely relevant in today’s era, when the world is moving toward low area and low energy circuits for use in edge devices.

关键词： Adders

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SCTrans: Multi-scale scRNA-seq Sub-vector Completion Transformer for Gene-selective Cell Type Annotation 33

SCTrans: Multi-scale scRNA-seq Sub-vector Completion Transfo...

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33rd International Joint Conference on Artificial Intelligence, IJCAI 2024

作者： Lin, Lu Xue, Wen Wei, Xindian Shen, Wenjun Liu, Cheng Wu, Si Wong, Hau San School of Computer Science and Engineering South China University of Technology China Department of Computer Science City University of Hong Kong Hong Kong Department of Bioinformatics Shantou University Medical College China Department of Computer Science Shantou University China

ISBN: (纸本)9781956792041

Cell type annotation is pivotal to single-cell RNA sequencing data (scRNA-seq)-based biological and medical analysis, e.g., identifying biomarkers, exploring cellular heterogeneity, and understanding disease mechanisms. The previous annotation methods typically learn a nonlinear mapping to infer cell type from gene expression vectors, and thus fall short in discovering and associating salient genes with specific cell types. To address this issue, we propose a multi-scale scRNA-seq Sub-vector Completion Transformer, and our model is referred to as SCTrans. Considering that the expressiveness of gene sub-vectors is richer than that of individual genes, we perform multi-scale partitioning on gene vectors followed by masked sub-vector completion, conditioned on unmasked ones. Toward this end, the multi-scale sub-vectors are tokenized, and the intrinsic contextual relationships are modeled via self-attention computation and conditional contrastive regularization imposed on an encoding transformer. By performing mutual learning between the encoder and an additional lightweight counterpart, the salient tokens can be distinguished from the others. As a result, we can perform gene-selective cell type annotation, which contributes to our superior performance over state-of-the-art annotation methods. © 2024 International Joint Conferences on Artificial Intelligence. All rights reserved.

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A Natural Light Simulation Mathematical Model for Mixed Reality Simulation Cockpit 13

A Natural Light Simulation Mathematical Model for Mixed Real...

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13th International Conference of Information and Communication technology, ICTech 2024

作者： Liu, Wenhai Li, Yang Feng, XinYuan Liang, GuiChong Li, Ning Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Shenyang China School of Information Science and Engineering East China University of Science and Technology Shanghai China School of Computer Science and Technology Shenyang University of Chemical Technology Shenyang China

ISBN: (纸本)9798350376258

This paper introduces a mathematical model for simulating natural light, grounded in the simple sky model and advanced sun positioning techniques. By processing inputs like time, geographical coordinates, and weather, it accurately computes natural light scenarios, replicating sun positioning, angles, intensity, and realistic environmental illumination. ©2024 IEEE.

关键词： Azimuth component illumination nature light simulation simulation solar energy sun position Zenith

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Using Recurrent Neural Networks to Forecast Climate Change: A Time Series Analysis of Global Temperature Variability 1

Using Recurrent Neural Networks to Forecast Climate Change: ...

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1st International Conference on Advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Rathore, Nitasha Anubha Singh, Kuljeet Kaur, Gaganpreet Sharma, Chetna Sinha, Anurag Bharatiya Vidyapeeth College of Engineering Department of Computer Science and Engineering New Delhi India KIET Group of Institutions India School of Sciences Christ University Department of Computer Science Delhi NCR India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India IGNOU Department of Computer science New Delhi India

ISBN: (纸本)9798331518578

Predicting the upcoming weather instances is very crucial. It depends on different climatic parameters like humidity, pressure, temperature, etc. In this paper, the historical data of the weather in the India area is used for future weather instances of the India for farmers' convenience in terms of the agricultural instance which depends on the weather and, functioning according to that which will restore the energy. For weather forecasting we have use the machine learning algorithm and probabilistic predictions of the predictive analytics based on soft computing and. NGBoost algorithm and. Linear models of the machine learning for predictive. Comparative weather incidence spaced on the historical data. We have also used, sliding window algorithm of the statistics for predicting the ideology of the concept of different contrasted windows and year. We've also used utility thirds and machine learning algorithm, classified for predicting the weather based on different features The overall implementation in this paper, shows the accuracy which we have gathered from the data set. An implementation of algorithm which ranges between 80% to 90% and the entire algorithm have been compared based on the feature instances. Work can be concluded on the measurement of the algorithm, which we have got after the implementation of Models. Which rely upon the different data features and thus it can be beneficial for preserving the energy and materials in the India agriculture area and forecasting the weather as per day Agricultural conditions. © 2025 IEEE.

关键词： Adversarial machine learning

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Design and Analysis of an Actuator with High Power Density Permanent Magnet motor for Bipedal Robots

Design and Analysis of an Actuator with High Power Density P...

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2024 International Conference on Electrical Machines, ICEM 2024

作者： He, Tianran Leng, Xiaokun Sun, Yangyu Wu, Yucong School of Electronics and Information Engineering Tongji University Shanghai China School of Computer Science and Technology Harbin Institute of Technology Harbin China University of Sheffield Department of Electrical and Electronic Engineering Sheffield United Kingdom Southern University of Science and Technology Department of Mechanical and Energy Engineering Shenzhen China

ISBN: (纸本)9798350370607

Recently, bipedal robots that utilize electric rotary actuators have attracted much attention since they can walk on any kind of path or terrain and can jump or step over obstacles. In addition, bipedal robots are designed to pick up and put down various types of heavy objects for industrial applications. To achieve the specified functions, the electric rotary actuators, as the core driving unit of bipedal robots, need to possess characteristics of high power density and large output torque. This paper presents the design of an electric rotary actuator based on an outer rotor permanent magnet motor for bipedal robots. The proposed electric rotary actuator includes a 36-slot/42-pole permanent magnet motor, a 10:1 planetary gearbox, and an integrated gear shaft. The electromagnetic performances and overload capability of the proposed 36-slot/42-pole outer rotor permanent magnet motor are analyzed by the finite element method. Finally, a prototype of the proposed electric rotary actuator is tested by a customed high-torque testing bench. The experimental results are in good agreement with the finite element analysis results. © 2024 IEEE.

关键词： Permanent magnets

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Enhancing Efficiency in 6G Radio Resource Allocation Using Machine Learning Algorithms 5th

Enhancing Efficiency in 6G Radio Resource Allocation Using M...

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5th International Conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Kamalraj, R. Nautiyal, Kshitij Taranum, Ayesha Savita Karnataka Bangalore India Department of Electrical Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9789819780426

Using machine gaining knowledge of algorithms with 6G radio resource allocation can assist to enhance network performance by using automating the selection making technique on a cease-to-stop foundation. This paper proposes a more suitable implementation of 6G radio aid allocation the usage of device getting to know algorithms to expand a framework for optimized useful resource utilization. The consequences of the experiments conducted in a real-world testbed validate the effectiveness of the proposed structure in achieving the desired goals consisting of improved community performance and decreased strength consumption. Moreover, the proposed improved implementation might be relevant in a huge range of other resource allocation eventualities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Resource allocation

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Comparison of Dynamic DES with Existing Symmetric Key Algorithms 9

Comparison of Dynamic DES with Existing Symmetric Key Algori...

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9th International Conference on Smart Computing and Communications, ICSCC 2023

作者： Sunny, Leya Elizabeth Paul, Varghese Soe Cochin University of Science and Technology Division of Information Technology Kochi India Rajagiri School of Engineering and Technology Department of Computer Science and Engineering Kochi India

ISBN: (纸本)9798350314090

The concept of security is fundamental to networking. The basic objective of security is to defend against various types of assaults on these networks that hold sensitive data. By altering criteria such as key size, altering the number of iterations and ultimately using confusion box as the S-box, the security of cryptographic algorithms can be increased. Data exists in different forms such as text, images, audio and video. In DES, S-boxes are used to increase the security of the data. All of these 8 S-boxes have 64 elements. Unique numbers in the 0-15 range can be found in each row. These S- boxes are generated dynamically by our suggested system depending on the key and no storage of these elements are required. The comparison of Dynamic DES with other symmetric algorithms like DES, AES and Blowfish yields better results for Dynamic DES in terms of Balance, Bit difference, Time and Memory needed for Execution. © 2023 IEEE.

关键词： Cryptography

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Temporal-Aware Deep Reinforcement Learning for Energy Storage Bidding in Energy and Contingency Reserve Markets

IEEE Transactions on Energy Markets, Policy and Regulation

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IEEE Transactions on Energy Markets, Policy and Regulation 2024年第3期2卷 392-406页

作者： Li, Jinhao Wang, Changlong Zhang, Yanru Wang, Hao Monash University Department of Data Science and AI Faculty of IT and Monash Energy Institute MelbourneVIC3800 Australia Monash University Department of Civil Engineering MelbourneVIC3800 Australia University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China

The battery energy storage system (BESS) has immense potential for enhancing grid reliability and security through its participation in the electricity market. BESS often seeks various revenue streams by taking part in multiple markets to unlock its full potential, but effective algorithms for joint-market participation under price uncertainties are insufficiently explored in the existing research. To bridge this gap, we develop a novel BESS joint bidding strategy that utilizes deep reinforcement learning (DRL) to bid in the spot and contingency frequency control ancillary services (FCAS) markets. Our approach leverages a transformer-based temporal feature extractor to effectively respond to price fluctuations in seven markets simultaneously and helps DRL learn the best BESS bidding strategy in joint-market participation. Additionally, unlike conventional 'black-box' DRL model, our approach is more interpretable and provides valuable insights into the temporal bidding behavior of BESS in the dynamic electricity market. We validate our method using realistic market prices from the Australian National Electricity Market. The results show that our strategy outperforms benchmarks, including both optimization-based and other DRL-based strategies, by substantial margins. Our findings further suggest that effective temporal-aware bidding can significantly increase profits in the spot and contingency FCAS markets compared to individual market participation. © 2023 IEEE.

关键词： Real time systems

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