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61st ACM/IEEE Design Automation Conference, DAC 2024

作者： Chen, Zihao Huang, Jiangli Liu, Yiting Yang, Fan Shang, Li Zhou, Dian Zeng, Xuan State Key Laboratory of Integrated Chips and Systems School of Microelectronics Fudan University Shanghai China China and Shanghai Key Laboratory of Data Science School of Computer Science Fudan University Shanghai China Department of Electrical Engineering University of Texas at Dallas RichardsonTX United States

ISBN: (纸本)9798400706011

This paper presents Artisan, an automated operational amplifier design framework using large language models (LLMs). We develop a bidirectional representation to align abstract circuit topologies with their structural and functional semantics. We further employ Tree-of-Thoughts and Chain-of-Thoughts approaches to model the design process as a hierarchical question-answer sequence, implemented by a mechanism of multi-agent interaction. A high-quality opamp dataset is developed to enhance the design proficiency of the Artisan-LLM. Experimental results demonstrate that Artisan outperforms state-of-the-art optimization-based methods and benchmark LLMs, in success rate, circuit performance metrics, and interpretability, while accelerating the design process by up to 50.1X. Artisan will be released for public access. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Semantics

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A connectivity index based on adjacent vertices in cubic fuzzy graph with an application

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 11025-11040页

作者： Guan, Hao Sadati, Seyed Hossein Talebi, Ali Asghar Shafi, Jana Khan, Aysha Institute of Computing Science and Technology Guangzhou University Guangzhou China School of Computer Science of Information Technology Qiannan Normal University for Nationalities Duyun China Department of Mathematics University of Mazandaran Babolsar Iran Department of Computer Engineering and Information College of Engineering in Wadi Alddawasir Prince Sattam Bin Abdulaziz University Wadi Alddawasir Saudi Arabia Department of Mathematics Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia

A cubic fuzzy graph is a type of fuzzy graph that simultaneously supports two different fuzzy memberships. The study of connectivity in cubic fuzzy graph is an interesting and challenging topic. This research generalized the neighborhood connectivity index in a cubic fuzzy graph with the aim of investigating the connection status of nodes with respect to adjacent vertices. In this survey, the neighborhood connectivity index was introduced in the form of two numerical and distance values. Some characteristics of the neighborhood connectivity index were investigated in cubic fuzzy cycles, saturated cubic fuzzy cycle, complete cubic fuzzy graph and complementary cubic fuzzy graph. The method of constructing a cubic fuzzy graph with arbitrary neighborhood connectivity index was the other point in this research. The results showed that the neighborhood connectivity index depends on the potential of nodes and the number of neighboring nodes. This research was conducted on the Central Bank's data regarding inter-bank relations and its results were compared in terms of neighborhood connectivity index. © 2024-IOS Press. All rights reserved.

关键词： Graph theory

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Bioinformatics exploration into potential mechanisms of Calamus draco for colorectal cancer treatment

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Procedia computer science 2024年 245卷 127-136页

作者： Christian Kenneth Rudi Nirwantono Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Biotechnology Department Faculty of Engineering Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

As one of the cancer types with the highest incidence rates, colorectal cancer (CRC) would benefit from treatments with fewer side effects and reduced treatment-resistant potential. One of the options is to harness the anti-CRC potential of natural products. Previous studies have shown that Calamus draco exudate, dragon's blood, has anticancer activity in liver cancer and acute myeloid leukemia, but its bioactivity has not been studied in CRC. Here we conduct a bioinformatics study based on network pharmacology to explore the anti-CRC potential and mechanism of C. draco -derived compounds. The bioinformatics pipeline is composed of compound and target collection, biological network evaluation, and enrichment analysis. We found that there are 43 bioactive compounds from C. draco targeting 91 CRC-related targets, of which most compounds target MEN1, PTGS2, and IDH1. Further analyses show that the targets of C. draco are involved in the cellular response to hypoxia. By inhibiting those targets, C. draco bioactive compounds can potentially hinder angiogenesis and increase treatment response efficacy.

关键词： bioinformatics network pharmacology Calamus draco colorectal cancer drug discovery

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Technical Note—Improved Sample-Complexity Bounds in Stochastic Optimization

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Operations Research 2025年第2期73卷 986-994页

作者： Baveja, Alok Chavan, Amit Nikiforov, Andrei Srinivasan, Aravind Xu, Pan Department of Supply Chain Management Rutgers Business School Rutgers The State University of New Jersey PiscatawayNJ08854 United States Snowflake Inc. San MateoCA94402 United States School of Business Rutgers The State University of New Jersey CamdenNJ08102 United States Department of Computer Science University of Maryland College ParkMD20742 United States Department of Computer Science New Jersey Institute of Technology NewarkNJ07102 United States

Real-world network-optimization problems often involve uncertain parameters during the optimization phase. Stochastic optimization is a key approach introduced in the 1950s to address such uncertainty. This paper presents improved upper bounds on the number of samples required for the sample-average approximation method in stochastic optimization. It enhances the sample complexity of existing approaches in this setting, providing faster approximation algorithms for any method that employs this framework. This work is particularly relevant for solving problems like the stochastic Steiner tree problem. © 2023 INFORMS.

关键词： Optimization algorithms

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Research on Image Encryption Algorithm Based on Chaotic System 4

Research on Image Encryption Algorithm Based on Chaotic Syst...

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4th IEEE International Conference on data science and computer Application, ICDSCA 2024

作者： Yihang, Wu Yanling, Sun Yingqi, Li School of Mechanical Engineering Shenyang Jianzhu University Shenyang China Department of Science Shenyang Jianzhu University Shenyang China Computer Science and Engineering Shenyang Jianzhu University Shenyang China

ISBN: (纸本)9798350368239

With the development of the internet, social software and media have become ubiquitous, making images increasingly important in daily life. The dissemination of a large number of images on social networks has made information security issues increasingly prominent, and image encryption technology has become increasingly important. This article mainly studies image encryption algorithms based on chaotic systems. Firstly, the background and history of image encryption were introduced, followed by an explanation of the basic concepts based on chaotic systems. Then, several chaos based image encryption schemes were comprehensively compared, and their advantages and disadvantages were analyzed. Subsequently, several common measures for evaluating the effectiveness of encryption schemes were introduced. Finally, the future development of image encryption algorithms based on chaotic systems is discussed. ©2024 IEEE.

关键词： Encryption algorithms

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Blockchain-Powered Student Feedback for Enhanced Learning Experiences

Blockchain-Powered Student Feedback for Enhanced Learning Ex...

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2024 International Conference on Innovations and Challenges in Emerging Technologies, ICICET 2024

作者： Naik, Girish R. Naik, Poornima G. KIT's College of Engineering Department of Mechanical Engineering Kolhapur India Csiber School of Computer Science and Applications Kolhapur India

ISBN: (纸本)9798350319019

In the contemporary landscape of education, the invaluable input provided by students through feedback is widely acknowledged as pivotal in elevating the quality of teaching and learning experiences. Yet, conventional feedback systems encounter notable constraints, notably in the realms of data security and transparency. Current research is dedicated to exploring the integration of blockchain technology as a robust and secure framework for collecting student feedback. Blockchain's distinct features, including its assurance of data integrity, transparency mechanisms, and the utilization of smart contracts, hold the promise of significantly refining the feedback process. This research meticulously outlines the academic significance of student feedback systems, defines essential components for effective feedback mechanisms, and elucidates the role of blockchain in constructing such a system. Moreover, it presents a conceptual model design, details the system's architecture, and elucidates the functionalities of smart contracts developed using Solidity. By leveraging blockchain, this innovative approach ensures the security, transparency, and streamlined efficiency of the feedback process, proclaiming unprecedented potential for transformative advancements in educational methodologies. © 2024 IEEE.

关键词： Smart contract

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Recurrent Neural Network-based Models as Bahasa Indonesia-Sundanese Language Neural Machine Translator

Recurrent Neural Network-based Models as Bahasa Indonesia-Su...

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2023 International Conference on computer science, Information Technology and Engineering, ICCoSITE 2023

作者： Wijanarko, Bambang Dwi Heryadi, Yaya Murad, Dina Fitria Tho, Cuk Hashimoto, Kiyota BINUS Online Learning Computer Science Department Bina Nusantara University Jakarta11480 Indonesia BINUS Graduate Program - Doctor of Computer Science Computer Science Department Bina Nusantara University Jakarta11480 Indonesia BINUS Online Learning Information Systems Department Bina Nusantara University Jakarta11480 Indonesia Prince of Songkla University Interdisciplinary Graduate School or Earth System Science and Andaman Natural Disaster Management Phuket83120 Thailand

ISBN: (纸本)9798350320954

Sequential data are available in many domains including weather observation data, stock price, gene sequence, and many others. Sequential data processing is very instrumental as a solution to a variety of downstream tasks in Natural Language Processing such as language translation. Translating a language to another language has become instrumental when peoples interact with other people who speak a different language. However, language translation is not an easy computation task when there is a language-resource gap. This paper presents some empirical results from the exploration of the Recurrent Neural Network (RNN), the Gated Recurrent Unit (GRU), the Long Short-term Memory (LSTM), and the Bidirectional Long Short-term Memory (Bi-LSTM) models as machine translation models from Bahasa Indonesia to the Sundanese language. These empirical results show that the Bi-LSTM achieved the highest performance (0.95 average training accuracy and 0.92 average testings BLEU score) compared to the RNN (0.93 average training accuracy and 0.91 average testings BLEU score), the GRU (0.88 average training accuracy and 0.89 average testings BLEU score), and the LSTM (0.92 average training accuracy and 0.91 average testings BLEU score) models. These results validate some previously reported studies that claim the Bi-LSTM model potentially outperforms the other recurrent neural network-based models when it is used to process sequence datasets. © 2023 IEEE.

关键词： Long short-term memory

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Fine classification of rice paddy using multitemporal compact polarimetric SAR C band data based on machine learning methods

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Frontiers of Earth science 2024年第1期18卷 30-43页

作者： Xianyu GUO Junjun YIN Kun LI Jian YANG Huimin ZOU Fukun YANG School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China Department of Electronic Engineering Tsinghua UniversityBeijing 100084China College of Global Change and Earth System Science Beijing Normal UniversityBeijing 100875China

Rice is an important food crop for human *** distinguishing different varieties and sowing methods of rice on a large scale can provide more accurate information for rice growth monitoring,yield estimation,and phenological monitoring,which has significance for the development of modern *** polarimetric(CP)synthetic aperture radar(SAR)provides multichannel information and shows great potential for rice monitoring and ***,the use of machine learning methods to build classification models is a controversial *** this paper,the advantages of CP SAR data,the powerful learning ability of machine learning,and the important factors of the rice growth cycle were taken into account to achieve high-precision and fine classification of rice ***,CP SAR data were simulated by using the seven temporal RADARSAT-2 C-band data ***,20-two CP SAR parameters were extracted from each of the seven temporal CP SAR data *** addition,we fully considered the change degree of CP SAR parameters on a time scale(ΔCP_(DoY)).Six machine learning methods were employed to carry out the fine classification of rice *** results show that the classification methods of machine learning based on multitemporal CP SAR data can obtain better results in the fine classification of rice paddies by considering the parameters ofΔCP_(DoY).The overall accuracy is greater than 95.05%,and the Kappa coefficient is greater than *** them,the random forest(RF)and support vector machine(SVM)achieve the best results,with an overall accuracy reaching 97.32%and 97.37%,respectively,and Kappa coefficient values reaching 0.965 and 0.966,*** the two types of rice paddies,the average accuracy of the transplant hybrid(T-H)rice paddy is greater than 90.64%,and the highest accuracy is 95.95%.The average accuracy of direct-sown japonica(D-J)rice paddy is greater than 92.57%,and the highest accuracy is 96.13%.

关键词： compact polarimetric(CP)SAR rice paddy machine learning fine classification multitemporal

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Adaptive Attribute-Based Honey Encryption: A Novel Solution for Cloud data Security

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computers, Materials & Continua 2025年第2期82卷 2637-2664页

作者： Reshma Siyal Muhammad Asim Long Jun Mohammed Elaffendi Sundas Iftikhar Rana Alnashwan Samia Allaoua Chelloug School of Computer Science and Engineering Central South UniversityChangsha410083China EIAS LAB College of Computer and Information Sciencesand Center of Excellence in Quantum and Intelligent ComputingPrince Sultan UniversityRiyadh11586Saudi Arabia Gianforte School of Computing Montana State UniversityBozemanMT 59717USA Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia

A basic procedure for transforming readable data into encoded forms is encryption, which ensures security when the right decryption keys are used. Hadoop is susceptible to possible cyber-attacks because it lacks built-in security measures, even though it can effectively handle and store enormous datasets using the Hadoop Distributed File System (HDFS). The increasing number of data breaches emphasizes how urgently creative encryption techniques are needed in cloud-based big data settings. This paper presents Adaptive Attribute-Based Honey Encryption (AABHE), a state-of-the-art technique that combines honey encryption with Ciphertext-Policy Attribute-Based Encryption (CP-ABE) to provide improved data security. Even if intercepted, AABHE makes sure that sensitive data cannot be accessed by unauthorized parties. With a focus on protecting huge files in HDFS, the suggested approach achieves 98% security robustness and 95% encryption efficiency, outperforming other encryption methods including Ciphertext-Policy Attribute-Based Encryption (CP-ABE), Key-Policy Attribute-Based Encryption (KB-ABE), and Advanced Encryption Standard combined with Attribute-Based Encryption (AES+ABE). By fixing Hadoop’s security flaws, AABHE fortifies its protections against data breaches and enhances Hadoop’s dependability as a platform for processing and storing massive amounts of data.

关键词： Cybersecurity data security cloud storage hadoop encryption and decryption privacy protection attribute-based honey encryption

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Joint Action Representation and Prioritized Experience Replay for Reinforcement Learning in Large Discrete Action Spaces 23

Joint Action Representation and Prioritized Experience Repla...

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7th International Conference on Machine Learning and Soft Computing, ICMLSC 2023

作者： Wei, Xueyu Xue, Wei Zhao, Wei Shen, Yuanxia Yu, Gaohang School of Computer Science and Technology Anhui University of Technology Maanshan243032 China Department of Mathematics School of Science Hangzhou Dianzi University Hangzhou310018 China

ISBN: (纸本)9781450398633

In dealing with the large discrete action spaces, a joint action representation and prioritized experience replay method is proposed in this paper, which consists of three modules. In the first module, we use the k-nearest neighbor method to reduce the dimensionality of the original action space, generating a compact action space, and then the critic network is introduced to further evaluate and filter this compact space to obtain the optimal action. Note that the optimal action may have inconsistency with the actual desired action. Then in the second module, we introduce a multi-step update technique to reduce the training variance when storing data in the replay buffer. In the third module, considering the existence of correlation between samples when sampling data, we assign the corresponding weight to the sample experience by calculating the absolute value of temporal difference error and use such a non-uniform sampling method to prioritize the samples for sampling. Experimental results on four benchmark environments demonstrate the effectiveness and efficiency of the proposed method in dealing with the large discrete action spaces. © 2023 ACM.

关键词： Reinforcement learning

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