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A Survey on Evolutionary Computation Based Drug Discovery

IEEE Transactions on Evolutionary Computation 2024年第3期29卷 1-1页

作者： Yu, Qiyuan Lin, Qiuzhen Ji, Junkai Zhou, Wei He, Shan Zhu, Zexuan Tan, Kay Chen College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China School of Computer Science University of Birmingham Bir-mingham UK Department of Computing The Hong Kong Poly-technic University Hong Kong Hong Kong

Drug discovery is an expensive and risky process. To combat the challenges in drug discovery, an increasing number of researchers and pharmaceutical companies recognize the benefits of utilizing computational techniques. Evolutionary computation (EC) offers promise as most drug discovery problems are essentially complex optimization problems beyond conventional optimization algorithms. EC methods have been widely applied to solve these complex optimization problems especially in lead com-pound generation and molecular virtual evaluation, substantially speeding up the process of drug discovery and development. This article presents a comprehensive survey of EC-based drug discovery methods. Particularly, a new taxonomy of the methods is provided and the advantages and limitations of the methods are reviewed. In addition, the potential future directions of EC-based drug discovery are discussed and the publicly available resources including databases and computational tools are compiled for the convenience of researchers seeking to pursue this field. IEEE

关键词： Lead compounds

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A Study on NLP Model Ensembles and Data Augmentation Techniques for Separating Critical Thinking from Conspiracy Theories in English Texts 25

A Study on NLP Model Ensembles and Data Augmentation Techniq...

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25th Working Notes of the Conference and Labs of the Evaluation Forum, CLEF 2024

作者： del Campo Sánchez-Hermosilla, Iñaki Panizo-Lledot, Angel Camacho, David Department of Computer System Engineering Universidad Politécnica de Madrid Calle de Alan Turing Madrid28031 Spain

Conspiracy theories propose that significant events are orchestrated by secretive, powerful groups, gaining traction especially during social upheaval and spreading rapidly via social media. These theories have real-world consequences, as seen in incidents like Pizzagate, where false claims led to a violent attack in a pizzeria in Washington, D.C., and COVID-19 vaccine conspiracies, which fueled public distrust and made the vaccination campaign more difficult. In the age of social media, distinguishing between conspiracy theories and critical thinking is crucial for accurate content moderation because misidentification can push individuals questioning legitimate issues towards conspiracy communities, highlighting the importance of developing effective methods for identifying conspiratorial content. Our study focuses on addressing this challenge by leveraging advanced NLP models. Specifically, we build ensembles using variations of the BERT model, including BERT-base, BERT-large, and RoBERTa. We experimented with different loss functions, such as cross-entropy, Mix-Up, and Supervised Contrastive Loss, and data augmentation techniques like synonym replacement and random word insertions. Our final model achieved a Matthews correlation coefficient (MCC) of 0.8149 on the competition set, securing 8th place in the ranking and demonstrating a considerable level of effectiveness in identifying conspiratorial content. © 2024 Copyright for this paper by its authors.

关键词： COVID-19

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A Reverse Path Planning Approach for Enhanced Performance of Multi-Degree-of-Freedom Industrial Manipulators

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computer Modeling in engineering & Sciences 2024年第5期139卷 1357-1379页

作者： Zhiwei Lin Hui Wang Tianding Chen Yingtao Jiang Jianmei Jiang Yingpin Chen Key Laboratory of Light Field Manipulation and System Integration Applications in Fujian Province School of Physics and Information EngineeringMinnan Normal UniversityZhangzhou363000China Department of Electrical and Computer Engineering University of NevadaLas Vegas89154USA College of Material Engineering Fujian Agriculture and Forestry UniversityFuzhou350000China

In the domain of autonomous industrial manipulators,precise positioning and appropriate posture selection in path planning are pivotal for tasks involving obstacle avoidance,such as handling,heat sealing,and *** Multi-Degree-of-Freedom(MDOF)manipulators offer kinematic redundancy,aiding in the derivation of optimal inverse kinematic solutions to meet position and posture requisites,their path planning entails intricate multiobjective optimization,encompassing path,posture,and joint motion *** satisfactory results in practical scenarios remains *** response,this study introduces a novel Reverse Path Planning(RPP)methodology tailored for industrial *** approach commences by conceptualizing the manipulator’s end-effector as an agent within a reinforcement learning(RL)framework,wherein the state space,action set,and reward function are precisely defined to expedite the search for an initial collision-free *** enhance convergence speed,the Q-learning algorithm in RL is augmented with ***,we formulate the cylindrical bounding box of the manipulator based on its Denavit-Hartenberg(DH)parameters and propose a swift collision detection ***,the motion performance of the end-effector is refined through a bidirectional search,and joint weighting coefficients are introduced to mitigate motion in high-power *** efficacy of the proposed RPP methodology is rigorously examined through extensive simulations conducted on a six-degree-of-freedom(6-DOF)manipulator encountering two distinct obstacle configurations and target *** results substantiate that the RPP method adeptly orchestrates the computation of the shortest collision-free path while adhering to specific posture constraints at the target ***,itminimizes both posture angle deviations and joint motion,showcasing its prowess in enhancing the operational performance of MDOF industrial manipulators.

关键词： Reverse path planning Dyna-Q bidirectional search posture angle joint motion

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Examination of Unsolicited Content on Public Service Platforms Across Multiple Devices 5

Examination of Unsolicited Content on Public Service Platfor...

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5th International Conference on Electronic Communication and Artificial Intelligence, ICECAI 2024

作者： Wu, Yi Wang, Yingshuo Cheng, Yanan Yao, Wangjun Liu, Hong Zhang, Zhaoxin Harbin Institute of Technology Faculty of Computing Weihai China National Computer System Engineering Research Institute of China Beijing China

ISBN: (纸本)9798350386943

Public service websites have become an integral part of people's everyday existence. And the proliferation of mobile devices has further facilitated access to these websites. These websites not only have formal differences between mobile and desktop, but more importantly, may lead to the differentiation and wider dissemination of unsolicited content. Although the existing studies have recognized the universality of the unsolicited content carried by them, they have not carried out a detailed analysis of the transmission forms of these unsolicited content and the defective rate of public websites. To address this issue, this paper use python collected 150 video websites and 150 novel websites through a combination of multiple search engines based on keyword algorithms. By analyzing the algorithm, it conducted mobile and desktop comparisons of the acquisition of unsolicited content on these two specific types of public service websites. The results show that both types of public service websites carry unsolicited content. Domain analysis of these unsolicited websites and their associated unsolicited content revealed that 92.86% of the IP addresses are located overseas. Moreover, the proportion of unsolicited websites on mobile devices can reach 60.42%, significantly higher than the 22.7% on desktop devices. Additionally, there are differences in the forms of unsolicited content displayed on mobile and desktop devices, with a discrepancy rate of up to 38.24%. Unsolicited websites on desktop devices exhibit a more diverse range of unsolicited content. This study provides the first in-depth study of these distinctions, facilitating the proposal of technical and regulatory solutions. © 2024 IEEE.

关键词： domain analysis Formalized differences inappropriate content Public service

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Will the fibers of the future be considered fibers?

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National Science Review 2024年第10期11卷 15-17页

作者： Dewen Xu Ya Liu Hailiang Wang Wei Yan Changjun Jiang Meifang Zhu State Key Laboratory for Modification of Chemical Fibers and Polymer Materials College of Materials Science and Engineering Donghua University Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Department of Computer Science and Technology Tongji University

Fiber materials are key materials that have changed human history and promoted the progress of human civilization. In ancient times, humans used feathers and animal skins for clothing, and later they widely employed natural fibers such as cotton, hemp, silk and wool to make fabrics(Fig. 1a). Chinese ancestors had mastered the art of natural fiber weaving as early as the Neolithic *** thousand years ago, people were already familiar with and adept at techniques for spinning natural fibers [1].

关键词： future will considered fibers

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Seer: Accelerating Block chain Transaction Execution by Fine-Grained Branch Prediction 51st

Seer: Accelerating Block chain Transaction Execution by Fine...

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51st International Conference on Very Large Data Bases, VLDB 2025

作者： Zhang, Shijie Cheng, Ru Liu, Xinpeng Xiao, Jiang Jin, Hai Li, Bo National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong

Increasingly popular decentralized applications (dApps) with complex application logic incur significant overhead for executing smart contract transactions, which greatly limits public block chain performance. Pre-executing transactions off the critical path can mitigate substantial I/O and computation costs during execution. However, pre-execution does not yield any state transitions, rendering the system state inconsistent with actual execution. This inconsistency can lead to deviations in pre-execution paths when processing smart contracts with multiple state-related branches, thus diminishing pre-execution effectiveness. In this paper, we develop Seer, a novel public block chain execution engine that incorporates fine-grained branch prediction to fully exploit pre-execution effectiveness. Seerpredicts state-related branches using a two-level prediction approach, reducing inconsistent execution paths more efficiently than executing all possible branches. To enable effective reuse of pre execution results, Seer employs checkpoint-based fast-path execution, enhancing transaction execution for both successful and unsuccessful predictions. Evaluations with realistic block chain workloads demonstrate that Seer delivers an average of 27.7× transaction-level speedup and an overall 20.6× speedup in the execution phase over vanilla Ethereum, outperforming existing block chain execution acceleration solutions. © 2025, VLDB Endowment, All rights reserved.

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Preventive Power Outage Estimation Based on A Novel Scenario Clustering Strategy

Preventive Power Outage Estimation Based on A Novel Scenario...

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2024 IEEE Power and Energy Society General Meeting, PESGM 2024

作者： Wang, Xiaofei Liu, Weijia Ding, Fei Yao, Yiyun Zhao, Junbo Power System Engineering Center National Renewable Energy Laboratory Golden United States Department of Electrical and Computer Engineering University of Connecticut Storrs United States

The increasing occurrence of extreme weather events is challenging power grid operation. For extreme weather events, the system operator is responsible for estimating the power outages and scheduling the restoration resources. This paper proposes an outage evaluation framework to identify the possible unserved load profiles, vulnerable areas, and mobile energy adequacy. The outputs of an outage prediction model tool are used to generate numerous faulted line scenarios. Next, each scenario's nodal unserved load profile is obtained by solving a three-phase restoration model that considers repair crews and mobile energy resources (MERs). Then, a novel scenario clustering strategy is developed to cluster the unserved load profiles into multiple representative profiles which the system operator can focus on. Finally, case studies on a distribution system evaluate the damage caused by an extreme weather event and verify the effectiveness of the proposed scenario clustering strategy. © 2024 IEEE.

关键词： Mobile energy resources (MERs) power system resilience preventive outage evaluation scenario clustering

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Analysis of Climate Change Misleading Information in TikTok 1

Analysis of Climate Change Misleading Information in TikTok

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1st Workshop on Countering Disinformation with Artificial Intelligence, CODAI 2024

作者： Baltasar, Clara D’Antonio Maceiras, Sergio Martín, Alejandro Camacho, David Department of Computer System Engineering Universidad Politécnica de Madrid Calle de Alan Turing Madrid28031 Spain

In today’s digital landscape, social media platforms have become important areas for disseminating information, ranging from legitimate discourse to misinformation, especially on critical topics such as Climate Change. This study employs claim detection and clustering techniques to analyze misleading information within an initial dataset of videos. Initially, the study identified 5,352 videos out of a total of 8,151 that warranted further investigation. Utilizing clustering methods, it was discovered that the prevalence of misinformation was surprisingly lower than anticipated. Most of the clusters showcases content that promotes sustainability and raises environmental awareness, strengthened by corroborated information of fact-checking agency EFE Verifica. Conversely, there are two clusters that focuses on videos propagating misinformation, conspiracy theories, active discussion and debate, highlighting the necessity of consuming media with caution. Looking ahead, combating misinformation necessitates the enhancement of digital literacy and the cultivation of critical thinking skills. This research aims to leverage technology and verified information from credible organizations to identify, analyze, and mitigate the influence of misleading content on social media, thus better understanding its dynamics and reducing its adverse impacts. © 2022 Copyright for this paper by its authors.

关键词： Climate change Environmental issues Misleading information TikTok videos

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A CNN-LSTM Network for Classification of Attention Deficit Hyperactivity Disorder from EEG Data 41

A CNN-LSTM Network for Classification of Attention Deficit H...

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第41届中国控制会议

作者： Yaru Zhang Xiangxin Tan Yue Gu Key Laboratory of Computer Vision and System (Ministry of Education) School of Computer Science and EngineeringTianjin University of Technology Engineering Research Center of Learning-Based Intelligent system Ministry of Education

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Attention deficit hyperactivity disorder(ADHD) is one of the most common neuropsychiatric disorders in children,with core symptoms of inattention,impulsivity,and *** a result,early detection and treatment are *** this point,ADHD can be diagnosed using an electroencephalogram(EEG).This study proposes a model for identifying the EEG of children with *** proposed model is made up of three parts:temporal convolutional blocks,spatial convolutional blocks,and Long Short Term Memory(LSTM),and because EEG contains more temporal information than other data,we build a model that can extract more temporal *** study included 24 subjects,including 12 children with ADHD and12 normal *** experimental results show that our proposed model outperforms Support Vector Machine(SVM) and EEGNet,with our model achieving correct classification rates of 92.29%,92.76%,and 90.91% on the three backs,respectively,and 94.25% on the full data,demonstrating the model's feasibility and applicability to the ADHD recognition problem.

关键词： Attention Deficit Hyperactivity Disorder Electroencephalogram Deep learning Classification

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Autonomous Drone Control for Wireless Charging Using Power Lines 18

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

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18th International Conference on Ubiquitous Information Management and Communication, IMCOM 2024

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

ISBN: (纸本)9798350331011

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. © 2024 IEEE.

关键词： Drones

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