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Protein Residue Contact Prediction Based on Deep Learning and Massive Statistical Features from Multi-Sequence Alignment

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Tsinghua science and Technology 2022年第5期27卷 843-854页

作者： Huiling Zhang Min Hao Hao Wu Hing-Fung Ting Yihong Tang Wenhui Xi Yanjie Wei Shenzhen Institutes of Advanced Technology Chinese Academy of SciencesShenzhen 518055China University of Chinese Academy of Sciences Beijing 100049China College of Electronic and Information Engineering Southwest UniversityChongqing 400715China School of Software Engineering University of Science and Technology of ChinaHefei 230051China Department of Computer Science The University of Hong KongHong Kong 999077China School of Computer Science Beijing University of Posts and TelecommunicationsBeijing 100876China

Sequence-based protein tertiary structure prediction is of fundamental importance because the function of a protein ultimately depends on its 3 D *** accurate residue-residue contact map is one of the essential elements for current ab initio prediction protocols of 3 D structure ***,with the combination of deep learning and direct coupling techniques,the performance of residue contact prediction has achieved significant ***,a considerable number of current Deep-Learning(DL)-based prediction methods are usually time-consuming,mainly because they rely on different categories of data types and third-party *** this research,we transformed the complex biological problem into a pure computational problem through statistics and artificial *** have accordingly proposed a feature extraction method to obtain various categories of statistical information from only the multi-sequence alignment,followed by training a DL model for residue-residue contact prediction based on the massive statistical *** proposed method is robust in terms of different test sets,showed high reliability on model confidence score,could obtain high computational efficiency and achieve comparable prediction precisions with DL methods that relying on multi-source inputs.

关键词： multi-sequence alignment residue-residue contact prediction feature extraction statistical information Deep Learning(DL) high computational efficiency

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A Twin Dual-Arm Robot Testbed to Collect the Human Demonstration Data for Imitation Learning 13

A Twin Dual-Arm Robot Testbed to Collect the Human Demonstra...

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Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2024

作者： Kim, Juhyung Heo, Sungwon Kim, Songwoo Jung, Ho-Jin Yoon, Han Ul Yonsei University Division of Computer and Telecomm. Engineering Mirae Campus Wonju Korea Republic of Graduate School Yonsei University Department of Computer Science Wonju Korea Republic of Yonsei University Division of Software Mirae Campus Wonju Korea Republic of

ISBN: (纸本)9798350373332

Collecting consistent human demonstrations has been a problematic issue in the field of a learning from demonstration. This paper presents a twin dual-arm robot testbed to collect human demonstration data while performing manual tasks in the activities of daily livings such as opening/closing a window, opening-up/closing-down a sauce bottle cap, etc. The testbed consists of the two sets of a dual-arm robot;for each robot, one-side arm has 6-DoF with a crab-gripper as its hand. Two dual-arm robots are mounted at the front and back of a table-type station. A human operator performs actions to complete a given task with the one at the back;the human operator's actions, meanwhile, are duplicated by the other robot at the front simultaneously. The experimental results show the testbed's potential to gather consistent human demonstration data for training a machine learning agent to manipulate the dual-arm robot under an imitation learning framework. © 2024 IEEE.

关键词： Bottles

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Learning Discriminative Representation for Few-Shot Knowledge Graph Completion 7

Learning Discriminative Representation for Few-Shot Knowledg...

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7th International Conference on Intelligent Information Processing, ICIIP 2022

作者： Wu, Yanping Tian, Ling Hui, Bei Wang, Zhaokun Song, Lexuan School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu Sichuan China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu Sichuan China

ISBN: (纸本)9781450396714

Few-Shot Knowledge Graph Completion (FKGC) aims to infer the missing facts of a relation with only few corresponding known instances. To accomplish this task, one prominent prerequisite is to fully explore and exploit useful information from these supporting instances. However, many existing methods usually treat the support set as a group of independent contents, and fail to explicitly model the interactions among instances. They also majorly focus on positive instances in the support set while overlooking negative ones. Generally, much useful information in the support set has not been well applied yet. Therefore, this work proposes a novel model FSDR to fully capture the fertile information within supporting sets. Extensive experiments on two benchmark datasets demonstrate our model is indeed effective and outperforms state-of-the-art FKGC approaches. © 2022 ACM.

关键词： Knowledge graph

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Secrecy Maximization for Pico Edge Users in 5G Backhaul HWNs: A Quantum RL Approach

Secrecy Maximization for Pico Edge Users in 5G Backhaul HWNs...

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2022 IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2022

作者： Sharma, Himanshu Pulkit Sharma, Gitika Kumar, Neeraj Thapar Institute of Engineering and Technology Computer Science and Engineering Department Punjab Patiala India MAQ Software Hyderabad India Bennett University School of Computer Science Engineering and Technology Greater Noida India

ISBN: (纸本)9781665473408

Picocells can enhance the capacity and spectral efficiency of heterogeneous wireless networks (HWNs) and reduce user equipment (UE) power consumption. Also, the wireless backhaul technology has emerged as an efficient way to boost the data rate in dense 5G HWNs. However, due to the dynamic and distributed nature of 5G HWNs, pico edge users (PEUs) cannot resist the eavesdropping attempts. Moreover, the PEUs are located far away from the PBS and receive considerably low signal-to-interference-plus-noise ratio (SINR) values. Thus, it is critical to improve the achievable secrecy rate (SR) of PEUs for reliable and secure transmission of confidential data in the existence of eavesdroppers. Aiming to enhance the SR of PEUs, this paper formulates a joint optimization problem of beamforming and power allocation of millimeter-wave (mmWave)enabled pico base station (PBS) having backhaul wireless links with macro base station (MBS). As the system is diverse and complex, addressing this non-convex optimization problem directly is challenging. Thus, we have used the Markov decision process (MDP) to transform this optimization problem into a multi-agent reinforcement learning (MARL) problem. Then, to determine the optimal policy for MARL problem of beamforming and power allocation, we propose a Quantum reinforcement learning (QRL) approach. It uses quantum parallelism and state superposition principles to maximize the SR of PEUs. Simulation results indicate that the proposed scheme achieves 28.12% and 7.9% better secrecy rate than MAQL and MADQL schemes, respectively. © 2022 IEEE.

关键词： Markov processes

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A Novel Approach Using Machine Learning for Accurate Detection of DDoS Attack in software Defined Network 1

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2nd International Conference on Cyber Intelligence and Information Retrieval, CIIR 2023

作者： Sharma, Hitesh Kumar Choudhury, Tanupriya Kotecha, Ketan Mahdi, Hussain Falih Dutta, Soumi Bhattacharya, Abhishek Sar, Ayan Energy Acres Bidholi Uttarakhand Bidholi India School of Computer Science University of Petroleum and Energy Studies Uttarakhand Dehradun India Pune India Pune India Department of Computer and Software Engineering University of Diyala Baquba Iraq Sister Nivedita University Chakpachuria India

ISBN: (数字)9789819776030

ISBN: (纸本)9789819776023

A denial Of Service (DoS) attack is termed an attack when the request-receiving server or network shuts down or becomes inaccessible for service. This kind of attack is caused by flooding the target server or network with huge traffic or sending information which can trigger a vulnerability to the server and lead to server crash. Different kinds of DoS attacks can occur on a network including a classic DoS attack which is carried out by a single client machine sending tons of requests and data to a server by itself to increase its processing to the extent that no new genuine requests are answered by the server and network is ultimately unreachable. Another kind of DoS attack is termed a DDoS (Distributed Denial Of Service) attack which includes a master host machine that leverages the network of machines around it to send multiple requests to the server from different machines which act as zombie machines for the attack as the machine won’t even have an idea itself that they are being used for an attack on the server. As multiple requests and data hits the server from different locations, the server becomes unavailable for processing and leads to network failure. These zombie machines are termed botnets which are connected online and are used to overwhelm a target website with fake traffic. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Denial-of-service attack

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Unlearnable 3D Point Clouds: Class-wise Transformation Is All You Need 38

Unlearnable 3D Point Clouds: Class-wise Transformation Is Al...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Wang, Xianlong Li, Minghui Liu, Wei Zhang, Hangtao Hu, Shengshan Zhang, Yechao Zhou, Ziqi Jin, Hai National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Cluster and Grid Computing Lab China Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China School of Computer Science and Technology Huazhong University of Science and Technology China

Traditional unlearnable strategies have been proposed to prevent unauthorized users from training on the 2D image data. With more 3D point cloud data containing sensitivity information, unauthorized usage of this new type data has also become a serious concern. To address this, we propose the first integral unlearnable framework for 3D point clouds including two processes: (i) we propose an unlearnable data protection scheme, involving a class-wise setting established by a category-adaptive allocation strategy and multi-transformations assigned to samples;(ii) we propose a data restoration scheme that utilizes class-wise inverse matrix transformation, thus enabling authorized-only training for unlearnable data. This restoration process is a practical issue overlooked in most existing unlearnable literature, i.e., even authorized users struggle to gain knowledge from 3D unlearnable data. Both theoretical and empirical results (including 6 datasets, 16 models, and 2 tasks) demonstrate the effectiveness of our proposed unlearnable framework. Our code is available at https://***/CGCL-codes/UnlearnablePC. © 2024 Neural information processing systems foundation. All rights reserved.

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Remote Sensing Image Compression Using ResNet and SEBlock 10

Remote Sensing Image Compression Using ResNet and SEBlock

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10th International Conference on computer and Communications, ICCC 2024

作者： Wang, Han Zhang, Qiang Wu, Junpu College of Architecture and Environment Sichuan University Chengdu China School of Computer Science and Software Engineering Southwest Petroleum University Chengdu China Petroleum Engineering School Southwest Petroleum University Chengdu China

This paper presents a remote sensing image compression method based on ResNet and the Squeeze-and-Excitation Block (SEBlock) to optimize bandwidth usage during satellite-to-ground transmission. By employing the ResNet model in the encoder and incorporating SEBlock to enhance feature representation, our model significantly reduces data size while preserving image quality. Experimental results demonstrate that the proposed method achieves an excellent trade-off between compression efficiency and image quality, with PSNR reaching 38.02 dB and SSIM reaching 0.9851. We tested scenarios with 2 to 5 ResNet layers, showing consistent performance improvements, with fewer layers resulting in higher PSNR and SSIM values. This approach offers a novel solution for efficient transmission of remote sensing imagery, suitable for high-resolution and bandwidth-limited satellite communications. © 2024 IEEE.

关键词： Band-width Optimization PSNR Remote Sensing Image Compression ResNet Satellite-to-Ground Transmission SEBlock SSIM

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An Efficient Deep Learning Model based Diagnosis System for Lung Cancer Disease 4

An Efficient Deep Learning Model based Diagnosis System for ...

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4th International Conference on Computing, Mathematics and engineering Technologies, iCoMET 2023

作者： Zaman Khan, Gul Ali Shah, Ibrar Farhatullah Ikram Ullah, Muhammad Ullah, Inam Ihtesham, Muhammad Junaid, Hazrat Yousafzai, Spogmay Sardar, Fouzia Department of Computer Software Engineering Uet Mardan Pakistan China University of Geosciences School of Automation Wuhan430074 China Dalian Jiaotong University School of Software Dalian116028 China Abdul Wali Khan University Department of Computer Science Maradan Pakistan University of Malakand Department of Computer Science and Information Technology Dir Lower Pakistan University of Malakand Department of Zoology Pakistan

ISBN: (纸本)9798350335316

Lung cancer illness seriously impacts people's health. Medical history-based detection of lung cancers has been utilized but it has unsatisfactory results. Artificial intelligence algorithms are more precise and efficient in classifying lung cancer patients and healthy persons. Additionally, the medical history-based diagnosis of lung cancer disease is costly and time consuming. The life of lung cancer disease is very short after detection. Artificial intelligence-based diagnosis systems can detect the lung cancer disease early and efficiently. However, previous research work as several limitations, for example, some techniques computation time is very high but their accuracy is good while some techniques have less computation time but accuracy is not good. The proposed work suggests a deep convolutional neural network-based diagnosis system for lung cancer disease early and accurate detection. We made use of publically available dataset downloaded from Kaggle online repository and applied deep convolutional neural network for accurate lung cancer detection. Furthermore, we have applied some preprocessing and features selection techniques such as max, min, standard deviation and variance threshold. The proposed CNN model achieved 99.2% validation accuracy, 99.8% training accuracy, 99% precision, and 99% recall in minimum computation time of 6 sec which is acceptable. © 2023 IEEE.

关键词： Convolutional neural networks

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Design of Vehicle-cloud Collaborative Framework for 5G Cross-border Multi-PLAN Scenarios 8

Design of Vehicle-cloud Collaborative Framework for 5G Cross...

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8th Annual International Conference on Network and Information Systems for computers, ICNISC 2022

作者： Du, Guotao Chu, Jinlong Ma, Jing Ning, Shiduo School of Mechanical Engineering Dalian University of Technology Dut Dalian China School of Engineering Computer & Mathematical Sciences Auckland University of Technology Department of Computer Science & Software Engineering Auckland New Zealand

ISBN: (纸本)9781665453516

The 5G-MOBIX project applies 5G network communication technology and defines five user stories and specific user scenarios in order to solve the cross-border problem of 5G driverless vehicles. Combined with the remote control user scenario in the remote driving user case, this paper explains the interconnect delay problem between PLMNS in this scenario. From the perspective of infrastructure, that is, the cooperation between vehicle infrastructure and cloud infrastructure, the CCAM architecture of remote control user story simulation test scenario is designed. The proposed solution of direct interconnection is verified and tested, and the cross-border test is simulated at the test site in China. The final test results show that the solution is feasible. © 2022 IEEE.

关键词： Remote control

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Neural Machine Translation by Fusing Key Information of Text

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computers, Materials & Continua 2023年第2期74卷 2803-2815页

作者： Shijie Hu Xiaoyu Li Jiayu Bai Hang Lei Weizhong Qian Sunqiang Hu Cong Zhang Akpatsa Samuel Kofi Qian Qiu Yong Zhou Shan Yang School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu610054China Science and Technology on Altitude Simulation Laboratory Sichuan Gas Turbine Establishment Aero Engine Corporation of ChinaMianyang621000China School of Power and Energy Northwestern Polytechnical UniversityXi’an710072China School of Computer Science Southwest Petroleum UniversityChengdu610500China Department of Chemistry Physics and Atmospheric SciencesJackson State UniversityJacksonMS 39217USA

When the Transformer proposed by Google in 2017,it was first used for machine translation tasks and achieved the state of the art at that *** the current neural machine translation model can generate high quality translation results,there are still mistranslations and omissions in the translation of key information of long *** the other hand,the most important part in traditional translation tasks is the translation of key *** the translation results,as long as the key information is translated accurately and completely,even if other parts of the results are translated incorrect,the final translation results’quality can still be *** order to solve the problem of mistranslation and missed translation effectively,and improve the accuracy and completeness of long sentence translation in machine translation,this paper proposes a key information fused neural machine translation model based on *** model proposed in this paper extracts the keywords of the source language text separately as the input of the *** the same encoding as the source language text,it is fused with the output of the source language text encoded by the encoder,then the key information is processed and input into the *** incorporating keyword information from the source language sentence,the model’s performance in the task of translating long sentences is very *** order to verify the effectiveness of the method of fusion of key information proposed in this paper,a series of experiments were carried out on the verification *** experimental results show that the Bilingual Evaluation Understudy(BLEU)score of the model proposed in this paper on theWorkshop on Machine Translation(WMT)2017 test dataset is higher than the BLEU score of Transformer proposed by Google on the WMT2017 test *** experimental results show the advantages of the model proposed in this paper.

关键词： Key information transformer fusion neural machine translation

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