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Research Square 2021年

作者： Xu, Yuan-Yuan Gu, Shen-Ming Li, Hua-Xiong Min, Fan School of Computer Science Southwest Petroleum University Chengdu610500 China Key Laboratory of Oceanographic Big Data Mining & Application of Zhejiang Province Zhejiang Ocean University Zhoushan316022 China Department of Control and Systems Engineering School of Management and Engineering Nanjing University Nanjing210093 China

Conversational recommendation is ubiquitous in e-commerce, while three-way recommendation provides friendly choices for service providers and users. However, their combination has not been studied yet. In this paper, we introduce the three-way conversational recommendation problem, and design the hybrid conversational recommendation (HTCR) algorithm to address it. First, a new recommendation problem is defined by considering the man-machine interaction as well as the misclassification and promotion costs. The optimization objective of the problem is to minimize the total cost. Second, a popularity-based technique is designed for user cold-start recommendation, where the user maturity is responsible for deciding when HTCR turns to the second technique. Third, an incremental matrix factorization technique is designed for regular recommendation. It is efficient since only a few rounds of training are needed for newly acquired user feedback. Experiments were undertaken on four well-known datasets, including Jester, MovieLens 100K, MovieLens 1M, and Yelp. Results demonstrated that our algorithm outperformed state-of-the-art ones in terms of average cost. © 2021, CC BY.

关键词： Matrix algebra

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CloudBrain-NMR: An Intelligent Cloud Computing Platform for NMR Spectroscopy Processing, Reconstruction and Analysis

arXiv

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arXiv 2023年

作者： Guo, Di Li, Sijin Liu, Jun Tu, Zhangren Qiu, Tianyu Xu, Jingjing Feng, Liubin Lin, Donghai Hong, Qing Lin, Meijin Lin, Yanqin Qu, Xiaobo School of Computer and Information Engineering Fujian Engineering Research Center for Medical Data Mining and Application Xiamen University of Technology Xiamen361024 China Department of Electronic Science Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance Xiamen University Xiamen361005 China The Key Laboratory for Chemical Biology of Fujian Province MOE Key Laboratory of Spectrochemical Analysis & Instrumentation College of Chemistry and Chemical Engineering Xiamen University Xiamen361005 China Department of Applied Marine Physics and Engineering College of Ocean and Earth Sciences Xiamen University Xiamen361102 China The China Mobile Group Xiamen361005 China

Nuclear Magnetic Resonance (NMR) spectroscopy has served as a powerful analytical tool for studying molecular structure and dynamics in chemistry and biology. However, the processing of raw data acquired from NMR spectrometers and subsequent quantitative analysis involves various specialized tools, which necessitates comprehensive knowledge in programming and NMR. Particularly, the emerging deep learning tools is hard to be widely used in NMR due to the sophisticated setup of computation. Thus, NMR processing is not an easy task for chemist and biologists. In this work, we present CloudBrain-NMR, an intelligent online cloud computing platform designed for NMR data reading, processing, reconstruction, and quantitative analysis. The platform is conveniently accessed through a web browser, eliminating the need for any program installation on the user side. CloudBrain-NMR uses parallel computing with graphics processing units and central processing units, resulting in significantly shortened computation time. Furthermore, it incorporates state-of-the-art deep learning-based algorithms offering comprehensive functionalities that allow users to complete the entire processing procedure without relying on additional software. This platform has empowered NMR applications with advanced artificial intelligence processing. CloudBrain-NMR is openly accessible for free usage at https://***/***. © 2023, CC BY.

关键词： Deep learning

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application of Deep Learning Methods for Distinguishing Gamma-Ray Bursts from Fermi/GBM TTE Data

arXiv

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arXiv 2023年

作者： Zhang, Peng Li, Bing Gui, Renzhou Xiong, Shaolin Zou, Ze-Cheng Wang, Xianggao Li, Xiaobo Cai, Ce Zhao, Yi Zhang, Yanqiu Xue, Wangchen Zheng, Chao Zhao, Hongyu College of Electronic and Information Engineering Tongji University Shanghai201804 China Key Laboratory of Particle Astrophysics Chinese Academy of Sciences Beijing100049 China School of Astronomy and Space Science Nanjing University Nanjing210023 China Guangxi Key Laboratory for Relativistic Astrophysics Nanning530004 China College of Physics Hebei Key Laboratory of Photophysics Research and Application Hebei Normal University Hebei Shijiazhuang050024 China School of Computer and Information Dezhou University Dezhou253023 China School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China

To investigate GRBs in depth, it is crucial to develop an effective method for identifying GRBs accurately. Current criteria, e.g., onboard blind search, ground blind search, and target search, are limited by manually set thresholds and perhaps miss GRBs, especially for sub-threshold events. We proposed a novel approach that utilizes convolutional neural networks (CNNs) to distinguish GRBs and non-GRBs directly. We structured three CNN models, plain-CNN, ResNet, and ResNet-CBAM, and endeavored to exercise fusing strategy models. Count maps of NaI detectors onboard Fermi/GBM were employed as the input samples of datasets and models were implemented to evaluate their performance on different time scale data. The ResNet-CBAM model trained on 64 ms dataset achieves high accuracy overall, which includes residual and attention mechanism modules. The visualization methods of Grad-CAM and t-SNE explicitly displayed that the optimal model focuses on the key features of GRBs precisely. The model was applied to analyze one-year data, accurately identifying approximately 98% of GRBs listed in the Fermi burst catalog, 8 out of 9 sub-threshold GRBs, and 5 GRBs triggered by other satellites, which demonstrated the deep learning methods could effectively distinguish GRBs from observational data. Besides, thousands of unknown candidates were retrieved and compared with the bursts of SGR J1935+2154 for instance, which exemplified the potential scientific value of these candidates indeed. Detailed studies on integrating our model into real-time analysis pipelines thus may improve their accuracy of inspection, and provide valuable guidance for rapid follow-up observations of multi-band telescopes. Copyright © 2023, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Securing the Private Key in Your Blockchain Wallet: A Continuous Authentication Approach Based on Behavioral Biometric

Securing the Private Key in Your Blockchain Wallet: A Contin...

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作者： Teng Hu Xiaolei Liu Weina Niu Kangyi Ding Yanping Wang Xiaosong Zhang Institute for Cyber Security School of Computer Science and EngineeringUniversity of Electronic Science and Technology of China Institute of Computer Application China Academy of Engineering Physics Peng Cheng Laboratory Cyberspace Security Research Center

As blockchain is increasingly valued, the application of blockchain and its security has become a research hotspot. Not only that, more and more ordinary users are paying attention to the blockchain and beginning to trade commodities in cryptocurrencies. The blockchain wallet is a program for users to manage and stores their private keys. Furthermore, the private key is the user's unique credential to the cryptocurrency in the blockchain. But for now, blockchain wallets are only using passwords to authenticate users, which poses a potential risk to private key security. To mitigate this risk, this paper proposes a continuous authentication approach based on user mouse behavioral biometrics. The approach was experimentally shown to be able to authenticate once per second with an average FAR and FRR of 6.92% and 6.18%, respectively.

关键词： A Continuous Authentication Approach Based on Behavioral Biometric Securing the Private Key in Your Blockchain Wallet Key

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A comprehensive survey analysis in image fusion using masking for future directions

引用

AIP Conference Proceedings 2024年第1期2512卷

作者： C. H. Dhawaleswar Rao Rudra Kalyan Nayak Ramamani Tripathy Department of Computer Science and Engineering Centurion University of Technology and Management Paralakhemundi Campus Village Alluri Nagar P.O. R Sitapur Gajapati District Odisha 761211 India School of CSE VIT Bhopal University Sehore Madhya Pradesh India Department of Master of Computer Application United School of Business Management Bhubaneswar Odisha India

In image processing fusion having a significant importance for different applications. Generally the process of image fusion is applied in multiple image and get output is one image. Now a day’s fusion is most popular in machine perception so we need accurate images after applying the fusion techniques. So in this work we compared different fusion techniques and analyzed their performance. Here different masks are applied in the images using discrete cosine transform (DCT) for better results. Out of different masking’s fanshaped mask gives better quality fused images. In multimodal identification systems requires the multiple image processing technques can be fused in this context the more accurated fused techniques required,so in this proposed work we provide the best fused techniques using masking techniques. Masking is a process of get some piece of information from the image based on the piece of information from the image modify the entire remaining image. In this paper study the different image processing fusion techniques and compare those fusion techniques given a comparative results. The results help full to the researcher which fusion techniques is better suitable for their work.

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Superior Performance of ZnGaO Solar-Blind Photodetectors by Implementing TFT Structure and Tunable ZNO Cycle Ratio

SSRN

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SSRN 2024年

作者： Fan, Hui-Chen Wang, Chen Xu, Yi-Hong Fan, Teng-Min Huang, Pao-Hsun Ruan, Yu-Jiao Wu, Tingzhu Kuo, Hao-Chung Wuu, Dong-Sing Lai, Feng-Min Lien, Shui-Yang Xiamen Key Laboratory of Development and Application for Advanced Semiconductor Coating Technology School of Opto-Electronic and Communication Engineering Xiamen University of Technology Xiamen361024 China School of Ocean Information Engineering Jimei University Jimei District Fujian Xiamen361021 China National Measurement and Testing Center for Flat Panel Display Industry Xiamen Institute of Measurement and Testing Xiamen361024 China National Integrated Circuit Industry and Education Integration Innovation Platform Department of Electronic Science Xiamen University Xiamen361005 China Department of Photonics Graduate Institute of Electro-Optical Engineering College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Department of Applied Materials and Optoelectronic Engineering National Chi Nan University Nantou54561 Taiwan Department of Biomedical Engineering Da-Yeh University Changhua51591 Taiwan

In this study, plasma enhanced atomic layer deposition was used to prepare ZnGaO thin films with different ZnO cycle ratios from 0% to 50%, which were employed to fabricate the MSM-type and TFT-type solar-blind photodetector. The properties of the films and the performance of the solar blind photodetector are greatly affected by the ZnO cycle ratio. Appropriate Zn doping content decreases the ratio of oxygen vacancies and Zn interstitials in Ga2O3, while excessive Zn doping degrades the quality of the film. Compared to MSM-type photodetector, an obviously improved performance of ZnGaO TFT-type photodetector has been observed due to its self-amplified photocurrent and the reduced dark current controlled by gate voltage. At a ZnO cycle ratio of 40%, a superior performance of TFT-type solar-blind photodetector with an ultra-low dark current ~4.5×10-13 A, an excellent light switching ratio ~2.2×107, and an outstandingly high sensitivity ~34.31 A/W has been obtained. These results demonstrate that the incorporation of Zn dopants can effectively modulate the performance of Ga2O3 TFT-type photodetector providing a great perspective for cost-effective and high-performance solar-blind PDs. © 2024, The Authors. All rights reserved.

关键词： Zinc oxide

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Adaptive PromptNet for Auxiliary Glioma Diagnosis Without Contrast-Enhanced MRI

Adaptive PromptNet for Auxiliary Glioma Diagnosis Without Co...

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IEEE International Symposium on Biomedical Imaging

作者： Yeqi Wang Weijian Huang Cheng Li Xiawu Zheng Yusong Lin Shanshan Wang School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou China Paul C. Lauterbur Research Center for Biomedical Imaging Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Peng Cheng Laboratory Shenzhen China School of Cyber Science and Engineering Zhengzhou University Zhengzhou China Collaborative Innovation Center for Internet Healthcare Zhengzhou University Zhengzhou China Hanwei IoT Institute Zhengzhou University Zhengzhou China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangdong China

Multi-contrast magnetic resonance imaging (MRI)-based automatic auxiliary glioma diagnosis plays an important role in the clinic. Contrast-enhanced MRI sequences (e.g., contrast-enhanced T1-weighted imaging) were utilized in most of the existing relevant studies, in which remarkable diagnosis results have been reported. Nevertheless, acquiring contrast-enhanced MRI data is sometimes not feasible due to the patient’s physiological limitations. Furthermore, it is more time-consuming and costly to collect contrast-enhanced MRI data in the clinic. In this paper, we propose an adaptive PromptNet to address these issues. Specifically, a PromptNet for glioma grading utilizing only non-enhanced MRI data has been constructed. PromptNet receives constraints from features of contrast-enhanced MR data during training through a designed prompt loss. To further boost the performance, an adaptive strategy is designed to dynamically weight the prompt loss in a sample-based manner. As a result, PromptNet is capable of dealing with more difficult samples. The effectiveness of our method is evaluated on a widely-used BraTS2020 dataset, and competitive glioma grading performance on NE-MRI data is achieved.

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Improve the Performance of LSM-Tree based on Key-Value via Multithreading

Improve the Performance of LSM-Tree based on Key-Value via M...

引用

第二十届分布式计算及其应用国际学术研讨会

作者： Yuan Gao Ping Xie Wendi Hua Meng Lv Jiating Lu College of Computer of Qinghai Normal University The State Key Laboratory of Tibetan Intelligent Information Processing and Application Key Laboratory of Internet of Things of Qinghai Province Academy of Plateau Science and Sustainability School of Physical and Electrical Engineering Qinghai Normal University

In the era of big data,key-value storage systems based on Log-Structure Merge tree(LSM-tree) are widely used in numerous ***-Tree is divided into two parts,one part is in the memory and the other part is on the hard *** writing data,first write to the memory,when the Immutable Memtable in the memory is full of data,then write to the disk to output a Sorted String Table(SSTable)***,the use of writing data from memory to disk is *** the Immutable Memtable writes data to the disk,it will block other *** article uses multiple Immutable Memtables and multi-threaded writing to solve the single-thread blocking *** running the benchmarks of LevelDB to analyze the experiment,the experimental results indicate a higher improvement.

关键词： Key-Value store Multi-Threading Write performance Read performance LSM-Tree Immutable Memtable

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Fault Localization of Statement Coverage Based on DNN

Fault Localization of Statement Coverage Based on DNN

引用

Electronic Information Technology and Smart Agriculture (ICEITSA), International Conference on

作者： Ling Peng Chunqiao Mi School of Computer Science and Engineering Huaihua University Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application Hunan Province Universities Huaihua China

ISBN: (纸本)9781665413015

In view of the inconvenience of parameter setting in the existing fault location method based on deep neural network, this paper combines the global random search ability of Genetic Algorithm, L2 regularization to prevent model overfitting and deep neural network to learn complex nonlinear ability, a fault location algorithm based on G-DNN is proposed. The optimal number of hidden layer neurons, learning rate, training period and L2 regularization coefficient of deep neural network are obtained by Genetic Algorithm; Input the statement coverage information and state values into the deep neural network to calculate the suspiciousness value of each executable statement; Locate defects from high to low according to the suspiciousness values. The Siemens Suite is used as the experimental sample, and the G-DNN is compared with the five defect location algorithms. The results show that the defect of the G-DNN can locate defects more accurately, and the positioning efficiency is improved.

关键词： Deep learning Training Location awareness Neurons Fault location Information technology Biological neural networks

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Frequency Fitness Assignment: Optimization without Bias for Good Solutions can be Efficient

arXiv

引用

arXiv 2021年

作者： Weise, Thomas Wu, Zhize Li, Xinlu Chen, Yan Lässig, Jörg Institute of Applied Optimization School of Artificial Intelligence and Big Data Hefei University Anhui Hefei230601 China Enterprise Application Development Group The Faculty of Electrical Engineering and Computer Science of the Hochschule Zittau/Görlitz Görlitz Germany

A fitness assignment process transforms the features (such as the objective value) of a candidate solution to a scalar fitness, which then is the basis for selection. Under Frequency Fitness Assignment (FFA), the fitness corresponding to an objective value is its encounter frequency in selection steps and is subject to minimization. FFA creates algorithms that are not biased towards better solutions and are invariant under all injective transformations of the objective function value. We investigate the impact of FFA on the performance of two theory-inspired, state-of-the-art EAs, the Greedy (2+1) GA and the Self-Adjusting (1+(λ,λ)) GA. FFA improves their performance significantly on some problems that are hard for them. In our experiments, one FFA-based algorithm exhibited mean runtimes that appear to be polynomial on the theory-based benchmark problems in our study, including traps, jumps, and plateaus. We propose two hybrid approaches that use both direct and FFA-based optimization and find that they perform well. All FFA-based algorithms also perform better on satisfiability problems than any of the pure algorithm *** Codes F.2.2, G.3, G.2.1, G.2.3, B.2.2 Copyright © 2021, The Authors. All rights reserved.

关键词： Formal logic

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