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Language Engineering (ESOLEC), International Conference on

作者： Abdulrahman Mahgoub Hesham Atef Abdulrahman Nasser Mohamed Yasser Walaa M. Medhat M. Saeed Darweesh Passent M. El-Kafrawy Computing and Information Systems Research Center (CIS) School of Information Technology and Computer Science Nile University Egypt Faculty of Computer and AI Benha University Cairo Egypt Wireless Intelligent Networks Center (WINC) School of Engineering and Applied Sciences Nile University Egypt Computer Science Department Effat College of Engineering SA

ISBN: (纸本)9781665453233

The market needs a deeper and more comprehensive grasp of its insight, where the analytics world and methodologies such as “Sentiment Analysis” come in. These methods can assist people especially “business owners” in gaining live insights into their businesses and determining wheatear customers are satisfied or not. This paper plans to provide indicators by gathering real world Amazon reviews from Egyptian customers. By applying both Bidirectional Encoder Representations from Transformers “Bert” and “Text Blob” sentiment analysis methods. The processes shall determine the overall satisfaction of Egyptian customers in the electronics department - in order to focus on a specific domain. The two methods will be compared for both the Arabic and English languages. The results show that people in *** are mostly satisfied with the percentage of 47%. For the performance, BERT outperformed Textblob indicating that word embedding model BERT is more superior than rule-based model Textblob with a difference of 15% - 25%.

关键词： Sentiment analysis Bit error rate Transformers Consumer electronics Business

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Activities Prediction of Drug Molecules by Using Automated Model Building with Descriptor Selection 1st

Activities Prediction of Drug Molecules by Using Automated M...

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1st BenchCouncil International Federated intelligent computing and Block Chain Conferences, FICC 2020

作者： Liu, Yue Tian, Wenjie Zhang, Hao School of Computer Engineering and Science Shanghai University Shanghai China Shanghai Institute for Advanced Communication and Data Science Shanghai China Shanghai Engineering Research Center of Intelligent Computing System Shanghai200444 China Shanghai Chempartner Co. Ltd. Shanghai201203 China

ISBN: (纸本)9789811611599

Machine learning is a powerful tool for simulating the quantitative structure activity relationship in drug discovery (QSAR). However, descriptor selection and model optimization remain two of most challenging tasks for domain experts to construct high-quality QSAR model. Therefore, we propose a QSAR-special automated machine learning method incorporating Automated Descriptor Selection with Automated Model Building (ADSMB) to efficiently and automatically build high-quality QSAR model. Automated Descriptor Selection provides a QSAR-special molecular descriptor selection mechanism to automatically obtain the descriptors without unique value, redundancy and low importance in QSAR dataset. Based on these QSAR-special descriptors, Automated Model Building constructs high-quality ensemble model of molecular descriptors and target activities under Bayesian optimization through Auto-Sklearn. Finally, we conduct experimental evaluation for our proposed method on Mutagenicity dataset. The results show ADSMB can obtain better and stable performance than the competing methods. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Machine learning

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Bi-directional Feature Fusion Generative Adversarial Network for Ultra-high Resolution Pathological Image Virtual Re-staining

Bi-directional Feature Fusion Generative Adversarial Network...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Kexin Sun Zhineng Chen Gongwei Wang Jun Liu Xiongjun Ye Yu-Gang Jiang Collaborative Innovation Center of Intelligent Visual Computing School of Computer Science & Shanghai Fudan University Shanghai Qi Zhi Institute Peking University People's Hospital Department of Urology National Cancer Center & National Clinical Research Center for Cancer Cancer Hospital Chinese Academy of Medical Sciences and Peking Union Medical College

The cost of pathological examination makes virtual restaining of pathological images meaningful. However, due to the ultra-high resolution of pathological images, traditional virtual restaining methods have to divide a WSI image into patches for model training and inference. Such a limitation leads to the lack of global information, resulting in observable differences in color, brightness and contrast when the re-stained patches are merged to generate an image of larger size. We summarize this issue as the square effect. Some existing methods try to solve this issue through overlapping between patches or simple postprocessing. But the former one is not that effective, while the latter one requires carefully tuning. In order to eliminate the square effect, we design a bidirectional feature fusion generative adversarial network (BFF-GAN) with a global branch and a local branch. It learns the inter-patch connections through the fusion of global and local features plus patch-wise attention. We perform experiments on both the private dataset RCC and the public dataset ANHIR. The results show that our model achieves competitive performance and is able to generate extremely real images that are deceptive even for experienced pathologists, which means it is of great clinical significance.

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Reinforced Label Denoising for Weakly-Supervised Audio-Visual Video Parsing

arXiv

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arXiv 2024年

作者： Gao, Yongbiao Sun, Xiangcheng Lv, Guohua Yu, Deng Niu, Sijiu Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Application [Southeast University Ministry of Education China School of Artificial Intelligence Shandong University China Shandong Key Laboratory of Ubiquitous Intelligent Computing Jinan China

Audio-visual video parsing (AVVP) aims to recognize audio and visual event labels with precise temporal boundaries, which is quite challenging since audio or visual modality might include only one event label with only the overall video labels available. Existing label denoising models often treat the denoising process as a separate preprocessing step, leading to a disconnect between label denoising and AVVP tasks. To bridge this gap, we present a novel joint reinforcement learning-based label denoising approach (RLLD). This approach enables simultaneous training of both label denoising and video parsing models through a joint optimization strategy. We introduce a novel AVVP-validation and soft inter-reward feedback mechanism that directly guides the learning of label denoising policy. Extensive experiments on AVVP tasks demonstrate the superior performance of our proposed method compared to label denoising techniques. Furthermore, by incorporating our label denoising method into other AVVP models, we find that it can further enhance parsing results. © 2024, CC BY.

关键词： Self-supervised learning

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NNL:a domain-specific language for neural networks

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High Technology Letters 2020年第2期26卷 160-167页

作者： Wang Bingrui Chen Yunji School of Computer Science and Technology University of Science and Technology of ChinaHefei 230027P.R.China Intelligent Processor Research Center Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190P.R.China

Recent years,neural networks(NNs)have received increasing attention from both academia and *** far significant diversity among existing NNs as well as their hardware platforms makes NN programming a daunting *** this paper,a domain-specific language(DSL)for NNs,neural network language(NNL)is proposed to deliver productivity of NN programming and portable performance of NN execution on different hardware *** productivity and flexibility of NN programming are enabled by abstracting NNs as a directed graph of *** language describes 4 representative and widely used NNs and runs them on 3 different hardware platforms(CPU,GPU and NN accelerator).Experimental results show that NNs written with the proposed language are,on average,14.5%better than the baseline implementations across these 3 ***,compared with the Caffe framework that specifically targets the GPU platform,the code can achieve similar performance.

关键词： artificial neural network(NN) domain-specific language(DSL) neural network(NN)accelerator

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Reciprocating Magnetic Fields in the Pulsar Wind Observed from the Black Widow Pulsar J1720-0534

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research in Astronomy and Astrophysics 2023年第10期23卷 255-266页

作者： Chen-Chen Miao Victoria Blackmon Wei-Wei Zhu Dong-Zi Li Ming-Yu Ge Xiao-Peng You Maura McLaughlin Di Li Na Wang Pei Wang Jia-Rui Niu M.Cruces Jian-Ping Yuan Jun-Tao Bai D.J.Champion Yu-Tong Chen Ming-Min Chi P.C.C.Freire Yi Feng Zhen-Ye Gan M.Kramer Fei-Fei Kou Yu-Xi Li Xue-Li Miao Ling-Qi Meng Chen-Hui Niu Sheng-Nan Sun Zhong-Yi Sun H.M.Tedila Shuang-Qiang Wang Qing-Dong Wu Jing-Bo Wang Zhi-Gang Wen Shen Wang Ya-Biao Wang Cheng-Jie Wang Meng-Yao Xue You-Ling Yue Mao Yuan Ju-Mei Yao Wen-Ming Yan Ru-Shuang Zhao Lei Zhang De Zhao National Astronomical Observatories Chinese Academy of SciencesBeijing 100101China School of Astronomy and Space Science University of Chinese Academy of SciencesBeijing 100049China West Virginia University 1500 University AveMorgantownWV 26506USA Institute for Frontiers in Astronomy and Astrophysics Beijing Normal UniversityBeijing 102206China Cahill Center for Astronomy and Astrophysics California Institute of Technology1216 E California BoulevardPasadenaCA 91125USA Key Laboratory of Particle Astrophysics Institute of High Energy PhysicsChinese Academy of SciencesBeijing 100049China School of Physical Science and Technology Southwest UniversityChongqing 400715China Research Center for Intelligent Computing Platforms Zhejiang LaboratoryHangzhou 311100China Xinjiang Astronomical Observatory Chinese Academy of SciencesUrumqi 830011China Max-Planck Institut für Radioastronomie Auf dem Hügel 69D-53121 BonnGermany School of Computer Science Fudan UniversityShanghai 200433China Tencent Youtu Lab Shanghai 200233China College of Physical Science and Technology Central China Normal UniversityWuhan 430079China Institute of Optoelectronic Technology Lishui UniversityLishui 323000China Guizhou Normal University Guiyang 550001China

We report the radio observations of the eclipsing black widow pulsar J1720-0534, a 3.26 ms pulsar in orbit with a low mass companion of mass 0.029 to 0.034 M⊙. We obtain the phase-connected timing ephemeris and polarization profile of this millisecond pulsar(MSP) using the Five-hundred-meter Aperture Spherical radio Telescope(FAST), the Green Bank Telescope(GBT), and the Parkes Telescope. For the first time from such a system, an oscillatory polarization angle change was observed from a particular eclipse egress with partial depolarization, indicating 10-milliGauss-level reciprocating magnetic fields oscillating in a length scale of 5 ×10^(3)km(assuming an orbital inclination angle of 90°) outside the companion's magnetosphere. The dispersion measure variation observed during the ingresses and egresses shows the rapid raising of the electron density in the shock boundary between the companion's magnetosphere and the surrounding pulsar wind. We suggest that the observed oscillatory magnetic fields originate from the pulsar wind outside the companion's magnetosphere.

关键词： (stars:)pulsars:general (stars:)pulsars:individual(J1720-0534) (stars:)binaries:eclipsing

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Real-Time Data Collection of Wsn Based on Trajectory Optimization of Laser-Charged Uav

SSRN

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SSRN 2023年

作者： Luo, Chuanwen Hong, Yi Hou, Yunan Zhu, Yuqing Li, Deying Yu, Dongxiao Wu, Weili School of Information Science and Technology Beijing Forestry University Beijing100083 China Engineering Research Center for Forestry-oriented Intelligent Information Processing of National Forestry and Grassland Administration Beijing100083 China Research Center of Applied Mathematics and Machine Intelligence Zhejiang Lab Zhejiang Hangzhou311121 China Department of Computer Science California State University Los AngelesCA90032 United States School of Information Renmin University of China Beijing100872 China School of Computer Science and Technology Institute of Intelligent Computing Shandong University Qingdao266510 China Department of Computer Science The University of Texas at Dallas RichardsonTX75080 United States

Unmanned Aerial Vehicle (UAV) can be used as wireless aerial mobile base station for collecting data from sensors in UAV-based Wireless Sensor Networks (WSNs), which is crucial for providing seamless services and improving the performance in the next generation wireless networks. However, since the UAV are powered by batteries with limited energy capacity, the UAV can not complete data collection tasks of all sensors without energy replenishment when a large number of sensors are deployed over large monitoring areas. To overcome this problem, this paper study the Real-time Data Collection with Laser-charging UAV (RDCL) problem, where the UAV is used to collect data from a given WSN and replenished energy by Laser Beam Directors (LBDs). The problem aims at optimizing the flight trajectory of UAV for ensuring the real-time performance of data, which is to design the detailed hovering and traveling plans of UAV for collecting data from sensors and charged by LBDs such that all sensory data of WSN are collected by UAV and transported to the base *** prove that the RDCL problem is NP-hard. To solve the problem, we first study two sub-problems, the Trajectory Optimization of UAV for Data Collection (TODC) problem and the Charging Trajectory Optimization of UAV (CTO) problem, whose objectives are to find the optimal flight plans of UAV in the data collection areas and charging areas, respectively. Then we propose an approximation algorithm to solve each of them with the constant factor. Afterwards, we propose an approximation algorithm to solve the RDCL problem based on TODC and CTO problems. Finally, the proposed algorithm is verified by extensive simulations. © 2023, The Authors. All rights reserved.

关键词： Wireless sensor networks

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Uncovering Neural Scaling Laws in Molecular Representation Learning

arXiv

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arXiv 2023年

作者： Chen, Dingshuo Zhu, Yanqiao Zhang, Jieyu Du, Yuanqi Li, Zhixun Liu, Qiang Wu, Shu Wang, Liang Center for Research on Intelligent Perception and Computing Institute of Automation Chinese Academy of Sciences China Department of Computer Science University of California Los Angeles United States The Paul G. Allen School of Computer Science and Engineering University of Washington United States Department of Computer Science Cornell University United States Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong

Molecular Representation Learning (MRL) has emerged as a powerful tool for drug and materials discovery in a variety of tasks such as virtual screening and inverse design. While there has been a surge of interest in advancing model-centric techniques, the influence of both data quantity and quality on molecular representations is not yet clearly understood within this field. In this paper, we delve into the neural scaling behaviors of MRL from a data-centric viewpoint, examining four key dimensions: (1) data modalities, (2) dataset splitting, (3) the role of pre-training, and (4) model capacity. Our empirical studies confirm a consistent power-law relationship between data volume and MRL performance across these dimensions. Additionally, through detailed analysis, we identify potential avenues for improving learning efficiency. To challenge these scaling laws, we adapt seven popular data pruning strategies to molecular data and benchmark their performance. Our findings underline the importance of data-centric MRL and highlight possible directions for future research. Copyright © 2023, The Authors. All rights reserved.

关键词： Benchmarking

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The Devil is in the Conflict: Disentangled Information Graph Neural Networks for Fraud Detection

The Devil is in the Conflict: Disentangled Information Graph...

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IEEE International Conference on Data Mining (ICDM)

作者： Zhixun Li Dingshuo Chen Qiang Liu Shu Wu School of Computer Science and Technology Beijing Institute of Technology Center for Research on Intelligent Perception and Computing National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences

Graph-based fraud detection has heretofore received considerable attention. Owning to the great success of Graph Neural Networks (GNNs), many approaches adopting GNNs for fraud detection has been gaining momentum. However, most existing methods are based on the strong inductive bias of homophily, which indicates that the context neighbors tend to have same labels or similar features. In real scenarios, fraudsters often engage in camouflage behaviors in order to avoid detection system. Therefore, the homophilic assumption no longer holds, which is known as the inconsistency problem. In this paper, we argue that the performance degradation is mainly attributed to the inconsistency between topology and attribute. To address this problem, we propose to disentangle the fraud network into two views, each corresponding to topology and attribute respectively. Then we propose a simple and effective method that uses the attention mechanism to adaptively fuse two views which captures data-specific preference. In addition, we further improve it by introducing mutual information constraints for topology and attribute. To this end, we propose a Disentangled Information Graph Neural Network (DIGNN) model, which utilizes variational bounds to find an approximate solution to our proposed optimization objective function. Extensive experiments demonstrate that our model can significantly outperform state-of-the-art baselines on real-world fraud detection datasets.

关键词： Adaptation models Network topology Fuses Semantics Graph neural networks Fraud Topology

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Verifying ReLU Neural Networks from a Model Checking Perspective

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Journal of computer science & Technology 2020年第6期35卷 1365-1381页

作者： Wan-Wei Liu Fu Song Tang-Hao-Ran Zhang Ji Wang College of Computer Science National University of Defense TechnologyChangsha 410073China School of Information Science and Technology ShanghaiTech UniversityShanghai 201210China Shanghai Engineering Research Center of Intelligent Vision and Imaging Shanghai 201210China State Key Laboratory for High Performance Computing Changsha 410073China

Neural networks, as an important computing model, have a wide application in artificial intelligence (AI) domain. From the perspective of computer science, such a computing model requires a formal description of its behaviors, particularly the relation between input and output. In addition, such specifications ought to be verified automatically. ReLU (rectified linear unit) neural networks are intensively used in practice. In this paper, we present ReLU Temporal Logic (ReTL), whose semantics is defined with respect to ReLU neural networks, which could specify value-related properties about the network. We show that the model checking algorithm for theΣ2∪Π2 fragment of ReTL, which can express properties such as output reachability, is decidable in EXPSPACE. We have also implemented our algorithm with a prototype tool, and experimental results demonstrate the feasibility of the presented model checking approach.

关键词： model checking rectified linear unit neural(ReLU)network temporal logic

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