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Is AI Robust Enough for Scientific Research?

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arXiv 2024年

作者： Zhang, Jun-Jie Song, Jiahao Wang, Xiu-Cheng Li, Fu-Peng Liu, Zehan Chen, Jian-Nan Dang, Haoning Wang, Shiyao Zhang, Yiyan Xu, Jianhui Shi, Chunxiang Wang, Fei Pang, Long-Gang Cheng, Nan Zhang, Weiwei Zhang, Duo Meng, Deyu Northwest Institute of Nuclear Technology No. 28 Pingyu Road Shaanxi Xi’an710024 China School of Telecommunications Engineering Xidian University No. 2 South Taibai Road Shaanxi Xi’an710071 China State Key Laboratory of ISN No. 2 South Taibai Road Shaanxi Xi’an710071 China Institute of Particle Physics Central China Normal University No. 152 Luoyu Road Hubei Wuhan30079 China School of Computer Science and Technology Xi’an Jiaotong University No. 28 Xianning West Road Shaanxi Xi’an710049 China Ministry of Education Key Lab of Intelligent Networks and Network Security Xi’an Jiaotong University No. 28 Xianning West Road Shaanxi Xi’an710049 China Guangzhou Institute of Geography Academy of Sciences No. 100 Xianlie Road Guangdong Guangzhou510070 China National Meteorological Information Center Beijing100044 China MDX Research Center for Element Strategy Institute of Integrated Research Institute of Science Tokyo Midori-ku Yokohama226-8503 Japan School of Physics and Information Technology Shaanxi Normal University No. 620 West Chang’an Avenue Shaanxi Xi’an710119 China School of Aeronautics Northwestern Polytechnical University No. 127 West Youyi Road Shaanxi Xi’an710072 China AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China School of Mathematics and Statistics Xi’an Jiaotong University No. 28 Xianning West Road Shaanxi Xi’an710049 China

We uncover a phenomenon largely overlooked by the scientific community utilizing AI: neural networks exhibit high susceptibility to minute perturbations, resulting in significant deviations in their outputs. Through an analysis of five diverse application areas—weather forecasting, chemical energy and force calculations, fluid dynamics, quantum chromodynamics, and wireless communication—we demonstrate that this vulnerability is a broad and general characteristic of AI systems. This revelation exposes a hidden risk in relying on neural networks for essential scientific computations, calling further studies on their reliability and security. © 2024, CC0.

关键词： Weather forecasting

Analysis of Stock Market Based on Machine Learning

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Analysis of Stock Market Based on Machine Learning

International Conference on Computer network, Electronic and Automation (ICCNEA)

作者： Wang Tianma Fangwei Wang Illinois Institute of Technology Chicago IL USA College of Computer and Cyber Security Hebei Normal University Shijiazhuang CHINA Key Laboratory of Network and Information Security in Hebei Province Shijiazhuang CHINA

This paper uses SPSS tools and factor analysis theory to make a comprehensive evaluation of the company's stock performance. Data come from 10 representative listed companies in different industries. Because of the high weight and strong correlation of the original data, it is impossible to obtain the original data directly. Therefore, it is necessary to reduce the number of variables, which not only completes the dimension reduction statistics, but also doesn't cause a large number of data loss, so that the factors of reorganization of the original variables ensure the weak linear relationship between them. Through data standardization, factor analysis and factor analysis, factor analysis model is obtained. From the final model, we can also see that in the comprehensive evaluation model, the proportion of comprehensive factors of enterprise profitability is the highest, and the proportion of comprehensive factors reflecting inventory turnover rate is the smallest, but the proportion of the four comprehensive factors is not much different. On the basis of practical analysis, the conclusion is reasonable, and the company's performance can be seen from the point of score.

关键词： Mathematical model Correlation Correlation coefficient Eigenvalues and eigenfunctions Dimensionality reduction Analytical models Principal component analysis

Clustering-based codebook design for MIMO communication system

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arXiv 2019年

作者： Jiang, Jing Wang, Xiaojing Sidhu, Guftaar Ahmad Sardar Zhen, Li Gao, Runchen Shaanxi Key Laboratory of Information Communication Network and Security Xi'An University of Posts and Telecommunications Xi'an China COMSATS Institute of Information Technology Islamabad Pakistan

Codebook design is one of the core technologies in limited feedback multi-input multi-output (MIMO) communication systems. However, the conventional codebook designs usually assume MIMO vectors are uniformly distributed or isotropic. Motivated by the excellent classfication and analysis ability of clustering algorithms, we propose a K-means clustering based codebook design. First, large amounts of channel state information (CSI) is stored as the input data of the clustering, and finally divided into N clusters according to the minimal distance. The clustering centroids are used as the statistic channel information of the codebook construction which the sum distance is minimal to the real channel information. Simulation results consist with theoretical analysis in terms of the achievable rate, and demonstrate that the proposed codebook design outperforms conventional schemes, especially in the non-uniform distribution of channel scenarios. Copyright © 2019, The Authors. All rights reserved.

关键词： K-means clustering

LR-RRA-CCA secure functional encryption for randomized functionalities from trapdoor HPS and LAF

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Science China(Information Sciences) 2018年第5期61卷 291-305页

作者： Huige WANG Kefei CHEN Baodong QIN Ziyuan HU Department of Network Engineering Anhui Science and Technology University Department of Computer Science and Engineering Shanghai Jiao Tong University Department of Mathematics Hangzhou Normal University Westone Cryptologic Research Center National Engineering Laboratory for Wireless Security Xi'an University of Posts and Telecommunications State Key Laboratory of Cryptology

Original public-key encryption is viewed as a tool to encrypt point-to-point *** data is aimed at a particular user,where a sender encrypts a message x under a specified public key P *** this case,only the owner of the(unique)secret key corresponding to P K can decrypt the resulting ciphertext to recover the message ***,on some occasions,encryption is necessary to implement more fine-grained control of the encrypted *** example,the sender

关键词： LAF CCA RRA

FedCM: A real-time contribution measurement method for participants in federated learning

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arXiv 2020年

作者： Yan, Bingjie Liu, Boyi Zhou, Yize Liang, Zhixuan Xu, Cheng-Zhong School of Compute Science and Cyberspace Security Hainan University Haikou China State Key Laboratory of Internet of Things for Smart City University of Macao China School of Science Hainan University Haikou China Department of Computing The Hong Kong Polytechnic University Hong Kong Faculty of Science and Technology University of Macao China

Federated Learning (FL) creates an ecosystem for multiple agents to collaborate on building models with data privacy consideration. The method for contribution measurement of each agent in the FL system is critical for fair credits allocation but few are proposed. In this paper, we develop a real-time contribution measurement method FedCM that is simple but powerful. The method defines the impact of each agent, comprehensively considers the current round and the previous round to obtain the contribution rate of each agent with attention aggregation. Moreover, FedCM updates contribution every round, which enable it to perform in real-time. Real-time is not considered by the existing approaches, but it is critical for FL systems to allocate computing power, communication resources, etc. Compared to the state-of-the-art method, the experimental results show that FedCM is more sensitive to data quantity and data quality under the premise of real-time. Furthermore, we developed federated learning open-source software based on FedCM. The software has been applied to identify COVID-19 based on medical images. © 2020, CC BY-NC-ND.

关键词： Open systems

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Saiqin Long Zhetao Li Zihao Liu Qingyong Deng Sangyoon Oh Nobuyoshi Komuro Key Laboratory of Hunan Province for Internet of Things and Information Security Hunan International Scientific and Technological Cooperation Base of Intelligent network School of Computer Science Xiangtan University Xiangtan China Science and Technology on Parallel and Distributed Processing Laboratory (PDL) National University of Defense Technology Changsha China Ajou University Suwon South Korea Graduate School of Advanced Integration Science Chiba University Chiba Japan

ISBN: (数字)9781728190747

ISBN: (纸本)9781728183824

Deduplication is a data redundancy elimination technique, designed to save system storage resources by reducing redundant data in cloud storage systems. With the development of cloud computing technology, deduplication has been increasingly applied to cloud data centers. However, traditional technologies face great challenges in big data deduplication to properly weigh the two conflicting goals of deduplication throughput and high duplicate elimination ratio. This paper proposes a similarity clustering-based deduplication strategy (named SCDS), which aims to delete more duplicate data without significantly increasing system overhead. The main idea of SCDS is to narrow the query range of fingerprint index by data partitioning and similarity clustering algorithms. In the data preprocessing stage, SCDS uses data partitioning algorithm to classify similar data together. In the data deletion stage, the similarity clustering algorithm is used to divide the similar data fingerprint superblock into the same cluster. Repetitive fingerprints are detected in the same cluster to speed up the retrieval of duplicate fingerprints. Experiments show that the deduplication ratio of SCDS is better than some existing similarity deduplication algorithms, but the overhead is only slightly higher than some high throughput but low deduplication ratio methods.

关键词： Cloud computing Clustering algorithms Estimation Fingerprint recognition Throughput Partitioning algorithms Classification algorithms

TCPModel: A short-term traffic congestion prediction model based on deep learning 1

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2nd CCF International Conference on Artificial Intelligence, CCF-ICAI 2019

作者： Xu, Xiujuan Gao, Xiaobo Xu, Zhenzhen Zhao, Xiaowei Pang, Wei Zhou, Hongmei School of Software Dalian University of Technology Dalian116620 China Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province Dalian116620 China School of Natural and Computing Sciences University of Aberdeen AberdeenAB24 3UE United Kingdom School of Transportation and Logistics Dalian University of Technology Dalian116024 China

ISBN: (数字)9789813292987

ISBN: (纸本)9789813292970

With the progress of the urbanization, a series of traffic problems have occurred because of the growing urban population and the far lower growth rate of roads than that of cars. One of the most prominent problems is traffic congestion problem. The prediction of traffic congestion is the key to alleviate traffic congestion. To ensure the real-time performance and accuracy of the traffic congestion prediction, we propose a short-term traffic congestion prediction model called TCPModel based on deep learning. By processing a massive amount of urban taxi transportation data, we extract the traffic volume and average speed of taxis which are the most important parameters for assessing of traffic flow prediction. After analyzing the temporal and spatial distribution characteristics of the traffic flow and average speed, we present a short-term traffic volume prediction model called TVPModel, and a short-term traffic speed prediction model called TSPModel. Both models are based on a deep learning method Stacked Auto Encoder (SAE). By comparing the other traffic flow forecasting methods and average speed forecasting methods, the methods proposed by this paper have improved the accuracy rate. For traffic congestion recognition, we use a novel model called TCPModel based on three traffic parameters (average speed, traffic flow and density), which uses standard function method to standardize the parameters and calculate the congestion comprehensive threshold to determine the congestion level by thresholds. According to the experiments, TVPModel and TSPModel in this paper got satisfied accuracy compared with other prediction models. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Traffic congestion

Competition on Dynamic Optimization Problems Generated by Generalized Moving Peaks Benchmark (GMPB)

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arXiv 2021年

作者： Yazdani, Danial Mavrovouniotis, Michalis Li, Changhe Chen, Guoyu Luo, Wenjian Omidvar, Mohammad Nabi Branke, Jürgen Yang, Shengxiang Yao, Xin The Business Intelligence Team WINC Australia Cyprus University of Technology Lemesos Cyprus ERATOSTHENES Centre of Excellence Cyprus The School of Artificial Intelligence Anhui University of Sciences & Technology Hefei China The Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies School of Computer Science and Technology Harbin Institute of Technology Peng Cheng Laboratory Shenzhen518055 China The School of Computing University of Leeds Leeds University Business School LeedsLS2 9JT United Kingdom The Operational Research and Management Sciences Group at Warwick Business school University of Warwick CoventryCV4 7AL United Kingdom School of Computer Science and Informatics De Montfort University LeicesterLE1 9BH United Kingdom The Department of Computing and Decision Sciences Lingnan University Hong Kong The CERCIA School of Computer Science BirminghamB15 2TT United Kingdom

The Generalized Moving Peaks Benchmark (GMPB) [1] is a tool for generating continuous dynamic optimization problem instances with controllable dynamic and morphological characteristics. GMPB has been used in recent Competitions on Dynamic Optimization at prestigious conferences, such as the IEEE Congress on Evolutionary Computation (CEC). This dynamic benchmark generator can create a wide variety of landscapes, ranging from simple unimodal to highly complex multimodal configurations, and from symmetric to asymmetric forms. It also supports diverse surface textures, from smooth to highly irregular, and can generate varying levels of variable interaction and conditioning. This document provides an overview of GMPB, emphasizing how its parameters can be adjusted to produce landscapes with customizable characteristics. The MATLAB implementation of GMPB is available on the EDOLAB platform [2]. © 2021, CC BY-NC-ND.

关键词： Optimization algorithms

Signed graph attention networks

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arXiv 2019年

作者： Huang, Junjie Shen, Huawei Hou, Liang Cheng, Xueqi CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

Graph or network data is ubiquitous in the real world, including social networks, information networks, traffic networks, biological networks and various technical networks. The non-Euclidean nature of graph data poses the challenge for modeling and analyzing graph data. Recently, Graph Neural network (GNNs) are proposed as a general and powerful framework to handle tasks on graph data, e.g., node embedding, link prediction and node classification. As a representative implementation of GNNs, Graph Attention networks (GATs) are successfully applied in a variety of tasks on real datasets. However, GAT is designed to networks with only positive links and fails to handle signed networks which contain both positive and negative links. In this paper, we propose Signed Graph Attention networks (SiGATs), generalizing GAT to signed networks. SiGAT incorporates graph motifs into GAT to capture two well-known theories in signed network research, i.e., balance theory and status theory. In SiGAT, motifs offer us the flexible structural pattern to aggregate and propagate messages on the signed network to generate node embeddings. We evaluate the proposed SiGAT method by applying it to the signed link prediction task. Experimental results on three real datasets demonstrate that SiGAT outperforms feature-based method, network embedding method and state-of-the-art GNN-based methods like signed graph convolutional network (SGCN). Copyright © 2019, The Authors. All rights reserved.

关键词： Graph neural networks