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arXiv 2023年

作者： Liu, Minghao Wei, Jiaheng Liu, Yang Davis, James Department of Computer Science and Engineering University of California Santa Cruz United States Data Science and Analytics Thrust Hong Kong University of Science and Technology Guangzhou China

Artificial intelligence (AI) models for computer vision trained with supervised machine learning are assumed to solve classification tasks by imitating human behavior learned from training labels. Most efforts in recent vision research focus on measuring the model task performance using standardized benchmarks such as accuracy. However, limited work has sought to understand the perceptual difference between humans and machines. To fill this gap, this study first analyzes the statistical distributions of mistakes from the two sources and then explores how task difficulty level affects these distributions. We find that even when AI learns an excellent model from the training data, one that outperforms humans in overall accuracy, these AI models have significant and consistent differences from human perception. We demonstrate the importance of studying these differences with a simple human-AI teaming algorithm that outperforms humans alone, AI alone, or AI-AI teaming. Copyright © 2023, The Authors. All rights reserved.

关键词： Adversarial machine learning

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NoxTrader: LSTM-Based Stock Return Momentum Prediction for Quantitative Trading

arXiv

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arXiv 2023年

作者： Wei-Ning, Chiu Hsiang-Hui, Liu Han-Jay, Shu Data dept. NoxLab Computer Science dept. NTHU Hsinchu Taiwan Infrastructure dept. NoxLab Computer Science dept. NTHU Hsinchu Taiwan Data dept. NoxLab Electrical Engineering and Computer Science dept. NTHU Hsinchu Taiwan

We introduce NoxTrader, a sophisticated system designed for portfolio construction and trading execution with the primary objective of achieving profitable outcomes in the stock market, specifically aiming to generate moderate to long-term profits. The underlying learning process of NoxTrader is rooted in the assimilation of valuable insights derived from historical trading data, particularly focusing on time-series analysis due to the nature of the dataset employed. In our approach, we utilize price and volume data of US stock market for feature engineering to generate effective features, including Return Momentum, Week Price Momentum, and Month Price Momentum. We choose the Long Short-Term Memory (LSTM) model to capture continuous price trends and implement dynamic model updates during the trading execution process, enabling the model to continuously adapt to the current market trends. Notably, we have developed a comprehensive trading backtesting system — NoxTrader, which allows us to manage portfolios based on predictive scores and utilize custom evaluation metrics to conduct a thorough assessment of our trading performance. Our rigorous feature engineering and careful selection of prediction targets enable us to generate prediction data with an impressive correlation range between 0.65 and 0.75. Finally, we monitor the dispersion of our prediction data and perform a comparative analysis against actual market data. Through the use of filtering techniques, we improved the initial -60% investment return to 325%. © 2023, CC BY-NC-SA.

关键词： Momentum

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Generalizing Personalized Federated Graph Augmentation via Min-max Adversarial Learning 25

Generalizing Personalized Federated Graph Augmentation via M...

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Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and data Mining V.1

作者： Liang Zhang Tao Long Yang Liu Lei Zhang Laizhong Cui Qingjiang Shi Shenzhen Research Institute of Big Data Shenzhen Guangdong China College of Computer Science and Software Engineering Shenzhen University Shenzhen Guangdong China Data Science Research Center Dukekunshan University Kunshan Jiangsu China School of Software Engineering Tongji University Yangpu Shanghai China

ISBN: (纸本)9798400712456

Federated learning (FL) enables the training of a global machine learning model among multiple local clients in a collaborative fashion without directly sharing the details of their data. Due to this advantage, it has been utilized in a wide range of applications where privacy is a critical concern and has attracted great attention for graph representation learning (GRL). Despite the offered advances, there still exist two major challenges in the FL for GRL across distributed graph data, including heterogeneity and complementarity. In order to tackle these challenges, a novel personalized federated graph augmentation (PFGA) framework is proposed in this work. Unlike existing techniques, it utilizes generative models as bridges to enable information sharing among clients, thereby facilitating the collaborative training of GRL models. Instead of directly using the generative model trained on each client individually, we aggregate them into the globally generative model to gain a global view of the entire graph, which effectively alleviates the heterogeneity and complementarity issues simultaneously. We formulate the training of the generative and GRL models as a min-max adversarial learning problem and theoretically prove the convergence. Furthermore, the effectiveness of the method is demonstrated using experimental results on six real-world datasets.

关键词： federated learning

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Generalizable Autonomous Driving System across Diverse Adverse Weather Conditions

arXiv

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arXiv 2024年

作者： Kou, Wei-Bin Zhu, Guangxu Ye, Rongguang Lin, Qingfeng Ren, Zeyi Tang, Ming Wu, Yik-Chung Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Shenzhen Research Institute of Big Data Shenzhen China

Various adverse weather conditions pose a significant challenge to autonomous driving (AD) street scene semantic understanding (segmentation). A common strategy is to minimize the disparity between images captured in clear and adverse weather conditions. However, this technique typically relies on utilizing clear image as a reference, which is challenging to obtain in practice. Furthermore, this method typically targets a single adverse condition, and thus perform poorly when confronting a mixture of multiple adverse weather conditions. To address these issues, we introduce a reference-free and Adverse weatherImmune scheme (called AdvImmu) that leverages the invariance of weather conditions over short periods (seconds). Specifically, AdvImmu includes three components: Locally Sequential Mechanism (LSM), Globally Shuffled Mechanism (GSM), and Unfolded Regularizers (URs). LSM leverages temporal correlations between adjacent frames to enhance model performance. GSM is proposed to shuffle LSM segments to prevent overfitting of temporal patterns. URs are the deep unfolding implementation of two proposed regularizers to penalize the model complexity to enhance across-weather generalization. In addition, to overcome the over-reliance on consecutive frame-wise annotations in the training of AdvImmu (typically unavailable in AD scenarios), we incorporate a foundation model named Segment Anything Model (SAM) to assist to annotate frames, and additionally propose a cluster algorithm (denoted as SBICAC) to surmount SAM’s category-agnostic issue to generate pseudo-labels. Extensive experiments demonstrate that the proposed AdvImmu outperforms existing state-of-the-art methods by 88.56% in mean Intersection over Union (mIoU). © 2024, CC BY-NC-ND.

关键词： Semantic Segmentation

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Screening Students for Stress Using Fitbit data

Screening Students for Stress Using Fitbit Data

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IEEE International Conference on Big data

作者： Rebecca Lopez Avantika Shrestha Kevin Hickey Xingtong Guo ML Tlachac Shichao Liu Elke A. Rundensteiner Data Science Worcester Polytechnic Institute Worcester MA USA Architectural Engineering Worcester Polytechnic Institute Worcester MA USA Information Systems and Analytics Bryant University Smithfield RI USA Computer Science and Data Science Worcester Polytechnic Institute Worcester MA USA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

The pressures faced by college students frequently lead to heightened levels of stress. Wearable devices, which collect sensor data in a non-intrusive manner, present an opportunity for early detection of stress. Nonetheless, there is a lack of diversity in current research concerning psychological assessments, physiological metrics, and time series features. In this work, we utilize a Fitbit dataset and evaluate its use in predicting stress through machine learning. Our results demonstrate that physiological data such as calories burned and sleep hold promise for stress screening, with F1 scores reaching up to 0.81. These findings illustrate the potential of wearable technology for continuous stress monitoring and emphasize the need for selecting appropriate data aggregation levels and physiological modalities for effective screening.

关键词： Performance evaluation Wearable Health Monitoring Systems Time series analysis Human factors Self-supervised learning Mental health Robustness Biomedical monitoring Monitoring Standards

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BitmapAligner:Bit-Parallelism String Matching with MapReduce and Hadoop

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computers, Materials & Continua 2021年第9期68卷 3931-3946页

作者： Mary Aksa Junaid Rashid Muhammad Wasif Nisar Toqeer Mahmood Hyuk-Yoon Kwon Amir Hussain Department of Computer Science COMSATS University IslamabadWah47040Pakistan Department of Computer Science AIR University IslamabadAerospace and Aviation Campus Kamra43570Pakistan Department of Computer Science National Textile UniversityFaisalabad37610Pakistan Department of Industrial Engineering and Research Center for Electrical and Information Technology Seoul National University of Science and TechnologyNowon-GuSeoul01811Korea Data Science and Cyber Analytics Research Group Edinburgh Napier UniversityEdinburghEH114DYUK

Advancements in next-generation sequencer(NGS)platforms have improved NGS sequence data production and reduced the cost involved,which has resulted in the production of a large amount of genome *** downstream analysis of multiple associated sequences has become a bottleneck for the growing genomic data due to storage and space utilization issues in the domain of *** traditional string-matching algorithms are efficient for small sized data sequences and cannot process large amounts of data for downstream *** study proposes a novel bit-parallelism algorithm called BitmapAligner to overcome the issues faced due to a large number of sequences and to improve the speed and quality of multiple sequence alignment(MSA).The input files(sequences)tested over BitmapAligner can be easily managed and organized using the Hadoop distributed file *** proposed aligner converts the test file(the whole genome sequence)into binaries of an equal length of the sequence,line by line,before the sequence alignment *** Hadoop distributed file system splits the larger files into blocks,based on a defined block size,which is 128 MB by *** can accurately process the sequence alignment using the bitmask approach on large-scale sequences after sorting the *** experimental results indicate that BitmapAligner operates in real time,with a large number of ***,BitmapAligner achieves the exact start and end positions of the pattern sequence to test the MSA application in the whole genome query *** MSA’s accuracy is verified by the bitmask indexing property of the bit-parallelism extended shifts(BXS)*** dynamic and exact approach of the BXS algorithm is implemented through the MapReduce function of Apache ***,the traditional seeds-and-extend approach faces the risk of errors while identifying the pattern sequences’***,the proposed model resolves the largescale data challen

关键词： Next-generation sequencing multiple sequence alignment MapReduce Hadoop whole-genome big data bit-parallelism

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引用

作者： Zhang, Tao Zhu, Tianqing Liu, Renping Zhou, Wanlei Centre for Cyber Security and Privacy School of Computer Science University of Technology SydneyNSW Australia School of Electrical and Data Engineering University of Technology SydneyNSW Australia

Differential privacy is a rigorous mathematical framework for evaluating and protecting data privacy. In most existing studies, there is a vulnerable assumption that records in a dataset are independent when differential privacy is applied. However, in real-world datasets, records are likely to be correlated, which may lead to unexpected data leakage. In this survey, we investigate the issue of privacy loss due to data correlation under differential privacy models. Roughly, we classify existing literature into three lines: (1) using parameters to describe data correlation in differential privacy, (2) using models to describe data correlation in differential privacy, and (3) describing data correlation based on the framework of Pufferfish. First, a detailed example is given to illustrate the issue of privacy leakage on correlated data in real scenes. Then our main work is to analyze and compare these methods, and evaluate situations that these diverse studies are applied. Finally, we propose some future challenges on correlated differential privacy. © 2020 John Wiley & Sons Ltd.

关键词： data privacy

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Smart Warehouse Solutions for Efficient Onion Buffer Stock Management System 7

Smart Warehouse Solutions for Efficient Onion Buffer Stock M...

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7th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2023

作者： Raman, Ramakrishnan Ramalingam, L. Kishorbhai Sutaria, Kamal Kamthan, Manika Sangeetha, S. Murugan, S. Maharashtra Pune India Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Parul Institute of Engineering and Technology Faculty of Engineering and Technology Parul University Gujarat Vadodara India Symbiosis Law School Maharashtra Pune India Karpagam College of Engineering Department of Computer Science and Engineering Tamil Nadu Coimbatore India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350340600

This research study presents an innovative smart warehouse system for onion buffer stock management. The system includes an RFID reader, a central controlling unit, a displaying unit, a temperature sensor, a moisture sensor, a relay, a cooling fan, storage nodes, and a cloud server. The RFID reader automatically tracks onion crates or pallets in the warehouse, enabling real-time visibility and traceability. The central controlling unit coordinates activities and collects data from sensors. Onions are stored at appropriate temperatures and moisture levels via sensors. Warehouse workers can monitor this data in real-time on the displaying unit once the central controlling unit processes it. Maintaining temperature and humidity need the relay and cooling fan. Based on sensor data, the main controlling unit activates the cooling fan to avoid onion spoiling. Storage nodes in the warehouse store onion bags and pallets. These nodes have RFID tags or sensors for convenient tracking of stock. data analysis, storage, and integration depend on the cloud server. It allows advanced data analytics, forecasting, and decision-making. It also helps suppliers, distributors, and retailers communicate and distribute products efficiently. Real-time visibility, smart monitoring, and proactive management of storage conditions advance efficient operations, prevent spoiling, and improve onion supply chain efficiency. © 2023 IEEE.

关键词： Decision making

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Predicting the Geometric Location of Critical Edges in Adaptive GDSW Overlapping Domain Decomposition Methods Using Deep Learning 26th

Predicting the Geometric Location of Critical Edges in Adapt...

引用

26th International Conference on Domain Decomposition Methods, 2020

作者： Heinlein, Alexander Klawonn, Axel Lanser, Martin Weber, Janine Faculty of Electrical Engineering Mathematics & Computer Science Delft Institute of Applied Mathematics Delft University of Technology Mekelweg 4 CD Delft72628 Netherlands Department of Mathematics and Computer Science University of Cologne Weyertal 86-90 Köln50931 Germany Center for Data and Simulation Science University of Cologne Köln Germany

ISBN: (纸本)9783030950248

For complex model problems with coefficient or material distributions with large jumps along or across the domain decomposition interface, the convergence rate of classic domain decomposition methods for scalar elliptic problems usually deteriorates. In particular, the classic condition number bounds [1, 12] will depend on the contrast of the coefficient function. As a remedy, different adaptive coarse spaces, e.g. [4, 13], have been developed which are obtained by solving certain generalized eigenvalue problems on local parts of the interface, i.e., edges and/or faces. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Domain decomposition methods

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Exploring Design Optimisation Techniques of a Radio Telescope Using Fixed Costing Constraints 2

Exploring Design Optimisation Techniques of a Radio Telescop...

引用

2nd International Conference on Artificial Intelligence, Computational Electronics and Communication System, AICECS 2023

作者： Iyer, Aditya Arun Prabhu, Gautham Manuru Gupta, Tanay Deshmukh, Shrey Rivankar, Rushit Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India Department of Information Communication Technology Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India

Cost optimization is a common problem encountered in the design of telescopes. This paper comprehensively discusses various radio telescope designs worldwide, focusing on their design and utilities. It contextualizes the Pulsar data challenge and subsequently discusses and develops a mathematical model for designing radio telescopes. The model assumes a fixed-cost budget. This paper expands current ideas of modeling the system using figure-of-merit equations and optimizing them based on a fixed budget to obtain optimal and affordable radio telescope designs. © Published under licence by IOP Publishing Ltd.

关键词： Radio telescopes

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