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41st International Conference on Machine Learning, ICML 2024

作者： Rahmani, Mohammad Reza Yassaee, Mohammad Hossein Maddah-Ali, Mohammad Ali Aref, Mohammad Reza Department of Electrical Engineering Sharif University of Technology Tehran Iran Sharif Center for Information Systems and Data Science Sharif Institute for Convergence Science & Technology Tehran Iran Department of Electrical and Computer Engineering University of Minnesota Minneapolis United States

Estimating high-dimensional covariance matrices is crucial in various domains. This work considers a scenario where two collaborating agents access disjoint dimensions of m samples from a high-dimensional random vector, and they can only communicate a limited number of bits to a central server, which wants to accurately approximate the covariance matrix. We analyze the fundamental trade-off between communication cost, number of samples, and estimation accuracy. We prove a lower bound on the error achievable by any estimator, highlighting the impact of dimensions, number of samples, and communication budget. Furthermore, we present an algorithm that achieves this lower bound up to a logarithmic factor, demonstrating its near-optimality in practical settings. Copyright 2024 by the author(s)

关键词： Budget control

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Fusing Spatio-Temporal Contexts into DeepFM for Taxi Pick-Up Area Recommendation

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computer systems science & engineering 2023年第6期45卷 2505-2519页

作者： Yizhi Liu Rutian Qing Yijiang Zhao Xuesong Wang Zhuhua Liao Qinghua Li Buqing Cao School of Computer Science and Engineering Hunan University of Science and TechnologyXiangtan411201China Northwestern Institute on Complex Systems Northwestern UniversityEvanstonIL60208USA Key Laboratory of Knowledge Processing and Networked Manufacturing in Hunan Province Xiangtan411201China

Short-term GPS data based taxi pick-up area recommendation can improve the efficiency and reduce the *** how to alleviate sparsity and further enhance accuracy is still *** at these issues,we propose to fuse spatio-temporal contexts into deep factorization machine(STC_DeepFM)offline for pick-up area recommendation,and within the area to recommend pick-up points online using factorization machine(FM).Firstly,we divide the urban area into several grids with equal ***-temporal contexts are destilled from pick-up points or points-of-interest(POIs)belonged to the preceding ***,the contexts are integrated into deep factorization machine(DeepFM)to mine high-order interaction relationships from *** a novel algorithm named STC_DeepFM is presented for offline pick-up area ***,we devise the architecture of offline-to-online(O2O)recommendation respectively based on DeepFM and FM model in order to tradeoff the accuracy and *** experiments are designed on the DiDi dataset to evaluate step by step the performance of spatio-temporal contexts,different recommendation models,and the O2O *** results show that the proposed STC_DeepFM algorithm exceeds several state-of-the-art methods,and the O2O architecture achieves excellent real-time performance.

关键词： Location-based service(LBS) trajectory data mining offline-toonline(O2O)recommendation deep factorization machine(DeepFM) spatiotemporal context

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Scheduling Tool for Deterministic Communication in Distributed Real-Time systems 10

Scheduling Tool for Deterministic Communication in Distribut...

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10th Workshop on software Quality Analysis, Monitoring, Improvement, and Applications, SQAMIA 2023

作者： Mach, Jan Tašková, Valéria Kohútka, Lukáš Institute of Computer Engineering and Applied Informatics Faculty of Informatics and Information Technologies Slovak University of Technology Bratislava Slovakia Institute of Informatics Information Systems and Software Engineering Faculty of Informatics and Information Technologies Slovak University of Technology Bratislava Slovakia

A network with critical data streams, where the timing of incoming and outgoing data is a necessity, is called a deterministic network. These networks are mostly used in association with real-time systems that use periodic hard-real-time data which need to be scheduled. The algorithm proposed in this paper is focused on achieving the lowest possible latency within the specified network topology depending on the physical parameters of the network and the periodic requirements of the communicating nodes. The algorithm allows to add priorities to the data flow between nodes and also the option to specify if preemption is allowed within the network, since not all devices support it. The result is a schedule of hard-real-time data communication within a specified network topology where all requirements of communicating nodes are met if such a solution is possible. © 2023 Copyright for this paper by its authors.

关键词： deadline deterministic scheduling Ethernet scheduling algorithm time-sensitive networking

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Why Should I Trust Your Explanation? An Evaluation Approach for XAI Methods Applied to Predictive Process Monitoring Results

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第4期5卷 1458-1472页

作者： Elkhawaga, Ghada Elzeki, Omar M. Abu-Elkheir, Mervat Reichert, Manfred Ulm University Institute of Databases and Information Systems Ulm89081 Germany Mansoura University Faculty of Computers and Information Mansoura35516 Egypt New Mansoura University Faculty of Computer Science and Engineering Mansoura35516 Egypt German University in Cairo Faculty of Media Engineering and Technology New Cairo11835 Egypt

As a use case of process mining, predictive process monitoring (PPM) aims to provide information on the future course of running business process instances. A large number of available PPM approaches adopt predictive models based on machine learning (ML). With the improved efficiency and accuracy of ML models usually being coupled with increasing complexity, their understandability becomes compromised. Having the user at the center of attention, various eXplainable artificial intelligence (XAI) methods emerged to provide users with explanations of the reasoning process of an ML model. Though there is a growing interest in applying XAI methods to PPM results, various proposals have been made to evaluate explanations according to different criteria. In this article, we propose an approach to quantitatively evaluate XAI methods concerning their ability to reflect the facts learned from the underlying stores of business-related data, i.e., event logs. Our approach includes procedures to extract features that are crucial for generating predictions. Moreover, it computes ratios that have proven to be useful in differentiating XAI methods. We conduct experiments that produce useful insights into the effects of the various choices made through a PPM workflow. We can show that underlying data and model issues can be highlighted using the applied XAI methods. Furthermore, we could penalize and reward XAI methods for achieving certain levels of consistency with the facts learned about the underlying data. Our approach has been applied to different real-life event logs using different configurations of the PPM workflow. © 2020 IEEE.

关键词： Process monitoring

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Cloud Security Service for Identifying Unauthorized User Behaviour

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computers, Materials & Continua 2022年第2期70卷 2581-2600页

作者： D.Stalin David Mamoona Anam Chandraprabha Kaliappan S.Arun Mozhi Selvi Dilip Kumar Sharma Pankaj Dadheech Sudhakar Sengan Department of Computer Science and Engineering IFET College of EngineeringVillupuram605108Tamil NaduIndia Department of Computer Sciences and Software Engineering Faculty of Basic and Applied SciencesInternational Islamic UniversityIslamabadPakistan Department of Information Technology Bannari Amman Institute of TechnologySathyamangalam638401Tamil NaduIndia Department of Computer Science Engineering DMI St.John the Baptist UniversityMangochiLilongweMalawi Department of Mathematics Jaypee University of Engineering and TechnologyGuna473226Madhya PradeshIndia Department of Computer Science and Engineering Swami Keshvanand Institute of TechnologyManagement and Gramothan(SKIT)Jaipur302017RajasthanIndia Department of Computer Science and Engineering PSN College of Engineering and TechnologyTirunelveli627152Tamil NaduIndia

Recently,an innovative trend like cloud computing has progressed quickly in *** a background of distributed networks,the extensive sprawl of internet resources on the Web and the increasing number of service providers helped cloud computing technologies grow into a substantial scaled Information Technology service *** cloud computing environment extracts the execution details of services and systems from end-users and ***,through the system’s virtualization accomplished using resource pooling,cloud computing resources become more *** attempt to design and develop a solution that assures reliable and protected authentication and authorization service in such cloud environments is described in this *** the help of multi-agents,we attempt to represent Open-Identity(ID)design to find a solution that would offer trustworthy and secured authentication and authorization services to software services based on the *** research aims to determine how authentication and authorization services were provided in an agreeable and preventive *** on attack-oriented threat model security,the evaluation *** considering security for both authentication and authorization systems,possible security threats are analyzed by the proposed security systems.

关键词： Cloud computing user behaviour access control security model

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Transfer Learning using BERT & Comparative Analysis of ML Algorithms for Opinion Mining 3

Transfer Learning using BERT & Comparative Analysis of ML Al...

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3rd IEEE Global Conference for Advancement in Technology, GCAT 2022

作者： Gandhi, Bhuvanjeet Singh Kumar, Shubham Victor, Akila Selvanambi, Ramani Vellore Institute of Technology Dept. of Computer Science Vellore India Vellore Institute of Technology Dept. of Software Systems Vellore India

ISBN: (数字)9781665468558

ISBN: (纸本)9781665468534

Sentiment Analysis is an ongoing field of research in text mining that is concerned with the computational treatment of textual views, sentiments, and subjectivity. It's the task of distinguishing between positive and negative viewpoints, emotions, and assessments. Sentiment analysis has been the topic of intensive research since its inception. During COVID-19, there has been a subtle increase in the usage and the time spent on the social networking sites by people as most of the daily operations have moved online. Moreover, in addition to the illness itself, the pandemic has led to dread, anxiety, stress, concern, repugnance, and poignancy in individuals all around the world. Considering these indicators, experts are paying close attention to Twitter data analysis during this pandemic. BERT is used as a transfer learning model and this work analyses the efficacy of fine-tuning it for the task of opinion mining by comparing it to a baseline model that includes a TF-IDF vectorizer and a Naïve Bayes classifier. Its performance is also compared to that of Naïve Bayes, Logistic Regression, K Nearest Neighbor, Decision Tree and XGBoost classifier. To determine the most effective settings for the BERT model, hyper-parameter tweaking is used. After two epochs of training at a learning rate of le-5 and batch size of 16, the maximum accuracy of 87.6% is attained. These results outperform all of the machine learning models examined in this study. This work tackles a comprehensive overview of the last update in this field. It can be beneficial to scholars in this domain because it encapsulates the most well-known Sentiment analysis methodologies and their comparison in single research work. © 2022 IEEE.

关键词： Sentiment analysis

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Sparse Crowd Flow Analysis of Tawaaf of Kaaba During the COVID-19 Pandemic

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computers, Materials & Continua 2022年第6期71卷 5581-5601页

作者： Durr-e-Nayab Ali Mustafa Qamar Rehan Ullah Khan Waleed Albattah Khalil Khan Shabana Habib Muhammad Islam Department of Computer Systems Engineering University of Engineering and TechnologyPeshawarPakistan Department of Computer Science College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Information Technology College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Information Technology and Computer Science Pak-Austria FachhochschuleInstitute of Applied Sciences and TechnologyHaripurPakistan Department of Electrical Engineering College of Engineering and Information TechnologyOnaizah CollegesAl-QassimSaudi Arabia

The advent of the COVID-19 pandemic has adversely affected the entire world and has put forth high demand for techniques that remotely manage crowd-related *** surveillance and crowd management using video analysis techniques have significantly impacted today’s research,and numerous applications have been developed in this *** research proposed an anomaly detection technique applied to Umrah videos in Kaaba during the COVID-19 pandemic through sparse crowd *** theKaaba rituals is crucial since the crowd gathers from around the world and requires proper analysis during these days of the *** Umrah videos are analyzed,and a system is devised that can track and monitor the crowd flow in *** crowd in these videos is sparse due to the pandemic,and we have developed a technique to track the maximum crowd flow and detect any object(person)moving in the direction unlikely of the major *** have detected abnormal movement by creating the histograms for the vertical and horizontal flows and applying thresholds to identify the non-majority *** algorithm aims to analyze the crowd through video surveillance and timely detect any abnormal activity tomaintain a smooth crowd flowinKaaba during the pandemic.

关键词： computer vision COVID sparse crowd crowd analysis flow analysis sparse crowd management tawaaf video analysis video processing

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Focusing Intracranial Aneurysm Lesion Segmentation by Graph Mask2Former with Local Refinement in DSA Images

Focusing Intracranial Aneurysm Lesion Segmentation by Graph ...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Mo, Yancheng Chen, Yanlin Hu, Yan Xu, Jiongfu Wang, Hao Dai, Limeng Liu, Jiang Southern University of Science and Technology Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Shenzhen China Shenzhen People's Hospital Shenzhen China

ISBN: (纸本)9798350337488

Intracranial Aneurysm (IA) lesion segmentation is significant for IA treatment, which is one of the high death rate and deformity cerebrovascular diseases. Segmenting the IA lesions accurately is still challenging in digital subtraction angiography (DSA) images due to blurred boundaries, imaging noise, and intracranial vascular morphologies. In this paper, we are the first time to propose a novel instance segmentation network architecture, Graph Mask2Former, to segment IA lesions automatically based on DSA images. Specifically, we apply a graph convolution module to reassign label information, aiming to adjust the confidence weight of error instances adaptively. Furthermore, we design a local refinement module to refine the coarse mask output. The extensive experiments on the clinical IA- DSA and LiTS datasets show that our method outperforms recent state-of-the-art methods. This paper also provides the visual analysis to explain the inherent behavior of our method. © 2023 IEEE.

关键词： DSA image Instance segmentation Intracranial aneurysm

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Text-Guided Attention is All You Need for Zero-Shot Robustness in Vision-Language Models 38

Text-Guided Attention is All You Need for Zero-Shot Robustne...

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38th Conference on Neural Information Processing systems, NeurIPS 2024

作者： Yu, Lu Zhang, Haiyang Xu, Changsheng School of Computer Science and Engineering Tianjin University of Technology China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation University of Chinese Academy of Sciences China

Due to the impressive zero-shot capabilities, pre-trained vision-language models (e.g. CLIP), have attracted widespread attention and adoption across various domains. Nonetheless, CLIP has been observed to be susceptible to adversarial examples. Through experimental analysis, we have observed a phenomenon wherein adversarial perturbations induce shifts in text-guided attention. Building upon this observation, we propose a simple yet effective strategy: Text-Guided Attention for Zero-Shot Robustness (TGA-ZSR). This framework incorporates two components: the Attention Refinement module and the Attention-based Model Constraint module. Our goal is to maintain the generalization of the CLIP model and enhance its adversarial robustness: The Attention Refinement module aligns the text-guided attention obtained from the target model via adversarial examples with the text-guided attention acquired from the original model via clean examples. This alignment enhances the model's robustness. Additionally, the Attention-based Model Constraint module acquires text-guided attention from both the target and original models using clean examples. Its objective is to maintain model performance on clean samples while enhancing overall robustness. The experiments validate that our method yields a 9.58% enhancement in zero-shot robust accuracy over the current state-of-the-art techniques across 16 datasets. Our code is available at https://***/zhyblue424/TGA-ZSR. © 2024 Neural information processing systems foundation. All rights reserved.

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Analysis of Significant Cell Differences Between Cancer Patients and Healthy Individuals 28th

Analysis of Significant Cell Differences Between Cancer Pati...

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28th International Conference on Technologies and Applications of Artificial Intelligence, TAAI 2023

作者： Sun, Pei-Chi Yan, Xiaowei Li, Yu-Wei Chen, Hao-Xiang Li, Hsu-Ching li, Cheng-Yu Cheng, Wan-Shu Department of Electronic Engineering National Kaohsiung University of Science and Technology Kaohsiung Taiwan Institute for Systems Biology SeattleWA United States Department of Computer Science and Information Management Providence University Taichung Taiwan

ISBN: (纸本)9789819717132

At the end of 2019, a global outbreak of a new coronavirus ravaged the world, and to this day, many people’s bodies are still deeply affected by the virus. In order to find out if there is a correlation between different levels of cancer diagnosis and healthy samples, and to further compare the differences between healthy samples and cancer samples, we have identified a significant set of genes that highlights the differences between the two samples. Therefore, we used statistics, immunology, and artificial intelligence to analyze the blood sampling data of cancer patients diagnosed with the new coronavirus during the diagnostic period, and compared them with healthy donors. We initially found that the proportion of significant cells in the two stem cell gene sets of the cancer samples was more than about two times greater than that of the healthy samples. Therefore, in the future, we will be able to further analyze the differences between the two samples and find out the key factors that can assist in medical diagnosis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Coronavirus

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