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23rd Eurographics Conference on Visualization, EuroVis 2021

作者： Yan, X.Y. Ma, Y.F. Department of Statistics and Data Science Southern University of Science and Technology Shenzhen China

ISBN: (纸本)9783038681441

Mentorship is an important collaborative relationship among scholars. The existing tools to visualize it mainly suffer from a waste of space, lack of overview representation, and less displayed attribute information. To solve these problems, we propose a novel elastic tree layout based on node-link diagrams, in which nodes and edges are represented as elastic rectangles and bands respectively. By stretching, compressing, aggregating, and expanding nodes and edges, we can: get a compact tree layout with high space-efficiency, display both the detailed subtree and compressed context in a single view, use labeling, charts, and node opacity to show multiple attributes. Besides, we designed various animated interactions to facilitate the exploration. © 2021 The Author(s) Eurographics Proceedings © 2021 The Eurographics Association.

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FasMe: fast and sample-efficient meta estimator for precision matrix learning in small sample settings 24

FasMe: fast and sample-efficient meta estimator for precisio...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Xiao Tan Yiqin Wang Yangyang Shen Dian Shen Meng Wang Peibo Duan Beilun Wang School of Computer Science and Engineering Southeast University Nanjing China and Department of Data Science & AI Monash University Melbourne Australia School of Computer Science and Engineering Southeast University Nanjing China College of Design and Innovation Tongji University Shanghai China Department of Data Science & AI Monash University Melbourne Australia

ISBN: (纸本)9798331314385

Precision matrix estimation is a ubiquitous task featuring numerous applications such as rare disease diagnosis and neural connectivity exploration. However, this task becomes challenging in small sample settings, where the number of samples is significantly less than the number of dimensions, leading to unreliable estimates. Previous approaches either fail to perform well in small sample settings or suffer from inefficient estimation processes, even when incorporating meta-learning techniques. To this end, we propose a novel approach FasMe for Fast and Sample-efficient Meta Precision Matrix Learning, which first extracts meta-knowledge through a multi-task learning diagram. Then, meta-knowledge constraints are applied using a maximum determinant matrix completion algorithm for the novel task. As a result, we reduce the sample size requirements to O(log p/K) per meta-training task and O(log |G|) for the meta-testing task. Moreover, the hereby proposed model only needs O(p log ε-1) time and O(p) memory for converging to anε-accurate solution. On multiple synthetic and biomedical datasets, FasMe is at least ten times faster than the four baselines while promoting prediction accuracy in small sample settings.

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Prefetching-based Adaptive Video Streaming Strategy on Edge Computing

Prefetching-based Adaptive Video Streaming Strategy on Edge ...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Xuanyu Yi Jipeng Zhou Department of Computer Science Jinan University Guangzhou China Department of Computer Science School of Data Science of Guangzhou Huashang College Jinan University Guangzhou China

Dynamic Adaptive Streaming over HTTP (DASH) is a widely adopted video streaming protocol. Adaptive Bitrate Streaming (ABR) algorithm is utilized to dynamically switch between different bitrates. However, traditional ABR algorithms have gradually failed to meet users’ demands for high-quality video transmission, especially in complex network environments. Thus, the current research focus has shifted towards enhancing the accuracy of algorithm. The advent of edge computing has brought new possibilities for DASH transmission. Edge computing-based algorithms can make decisions from a more macroscopic perspective, which can enhance algorithm efficiency. In this paper, we propose an edge computing-based strategy that enables the edge server to obtain the actual file size of the next segment at every bitrate. As a result, edge servers can obtain more informations. We further propose an edge-based system model that assists the ABR algorithm in achieving better operational efficiency. With additional information, the algorithm located at the edge node possesses the capability to make more precise decisions, which is advantageous for enhancing the quality of experience (QoE) for users. Our experiments demonstrate that the proposed strategy can significantly enhance QoE and network resource utilization.

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Flying Neural Network-Based Optimistic Financial Early Alert System in AI Model 6

Flying Neural Network-Based Optimistic Financial Early Alert...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Boggavarapu, Saikrishna Ali, S. Shabbir Manikandan, G. Mohanraj, R. Singh, Devesh Pratap Revathi, R. University of the Cumberlands Department of Information Technology Prospect HeightsIL United States KL University Department of Electronics & Communication Engineering Guntur India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai Thandalam India St.Martin's Engineering College Department of Artificial Intelligence and Data Science Telangana Secunderabad India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttrakhand Dehradun India Karpagam Academy of Higher Education Department of Computer Science Coimbatore India

ISBN: (纸本)9798350304480

An innovative method for early warnings in the area of financial risk management is the Flying Neural Network-based Optimistic Financial Early Alert System (FNN-OFEAS). The FNN-OFEAS outperforms conventional models and rule-based systems because it makes use of the Flying Neural Network architecture and incorporates sentiment analysis. Using a fictitious dataset including past stock prices, financial ratios, economic indicators, and sentiment analysis data, this study details the development, use, and findings of the FNN-OFEAS. The results show that the system is effective at capturing complicated temporal correlations, recognising non-linear patterns, and factoring in market sentiment, all of which contribute to improved risk identification in a timely and accurate manner. Early risk identification, investment decision assistance, regulatory compliance, and financial stability evaluation are just few of the real-world uses for the FNN-OFEAS. Its application is beneficial for financial institutions, investors, regulators, and policymakers since it enhances decision-making, reduces risk, and sheds light on previously hidden information. More work has to be done to improve the model architecture, include other data sources, provide real-time implementations, evaluate the model's resilience, and make it easier to comprehend the results. Addressing financial risks and bolstering financial stability in volatile market settings is one area where the FNN-OFEAS shows promise. © 2023 IEEE.

关键词： 'Flying Neural Network Optimistic Financial Early Alert System (OFEAS)' conventional models early alerts economic indicators Financial anomalies financial ratios financial risk management financial stability historical stock prices hypothetical dataset investment decision support market sentiment practical applications risk detection rule-based systems sentiment analysis

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AGE OF GOSSIP FROM CONNECTIVE PROPERTIES VIA FIRST PASSAGE PERCOLATION

arXiv

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arXiv 2024年

作者： Maranzatto, Thomas Jacob Michelen, Marcus University of Maryland Department of Electrical and Computer Engineering United States University of Illinois Department of Mathematics Statistics and Computer science Chicago United States

In gossip networks, a source node forwards time-stamped updates to a network of observers according to a Poisson process. The observers then update each other on this information according to Poisson processes as well. The Age of Information (AoI) of a given node is the difference between the current time and the most recent time-stamp of source information that the node has received. We provide a method for evaluating the AoI of a node in terms of first passage percolation. We then use this distributional identity to prove matching upper and lower bounds on the AoI in terms of connectivity properties of the underlying network. In particular, if one sets Xv to be the AoI of node v on a finite graph G with n nodes, then we define m* = min{m: m · |Bm(v)| ≥ n} where Bm(v) is the ball of radius m in G. In the case when the maximum degree of G is bounded by ∆ we prove EXv = Θ∆(m*). As corollaries, we solve multiple open problems in the literature such as showing the age of information on a subset of Zd is Θ(n1/(d+1)). We also demonstrate examples of graphs with AoI scaling like nα for each α (0, 1/2). These graphs are not vertex-transitive and in fact we show that if one considers the AoI on a graph coming from a vertex-transitive infinite graph then either EXv = Θ(n1/k) for some integer k ≥ 2 or EXv = no(1) © 2024, CC BY.

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Perceptual Video Summarization Using Keyframes Extraction Technique 3

Perceptual Video Summarization Using Keyframes Extraction Te...

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3rd International Conference on Innovative Practices in Technology and Management, ICIPTM 2023

作者： Rajitha Jasmine, R. Nimmagadda, Padmaja Sudhakar, K. Benitha Christinal, J. Rajasekar, P. Syed Musthafa, A. R. M. K. Engineering College Department of Information Technology Kavaraipettai India Mohan Babu University Department of Electronics and Communication Tirupati India Madanapalle Institute of Technology Department of Computer Science Madanapalle India Panimalar Engineering College Department of Information Technology Chennai India Srm Institute of Science and Technology Department of Data Science and Business Systems Chengalpattu India M. Kumarasamy College of Engineering Department of Information Technology Karur India

ISBN: (纸本)9798350336238

The growth of video content in recent years is a challenging problem due to increased memory storage and time consuming for analyzing content of the video. Therefore, there is a need to reduce the content for human usage. This paper presents a keyframes technique that uses co-occurrence matrix and permutation computation for perceptual video summarization (PVS). The Human Visual System (HVS) needs to incorporate the PVS. It helps to allow for the importance of perceptually significant contents. The proposed method uses different kinds of videos to evaluate the effectiveness of the work. The subjective evaluation scores have evaluated the proposed work. © 2023 IEEE.

关键词： Video recording

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Efficient Weighting Schemes for Auditing Instant-Runoff Voting Elections

arXiv

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arXiv 2024年

作者： Ek, Alexander Stark, Philip B. Stuckey, Peter J. Vukcevic, Damjan Department of Econometrics and Business Statistics Monash University Clayton Australia Department of Statistics University of California BerkeleyCA United States Department of Data Science and AI Monash University Clayton Australia

Various risk-limiting audit (RLA) methods have been developed for instant-runoff voting (IRV) elections. A recent method, AWAIRE, is the first efficient approach that can take advantage of but does not require cast vote records (CVRs). AWAIRE involves adaptively weighted averages of test statistics, essentially "learning" an effective set of hypotheses to test. However, the initial paper on AWAIRE only examined a few weighting schemes and parameter settings. We explore schemes and settings more extensively, to identify and recommend efficient choices for practice. We focus on the case where CVRs are not available, assessing performance using simulations based on real election data. The most effective schemes are often those that place most or all of the weight on the apparent "best" hypotheses based on already seen data. Conversely, the optimal tuning parameters tended to vary based on the election margin. Nonetheless, we quantify the performance trade-offs for different choices across varying election margins, aiding in selecting the most desirable trade-off if a default option is needed. A limitation of the current AWAIRE implementation is its restriction to a small number of candidates—up to six in previous implementations. One path to a more computationally efficient implementation would be to use lazy evaluation and avoid considering all possible hypotheses. Our findings suggest that such an approach could be done without substantially compromising statistical performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Runoff

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Comprehensive Analysis for Diagnosing Attention Deficit Hyperactivity Disorder Using Support Vector Machine and Multilayer Perceptron

Comprehensive Analysis for Diagnosing Attention Deficit Hype...

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International Conference on computer and Communications Technologies (ICCCT)

作者： R. Lakshmi B. Vanathi Department of Artificial Intelligence & Data Science SRM Valliammai Engineering College Kattankulathur Chennai Department of Computer Science & Engineering SRM Valliammai Engineering College Kattankulathur Chennai

ISBN: (数字)9798331537579

ISBN: (纸本)9798331537586

ADHD or Attention Deficit Hyperactivity Disorder, is a nervous system disorder that primarily creates problems for youngsters. It is associated with behavioral disorders, which are mostly typified by inattention and hyperactivity. This study's primary goals are to (i) identify and detect individuals with ADHD based on risk variables and (ii) provide a suitable machine-learning method for ADHD prediction and detection. Using logistic regression, the characteristics of people with ADHD are extracted. Numerous machine learning methods, including Random Forest (RF), Naïve Bayes (NB), Support Vector Machines (SVM), and K-Nearest Neighbours (KNN), can be used to predict ADHD. To predict ADHD, current techniques have used neuroimaging and behavioral analysis. According to the study, MLP offers great accuracy in ADHD classification and prediction. According to the study, a genetic component is crucial in identifying ADHD. Different parameters received from functional magnetic resonance imaging(fMRI), electroencephalography (EEG), medical notes, video, and speech, can be used in investigation for prediction and detection of ADHD.

关键词： Support vector machines Radio frequency Neuroimaging Accuracy Nearest neighbor methods Nervous system Electroencephalography Real-time systems Bayes methods Random forests

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Comparative Analysis of Machine Learning Models for Detecting Mobile Messaging Spam In Swahili SMS 1

Comparative Analysis of Machine Learning Models for Detectin...

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1st International Conference on the Advancements of Artificial Intelligence in African Context, AAIAC 2023

作者： Kalolo, Christopher Mbelwa, Jimmy Eastern Africa Statistical Training Centre Department of Official Statistics Dar es Salaam Tanzania United Republic of University of Dar Es Salaam Department of Computer Science and Engineering Dar es Salaam Tanzania United Republic of

ISBN: (纸本)9798350330137

The prevalence of spam messages is a worldwide issue. Unsolicited text messages in Swahili via SMS have emerged as a significant concern for mobile phone users. This surge can be attributed to the increasing number of mobile phone users who favor SMS as their primary means of communication. Various techniques and strategies have been developed to counteract unwanted SMS messages in various languages. Nonetheless, languages with limited research and a lack of publicly available Swahili SMS datasets are more susceptible to spam texts. This research conducted a comparative examination of machine learning models for addressing spam in Swahili mobile messages. We employed six machine learning models and assessed their performance using various classification metrics. The comprehensive assessment favored the Support Vector Classifier (SVC) model as the top performer. The SVC model achieved an accuracy rate of 96.19%, a precision of 97.39%, a recall of 94.92%, an F-Measure of 96.14%, and an AUC-ROC score of 0.9619. Following closely was the Logistic Regression model, boasting an accuracy rate of 95.35%, precision of 96.93%, recall of 93.64%, an F-Measure of 95.26%, and an AUC-ROC score of 0.9535. The overall evaluation indicated that all machine learning models displayed promising performance. Results demonstrated that these models exhibit strong discriminatory capabilities between positive and negative classes, as evidenced by their higher AUC-ROC scores. In conclusion, we recommend further research in this domain and in other natural language processing tasks specific to the Swahili language. © 2023 IEEE.

关键词： Learning algorithms

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Machine Learning With data Assimilation and Uncertainty Quantification for Dynamical Systems:A Review

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IEEE/CAA Journal of Automatica Sinica 2023年第6期10卷 1361-1387页

作者： Sibo Cheng César Quilodrán-Casas Said Ouala Alban Farchi Che Liu Pierre Tandeo Ronan Fablet Didier Lucor Bertrand Iooss Julien Brajard Dunhui Xiao Tijana Janjic Weiping Ding Yike Guo Alberto Carrassi Marc Bocquet Rossella Arcucci Data Science Institute Department of ComputingImperial College LondonSW72AZ London Department of Earth Science and Engineering Imperial College LondonSW72AZ London Department of Computer Science and Engineering Hong Kong University of Science and TechnologyHong Kong 999077China the IMT Atlantique Lab-STICCUMR CNRS 6285France and OdysseyInria/IMTFrance.P.Tandeo is also with RIKEN Center for Computational ScienceKobeJapan the CEREA École des Ponts and EDF R&Dîle-de-FranceFrance the Laboratoire Interdisciplinaire des Sciences du Numérique CNRSParis-Saclay UniversityF-91403OrsayFrance the Electricitéde France(EDF) 78401 ChatouFranceInstitut de Mathématiques de Toulouse31062 ToulouseFrance and SINCLAIR AI LabSaclayFrance the Sorbonne University ParisFranceand also with Nansen Environmental and Remote Sensing Center(NERSC)BergenNorway the School of Mathematical Sciences Tongji UniversityShanghai 200092China the Mathematical Institute for Machine Learning and Data Science KU Eichstaett-IngolstadtBavariaGermany the School of Information Science and Technology Nantong UniversityNantong 226019China the Department of Physics and Astronomy“Augusto Righi” University of Bologna40124 BolognaItaly

data assimilation(DA)and uncertainty quantification(UQ)are extensively used in analysing and reducing error propagation in high-dimensional spatial-temporal *** applications span from computational fluid dynamics(CFD)to geoscience and climate ***,much effort has been given in combining DA,UQ and machine learning(ML)*** research efforts seek to address some critical challenges in high-dimensional dynamical systems,including but not limited to dynamical system identification,reduced order surrogate modelling,error covariance specification and model error correction.A large number of developed techniques and methodologies exhibit a broad applicability across numerous domains,resulting in the necessity for a comprehensive *** paper provides the first overview of state-of-the-art researches in this interdisciplinary field,covering a wide range of *** review is aimed at ML scientists who attempt to apply DA and UQ techniques to improve the accuracy and the interpretability of their models,but also at DA and UQ experts who intend to integrate cutting-edge ML approaches to their ***,this article has a special focus on how ML methods can overcome the existing limits of DA and UQ,and vice *** exciting perspectives of this rapidly developing research field are also *** Terms-data assimilation(DA),deep learning,machine learning(ML),reduced-order-modelling,uncertainty quantification(UQ).

关键词： assimilation overcome apply

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