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Proceedings of the 2020 4th International Symposium on Computer science and Intelligent Control

作者： Bekkouch Imad Ibrahim Dragoş Constantin Nicolae Adil Khan Syed Imran Ali Asad Khattak Machine Learning and Knowledge Representation Lab Institute of Data Science & AI Innopolis Tatarstan Russia Institutul de cercetări pentru Inteligenta Artificiala 'Mihai Draganescu' România Department of Computer Engineering Kyung Hee University Yongin-si South Korea College of Technological Innovations Zayed University Abu Dhabi United Arab Emirates

ISBN: (纸本)9781450388894

Outlier detection is one of the main fields in machine learning and it has been growing rapidly due to its wide range of applications. In the last few years, deep learning-based methods have outperformed machine learning and handcrafted outlier detection techniques, and our method is no different. We present a new twist to generative models which leverages variational autoencoders as a source for uniform distributions which can be used to separate the inliers from the outliers. Both the generative and adversarial parts of the model are used to obtain three main losses (Reconstruction loss, KL-divergence, Discriminative loss) which in return are wrapped with a one-class SVM which is used to make the predictions. We evaluated our method against several datasets both for images and tabular data and it has shown great results for the zero-shot outlier detection problem and was able to easily generalize it for supervised outlier detection tasks on which the performance has increased. For comparison, we evaluated our method against several of the common outlier detection techniques such as DBSCAN-based outlier detection, GMM, K-means and one class SVM directly, and we have outperformed all of them on all datasets.

关键词： Deep Learning Generative Models Machine Learning Outlier Detection

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On multi-objective minimum spanning tree under the framework of uncertainty theory

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AIP Conference Proceedings 2023年第1期2851卷

作者： Swapna Halder Saibal Majumder Arindam Biswas Bijoy Kumar Mandal Sheng-Lung Peng Department of Computer Science and Engineering St. Thomas College of Engineering & Technology Kolkata West Bengal India Centre for IOT and AI Integration with Education -Industry-Agriculture Kazi Nazrul University Asansol West Bengal India Department of Computer Science and Engineering (Data Science) Dr. B. C. Roy Engineering College Durgapur West Bengal India School of Mines and Metallurgy Kazi Nazrul University Asansol West Bengal India Department of Computer Science and Engineering NSHM Knowledge Campus Durgapur West Bengal India Department of Creative Technologies and Product Design National Taipei University of Business Taoyuan Taiwan

Aminimum spanning tree with multiple objectives is one type of NP hard problem, and it has a wide application area in ourdaily life. Modelling real world problems, requires many associated parameters which are not well-defined and are indeterminate in nature. Thus, it is rational to consider these indeterminate parameters while modelling real life problems. This article, investigates onthe study of minimum spanning tree with multiple objectives which are uncertain in nature. The proposed multiple objectives minimum spanning tree has two uncertain parameters associated with it, they are time and cost. Both of these parameters are indeterminate in nature and characterizedas uncertain variables. The expected value model (EVM) of multiple objectives minimum spanning tree is formulatedin this study and convex combination method is used to solve this problem. Finally to achieve the nondominated solutions of this problem, we have solved the EVM by varying the related weights of its objectives.

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Detecting anomalous events over time using RDF triple extraction and a dynamic implementation of oddball 30

Detecting anomalous events over time using RDF triple extrac...

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30th Benelux Conference on Artificial Intelligence, BNAIC 2018

作者： Heinrichs, Benedikt Scholtes, Jan C. Department of Data Science and Knowledge Engineering Maastricht University Netherlands

This paper shows a new approach for anomaly detection by combining the extraction of so-called triples consisting of a subject, predicate, and object using dynamic anomaly-detection. First, the methods used to extract triples and general principles of anomaly detection and event detection are discussed. Next, a novel approach is presented where extracted triples are converted into time-lapsed networks of triples on which anomaly and event detection methods from social network analysis are applied. Subsequently, the results of the experiments are presented together with the evaluation method used. Considering the results of the tested methods, the dynamic variation of the OddBall algorithm, which measures network changes over time, displays the connection between the predictions of our model and real-life events accurately. © 2018 University of Groningen. All rights reserved.

关键词： Anomaly detection

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Higgs Boson Discovery using Machine Learning Methods with Pyspark

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Procedia Computer science 2020年 170卷 1141-1146页

作者： Mourad Azhari Abdallah Abarda Badia Ettaki Jamal Zerouaoui Mohamed Dakkon Laboratory of Engineering Sciences and Modeling Faculty of Sciences- Ibn Tofail University Campus Universitaire BP 133 Kenitra Morocco Laboratoire de Modélisation Mathématiques et de Calculs Economiques FSJES Université Hassan 1er Settat Morocco Laboratory of Research in Computer Science Data Sciences and Knowledge Engineering Department of Data Content and knowledge Engineering School of Information Sciences Rabat Morocco Département de Statistique et Informatique de Gestion Université Abdelmalek Essaadi Tetouan Morocco

Higgs Boson is an elementary particle that gives the mass to everything in the natural world. The discovery of the Higgs Boson is a major challenge for particle physics. This paper proposes to solve the Higgs Boson Classification Problem with four Machine Learning (ML) Methods, using the Pyspark environment: Logistic Regression (LR), Decision Tree (DT), Random Forest (RF) and Gradient Boosted Tree (GBT). We compare the accuracy and AUC metrics of those ML Methods. We use a large dataset as Higgs Boson, collected from public site UCI and Higgs dataset downloaded from Kaggle site, in the experimentation stage.

关键词： Boson Higgs Spark Pyspark Machine Learning (ML) Logistic Regression (LR) Decision Tree (DT) Random Forest (RF) Gradient Boosted Tree (GBT) AUC Accuracy

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Social emotion mining techniques for facebook posts reaction prediction 10

Social emotion mining techniques for facebook posts reaction...

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10th International Conference on Agents and Artificial Intelligence, ICAART 2018

作者： Krebs, Florian Lubascher, Bruno Moers, Tobias Schaap, Pieter Spanakis, Gerasimos Department of Data Science and Knowledge Engineering Maastricht University Maastricht Netherlands

ISBN: (纸本)9789897582752

As of February 2016 Facebook allows users to express their experienced emotions about a post by using five so-called ‘reactions’. This research paper proposes and evaluates alternative methods for predicting these reactions to user posts on public pages of firms/companies (like supermarket chains). For this purpose, we collected posts (and their reactions) from Facebook pages of large supermarket chains and constructed a dataset which is available for other researches. In order to predict the distribution of reactions of a new post, neural network architectures (convolutional and recurrent neural networks) were tested using pretrained word embeddings. Results of the neural networks were improved by introducing a bootstrapping approach for sentiment and emotion mining on the comments for each post. The final model (a combination of neural network and a baseline emotion miner) is able to predict the reaction distribution on Facebook posts with a mean squared error (or misclassification rate) of 0.135. Copyright © 2018 by SCITEPRESS – science and Technology Publications, Lda. All rights reserved.

关键词： Recurrent neural networks

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Machine Memory Intelligence: Inspired by Human Memory Mechanisms

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engineering 2025年

作者： Qinghua Zheng Huan Liu Xiaoqing Zhang Caixia Yan Xiangyong Cao Tieliang Gong Yong-Jin Liu Bin Shi Zhen Peng Xiaocen Fan Ying Cai Jun Liu School of Computer Science and Technology Xi’an Jiaotong University Xi’an 710049 China Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong University Xi’an 710049 China Translational Research Institute of Brain and Brain-Like Intelligence Shanghai Fourth People’s Hospital Shanghai 200434 China School of Medicine Tongji University Shanghai 200092 China Department of Computer Science and Technology Tsinghua University Beijing 100084 China Department of Computer Science Iowa State University Ames IA 50011 USA

Large models, exemplified by ChatGPT, have reached the pinnacle of contemporary artificial intelligence (AI). However, they are plagued by three inherent drawbacks: excessive training data and computing power consumption, susceptibility to catastrophic forgetting, and a deficiency in logical reasoning capabilities within black-box models. To address these challenges, we draw insights from human memory mechanisms to introduce “machine memory,” which we define as a storage structure formed by encoding external information into a machine-representable and computable format. Centered on machine memory, we propose the brand-new machine memory intelligence (M 2 I) framework, which encompasses representation, learning, and reasoning modules and loops. We explore the key issues and recent advances in the four core aspects of M 2 I, including neural mechanisms, associative representation, continual learning, and collaborative reasoning within machine memory. M 2 I aims to liberate machine intelligence from the confines of data-centric neural networks and fundamentally break through the limitations of existing large models, driving a qualitative leap from weak to strong AI.

关键词： Machine memory intelligence Neural mechanism Associative representation Continual learning Collaborative reasoning

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A Continuous Information Gain Measure to Find the Most Discriminatory Problems for AI Benchmarking

A Continuous Information Gain Measure to Find the Most Discr...

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Congress on Evolutionary Computation

作者： Matthew Stephenson Damien Anderson Ahmed Khalifa John Levine Jochen Renz Julian Togelius Christoph Salge Department of Data Science and Knowledge Engineering Maastricht University Maastricht the Netherlands Computer and Information Sciences Department University of Strathclyde Glasgow UK Game Innovation Lab Tandon School of Engineering New York University New York USA Research School of Computer Science Australian National University Canberra Australia

ISBN: (数字)9781728169293

ISBN: (纸本)9781728169309

This paper introduces an information-theoretic method for selecting a subset of problems which gives the most information about a group of problem-solving algorithms. This method was tested on the games in the General Video Game AI (GVGAI) framework, allowing us to identify a smaller set of games that still gives a large amount of information about the abilities of different game-playing agents. This approach can be used to make agent testing more efficient. We can achieve almost as good discriminatory accuracy when testing on only a handful of games as when testing on more than a hundred games, something which is often computationally infeasible. Furthermore, this method can be extended to study the dimensions of the effective variance in game design between these games, allowing us to identify which games differentiate between agents in the most complementary ways.

关键词： Games Correlation Artificial intelligence Testing Task analysis Noise measurement Technological innovation

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Culture versus Policy: More Global Collaboration to Effectively Combat COVID-19

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The Innovation 2020年第2期1卷 15-16页

作者： Jianping Li Kun Guo Enrique Herrera Viedma Heesoek Lee Jiming Liu Ning Zhong Luiz Flavio Autran Monteiro Gomes Florin Gheorghe Filip Shu-Cherng Fang Mujgan SagirÖzdemir Xiaohui Liu Guoqing Lu Yong Shi School of Economics and Management University of Chinese Academy of SciencesBeijing 100190China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of SciencesBeijing 100190China Research Center on Fictitious Economy&Data Science Chinese Academy of SciencesBeijing 100190China Department of Computer Science and Artificial Intelligence E.T.S.de Ingenieria Informatica y de TelecomunicacionesUniversity of Granada18071 GranadaSpain Department of Information Management Korea Advanced Institute of Science and TechnologySeoul 207-43 Korea Department of Computer Science and HKBU-CSD&NIPD Joint Research Laboratory for Intelligent Disease Surveillance and Control Hong Kong Baptist UniversityHong KongChina Department of Life Science and Informatics Maebashi Institute of TechnologyMaebashi 371-0816Japan Ibmec University Center Av.Presidente Wilson118Office#111020030-020 Rio de JaneiroBrazil The Romanian Academy Bucharest010071Romania Industrial and Systems Engineering Department North Carolina State UniversityRaleighNC 27695USA Department of Industrial Engineering Eskisehir Osmangazi University26480 EskisehirTurkey Department of Computer Science Brunel University LondonLondonUB83PHUK Department of Biology and School of Interdisciplinary Informatics University of Nebraska at OmahaOmahaNE 68182USA College of Information Science and Technology University of Nebraska at OmahaOmahaNE 68182USA

The outbreak of COVID-19 seriously challenges every government with regard to capacity and management of public health systems facing the catastrophic *** and anti-epidemic policy do not necessarily conflict with each *** countries and governments should be more tolerant to each other in seeking cultural and political consensus to overcome this historically tragic pandemic together.

关键词： seeking overcome policy

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Autoregressive convolutional recurrent neural network for univariate and multivariate time series prediction

arXiv

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arXiv 2019年

作者： Maggiolo, Matteo Spanakis, Gerasimos Department of Data Science and Knowledge Engineering Maastricht University Maastricht6200MD Netherlands

Time Series forecasting (univariate and multivariate) is a problem of high complexity due the different patterns that have to be detected in the input, ranging from high to low frequencies ones. In this paper we propose a new model for timeseries prediction that utilizes convolutional layers for feature extraction, a recurrent encoder and a linear autoregressive component. We motivate the model and we test and compare it against a baseline of widely used existing architectures for univariate and multivariate timeseries. The proposed model appears to outperform the baselines in almost every case of the multivariate timeseries datasets, in some cases even with 50% improvement which shows the strengths of such a hybrid architecture in complex timeseries. Copyright © 2019, The Authors. All rights reserved.

关键词： Convolution

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Aerial Active STAR-RIS-assisted Satellite-Terrestrial Covert Communications

arXiv

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arXiv 2025年

作者： Zhang, Chuang Sun, Geng Li, Jiahui Wang, Jiacheng Zhang, Ruichen Niyato, Dusit Mao, Shiwen Quek, Tony Q.S. Computer Science and Technology Jilin University Changchun130012 China Singapore University of Technology and Design Singapore487372 Singapore College of Computer Science and Technology Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China Department of Electrical and Computer Engineering Auburn University AuburnAL36849 United States Information System Technology and Design Pillar Singapore University of Technology and Design Singapore487372 Singapore

An integration of satellites and terrestrial networks is crucial for enhancing performance of next generation communication systems. However, the networks are hindered by the long-distance path loss and security risks in dense urban environments. In this work, we propose a satellite-terrestrial covert communication system assisted by the aerial active simultaneous transmitting and reflecting reconfigurable intelligent surface (AASTAR-RIS) to improve the channel capacity while ensuring the transmission covertness. Specifically, we first derive the minimal detection error probability (DEP) under the worst condition that the Warden has perfect channel state information (CSI). Then, we formulate an AASTAR-RIS-assisted satellite-terrestrial covert communication optimization problem (ASCCOP) to maximize the sum of the fair channel capacity for all ground users while meeting the strict covert constraint, by jointly optimizing the trajectory and active beamforming of the AASTAR-RIS. Due to the challenges posed by the complex and high-dimensional state-action spaces as well as the need for efficient exploration in dynamic environments, we propose a generative deterministic policy gradient (GDPG) algorithm, which is a generative deep reinforcement learning (DRL) method to solve the ASCCOP. Concretely, the generative diffusion model (GDM) is utilized as the policy representation of the algorithm to enhance the exploration process by generating diverse and high-quality samples through a series of denoising steps. Moreover, we incorporate an action gradient mechanism to accomplish the policy improvement of the algorithm, which refines the better state-action pairs through the gradient ascent. Simulation results demonstrate that the proposed approach significantly outperforms important benchmarks, and also validate the performance and practicality under different algorithm parameters and environment settings. Copyright © 2025, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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