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arXiv 2023年

作者： Paluzo-Hidalgo, Eduardo Gonzalez-Diaz, Rocio Gutiérrez-Naranjo, Miguel A. Department of Quantitative Methods Universidad Loyola Andalucía Campus Sevilla Dos Hermanas Seville Spain Department of Applied Mathematics I School of engineering University of Seville Seville Spain Department of Computer Science and Artificial Intelligence School of Engineering University of Seville Seville Spain

Simplicial map neural networks (SMNNs) are topology-based neural networks with interesting properties such as universal approximation ability and robustness to adversarial examples under appropriate conditions. However, SMNNs present some bottlenecks for their possible application in high-dimensional datasets. First, SMNNs have precomputed fixed weight and no SMNN training process has been defined so far, so they lack generalization ability. Second, SMNNs require the construction of a convex polytope surrounding the input dataset. In this paper, we overcome these issues by proposing an SMNN training procedure based on a support subset of the given dataset and replacing the construction of the convex polytope by a method based on projections to a hypersphere. In addition, the explainability capacity of SMNNs and effective implementation are also newly introduced in this paper. © 2023, CC BY.

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Analysis Pre and Post COVID-19 Pandemic Rorschach Test Data of Using EM Algorithms and GMM Models

Analysis Pre and Post COVID-19 Pandemic Rorschach Test Data ...

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2022 Scholar's Yearly Symposium of Technology, Engineering and Mathematics, SYSTEM 2022

作者： Ponzi, Valerio Russo, Samuele Wajda, Agata Brociek, Rafal Napoli, Christian Department of Computer Control and Management Engineering Sapienza University of Rome Via Ariosto 25 Roma00185 Italy Department of Psychology Sapienza University of Rome Via dei Marsi 78 Roma00185 Italy Institute of Energy and Fuel Processing Technology Zabrze41-803 Poland Department of Mathematics Applications and Methods for Artificial Intelligence Faculty of Applied Mathematics Silesian University of Technology Gliwice44-100 Poland Institute for Systems Analysis and Computer Science Italian National Research Council Via dei Taurini 19 Roma00185 Italy

The global spread of the COVID-19 virus has become one of the greatest challenges that humanity has faced in recent years. The unprecedented circumstances of forced isolation and uncertainty that it has imposed on us continue to impact our mental well-being, whether or not we have been directly affected by the virus. Over a period of nearly three years (2017-2020), data was collected from multiple administrations of the Rorschach test, one of the most renowned and extensively studied psychological tests. This study involved the clustering of data, collected through the RAP3 software, to analyze the distinctive trends in data recorded before and after the pandemic. This was achieved through the implementation of the well-established machine learning algorithm, Expectation-Maximization. The proposed solution effectively identifies the key variables that significantly influence the subject's score and provides a reliable solution. Additionally, the solution offers an intuitive visualization that can assist psychologists in accurately interpreting shifts in trends and response distributions within a large amount of data in the two periods. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0)

关键词： Principal component analysis

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Equivariant Spatio-Temporal Attentive Graph Networks to Simulate Physical Dynamics

arXiv

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arXiv 2024年

作者： Wu, Liming Hou, Zhichao Yuan, Jirui Rong, Yu Huang, Wenbing Gaoling School of Artificial Intelligence Renmin University of China China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China Department of Computer Science North Carolina State University United States Tsinghua University China Tencent AI Lab China

Learning to represent and simulate the dynamics of physical systems is a crucial yet challenging task. Existing equivariant Graph Neural Network (GNN) based methods have encapsulated the symmetry of physics, e.g., translations, rotations, etc, leading to better generalization ability. Nevertheless, their frame-to-frame formulation of the task overlooks the non-Markov property mainly incurred by unobserved dynamics in the environment. In this paper, we reformulate dynamics simulation as a spatio-temporal prediction task, by employing the trajectory in the past period to recover the Non-Markovian interactions. We propose Equivariant Spatio-Temporal Attentive Graph Networks (ESTAG), an equivariant version of spatio-temporal GNNs, to fulfill our purpose. At its core, we design a novel Equivariant Discrete Fourier Transform (EDFT) to extract periodic patterns from the history frames, and then construct an Equivariant Spatial Module (ESM) to accomplish spatial message passing, and an Equivariant Temporal Module (ETM) with the forward attention and equivariant pooling mechanisms to aggregate temporal message. We evaluate our model on three real datasets corresponding to the molecular-, protein- and macro-level. Experimental results verify the effectiveness of ESTAG compared to typical spatio-temporal GNNs and equivariant GNNs. Copyright © 2024, The Authors. All rights reserved.

关键词： Message passing

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Hierarchical Encryption in a Residual Number System

Hierarchical Encryption in a Residual Number System

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International Conference on Advanced computer Information Technologies (ACIT)

作者： Igor Yakymenko Olesya Martyniuk Serhii Martyniuk Andrii Martyniuk Yurii Yakymenko Mykhailo Kasianchuk Department of Cyber Security West Ukrainian National University Ternopil Ukraine Department of Applied Mathematies West Ukrainian National University Ternopil Ukraine Department of Computer Science and Teaching Methods Ternopil Volodymyr Hnatiuk National Pedagogical University Ternopil Ukraine Faculty of Mechanics and Mathematics Taras Shevchenko National University of Kyiv Kyiv Ukraine

ISBN: (数字)9798350350043

ISBN: (纸本)9798350350050

Within this article, a method of symmetric encryption of information based on a hierarchical residue number system has been developed. The corresponding mathematical and algorithmic framework is presented. The proposed approach simplifies hardware implementation and enhances operational speed by reducing modulus sizes and consequently numerical operands at each subsequent level, as well as by parallelizing computational processes. Utilizing this encryption method enables the creation of reliable, efficient, and resistant-to-cryptanalysis systems for protecting information streams. A encryption/decryption scheme and an application example of the method are provided.

关键词： Hardware Encryption Reliability Information technology Streams

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The KANDY Benchmark: Incremental Neuro-Symbolic Learning and Reasoning with Kandinsky Patterns

arXiv

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arXiv 2024年

作者： Lorello, Luca Salvatore Lippi, Marco Melacci, Stefano Department of Computer Science University of Pisa Largo B. Pontecorvo 3 Pisa56127 Italy Department of Sciences and Methods for Engineering University of Modena and Reggio Emilia via Amendola 2 Reggio Emilia42122 Italy Department of Information Engineering and Mathematics University of Siena via Roma 56 Siena53100 Italy

Artificial intelligence is continuously seeking novel challenges and benchmarks to effectively measure performance and to advance the state-of-the-art. In this paper we introduce KANDY, a benchmarking framework that can be used to generate a variety of learning and reasoning tasks inspired by Kandinsky patterns. By creating curricula of binary classification tasks with increasing complexity and with sparse supervisions, KANDY can be used to implement benchmarks for continual and semi-supervised learning, with a specific focus on symbol compositionality. Classification rules are also provided in the ground truth to enable analysis of interpretable solutions. Together with the benchmark generation pipeline, we release two curricula, an easier and a harder one, that we propose as new challenges for the research community. With a thorough experimental evaluation, we show how both state-of-the-art neural models and purely symbolic approaches struggle with solving most of the tasks, thus calling for the application of advanced neuro-symbolic methods trained over time. © 2024, CC BY-NC-SA.

关键词： Curricula

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Use of digital platforms for enhancing academic performance. Systematic Review

Use of digital platforms for enhancing academic performance....

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International IEEE Conference and Workshop: Óbuda on Electrical and Power Engineering (CANDO-EPE)

作者： Cristina Anamaria Costescu Mălina Șogor Sabina Pălimariu Jozsef Katona Ioana Tufar Carla Maria Avram Special Education Department Babeș-Bolyai University Cluj-Napoca Romania Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Obuda University Budapest Hungary Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary GAMF Faculty of Engineering and Computer Science John Von Neumann University Kecskemet Hungary Department of Applied Quantitative Methods Faculty of Finance and Accountancy Budapest Business University Budapest Hungary

ISBN: (数字)9798331531119

ISBN: (纸本)9798331531126

The purpose of this study was to determine the effect of technology-based interventions on academic performance. To achieve this purpose a systematic review was conducted. Within the scope of this study EBSCO, Springer, ProQuest, science Direct, Wiley Online Library, Taylor and Francis Online, databases were scanned, and 18 studies were included in the review. Our results show that the iPad is the most preferred digital device for enhancing math and reading skills and significant results can be seen when used. Similar positive effects were observed with other technological devices, reinforcing the effectiveness of technology as a tool for supporting academic performance. This evidence underscores the value of incorporating digital devices in educational strategies, particularly for improving key academic skills.

关键词： Performance evaluation Power engineering Databases Personal digital devices Tablet computers Libraries

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ESCAPED: Efficient Secure and Private Dot Product Framework for Kernel-based Machine Learning Algorithms with Applications in Healthcare 35

ESCAPED: Efficient Secure and Private Dot Product Framework ...

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35th AAAI Conference on Artificial Intelligence, AAAI 2021

作者： Ünal, Ali Burak Akgün, Mete Pfeifer, Nico Methods in Medical Informatics Department of Computer Science University of Tuebingen Germany Translational Bioinformatics University Hospital Tuebingen Tuebingen Germany Statistical Learning in Computational Biology Max Planck Institute for Informatics Saarbrücken Germany

ISBN: (纸本)9781713835974

Training sophisticated machine learning models usually requires many training samples. Especially in healthcare settings these samples can be very expensive, meaning that one institution alone usually does not have enough. Merging privacy-sensitive data from different sources is usually restricted by data security and data protection measures. This can lead to approaches that reduce data quality by putting noise onto the variables (e.g., in Ε-differential privacy) or omitting certain values (e.g., for k-anonymity). Other measures based on cryptographic methods can lead to very time-consuming computations, which is especially problematic for larger multi-omics data. We address this problem by introducing ESCAPED, which stands for Efficient SeCure And PrivatE Dot product framework. ESCAPED enables the computation of the dot product of vectors from multiple sources on a third-party, which later trains kernel-based machine learning algorithms, while neither sacrificing privacy nor adding noise. We have evaluated our framework on drug resistance prediction for HIV-infected people and multi-omics dimensionality reduction and clustering problems in precision medicine. In terms of execution time, our framework significantly outperforms the best-fitting existing approaches without sacrificing the performance of the algorithm. Even though we only present the benefit for kernel-based algorithms, our framework can open up new research opportunities for further machine learning models that require the dot product of vectors from multiple sources. Copyright © 2021, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Sensitive data

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Intensionalizing Abstract Meaning Representations: Non-Veridicality and Scope 15

Intensionalizing Abstract Meaning Representations: Non-Verid...

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Joint 15th Linguistic Annotation Workshop and 3rd Designing Meaning Representations Workshop, LAW-DMR 2021

作者： Williamson, Gregor Elliott, Patrick Ji, Yuxin Department of Computer Science Emory University AtlantaGA30322 United States Department of Linguistics and Philosophy Massachusetts Institute of Technology CambridgeMA02139 United States Department of Quantitative Theory and Methods Emory University AtlantaGA30322 United States

ISBN: (纸本)9781954085855

Meaning Representation (AMR) is a graphical meaning representation language designed to represent propositional information about argument structure. However, at present it is unable to satisfyingly represent non-veridical intensional contexts, often licensing inappropriate inferences. In this paper, we show how to resolve the problem of non-veridicality without appealing to layered graphs through a mapping from AMRs into Simply-Typed Lambda Calculus (STLC). At least for some cases, this requires the introduction of a new role:content which functions as an intensional operator. The translation proposed is inspired by the formal linguistics literature on the event semantics of attitude reports. Next, we address the interaction of quantifier scope and intensional operators in so-called de re/de dicto ambiguities. We adopt a scope node from the literature and provide an explicit multidimensional semantics utilizing Cooper storage which allows us to derive the de re and de dicto scope readings as well as intermediate scope readings which prove difficult for accounts without a scope node. © 2021 Association for Computational Linguistics.

关键词： Semantics

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Time-Series Anomaly Detection of Mozi Malware in IoT Devices Using Arima and Local Outlier Factor

Time-Series Anomaly Detection of Mozi Malware in IoT Devices...

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International Symposium on Applied Computational Intelligence and Informatics ( SACI)

作者： Tünde Kaufman Jozsef Katona National Media and Infocommunications Authority Budapest Hungary Kandó Kálmán Faculty of Electrical Engineering Institute of Electronics and Communication Systems Obuda University Budapest Hungary Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary GAMF Faculty of Engineering and Computer Science John Von Neumann University Kecskemet Hungary Department of Applied Quantitative Methods Faculty of Finance and Accountancy Budapest University of Economics and Business Budapest Hungary

ISBN: (数字)9798331515478

ISBN: (纸本)9798331515485

The increasing demand for IoT devices in industries has generated a high level of cybersecurity threats, with botnets including Mozi exploiting poor passwords and unpatched vulnerabilities to target networked infrastructure. Peer-to-peer (P2P) topology of Mozi, employing Distributed Hash Tables (DHTs) for Command-and-Control (C2) communications, makes it resistant to takedown operations and capable of high-scale Distributed Denial-of-Service (DDoS) attack, data exfiltration, and remote command execution. Traditional one-class, machine learning techniques such as OneClass Support Vector Machine (OCSVM) and Local Outlier Factor (LOF) have been widely used for discovering malwarerelated anomalies. However, such techniques require high labelled training datasets, whose availability in dynamically changing environments of malwares proves challenging. In this work, an AutoRegressive Integrated Moving Average (ARIMA)-based mechanism for discovering time-series anomalies in IoT-infected devices with Mozi infection is proposed. In an experimental setup using a Raspberry Pi running Pi-hole, baseline profiles of CPU consumption, RAM usage, and network activity are first established under normal conditions. Next, a system is subjected to a simulation of infection with Mozi malware and monitored for deviation with ARIMA forecasting and Local Outlier Factor (LOF) for discovering anomalies. By comparing predicted values with actual values, effective intrusion detection for malware-related anomalies is attained, providing a real-time, adaptive mechanism for early intrusion detection. In conclusion, with its effectiveness in discovering stealthy behaviour of malwares, employing ARIMA for discovering time-series anomalies in IoTinfected devices proves effective, and its employment in IoT cybersecurity aids in providing real-time mechanism for discovering threats for system admins and security professionals. As future work, a combination of ARIMA with deep neural networks such as Long

关键词： Training Botnet Intrusion detection Malware Real-time systems Peer-to-peer computing Topology Internet of Things Long short term memory Anomaly detection

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Convolutional Motif Kernel Networks

arXiv

引用

arXiv 2021年

作者： Ditz, Jonas C. Reuter, Bernhard Pfeifer, Nico Methods in Medical Informatics Department of Computer Science University of Tübingen Tübingen Germany

Artificial neural networks show promising performance in detecting correlations within data that are associated with specific outcomes. However, the black-box nature of such models can hinder the knowledge advancement in research fields by obscuring the decision process and preventing scientist to fully conceptualize predicted outcomes. Furthermore, domain experts like healthcare providers need explainable predictions to assess whether a predicted outcome can be trusted in high stakes scenarios and to help them integrating a model into their own routine. Therefore, interpretable models play a crucial role for the incorporation of machine learning into high stakes scenarios like healthcare. In this paper we introduce Convolutional Motif Kernel Networks, a neural network architecture that involves learning a feature representation within a subspace of the reproducing kernel Hilbert space of the position-aware motif kernel function. The resulting model enables to directly interpret and evaluate prediction outcomes by providing a biologically and medically meaningful explanation without the need for additional post-hoc analysis. We show that our model is able to robustly learn on small datasets and reaches state-of-the-art performance on relevant healthcare prediction tasks. Our proposed method can be utilized on DNA and protein sequences. Furthermore, we show that the proposed method learns biologically meaningful concepts directly from data using an end-to-end learning scheme. © 2021, CC BY.

关键词： Forecasting

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