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26th International Conference of the Catalan Association for Artificial Intelligence, CCIA 2024

作者： Angerri, Xavier Delgado, Oscar Gibert, Karina Knowledge Engineering and Machine Learning Group Intelligent Data Science and Artificial Intelligence Research Center Universtitat Politècnica de Catalunya Spain

ISBN: (纸本)9781643685434

When a knowledge Discovery from data (KDD) (Fayyad, Piatetsky-Shapiro, & Smyth, 1996) process is being applied to get knowledge, several methods could be used (Gibert, et al., 2018). A simple and fast way to obtain preliminary insights from data before using KDD models is by generating a basic descriptive analysis. It is one of the most popular ways to describe experimental data and should be the beginning of all data projects. Nevertheless some of the main knowledge that can be extracted in a descriptive analysis is hidden due to underlying multivariate structures which could be elicited through multivariate analysis techniques. Moreover, the domain expert is key for a proper interpretation of descriptive results. At the same time, there is a lack of automatic reporting techniques that can report and help in the interpretation of complex patterns and the use of advanced multivariate techniques. This paper shows the tool developed to generate automatic interpretation of Multiple Correspondence Analysis (MCA) and Principal Components Analysis (PCA) by using RMarkdown. This tool generates a Word document which contains the automatic interpretation of the results, built on the basis of regular expressions ellaborating over the R analytical outputs (either numerical or graphical results). The proposal is being applied with some real data, like INSESS database on social vulnerabilities of the Catalan population. In conclusion, the developed tool contributes to facilitate the factorial methods results, avoiding the misinterpretation of the results and the involuntary skipping of conclusions due to the large amount of knowledge that can be extracted from a complete factorial analysis. Also, this software enables non-expert users to read multivariate analysis results in a friendly way. Moreover, this tool saves time in the interpretation step and is a basis to support the expert to start the report with the results, even the output of the software could become the report or

关键词： automatic interpretation Automatic reporting explainability

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From Clustering to Intelligent Decision Support System: An Application to 3D Printing 26

From Clustering to Intelligent Decision Support System: An A...

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26th International Conference of the Catalan Association for Artificial Intelligence, CCIA 2024

作者： Karna, Ashutosh Gibert, Karina Knowledge Engineering and Machine Learning Group Intelligent Data Science and Artificial Intelligence Research Centre Universitat Politècnica de Catalunya Barcelona Spain

ISBN: (纸本)9781643685434

This study focuses on developing an intelligent decision support system (IDSS) that helps a human operator make data-driven decisions. To put IDSS in production, it is necessary to develop two additional components: one oriented to recognize the cluster of new data and the other a knowledge-based resulting from the interpretation of clusters and further association of actions to each cluster, constituting a knowledge base with the alerts and recommendations associated to every profile. Bootstrap-CURE technique is used to handle the initial component, whereas a meta-clustering framework is suggested for interpreting the clusters and providing recommendations. A detailed strategy is presented for handling a print job, examining patterns, and executing actions through IDSS, thus improving predictive accuracy and operational efficiency. Two distinct machine learning models were developed, one to detect the operational mode and another to choose the best meta-cluster for the type of printing jobs and detained steps are provided for implementing the recommendations. © 2024 The Authors.

关键词： Printing presses

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Iotpredictor: A Security Framework for Predicting Iot Device Behaviours and Detecting Malicious Devices Against Cyber Attacks

SSRN

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SSRN 2024年

作者： Kalaria, Rudri Kayes, A.S.M. Rahayu, Wenny Pardede, Eric Salehi, Ahmad S. La Trobe University VIC3086 Australia Data Engineering and Knowledge Management Group Australia

Securing Internet of Things (IoT) devices is paramount to mitigate unauthorised access and potential cyber threats, safeguarding the integrity of transmitted and processed data within interconnected devices. Identifying malicious IoT devices necessitates vigilant monitoring of network traffic, behaviour analysis, and implementing security measures, including Anomaly Detection Systems (ADSs), Intrusion Detection Systems (IDSs), and regular firmware updates. Traditional security approaches need to be revised for safeguarding IoT systems due to their inherent limitations in accommodating the resource-constrained nature of these *** introduce IoTPredictor, an advanced security approach designed to predict and detect malicious activities in IoT devices. Leveraging Hidden Markov Models (HMMs), IoTPredictor integrates an ADS to proactively detect and thwart attacks within the complex IoT-fog computing landscape. Our conceptual approach begins with categorising IoT devices into genuine, compromised, and counterfeit. We propose an HMM-based state transition model that captures potential transitions between states, such as normal, compromised, or counterfeit operations. We introduce an algorithm for estimating probabilities associated with next-state predictions to facilitate predictive ***, we present a formal approach for analysing communications between different states, enhancing the precision of the security framework. To validate the effectiveness of IoTPredictor, we conduct a series of experiments and provide a comprehensive evaluation. The results demonstrate the robustness and efficiency of our proposed security framework in predicting and preventing malicious activities, thereby contributing to the overall security enhancement of IoT devices within the complex IoT-fog computing network. © 2024, The Authors. All rights reserved.

关键词： Hidden Markov models

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Selecting Features by their Resilience to the Curse of Dimensionality

arXiv

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arXiv 2023年

作者： Stubbemann, Maximilian Hille, Tobias Hanika, Tom Knowledge & Data Engineering Group University of Kassel Hesse Kassel Germany

Real-world datasets are often of high dimension and effected by the curse of dimensionality. This hinders their comprehensibility and interpretability. To reduce the complexity feature selection aims to identify features that are crucial to learn from said data. While measures of relevance and pairwise similarities are commonly used, the curse of dimensionality is rarely incorporated into the process of selecting features. Here we step in with a novel method that identifies the features that allow to discriminate data subsets of different sizes. By adapting recent work on computing intrinsic dimensionalities, our method is able to select the features that can discriminate data and thus weaken the curse of dimensionality. Our experiments show that our method is competitive and commonly outperforms established feature selection methods. Furthermore, we propose an approximation that allows our method to scale to datasets consisting of millions of data points. Our findings suggest that features that discriminate data and are connected to a low intrinsic dimensionality are meaningful for learning procedures. Copyright © 2023, The Authors. All rights reserved.

关键词： Feature Selection

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Correction: Research topic flows in co‑authorship networks

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Scientometrics 2022年第9期128卷 5079-5080页

作者： Schäfermeier, Bastian Hirth, Johannes Hanika, Tom Knowledge and Data Engineering Group University of Kassel Kassel Germany

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HardnessTesterV: A Web Machine Learning application for Vickers Hardness Prediction of a Metallic Alloy Using Flask API 4

HardnessTesterV: A Web Machine Learning application for Vick...

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4th International Conference on Electrical, Communication and Computer engineering, ICECCE 2023

作者： Olanipekun, Ayorinde Tayo Mashao, Daniel Data Science Across Disciplines Research Group Institute for the Future of Knowledge Faculty of Engineering and the Built Environment University of Johannesburg Auckland Park South Africa Faculty of Engineering and the Built Environment University of Johannesburg Auckland Park South Africa

ISBN: (纸本)9798350369694

This research presents the HardnessTesterV app- a web application for predicting the Vicker hardness of Laser welded Metallic alloy using Flask API, HTML, and CSS to build the front end and back end. Vickers hardness is an important property in the manufacturing industry, and predicting it accurately is vital for material selection and design. The Web application takes some of the important processing parameters as the input features and uses a random forest Machine learning model to predict its Vickers hardness. Flask API was used to create a RESTful interface that provides a user-friendly platform for users to submit the combinations of the processing parameters and receive predictions. HTML and CSS were used to design the front end of the web application to provide a visually appealing interface for users. The application was deployed on a PythonAnywhere cloud server, making it easily accessible to users. The application was deployed on a cloud server, making it easily accessible to users worldwide. The accuracy of the predictions was evaluated using various metrics and the results showed that the developed model can accurately predict the Vickers hardness of metallic alloys. HardnessTesterV web app measures the Vickers hardness of 2507 Duplex stainless steel material. The web application is expected to be a valuable tool for engineers and researchers in the manufacturing industry for material selection and design. © 2023 IEEE.

关键词： Machine learning

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The Geometric Structure of Topic Models

arXiv

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arXiv 2024年

作者： Hirth, Johannes Hanika, Tom Knowledge & Data Engineering Group University of Kassel Kassel Germany Intelligent Information Systems University of Hildesheim Hildesheim Germany

Topic models are a popular tool for clustering and analyzing textual data. They allow texts to be classified on the basis of their affiliation to the previously calculated topics. Despite their widespread use in research and application, an in-depth analysis of topic models is still an open research topic. State-of-the-art methods for interpreting topic models are based on simple visualizations, such as similarity matrices, top-term lists or embeddings, which are limited to a maximum of three dimensions. In this paper, we propose an incidence-geometric method for deriving an ordinal structure from flat topic models, such as non-negative matrix factorization. These enable the analysis of the topic model in a higher (order) dimension and the possibility of extracting conceptual relationships between several topics at once. Due to the use of conceptual scaling, our approach does not introduce any artificial topical relationships, such as artifacts of feature compression. Based on our findings, we present a new visualization paradigm for concept hierarchies based on ordinal motifs. These allow for a top-down view on topic spaces. We introduce and demonstrate the applicability of our approach based on a topic model derived from a corpus of scientific papers taken from 32 top machine learning venues. Copyright © 2024, The Authors. All rights reserved.

关键词： Visualization

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A Cohesive Distillation Architecture for Neural Language Models

arXiv

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arXiv 2023年

作者： Wahle, Jan Philip Data & Knowledge Engineering Group Faculty of Electrical Information and Media Engineering Bergische Universität Wuppertal Germany

A recent trend in Natural Language Processing is the exponential growth in Language Model (LM) size, which prevents research groups without a necessary hardware infrastructure from taking part in the development process. This study investigates methods for knowledge Distillation (KD) to provide efficient alternatives to large-scale models. In this context, KD means the extraction of information about language encoded in a Neural Network and Lexical knowledge databases. To test our hypothesis that efficient architectures can gain knowledge from LMs and extract valuable information from lexical sources, we developed two methods. First, we present a technique to learn confident probability distribution for Masked Language Modeling by prediction weighting of multiple teacher networks. Second, we propose a method for Word Sense Disambiguation (WSD) and lexical KD that is general enough to be adapted to many LMs. Our results show that KD with multiple teachers leads to an improved training convergence. When using our lexical pre-training method, LM characteristics are not lost, leading to increased performance in Natural Language Understanding (NLU) tasks over the state-of-the-art while adding no parameters. Moreover, the improved semantic understanding of our model increased the task performance beyond WSD and NLU in a real-problem scenario (Plagiarism Detection). This study suggests that sophisticated training methods and network architectures can be superior over scaling trainable parameters. On this basis, we suggest the research area should encourage the development and use of efficient models and rate impacts resulting from growing LM size equally against task performance. © 2023, CC BY-SA.

关键词： Semantics

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The Birkhoff completion of finite lattices

arXiv

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arXiv 2024年

作者： Abdulla, Mohammad Hirth, Johannes Stumme, Gerd Knowledge & Data Engineering Group University of Kassel Germany Interdisciplinary Research Center for Information System Design University of Kassel Germany

We introduce the Birkhoff completion as the smallest distributive lattice in which a given finite lattice can be embedded as semi-lattice. We discuss its relationship to implicational theories, in particular to R. Wille's simply-implicational theories. By an example, we show how the Birkhoff completion can be used as a tool for ordinal data science. Copyright © 2024, The Authors. All rights reserved.

关键词： Formal concept analysis

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DC-Instruct: An Effective Framework for Generative Multi-intent Spoken Language Understanding

DC-Instruct: An Effective Framework for Generative Multi-int...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Xing, Bowen Liao, Lizi Huang, Minlie Tsang, Ivor W. Beijing Key Laboratory of Knowledge Engineering for Materials Science School of Computer and Communication Engineering University of Science and Technology Beijing China Singapore Management University Singapore Group Tsinghua University China CFAR and IHPC Agency for Science Technology and Research Singapore College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (纸本)9798891761643

In the realm of multi-intent spoken language understanding, recent advancements have leveraged the potential of prompt learning frameworks. However, critical gaps exist in these frameworks: the lack of explicit modeling of dual-task dependencies and the oversight of task-specific semantic differences among utterances. To address these shortcomings, we propose DC-Instruct, a novel generative framework based on Dual-task Inter-dependent Instructions (DII) and Supervised Contrastive Instructions (SCI). Specifically, DII guides large language models (LLMs) to generate labels for one task based on the other task's labels, thereby explicitly capturing dual-task inter-dependencies. Moreover, SCI leverages utterance semantics differences by guiding LLMs to determine whether a pair of utterances share the same or similar labels. This can improve LLMs on extracting and discriminating task-specific semantics, thus enhancing their SLU reasoning abilities. Extensive experiments on public benchmark datasets show that DC-Instruct markedly outperforms current generative models and state-of-the-art methods, demonstrating its effectiveness in enhancing dialogue language understanding and reasoning. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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