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Joint Ontology Workshops 2023, Episode IX: The Quebec Summer of Ontology, JOWO 2023

作者： Bella, Giampaolo Castiglione, Gianpietro Santamaria, Daniele Francesco Department of Mathematics and Computer Science University of Catania Viale Andrea Doria 6 Catania95125 Italy

Large documents written in juridical language are difficult to interpret, with long sentences leading to intricate and intertwined relations between the nouns. The present paper frames this problem in the context of recent European security directives. The complexity of their language is here thwarted by automating the extraction of the relevant information, namely of the parts of speech from each clause, through a specific tailoring of Natural Language Processing (NLP) techniques. These contribute, in combination with ontology development principles, to the design of our automated method for the representation of security directives as ontologies. The method is showcased on a practical problem, namely to derive an ontology representing the NIS 2 directive, which is the peak of cybersecurity prescripts at the European level. Although the NLP techniques adopted showed some limitations and had to be complemented by manual analysis, the overall results provide valid support for directive compliance in general and for ontology development in particular. © 2023 Copyright for this paper by its authors.

关键词： Ontology

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K-means Clustering for Large Data: Anomaly Detection in Supervisory Control and Data Acquisition Systems

K-means Clustering for Large Data: Anomaly Detection in Supe...

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2023 Congress in computer science, computer Engineering, and Applied Computing, CSCE 2023

作者： Stanford, Corwin Tanner, April Jackson State University Department of Mathematics & Statistical Sciences United States Jackson State University Department of Electrical & Computer Engineering and Computer Science United States

ISBN: (纸本)9798350327595

K-means is a common unsupervised method for partitioning low dimensional datasets. However, it is usually not used for higher dimensional datasets, such as those found in Supervisory Control and Data Acquisition (SCADA) systems. Here we examine its application to a large dataset for the purposes of detecting cyber attacks using anomaly-based intrusion detection in the Battle of the Attack Algorithms (BATADAL) dataset. Additionally, dimensionality reduction using Principal Component Analysis (PCA) is examined as a method of improving anomaly detection performance. Emphasis is placed on methods for selecting parameters without using information obtained from examination of attack labels. © 2023 IEEE.

关键词： BATADAL cyberattacks machine learning operational technology SCADA systems

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Information Centric Networking Based Cooperative Caching Framework for 5G Communication Systems

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computers, Materials & Continua 2024年第9期80卷 3945-3966页

作者： R.Mahaveerakannan Thanarajan Tamilvizhi Sonia Jenifer Rayen Osamah Ibrahim Khalaf Habib Hamam Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennai602105India Department of Computer Science and Engineering Panimalar Engineering CollegeChennai600123India Department of Computer Science and Engineering Sathyabama Institute of Science and TechnologyChennai600119India Department of Solar Energy Al-Nahrain Research Center for Renewable EnergyAl-Nahrain UniversityJadriyaBaghdad64074Iraq Faculty of Engineering Universitéde MonctonNew BrunswickE1A3E9Canada Hodmas University College Mogadishu521376Somalia Bridges for Academic Excellence Tunis1002Tunisia School of Electrical Engineering University of JohannesburgAuckland ParkJohannesburg2006South Africa

The demands on conventional communication networks are increasing rapidly because of the exponential expansion of connected multimedia *** light of the data-centric aspect of contemporary communication,the information-centric network(ICN)paradigm offers hope for a solution by emphasizing content retrieval by name instead of *** 5G networks are to meet the expected data demand surge from expanded connectivity and Internet of Things(IoT)devices,then effective caching solutions will be required tomaximize network throughput andminimize the use of ***,an ICN-based Cooperative Caching(ICN-CoC)technique has been used to select a cache by considering cache position,content attractiveness,and rate *** findings show that utilizing our suggested approach improves caching regarding the Cache Hit Ratio(CHR)of 84.3%,Average Hop Minimization Ratio(AHMR)of 89.5%,and Mean Access Latency(MAL)of 0.4 *** a framework,it suggests improved caching strategies to handle the difficulty of effectively controlling data consumption in 5G *** improvements aim to make the network run more smoothly by enhancing content delivery,decreasing latency,and relieving *** improving 5G communication systems’capacity tomanage the demands faced by modern data-centric applications,the research ultimately aids in advancement.

关键词： Information-centric networking caching schemes 5G communication non-negative matrix factorization(NMF) weighted clustering algorithm

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From Infants to AI: Incorporating Infant-like Learning in Models Boosts Efficiency and Generalization in Learning Social Prediction Tasks

arXiv

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arXiv 2025年

作者： Treger, Shify Ullman, Shimon Department of Computer Science and Applied Mathematics Weizmann Institute of Science

Early in development, infants learn a range of useful concepts, which can be challenging from a computational standpoint. This early learning comes together with an initial understanding of aspects of the meaning of concepts, e.g., their implications, causality, and using them to predict likely future events. All this is accomplished in many cases with little or no supervision, and from relatively few examples, compared with current network models. In learning about objects and human-object interactions, early acquired and possibly innate concepts are often used in the process of learning additional, more complex concepts. In the current work, we model how early-acquired concepts are used in the learning of subsequent concepts, and compare the results with standard deep network modeling. We focused in particular on the use of the concepts of animacy and goal attribution in learning to predict future events. We show that the use of early concepts in the learning of new concepts leads to better learning (higher accuracy) and more efficient learning (requiring less data). We further show that this integration of early and new concepts shapes the representation of the concepts acquired by the model. The results show that when the concepts were learned in a human-like manner, the emerging representation was more useful, as measured in terms of generalization to novel data and tasks. On a more general level, the results suggest that there are likely to be basic differences in the conceptual structures acquired by current network models compared to human learning. © 2025, CC BY.

关键词： Contrastive Learning

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Enhancing the Heart Disease Classification Using Multi-level Perceptron and Principal Component Analysis 1

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International Conference on Advanced Engineering, Technology and Applications, ICAETA 2024

作者： Jaiswal, Vanshika Gupta, Sanyam Gupta, Shilpa Kumar, Deepika Hameed, Alaa Ali Department of Computer Science and Engineering JIMS Engineering Management Technical Campus U.P. Greater Noida India Department of Computer Science and Engineering Bharati Vidyapeeth’s College of Engineering New Delhi India Department of Computer Engineering Faculty of Engineering and Natural Sciences Istinye University Istanbul34396 Turkey

ISBN: (数字)9783031709241

ISBN: (纸本)9783031709234

The ability to detect cardiac disease early is vital to saving lives. Heart attacks are one of the leading reasons for high death rates worldwide due to the high cost of identifying cardiac disorders, which is crucial to the healthcare industry, as well as the shortage of human and logistical resources. In order to find the best machine learning algorithm for early-stage heart disease prediction, a model is presented in this study. In order to train and evaluate the model based on the data for efficient decision-making, machine learning techniques are utilized. The goal of this research is to combine Principal Component Analysis and Multilevel Perceptron model to propose an updated diagnosis and classification model based on machine learning (ML) for predicting heart failure and heart disease detection. Using cutting-edge techniques and foundational classifiers like Support Vector machines, Random forests, and K-Nearest Neighbors, an empirical evaluation of the suggested methodology was carried out with accuracy, precision, recall, and F1-score serving as the evaluation criteria. The proposed method has shown the maximum accuracy of 95.1%. This innovative approach not only streamlines the diagnostic process but also offers personalized medical assistance, facilitating early detection and intervention in heart disease, thereby optimizing healthcare delivery and improving patient outcomes. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Support vector machines

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CHARACTERIZING OVERFITTING IN KERNEL RIDGELESS REGRESSION THROUGH THE EIGENSPECTRUM

arXiv

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arXiv 2024年

作者： Cheng, Tin Sum Lucchi, Aurelien Kratsios, Anastasis Belius, David Department of Mathematics and Computer Science University of Basel Switzerland Department of Mathematics and Statistics McMaster University Vector Institute Canada Faculty of Mathematics and Computer Science UniDistance Suisse Switzerland

We derive new bounds for the condition number of kernel matrices, which we then use to enhance existing non-asymptotic test error bounds for kernel ridgeless regression (KRR) in the over-parameterized regime for a fixed input dimension. For kernels with polynomial spectral decay, we recover the bound from previous work;for exponential decay, our bound is non-trivial and novel. Our contribution is two-fold: (i) we rigorously prove the phenomena of tempered overfitting and catastrophic overfitting under the sub-Gaussian design assumption, closing an existing gap in the literature;(ii) we identify that the independence of the features plays an important role in guaranteeing tempered overfitting, raising concerns about approximating KRR generalization using the Gaussian design assumption in previous literature. © 2024, CC BY.

关键词： Error analysis

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Survey semantic Arabic language in deep learning based recommendation system 17

Survey semantic Arabic language in deep learning based recom...

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17th International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2023

作者： Kadhem, Rasha Mallat, Souheyl Zrigu, Mounir University of ALQadisiyah Department of Computer Science Iraq Research Laboratory in Algebra Numbers Theory and Intelligent Systems Monastir Tunisia University of Monastir Faculty of Sciences of Monastir Department of Computer Science Tunisia

ISBN: (纸本)9798350338904

Due to problems, Arabic-speaking internet users have surged, although nothing is done on it. It is challenging to develop a repliable recognition system (RS) for cursive languages such as Arabic. Variations in text size, fonts, word semantics, user Arabic region, etc. complicate these issues. Deep learning models can model big datasets and handle them. Good features can be selected and learned consecutively by both CNNs and RNNs. In numerous studies, both of these neural networks have proven to be superior than their counterparts. This is true for text recognition, voice recognition, and several NLP tasks (NLP), but they are not qualified to deal with the semantics of the text, so we decided to find the best DL technique for semantic Arabic language from a lot of research to be a survey to other searchers. Our paper compared different algorithms and their accuracy. © 2023 IEEE.

关键词： Semantics

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AraDS: Arabic Datasets for Text Mining Approaches

AraDS: Arabic Datasets for Text Mining Approaches

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2023 International Conference on Smart Computing and Application, ICSCA 2023

作者： Yafooz, Wael M.S. Alsaeedi, Abdullah Emara, Abdel-Hamid M. College of Computer Science and Engineering Taibah University Computer Science Department Medina Saudi Arabia Al-Azhar University Faculty of Engineering Department of Computers and Systems Engineering Cairo11884 Egypt

ISBN: (纸本)9798350347050

With the digital transformation, life started depending on the digital world. Hence, there is a massive amount of unstructured textual data produced and accumulated faster. Such data used in many applications such sentiment analysis, topic modeling, summarization, classifications, and clustering. However, researcher's wastes time and effort in collecting data and constructing a dataset to examine and evaluate their models or algorithms. In the Universal Language of English, there are many benchmark datasets. The Arabic language lacks datasets in many domains. This paper introduces an Arabic dataset called AraDS. AraDS consist of three Arabic datasets namely;Arabic Dataset on Herbal Treatments for Diabetes (ADHTD), Arabic Multi-Classification Dataset (AMCD), and the Arabic Sentiment Dataset-Khat (ASDK). These datasets were collected from social media platforms such as YouTube. It contains user-generated comments and video metadata. AraDS publicly available dataset. The assessment of the annotation process has been carried out and evaluated using four methods. They are Cohen's kappa, three Arabic native speaker annotators, accuracy, and F-measure. AMCD and ASDK datasets are balanced, whereas ADHTD is an imbalanced dataset. Two datasets are sided with binary classes and the third one is a multi-class dataset. These datasets can be beneficial for many data, text mining, and sentiment analysis researchers to apply methods and algorithms. © 2023 IEEE.

关键词： Sentiment analysis

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Development of Voice Chatbot for E-Concierge System of Front Office Hotel

Development of Voice Chatbot for E-Concierge System of Front...

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2023 International Seminar on Application for Technology of Information and Communication, iSemantic 2023

作者： Gunawan, Alexander A.S. Gosjen, Daniel Ignasius Haryadi, Kelvin Hizkia Nicholas Adianti, Maria Pia Saputri, Hanis Amalia Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Faculty of Economics & Communication Hotel Management Department Jakarta11480 Indonesia

ISBN: (纸本)9798350339215

The rapid advancement of technology has simplified the process of accessing hotel information, but many hotel websites still lack robust customer support. Chatbots present a solution to reduce the workload of concierge staff. Our study aims to design a chatbot using Android Studio, Flutter, Dialogflow, and Firebase. This chatbot facilitates reservations, supports user interaction via menu-driven options, and delivers comprehensive hotel information. The Binus Hotel's chatbots have been fully integrated with Google Assistant, enabling guests to engage with them through Google Home and smartphones as a Voicebot. The Binus Hotel Chatbot not only alleviates the burden on concierge staff but also provides guests with in-depth hotel information. In conclusion, the Binus Hotel Chatbot emerges as an innovative communication medium for guests, streamlining the reservation process by offering timely and pertinent details. © 2023 IEEE.

关键词： Hotels

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Knowledge-aware Recommendations: Exploring the Interplay between Utility, Explanation Quality, and Fairness in Path Reasoning Methods 13

Knowledge-aware Recommendations: Exploring the Interplay bet...

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13th Italian Information Retrieval Workshop, IIR 2023

作者： Balloccu, Giacomo Boratto, Ludovico Cancedda, Christian Fenu, Gianni Marras, Mirko Department of Mathematics and Computer Science University of Cagliari V. Ospedale 72 Cagliari09124 Italy

Adopting Knowledge Graphs (KGs) in recommender systems has engendered the emergence of sophisticated techniques, such as path reasoning, designed to navigate KGs and model complex relationships. KGs enable the representation of intricate connections, while path reasoning approaches adeptly learn to traverse these graphs, constructing detailed user-product relationships by discerning reasoning paths linking recommended products with those previously experienced by users. These identified paths are subsequently converted into well-articulated textual explanations, facilitating a deeper and more comprehensive understanding for the users. Despite its potential, the field is hindered by disparate and insufficient evaluation protocols, complicating efforts to assess the impact of existing methodologies. In this paper, we summarize our previous work on replicating and evaluating three state-of-the-art path reasoning recommendation approaches, originally presented at prestigious conferences, using a standardized protocol based on two publicly available datasets and benchmarking them against other knowledge-aware techniques. Our analysis encompasses recommendation utility, explanation quality, and fairness considerations for both consumers and providers. This investigation offers a comprehensive overview of the progress in the field, emphasizing key challenges and potential avenues for future exploration. Source code is available at https://***/giacoballoccu/rep-path-reasoning-recsys. © 2023 Copyright for this paper by its authors.

关键词： Recommender systems

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