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Reasoning inWarded Datalog with Harmful Joins 30

Reasoning inWarded Datalog with Harmful Joins

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30th Italian Symposium on Advanced Database Systems, SEBD 2022

作者： Baldazzi, Teodoro Bellomarini, Luigi Sallinger, Emanuel Atzeni, Paolo Università Roma Tre Department of Computer Science and Engineering Rome Italy Banca d'Italia Italy TU Wien Faculty of Informatics Vienna Austria University of Oxford Department of Computer Science Oxford United Kingdom

Warded Datalog is a powerful member of the Datalog family of logic languages, featuring full support for recursion and existential quantification. As a result of the promising trade-off between expressive power and data complexity offered, it recently rose as a relevant candidate for ontological reasoning on large knowledge graphs and is employed as logic core of the Vadalog system, a well-known state-of-the-art reasoner. To achieve decidability and data tractability in practice over recursive settings, reasoners such as Vadalog adopt specialized strategies that control the effects of recursion and ensure reasoning termination with small memory footprint. However, exploiting the theoretical underpinnings of the Warded fragment, to enable these strategies, requires the settings not to contain "harmful" joins, i.e., on variables affected by existential quantification. To support reasoning decidability and the full expressive power of the language, we developed the Harmful Join Elimination, an algorithm that removes such joins while preserving the correctness of the task, and we integrated it into the Vadalog system. © 2022 CEUR-WS. All rights reserved.

关键词： Ontology

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An Overview of Vadalog: A System for Reasoning over Large Knowledge Graphs 30

An Overview of Vadalog: A System for Reasoning over Large Kn...

引用

30th Italian Symposium on Advanced Database Systems, SEBD 2022

作者： Bellomarini, Luigi Benedetto, Davide Sallinger, Emanuel Banca d'Italia Italy Università Roma Tre Department of Computer Science and Engineering Rome Italy TU Wien Faculty of Informatics Vienna Austria University of Oxford Department of Computer Science Oxford United Kingdom

As a main requirement, a knowledge representation and reasoning language must exhibit a good tradeoff between expressive power and computational complexity of reasoning. Warded Datalog, a fragment from the Datalog family, satisfies these requirements, by offering high expressive power and at the same time preserving tractability of query answering. The Vadalog system, a knowledge graph management system developed by the University of Oxford and Banca d'Italia, adopts Warded Datalog as its core language. This enables Vadalog to solve many ontological reasoning tasks including those where complex graph traversal operations are involved. To control graph navigation, Vadalog combines different rules prioritization policies and join implementations. In this paper, a short version of our recent contribution appeared in the Journal of Information Systems, we describe Vadalog from an architectural point of view, focusing on the execution model, graph traversal strategies, and join algorithms. We also provide an experimental evaluation of the system. © 2022 CEUR-WS. All rights reserved.

关键词： Knowledge graph

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A Framework for Democratizing Open-Source Decision-Making using Decentralized Autonomous Organization

A Framework for Democratizing Open-Source Decision-Making us...

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Computing and informatics (ICCI), International Conference on

作者： Khalifa H. Afifi Khaled M. Fouad Heba K. Aslan Faculty of Information Technology and Computer Science Nile University Cairo Egypt Faculty of Computers and Artificial Intelligence Benha University Faculty of Computers and Engineering New Mansoura University Egypt Informatics Department Electronics Research Institute (ERI) Cairo Egypt Center of Informatics Science Faculty of Information Technology and Computer Science Nile University Cairro Egypt

The open Source Software (OSS) became the backbone of the most heavily used technologies, including operating systems, cloud computing, AI, Blockchain, Bigdata Systems, IoT, and many more. Although the OSS individual contributors are the primary power for developing the OSS projects, they do not contribute to the OSS project's decisionmaking as much as their contributions in the OSS Projects development. This paper proposes a framework to democratize the OSS Project's decision-making using a blockchain-related technology called Decentralized Autonomous Organization (DAO). Using DAO, contributors get incentive tokens as a reward in return for contributions. The earned tokens can be used in the process of decision-making governance. This governance model tends to allow OSS individual contributors to have a public voice in the OSS project's development roadmap in particular, and the OSS decision making governance in general.

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ADOD: Adaptive Density Outlier Detection 24

ADOD: Adaptive Density Outlier Detection

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24th IEEE International Conference on Data Mining, ICDM 2024

作者： Qian, Li Qian, Jing Sun, Xin Guo, Wengang Bohm, Christian Institute of Informatics Ludwig Maximilian University of Munich Munich Germany University of Vienna Faculty of Computer Science Vienna Austria Carbon Neutral R&D Center China Railway Hi-Tech Industry Corporation Limited Beijing China City University of Macau Faculty of Data Science China College of Electronic and Information Engineering Tongji University Shanghai China

ISBN: (纸本)9798331506681

Outlier detection plays a dual role in data analysis: cleansing data to optimize the performance of downstream tasks and identifying potentially rare valuable events or patterns. Proximity-based methods, which are independent of data distribution assumptions, are plagued by parameter selection and performance challenges when handling data with varying densities. This study proposed a novel unsupervised algorithm named Adaptive Density Outlier Detection (ADOD) to address these challenges. The core innovation of ADOD involves two main aspects: adaptive neighborhood boundaries and density consistency scoring. First, instead of relying on a predefined fixed radius, ADOD employs perplexity to calculate the local scale of each data point. It then dynamically adjusts the neighborhood boundaries according to this scale to adapt to data with varying densities. Second, ADOD estimates local density using a mutual neighbor graph and combines the density differences between data points and their neighbors to compute outlier scores, effectively distinguishing outliers that significantly deviate from their surroundings. This study evaluated ADOD on one synthetic and 32 real datasets, and compared it with 14 classical and state-of-the-art algorithms from different categories. Extensive experimental results demonstrated the superior performance of ADOD, achieving the highest average accuracy across ROC, P@N, and AP metrics. This study promotes the development of outlier detection techniques and expands their potential for real-time applications. © 2024 IEEE.

关键词： Adversarial machine learning

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Optimal selection of seed nodes by reducing the influence of common nodes in the influence maximization problem 13

Optimal selection of seed nodes by reducing the influence of...

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13th International Conference on Information and Knowledge Technology, IKT 2022

作者： Kazemzadeh, Farzaneh Safaei, Ali Asghar Mirzarezaee, Mitra Islamic Azad University Science and Research Branch Department of Computer Engineering Tehran Iran Tarbiat Modares University Faculty of Medical Sciences Department of Medical Informatics Tehran Iran

ISBN: (纸本)9798350320626

The Internet and social networks play an important role in today's world. Many large companies and organizations use the Internet to manage and implement their activities. Moreover, businesses in these organizations have been significantly expanded through social networks and the appropriate conditions provided by these networks for extensive advertising among a large number of users. Influence maximization (IM) can further spread news and advertisements in social networks by the optimal discovery of a set of k-member of users who have the most diffusion in the social network. Due to its importance, many researchers have investigated this problem and presented algorithms that cannot be used in the real world because they are not optimal and lack proper efficiency. Therefore, this paper reviews and studies the influence maximization problem (IMP) to provide an optimal solution to present an algorithm. The optimal solution is provided by the IBN algorithm at a suitable time. This algorithm prevents the diffusion to be limited among some nodes with common neighbors in a dense part of the network. This method solves the algorithm and selects a set of K members of influential nodes in two steps by scoring for each node and adjusting the scores according to the number of common neighbors between the nodes. The results obtained from this algorithm show that the proposed idea has an optimal solution for this problem, including very high accuracy in selecting the optimal nodes and suitable execution time compared to other algorithms. © 2022 IEEE.

关键词： Optimal systems

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The Impact of Weather Conditions on Renewable Energy Production: A Machine Learning Approach

The Impact of Weather Conditions on Renewable Energy Product...

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IEEE International Conference on Big Data

作者： Recep Özbay Merve Çelebi Uraz Yavanoğlu Dept. Of Computer Forensics Graduate School of Informatics Gazi University Ankara Turkey Dept. Of Computer Education and Instructional Technologies Faculty of Education Hatay Mustafa Kemal University Hatay Turkey Dept. of Computer Engineering Faculty of Engineering Gazi University Ankara Turkey

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

In recent decades, obtaining energy from wind, solar, and hydro energy has brought hope for a sustainable future. Machine learning (ML) significantly impacts the field of energy systems. ML techniques have emerged as a powerful tool for analyzing and optimizing energy systems. This approach can potentially reduce the cost of analyzing a device by focusing solely on the data generated during its operation. This paper investigates the application of regression models to predict renewable energy production based on weather conditions, aiming to enhance the reliability and efficiency of renewable energy systems. Experiments are performed using Decision Tree (DT), k-nearest Neighbors (KNN), Multiple Linear (MLR), Polynomial (PR), Random Forest (RF), and Support Vector (SVR) Regression algorithms. The performances of ML models are compared based on the evaluations obtained through the Coefficient of Determination (R2), and error measures namely Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), and Root Mean Square Error (RMSE). RF is the most successful algorithm in predicting renewable energy production depending on weather conditions as it has the highest R2 value and the lowest error measures.

关键词： Radio frequency Renewable energy sources Costs Wind energy Biological system modeling Measurement uncertainty Production Nearest neighbor methods Prediction algorithms Reliability

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Analysis of YouTube Video Comments with NLP Methods

Analysis of YouTube Video Comments with NLP Methods

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International Conference on Electronics, computers and Artificial Intelligence (ECAI)

作者： Pirnau Mironela Priescu Iustin Priescu Catalina Mihaela Pirnau Claudiu Ghiculescu Liviu Daniel Department of Informatics Faculty of Informatics “Titu Maiorescu” University Bucharest Romania Faculty of Automatic Control and Computer Science National University of Science and Technology POLITEHNICA Bucharest Bucharest Romania Manufacturing Engineering Department Faculty of Industrial Engineering and Robotics National University of Science and Technology POLITEHNICA Bucharest Bucharest Romania

ISBN: (数字)9798350371154

ISBN: (纸本)9798350371161

This study proposes methods that can be used to examine and interpret comments that users have made after watching videos on YouTube on a particular topic. YouTube tutorials are very popular among young people. They have become an important pillar in informal education, thus contributing to the rapid acquisition of skills and knowledge. Under these circumstances, we were interested in analysing the YouTube videos comments, knowing that the platform has a continuous increase in popularity, which is also due to the opportunities of sharing them. Since ChatGPT-themed YouTube videos have seen a significant surge in popularity since 2022, we were interested to analyse some videos that approach the topics of “ChatGPT, AI clone, AI robot and Deep Learning.”

关键词： Video on demand Social networking (online) Education Tutorials Chatbots Web sites Artificial intelligence

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Heuristic-based Reasoning on Financial Knowledge Graphs

Heuristic-based Reasoning on Financial Knowledge Graphs

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2022 Workshops of the EDBT/ICDT Joint Conference, EDBT/ICDT-WS 2022

作者： Baldazzi, Teodoro Benedetto, Davide Brandetti, Matteo Vlad, Adriano Bellomarini, Luigi Sallinger, Emanuel Università Roma Tre Department of Computer Science and Engineering Rome Italy TU Wien Faculty of Informatics Vienna Austria University of Oxford Department of Computer Science Oxford United Kingdom Banca d’Italia Italy

When reasoning over large knowledge graphs, Datalog+/- languages offer a good trade-off between expressive power and computational complexity. However, in case of considerably large inputs and in the presence of recursive rules, even state-of-the-art reasoners struggle to accomplish reasoning tasks, e.g. answering Boolean Conjuctive Queries (BCQs). To address this problem, we introduce the notion of heuristic-based reasoning, that is, evaluating Datalog+/- rules according to an order of the facts determined via a user-defined heuristic. We show that adopting a fitting heuristic that guides the reasoning process provides an optimized way to answer a BCQ. To achieve this behaviour in practice, we enrich Datalog+/- programs with a context-aware operator, which we name Dynamic Hint Operator. We apply our new methodology to efficiently solve the Close Link problem on the knowledge graph of Italian companies, a relevant financial problem that estimates the risk to grant a specific loan to a company that is backed by collateral issued by another company. © 2022 Copyright for this paper by its authors.

关键词： Knowledge graph

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Emotion Recognition from Video Frame Sequence using Face Mesh and Pre-Trained Models of Convolutional Neural Network

Emotion Recognition from Video Frame Sequence using Face Mes...

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International Seminar on Intelligent Technology and Its Applications (ISITIA)

作者： Derry Pramono Adi Eko Mulyanto Yuniarno Diah Puspito Wulandari Dept. of Electrical Engineering Faculty of Intelligent Electrical and Informatics Technology Institut Teknologi Sepuluh Nopember Surabaya Indonesia Dept. of Electrical Engineering Dept. of Computer Engineering Faculty of Intelligent Electrical and Informatics Technology Institut Teknologi Sepuluh Nopember Surabaya Indonesia Dept. of Computer Engineering Faculty of Intelligent Electrical and Informatics Technology Institut Teknologi Sepuluh Nopember Surabaya Indonesia

Emotions are a collection of subjective cognitive experiences and psychological and physiological characteristics that express a wide range of feelings, thoughts, and behaviors in human interaction. Emotions can be represented through several means, such as facial expressions, tone of voice, and behavior. Deep Learning (DL) research has focused on incorporating facial expressions. Images with facial expressions are commonly used as data input for the DL model. Unfortunately, most DL models in Facial Emotion Recognition (FER) use static images. This method does not take into consideration all conceivable facial expressions. The static image of facial expressions is insufficient for recognizing emotions, but a sequential image from a video is required. In this study, we extract MediaPipe’s face mesh feature, the state-of-the-art multidimensional expression key points embedded in the video image sequence. Furthermore, we feed sequence image data into the pre-trained Convolutional Neural Network (CNN) model. The data we used is from The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) with the emotion classes of “Anger,” “Fearful,” “Happy,” and “Sad.” For this specific FER task, we found that the best pre-trained CNN model achieved 92.8% accuracy (using the VGG-19 model), with the fastest runtime of $\sim2.3$ seconds (achieved using the SqueezeNet model).

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Applications of Particle Swarm Optimization Algorithms in Coordination between Power System Stabilizer and Flexible AC Transmission 21

Applications of Particle Swarm Optimization Algorithms in Co...

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21st IEEE International Symposium on Intelligent Systems and informatics, SISY 2023

作者： Moazzami, Majid Shahgholian, Ghazanfar Heydaran-Daroogheh-Amnyieh, Zahra Mosavi, Amirhosein Fathollahi, Arman Islamic Azad University Najafabad Branch Smart Microgrid Research Center Najafabad Iran Islamic Azad University Najafabad Branch Department of Electrical Engineering Najafabad Iran Islamic Azad University Dolatabad Branch Department of Computer Engineering Dolatabad Iran Obuda University John von Neumann Faculty of Informatics Budapest Hungary Aarhus University Department of Electrical and Computer Engineering Aarhus Denmark

ISBN: (纸本)9798350343366

Interconnected power systems require the damping of oscillations to ensure secure operation. Traditional power system stabilizers (PSSs) may, however, not be able to sufficiently dampen inter-Area oscillations in multi-machine energy networks. PSSs and flexible AC transmission systems (FACTS) devices are frequently utilized to improve system stability. Meta-heuristic algorithms using search strategies are utilized to find near-optimal solutions. These strategies typically rely on a fitness function to evaluate candidate solutions and are inspired by natural systems. In this work, we furnish a concise overview of particle swarm optimization algorithms applied for the coordinated arrangement of PSS and FACTS controllers, presenting new directions for researchers working on meta-heuristic algorithms and coordination design of FACTS and PSS controllers for inter-Area oscillation damping in power systems. © 2023 IEEE.

关键词： Soft computing

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