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UPB Scientific Bulletin, Series C: Electrical Engineering and computer Science 2024年第2期86卷 61-72页

作者： Dorobăț, Ilie Cristian Posea, Vlad Boncea, Radu Dept. of Computer Science National University of Science and Technology POLITEHNICA Bucharest Romania National Institute for Research and Development in Informatics-ICI Bucharest Romania

The paper introduces the Cultural Leaf Semantic Portal whose aim is to provide a consolidated user-friendly solution for exploring the 36,132 Romanian artifacts and the 1,086 cultural entities that house them. Using the eCHO framework, the data collected from official sources are subjected to a transformation process through which they are sanitized, temporal expressions are normalized, thus reducing the number of forms in which the centuries and millennia are expressed from 1,733 to only 67, and then they are transformed into semantic structures. Finally, the resulting data is accessible via both a SPARQL endpoint and a semantic portal. © 2024, Politechnica University of Bucharest. All rights reserved.

关键词： Semantic Web

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Shared Parking Concept in the Smart City Environment 8th

Shared Parking Concept in the Smart City Environment

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8th International Congress on Information and Communication technology, ICICT 2023

作者： Špitálová, Zuzana Mandová, Lucia Opatovský, Martin Faculty of Informatics and Information Technology Institute of Computer Engineering and Applied Informatics Slovak University of Technology Bratislava Slovakia

ISBN: (纸本)9789819930906

With the modern city infrastructure and increasing number of subjects in the traffic environment, there is a need for increased number of parking places. As the parking place is usually not used for a whole day, it could be shared with the others in the certain community. As it eliminates the useless driving due to the finding of free parking place, the traffic becomes more effective and produces less emissions. We designed the application for smart city environment, which helps to achieve the described situation. It consists of two parts, the frontend and backend. The data processed by the backend part can be further used for the city infrastructure improvements. The front-end part, designed in React Native, represents the graphical interface. The back-end part, designed in PostgreSQL, represents the database. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Smart city

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A Deep Ensemble Learning Approach for Venomous & Non-Venomous Serpents and Insect Species Identification 3

A Deep Ensemble Learning Approach for Venomous & Non-Venomou...

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3rd IEEE Asian Conference on Innovation in technology, ASIANCON 2023

作者： Samarasinghe, Buddhin Saroj Lakmali, K.B.N. Informatics Institute of Technology Dept. of Computer Science Colombo 06 Sri Lanka Informatics Institute of Technology Department of Computer Science Colombo 06 Sri Lanka

ISBN: (纸本)9798350302288

In recent years snake and insect attacks have become a huge problem worldwide. Most species have similar colors and shapes, which makes it hard to tell them apart using typical techniques. Similarly, identifying different species of bees and wasps can be challenging, especially for non-experts in the field. Therefore, developing a reliable and effective method for recognizing these animals is essential to reducing the issues caused by snake and insect attacks and supporting wildlife conservation efforts. The proposed method in this research utilizes deep ensemble learning and transfer learning techniques to identify snakes, wasps, and bees accurately. Deep ensemble learning involves combining several machine learning models to make better predictions, and transfer learning is a technique that allows pre-trained models to be re-used for new tasks, saving time and computational resources. The proposed method utilizes deep ensemble learning and uses three base transfer learning models, DenseNet201, MobileN etV2, and InceptionResN etV2, for classification. The study obtained a training accuracy of 93%. The research classified 11 types of snakes, four types of bees, and four types of wasps, including all 19 classes. Moreover, the author developed a mobile application for user interaction and utilized Flask API for the logic tier. The mobile application enables users to take pictures or upload images of a snake, wasp, or bee and identify the species accurately. This feature makes the proposed method more accessible to people who encounter these animals daily. Overall, this research represents a promising step towards developing an accurate and effective system for identifying snakes, wasps, and bees, with practical implications for human safety and biodiversity conservation. © 2023 IEEE.

关键词： Learning systems

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Use of gamification and game-based learning in educating Generation Alpha: A systematic literature review

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Educational technology and Society 2024年第2期27卷 114-132页

作者： Fernando, Pumudu A. Premadasa, H. K. Salinda Department of Computing Informatics Institute of Technology Sri Lanka Centre for Computer Studies Sabaragamuwa University Sri Lanka

Children born after 2010 are labelled as members of Generation Alpha, who currently pursue their primary education. Gamification and game-based learning methodologies have gained popularity in the global education sector in the recent past. The goal of this study is to investigate the present state of adopting gamification and game-based learning for primary education student cohorts, with the aid of recent peer-reviewed research publications. By employing a systematic mapping design, reviewed paper results are categorized and further evaluated in relation to the attributes such as type of gamification, game mechanics and elements used, evaluation context, type of experimental outcome, gamified academic subject area, and type of application. With the aid of review results, this study proposes a mapping of gamification learning mechanics, suited to address Generation Alpha traits. It also recommends future research directions, which include investigating the realistic learning preferences of Generation Alpha, the need for designing novel educational game elements and adaptive gamification learning strategies, and the importance of long-term studies with diverse learner samples. The findings of this study will support prospective stakeholders, such as researchers, educators, parents, and educational policymakers, to utilize gamification approaches effectively in educating Generation Alpha. © (2023), (International Forum of Educational technology and Society). All Rights Reserved.

关键词： Adaptive learning Game-based learning Gamification Generation Alpha Primary education

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Optimized Price Prediction of Cryptocurrencies using Deep Learning on High-Volume Time Series Data 15

Optimized Price Prediction of Cryptocurrencies using Deep Le...

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15th International Conference on Information, Intelligence, Systems and Applications, IISA 2024

作者： Vonitsanos, Gerasimos Kanavos, Andreas Grivokostopoulou, Foteini Sioutas, Spyros University of Patras Computer Engineering and Informatics Department Patras Greece Ionian University Department of Informatics Corfu Greece Computer Technology Institute and Press 'Diophantus' Patras Greece

ISBN: (纸本)9798350368833

Statistical models, enhanced by deep learning techniques, have become pivotal in various predictive tasks, including financial forecasting. This paper addresses the challenge of predicting cryptocurrency prices, utilizing a dataset comprising various cryptocurrencies treated as time series. We employ several deep neural network architectures-including Multilayer Perceptrons (MLP), Recurrent Neural Networks (RNN), Long Short-Term Memory networks (LSTM), and Bidirectional LSTM networks-to forecast future prices. Our methodology involves a detailed analysis of cryptocurrency time series to inform the design of these networks. The performance of each model is rigorously compared, highlighting their predictive capabilities in the context of cryptocurrency markets. This study not only contributes to the empirical literature by applying advanced neural networks to high-volume financial data but also provides a comparative analysis that may guide future applications of deep learning in economic forecasting. © 2024 IEEE.

关键词： Costs

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Memoization on Shared Subtrees Accelerates Computations on Genealogical Forests 24

Memoization on Shared Subtrees Accelerates Computations on G...

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24th International Workshop on Algorithms in Bioinformatics, WABI 2024

作者： Hübner, Lukas Stamatakis, Alexandros Institute of Theoretical Informatics Karlsruhe Institute of Technology Germany Heidelberg Institute for Theoretical Studies Germany Institute of Computer Science Foundation for Research and Technology-Hellas Heraklion Greece

ISBN: (纸本)9783959773409

The field of population genetics attempts to advance our understanding of evolutionary processes. It has applications, for example, in medical research, wildlife conservation, and - in conjunction with recent advances in ancient DNA sequencing technology - studying human migration patterns over the past few thousand years. The basic toolbox of population genetics includes genealogical trees, which describe the shared evolutionary history among individuals of the same species. They are calculated on the basis of genetic variations. However, in recombining organisms, a single tree is insufficient to describe the evolutionary history of the whole genome. Instead, a collection of correlated trees can be used, where each describes the evolutionary history of a consecutive region of the genome. The current corresponding state of-the-art data structure, tree sequences, compresses these genealogical trees via edit operations when moving from one tree to the next along the genome instead of storing the full, often redundant, description for each tree. We propose a new data structure, genealogical forests, which compresses the set of genealogical trees into a DAG. In this DAG identical subtrees that are shared across the input trees are encoded only once, thereby allowing for straight-forward memoization of intermediate results. Additionally, we provide a C++ implementation of our proposed data structure, called gfkit, which is 2.1 to 11.2 (median 4.0) times faster than the state-of-the-art tool on empirical and simulated datasets at computing important population genetics statistics such as the Allele Frequency Spectrum, Patterson's f, the Fixation Index, Tajima's D, pairwise Lowest Common Ancestors, and others. On Lowest Common Ancestor queries with more than two samples as input, gfkit scales asymptotically better than the state-of-the-art, and is thus up to 990 times faster. In conclusion, our proposed data structure compresses genealogical trees by storing shared subtree

关键词： algorithms ases bioinformatics population genetics

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Distributed computing and shared memory-based utility list buffer miner with parallel frameworks for high utility itemset mining

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International Journal of Business Intelligence and Data Mining 2023年第2期23卷 125-149页

作者： Atmaja, Eduardus Hardika Sandy Sonawane, Kavita Department of Computer Engineering St. Francis Institute of Technology Mumbai India Department of Informatics Sanata Dharma University Yogyakarta Indonesia

High utility itemset mining (HUIM) is a well-known pattern mining technique. It considers the utility of the items that leads to finding high profit patterns which are more useful for real conditions. Handling large and complex dataset are the major challenges in HUIM. The main problem here is the exponential time complexity. Literature review shows multi-core approaches to solve this problem by parallelising the tasks, but it is limited to single machine resources and also needs a novel strategy. To address this problem, we proposed new strategies namely distributed computing (DC-PLB) and shared memory (SM-PLB)-based utility list buffer miner with parallel frameworks (PLB). It utilises cluster nodes to parallelise and distribute the tasks efficiently. Thorough experiments with results proved that the proposed frameworks achieved better runtime (448 s) in dense datasets compared to the existing PLB (2,237 s). It has effectively addressed the challenges of handing large and complex datasets. © 2023 Inderscience Enterprises Ltd.

关键词： Data mining

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The Generalized Fixed Point Iteration Method for AVE

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Journal of Mathematical Research with Applications 2024年第6期44卷 837-849页

作者： Changfeng MA Jing KANG School of Data and Computer Science Xiamen Institute of Technology College of Mathematics and Informatics Fujian Normal University

In this paper, based on the previous published work by Ke et al.(2019) and Li et al.(2022), by using the matrix splitting technique, generalized fixed point iteration method(GFPI) is established to solve the absolute value equation(AVE). The proposed method not only includes SOR-like method, FPI method, MFPI method and so on, but also generates some special versions. Some convergence conditions of the proposed method with different iteration error norms are presented. Furthermore, methods corresponding to other splitting methods are studied in detail. The effectiveness and feasibility of the proposed method are confirmed by some numerical experiments.

关键词： absolute value equations fixed point iteration convergence

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Immersive English Online Teaching Model Using Original Film as Teaching Resources

International Journal of Emerging Technologies in Learning

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International Journal of Emerging Technologies in Learning 2023年第2期18卷 97-114页

作者： Song, Rongrong Otair, Mohammad School of Foreign Languages Xiamen Institute of Technology Xiamen China Faculty of Computer Sciences and Informatics Amman Arab University Amman Jordan

The traditional English teaching mode generally consists of reciting words, phrases and texts with high intensity, mechanically memorizing grammar and doing a lot of exercises. This way not only causes the majority of students lack of practical ability to use English, but also difficult to stimulate students’ interest in learning. In this study, an immersive on-line teaching mode of audio-visual film English is constructed. Compared with the general on-line teaching mode, the model has a learning state feedback module based on convolutional neural network and support vector machine. Teachers can pay attention to students’ learning state in time and adjust teaching strategies according to their learning state. Three classes of the same school and grade were selected for comparative experiment. Offline teaching, traditional online teaching and improved online teaching were adopted in one semester respectively. The effectiveness of the improved method was proved by analyzing the class status and final grades of students in the whole semester. Through network training and practical tests, compared with the students who adopt traditional online teaching, the students who adopt this mode have greatly improved their learning state and final exam scores. At a time when the epidemic is still lingering, this mode can provide a positive reference for the development of online teaching © 2023, International Journal of Emerging Technologies in *** Rights Reserved.

关键词： Students

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High-Quality Hypergraph Partitioning

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ACM Journal of Experimental Algorithmics 2023年第11期27卷 1-39页

作者： Schlag, Sebastian Heuer, Tobias Gottesbüren, Lars Akhremtsev, Yaroslav Schulz, Christian Sanders, Peter Karlsruhe Institute of Technology Institute of Theoretical Informatics Postfach 6980 Karlsruhe76128 Germany Heidelberg University Faculty for Mathematics and Computer Science Heidelberg69120 Germany

Hypergraphs are a generalization of graphs where edges (aka nets) are allowed to connect more than two vertices. They have a similarly wide range of applications as graphs. This article considers the fundamental and intensively studied problem of balanced hypergraph partitioning (BHP), which asks for partitioning the vertices into k disjoint blocks of bounded size while minimizing an objective function over the hyperedges. Here, we consider the two most commonly used objectives: the cut-net metric and the connectivity *** describe our open-source hypergraph partitioner KaHyPar which is based on the successful multi-level approach - driving it to the extreme of using one level for (almost) every vertex. Using carefully designed data structures and dynamic update techniques, this approach turns out to have a very good time-quality tradeoff. We present two preprocessing techniques - pin sparsification using locality-sensitive hashing (LSH) and community detection based on the Louvain algorithm. The community structure is used to guide the coarsening process that incrementally contracts vertices. Portfolio-based partitioning of the contracted hypergraph then already achieves a good initial solution. While reversing the contraction process, a combination of several refinement techniques achieves a good final partitioning. In particular, we support a highly-localized local search that can directly produce a k-way partitioning and complement this with flow-based techniques that take a more global view. Optionally, a memetic algorithm evolves a pool of solution candidates to an overall good *** evaluate KaHyPar for a large set of instances from a wide range of application domains. With respect to quality, KaHyPar outperforms all previously considered systems that can handle large hypergraphs such as hMETIS, PaToH, Mondriaan, or Zoltan. Somewhat surprisingly, to some extend, this even extends to graph partitioners such as KaHIP when considering the special case

关键词： Coarsening

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