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47th ICT and Electronics Convention, MIPRO 2024

作者： Sirotić, Zlatko Sovilj, Siniša Oršulić, Matija Pripužić, Krešimir Istra Tech D.o.o. Pula Croatia Juraj Dobrila University Of Pula Faculty Of Informatics Pula Croatia University Of Zagreb Faculty Of Electrical Engineering And Computing Zagreb Croatia

ISBN: (纸本)9798350382495

This study aims to analyze covariance with non-generic parameters across seven renowned statically-typed object-oriented languages: C++, Eiffel, Java, C#, Scala, Kotlin, and Swift, presented in chronological order from oldest to newest. In the original examples, an attempt is made to apply covariance to two interconnected superclass/subclass pairs, specifically the Patient/HeartPatient and FoodForPatients / FoodForHeartPatients classes. Within a method of the Patient class, a parameter of the FoodForPatients class (or type) is introduced. We asked whether in the aforementioned languages, it is possible to create an overridden method in the HeartPatient subclass that utilizes the FoodForHeartPatients class in place of the FoodForPatients class. It is essential to emphasize that while the utilization of generic parameters and their covariance represents a viable solution, it is not considered the optimal one due to significant practical drawbacks. © 2024 IEEE.

关键词： Polymorphism

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Learning Algorithms Concepts in a Virtual Reality Escape Room

Learning Algorithms Concepts in a Virtual Reality Escape Roo...

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Proceedings of the International Convention MIPRO

作者： Emilia Haramina Mateo Paladin Zdravko Petričušić Fran Posarić Antun Drobnjak Ivica Botički Faculty of Electrical Engineering and Computing Zagreb Croatia

ISBN: (数字)9798350382501

ISBN: (纸本)9798350382518

Although the standard way to learn algorithms is by coding, learning through games is another way to obtain knowledge while having fun. Virtual reality is a computer-generated three-dimensional environment in which the player is fully immersed by having external stimuli mostly blocked out. In the game presented in this paper, players are enhancing their algorithms skills by playing an escape room game. The goal is to complete the room within the designated time by solving puzzles. The puzzles change for every playthrough with the use of generative artificial intelligence to provide every player with a unique experience. There are multiple types of puzzles such as. time complexity, sorting algorithms, searching algorithms, and code execution. The paper presents the results of a study indicating students’ preference for learning through gaming as a method of acquiring algorithms knowledge.

关键词： Codes Generative AI Virtual environments External stimuli Games Learning (artificial intelligence) Encoding

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A New Benchmark and Algorithm for Clothes-Changing Video Person Re-Identification

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IEEE Transactions on Information Forensics and Security 2025年 20卷 1993-2005页

作者： Wang, Likai Zhang, Xiangqun Han, Ruize Wei, Yanjie Wang, Song Feng, Wei Tianjin University College of Intelligence and Computing Tianjin300350 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518107 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China

Person re-identification (Re-ID) is a classical computer vision task and has significant applications for public security and information forensics. Recently, long-term Re-ID with clothes-changing has attracted increasing attention. However, existing methods mainly focus on image-based setting, where richer temporal information is overlooked. In this paper, we focus on the relatively new yet practical problem of Clothes-Changing Video-based Re-ID (CCVReID), which is less studied. First, given the dataset shortage, we build two new benchmark datasets for CCVReID problem, including a large-scale synthetic video dataset and a real-world one, both containing human sequences with various clothing changes. Moreover, we systematically study this problem by simultaneously considering the classical appearance feature and temporal feature contained in the video. We develop a dual-branch fusion framework that makes use of the information from both clothes-aware appearance feature and clothes-free gait feature. For better information fusion, a confidence-guided re-ranking strategy is proposed to adaptively balance the weight of these two categories of features. We have released the benchmark and code proposed in this work to the public at https://***/kkw98/CCVReID. © 2025 IEEE.

关键词： Reamers

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Early Detection of Down Syndrome Through Ultrasound Imaging Using Deep Learning Strategies - A Review 2

Early Detection of Down Syndrome Through Ultrasound Imaging ...

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2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Vidhyasagar, B.S. Madamanchi, Sree Chandana Kalimuthu, Sivakumar Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Build Environment and Information Technology SEGi University Damansara Faculty of Engineering Kuala Lumpur Malaysia

ISBN: (纸本)9798350328202

Another name for Down syndrome is Trisomy 21, which is a complex chromosomal disorder that poses a substantial challenge in prenatal care due to its widespread prevalence. An additional copy of chromosome 21 is the defining feature of this disorder, leading to numerous physical and cognitive impairments. Typical physical traits of Down syndrome include a flattened facial structure, most notably in the nose bridge region, as well as upward-slanted almond-shaped eyes and a shortened neck. Apart from these features, individuals with Down syndrome also experience a range of intellectual disabilities, spanning from mild to severe. These challenges manifest in various areas, such as expressive communication, verbal memory, and attention. Furthermore, children with this disorder may exhibit behavioral issues that require special attention and care. Early and accurate detection of this condition is essential for informed decision-making by expectant parents. This review explores an overview of the data sets and results of recent studies that have used deep learning and machine learning methodologies to create effective models for the identification of Down syndrome. © 2024 IEEE.

关键词： Chromosomes

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Dialect recognition based on improved GFCC with VMD and Teager energy operator cepstral coefficients 16

Dialect recognition based on improved GFCC with VMD and Teag...

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16th International Conference on Signal Processing Systems, ICSPS 2024

作者： Zhang, Liyan Qi, Chao Liu, Zengli Zhao, Xuanzhi Rong, Shaowei Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Kunming University of Science and Technology Computing Center Kunming650500 China

ISBN: (纸本)9781510689251

Aiming at the problems of low recognition rate of Chinese dialects and poor robustness in noisy environment, this paper proposes an improved feature extraction algorithm, which combines the Sparrow Search Algorithm (SSA) and Variational Mode Decomposition (VMD) with the improved Gamma-tone Frequency Cepstral Coefficients (GFCC) and Teager Energy Operator Cepstral Coefficient (TEOCC). Firstly, the VMD parameters are optimized by SSA, the dialect signal is decomposed into different modal components, and the correlation coefficients with the original signal are calculated. Then, the component of the dominant signal is screened according to the correlation, which is used to calculate the GFCC characteristics. Subsequently, the improved GFCC features are fused with TEOCC, and Principal Component Analysis (PCA) is used to reduce the dimension of the fusion features. Finally, the Gaussian mixture model-universal background model (GMM-UBM) is used for dialect recognition. The experimental results show that the proposed method can improve the recognition rate compared with the traditional GFCC and single feature in both noise-free and noise-free environments, and verify the effectiveness of the improved algorithm in dialect recognition. © 2025 SPIE.

关键词： Variational mode decomposition

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Deep Learning-Based E-Learning Solution for Identifying and Bridging the Knowledge Gap in Primary Education

Deep Learning-Based E-Learning Solution for Identifying and ...

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2023 International Research Conference on Smart computing and Systems engineering, SCSE 2023

作者： Arunoda, D.P.H. Walpola, S.R. Piumira, S.M.I. Athukorala, A.D.M.P. Thilakarathna, Thusithanjana Chandrasiri, Sanjeevi Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Department of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350341454

Educational teaching apps are primarily available in app stores to educate students in various contexts. Lack of educational resources, physical and mental health conditions, and poverty cause some students to skip school and move on to the next school grade without completing the course content of the previous grade. Most of the available apps focus on specific content to cover. The Smart Primary Education Tutor (SPET) teaching app specifically focuses on the missed content by analyzing their knowledge gap and providing lessons to cover the missed content. The main objective of SPET is to develop a methodology to identify the gap in student knowledge and fill the knowledge gap by teaching using smart techniques. SPET is determined to identify students' interactions (attention, emotions) with the system to identify students' ability to use the learning tool, identifying gaps in students' knowledge levels compared to their actual grades using activities and voice-based technologies, teaching to cover the knowledge gap by providing engaging activities and lessons and evaluating students by conducting a final assessment and analyze students' knowledge and performance obtained through the system. Students between the ages of 5 and 8 are targeted in the community to apply. The solution embeds deep learning-based models including attention classification models using head posture estimation, facial expression recognition, and eye gaze estimation, speech recognition models to identify provided verbal answers, handwriting recognition models to evaluate student performance, and smart teaching. The child emotion recognition model achieved 93% accuracy. The Attention span evaluation model achieved 85% accuracy. The handwritten numerical and English character data recognition model which detects answers for the final assessment paper achieved 85% percent of accuracy. © 2023 IEEE.

关键词： Students

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Vessel Registration Number Detection and Recognition System

Vessel Registration Number Detection and Recognition System

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2025 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops, WACVW 2025

作者： Fabijanić, Matej Magdalenić, Maja Obradović, Juraj Kapetanović, Nadir Ferreira, Fausto Mišković, Nikola University of Zagreb Faculty of Electrical Engineering and Computing Croatia

ISBN: (纸本)9798331536626

Accurate identification of ships is vital for global trade and maritime security. More specifically, reliable recognition of the vessel registration number helps in orderly navigation and port use for maritime transportation activities. The purpose of this research was to develop and analyze different methods for detecting vessel registration numbers with a monocular camera. We used existing OCR tools available online and modified them to improve their out-of-the-box performance. Three neural network-based text detection methods were developed and tested. All three methods use different text region detection modules for real scenes, while available OCR software is used in all of them for specific character recognition. The methods are compared using standard optical character recognition metrics. The best method that was developed uses a deep neural network model to detect a vessel, then another fine-tuned neural network model to detect a text region, coupled with deterministic image processing methods to improve picture quality. In conclusion, with an accuracy of 72.69% recognized characters of vessel registrations using a limited image dataset, we achieve satisfactory results. Further effort focusing on collecting and annotating diverse maritime environment datasets featuring footage of vessels with visible registration numbers is needed to have a more reliable and robust system. © 2025 IEEE.

关键词： Optical character recognition

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Anomaly Detection in Hotel Reviews: Applying Data Science for Enhanced Review Integrity 32

Anomaly Detection in Hotel Reviews: Applying Data Science fo...

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32nd Telecommunications Forum, TELFOR 2024

作者： Nikolic, Milena Stojanovic, Milos Marjanovic, Marina The Academy of Applied Technical and Preschool Studies Department of Information and Communication Technologies Niš Serbia Singidunum University Faculty of Technical Sciences Engineering Management and Electrical Engineering and Computing Belgrade Serbia

ISBN: (纸本)9798350391053

As the hotel industry increasingly relies on online reviews to attract and retain customers, distinguishing between genuine and fraudulent feedback has become critical. This paper presents a methodology for anomaly detection in hotel reviews by employing advanced exploratory data analysis (EDA). Key aspects of our analysis include evaluating sentiment and review similarity, uncovering unusual behavioral patterns, and identifying patterns in temporal posting trends and distributions. Time series analysis helps to recognize unusual spikes and shifts in user review activity. Additionally, we address issues such as misleading titles and ratings by tracing correlations among crucial review elements. This methodology provides actionable insights and recommendations for improving review filtering processes and addressing fraudulent content, offering a comprehensive analysis of online review patterns without relying on predictive modeling. © 2024 IEEE.

关键词： Data integrity

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Charting Uncharted Territory: Defining the Scope of the Metaverse in Education

Charting Uncharted Territory: Defining the Scope of the Meta...

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IEEE Education engineering (EDUCON)

作者： Bartol Boras Antun Drobnjak Ivica Boticki Department of Applied Computing Faculty of Electrical Engineering & Computing University of Zagreb Zagreb Croatia

ISBN: (数字)9798331539498

ISBN: (纸本)9798331539504

The metaverse is a concept that has recently emerged as a key topic of discussion across various industries, including education. Although there is no universally accepted definition of the metaverse, it is generally understood as a collective technology-supported virtual space that facilitates interactive communication, collaborative learning, and innovative content creation. This paper seeks to investigate multiple perspectives on the definition of the metaverse, placing special emphasis on its applications within the educational sector, and employing keyword analysis from relevant academic literature to highlight current trends and key insights. The need for this study stems from the potential of the metaverse to transform educational experiences by offering personalized, immersive, and engaging learning opportunities. Nonetheless, successful implementation of the metaverse in education requires overcoming a range of technical, security, and social challenges. Educational institutions must invest in the necessary infrastructure, training, and resources to effectively integrate these technologies into their curricula. This paper contributes to the body of research with a coherent overview of how the metaverse can be defined and applied in educational processes, and by analyzing key terms and trends, aims to highlight the essential technologies and approaches that shape this emerging field. The research findings indicate that the metaverse in education is increasingly linked to the development and application of cutting-edge technologies that foster richer interactive experiences. Key technologies such as virtual reality (VR) and augmented reality (AR), as well as artificial intelligence (AI), Internet-of-Things (IoT), blockchain technologies and others, are revolutionizing traditional educational paradigms by providing immersive environments where students can engage with content in new and innovative ways. Critical issues like ethical concerns, data privacy, AI b

关键词： Ethics Data privacy Metaverse Scalability Education Psychology Transforms Market research Blockchains Artificial intelligence

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An Ensemble-Based Extra Feature Selection Approach for Predicting Heart Disease 6th

An Ensemble-Based Extra Feature Selection Approach for Predi...

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6th International Conference on Recent Innovations in computing, ICRIC 2023

作者： Abubaker, Howida Singh, Jyotsna Muchtar, Farkhana Fattah, Salmah Faculty of Computing Universiti Teknologi Malaysia Johor Bahru Johor81310 Malaysia School of Technology Management and Engineering Narsee Monjee Institute of Management Studies Chandigarh Chandigarh India Faculty of Computing and Informatics Universiti Malaysia Sabah Jalan UMS Kota Kinabalu Sabah88400 Malaysia

ISBN: (纸本)9789819728381

Heart disease is a severe condition that has a significant impact on human life and is the leading cause of death in many countries. Clinicians can access clinical datasets to assist in diagnosing cardiovascular conditions and reduce the spread of this disease. Understanding these datasets and their patterns is essential in predicting the illness accurately. However, the proliferation of medical datasets in the era of big data has posed challenges for medical professionals in identifying the most relevant attributes for predicting heart disease. Therefore, this study aims to identify the most distinguishing characteristics within a high-dimensional dataset that will facilitate the accurate classification of patients with heart disease and non-patients with minimal complexity. The study conducted an experimental assessment of model performance generated through different classification algorithms, using both relevant features selected and the complete features set. An ensemble-based extra tree feature selection approach was employed for this purpose. The experiment utilized four datasets from reputable sources, employing various machine learning classification models with both complete and reduced feature subsets as inputs for the analysis. The evaluation of the classification models was based on accuracy, precision, and recall. The proposed feature selection technique achieved the highest classification accuracy, precision, and recall, reaching 97% when utilizing the extra tree classifier algorithm. These results underscore the potential of the feature selection method in identifying the most significant, related, and well-represented features for the target class, thereby enhancing classification performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Feature Selection

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