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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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Designing Relocation Rules with Genetic Programming for the Online Container Relocation Problem 13

Designing Relocation Rules with Genetic Programming for the ...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Durasević, Marko Dumić, Mateja Gil-Gala, Francisco Javier University of Zagreb Faculty of Electrical Engineering and Computing Zagreb Croatia School of Applied Mathematics and Informatics J. J. Strossmayer University of Osijek Osijek Croatia University of Oviedo Department of Computing Gijon Spain

ISBN: (纸本)9798350308365

The container relocation problem (CRP) in shipping terminals is becoming increasingly important due to the growing amount of transferred goods. Until now, the most commonly investigated problem variant has been the offline CRP, in which the order in which the containers need to be retrieved is known beforehand. However, in many real-world situations this is not the case, which is modelled using the online CRP variant. In this variant, not all information is available from the beginning, but rather, it becomes available as the problem is being solved. Unfortunately, many traditional metaheuristic solution methods can not be applied to such a problem variant, which prompts the application of problem-specific heuristics called relocation rules (RRs). However, RRs are challenging to design manually, which prompted the application of genetic programming (GP) to design them automatically. Since GP was used only to design RRs for the offline problem variant, we apply GP to design relocation rules for the online variant in this study. The performance of GP is investigated under different levels of information availability to measure its performance. The results demonstrate that GP can evolve RRs that perform better than existing manually designed ones. Furthermore, the results show that in certain cases, rules generated for one level of information availability perform well for other levels, demonstrating that the evolved rules exhibit a good generalisation capability. © 2024 IEEE.

关键词： Genetic programming

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Constructing Ensembles of Automatically Designed Relocation Rules for the Container Relocation Problem 13

Constructing Ensembles of Automatically Designed Relocation ...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

ISBN: (纸本)9798350308365

Automated design of heuristics with genetic programming (G P) has, in recent years, become an intensively researched research area. One of the most recent applications of this methodology is to generate relocation rules (RRs) for the container relocation problem (CRP). CRP is an important combinatorial optimisation problem that is found in ship ter-minals and warehouses. RRs are simple constructive heuristic methods that provide a good solution quickly, thus representing an alternative to computationally expensive exact or metaheuris-tic methods. Even though the RRs designed by GP provide an improvement over existing manually designed rules, they have limited performance. An efficient way to improve the performance of RRs generated by GP is to use ensemble learning. In this study, we apply ensemble learning on RRs generated by GP for CRP to improve the performance of individual rules. We investigate how different ensemble sizes and combination methods affect the quality of the results, as well as which rules are selected to form ensembles. The experimental study shows that ensembles constructed out of automatically designed RRs significantly improve performance compared to the individual rules. © 2024 IEEE.

关键词： Combinatorial optimization

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Bilingual phrase induction with local hard negative sampling

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CAAI Transactions on Intelligence Technology 2025年第1期10卷 147-159页

作者： Hailong Cao Hualin Miao Weixuan Wang Liangyou Li Wei Peng Tiejun Zhao Faculty of Computing Harbin Institute of TechnologyHarbinHeilongjiangChina School of Informatics University of EdinburghEdinburghScotlandUK Huawei Noah's Ark Lab Hong KongChina IT Innovation and Research Center Huawei Technologies Co.Ltd.ShenzhenGuangdongChina

Bilingual lexicon induction focuses on learning word translation pairs,also known as bitexts,from monolingual corpora by establishing a mapping between the source and target embedding *** recent advancements,bilingual lexicon induction is limited to inducing bitexts consisting of individual words,lacking the ability to handle semantics-rich *** bridge this gap and support downstream cross-lingual tasks,it is practical to develop a method for bilingual phrase induction that extracts bilingual phrase pairs from monolingual corpora without relying on cross-lingual *** this paper,the authors propose a novel phrase embedding training method based on the skip-gram ***,a local hard negative sampling strategy that utilises negative samples of central tokens in sliding windows to enhance phrase embedding learning is *** proposed method achieves competitive or superior performance compared to baseline approaches,with exceptional results recorded for distant ***,we develop a phrase representation learning method that leverages multilingual pre-trained language *** mPLMs-based representations can be combined with the above-mentioned static phrase embeddings to further improve the accuracy of the bilingual phrase induction *** manually construct a dataset of bilingual phrase pairs and integrate it with MUSE to facilitate the bilingual phrase induction task.

关键词： artificial intelligence local hard negative sampling natural language processing phrase embedding pre-trained language models

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Intelligence COVID-19 Monitoring Framework Based on Deep Learning and Smart Wearable IoT Sensors

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Computers, Materials & Continua 2023年第10期77卷 583-599页

作者： Fadhil Mukhlif Norafida Ithnin Roobaea Alroobaea Sultan Algarni Wael Y.Alghamdi Ibrahim Hashem Information Assurance and Security Research Group(IASRG) Faculty of ComputingUniversiti Teknologi Malaysia(UTM)Johor BahruJohor81310Malaysia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia College of Computing and Informatics Department of Computer ScienceUniversity of SharjahSharjah27272United Arab Emirates

The World Health Organization(WHO)refers to the 2019 new coronavirus epidemic as COVID-19,and it has caused an unprecedented global crisis for several *** every country around the globe is now very concerned about the effects of the COVID-19 outbreaks,which were previously only experienced by Chinese *** of these nations are now under a partial or complete state of lockdown due to the lack of resources needed to combat the COVID-19 epidemic and the concern about overstretched healthcare *** time the pandemic surprises them by providing new values for various parameters,all the connected research groups strive to understand the behavior of the pandemic to determine when it will *** prediction models in this research were created using deep neural networks and Decision Trees(DT).DT employs the support vector machine method,which predicts the transition from an initial dataset to actual figures using a function trained on a *** short-term memory networks(LSTMs)are a special sort of recurrent neural network(RNN)that can pick up on long-term *** an added bonus,it is helpful when the neural network can both recall current events and recall past events,resulting in an accurate prediction for *** provided a solid foundation for intelligent healthcare by devising an intelligence COVID-19 monitoring *** developed a data analysis methodology,including data preparation and dataset *** examine two popular algorithms,LSTM and Decision tree on the official ***,we have analysed the effectiveness of deep learning and machine learning methods to predict the scale of the *** issues and challenges are discussed for future *** is expected that the results these methods provide for the Health Scenario would be reliable and credible.

关键词： Healthcare framework AI COVID-19 machine&deep learning LSTM RNN decision tree

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Steam players count prediction using GRU optimized by modified PSO 32

Steam players count prediction using GRU optimized by modifi...

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32nd Telecommunications Forum, TELFOR 2024

作者： Jovanovic, Luka Zivkovic, Miodrag Zivkovic, Tamara Bacanin, Nebojsa Dubljanin, Dobrivoje Malisic, Snezana Singidunum University Faculty of Technical Sciences Belgrade Serbia Singidunum University Faculty of Informatics and Computing Belgrade Serbia University of Belgrade School of Electrical Engineering Belgrade Serbia University of Belgrade Technical Faculty in Bor Bor Serbia

ISBN: (纸本)9798350391053

Accurate prediction of Steam players count is vital for efficient resource management, informed decision-making, and ability to maximize the potential of each game over its entire lifecycle. From the business point of view, this can directly affect profitability, player retention and strategic planning. This paper examines potential of GRU tuned by an improved version of renowned particle swarm optimization algorithm. The proposed model was placed into comparative simulations with other potent optimization techniques, and obtained experimental outcomes clearly pointed out the supreme performance of the suggested technique. © 2024 IEEE.

关键词： GRU metaheuristics algorithms players count prediction PSO Steam

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MSSThreD: Multitask Social Media Sentiment Analysis with Sarcasm for Public Security Threat Detection 11

MSSThreD: Multitask Social Media Sentiment Analysis with Sar...

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11th International Conference on Information Technology, Computer and Electrical Engineering, ICITACEE 2024

作者： Suhaimin, Mohd Suhairi Md Hijazi, Mohd Hanafi Ahmad Moung, Ervin Gubin Politeknik Kota Bharu College Education Department of Polytechnic and Community Kelantan Kota Bharu Malaysia Universiti Malaysia Sabah Data Technology and Applications Research Group Creative Advanced Machine Intelligence Research Centre Faculty of Computing and Informatics Sabah Malaysia Universiti Malaysia Sabah Data Technology and Applications Research Group Faculty of Computing and Informatics Sabah Malaysia

ISBN: (纸本)9798350389289

Public security threat detection is critical for maintaining safety of citizens from possible natural or non-natural disasters. Social media, with its vast and diverse content, serves as a significant source for communication and information dissemination. Early detection of threats through social media analysis can greatly benefit in prevention before the event, or response and rescue during the event. However, the noisy textual with linguistic nature of social media content presents substantial challenges for sentiment analysis. Coupling with sarcasm can reverse the polarity of sentiments, complicating threat detection efforts. To address these challenges, we introduce an application framework for public security threat detection that embeds multitask learning, incorporating sentiment analysis and sarcasm detection. This application utilized preprocessing and feature engineering techniques based on Natural Language Processing to capture the nuance of sentiment and sarcasm, with deep learning network model of Bidirectional Long Short-Term Memory and Gated Recurrent Unit combination. The sentiment filtering and identifying threat-related keywords, producing monitoring reports that highlight threats earlier than traditional web news sources, as evidenced by our case study results. This application demonstrates the practical utility and impact of the framework in enhancing public security through timely threat detection. © 2024 IEEE.

关键词： Long short-term memory

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Application of Speech Augmented Reality for Sabah Tourism Industry 1

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8th International Conference on Computational Science and Technology, ICCST 2021

作者： Saimon, Rianty Ng, Giap Weng Tanalol, Siti Hasnah Faculty of Computing and Informatics University Malaysia Sabah Sabah Kota Kinabalu Malaysia

ISBN: (数字)9789811685156

ISBN: (纸本)9789811685149

This work focuses on developing and researching the potential of hybrid application with speech and augmented reality technology to solve the problem that found in Sabah tourism industry. Tourism in Sabah, Malaysia in fact has been growing rapidly and becoming one of the most important industry in Sabah. Although tourism in Sabah is growing steadily, there are some problems found within the industry especially in term of technology advancement. First problem that found by the researcher is there still a few numbers of applications has been developed for Sabah Tourism. Second, lack of interaction between tourists and tourism place such as Museum Sabah, Mount Kinabalu and so on. There are two objectives involve in this research. First objective is to develop a prototype of speech AR application based on Sabah tourism industry. This include the information of the tourism places and 3D virtual object that represent the environment and landscape of tourism places in Sabah. Second objective is to evaluate the potential of developed prototype for Sabah tourism industry. To achieve the second objective, semi-structured interview was conducted to evaluate the potential of the hybrid application. The potential of application with speech and augmented reality technology determined based on the response from participants that involved in an interview. A great potential of an application can be concluded if positive response acquires from the participants. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Speech recognition

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Copy-Move Forgery Detection Optimization Using Harmony Search with Clonal Selection Algorithm 6

Copy-Move Forgery Detection Optimization Using Harmony Searc...

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6th IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2024

作者： Hijazi, Mohd Hanafi Ahmad Josmin, Ezlyn Sherlyn Data Technology and Applications Research Group Creative Advanced Machine Intelligence Research Center Faculty of Computing and Informatics Universiti Malaysia Sabah Kota Kinabalu Malaysia

ISBN: (纸本)9798350389692

In today's world, where technology is advancing rapidly, the extensive use of digital images on social media brings up concerns about their authenticity. One common form of deception is image forgery, especially copy-move forgery, where parts of an image are duplicated and inserted elsewhere within the same image. Traditional detection methods, like randomized search algorithms, often struggle with adaptability. This study presents a new approach that uses the Harmony Search with Clonal Selection Algorithm (HSCSA) to improve the detection of copy-move forgeries by fine -tuning the hyperparameters of Convolutional Neural Network (CNN) models. Our method involves preprocessing the CoMoFoD dataset, optimizing essential hyperparameters using HSCSA, and comparing the performance of the optimized model with standard CNN models. The results showed a notable improvement, with accuracy rising from 67.72% to 78.20%. © 2024 IEEE.

关键词： Convolutional neural networks

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Intrusion Detection System for Cyber-attacks in the Internet of Vehicles (IoV) Environment

Intrusion Detection System for Cyber-attacks in the Internet...

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Cyber Security and computing (CyberComp), International Conference on

作者： Mueez Hanifa H.T. Zubair Faculty of Computing and Informatics Multimedia University Cyberjaya Malaysia

ISBN: (数字)9798350387728

ISBN: (纸本)9798350387735

This report presents the development of an Intrusion Detection System (IDS) designed to detect cyber-attacks in an Internet of Vehicles (IoV) environment. The IDS employs machine learning techniques, specifically Support Vector Machines (SVM), and is implemented on two low-memory, low-speed microcontroller systems connected to a CAN bus. The model is trained on real-world data, sourced from a publicly available dataset collected from a Hyundai Avante CN7, using Python-based tools on a computer. The resulting coefficients and biases are extracted and embedded into a C program, which runs on the microcontrollers. The final implementation achieved an accuracy of 83%, with a precision of 97% and a recall of 68%.

关键词： Support vector machines Microcontrollers Tiny machine learning Intrusion detection Data models Real-time systems Hardware Security Cyberattack Internet of Vehicles

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