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Enhancing Pretrained Multilingual Machine Translation Model with Code-Switching: A Study on Chinese, English and Malay Language 24

Enhancing Pretrained Multilingual Machine Translation Model ...

引用

13th International Conference on computing and Pattern Recognition, ICCPR 2024

作者： Liu, Haijing Seman, Noraini Department of Computer Engineering Taiyuan Institute of Technology Shanxi Taiyuan China College of Computing Informatics and Mathematics University Technology MARA Shah Alam Selangor Malaysia

ISBN: (纸本)9798400717482

In the field of multilingual machine translation, many pretrained language models have achieved the inspiring results. However, the results based on pretrained models are not yet very satisfactory for low-resource languages. This paper investigates how to leverage code-switching data to fine-tune pretrained multilingual machine translation model, in order to boost the performance of few-shot low resource machine translation. By utilizing a multilingual mixed corpus, the code-switching method can enhance the cross-linguistic generalization ability of the model and improve its overall understanding of the language. In this paper, on the smaller size model of FLORES-101 benchmark, we use the code-switching data augmentation method to achieve the results of benchmark's larger model on six direction pairs of three languages, Chinese, English and Malay. This paper studied various corpus mixture mechanisms to construct the data in code-switching, and the experimental findings show that the results using the code-switching fine-tuning model improve the spBLEU score by an average of 2 to 3 points over the results without code-switching. © 2024 Copyright held by the owner/author(s).

关键词： Machine translation

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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 ...

引用

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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Paddy Diseases Multi-Class Classification using CNN Variants 24

Paddy Diseases Multi-Class Classification using CNN Variants

引用

24th International Arab Conference on Information Technology, ACIT 2023

作者： Aziz, Danial Izzat Anwar Bin Abdul Yusoff, Marina Ibrahim, Nurain Alazaidah, Raed College of Computing Informatics and Mathematics Universiti Teknologi Mara Shah Alam Malaysia College of Computing Informatics and Mathematics Universiti Teknologi Mara Shah Alam Malaysia Zarqa University Faculty of Information Technology Department of Computer Science Zarqa Jordan

ISBN: (纸本)9798350384307

Agriculture is critical to ensuring and securing food supply for human consumption. Paddy crops are an agricultural practice that produces one of the highest food yields in agriculture. Paddy diseases must now be detected and diagnosed as soon as possible rather than later. Farmers currently use the naked eye to observe suspected infected paddies. The suspected infected paddy will then be sampled and sent to the laboratory for further examination. These methods are highly inefficient, time-consuming, and error prone. Most paddy farmers lack the knowledge to make an informed and accurate diagnosis of which paddies are infected by which diseases. This paper explores using deep learning algorithms to detect and classify various paddy diseases automatically. This method will take an image of paddy leaves and classify it based on the disease category indicated by the idea. The PaddyDoctor dataset from the Kaggle website was used to train and test the deep-learning models. Convolutional Neural Network (CNN) and Transfer Learning models, including MobileNet, MobileNetv2, and InceptionV3, have been constructed. The results of an experiment involving different dataset split ratios, learning rates, and batch sizes. CNN with MobileNetV2 outperformed CNN with MobileNetV1 and CNN with InceptionV3 by 98.05 percent, 80.08 percent, and 83.98 percent, respectively. © 2023 IEEE.

关键词： Agriculture Convolutional Neural Network Deep Learning Paddy Disease

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Parkinson's Disease Induced Gain Freezing Detection using Gated Recurrent Units Optimized by Modified Crayfish Optimization Algorithm 5

Parkinson's Disease Induced Gain Freezing Detection using Ga...

引用

5th International Conference on Mobile computing and Sustainable informatics, ICMCSI 2024

作者： Bacanin, Nebojsa Petrovic, Aleksandar Jovanovic, Luka Zivkovic, Miodrag Zivkovic, Tamara Sarac, Marko Singidunum University Department of Informatics and Computing Belgrade Serbia Singidunum University Faculty of Technical Sciences Belgrade Serbia School of Electrical Engineering Belgrade Serbia

ISBN: (纸本)9798350395235

Parkinson's disease belongs to the group of health problems that are incurable but can be mitigated if treated properly. While there is no way of curing the damage caused by the disease, patient's life quality can be improved if diagnosed and treated properly and on time. The role of artificial intelligence (AI) in medicine is increasing. Deep learning algorithms may be utilized to automatically detect freezing of gait episodes. This study is focused on Parkinson's disease diagnosis based on the disturbances in the patient's gait which is affected by this disease. A hybrid deep and machine learning AI solution that employs the gated recurrent unit (GRU) neural network optimized by a hybrid swarm intelligence solution between the crayfish optimization algorithm and the firefly algorithm has been proposed. The proposed solution is compared to other high-performing optimization algorithms to establish objective grounds for comparison. The proposed framework results in the overall best performance and confirms the made improvements. The best-constructed model attained an accuracy of 87.08%. © 2024 IEEE.

关键词： Patient treatment

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Comparative analysis of Start-up Success Rate Prediction Using Machine Learning Techniques 3

Comparative analysis of Start-up Success Rate Prediction Usi...

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3rd International Conference on Smart Technologies and Systems for Next Generation computing, ICSTSN 2024

作者： Belgaum, Mohammad Riyaz Viswanath, Ediga Kasi Chakradhar, Pavukolla Goud, Ediga Chenna Kesava Chandu, Kuruva G. Pullaiah College of Engineering and Technology Department of Computer Science and Engineering Kurnool India Multimedia University Faculty of Computing and Informatics Cyberjaya Malaysia

ISBN: (纸本)9798350391565

Software startups create innovative products or services using programming, which can lead to a need for products from potential customers. Researchers suggest that trial and error methodology can increase the success rate of these organizations by evaluating assumptions about client needs before fostering a product. However, programming new businesses typically do not require trial and error. The study aims to predict the success of startup companies in their early development stages by analyzing their success based on initial funding. A study of 106 unsuccessful software start-ups was conducted to predict the success rate. The experimentation progression model (X Pro) was developed to show that effective experimentation requires understanding trial and error, setting objectives, conducting tests, analyzing results, and acting based on new knowledge. The findings provide insights into supporting procedures and approaches that enhance trial- and-error reception in developing new business models. Researchers can become aware of potential factors leading to startup disappointment and take precautions to avoid them. The research focuses on using web-based open sources, including social networking services and the web, to model the task. The study uses machine learning techniques like Random Forest, Gradient Boost Classifier, XG Boost, and Ada Boost Classifier. The analysis provides insights into the types of useful signals discovered on the web and market mechanisms underlying the funding process. This approach offers a more comprehensive approach to predicting startup success. © 2024 IEEE.

关键词： Ada (programming language)

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The Role of Privacy and Security Concerns and Trust in Online Teaching: Experiences of Higher Education Students in the Kingdom of Saudi Arabia 16

The Role of Privacy and Security Concerns and Trust in Onlin...

引用

16th International Conference on Computer Supported Education, CSEDU 2024

作者： Almekhled, Basmah Petrie, Helen Department of Computer Science University of York Heslington East York United Kingdom College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia

ISBN: (纸本)9789897586972

Higher education institutions (HEIs) are increasingly using online teaching, particularly since the COVID-19 pandemic. Numerous digital technologies are now used in online teaching, such as videoconferencing for online classes. This has raised privacy and security concerns for students, as well as a reluctance to have webcams on during online classes. This study investigated the privacy and security concerns in online teaching of HEI students in the Kingdom of Saudi Arabia (KSA), as well as their trust in a range of actors and entities involved in online teaching. It also investigated their use of webcams and their reasons for having their webcams off during online classes. The study was conducted in the real-world context of online courses at a HEI in KSA. It found high levels of concern about online privacy in relation to the institution, but moderate levels in relation to instructors and classmates and in relation to online security. Complex, unexpected relationships were found between online privacy and security concerns and trust. As with previous research, students were reluctant to have their webcams on for a variety of reasons, often concerned with privacy of personal information. Only trust in instructors was a significant predictor of whether students were likely to have their webcams on during online classes. Copyright © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Students

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Few-Shot Object Detection for Classifying Reported Urban Problems 12

Few-Shot Object Detection for Classifying Reported Urban Pro...

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12th IEEE International Conference on Communication, Networks and Satellite, COMNETSAT 2023

作者： Novalina, Nadya Bhaswara, Irfan Dwiki Husna, Nur Laily Romadhotul Fitriyya, Annida Nasution, Bahrul Ilmi Nugraha, Yudhistira Kanggrawan, Juan Intan Telkom University School of Computing Bandung Indonesia Jakarta Smart City Department of Communications Informatics and Statistics Jakarta Indonesia

ISBN: (纸本)9798350341102

These days, one of the government-citizen engagements is a digital report submitted by citizens. Urban problem detection is a process to classify images reported in an application to be followed up by the Government. The detection is crucial for maintaining livable and sustainable cities. However, annotating large-scale datasets for object detection models is labor-intensive. This paper proposes a few-shot object detection model for urban problem detection, overcoming data limitations. Our model demonstrates promising performance across 13 selected categories, including 10 base classes and 3 novel classes, using a dataset from the Jakarta Kini (JAKI) application. We evaluate the model on familiar (base classes) and unseen (novel classes) categories, showcasing its generalization capabilities. With increased instances (n-shot), our model achieves high average precision (AP) scores, with the highest performance observed at 30-shot. The validation evaluation yields AP scores of 54.5% for all classes, 65.3% for base classes, and 71.6% for novel classes. Similarly, the test evaluation results in AP scores of 42.5% for all classes, 51.4% for base classes, and 69.8% for novel classes. The performance differences between the validation and test datasets stem from unique features and category challenges. Our few-shot object detection model holds promise for real-world urban problem detection and can be extended to other urban-related tasks and datasets. © 2023 IEEE.

关键词： Object detection

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DualFraud: Dual-Target Fraud Detection and Explanation in Supply Chain Finance Across Heterogeneous Graphs 28th

DualFraud: Dual-Target Fraud Detection and Explanation in ...

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28th International Conference on Database Systems for Advanced Applications, DASFAA 2023

作者： Wu, Bin Chao, Kuo-Ming Li, Yinsheng School of Computer Science Fudan University Shanghai China Department of Computing and Informatics Bournemouth University Bournemouth United Kingdom

ISBN: (纸本)9783031306778

In supply chain finance, detecting fraudulent enterprises and transactions is crucial to minimize financial loss. Enterprises and transactions have heterogeneous information and fraud labels, thus, leveraging such information well can simultaneously improve fraud detection performance in enterprise and transaction domains. This paper describes our newly proposed multitask learning framework, DualFraud, which detects fraudulent enterprises and transactions with explainability based on heterogeneous graphs in supply chain finance. The main contributions of this work are the proposed framework that can facilitate these two domains to share and enhance learning and modelling capabilities to improve fraud projection. The explainer component is attached to generate rich and meaningful explanations for risk controllers across enterprise and transaction graphs. Experiments on datasets prove the effectiveness of fraud detection and explainability in both enterprises and transactions. The proposed DualFraud outperforms the other methods in the selected criteria. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Supply chains

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Jointly Preserving Subject and Object Anonymity in Nearby Friends Querying for Curious Service Providers

Jointly Preserving Subject and Object Anonymity in Nearby Fr...

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2024 Cognitive Models and Artificial Intelligence Conference, AICCONF 2024

作者： Abul, Osman Department of Computer Science College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Department of Computer Engineering TOBB University of Economics and Technology Ankara Ankara Turkey

ISBN: (纸本)9798400716928

This work studies the urban area location privacy preserving location updates and nearby friends (NF) querying for the centralized proximity based services (PBSs). The urban area constraint forces the user mobility to be on the city road network, modeled as a weighted directed graph. The location privacy model exploits location anonymity through subgraph cloaking. Two kinds of location privacy issues have been identified and handled in a unified framework, (i) Subject Anonymity on location updates, i.e., providing location anonymity of the location updater (against the curious PBS provider), and (ii) Object Anonymity on NF querying, i.e., providing location anonymity of friends in NF query responses (against the curious query issuer). In the former, the user simulates the PBS provider's belief (a subgraph) on his location whereabouts, and locally blocks anonymity violating location update requests which is understood by a simulated attack. In the latter, as the NF query processor, the PBS provider simulates the user's mutual beliefs (subgraphs) on friends' location whereabouts obtained through previous NF queries. By a simulated attack, some friends' existence are suppressed from the NF query responses. An experimental evaluation on a real dataset has been conducted. © 2024 ACM.

关键词： Location

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