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2nd International Conference on Advances in Computing Research, ACR 2024

作者： Golding, Louis Polding, Robert Department of Data and Business Analytics School of Science and Technology IE University Madrid Spain

ISBN: (纸本)9783031569494

Artificial Intelligence (AI) technology is constantly advancing. Intelligent hardware and software that reason like human beings is beginning to affect many critical areas of human life. However, there are many challenges accompanying these innovations. The challenges that need to be vitally considered to get broader advantages of AI are technical and ethical. Learning, adoption, and retention are essential in the technical challenges that we face. To learn humans’ goals, AI must not merely reason about what we do, but also about why we do it. Humans carry out this task without realizing how complicated it is for computing systems to do. It is thus hard for AI systems to strictly digest our motives, but it could be conceivably argued that if intelligent systems are provided with huge amounts of data, they would possibly be able to come up with reasonable conclusions. With regards to adoption, even if AI systems can absorb our goals, there would be no assurance they can inevitably adopt them. To discuss retention, let’s assume an AI system that can learn and adopt our goals is constructed. Can these systems maintain these goals regardless of the endless enhancements these systems endure? When AI systems continue to be more intelligent, it is likely their potential to fulfill our goals will increase. However, their comprehension of the nature of these goals may progress ahead of our knowledge. Ethical challenges deal with expressing morals that AI systems should meet. In this case, it is hard to envision the types of morals that need to be encoded in AI systems, the people who will identify them, their accomplishments on an international scale, and employing consistent laws and morals in different countries and cultures. As a result, there is a great demand for sensible solutions to these requirements. For AI to be beneficial, these solutions should be sought promptly. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Intelligent systems

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Information Flows during Constraint Satisfaction Search with Dynamic Heuristics: Information from Preprocessing 1

Information Flows during Constraint Satisfaction Search with...

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1st International Workshop on Artificial Intelligence for Climate Change, 12th Italian Workshop on Planning and Scheduling, 31st RCRA Workshop on Experimental Evaluation of Algorithms for Solving Problems with Combinatorial Explosion, and SPIRIT Workshop on Strategies, Prediction, Interaction, and Reasoning in Italy, AI4CC-IPS-RCRA-SPIRIT 2024

作者： Wallace, Richard J. Insight Centre for Data Analytics School of Computer Science and IT University College Cork Ireland

We still do not have an adequate understanding of heuristic methods used for solving constraint satisfaction problems (CSPs). An example of this involves the effects of preprocessing, an essential means of improving CSP search. The traditional explanation for its beneficial effect is that the resulting "problem reduction" leaves fewer possibilities to explore. Recently, however, it was shown that when dynamic variable ordering heuristics are used, other factors related to domain size reduction are much more important. This paper extends this earlier work and clarifies what is involved. The key idea is that the pattern of domain reductions produced by preprocessing constitutes information that by itself enables heuristics to make better decisions. Treating domain reduction as a code and the set of reductions as a message of length n transmitted to the search algorithm, we can distinguish two factors, message discriminability and code quality. We provide evidence that greater problem reduction enhances both. © 2024 Copyright for this paper by its authors.

关键词： Constraint satisfaction problems

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A comprehensive study on fault tolerance in stream processing systems

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Frontiers of Computer science 2022年第2期16卷 80-97页

作者： Xiaotong WANG Chunxi ZHANG Junhua FANG Rong ZHANG Weining QIAN Aoying ZHOU School of Data Science and Engineering East China Normal UniversityShanghai 200062China Advanced Data Analytics Laboratory of Soochow University Suzhou 215006China

Stream processing has emerged as a useful technology for applications which require continuous and low latency computation on infinite streaming *** stream processing systems(SPSs)usually require distributed deployment on clusters of servers in face of large-scale of data,it is especially common to meet with failures of processing nodes or communication networks,but should be handled seriously considering service quality.A failed system may produce wrong results or become unavailable,resulting in a decline in user experience or even significant financial ***,a large amount of fault tolerance approaches have been proposed for *** approaches often have their own priorities on specific performance concerns,e.g.,runtime overhead and recovery ***,there is a lack of a systematic overview and classification of the state-of-the-art fault tolerance approaches in SPSs,which will become an obstacle for the development of ***,we investigate the existing achievements and develop a taxonomy of the fault tolerance in ***,we propose an evaluation framework tailored for fault tolerance,demonstrate the experimental results on two representative open-sourced SPSs and exposit the possible disadvantages in current ***,we specify future research directions in this domain.

关键词： fault tolerance performance evaluation stream processing

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Knowledge Pyramid Perspective of the Political data Ecosystem: A Case Study of Bhutan 16

Knowledge Pyramid Perspective of the Political Data Ecosyste...

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16th International Conference on Knowledge Management and Information Systems, KMIS 2024 as part of 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2024

作者： Namgay, Phub Wangdi, Pema Department of Informatics and Media Uppsala University Sweden School of Data Science and Data Analytics Sherubtse College Royal University of Bhutan Bhutan

ISBN: (纸本)9789897587160

This study examines the dynamics of data management and knowledge flow in the political data ecosystem through the lens of the Knowledge Pyramid. We used open-government electoral documents and polling data for granular insights into how data, information, knowledge, and wisdom (DIKW) are managed in Bhutan’s political data ecosystem. Bhutan’s electoral stakeholders and political parties manage and use DIKW of varying types, sizes, and complexities. In particular, political parties use information systems, websites, and social media to manage data and construct and use knowledge for political activities. Democracy is still young and gaining a foothold in Bhutan. The political parties do not employ complex data technologies and rich human resources to manage DIKW emanating from the political data ecosystem. Thus, scope exists for electoral stakeholders and political actors to explore and adopt effective and efficient knowledge management infrastructures to deal with DIKW elements in the political arena, namely the complex dynamics of turning raw data into higher elements of the Knowledge Pyramid. In addition to contributing to the knowledge management literature through an in-depth account of the DIKW aspects in the political space, this paper demonstrated the analytical and explanatory power of the Knowledge Pyramid for discourse on the political data ecosystem. © 2024 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Bhutan Democratic Electoral Process Knowledge Management Knowledge Pyramid Political data Ecosystem

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T³Planner: Multi-Phase Planning Across Structure-Constrained Optical, IP, and Routing Topologies

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IEEE Journal on Selected Areas in Communications 2025年第5期43卷 1823-1839页

作者： Hao, Yijun Yang, Shusen Li, Fang Zhang, Yifan Zhao, Cong Ren, Xuebin Zhao, Peng Xu, Chenren Wang, Shibo Xi’an Jiaotong University National Engineering Laboratory for Big Data Analytics Xi’an710049 China Xi’an Jiaotong University National Engineering Laboratory for Big Data Analytics Ministry of Education Key Laboratory for Intelligent Networks and Network Security Xi’an710049 China Peking University School of Computer Science Beijing100871 China The Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong National Engineering Laboratory for Big Data Analytics Xi'an Jiaotong University Xi'an710049 China

Network topology planning is an essential multi-phase process to build and jointly optimize the multi-layer network topologies in wide-area networks (WANs). Most existing practices target single-phase/layer planning, and are incapable of satisfying all rigorous topological structure constraints (e.g., dual-homing rings) defined by network standards and operators, especially in large-scale networks. These significantly limit their usability and performance in production networks. We consider a general topology planning problem with typical structure constraints over three essential phases (greenfield, reconfiguration, and site expansion) and topological layers (optical, IP, and routing topologies). We present, T3Planner, a novel practical solver to this problem in production. Specifically, we develop a structure-driven encoder based on graph neural network (GNN) for concise structure encoding, and design a new learning framework with optical-centric layer compression/reconstruction and rule-aided reinforcement learning (RL) for fast convergence and high performance. Extensive experiments on nine real topologies demonstrate that T3Planner scales to large optical networks with hundreds of sites, saves 46.6% cost, and supports 3.12x more demand when compared to related existing approaches. © 1983-2012 IEEE.

关键词： Network topology

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Synthetically Augmented Self-Supervised Fine-Tuning for Diverse Text OCR Correction 27

Synthetically Augmented Self-Supervised Fine-Tuning for Dive...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Guan, Shuhao Greene, Derek Insight Centre for Data Analytics Dublin School of Computer Science University College Dublin Ireland

ISBN: (纸本)9781643685489

The adoption of Optical Character Recognition (OCR) tools has been central to the increased digitization of historical documents. However, the errors introduced during OCR, particularly in texts with a specialized vocabulary (SV), necessitate effective post-OCR correction methodologies. This study introduces a novel approach that leverages weak supervision and self-supervised finetuning to enhance post-OCR correction without the need for substantial manual annotations. By using multi-noise-level synthetic data, generated through automatically-extracted OCR errors and applied to clean texts, we can train robust models tailored for post-OCR tasks. Furthermore, we propose a unique self-supervised fine-tuning strategy, applied specifically to long texts, enables models to adeptly handle out-of-vocabulary problems and SV. Additionally, we tested the performance of the GPT model on post-OCR tasks. © 2024 The Authors.

关键词： Optical character recognition

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Software Defect Detection Using Machine Learning on data from Open-Source Programs 14th

Software Defect Detection Using Machine Learning on Data fro...

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14th International Conference on Information, Intelligence, Systems, and Applications, IISA 2023

作者： Vasileiou, Maria Papageorgiou, George Tjortjis, Christos The Data Mining and Analytics Research Group School of Science and Technology International Hellenic University Thessaloniki Greece

ISBN: (纸本)9783031674259

To identify software defects, this paper looks at an effective machine learning (ML) approach. It focuses on distinguishing between software files that are defective and those that are not, and it also explores the idea of employing only structural code metrics to achieve that. To categorize files as defective or non-defective, two real-life datasets were used: one from the source code of twenty-six releases of Notepad++ and one from the source code of twenty-six releases of JuCi++ created using the LocMetrics tool. Using accuracy, precision, recall, F1-score, and their macro/weighted metrics, seven ML models—Decision Tree (DT), Support Vector Machine (SVM), Logistic Regression (LR), Naïve Bayes (NB), Random Forest (RF), and Extra Trees (ET)—were deployed and benchmarked. Randomized Grid Search (RGS) was utilized to optimize each model, followed by 10-fold Cross Validation (CV) for training. The models were assessed twice: first using test data and again using unseen data. The ET model surpassed the other models with an accuracy of 82.09% and continued to dominate the validation and evaluation processes, according to the results from the Notepad++ dataset. On the other hand, the results from the JuCi++ dataset showed that the LR model surpassed others in validation and evaluation procedures. Last, the most important predictors for Notepad++ were the type of source codes (presence of C++), BLOC, and SLOC-L, while for JuCi++ were the type of the file (hpp and cpp extension) and HCWORD, illustrating that the programming language, the clarity, and readability of the code (denoted by the number of blank lines), the logical source lines of code and the header files and their header comments play crucial roles in determining the likelihood of software defects. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Machine learning Software defect detection Structural code

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Traffic Congestion Prediction: A Machine Learning Approach 14th

Traffic Congestion Prediction: A Machine Learning Approach

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14th International Conference on Information, Intelligence, Systems, and Applications, IISA 2023

作者： Geromichalou, Olga Mystakidis, Aristeidis Tjortjis, Christos The Data Mining and Analytics Research Group School of Science and Technology International Hellenic University Thessaloniki Greece

ISBN: (纸本)9783031674259

In this study, in order to forecast traffic flow, we employed univariable and multivariable regression models, including Linear Regression (LR), Random Forest (RF), Multi-Layer Perceptron (MLP), Gradient Boosting (GB), Long short-term memory (LSTM), and Gated Recurrent Unit (GRU). With timestamp information being unavailable, we use temporal characteristics and seasonality time gaps between timeseries data to improve the results. GB outperformed the other models with regards to the coefficient of determination (R2), Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), as well as Explained Variance Score (EVS). One extra univariable approach was utilized, combining data from other highly correlated intersections. This strategy led to better models with regard to several metrics. All in all, the multivariable and highly correlated approach produced better results compared to univariable regression. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Adversarial machine learning

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English-to-Low-Resource Translation: A Multimodal Approach for Hindi, Malayalam, Bengali, and Hausa 9

English-to-Low-Resource Translation: A Multimodal Approach f...

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9th Conference on Machine Translation, WMT 2024

作者： Hatami, Ali Banerjee, Shubhanker Arcan, Mihael Buitelaar, Paul McCrae, John Philip Insight SFI Research Centre for Data Analytics Data Science Institute University of Galway Ireland ADAPT Centre Data Science Institute University of Galway Ireland Lua Health Galway Ireland School of Computer Science University of Galway Ireland

ISBN: (纸本)9798891761797

Multimodal machine translation leverages multiple data modalities to enhance translation quality, particularly for low-resourced languages. This paper uses a multimodal model that integrates visual information with textual data to improve translation accuracy from English to Hindi, Malayalam, Bengali, and Hausa. This approach employs a gated fusion mechanism to effectively combine the outputs of textual and visual encoders, enabling more nuanced translations that consider both language and contextual visual cues. The model’s performance was evaluated against the text-only machine translation model based on BLEU, ChrF2 and TER. Experimental results demonstrate that the multimodal approach consistently outperforms the text-only baseline, highlighting the potential of integrating visual information in low-resourced language translation tasks. ©2024 Association for Computational Linguistics.

关键词： Machine translation

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Testing Most Distant Neighbor (MDN) Variants for Semi-Factual Explanations in XAI 32

Testing Most Distant Neighbor (MDN) Variants for Semi-Factua...

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Workshops at the 32nd International Conference on Case-Based Reasoning, ICCBR-WS 2024

作者： Aryal, Saugat Keane, Mark T. School of Computer Science University College Dublin Dublin Ireland Insight Centre for Data Analytics Dublin Ireland

Recently, Semi-factual explanations have gained popularity in the eXplainable AI (XAI) community. They provide "even if" justifications to indicate what key input features could change without changing the outcome. Although several methods have now been proposed to compute semi-factuals, the instance-based, Most Distant Neighbors (MDN) method has emerged in recent comprehensive tests to be quite competitive, even though was originally proposed as a naive benchmark. However, on some metrics MDN comes bottom of the class (e.g., sparsity). In this paper, we explore nine variants of the MDN method to determine whether its performance on some metrics can be improved relative to older methods by performing comprehensive tests on key metrics. The results show that there are MDN variants that perform better on some key metrics, but that some of the historical methods still do better. © 2024 Copyright for this paper by its authors.

关键词： Most Distant Neighbor Semi-Factual XAI XCBR

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