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Eliciting Semantic Types of Legal Norms in Korean Legislation with Deep Learning 1

12th International Joint Conference on Knowledge Discovery, Knowledge engineering and Knowledge Management, IC3K 2020

作者： lam, Ho-Pun Phan, Thi Thuy Hashmi, Mustafa The, Kiet Hoang Lo, Sin Kit Choi, Yongsun Data61 CSIRO Sydney Australia CMC Ciber Hanoi Viet Nam La Trobe Law School La Trobe University Melbourne Australia EM & AI JSC Da Nang Viet Nam School of Computer Science and Engineering UNSW Sydney Australia Inje University Gimhae Korea Republic of

ISBN: (数字)9783031146022

ISBN: (纸本)9783031146015

Automating information extraction from legal documents and formalising them into a machine understandable format has long been an integral challenge to legal reasoning. Most approaches in the past consist of highly complex solutions that use annotated syntactic structures and grammar to distil rules. The current research trend is to utilise state-of-the-art natural language processing (NLP) approaches to automate these tasks, with minimum human interference. In this paper, based on its functional features, we propose a taxonomy of semantic type in korean legislation, such as obligations, rights, permissions, penalties, etc. Based on this, we performed automatic classification of legal norms with a rule-based classifier using a manually labelled dataset formed by three korean acts, i.e., Insurance Business Act, Banking Act and Financial Holding Companies Act, of the Korean legislation (n= 1237 ) and a performance of F1= 0.97 was reached. In contrast, several supervised machine learning based classifiers were implemented and a performance of F-measure = 0.99 was achieved. © 2022, Springer Nature Switzerland AG.

关键词： laws and legislation

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Privacy-Preserving Quantum Two-Party Geometric Intersection

arXiv

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arXiv 2023年

作者： Liu, Wen-Jie Xu, Yong Yang, James C.N. Yu, Wen-Bin Chi, Lian-Hua Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science & Technology Nanjing210044 China School of Computer and Software Nanjing University of Information Science and Technology Nanjing210044 China Department of Computer Science and Information Engineering National Dong Hwa University Hualien974 Taiwan Department of Computer Science and Information Technology La Trobe University VIC3086 Australia

Privacy-preserving computational geometry is the research area on the intersection of the domains of secure multi-party computation (SMC) and computational geometry. As an important field, the privacy-preserving geometric intersection (PGI) problem is when each of the multiple parties has a private geometric graph and seeks to determine whether their graphs intersect or not without revealing their private information. In this study, through representing Alice’s (Bob’s) private geometric graph GA (GB ) as the set of numbered grids SA (SB ), an efficient privacy-preserving quantum two-party geometric intersection (PQGI) protocol is proposed. In the protocol, the oracle operation OA (OB ) is firstly utilized to encode the private elements of SA (a0, a1,, aM1) (SB (b0,b1,bN1)) into the quantum states, and then the oracle operation Of is applied to obtain a new quantum state which includes the XOR results between each element of SA and SB . Finally, the quantum counting is introduced to get the amount (t ) of the states ai bj equaling to 0, and the intersection result can be obtained by judging t 0 or not. Compared with classical PGI protocols, our proposed protocol not only has higher security, but also holds lower communication complexity. © 2023, CC BY.

关键词： Computational geometry

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Optimization of indirect wastewater characterization using led spectrophotometry: a comparative analysis of regression, scaling, and dimensionality reduction methods

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Environmental science and Pollution Research 2024年第42期31卷 54481-54501页

作者： Carreres-Prieto, Daniel Fernandez-Blanco, Enrique Rivero, Daniel Rabuñal, Juan R. Anta, Jose García, Juan T. Department of Engineering and Applied Techniques Centro Universitario de la Defensa Universidad Politécnica de Cartagena C/ Coronel López Peña S/N Base Aérea de San Javier Santiago de La Ribera Murcia30720 Spain Department of Computer Science and Information Technologies Universidade da Coruña CITIC A Coruña15071 Spain University of A Coruña A Coruña15071 Spain Civil Engineering School Universidade da Coruña CITEEC A Coruña15071 Spain Department of Mining and Civil Engineering Universidad Politécnica de Cartagena Cartagena30202 Spain

LED spectrophotometry is a robust technique for the indirect characterization of wastewater pollutant load through correlation modeling. To tackle this issue, a dataset with 1300 samples was collected, from both raw and treated wastewater from 45 wastewater treatment plants in Spain and Chile collected over 4 years. The type of regressor, scaling, and dimensionality reduction technique and nature of the data play crucial roles in the performance of the processing pipeline. Eighty-four pipelines were tested through exhaustive experimentation resulting from the combination of 7 regression techniques, 3 scaling methods, and 4 possible dimensional reductions. Those combinations were tested on the prediction of chemical oxygen demand (COD) and total suspended solids (TSS). Each pipeline underwent a tenfold cross-validation on 15 sub-datasets derived from the original dataset, accounting for variations in plants and wastewater types. The results point to the normalization of the data followed by a conversion through the PCA to finally apply a Random Forest Regressor as the combination which stood out These results highlight the importance of modeling strategies in wastewater management using techniques such as LED spectrophotometry. © The Author(s) 2024.

关键词： Chemical oxygen demand

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Tensor Extended Kalman Filter and its Application to Traffic Prediction

引用

IEEE Transactions on Intelligent Transportation Systems 2023年第12期24卷 13813-13829页

作者： Chang, Shih Yu Wu, Hsiao-Chun Kao, Yi-Chih San Jose State University Department of Applied Data Science San JoseCA95192 United States Louisiana State University School of Electrical Engineering and Computer Science Baton RougeLA70803 United States Yuan Ze University Innovation Center for Ai Applications Taoyuan32003 Taiwan National Yang Ming Chiao Tung University Information Technology Service Center Hsinchu30010 Taiwan

Traffic prediction is a very important mechanism in intelligent transportation systems for applications including routing planning and traffic control. In order to infer multifarious traffic information, one/two-relational traffic data in the vector/matrix form need to be expanded to multi-relational traffic data in an arbitrary tensor form. However, none of the existing approaches is capable of performing traffic prediction by characterizing and tracking the inherent nonlinear dynamics which are often encountered in realistic time-series analysis. Although the extended Kalman filter (EKF) has been proven to be quite promising in inferring nonlinear dynamics from time series, but the current EKF approach still suffers, unfortunately, from a serious drawback that state variables have to be represented in vector form. In fact, the characteristics of multi-relational states in practice can never been manifested accurately in practice and the performance of an EKF would be greatly restricted thereby. In this work, we introduce a new tensor extended Kalman filter (TEKF) approach to accommodate arbitrary input, output, and state variables all in arbitrary tensor forms. We also propose a new tensor-based expectation-maximization (EM) algorithm to estimate the nonlinear state-transition and observation-model mappings. The computational and memory complexities of the proposed TEKF approach are also studied in this paper. Finally, numerical experiments are conducted to evaluate the traffic prediction performance of the proposed new TEKF approach over the simulated and realworld traffic datasets in comparison with three other existing deep-learning prediction methods. © 2000-2011 IEEE.

关键词： Tensors

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Shape-aware contrastive deep supervision for esophageal tumor segmentation from CT scans

Shape-aware contrastive deep supervision for esophageal tumo...

引用

2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Jin, Qiangguo Cui, Hui Sun, Changming Huang, Jiapeng Xuan, Ping Xu, Yiyue Wang, Linlin Cao, Leilei Wei, Leyi Su, Ran Northwestern Polytechnical University School of Software Shaanxi China Yangtze River Delta Research Institute of Northwestern Polytechnical University Taicang China La Trobe University Department of Computer Science and Information Technology Melbourne Australia Csiro Data61 Sydney Australia Shantou University School of Engineering Department of Computer Science Guangdong China Shandong First Medical University Shandong Academy of Medical Sciences Shandong Cancer Hospital and Institute Department of Radiation Oncology Shandong China Zhejiang University Innovation Center of Yangtze River Delta Zhejiang China Shandong University School of Software Shandong China Tianjin University School of Computer Software College of Intelligence and Computing Tianjin China

ISBN: (纸本)9798350337488

Accurate tumor segmentation is crucial for esophageal cancer radiotherapy treatment planning. The low contrast among the esophagus, tumors, and surrounding tissues, and irregular tumor shapes limit the performance of automatic segmentation methods. In this paper, we aim to exploit the irregular shapes of tumors to facilitate accurate segmentation. We propose a simple and pluggable shape-aware contrastive deep supervision network (SCDSNet) with shape-aware regularization and voxel-to-voxel contrastive deep supervision. Specifically, the shape-aware regularization with an uncertainty minimization strategy encourages the precise predictions of an additional shape-aware head. The voxel-to-voxel contrastive deep supervision enhances the multi-scale shape-tumor contrast for better voxel-to-voxel prediction of shapes. The proposed method is simple and highly pluggable, which can easily be extended to other frameworks. Further, we establish a large in-house dataset on esophageal cancer to validate the effectiveness of our proposed method. The quantitative and qualitative experimental results demonstrate the effectiveness of SCDSNet on the esophageal cancer dataset. © 2023 IEEE.

关键词： Deep supervision Esophagus tumor segmentation Shape-aware regularization Voxel-to-voxel contrastive learning

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Adaptive Iterated Local Search Algorithm for Dynamic Patient Admission Scheduling Problems

SSRN

引用

SSRN 2023年

作者： Turky, Ayad Sabar, Nasser R. Song, Andy Hussain, Abir Liatsis, Panos Department of Computer Science College of Computing and Informatics University of Sharjah United Arab Emirates Department of Computer Science and Information Technology La Trobe University MelbourneVIC3086 Australia School of Computing Technologies RMIT University Melbourne Australia Department of Electrical Engineering University of Sharjah Sharjah United Arab Emirates Department of Electrical Engineering and Computer Science Khalifa University United Arab Emirates

Healthcare resource management is essential for ensuring the quality of patient care. However, it can be a complex and costly task. This work addresses the dynamic patient admission scheduling (PAS) problem, which is a challenging healthcare resource management problem. PAS aims to allocate patients to hospital beds within a planning horizon, subject to a variety of healthcare constraints. The goal is to maximize management efficiency and patient comfort, in order to improve medical treatment. In this work, we consider a practical variant of PAS, known as dynamic PAS (DPAS). DPAS takes into account several factors and constraints, such as the daily registration of new patients, urgent patients, uncertainties in stay lengths, operating theatre resources, and the surgery scheduling process. An effective and efficient adaptive iterated local search (AILS) algorithm is proposed to solve DPAS. To enable the search to efficiently explore the search space, the proposed AILS adaptively integrates a number of components. Two adaptive perturbation strategies are devised to locate unexplored areas in the search space. To effectively exploit the newly discovered areas, we propose an adaptive local search mechanism as an intensification strategy to find a high-quality solution. The proposed AILS algorithm is compared to benchmark problems used by existing algorithms. The experimental results demonstrate the effectiveness and efficiency of the proposed approach. Specifically, out of 30 tested instances, AILS obtains 17 of the best-known results by using less computational time. © 2023, The Authors. All rights reserved.

关键词： Learning algorithms

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Developing educational serious games as a proposal for university training in the area of Child computer Interaction

引用

International Journal of Child-computer Interaction 2024年 41卷

作者： Sanz, Cecilia Verónica Artola, Verónica Baldassarri, Sandra Institute of Research at Computer Science III-LIDI School of Computer Science National University of La Plata CIC Engineering Research Institute of Aragon (I3A) Department of Computer Science and Systems Engineering University of Zaragoza

Training in the area of CCI has become a current topic of debate. It is necessary to address the specificities related to this target audience in terms of ethical aspects, evaluation techniques and instruments and the appropriate approach to content, forms of interaction, among others. This work focuses on a project-based learning experience that is being developed with students in the area of computer science, who work on the development of educational serious games aimed at children. Different axes related to CCI training and educational game design are approached in a phase-based process and an analysis of cognitive residues that are addressed is also included. The project developed in 2023, related to healthy eating, is presented as an example of the experiences carried out with students. The results indicate that this type of proposals are attractive and motivating for the students involved, who participate in real projects with stakeholders and children. © 2024 Elsevier B.V.

关键词： Child–computer interaction Educational serious games Nutrition and healthy eating Project-based learning Tangible interaction

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Comprehensive study and analysis for StarGAN and StarGAN-v2 in the task of image generation 2

Comprehensive study and analysis for StarGAN and StarGAN-v2 ...

引用

2nd International Conference on Computing Innovation and Applied Physics, CONF-CIAP 2023

作者： Cai, Yuanan Li, Yepeng Zhang, Zhenyu Department of Computer Engineering Baskin School of Engineering University of California Santa CruzCA95064 United States Department of Cognitive Science University of California - San Diego La Jolla CA92092 United States Department of Information Technology Monash University Wellington Rd ClaytonVIC3800 Australia

With the continuous development of deep learning and artificial intelligence industry, more and more neural networks have been developed. Among them, one typical network widely used is StarGAN. It solves the problem of requiring multiple generators and discriminators for multiple domains and style transitions. In this article, we will analyze and explore StarGAN v1 and StarGAN v2. including StarGAN's development history, advantages and disadvantages in the application, etc. At the same time, we will compare StarGAN with other neural networks such as CycleGAN and MSGAN through some discrimination criteria, and clearly demonstrate the influence of different networks on the generation of image data sets through quantitative and qualitative analysis. In addition, we also found a representative code on some of the actual uses of StarGAN for hair replacement through online sources. Since this code may be used to confuse the use of StarGAN v1 and v2, we will also improve this code in the article and put forward suggestions for changes. In this paper, we carried out the specific analysis of StarGAN's loss function based on the existing data and put forward some relevant insights. By taking the data we searched for StarGAN, it can be found that StarGAN could use as few resources as possible to achieve clearer and more varied results during the image generation task © Published under licence by IOP Publishing Ltd.

关键词： Deep learning

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Adaptive Video Streaming: An AI-Driven Approach Leveraging Cloud and Edge Computing

Adaptive Video Streaming: An AI-Driven Approach Leveraging C...

引用

Artificial Intelligence, Blockchain, and Internet of Things (AIBThings), IEEE International Conference on

作者： Mahmoud Darwich Kasem Khalil Yasser Ismail Magdy Bayoumi Department of Mathematics and Computer Science University of Mount Union Alliance OH Department of Electrical and Computer Engineering University of Mississippi University MS Electrical Engineering Department Southern University and A&M College Baton Rouge LA Department of Electrical and Computer Engineering University of Louisiana at Lafayette LA

Video streaming efficiency remains a major challenge, with increasing demands for high-resolution content and minimal buffering times. We propose a novel solution to enhance user experiences. Our approach combines adaptive streaming, AI algorithms predicting network conditions, and edge computing to optimize task allocation. These technologies aim to reduce latency, lower network traffic, and facilitate seamless transitions in video quality. Our results improve the streaming services and enable real-time video applications in remote or mobile environments, presenting substantial implications for the future of video streaming.

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Orthogonal projection for anomaly detection in networking datasets

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Journal of Ambient Intelligence and Humanized Computing 2023年第6期14卷 7957-7966页

作者： Cortes-Polo, David Jimenez, Luis I. Paoletti, Mercedes E. Calle-Cancho, Jesus Rico-Gallego, Juan A. Department of Signal Theory and Communication University Rey Juan Carlos Cam. del Molino 5 Madrid Fuenlabrada28942 Spain Intelligent Systems Group Department of Computer Science University of Valladolid P.O de Belén 15 Valladolid47011 Spain Hyperspectral Computing Laboratory Department of Technology of Computers and Communication University of Extremadura Av. de la Universidad s/n Caceres10003 Spain Calle Sola 1 Extremadura Trujillo10200 Spain Computer and Telematics Systems Engineering University of Extremadura Av. de la Universidad s/n Caceres10003 Spain

In recent years, the impressive growth of new wireless technologies, together with the appearance of new requirements in applications and services, is progressively changing the use of networks. Due to the high mobility required, the network must adapt to the infrastructure to meet the demands of the users. As a result, service providers currently have to over-provision network capacity, which is costly. In addition, considering efficient resource planning in advance involves a lot of labor-intensive efforts. Consequently, network usage analysis is a very useful tool that allows network administrators to find patterns and anomalies. Whilst pattern detection provides administrators the ability to define the infrastructure, anomaly detection provides rich and valuable information for certain applications, for example, to avoid network saturation in urban areas during peak hours. This article proposes a new methodology based on orthogonal projections over Call Detail Records (CDR) for anomaly detection to help in the dynamic management of the network in an urban area. The method is evaluated in a real scenario provided by an Italian telecommunications operator, considering different locations in the Milan metropolitan area, differentiated by the geographic resolution of the data, reaching F1 scores above 0.8. In addition, a new ground truth is presented, hoping it will become a reference data set for the community, in the form of a set of locations that have been corroborated for use in evaluating anomaly detection techniques. © 2023, The Author(s).

关键词： Anomaly detection

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