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Computing and Information Technology (ICCIT-1441), 2020 International Conference on

作者： Saeed Hamood Alsamhi Ammar Hawbani Mohan Timilsina Edward Curry Insight Centre for Data Analytics University of Galway Galway Ireland School of Computer Science Shenyang Aerospace University Shenyang China

ISBN: (数字)9798350353839

ISBN: (纸本)9798350353846

The emergence of the Metaverse as a new platform for human interaction could revolutionize several sectors. This paper provides a new “Metaverse Knowledge Graph” (MateKG) framework to enhance user interaction, data management, and knowledge representation in the virtual environment. The building and interacting MetaKGs is proposed to allow for efficient and successful data management, organization, and exploitation. The novel framework comprises several layers: infrastructure, data gathering, Knowledge Graph (KG), user interaction, evaluation and enhancement, and applications. We discuss several approaches, storage options, ontology designs for KG creation, and data-collecting sources. We look at customization strategies, accessibility concerns for the user interaction layer, and user interface design. Furthermore, we showcase case studies and MetaKG applications in e-commerce, smart cities, healthcare, and entertainment. We provide a starting point for creating reliable and efficient MetaKGs that serve a range of applications and domains.

关键词： Metaverse Smart cities Soft sensors Buildings Entertainment industry Knowledge graphs Medical services data collection Electronic commerce Reliability

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Energy-Efficient Multi-Objective Hybrid Flowshop Scheduling Problem with Blocking Constraints 9

Energy-Efficient Multi-Objective Hybrid Flowshop Scheduling ...

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9th International Conference on Control, Decision and Information Technologies, CoDIT 2023

作者： Missaoui, Ahmed Ozturk, Cemalettin O'Sullivan, Barry University College Cork Insight Centre for Data Analytics Cork Ireland Munster Technological University Process Energy Transport Engineering Cork Ireland School of Computer Science & It University College Cork Cork Ireland

ISBN: (纸本)9798350311402

The social context in relation to energy policies, energy supply, and sustainability concerns, as well as advances in more energy-efficient technologies is driving a need for a change in the manufacturing industry. In this paper, a hybrid ftowshop production system with blocking constraints is investigated from both sustainability and productivity dimensions. For an efficient solution to this proven NP-hard problem, we implemented a multi-objective iterated greedy algorithm first time to minimize the makespan and total energy consumption simultaneously. We provide small, medium, and large instances to benchmark our approach against non-dominated sorting genetic algorithm-II (NSGA-II) which shows efficiency of the proposed methodology. © 2023 IEEE.

关键词： Energy efficiency

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New regularization techniques for ill-conditioning problems and their applications: Choices of regularization parameters

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Engineering Analysis with Boundary Elements 2023年第1期152卷 347-361页

作者： Zhang, Li-Ping Li, Zi-Cai Wei, Yimin Huang, Hung-Tsai School of Mathematical Sciences Qufu Normal University Qufu Shandong273165 China Department of Applied Mathematics National Sun Yat-sen University Kaohsiung80424 Taiwan Shanghai Key Laboratory of Contemporary Mathematics and School of Mathematics Fudan University Shanghai200433 China Department of Data Science and Analytics I-Shou University Kaohsiung84001 Taiwan

This is a continued study of Li et al. (2011) for the regularization of ill-conditioning problems, where the minimal singular value σmin of discrete matrices is infinitesimal. To remove the effects of high frequency caused by the singular vector of σmin better, we combine the Tikhonov regularization (TR) with the truncated singular value decomposition (TSVD) (simply denoted by T-TR). New computational formulas of the traditional condition number (Cond) and the effective condition number (Cond_eff) are derived, and a brief error analysis is made. The regularization parameter λ is involved in the TR, and its better (or optimal) choices are essential in both theory and computation. For ill-conditioning problems, stability is more imperative than accuracy. The most important criterion is to reduce the Cond. Consider the linear algebraic equations Ax = b. The optimal regularization parameter for the TR is derived as λ=σmaxσmin, where σmax and σmin(>0) are the maximal and the minimal singular values of matrix A, respectively. The L-curve techniques emphasize the ill-conditioning of ‖xλ‖ (see Hansen (1998)). Note that the norm ‖xλ‖ is only part of the Cond in stability. In fact, by using regularization, the Cond may be greatly reduced, while the errors do not increase much. The second important criterion is for both stability and solution accuracy. The sensitivity index proposed in Zhang et al. (2021) indicates the severity of ill-conditioning via accuracy for comparing different numerical methods and techniques. The sensitivity index is effective not only to select the source nodes in the method of fundamental solutions (MFS), but also to select the regularization parameter in the TR. The regularization choices of the T-TR are similar to those of the TR because differences of the Cond and the errors between the TR and the T-TR are insignificant. Numerical experiments are reported by the MFS for Laplace's equation, to support the new regularization techniques proposed. For

关键词： Errors

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A Symbolic Rule Integration Framework with Logic Transformer for Inductive Relation Prediction 24

A Symbolic Rule Integration Framework with Logic Transformer...

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33rd ACM Web Conference, WWW 2024

作者： Pan, Yudai Liu, Jun Zhao, Tianzhe Zhang, Lingling Lin, Yun Dong, Jin Song School of Computer Science and Technology Xi'an Jiaotong University Shaanxi Xi'an China National Engineering Lab for Big Data Analytics Xi'an Jiaotong University Shaanxi Xi'an China Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Xi'an Jiaotong University Shaanxi Xi'an China Shanghai Jiao Tong University Shanghai China National University of Singapore Singapore Singapore

ISBN: (纸本)9798400701719

Relation prediction in knowledge graphs (KGs) aims at predicting missing relations in incomplete triples, whereas the dominant paradigm by KG embeddings has a limitation to predict the relation between unseen entities. This situation is called an inductive setting, which is more common in the real-world scenario. To handle this issue, implicit symbolic rules have shown great potential in capturing the inductive capability. However, it is still challenging to obtain precise representations of logic rules from KGs. The argument variability and predicate non-commutativity in symbolic rule integration make the modeling of component symbols difficult. To this end, we propose a novel inductive relation prediction model named SymRITa with a logic transformer integrating rules. SymRITa firstly extracts the subgraph, whose embeddings are captured by a graph network. Meanwhile, symbolic rule graphs in the subgraph can be generated. Then, the symbolic rules are modeled by a proposed logic transformer. Specifically, the input format based on the subgraph-based embeddings is to focus on the argument variability in symbolic rules. In addition, a conjunction attention mechanism in the logic transformer can resolve predicate non-commutativity in the symbolic rule integration process. Finally, the subgraph-based and symbol-based embeddings obtained from the previous steps are combined for the training regime, and prediction results as well as rules explaining the reasoning process are explicitly output. Extensive experiments on twelve inductive datasets show that SymRITa achieves outstanding effectiveness compared to state-of-the-art inductive baselines. Moreover, the logic rules with corresponding confidences provide an interpretable paradigm. © 2024 ACM.

关键词： Forecasting

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FAIRifying STEM data Ecosystem to Enhance data Reuse

FAIRifying STEM Data Ecosystem to Enhance Data Reuse

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Frontiers in Education (FIE) Conference

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

In the science, technology, engineering, and mathematics (STEM) community, academic and research workflows and work practices are increasingly mediated and informed by data. However, making digital materials and resources findable, accessible, interoperable, and reusable (FAIR) for teaching, learning, and research is an under-researched area. Thus, it is vital to examine the current data practices of STEM students and faculties and acquaint them with the FAIR data concept. FAIR data Principles is a set of guidelines that underscore metadata, vocabularies, licences, and standards to enhance data reuse. A study was conducted among students and faculties in the STEM community of the Royal University of Bhutan (RUB) to unpack their current data practices and explore areas for improvement using the FAIR data Principles. The STEM students and faculties of the RUB share and reuse digital materials and resources for teaching, learning, and research. Nevertheless, their data practice is not as widespread or desired in the literature on optimum data reuse. Moreover, the compliance of current data practices to the tenets of FAIR data Principles is not satisfactory. A pragmatic solution is complementing data practices with policies and infrastructural systems that underscore FAIR data Principles. A sensitisation programme such as seminars and hands-on exercises on data FAIRification is crucial to familiarise people with the essentialness of FAIR data, and doing so will provide a platform to develop their repertoire for FAIRifying data and encourage systematic sharing and reuse of data. An in-depth account of the FAIRifying STEM data ecosystem in the study contributes to the growing knowledge base on adopting FAIR data Principles in other areas of data-informed work and life.

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Graph-Based Visualization & Analysis of Azerbaijani Web

Graph-Based Visualization & Analysis of Azerbaijani Web

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International Conference on Application of Information and Communication Technologies (AICT)

作者： Anar Shikhaliyev Abzatdin Adamov Master of Computer Science and Data Analytics ADA University Baku Azerbaijan School of IT & Engineering ADA University Baku Azerbaijan

ISBN: (数字)9798350387537

ISBN: (纸本)9798350387544

Understanding the dynamics of the Azerbaijani internet landscape is crucial for analyzing information dissemination patterns and assessing national cybersecurity resilience. This study employs web crawling techniques and graphical analysis to construct a comprehensive network diagram of local websites. The objective is twofold: first, to catalog and analyze local web entities using a Python-based crawler integrated with databases; second, to visualize the web structure using graph theory and evaluate node importance through ranking algorithms. data extraction from Ministry of Communication & Information Technologies' databases feeds into both Postgres and Neo4j databases, facilitating graph representation where nodes and edges denote websites and their relationships. The results emphasize visualization insights with ongoing analytical implementations for comprehensive web mapping.

关键词： Ecosystems data visualization Strategic planning Information retrieval Graph theory Information and communication technology Visual databases Servers Feeds Resilience

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High-Order Resting-State Functional Connectivity is Predictive of Working Memory Decline After Brain Tumor Resection

High-Order Resting-State Functional Connectivity is Predicti...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Triet M. Tran Thi T. Tran Sina Khanmohammadi Data Science And Analytics Institute University of Oklahoma Norman OK USA School of Computer Science University of Oklahoma Norman OK USA

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Surgical resection is one of the main treatment options for brain tumors. However, there is a risk of postoperative cognitive deterioration associated with resective surgery. Recent studies suggest that pre-surgery brain dynamics captured using functional Magnetic Resonance Imaging (fMRI) could provide valuable information about the risk of post-surgery cognitive decline. However, most of these studies are based on simple regression analysis of the raw fMRI signals that do not capture the underlying complex brain dynamics. Here, we investigated the role of higher-order functional brain networks in predicting cognitive decline after surgical resection of brain tumors. More specifically, we looked at the predictive power of second-order functional brain networks in estimating post-surgery working memory (WM) performance. Our results show that the second-order functional brain networks can accurately predict the working memory decline in patients with glioma and meningioma tumors. These findings suggest that there is an interesting relationship between pre-surgical higher-order brain dynamics and the risk of cognitive decline after surgery, which could potentially yield a better prognostic marker for treatment planning of brain tumor patients.

关键词： Surgery Functional magnetic resonance imaging Planning Regression analysis Neurosurgery Engineering in medicine and biology Monitoring

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Inductive Relation Prediction with Logical Reasoning Using Contrastive Representations

Inductive Relation Prediction with Logical Reasoning Using C...

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2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Pan, Yudai Liu, Jun Zhang, Lingling Zhao, Tianzhe Lin, Qika Hu, Xin Wang, Qianying School of Computer Science and Technology Xi'an Jiaotong University China National Engineering Lab for Big Data Analytics China Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering China Lenovo Research Beijing China

Relation prediction in knowledge graphs (KGs) aims at predicting missing relations in incomplete triples, whereas the dominant embedding paradigm has a restriction on handling unseen entities during testing. In the real-world scenario, the inductive setting is more common because entities in the training process are finite. Previous methods capture an inductive ability by implicit logic in KGs. However, it would be challenging to preciously acquire entity-independent relational semantics of compositional logic rules and to deal with the deficient supervision of logic caused by the scarcity of relational semantics. To this end, we propose a novel graph convolutional network (GCN)-based model LogCo with logical reasoning by contrastive representations. LogCo firstly extracts enclosing subgraphs and relational paths between two entities to supply the entity-independence. Then a contrastive strategy for relational path instances and the subgraph is proposed for the issue of deficient supervision. The contrastive representations are learned for a joint training regime. Finally, prediction results and logic rules for reasoning are attained. Comprehensive experiments on twelve inductive datasets show that LogCo achieves outstanding performance comparing with SOTA inductive baselines. © 2022 Association for Computational Linguistics.

关键词： Forecasting

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Single-Stage Grasping Algorithm for the Manipulator Based on Rotational Gaussian Encoding 13

Single-Stage Grasping Algorithm for the Manipulator Based on...

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13th IEEE International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2023

作者： Liu, Zhenghao Liu, Yizhe Yang, Guowei Chen, Dongyue Jia, Tong College of Information Science and Engineering Northeastern University Shenyang China Foshan Graduate School of Innovation Northeastern University China Northeastern University Key Laboratory of Data Analytics and Optimization for Smart Industry Ministry of Education China

ISBN: (纸本)9798350315196

As we all know, there is an unsolved problem for robots to accurately and quickly grasp unknown objects in an unstructured environment. In order to describe the pose information of objects more accurately, a rotational Gaussian encoding method is proposed in this paper. Different from the traditional 2-D Gaussian encoding to represent ground-truth, this method introduces rotation information based on 2-D Gaussian. Grasping is made more accurate and robust by treating the grasp pose as a rotating bounding box in the image plane. In addition, a balanced loss function is introduced in this paper. This loss function not only solves the problem of positive and negative sample imbalance in the traditional cross-entropy loss but also reduces the penalty of points around the Gaussian center location. In this paper, a pixel-level single-stage grasping algorithm is designed in an end-to-end manner without the intermediate process of using anchor points, which saves time and improves the accuracy of grasping simultaneously. Then, the proposed model in this paper is evaluated on two standard datasets, Cornell dataset and Jacquard dataset, achieving 96.8% and 94.7% accuracy, respectively. Finally, we use a 6 DoF UR5e robotic arm for real-world grasping experiments. The success rate of single-object grasping is 94.7% and the time to generate the grasp frame is 8 ms. Experiments demonstrat that the algorithm is equally effective in real-world environments and has a faster detection time. © 2023 IEEE.

关键词： Signal encoding

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Binocular active stereo matching based on multi-scale random forest 13

Binocular active stereo matching based on multi-scale random...

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13th IEEE International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2023

作者： Liu, Jiatong Pu, Mingqi Wang, Ghutian Chen, Dongyue Jia, Tang College of Information Science and Engineering Northeastern University Shenyang China Foshan Graduate School of Innovation Northeastern University China Northeastern University Key Laboratory of Data Analytics and Optimization for Smart Industry Ministry of Education China

ISBN: (纸本)9798350315196

With the rapid development of 3D vision technology, the existing passive binocular cameras can no longer meet the practical needs of depth perception. Therefore, this paper proposes a binocular active stereo matching method based on multi-scale random forest. Firstly, a binocular active vision system consists of a near-infrared random speckle projector and a binocular camera is constructed, and the system was calibrated using Zhang's calibration method. Secondly, gamma image enhancement and image difference method are used for processing to reduce the impact of ambient light on the measurement results. Next, extract the points of interest and use the multi-scale random forest algorithm to match the window where the points of interest are located to generate a sparse structured light anchor disparity map globally. Finally, using the Census transforms as the matching cost, iterate continuously, refine the disparity and get a dense disparity map. The experimental results show that this system can achieve good depth perception accuracy and robustness under complex indoor lighting conditions. © 2023 IEEE.

关键词： Binoculars

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