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International Conference on ICT For Smart Society (ICISS)

作者： Wikan Danar Sunindyo Alfrojems Knowledge and Software Engineering Research Group School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia Indonesia Business Coalition for Women Empowerment South Jakarta Indonesia

Accelerating regional economic growth is needed to be able to catch up with a region, especially if the region has economic potential, especially in terms of natural capital, such as a beautiful environment. However, in practice, a development process will experience obstacles as an inseparable part. This condition certainly requires appropriate treatment efforts based on empirical evidence data. This also happened to West Lombok. Where in fact the regions that get the opportunity to become Smart City pilots certainly have a positive impact, but the challenges that must be carried out also go hand in hand with the process of implementing these efforts. Referring to this background, the purpose of this research is to be able to explore and analyze the challenges experienced by West Lombok in implementing the Smart City program in its region. The method used is qualitative with data collection techniques in the form of documentation studies, observations, interviews, and focus group discussions. This study involved 50 informants with various backgrounds but still in accordance with the characteristics of the informants determined by the researcher through the use of purposive sampling technique. The results of this study are that there are four challenges that need to get more attention from the local government of West Lombok and its stakeholders as an effort to realize West Lombok as a smart city. The four challenges include policy, infrastructure, human capital, and environment. The need for strategic efforts carried out by West Lombok to be able to encourage the achievement of the target of becoming a smart city in Indonesia, especially in West Nusa Tenggara Province.

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LLMs4Life: Large Language Models for Ontology Learning in Life Sciences

arXiv

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arXiv 2024年

作者： Fathallah, Nadeen Staab, Steffen Algergawy, Alsayed Analytic Computing Institute for Artificial Intelligence University of Stuttgart Stuttgart Germany University of Southampton Southhampton United Kingdom Data and Knowledge Engineering University of Passau Passau Germany Institute for Informatics Friedrich-Schiller-University Jena Jena Germany

Ontology learning in complex domains, such as life sciences, poses significant challenges for current Large Language Models (LLMs). Existing LLMs struggle to generate ontologies with multiple hierarchical levels, rich interconnections, and comprehensive class coverage due to constraints on the number of tokens they can generate and inadequate domain adaptation. To address these issues, we extend the NeOn-GPT pipeline for ontology learning using LLMs with advanced prompt engineering techniques and ontology reuse to enhance the generated ontologies’ domain-specific reasoning and structural depth. Our work evaluates the capabilities of LLMs in ontology learning in the context of highly specialized and complex domains such as life science domains. To assess the logical consistency, completeness, and scalability of the generated ontologies, we use the AquaDiva ontology developed and used in the collaborative research center AquaDiva 1 as a case study. Our evaluation shows the viability of LLMs for ontology learning in specialized domains, providing solutions to longstanding limitations in model performance and scalability. © 2024, CC BY.

关键词： Contrastive Learning

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Multi-Label Feature Selection for High-Dimensional Biological data via Global Relevance and Redundancy Optimization Based on JS Divergence

Multi-Label Feature Selection for High-Dimensional Biologica...

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International Conference on Information Science and Technology (ICIST)

作者： Man Yang Yibo Wang Yadi Wang Xiaoding Guo Huiyu Mu Hangjun Che Institute of Data and Knowledge Engineering School of Computer and Information Engineering Henan University Kaifeng China Computer Science and Technology School of International Business Henan University Zhengzhou China College of Electronic and Information Engineering Southwest University Chongqing China

ISBN: (数字)9798350353334

ISBN: (纸本)9798350353341

In recent years, multi-label feature selection has been widely used in fields such as bioinformatics, information retrieval, and multimedia annotation. Most of the previous multi-label feature selection methods are directly transformed from the traditional single-label feature selection methods, or they cannot make full use of label information. As a result, the selected feature subset involves features that are redundant or irrelevant to label information. Moreover, most algorithms do not use discretization when processing continuous data sets, so they cannot effectively eliminate the interference of abnormal data. In order to solve these problems, based on the GRRO (Global Relevance and Redundancy Optimization) algorithm, the JS divergence is used to better measure the overall similarity between two distributions and introduces data discretization preprocessing operations for continuous data sets. The experimental results after experimental verification on ten typical high-dimensional biological data sets show that the GRRO-JS algorithm is superior to the traditional multi-label feature selection method and the GRRO algorithm in terms of accuracy and efficiency and has high practical value.

关键词： Correlation Accuracy Termination of employment Interference Parallel processing Feature extraction Biology Time measurement Time complexity Optimization

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Unitig-centered pan-genome machine learning approach for predicting antibiotic resistance and discovering novel resistance genes in bacterial strains

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Computational and Structural Biotechnology Journal 2024年 23卷 1864-1876页

作者： Do, Duyen Thi Yang, Ming-Ren Vo, Tran Nam Son Le, Nguyen Quoc Khanh Wu, Yu-Wei Graduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University Taipei Taiwan Department of Electrical Engineering National Taiwan University of Science and Technology Taipei Taiwan Department of Business Administration College of Management Lunghwa University of Science and Technology Taoyuan City Taiwan Professional Master Program in Artificial Intelligence in Medicine College of Medicine Taipei Medical University Taipei Taiwan Clinical Big Data Research Center Taipei Medical University Hospital Taipei Taiwan TMU Research Center for Digestive Medicine Taipei Medical University Taipei Taiwan

In current genomic research, the widely used methods for predicting antimicrobial resistance (AMR) often rely on prior knowledge of known AMR genes or reference genomes. However, these methods have limitations, potentially resulting in imprecise predictions owing to incomplete coverage of AMR mechanisms and genetic variations. To overcome these limitations, we propose a pan-genome-based machine learning approach to advance our understanding of AMR gene repertoires and uncover possible feature sets for precise AMR classification. By building compacted de Brujin graphs (cDBGs) from thousands of genomes and collecting the presence/absence patterns of unique sequences (unitigs) for Pseudomonas aeruginosa, we determined that using machine learning models on unitig-centered pan-genomes showed significant promise for accurately predicting the antibiotic resistance or susceptibility of microbial strains. Applying a feature-selection-based machine learning algorithm led to satisfactory predictive performance for the training dataset (with an area under the receiver operating characteristic curve (AUC) of > 0.929) and an independent validation dataset (AUC, approximately 0.77). Furthermore, the selected unitigs revealed previously unidentified resistance genes, allowing for the expansion of the resistance gene repertoire to those that have not previously been described in the literature on antibiotic resistance. These results demonstrate that our proposed unitig-based pan-genome feature set was effective in constructing machine learning predictors that could accurately identify AMR pathogens. Gene sets extracted using this approach may offer valuable insights into expanding known AMR genes and forming new hypotheses to uncover the underlying mechanisms of bacterial AMR. © 2024 The Authors

关键词： Genes

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Mitigating Label-Noise for Facial Expression Recognition in the Wild

Mitigating Label-Noise for Facial Expression Recognition in ...

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2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Yan, Huan Gu, Yu Zhang, Xiang Wang, Yantong Ji, Yusheng Ren, Fuji School of Computer and Information Hefei University of Technology S2AC Lab China Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data China Anhui Province Key Laboratory of Cyberspace Security Situation Awareness and Evaluation China National Institute of Informatics Tokushima University Japan

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Label-noise constitutes a major challenge for facial expression recognition in the wild due to the ambiguity of facial expressions worsened by low-quality images. To deal with this problem, we propose a simple but effective Label-noise Robust Network (LRN) which explores the inter-class correlations for mitigating ambiguity that usually happens between morphologically similar classes. Specifically, LRN leverages a multivariate normal distribution to model such correlations at the final hidden layer of the neural network to suppress the heteroscedastic uncertainty caused by inter-class label noise. Furthermore, LRN utilizes a confidence-based label-free loss to extract compact intra-class feature representations under label noise while preserving the intrinsic inter-class relationships. Experiments on three in-the-wild facial expression datasets demonstrates the superiority of our method. © 2022 IEEE.

关键词： Face recognition

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Quantifying societal emotional resilience to natural disasters from geo-located social media content

arXiv

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arXiv 2022年

作者： Bathina, Krishna C. ten Thij, Marijn Bollen, Johan School of Informatics Computing and Engineering Indiana University United States Delft Institute of Applied Mathematics Delft University of Technology Netherlands Department of Data Science and Knowledge Engineering Maastricht University Netherlands

Natural disasters can have devastating and long-lasting effects on a community’s emotional well-being. These effects may be distributed unequally, affecting some communities more profoundly and possibly over longer time periods than others. Here, we analyze the effects of four major US hurricanes, namely, Irma, Harvey, Florence, and Dorian on the emotional well-being of the affected communities and regions. We show that a community’s emotional response to a hurricane event can be measured from the content of social media that its population posted before, during, and after the hurricane. For each hurricane making landfall in the US, we observe a significant decrease in sentiment in the affected areas before and during the hurricane followed by a rapid return to pre-hurricane baseline, often within 1-2 weeks. However, some communities exhibit markedly different rates of decline and return to previous equilibrium levels. This points towards the possibility of measuring the emotional resilience of communities from the dynamics of their online emotional response. © 2022, CC BY.

关键词： Hurricanes

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Scientific Value Weights more than Being Open or Toll Access:An analysis of the OA advantage in Nature and Science

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Journal of data and Information Science 2021年第4期6卷 62-75页

作者： Howell Y.Wang Shelia X.Wei Cong Cao Xianwen Wang Fred Y.Ye International Joint Informatics Laboratory&Jiangsu Key Laboratory of Data Engineering and Knowledge Service School of Information ManagementNanjing UniversityNanjing 210023China Nottingham University Business School China University of Nottingham Ningbo ChinaNingbo315100China WISE Lab. Dalian University of TechnologyDalian 116024China

Purpose:We attempt to find out whether OA or TA really affects the dissemination of scientific ***/methodology/approach:We design the indicators,hot-degree,and R-index to indicate a topic OA or TA ***,according to the OA classification of the Web of Science(WoS),we collect data from the WoS by downloading OA and TA articles,letters,and reviews published in Nature and Science during 2010–*** papers are divided into three broad disciplines,namely biomedicine,physics,and ***,taking a discipline in a journal and using the classical Latent Dirichlet Allocation(LDA)to cluster 100 topics of OA and TA papers respectively,we apply the Pearson correlation coefficient to match the topics of OA and TA,and calculate the hot-degree and R-index of every OA-TA topic ***,characteristics of the discipline can be *** qualitative comparison,we choose some high-quality papers which belong to Nature remarkable papers or Science breakthroughs,and analyze the relations between OA/TA and citation ***:The result shows that OA hot-degree in biomedicine is significantly greater than that of TA,but significantly less than that of TA in *** on the R-index,it is found that OA advantages exist in biomedicine and TA advantages do in ***,the dissemination of average scientific discoveries in all fields is not necessarily affected by OA or ***,OA promotes the spread of important scientific discoveries in high-quality *** limitations:We lost some citations by ignoring other open sources such as arXiv and *** limitation came from that Nature employs some strong measures for access-promoting subscription-based articles,on which the boundary between OA and TA became *** implications:It is useful to select hot topics in a set of publications by the hotdegree *** finding comprehensively reflects the differences of OA and TA in different disciplines,which is a u

关键词： Open access Toll access Scientific discovery Academic dissemination data analysis

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SALI: A Scalable Adaptive Learned Index Framework based on Probability Models

arXiv

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arXiv 2023年

作者： Ge, Jiake Zhang, Huanchen Shi, Boyu Luo, Yuanhui Guo, Yunda Chai, Yunpeng Chen, Yuxing Pan, Anqun Key Laboratory of Data Engineering and Knowledge Engineering MOE China Engineering Research Center of Database and Business Intelligence MOE China School of Information Renmin University of China China China and Shanghai Qi Zhi Institute China Tsinghua University China Tencent Inc China

The growth in data storage capacity and the increasing demands for high performance have created several challenges for concurrent indexing structures. One promising solution is learned indexes, which use a learning-based approach to fit the distribution of stored data and predictively locate target keys, significantly improving lookup performance. Despite their advantages, prevailing learned indexes exhibit constraints and encounter issues of scalability on multi-core data storage. This paper introduces SALI, the Scalable Adaptive Learned Index framework, which incorporates two strategies aimed at achieving high scalability, improving efficiency, and enhancing the robustness of the learned index. Firstly, a set of node-evolving strategies is defined to enable the learned index to adapt to various workload skews and enhance its concurrency performance in such scenarios. Secondly, a lightweight strategy is proposed to maintain statistical information within the learned index, with the goal of further improving the scalability of the index. Furthermore, to validate their effectiveness, SALI applied the two strategies mentioned above to the learned index structure that utilizes fine-grained write locks, known as LIPP. The experimental results have demonstrated that SALI significantly enhances the insertion throughput with 64 threads by an average of 2.04× compared to the second-best learned index. Furthermore, SALI accomplishes a lookup throughput similar to that of LIPP+. © 2023, CC BY-NC-ND.

关键词： Scalability

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Edge-Minimum Saturated k-Planar Drawings 29th

Edge-Minimum Saturated k-Planar Drawings

引用

29th International Symposium on Graph Drawing and Network Visualization, GD 2021

作者： Chaplick, Steven Klute, Fabian Parada, Irene Rollin, Jonathan Ueckerdt, Torsten Department of Data Science and Knowledge Engineering Maastricht University Maastricht Netherlands Utrecht University Utrecht Netherlands TU Eindhoven Eindhoven Netherlands Department of Mathematics and Computer Science FernUniversität in Hagen Hagen Germany Institute of Theoretical Informatics Karlsruhe Institute of Technology Karlsruhe Germany

ISBN: (纸本)9783030929305

For a class D of drawings of loopless (multi-)graphs in the plane, a drawing D∈ D is saturated when the addition of any edge to D results in D′∉ D —this is analogous to saturated graphs in a graph class as introduced by Turán (1941) and Erdős, Hajnal, and Moon (1964). We focus on k-planar drawings, that is, graphs drawn in the plane where each edge is crossed at most k times, and the classes D of all k-planar drawings obeying a number of restrictions, such as having no crossing incident edges, no pair of edges crossing more than once, or no edge crossing itself. While saturated k-planar drawings are the focus of several prior works, tight bounds on how sparse these can be are not well understood. We establish a generic framework to determine the minimum number of edges among all n-vertex saturated k-planar drawings in many natural classes. For example, when incident crossings, multicrossings and selfcrossings are all allowed, the sparsest n-vertex saturated k-planar drawings have 2k-(kmod2)(n-1) edges for any k≥ 4, while if all that is forbidden, the sparsest such drawings have 2(k+1)k(k-1)(n-1) edges for any k≥ 6. © 2021, Springer Nature Switzerland AG.

关键词： Drawing (graphics)

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Applying data mining techniques in the context of social media to improve situational awareness at large-scale events

Applying data mining techniques in the context of social med...

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2021 IEEE International Conference on Electrical, Computer, Communications and Mechatronics engineering, ICECCME 2021

作者： Simon, Rainer Ignjatovic, Drazen Neubauer, Georg Gutschi, Clemens Pan, Johannes Vossner, Siegfried Data Science Artificial Intelligence Center for Digital Safety Security Austrian Institute of Technology GmbH Vienna Austria Cooperative Digital Technologies Center for Digital Safety Security Austrian Institute of Technology GmbH Vienna Austria Engineering- and Business Informatics Graz University of Technology Graz Austria

ISBN: (纸本)9781665412629

Having relevant information plays an important role in every aspect of human life. data mining supports the extraction of additional information by discovering patterns and correlations between data within the dataset under investigation. Information obtained in this way is used in various applications ranging from education, business to crisis management etc. Enhanced situational awareness is a foundation for improved decision making. A prerequisite for optimized decision making is to quickly obtain valid information on a given topic, but also to have it presented in an understandable and easily digestible form. In this paper, we propose a method for applying data mining techniques in the context of social media to improve situational awareness in large-scale events by describing a solution for an intelligent situational information portal that leverages open data information sources and channels for improving decision support. © 2021 IEEE.

关键词： Sentiment analysis

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