Article
Biochemistry & Molecular Biology
Zehong Zhang, Lifan Chen, Feisheng Zhong, Dingyan Wang, Jiaxin Jiang, Sulin Zhang, Hualiang Jiang, Mingyue Zheng, Xutong Li
Summary: This article provides an overview of the application of deep neural networks and graph neural networks in drug-target interaction (DTI) prediction. The use of graph neural networks has proven effective in predicting DTIs, finding repositioning drugs, and accelerating drug discovery. The article also highlights current challenges and future directions for further development in this field.
CURRENT OPINION IN STRUCTURAL BIOLOGY
(2022)
Review
Biochemistry & Molecular Biology
Lorenzo Boninsegna, Asli Yildirim, Yuxiang Zhan, Frank Alber
Summary: Advancements in technology have allowed us to have a better understanding of the structure and dynamics of genomes. These developments have enabled the creation of quantitative models of nuclear organization, leading to deeper insights into genome functions and spatial organization.
Article
Computer Science, Cybernetics
Marina Martin, Jose A. Macias
Summary: This paper presents a supporting tool based on the Card Sorting method, which implements predictive analysis of results through advanced statistics and machine learning techniques, providing comprehensive reports to enhance decision-making.
INTERNATIONAL JOURNAL OF HUMAN-COMPUTER INTERACTION
(2023)
Article
Information Science & Library Science
Vanda Broughton
Summary: This paper discusses the disparity in the use and understanding of concepts such as facet, facet analysis, and faceted classification in information and knowledge organization. The paper traces the history of these ideas and explores different interpretations. It not only focuses on the concept and purpose of facet analysis, but also on the language utilized to describe and explain it.
CATALOGING & CLASSIFICATION QUARTERLY
(2023)
Article
Social Sciences, Interdisciplinary
Gisela Boehm, Hans-Ruediger Pfister, Vanessa Ayres-Pereira, Ingvar Tjostheim
Summary: This study investigates how users perceive the risks to privacy posed by smartphones and social media. The researchers define three facets of e-privacy risks and use a questionnaire to analyze people's perception of these risks. The results suggest that people have different perceptions of risks depending on the type of data disclosed and the type of actor misusing the information.
JOURNAL OF RISK RESEARCH
(2023)
Article
Computer Science, Information Systems
Travis L. Wagner, Diana Marsh, Lydia Curliss
Summary: This paper explores the role of Indigenous and queer embodiment in understanding the current limitations of sociotechnical systems as they relate to cultural heritage institutions. The paper highlights how the ideologies of colonialism and cisnormativity render Indigenous and queer identities invisible within cultural heritage institutions through a critical case study. It identifies methods to challenge such inequities through community-led, Indigenous, and queer affirming descriptive practices and theorizes methods for addressing broader inequities within sociotechnical systems.
JOURNAL OF THE ASSOCIATION FOR INFORMATION SCIENCE AND TECHNOLOGY
(2023)
Review
Psychology, Multidisciplinary
Rui Qiao
Summary: The role of teachers in developing positive traits in EFL students, such as grit and academic engagement, is widely recognized. However, there is a lack of theoretical and systematic review studies on the role of EFL teachers' approaches in strengthening these traits. This study aims to fill this gap by reviewing the theoretical and empirical foundations of students' grit and engagement and providing practical teaching approaches for EFL teachers in various contexts.
FRONTIERS IN PSYCHOLOGY
(2022)
Review
Chemistry, Multidisciplinary
Kadi L. Saar, Daoyuan Qian, Lydia L. Good, Alexey S. Morgunov, Rosana Collepardo-Guevara, Robert B. Best, Tuomas P. J. Knowles
Summary: Biomolecular condensation processes are fundamental mechanisms that living cells utilize to organize biomolecules in time and space, leading to the formation of membraneless organelles. Computational methods, including theoretical methods, physics-driven simulations, and data-driven machine learning methods, provide a unique perspective to study biomolecular condensation and offer advantages such as high resolution and scale. This review discusses the recent progress and limitations of these computational approaches and highlights the challenges in understanding the molecular driving forces and biological roles of biomolecular condensation in health and disease.
Review
Agriculture, Dairy & Animal Science
Suresh Neethirajan, Bas Kemp
Summary: Social behaviour has a significant impact on livestock management and animal welfare. The use of sensing technologies in social network analysis of farm animals allows for a better understanding of animal interactions and behavioural dynamics. This can ultimately lead to improvements in welfare and farm management processes.
Article
Multidisciplinary Sciences
Erik A. Wing, Ford Burles, Jennifer D. Ryan, Asaf Gilboa
Summary: This study demonstrates that prior knowledge has a significant influence on memory, altering the relationship between psychological similarity and memory. Experts are influenced by taxonomic features, while controls are more influenced by bird color. Additionally, expert memory is regulated by species-level name knowledge and the organization of conceptual space.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
(2022)
Article
Computer Science, Artificial Intelligence
Siripinyo Chantamunee, Kok Wai Wong, Chun Che Fung
Summary: Collaborative-based personalization is a successful technique in building personalized recommender systems and facet selection. However, there is a challenge in multiple facet selection, where predictions need to be based on the similarity among different groups of users and facets. This study proposes a new collaborative-based personalization model called Relation-aware Collaborative Autoencoder (RCAE) Model, which utilizes multiple facet relationships to improve the accuracy of the personalized model.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Chemistry, Physical
Chaonan Zhang, Shaokang Yang, Dewei Rao
Summary: Energy depletion and environmental pollution are serious challenges for humans. The application of hydrogen energy is a promising strategy to address these issues. However, hydrogen production is a drawback for hydrogen energy. The hydrogen evolution reaction (HER) based on electrocatalysis is an effective way to enhance hydrogen generation with low energy consumption. Many efforts have been made to develop high-performance catalysts for HER processes.
INTERNATIONAL JOURNAL OF HYDROGEN ENERGY
(2022)
Article
Economics
Mitchell Hoffman, Steven Tadelis
Summary: The study shows that having good people management skills can lower employee turnover rate, while also leading to better performance evaluations, promotion opportunities, and salary increases for the managers themselves.
JOURNAL OF POLITICAL ECONOMY
(2021)
Article
Nursing
Peter Kokol
Summary: This study conducted a bibliometric analysis on the use of meta-approaches in nursing research literature, revealing an exponential growth in the use of meta-analysis, a linear growth in the use of meta-synthesis, and a constant use of meta-ethnography. The most productive countries were the United States, United Kingdom, and China, with the top publications appearing in Journal of Advanced Nursing, International Journal of Nursing Studies, and Journal of Clinical Nursing.
Review
Health Care Sciences & Services
Sultana Al Sabahi, Michael G. Wilson, John N. Lavis, Fadi El-Jardali, Kaelan Moat, Marcela Velez
Summary: This study aims to develop a conceptual framework to guide the process of establishing a policy support organization (PSO). The findings suggest that the PSO establishment process has four interconnected stages and is influenced by political, research, and health systems contextual factors.
INTERNATIONAL JOURNAL OF HEALTH POLICY AND MANAGEMENT
(2022)
Article
Computer Science, Information Systems
Jonathan Furner, Birger Hjorland
Summary: This article examines the flaws in the logical consistency of the Library of Congress Subject Headings (LCSH), the most widely used subject heading system, in representing information science (IS) and knowledge organization (KO). The study employs a method that checks for the presence of core concepts in the system, looks for alternative terms for missing concepts, and identifies semantic relations between subject headings. The findings have implications for controlled vocabularies in general.
JOURNAL OF DOCUMENTATION
(2023)
Article
Information Science & Library Science
Paula Carina de Araujo, Renata Cristina Gutierres Castanha, Birger Hjorland
Summary: This article provides an overview of citation indexes, comparing major ones and discussing their role in knowledge organization and information retrieval. It also explores citation behavior and the impact of citation indexes on the scientific information ecosystem.
KNOWLEDGE ORGANIZATION
(2021)
Article
Computer Science, Information Systems
Birger Hjorland
Summary: Information retrieval (IR) focuses on using techniques to search for information, while knowledge organization (KO) aims to reflect contemporary scholarship. The two fields are related but have different research focuses.
Letter
Information Science & Library Science
Birger Hjorland
KNOWLEDGE ORGANIZATION
(2019)
Article
Computer Science, Information Systems
Birger Hjorland
JOURNAL OF DOCUMENTATION
(2019)
Review
Information Science & Library Science
Birger Hjorland
KNOWLEDGE ORGANIZATION
(2018)
Article
Information Science & Library Science
Birger Hjorland
KNOWLEDGE ORGANIZATION
(2018)
Review
Information Science & Library Science
Birger Hjorland
KNOWLEDGE ORGANIZATION
(2018)
Article
Information Science & Library Science
Birger Hjorland
KNOWLEDGE ORGANIZATION
(2017)
Correction
Information Science & Library Science
Birger Hjorland
KNOWLEDGE ORGANIZATION
(2017)
Correction
Information Science & Library Science
Birger Hjorland
KNOWLEDGE ORGANIZATION
(2017)
Review
Information Science & Library Science
Birger Hjorland
KNOWLEDGE ORGANIZATION
(2017)
Article
Information Science & Library Science
Birger Hjorland
INFORMATION RESEARCH-AN INTERNATIONAL ELECTRONIC JOURNAL
(2017)
Article
Information Science & Library Science
Birger Hjorland
KNOWLEDGE ORGANIZATION
(2020)
Review
Information Science & Library Science
Birger Hjorland
KNOWLEDGE ORGANIZATION
(2018)
Article
Computer Science, Information Systems
Sang-Bing Tsai, Xusen Cheng, Yanwu Yang, Jason Xiong, Alex Zarifis
Summary: This article structurally concludes the methods proposed and evidenced to develop digital entrepreneurship from a socio-technical perspective. The technology itself and the process of utilization should be carefully considered. From a social perspective, fulfilling the needs of customers in social interaction and nurturing characteristics and social skills for the digital work environment are crucial.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Xiaochang Fang, Hongchen Wu, Jing Jing, Yihong Meng, Bing Yu, Hongzhu Yu, Huaxiang Zhang
Summary: This study proposes a novel fake news detection framework, utilizing news semantic environment perception (NSEP) to identify fake news content. The framework consists of steps such as dividing the semantic environment into macro and micro levels, applying graph convolutional networks, and utilizing multihead attention. Empirical experiments show that the NSEP framework achieves high accuracy in detecting Chinese fake news, outperforming other baseline methods and highlighting the importance of both micro and macro semantic environments in early detection of fake news.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Xudong Sun, Alladoumbaye Ngueilbaye, Kaijing Luo, Yongda Cai, Dingming Wu, Joshua Zhexue Huang
Summary: This paper proposes a scalable distributed frequent itemset mining (ScaDistFIM) algorithm to address the data scalability and flexibility issues in basket analysis in the big data era. Experiment results demonstrate that the ScaDistFIM algorithm is more efficient compared to the Spark FP-Growth algorithm.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Boxu Guan, Xinhua Zhu, Shangbo Yuan
Summary: This paper aims to improve the interpretability of machine reading comprehension models by utilizing the pre-trained T5 model for evidence inference. They propose an interpretable reading comprehension model based on T5, which is trained on a more accurate evidence corpus and can infer precise interpretations for answers. Experimental results show that their model outperforms the baseline BERT model on the SQuAD1.1 task.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Yanhao Wang, Baohua Zhang, Weikang Liu, Jiahao Cai, Huaping Zhang
Summary: In this study, we propose a data augmentation-based semantic text matching model called STMAP. By using Gaussian noise and noise mask signal for data augmentation, as well as employing an adaptive optimization network for training target optimization, our model achieves good performance in few-shot learning and semantic deviation problems.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Jiahao Yang, Shuo Feng, Wenkai Zhang, Ming Zhang, Jun Zhou, Pengyuan Zhang
Summary: To pursue profit from stock markets, researchers utilize deep learning methods to forecast asset price movements. However, there are two issues in current research, the discrepancy between forecasting results and profits, and heavy reliance on prior knowledge. To address these issues, researchers propose a novel optimization objective and modeling method, and conduct experiments to validate their approach.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Heng Zhang, Chengzhi Zhang, Yuzhuo Wang
Summary: This study provides an accurate analysis of technology development in the field of Natural Language Processing (NLP) from an entity-centric perspective. The findings indicate an increase in the average number of entities per paper, with pre-trained language models becoming mainstream and the impact of Wikipedia dataset and BLEU metric continuing to rise. There has been a surge in popularity for new high-impact technologies in recent years, with researchers accepting them at an unprecedented speed.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Davide Buscaldi, Danilo Dessi, Enrico Motta, Marco Murgia, Francesco Osborne, Diego Reforgiato Recupero
Summary: In scientific papers, citing other articles is a common practice to support claims and provide evidence. This paper proposes two automatic methods using Transformer models to address citation placement, and achieves significant improvements in experiments.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Baozhuang Niu, Lingfeng Wang, Xinhu Yu, Beibei Feng
Summary: This paper examines whether the incumbent brand should adopt digital technology to forecast demand and adjust order decisions in the face of soaring demand for medical supply caused by frequent outbreaks of regional COVID-19 epidemic. The study finds that digital transformation can lead to a triple-win situation among the incumbent brand, social welfare, and consumer surplus, as well as bring benefits to the manufacturer. Furthermore, the research provides insights for firms' digital entrepreneurship decisions through theoretical optimization and data processing/policy simulation.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Xueyang Qin, Lishang Li, Fei Hao, Meiling Ge, Guangyao Pang
Summary: Image-text retrieval is important in connecting vision and language. This paper proposes a method that utilizes prior knowledge to enhance feature representations and optimize network training for better retrieval results.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Review
Computer Science, Information Systems
Gang Ren, Lei Diao, Fanjia Guo, Taeho Hong
Summary: This paper proposes a novel approach for predicting the helpfulness of reviews by utilizing both textual and image features. The proposed method considers the correlation between features through self-attention and co-attention mechanisms, and fuses multi-modal features for prediction. Experimental results demonstrate the superior performance of the proposed method compared to benchmark methods.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Zhongquan Jian, Jiajian Li, Qingqiang Wu, Junfeng Yao
Summary: Aspect-Level Sentiment Classification (ALSC) is a crucial challenge in Natural Language Processing (NLP). Most existing methods fail to consider the correlations between different instances, leading to a lack of global viewpoint. To address this issue, we propose a Retrieval Contrastive Learning (RCL) framework that extracts intrinsic knowledge across instances for improved instance representation. Experimental results demonstrate that training ALSC models with RCL leads to substantial performance improvements.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Ying Hu, Yanping Chen, Ruizhang Huang, Yongbin Qin, Qinghua Zheng
Summary: Biomedical relation extraction aims to extract the interactive relations between biomedical entities in a sentence. This study proposes a hierarchical convolutional model to address the semantic overlapping and data imbalance problems. The model encodes both local contextual features and global semantic dependencies, enhancing the discriminability of the neural network for biomedical relation extraction.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Zhou Yang, Yucai Pang, Xuehong Li, Qian Li, Shihong Wei, Rong Wang, Yunpeng Xiao
Summary: This study proposes a rumor detection model based on topic audiolization, which transforms the topic space into audio-like signals. Experimental results show that the model achieves significant performance improvements in rumor identification.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Alistair Moffat
Summary: This paper proposes the buying power metric for assessing the quality of product rankings on e-commerce sites. It discusses the relationship between the buying power metric and user reactions, and introduces an alternative product ranking effectiveness metric.
INFORMATION PROCESSING & MANAGEMENT
(2024)
