Top 10 downloads from ACM's digital library

This study investigated into the effects of the "Advanced search" feature of three digital libraries on users' search performance and search efforts. Three operational digital libraries, i.e., the ACM digital library, the IEEE Computer Society digital library, and the IEEE Xplore digital library, were used in this study. Thirty-five students participated in an experiment and completed the assigned search task, using their preferred search feature(s). The results demonstrate that for ACM and IEEE CS, the use of Advanced search did not have a significant effect on improving search performance. Only in Xplore, the use of Advanced search significantly improved search performance. The search efforts increased significantly for the combined-use of Advanced search and Basic search in ACM and IEEE CS. The reasons leading to the results are discussed.

Automatic assessment of pain based on deep learning methods: A systematic review

BackgroundThe Unified Medical Language System (UMLS) has been a critical tool in biomedical and health informatics, and the year 2021 marks its 30th anniversary. The UMLS brings together many broadly used vocabularies and standards in the biomedical field to facilitate interoperability among different computer systems and applications.ObjectiveDespite its longevity, there is no comprehensive publication analysis of the use of the UMLS. Thus, this review and analysis is conducted to provide an overview of the UMLS and its use in English-language peer-reviewed publications, with the objective of providing a comprehensive understanding of how the UMLS has been used in English-language peer-reviewed publications over the last 30 years.MethodsPubMed, ACM Digital Library, and the Nursing & Allied Health Database were used to search for studies. The primary search strategy was as follows: UMLS was used as a Medical Subject Headings term or a keyword or appeared in the title or abstract. Only English-language publications were considered. The publications were screened first, then coded and categorized iteratively, following the grounded theory. The review process followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines.ResultsA total of 943 publications were included in the final analysis. Moreover, 32 publications were categorized into 2 categories; hence the total number of publications before duplicates are removed is 975. After analysis and categorization of the publications, UMLS was found to be used in the following emerging themes or areas (the number of publications and their respective percentages are given in parentheses): natural language processing (230/975, 23.6%), information retrieval (125/975, 12.8%), terminology study (90/975, 9.2%), ontology and modeling (80/975, 8.2%), medical subdomains (76/975, 7.8%), other language studies (53/975, 5.4%), artificial intelligence tools and applications (46/975, 4.7%), patient care (35/975, 3.6%), data mining and knowledge discovery (25/975, 2.6%), medical education (20/975, 2.1%), degree-related theses (13/975, 1.3%), digital library (5/975, 0.5%), and the UMLS itself (150/975, 15.4%), as well as the UMLS for other purposes (27/975, 2.8%).ConclusionsThe UMLS has been used successfully in patient care, medical education, digital libraries, and software development, as originally planned, as well as in degree-related theses, the building of artificial intelligence tools, data mining and knowledge discovery, foundational work in methodology, and middle layers that may lead to advanced products. Natural language processing, the UMLS itself, and information retrieval are the 3 most common themes that emerged among the included publications. The results, although largely related to academia, demonstrate that UMLS achieves its intended uses successfully, in addition to achieving uses broadly beyond its original intentions.

Creativity support is an important and challenging emerging area of research. combinFormation is being developed as a tool to support creativity through a mixed-initiative composition space. The system combines searches and information feeds, representing relevant information collections as compositions of image and text surrogates. The composition space affords human manipulation. This method has been shown to support information discovery in The Design Process, an interdisciplinary undergraduate course. In this demo, we demonstrate how combinFormation can be used to explore and discover information in digital libraries such as ACM Digital Library and the International Children's Digital Library.

No abstract available.

We are very happy to present the proceedings of the 4th Augmented Human International Conference (Augmented Human 2013). Augmented Human 2013 focuses on augmenting human capabilities through technology for increased well-being and enjoyable human experience. The conference is in cooperation with ACM SIGCHI, with its proceedings to be archived in ACM's Digital Library. With technological advances, computing has progressively moved beyond the desktop into new physical and social contexts. As physical artifacts gain new computational behaviors, they become reprogrammable, customizable, repurposable, and interoperable in rich ecologies and diverse contexts. They also become more complex, and require intense design effort in order to be functional, usable, and enjoyable. Designing such systems requires interdisciplinary thinking. Their creation must not only encompass software, electronics, and mechanics, but also the system's physical form and behavior, its social and physical milieu, and beyond.

No abstract available.

No abstract available.

The following abstracts are from recent issues and the forthcoming issue of ACM's Transactions of Computer Human Interaction (ToCHI). They are included here to alert interactions' readers to what research is being done in the field of Computer Human Interaction. The complete papers, when published, can be found in ACM's Digital Library at www.acm.org/pubs/contents/journals/tochi/

No abstract available.

The convergence of drone technology, cloud computing, and intelligent decision-making is revolutionizing precision forestry. However, deploying large-scale drone networks in smart forestry faces challenges such as trust, security, data integrity, and autonomous coordination. This survey examines how combining Blockchain technology with deep Q-learning (DQL) can address these issues within cloud-enabled drone networks. Drawing on 102 peer-reviewed sources published between 2022 and 2025 from reputable platforms such as ACM Digital Library, Frontiers, Wiley Online Library, PLoS, Nature, Springer, ScienceDirect, MDPI, IEEE Xplore Digital Library, Taylor & Francis, Sage, and Google Scholar, this work highlights recent advancements in secure and intelligent drone ecosystems. Blockchain provides a decentralized, tamper-resistant framework for validating transactions and securing data exchange among autonomous drones, ensuring the integrity, confidentiality, and authenticity of environmental data. This is critical in forestry, where data manipulation and unauthorized access pose significant risks. Complementing this, DQL enables drones to make autonomous decisions by interpreting real-time environmental data and learning from past experiences, allowing drones to adjust their flight paths, optimize resource utilization, and enhance data collection in dynamic forest environments, such as wildfires or illegal logging operations. Together, Blockchain and DQL create a resilient, scalable architecture that supports secure, real-time, and intelligent forest monitoring. This framework lays the groundwork for developing autonomous and trustworthy drone networks that promote sustainable and climate-smart forestry management.

Smartwatches are wearable devices of emerging mobile technology and are increasingly in people's daily lives. They allow you to perform various tasks; however, the interaction with these devices still proves to be a significant challenge. Several studies carried out interaction studies with smartwatches using gesture recognition. Thus, this work presents a comprehensive Systematic Review of Literature (SRL) on interaction with smartwatches using gesture recognition, showing what has already been developed and what is state of the art. We searched in four databases with relevant scientific scope: ACM Digital Library, IEEE Xplore Digital Library, Science Direct, and Scopus. Work on this theme is diverse; gesture recognition is used to perform operations on smartwatches itself or even control other devices or virtual environments. We hope that this work will provide a rich base on the methods developed and that it will become a source of research for future researchers and that can support the development of other methods and applications.

Deep Learning Artificial Neural Networks has pushed forward researches in the field of pattern recognition, furthermore in human handwriting recognition. From online to offline approach, signature verification, writing or writer identification, segmentation or features extraction, a multitude of Artificial Neural Networks (ANNs) models are applied in the process. This paper focuses on the literature review of human handwriting recognition with ANN's over the last decade. We propose an exploratory analysis of 294 research papers collected from five indexed research engines: ACM Digital Library, IEEE digital library, Science Direct, Scopus and Web of Science. Our aim is to provide a research papers distribution across years and journals, a Keywords frequency analysis using cloud visualization, and a Natural Language Processing Topic Modeling using Non-Negative Matrix Factorization (NMF). The results of this study show that the number of research papers reached noticeably a peak in the 2010 with 44 published papers; also Pattern Recognition was the top publishing journal with 12 published papers. As for the topic modeling using NMF we obtained 3 topics listed as follows: 1) Feature Extraction and segmentation techniques for Handwritten Texts Recognition; 2) Signature Verification in Biometric security for Off-line Authentication; 3) Assessment Systems for Student Identification

As patients are receiving their own health data more often and in larger amounts, visualizations are generating enthusiasm for their ability to help patients understand the information. It is crucial to assess these representations to guarantee that patients can comprehend and, when suitable, take action based on health data in a secure and efficient way. The aim of this systematic study was to assess and analyze the current status of patient-facing representations of personal health data. We conducted a thorough search on five reputable academic databases, namely PubMed, Embase, Scopus, ACM Digital Library (Association for Computing Machinery Digital Library), and IEEE Computational Index (Institute of Electrical and Electronics Engineers Computational Index). We included English-language papers that created or examined patient-oriented visual representations for personal health information. The results of the article indicated that a greater proportion of patients were able to comprehend number lines and bar graphs as opposed to line graphs. Additionally, it was shown that the use of color was useful in conveying danger, enhancing understanding, and boosting confidence in interpretation. This review provides a concise overview of the many kinds and components of visuals that are directly accessible to patients. Additionally, it outlines the different approaches used in the production and assessment of these visualizations, as described in the examined publications. In addition, we provide suggestions for future research on the collection and presentation of data, exploration of clinically significant thresholds for various kinds of data, and use of data science techniques. This effort will be crucially significant as the use of patient portals and mobile devices for accessing personal health data continues to increase.

In the industrial work context, Augmented Reality (AR) can support work processes and employees' cognitive relief through the location-specific and context-related superimposition of real objects with virtual information. The AR-based support of industrial work processes ranges over product development, manufacturing, assembly, maintenance, and training. In all these areas, numerous location-based AR support functions are being prototypically implemented, aiming to improve work efficiency, communication in mobile work situations, or employee qualification in the work process. In contrast to the increasing number of developed AR solutions in recent years, there is no widespread use of these solutions in industrial practice. AR systems' successful introduction is closely related to user acceptance, which has not been comprehensively considered over the system development process. In addition to improving AR hardware ergonomic features, usability or user interface design play an essential role in user acceptance. Particularly in the context of employee qualification, increasing employee engagement can be named as a success factor. Previous user studies of industrial AR systems only include individual user acceptance aspects. The use of game elements has not been widely addressed in connection with manual tasks in production environments, including AR-based assistance systems. This paper aims to examine user acceptance of industrial AR systems and the relevant factors for investigating user acceptance, e.g., ease of use or enjoyment, based on a systematic literature review. An analysis of existing review articles on industrial AR systems elaborates the current state of the art and identifies the research gap. This review of 109 scientific articles from 2011 to 2020 provides an overview of the current state of research on the inclusion of user acceptance in industrial AR systems. The identified papers from the scientific databases, Scopus, Web of Science, IEEE Xplore Digital Library, ACM Digital Library, and Science Direct, are evaluated for their relevance and selected for further analysis based on inclusion and exclusion criteria, e.g., year of publication. This review presents the current challenges regarding user acceptance of industrial AR systems and future possibilities for the comprehensive integration of user acceptance factors into the development, evaluation, and implementation process.