Association For Computing Machinery Research Articles

A Review on Deep Learning Approaches and Optimization Techniques For Political Security Threat Prediction

In an era of complex geopolitical dynamics and evolving security threats, the accurate prediction and proactive management of political security risks are imperative. This article provides a comprehensive review of the application of deep learning methodologies and optimization techniques to enhance political security threat prediction. Beginning with analyzing the dynamic landscape of political security threats, the paper emphasizes the necessity for adaptive, data-driven predictive tools. It then delves into the fundamentals of deep learning, elucidating core principles, notable architectural frameworks, and their diverse applications across domains. Expanding upon this foundation, the study evaluates the suitability of deep learning models for addressing the multifaceted challenges associated with political security threat prediction. To maximize the utility of these models, the article explores optimization techniques encompassing hyperparameter tuning, transfer learning, and ensemble strategies, assessing their effectiveness in fine-tuning predictions and bolstering the resilience of threat prediction systems. This review involved the utilization of four journal databases: IEEE, Science Direct, Association for Computing Machinery (ACM), and SpringerLink. We analyzed and examined 39 articles, paying close attention to the different patterns and techniques found within the chosen research framework. Through a critical synthesis of existing research, this review offers insights into the strengths, limitations, and future directions of deep learning-based political security threat prediction, contributing to the ongoing discourse on leveraging artificial intelligence for safeguarding global stability and security.

Research artifacts for human-oriented experiments in software engineering: An ACM badges-driven structure proposal

Context:The Open Science (OS) movement promotes the value of making public the research artifacts (datasets, analysis scripts, guidelines, etc.) used during empirical studies. OS is widely known in areas such as Medicine or Biology, where the process of sharing research artifacts is subject to strict protocols. Unfortunately, in Software Engineering (SE), this process is carried out in a non-systematic way, resulting in incomplete or inaccurate material shared by researchers, which hinders the reproducibility and replicability of empirical studies. Nevertheless, in recent years, it seems that the Empirical Software Engineering (ESE) community is embracing some of the proposed OS initiatives, such as the one proposed by the Association for Computing Machinery (ACM), which provides a badge system to evaluate the quality of a research artifact. This badge system has been adopted by several SE conferences as a method of assessing research artifacts. Aims:Focusing on human-oriented experiments (HOEs) in SE, whose research artifacts are more complex than those for computational experiments, this work applies Design Science Research (DSR) with a twofold purpose: (i) review the current status of HOEs research artifacts publication through evaluation of this practice in the most relevant ESE journals , and (ii) propose a structured outline for HOEs research artifacts driven by the aforementioned ACM badging policy. Method:Regarding the first purpose, we carried out a survey to analyze the current status of the publication of research artifacts considering relevant peer review journals and the quality of 106 research artifacts published in these journals with respect to the ACM badging policy. For the second purpose, an iterative process was carried out to review the content of 106 research artifacts research and their concordance with ACM badges, obtaining a structured scheme for HOEs research artifacts that has been validated through a detailed review of 12 research artifacts obtained from some of those of ACM badges in relevant SE conferences. In addition, we validated the proposal in the research artifacts of 2 of our own experiments. Results:Our survey reveals issues such as the 39,70% of journal studies making completely accessible their research artifacts; most of the analyzed research artifacts are incomplete; the most common repositories used in the ESE community to share the research artifacts are GitHub, institutional repositories, and Zenodo. On the other hand, the validated and structured research artifact outline consists of a list of ordered sections containing a set of artifacts, which can be mandatory or not to achieve a particular ACM badge. For its internal validation, several improvement iterations on the first release of the outline have been carried out based on the conference guidelines, the ACM badging policy, and other relevant proposals. Conclusions:Although the ESE community is making great efforts in standardization, review, and digital publishing related to OS, the availability and completeness of research artifacts can be improved. Our proposal for the elaboration of structured research artifact outline meets the requirements of HOEs in SE. Nevertheless, further research is needed not only to improve and externally validate it but also to disseminate its use among the research community.

A scoping review of digital fabrication techniques applied to prosthetics and orthotics: Part 1 of 2-Prosthetics.

Traditionally, the manufacture of prostheses is time-consuming and labor-intensive. One possible route to improving access and quality of these devices is the digitalizing of the fabrication process, which may reduce the burden of manual labor and bring the potential for automation that could help unblock access to assistive technologies globally. To identify where there are gaps in the literature that are creating barriers to decision-making on either appropriate uptake by clinical teams or on the needed next steps in research that mean these technologies can continue on a pathway to maturity. Scoping literature review. A comprehensive search was completed in the following databases: Allied and Complementary Medicine Database, MEDLINE, Embase, Global Health Archive, CINAHL Plus, Cochrane Library, Web of Science, Association for Computing Machinery, Institute of Electrical and Electronics Engineers, and Engineering Village, resulting in 3487 articles to be screened. After screening, 130 lower limb prosthetic articles and 117 upper limb prosthetic articles were included in this review. Multiple limitations in the literature were identified, particularly a lack of long-term, larger-scale studies; research into the training requirements for these technologies and the necessary rectification processes; and a high range of variance of production workflows and materials which makes drawing conclusions difficult. These limitations create a barrier to adequate evidence-based decision-making for clinicians, technology developers, and wider policymakers. Increased collaboration between academia, industry, and clinical teams across more of the pathway to market for new technologies could be a route to addressing these gaps.

IoT Attack Detection using Machine Learning and Deep Learning in Smart Home

The Internet of Things (IoT) has revolutionized the traditional Internet, pushing past its former boundaries by implementing smart linked gadgets. The IoT is steadily becoming a staple of everyday life, having been implemented into various diverse applications, such as cities, smart homes, and transportation. However, despite the technological advancements that the IoT brings, various new security risks have also been introduced due to the development of new types of attacks. This prevents current intelligent IoT applications from adaptively learning from other intelligent IoT applications, which leaves them in a volatile state. In this paper, we conducted a structured literature review (SLR) on Smart Home's IoT attack detection using machine learning and deep learning. Four journal databases were used to perform this review: IEEE, Science Direct, Association for Computing Machinery (ACM), and SpringerLink. Sixty articles were selected and studied, where we noted the various patterns and techniques present in the framework of the selected research. We also took note of the different machine learning and deep learning methods, the types of attacks, and the Network layers present in Smart Home. By conducting an SLR, we analyzed the numerous techniques of IoT attack detection for smart homes proposed by various theoretical studies. We enhanced the studied literature by proposing a new solution for better IoT attack detection in smart homes.

Sensor-based systems for the measurement of Functional Reach Test results: a systematic review.

The measurement of Functional Reach Test (FRT) is a widely used assessment tool in various fields, including physical therapy, rehabilitation, and geriatrics. This test evaluates a person's balance, mobility, and functional ability to reach forward while maintaining stability. Recently, there has been a growing interest in utilizing sensor-based systems to objectively and accurately measure FRT results. This systematic review was performed in various scientific databases or publishers, including PubMed Central, IEEE Explore, Elsevier, Springer, the Multidisciplinary Digital Publishing Institute (MDPI), and the Association for Computing Machinery (ACM), and considered studies published between January 2017 and October 2022, related to methods for the automation of the measurement of the Functional Reach Test variables and results with sensors. Camera-based devices and motion-based sensors are used for Functional Reach Tests, with statistical models extracting meaningful information. Sensor-based systems offer several advantages over traditional manual measurement techniques, as they can provide objective and precise measurements of the reach distance, quantify postural sway, and capture additional parameters related to the movement.

Classification of BI-RADS using convolutional neural network and effecientNet-B7

From its development, image processing technology has become increasingly complex, such as detecting cancer in an image. In this context, the American College of Radiology (ACR) has developed a standard format and terminology called the breast imaging reporting and data system (BI-RADS). The aim of this research is to determine whether CNN can classify BI-RADS types (C1, C2, C3, C4), the best model, and the classification results of BI-RADS types on mammograms using CNN, and the best values for accuracy, sensitivity, specificity, and precision. Google Colaboratory and machine learning libraries were used in this research to create machine learning for BI-RADS classification. Then, a dataset from Kaggle titled "Mini-DDSM: Mammography-based Automatic Age Estimation" by Lekamlage et al., from the Association for Computing Machinery (ACM), Kyoto, Japan, was utilized. Based on the research, an accuracy of 85.9% was achieved for Category 1 classification. Category 2 classification attained an accuracy of 77.9%. The accuracy for Category 3 classification was 81.4%, and for Category 4, it reached 90.8%.

Routing Techniques in Network-On-Chip Based Multiprocessor-System-on-Chip for IOT: A Systematic Review

Routing techniques (RTs) play a critical role in modern computing systems that use network-on-chip (NoC) communication infrastructure within multiprocessor system-on-chip (MPSoC) platforms. RTs contribute greatly to the successful performance of NoC-based MPSoCs due to traffic congestion avoidance, quality-of-service assurance, fault handling and optimisation of power usage. This paper outlines our efforts to catalogue RTs, limitations, recommendations and key challenges associated with these RTs used in NoC-based MPSoC systems for the IoT domain. We utilized the PRISMA method to collect data from credible resources, including IEEE Xplore ®, ScienceDirect, Association for Computing Machinery and Web of Science. Out of the 906 research papers reviewed, only 51 were considered relevant to the investigation on NoC RTs. The study addresses issues related to NoC routing and suggests new approaches for in-package data negotiating. In addition, it gives an overview of the recent research on routing strategies and numerous algorithms that can be used for NoC-based MPSoCs. The literature analysis addresses current obstacles and delineates potential future avenues, recommendations, and challenges analyzing techniques to assess performance utilizing metrics within the TCCM framework.

Teaching Ethics in Computing: A Systematic Literature Review of ACM Computer Science Education Publications

The computing education research community now has at least 40 years of published research on teaching ethics in higher education. To examine the state of our field, we present a systematic literature review of papers in the Association for Computing Machinery computing education venues that describe teaching ethics in higher-education computing courses. Our review spans all papers published to SIGCSE, ICER, ITiCSE, CompEd, Koli Calling, and TOCE venues through 2022, with 100 papers fulfilling our inclusion criteria. Overall, we found a wide variety in content, teaching strategies, challenges, and recommendations. The majority of the papers did not articulate a conception of “ethics,” and those that did used many different conceptions, from broadly applicable ethical theories to social impact to specific computing application areas (e.g., data privacy and hacking). Instructors used many different pedagogical strategies (e.g., discussions, lectures, assignments) and formats (e.g., stand-alone courses, incorporated within a technical course). Many papers identified measuring student knowledge as a particular challenge, and 59% of papers included mention of assessments or grading. Of the 69% of papers that evaluated their ethics instruction, most used student self-report surveys, course evaluations, and instructor reflections. While many papers included calls for more ethics content in computing, specific recommendations were rarely broadly applicable, preventing a synthesis of guidelines. To continue building on the last 40 years of research and move toward a set of best practices for teaching ethics in computing, our community should delineate our varied conceptions of ethics, examine which teaching strategies are best suited for each, and explore how to measure student learning.

ACM SIGMETRICS 2023 Student Research Competition

Every year, the Association for Computing Machinery (ACM) spearheads a series of Student Research Competitions (SRCs) at ACM-sponsored or co-sponsored conferences. These SRCs provide graduate (Masters or PhD program) and undergraduate students an opportunity to present their original research to a panel of judges. Participating students present posters and oral presentations of their research for the opportunity to win cash prizes. In each category (graduate and undergraduate), the 1st, 2nd, and 3rd place winners receive prizes of 500, 300, and 200, respectively. Additionally, first place undergraduate and graduate student winners from the SRCs held during the year are invited to present their work at the SRC Grand Finals, where a different panel of judges evaluates these winners against each other.

Social support via Internet communication technology for diabetes self-management: a scoping review.

The global prevalence of diabetes has been increasing over the past 30 years, leading to a rise in complications; diabetes is the leading cause of amputations, blindness, and kidney failure in developed countries. Diabetes self-management is challenging due to the complex lifestyle changes required. Social support from family and friends plays a crucial role in overcoming barriers to healthy behavior choices. Integrating Internet and communication technologies with social support interventions has the potential to improve diabetes self-management. A scoping review was conducted by searching PubMed, Ovid, Cumulative Index to Nursing and Allied Health Literature (CINAHL), and Association for Computing Machinery (ACM) Digital Library databases for studies combining diabetes, Internet and communication technologies, and social support interventions. Inclusion criteria focused on adult patients with diabetes and primarily Internet and communication technologies-based strategies to initiate and enhance social support. Data abstraction included study population, design, outcomes, social support domains emphasized, support relationship intervention strategy, and Internet and communication technologies modality. The review identified 39 articles and 33 unique studies representing 27 unique interventions utilizing peer web forums, interactive voice recordings, messaging-based interventions (including Short Message Service and instant messaging), and email as Internet and communication technologies modalities. Various outcomes were reported, including improvements in perception of social support, psychosocial well-being, behavior changes, and clinical outcomes. Existing support relationships may be more effective in promoting behavior change and clinical outcomes compared to developing new relationships. Studies that explicitly measured patients' perception of support consistently showed improvements in psycho-social, behavioral, and clinical outcomes. This scoping review highlights the pivotal role of social support in diabetes self-management. By integrating Internet and communication technologies into interventions, diverse modalities such as web forums and text messaging have shown promise in enhancing patients' perception of support and improving psychosocial well-being, behavior changes, and clinical outcomes. The emphasis on leveraging existing support relationships, rather than establishing new ones, underscores the effectiveness of personalized, patient-centered approaches. These findings provide essential insights for healthcare strategies, emphasizing the need to harness technology and existing social networks to empower individuals in managing diabetes effectively.

Methods for the Clinical Validation of Digital Endpoints: Protocol for a Scoping Review Abstract.

Clinical trials often use digital technologies to collect data continuously outside the clinic and use the derived digital endpoints as trial endpoints. Digital endpoints are also being developed to support diagnosis, monitoring, or therapeutic interventions in clinical care. However, clinical validation stands as a significant challenge, as there are no specific guidelines orienting the validation of digital endpoints. This paper presents the protocol for a scoping review that aims to map the existing methods for the clinical validation of digital endpoints. The scoping review will comprise searches from the electronic literature databases MEDLINE (PubMed), Scopus (including conference proceedings), Embase, IEEE (Institute of Electrical and Electronics Engineers) Xplore, ACM (Association for Computing Machinery) Digital Library, CENTRAL (Cochrane Central Register of Controlled Trials), Web of Science Core Collection (including conference proceedings), and Joanna Briggs Institute Database of Systematic Reviews and Implementation Reports. We will also include various sources of gray literature with search terms related to digital endpoints. The methodology will adhere to the Joanna Briggs Institute Scoping Review and the Guidance for Conducting Systematic Scoping Reviews. A search for reviews on the existing evidence related to this topic was conducted and has shown that no such review was previously undertaken. This review will provide a systematic assessment of the literature on methods for the clinical validation of digital endpoints and highlight any potential need for harmonization or reporting of methods. The results will include the methods for the clinical validation of digital endpoints according to device, digital endpoint, and clinical application goal of digital endpoints. The study started in January 2023 and is expected to end by December 2023, with results to be published in a peer-reviewed journal. A scoping review of methodologies that validate digital endpoints is necessary. This review will be unique in its breadth since it will comprise digital endpoints collected from several devices and not focus on a specific disease area. The results of our work should help guide researchers in choosing validation methods, identify potential gaps in the literature, or inform the development of novel methods to optimize the clinical validation of digital endpoints. Resolving these gaps is the key to presenting evidence in a consistent way to regulators and other parties and obtaining regulatory acceptance of digital endpoints for patient benefit. PRR1-10.2196/47119.

Mind the Gap: Understanding Coverage Breaks of Newly Launched Engineering and Computer Science Journals in Core Databases

Journal coverage is an important factor to consider when evaluating a database. In this study, we counted the number of articles (including early access articles) in newly-launched journals (2018-2022) from two major association publishers in engineering and computer science: the Institute of Electrical and Electronics Engineers (IEEE) and the Association for Computing Machinery (ACM). We then compared these numbers with the number of articles from these journals in Web of Science (WOS) Core Collection, Scopus, and Google Scholar (GS). Results indicated that GS had higher percentages for coverage for all the journals that were assessed and outperformed WOS and Scopus in terms of how fast newly-launched journals were indexed. Findings from this study will help librarians evaluate each database in reference, instruction, and collection development.

A method of water resources accounting based on deep clustering and attention mechanism under the background of integration of public health data and environmental economy.

Water resource accounting constitutes a fundamental approach for implementing sophisticated management of basin water resources. The quality of water plays a pivotal role in determining the liabilities associated with these resources. Evaluating the quality of water facilitates the computation of water resource liabilities during the accounting process. Traditional accounting methods rely on manual sorting and data analysis, which necessitate significant human effort. In order to address this issue, we leverage the remarkable feature extraction capabilities of convolutional operations to construct neural networks. Moreover, we introduce the self-attention mechanism module to propose an unsupervised deep clustering method. This method offers assistance in accounting tasks by automatically classifying the debt levels of water resources in distinct regions, thereby facilitating comprehensive water resource accounting. The methodology presented in this article underwent verification using three datasets: the United States Postal Service (USPS), Heterogeneity Human Activity Recognition (HHAR), and Association for Computing Machinery (ACM). The evaluation of Accuracy rate (ACC), Normalized Mutual Information (NMI), and Adjusted Rand Index (ARI) metrics yielded favorable results, surpassing those of K-means clustering, hierarchical clustering, and Density-based constraint extension (DCE). Specifically, the mean values of the evaluation metrics across the three datasets were 0.8474, 0.7582, and 0.7295, respectively.

A systematic review of clinical health conditions predicted by machine learning diagnostic and prognostic models trained or validated using real-world primary health care data.

With the advances in technology and data science, machine learning (ML) is being rapidly adopted by the health care sector. However, there is a lack of literature addressing the health conditions targeted by the ML prediction models within primary health care (PHC) to date. To fill this gap in knowledge, we conducted a systematic review following the PRISMA guidelines to identify health conditions targeted by ML in PHC. We searched the Cochrane Library, Web of Science, PubMed, Elsevier, BioRxiv, Association of Computing Machinery (ACM), and IEEE Xplore databases for studies published from January 1990 to January 2022. We included primary studies addressing ML diagnostic or prognostic predictive models that were supplied completely or partially by real-world PHC data. Studies selection, data extraction, and risk of bias assessment using the prediction model study risk of bias assessment tool were performed by two investigators. Health conditions were categorized according to international classification of diseases (ICD-10). Extracted data were analyzed quantitatively. We identified 106 studies investigating 42 health conditions. These studies included 207 ML prediction models supplied by the PHC data of 24.2 million participants from 19 countries. We found that 92.4% of the studies were retrospective and 77.3% of the studies reported diagnostic predictive ML models. A majority (76.4%) of all the studies were for models' development without conducting external validation. Risk of bias assessment revealed that 90.8% of the studies were of high or unclear risk of bias. The most frequently reported health conditions were diabetes mellitus (19.8%) and Alzheimer's disease (11.3%). Our study provides a summary on the presently available ML prediction models within PHC. We draw the attention of digital health policy makers, ML models developer, and health care professionals for more future interdisciplinary research collaboration in this regard.

Perceived Barriers and Facilitators in Using Patient-Reported Outcome Systems for Cancer Care: Systematic Mapping Study.

Cancer is a major global health problem. Patient-reported outcome (PRO) systems have been developed to support the treatment of patients with cancer. Although clear evidence of the benefits of the routine use of electronic patient-reported outcomes (ePROs) exists, engaging physicians in using these systems has been challenging. This study aims to identify and analyze what is currently known about health care professionals' (HCPs) perceived barriers and facilitators that exist and influence the use of ePRO systems for cancer care. We carried out a systematic mapping study by conducting searches of 3 databases (Association for Computing Machinery, PubMed, and Scopus). Eligible papers were published between 2010 and 2021, and they described HCPs' perspectives on using ePROs. The data on the included papers were extracted, a thematic meta-synthesis was performed, and 7 themes were summarized into 3 categories. A total of 17 papers were included in the study. The HCPs' perceived barriers and facilitators of using ePROs can be summarized into 7 themes: clinical workflow, organization and infrastructure, value to patients, value to HCPs, digital health literacy, usability, and data visualization and perceived features. These themes can be further summarized into 3 categories: work environment, value to users, and suggested features. According to the study, ePROs should be interoperable with hospital electronic health records and adapted to the hospital workflow. HCPs should get appropriate support for their use. Additional features are needed for ePROs, and special attention should be paid to data visualization. Patients should have the option to use web-based ePROs at home and complete it at the time most valuable to the treatment. Patients' ePRO notes need attention during clinical visits, but ePRO use should not limit patient-clinician face-to-face communication. The study revealed that several aspects need improvement in ePROs and their operating environments. By improving these aspects, HCPs' experience with ePROs will enhance, and thus, there will be more facilitating factors for HCPs to use ePROs than those available today. More national and international knowledge about using ePROs is still needed to cover the need for information to develop them and their operating environments to meet the needs of HCPs.

A term function–aware keyword citation network method for science mapping analysis

Various keyword network methods are used to map scientific fields, but few studies have considered the semantic roles of keywords in such networks. This study proposes a term function–aware keyword citation network to fill this research limitation. Specifically, we first used a term function identification method to identify research questions and methods from scientific articles. Then, we constructed a question-method term citation network to represent the correlation structure of keywords. Next, we explored the topology characteristics, question-method bipartite network, and knowledge community structure of the generated network to validate its superiority in science mapping analysis. A dataset of 299,567 conference proceedings collected from the Association for Computing Machinery (ACM) digital library is used to evaluate the effectiveness of our methods. The results show that the term function identification model based on Bidirectional Encoder Representations from Transformers (BERT) achieves a score of 0.90 F1. And the question-method term citation network outperforms existing keyword citation methods in revealing association patterns between scientific knowledge and improving the interpretability of the knowledge structure of the computing field. We believe that our work expands the methodology of keyword citation network and science mapping analysis and provides guidance for considering the term function in various scenarios.

Software Test Case Generation Tools and Techniques: A Review

Software Industry is evolving at a very fast pace since last two decades. Many software developments, testing and test case generation approaches have evolved in last two decades to deliver quality products and services. Testing plays a vital role to ensure the quality and reliability of software products. In this paper authors attempted to conduct a systematic study of testing tools and techniques. Six most popular e-resources called IEEE, Springer, Association for Computing Machinery (ACM), Elsevier, Wiley and Google Scholar to download 738 manuscripts out of which 125 were selected to conduct the study. Out of 125 manuscripts selected, a good number approx. 79% are from reputed journals and around 21% are from good conference of repute. Testing tools discussed in this paper have broadly been divided into five different categories: open source, academic and research, commercial, academic and open source, and commercial & open source. The paper also discusses several benchmarked datasets viz. Evosuite 10, SF100 Corpus, Defects4J repository, Neo4j, JSON, Mocha JS, and Node JS to name a few. Aim of this paper is to make the researchers aware of the various test case generation tools and techniques introduced in the last 11 years with their salient features.

A Systematic Literature Review of Automation Quality of Service in Computer Networks: Research Trends, Datasets, and Methods

The article is a systematic literature review of the use of automation for quality of service (QoS) in computer networks. It summarizes the research trends, datasets, and methods used in the field and provides an overview of the current state of the art. The focus of the review is on the use of automation for QoS management and improvement in computer networks, including the use of machine learning, artificial intelligence, and other computational techniques. The review highlights the need for further research and development in this area and provides insights into future directions for the field. The review covered a wide range of studies, including research papers and conference proceedings, and involved a comprehensive database search of the Scopus database covering journals and proceedings such as the Institute of Electrical and Electronics Engineers (IEEE) Xplore, Association for Computing Machinery (ACM) Digital Library, Springer, and ScienceDirect databases between 2017 and September 2022. From these databases, 1856 metadata were found, which eventually became seventy-three metadata after going through the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol.

Mobile Application to identify and recognize emotions for children with autism: A systematic review

IntroductionEmotions are a vital component of human interaction. Children with Autism Spectrum Disorder (ASD) face severe difficulties in sensing and interpreting the emotions of others, as well as responding emotionally appropriately. Developers are producing many mobile applications to assist ASD children in improving their facial expression detection and reaction abilities and increasing their independence.ObjectiveThis systematic review aims to explore the mobile application in helping children with ASD to identify and express their feeling.MethodsThe inclusion and exclusion articles for our analysis were mapped using the PRISMA Preferred Reporting Items for Systematic Reviews and Meta-Analysis diagram. The studies were retrieved from the following four databases: Google Scholar, Scopus, Association for Computing Machinery (ACM), and Institute of Electrical and Electronics Engineers (IEEE). Additionally, two screening processes were used to determine relevant literature. Reading the title and abstract was the initial step, followed by reading the complete content. Finally, the authors display the results using a narrative synthesis.ResultsFrom four electronic databases, we retrieved 659 articles. six studies that met our inclusion criteria were included in the systematic review. More details about inclusion and exclusion criteria can be found in the Eligibility criteria.ConclusionThis systematic review sheds light on current research that employed mobile applications to improve emotion detection and expression in children with ASD. This smartphone application has the potential to empower autistic children by assisting them in expressing their emotions and enhancing their ability to recognize emotions. However, it is currently deemed essential to assess the effectiveness of mobile applications for remediation through more rigorous methodological research. For example, most included studies were quantitative and focused on statical measurements. However, there is an immediate need for more incredible research in this area to include qualitative research and to consider large samples, control groups and placebo, prolonged treatment durations, and follow-up to see whether improvements are sustainable and to ensure the effectiveness of applications.

Mining Sensor Data to Assess Changes in Physical Activity Behaviors in Health Interventions: Systematic Review.

Sensors are increasingly used in health interventions to unobtrusively and continuously capture participants' physical activity in free-living conditions. The rich granularity of sensor data offers great potential for analyzing patterns and changes in physical activity behaviors. The use of specialized machine learning and data mining techniques to detect, extract, and analyze these patterns has increased, helping to better understand how participants' physical activity evolves. The aim of this systematic review was to identify and present the various data mining techniques employed to analyze changes in physical activity behaviors from sensors-derived data in health education and health promotion intervention studies. We addressed two main research questions: (1) What are the current techniques used for mining physical activity sensor data to detect behavior changes in health education or health promotion contexts? (2) What are the challenges and opportunities in mining physical activity sensor data for detecting physical activity behavior changes? The systematic review was performed in May 2021 using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. We queried the Association for Computing Machinery (ACM), IEEE Xplore, ProQuest, Scopus, Web of Science, Education Resources Information Center (ERIC), and Springer literature databases for peer-reviewed references related to wearable machine learning to detect physical activity changes in health education. A total of 4388 references were initially retrieved from the databases. After removing duplicates and screening titles and abstracts, 285 references were subjected to full-text review, resulting in 19 articles included for analysis. All studies used accelerometers, sometimes in combination with another sensor (37%). Data were collected over a period ranging from 4 days to 1 year (median 10 weeks) from a cohort size ranging between 10 and 11615 (median 74). Data preprocessing was mainly carried out using proprietary software, generally resulting in step counts and time spent in physical activity aggregated predominantly at the daily or minute level. The main features used as input for the data mining models were descriptive statistics of the preprocessed data. The most common data mining methods were classifiers, clusters, and decision-making algorithms, and these focused on personalization (58%) and analysis of physical activity behaviors (42%). Mining sensor data offers great opportunities to analyze physical activity behavior changes, build models to better detect and interpret behavior changes, and allow for personalized feedback and support for participants, especially where larger sample sizes and longer recording times are available. Exploring different data aggregation levels can help detect subtle and sustained behavior changes. However, the literature suggests that there is still work remaining to improve the transparency, explicitness, and standardization of the data preprocessing and mining processes to establish best practices and make the detection methods easier to understand, scrutinize, and reproduce.