Reviewerly/exHarmony huggingface dataset:
Blockchain technology in the AEC industry: Scientometric analysis of research activities

The architectural, engineering and construction (AEC) industry is plagued with complex and difficult problems such as late payments, inadequate information sharing and supply chain issues. Thus, advances in blockchain technology are increasingly investigated as a viable digital technology to address such problems. This advantage has led to blockchain gaining significant traction within the AEC industry thus piquing the interests of researchers. Although prior reviews on blockchain have been well appreciated, those studies do not give a complete picture of blockhain in the AEC industry as a whole. For example, whereas some researchers focused their studies on the construction industry, others did so across multiple domains. And researchers who attempted to review blockchain in the AEC industry employed a qualitative methodology which have been criticized for its lack of reproducibility and susceptibility to subjective biases. Moreover, those studies which utilized a quantitative approach employed a single science mapping tool for its analysis, which did not meet the criteria for a robust science mapping research. Therefore, there is the need for further review research efforts to supplement the limitations of the previous works. Thus, this study presents a robust science-mapping based analysis of the state-of-the art research on blockchain technology in the AEC industry, by combining three scientometric tools to analyze quantitatively, 12,549 relevant bibliographic data retrieved from the Web of Science (WoS) database. The results revealed that blockchain can be used as an optimization technology to optimize processes, systems, activities, and decision-making in the AEC industry. The findings confirmed that blockchain is being utilized to resolve AEC management problems in supply chain, projects, risk, cost, privacy, and security. The study further disclosed Industry 4.0 technologies that have presently been integrated with blockchain to improve its practicability in the AEC industry, as well as the understudied research areas. Within the existing corpus of literature on blockchain, the research findings are informative in identifying and comprehending trends and patterns, including core research topics, countries, and institutions and their interconnection. This study contributes to the global body of knowledge in blockchain by providing a holistic view of the state-of-the-art development of blockchain and proposes the directions of future research efforts, while promoting the consciousness of blockchain in the AEC industry.
Local-to-global spatial learning for whole-slide image representation and classification

Whole-slide image (WSI) provides an important reference for clinical diagnosis. Classification with only WSI-level labels can be recognized for multi-instance learning (MIL) tasks. However, most existing MIL-based WSI classification methods have moderate performance on correlation mining between instances limited by their instance- level classification strategy. Herein, we propose a novel local-to-global spatial learning method to mine global position and local morphological information by redefining the MIL-based WSI classification strategy, better at learning WSI-level representation, called Global-Local Attentional Multi-Instance Learning (GLAMIL). GLAMIL can focus on regional relationships rather than single instances. It first learns relationships between patches in the local pool to aggregate region correlation (tissue types of a WSI). These correlations then can be further mined to fulfill WSI-level representation, where position correlation between different regions can be modeled. Furthermore, Transformer layers are employed to model global and local spatial information rather than being simply used as feature extractors, and the corresponding structure improvements are present. In addition, we evaluate GIAMIL on three benchmarks considering various challenging factors and achieve satisfactory results. GLAMIL outperforms state-of-the-art methods and baselines by about 1 % and 10 %, respectively.
A zero-watermarking scheme based on spatial topological relations for vector dataset

Zero-watermarking technology uses the important features of the original data itself to construct watermark information. In the process of watermark construction, no modification is made to the original data, which can effectively solve the contradiction between the imperceptibility and robustness of the traditional embedded watermark. It is a hot spot in the field of high-precision geographic vector data protection. However, most of the existing zero-watermarking algorithms for vector geographic data are designed for one or several types of elements in vector geographic dataset and still cannot be applied to dataset with point, line, and polygon data types. To address this issue, this paper proposes a vector geographic data zero watermarking algorithm based on topology and metrics. Firstly, the topological relationship is used to describe the spatial relationship between different layer objects. Then, based on the rough classification of topological relations, metric parameters are introduced to enhance the distinguishability of spatial relations. Finally, the different spatial metric parameters obtained are combined to generate zero-watermark information about the data. The experimental results show that the proposed algorithm effectively solves the problem that the existing algorithm cannot be applied to the dataset and has good uniqueness and robustness. Under the applicable conditions of the existing algorithm, the efficiency of the proposed algorithm is 3–8 times that of the existing algorithm for the dataset with more graph layers and has excellent watermark extraction efficiency.
Few-Shot Learning for Clinical Natural Language Processing Using Siamese Neural Networks: Algorithm Development and Validation Study

Background Natural language processing (NLP) has become an emerging technology in health care that leverages a large amount of free-text data in electronic health records to improve patient care, support clinical decisions, and facilitate clinical and translational science research. Recently, deep learning has achieved state-of-the-art performance in many clinical NLP tasks. However, training deep learning models often requires large, annotated data sets, which are normally not publicly available and can be time-consuming to build in clinical domains. Working with smaller annotated data sets is typical in clinical NLP; therefore, ensuring that deep learning models perform well is crucial for real-world clinical NLP applications. A widely adopted approach is fine-tuning existing pretrained language models, but these attempts fall short when the training data set contains only a few annotated samples. Few-shot learning (FSL) has recently been investigated to tackle this problem. Siamese neural network (SNN) has been widely used as an FSL approach in computer vision but has not been studied well in NLP. Furthermore, the literature on its applications in clinical domains is scarce. Objective The aim of our study is to propose and evaluate SNN-based approaches for few-shot clinical NLP tasks. Methods We propose 2 SNN-based FSL approaches, including pretrained SNN and SNN with second-order embeddings. We evaluate the proposed approaches on the clinical sentence classification task. We experiment with 3 few-shot settings, including 4-shot, 8-shot, and 16-shot learning. The clinical NLP task is benchmarked using the following 4 pretrained language models: bidirectional encoder representations from transformers (BERT), BERT for biomedical text mining (BioBERT), BioBERT trained on clinical notes (BioClinicalBERT), and generative pretrained transformer 2 (GPT-2). We also present a performance comparison between SNN-based approaches and the prompt-based GPT-2 approach. Results In 4-shot sentence classification tasks, GPT-2 had the highest precision (0.63), but its recall (0.38) and F score (0.42) were lower than those of BioBERT-based pretrained SNN (0.45 and 0.46, respectively). In both 8-shot and 16-shot settings, SNN-based approaches outperformed GPT-2 in all 3 metrics of precision, recall, and F score. Conclusions The experimental results verified the effectiveness of the proposed SNN approaches for few-shot clinical NLP tasks.
Finetuning Analytics Information Systems for a Better Understanding of Users: Evidence of Personification Bias on Multiple Digital Channels

Abstract Although the effect of hyperparameters on algorithmic outputs is well known in machine learning, the effects of hyperparameters on information systems that produce user or customer segments are relatively unexplored. This research investigates the effect of varying the number of user segments on the personification of user engagement data in a real analytics information system, employing the concept of persona. We increment the number of personas from 5 to 15 for a total of 330 personas and 33 persona generations. We then examine the effect of changing the hyperparameter on the gender, age, nationality, and combined gender-age-nationality representation of the user population. The results show that despite using the same data and algorithm, varying the number of personas strongly biases the information system’s personification of the user population. The hyperparameter selection for the 990 total personas results in an average deviation of 54.5% for gender, 42.9% for age, 28.9% for nationality, and 40.5% for gender-age-nationality. A repeated analysis of two other organizations shows similar results for all attributes. The deviation occurred for all organizations on all platforms for all attributes, as high as 90.9% in some cases. The results imply that decision makers using analytics information systems should be aware of the effect of hyperparameters on the set of user or customer segments they are exposed to. Organizations looking to effectively use persona analytics systems must be wary that altering the number of personas could substantially change the results, leading to drastically different interpretations about the actual user base.
On the Use of Transformer-Based Models for Intent Detection Using Clustering Algorithms

Chatbots are becoming increasingly popular and require the ability to interpret natural language to provide clear communication with humans. To achieve this, intent detection is crucial. However, current applications typically need a significant amount of annotated data, which is time-consuming and expensive to acquire. This article assesses the effectiveness of different text representations for annotating unlabeled dialog data through a pipeline that examines both classical approaches and pre-trained transformer models for word embedding. The resulting embeddings were then used to create sentence embeddings through pooling, followed by dimensionality reduction, before being fed into a clustering algorithm to determine the user’s intents. Therefore, various pooling, dimension reduction, and clustering algorithms were evaluated to determine the most appropriate approach. The evaluation dataset contains a variety of user intents across different domains, with varying intent taxonomies within the same domain. Results demonstrate that transformer-based models perform better text representation than classical approaches. However, combining several clustering algorithms and embeddings from dissimilar origins through ensemble clustering considerably improves the final clustering solution. Additionally, applying the uniform manifold approximation and projection algorithm for dimension reduction can substantially improve performance (up to 20%) while using a much smaller representation.
Augmented reality technology in enhancing learning retention and critical thinking according to STEAM program

Abstract According to the science, technology, engineering, arts, and mathematics (STEAM) program, this experimental research aims to advocate e-content based on augmented reality (AR) technology to enhance retention learning (LR) and reinforce critical thinking in the intermediate stage in Ha’il, KSA. Then, we study the interaction between the technology of AR design (image/mark) and the mental capacity of learners (high/low) in developing critical thinking (CT) and practical skills, i.e., the interaction between AR (image/mark) and gender. The study’s sample consisted of 120 8th-grade junior high school students from six schools in Ha’il. 63 of the 120 participants are females, while 57 are males. They were divided into 2 control and 8 experimental groups. Our analysis revealed that students’ LR and CT skills after using AR were better than before using AR. The first result we found was that implementing AR in educational realms impacted students’ LR. Furthermore, statistically significant differences were exhibited in overall CT skills between those with high and low mental capacity (MC), favoring those with high MC. Even more interestingly, according to the STEAM program, male students’ outcomes in science learning were more reinforced by AR than females’. Future research could quantify learning outcomes and look at underserved communities. Moreover, future studies could reveal the educational benefits of augmented reality-based active learning.
Cyborg Assemblages: How autistic adults construct sociotechnical networks to support cognitive function

Autism has become a popular context for accessible technology researchers, yet a majority of HCI projects for autism and ADHD do not engage in participatory methods or otherwise involve disabled stakeholders in the project and research design. Prior inquiry has identified executive function as a common difficulty for which technologies may provide novel benefits. In this study, we explore how autistic adults currently use technologies, broadly defined, to augment executive function and support themselves in day-to-day tasks. We collect qualitative data from narratives elicited during informal asynchronous interviews to conduct a digital ethnomethodology. Following from principles of Design Justice, crip technoscience, and cyborg assemblage theory, we investigate how autistic adults articulate their own sociotechnical environments into technologically mediated assemblages of executive function and interpersonal webs of care. These patterns of sociotechnical formation inform future work in research and design for tools that can mediate executive function for all users.
Blockchain‐based internet of vehicles (<scp>BIoV</scp>): A systematic review of surveys and reviews

Abstract The Internet of vehicles (IoV) has appeared as an effective method of obtaining an intelligent transportation system able to deliver various innovative solutions and enable several applications as a replacement for vehicular ad‐hoc networks (VANETs). To help IoV contexts, enormous quantities of information are created and transmitted between various communication components wirelessly across multiple channels, which may entice attackers and put the network at risk. Security is one of the vital concerns and critical issues in VANETs and IoV networks. Blockchain is employed to create a distributed and secure IoV to overcome some centralized concerns and enhance the network architecture. This article presents a systematic and detailed review by selecting 28 review articles from 2018 to 2022 on blockchain‐based IoV (BIoV). We investigate the latest review and survey articles regarding their aspects, contributions, findings, limitations, and strong points to address unsolved problems of BIoV. In this article, the review/survey articles are systematically reviewed to resolve taxonomy aspects intended for BIoV. We provide a new taxonomy and discuss security concerns, open issues, and future directions so that researchers in this field can quickly and easily access recent content and do not need to read numerous review articles.
Correlates of Person-Specific Rates of Change in Sensor-Derived Physical Activity Metrics of Daily Living in the Rush Memory and Aging Project

This study characterized person-specific rates of change of total daily physical activity (TDPA) and identified correlates of this change. TDPA metrics were extracted from multiday wrist-sensor recordings from 1083 older adults (average age 81 years; 76% female). Thirty-two covariates were collected at baseline. A series of linear mixed-effect models were used to identify covariates independently associated with the level and annual rate of change of TDPA. Though, person-specific rates of change varied during a mean follow-up of 5 years, 1079 of 1083 showed declining TDPA. The average decline was 16%/year, with a 4% increased rate of decline for every 10 years of age older at baseline. Following variable selection using multivariate modeling with forward and then backward elimination, age, sex, education, and 3 of 27 non-demographic covariates including motor abilities, a fractal metric, and IADL disability remained significantly associated with declining TDPA accounting for 21% of its variance (9% non-demographic and 12% demographics covariates). These results show that declining TDPA occurs in many very old adults. Few covariates remained correlated with this decline and the majority of its variance remained unexplained. Further work is needed to elucidate the biology underlying TDPA and to identify other factors that account for its decline.
MOPISDE: A collaborative multi-objective information-sharing DE algorithm for software clustering

The software module clustering problem (SMCP) aims to improve the internal quality of software while helping software engineers understand the system architecture and facilitating software system maintenance. However, most current methods ignore modular stability in software evolution and the topological properties of the software architecture and hence obtain decompositions much worse than the expert. Therefore, we propose a collaborative multi-objective information-sharing differential evolution (MOPISDE) algorithm for SMCP with global stability and path complexity as two new objective functions. Specifically, two new concepts are defined for SMCP as two objectives of populations and modular quality (MQ) as an objective of the third population. Population-sharing technology is designed to collaboratively exchange information among different populations to address the lack of diversity in a single population. An information-sharing three-stage differential evolution strategy is presented to reduce the search space and improve search performance by sharing good substructures among elite individuals. New mutation strategies are proposed to utilize the different substructures between two random individuals as a new community to further improve the search performance. Experiments on various projects demonstrate the superiority of the proposed algorithm. The proposed method not only has fast convergence but also provides stable and accurate modularity that is somewhat closer to expert decomposition than that of other methods.
ReactiveFnJ: A choreographed model for Fork-Join Workflow in Serverless Computing

Abstract Function-as-a-Service (FaaS) is an event-based reactive programming model where functions run in ephemeral stateless containers for short duration. For building complex serverless applications, function composition is crucial to coordinate and synchronize the workflow of an application. Some serverless orchestration systems exist, but they are in their primitive state and do not provide inherent support for non-trivial workflows like, Fork-Join. To address this gap, we propose a fully serverless and scalable design model ReactiveFnJ for Fork-Join workflow. The intent of this work is to illustrate a design which is completely choreographed, reactive, asynchronous, and represents a dynamic composition model for serverless applications based on Fork-Join workflow. Our design uses two innovative patterns, namely, Relay Composition and Master-Worker Composition to solve execution time-out challenges. As a Proof-of-Concept (PoC), the prototypical implementation of Split-Sort-Merge use case, based on Fork-Join workflow is discussed and evaluated. The ReactiveFnJ handles embarrassingly parallel computations, and its design does not depend on any external orchestration services, messaging services, and queue services. ReactiveFnJ facilitates in designing fully automated pipelines for distributed data processing systems, satisfying the Serverless Trilemma in true essence. A file of any size can be processed using our effective and extensible design without facing execution time-out challenges. The proposed model is generic and can be applied to a wide range of serverless applications that are based on the Fork-Join workflow pattern. It fosters the choreographed serverless composition for complex workflows. The proposed design model is useful for software engineers and developers in industry and commercial organizations, total solution vendors and academic researchers.
ARRAY: Adaptive triple feature-weighted transfer Naive Bayes for cross-project defect prediction

Cross-project defect prediction (CPDP) aims to predict defects of target data by using prediction models trained on the source dataset. However, owing to the huge distribution difference, it is still a challenge to build high-performance CPDP models. We propose a novel high-performance CPDP method named adaptive triple feature-weighted transfer naive Bayes (ARRAY). ARRAY is characterized by feature weighted similarity, feature weighted instance weight, and the model adaptive adjustment. Experiments are performed on 34 defect datasets. We compare ARRAY with seven state-of-the-art CPDP methods in terms of area under ROC curve (AUC), F1, and Matthews correlation coefficient (MCC) with statistical testing methods. Experimental results show that: (1) on average, ARRAY separately improves MCC, AUC, and F1 over the baselines by at least 18.4%, 6.5%, and 4.5%; (2) ARRAY significantly performs better than each baseline on most datasets; (3) ARRAY significantly outperforms all baselines with non-negligible effect size according to post-hoc test. It can be concluded that: (1) the proposed feature weighted similarity, feature weighted instance weight, and the model adaptive adjustment are very helpful for improving the performance of CPDP models; (2) ARRAY is a more promising alternative for CPDP with common metrics.