Reviewerly/exHarmony huggingface dataset:
Panorama of mobile device applications (Apps) for the construction industry

Purpose This study was conducted to identify, summarise, analyse and categorise mobile device applications (Apps), relevant to the construction industry and to explore their uses and exposure levels. Design/methodology/approach The research method involved reviewing literature and searching for Apps. The construction Apps were found by developing key phrases. These key phrases were used to develop search strategies, which were then used to find the construction Apps. The Apps found were categorised based on the similarity of their uses. Findings The 136 Apps identified were summarised, analysed, and categorised into 11 groups of distinct construction operations and tasks. The “Design and Drawing Apps”, “Measurement and Estimation Apps”, “Management Apps”, “All Round Apps” and “Construction Site Apps” recorded 29, 28, 26, 21 and 11 numbers of Apps, respectively. The Autodesk Sketchbook, GPS Field Area Measure, MagicPlan, Measure and TSheets were the top five in terms of the number of downloads. These Apps in terms of their exposure levels in the construction industry record 4.76%, 2.38%, 0.52%, 0.48% and 0.42%, respectively. Originality/value This paper provides a catalogue of the continuum of construction Apps for a wide variety of construction operations/activities which are available for construction professionals and provide guidance on their uses to assist in selecting appropriate Apps for specified operation/tasks/activities in the construction industry. Construction professionals may benefit from increased productivity, efficiency and ease of working.
The Cognitive Transformation of Japanese Language Education by Artificial Intelligence Technology in the Wireless Network Environment

This study aims to solve the multiscale problems faced by the current classroom student behavior target detection based on the convolutional neural network (CNN) in the wireless network environment. Firstly, the recent reform of Japanese language education is introduced. Secondly, the multiscale problem research of classroom student behavior target detection is discussed. A CNN-based new extraction network is designed based on dilated convolution and pyramid features. An anchor reconstruction algorithm based on improved K-means clustering is presented for the self-made student behavior dataset. Finally, the performance of the designed algorithm is tested. The anchor reconstruction algorithm’s mean average precision is 83.2%, and the average intersection over union is 73.7%. The experimental results of this scheme outperform the original single-shot multibox detector and K-means algorithms. Compared with other algorithms, the designed multiscale detection algorithm of classroom student behavior has the best detection effect on Pascal visual object classes (VOC) dataset. The detection accuracy of the entire dataset is 79.8%. Overall, the multiscale detection algorithm for classroom student behavior has a better detection effect on the Pascal VOC dataset and has good generalization ability and robustness. This research can guide students to recognize their class status and make corresponding adjustments to improve their learning efficiency, which has essential research significance and application value.
Color Image Mosaic Detection Algorithm Based on Cascaded Multiscale Residual Neural Networks

The appearance of a large number of image editing software packages allows people to easily tamper with image content information, resulting in a significant decrease in image credibility. A color image mosaic detection model based on CNN is proposed in this study. The cascade network structure of shallow thin neurons replaces the single network structure of deep multineurons in this study, and it compensates for the shortcomings of the previous image tampering detection algorithm using the single network structure of deep multineurons by relearning the characteristics of difficult samples. A multiscale convolution layer and a residual module are included in the model at the same time. Feature maps with different receptive fields can be fused with the multiscale convolution layer. By establishing a short connection between the input and output feature maps, the residual module can effectively reduce the risk of gradient disappearance in the model’s training process while also speeding up the network’s convergence speed. The simulation results show that this algorithm has an accuracy of 92.14% and an F1 value of 95.7%. This detection method outperforms other detection methods in terms of detection ability, reliability, and usability. This research gives users more information on which to base their judgments on when judging color mosaic images.
DIGITALISATION OF LIBRARIES (DIGITAL LIBRARIES) AND ITS SCOPE

In comparison to traditional libraries, digital libraries provide efficient and qualitative services by collecting, organizing, storing, disseminating, retrieving and preserving the information. Digital libraries support preservation besides making information retrieval and delivery more comfortable. It provides online access to historical and cultural documents whose existence is endangered due to physical decay. The major areas which offer digital libraries great exploitation are: Information retrieval, multimedia database, data mining, data warehouse, on-line information repositories, image processing, hypertext, World Wide Web and Wide Area Information Services (WAIS). Digital libraries necessarily include a strong focus on the management of digital content, just as traditional libraries have focused for long on the management of content in physical forms. Most of the digital content that is being managed includes human language, either in the form of character-coded electronic text, scanned versions of printed or handwritten text, or digital representations of human speech. Language technology therefore plays a major role in managing digital content. This comes as no surprise, of course. Digital libraries today make good use of what we know about searching large collections, and techniques such as machine-assisted indexing are employed increasingly often as we strive to extend our reach to progressively larger collections. But we are on the verge of a new era, one in which our machines will learn from what we do and then apply those capabilities to enable the management of digital content at a far larger scale than we could ever hope to do ourselves. Few advantages of digital libraries according to Haddouti are:User can access the information anywhere Reduces bureaucracy by providing access to the information The information is not necessarily located in same place Understanding the catalogue structure is not necessary Cross references to other documents speed up the work of users Full text search Protected information source Wide exploration and exploitation of the information The knowledge dissemination is an integral part of the success story of the popularity of creating digital libraries. The aim is to provide universal access to human knowledge, and given the advancement of digital storage and communications this goal is now achievable. KEY WORDS: Digitalization of Libraries, Role of community information centers in digitalization of libraries.
Factors affecting the success of VR-learning implementation in institutes of higher learning in Jordan

The goal of this study is to determine the importance of IS success and its relationships with the intent to use, utilization, user happiness, and net benefits of VR-learning at six Jordanian universities. This study focuses on the use of cultural and social elements that have been identified as important in improving students' learning processes, with the goal of persuading university administrators to pay attention to factors that influence the usage of VR-learning. At Jordan, however, there has been little success with VR-learning adoption in higher education institutions. In addition, only a few research have looked at the aspects that influence the success of VR-learning deployment.
Exploits of blockchain technology in supply chain finance: a blockchain-as-a-service perspective for Ghana’s SMEs

The implementation of blockchain technology in supply chain finance (BcSCF) offers a conducive way for entrepreneurial financing. Extending the resource-based view (RBV), a traditional perspective approach is espoused to trigger scholarly discussions, pining the development of research fronts on Blockchain-as-a-Service (BaaS) in addressing small and medium-sized enterprises (SME) financing constraints. This paper suggests that, despite the operational challenges of blockchains and the necessity to focus on core business activities, BaaS is promising, with significant efforts in terms of cost-savings, efficiency, and reliability with reduced risks. This study provides insight on BaaS contributions to fostering entrepreneurial finance as well as contributing to the literature on the accomplishments of BaaS within BcSCF provisions for particularly Ghanaian SMEs. Therefore, detailed studies into the viability of technologically-enabled types of SME finance are essential, necessitating more research. Resultantly, some forward-thinking research issues for researchers in the disciplines of innovation, technology, entrepreneurship, finance, and decision science have been presented.
Generalization to translation shifts: a study in architectures and augmentations

We study how effective data augmentation is at capturing the inductive bias of carefully designed network architectures for spatial translation invariance. We evaluate various image classification architectures (antialiased, convolutional, vision transformer, and fully connected MLP networks) and data augmentation techniques towards generalization to large translation shifts. We observe that: (a) without data augmentation, all architectures, including convolutional networks with antialiased modification suffer some degradation in performance when evaluated on translated test distributions. Understandably, both the in-distribution accuracy and degradation to shifts is significantly worse for non-convolutional models. (b) The robustness of performance is improved by even a minimal augmentation of $4$ pixel random crop across all architectures. In some instances, even $1-2$ pixel random crop is sufficient. This suggests that there is a form of meta generalization from augmentation. For non-convolutional architectures, while the absolute accuracy is still low with this basic augmentation, we see substantial improvements in robustness to translation shifts. (c) With a sufficiently advanced augmentation pipeline ($4$ pixel crop+RandAugmentation+Erasing+MixUp), all architectures can be trained to have competitive performance in terms of in-distribution accuracy as well as generalization to large translation shifts.
Experiential program “Living values and life skill education” for primary students based on the current education reform

The article introduces an overview of the experiential program “Living values and life skill education” for elementary students of the group “Hand in hand”, the program was approved by the scientific committee of the Vietnam Association of Psychology and Education since 2016, adjusted annually and printed into books to educate students in many primary schools over the country. The article focuses on analysis and discussion on: Scientific basis of the program, approach to program development, program objectives, overall content of the program, educational principles, methods, and educational organization of this program. The article also analyzes and describes the consistency of the program with the world education orientation and the goals and orientations of the current general education reform program issued by the Vietnam Ministry of Education and Training; At the same time, some proposals for the implementation of this experiential program in schools and educational institutions in the future will also be analyzed and discussed.
Image-Text Retrieval via Contrastive Learning with Auxiliary Generative Features and Support-set Regularization

In this paper, we bridge the heterogeneity gap between different modalities and improve image-text retrieval by taking advantage of auxiliary image-to-text and text-to-image generative features with contrastive learning. Concretely, contrastive learning is devised to narrow the distance between the aligned image-text pairs and push apart the distance between the unaligned pairs from both inter- and intra-modality perspectives with the help of cross-modal retrieval features and auxiliary generative features. In addition, we devise a support-set regularization term to further improve contrastive learning by constraining the distance between each image/text and its corresponding cross-modal support-set information contained in the same semantic category. To evaluate the effectiveness of the proposed method, we conduct experiments on three benchmark datasets (i.e., MIRFLICKR-25K, NUS-WIDE, MS COCO). Experimental results show that our model significantly outperforms the strong baselines for cross-modal image-text retrieval. For reproducibility, we submit the code and data publicly at: \urlhttps://github.com/Hambaobao/CRCGS.
Dual Master’s Programs as a Tool for the Formation of a Single Educational Space of the EAEU

The relevance of the topic of the article is due to the key role of higher education in the formation of the necessary compe tencies for the scientific, technological and industrial sphere of the economy of a modern state. Aim. The purpose of the article is to consider the methodological and practical issues of designing double degree master’s programs in the field of business education as one of the effective tools for the formation of a single educational space of the Eurasian Economic Union. The issues of development and implementation of joint educational programs are considered on the example of an international educational project developed at the Department of Business Informatics of the North-Western Institute of Management of the RANEPA. A substantive description of the project is given, the choice of a partner organization is substantiated, criteria for evaluating the effectiveness of the project implementation are formulated. Tasks. Give a meaningful description of the international educational project (relevance, relevance, structure, methodology and methodological novelty, competencies being formed, etc.), justify the choice of a partner organization and formulate criteria for evaluating the effectiveness of the implementation of an educational project. Methods. System approach and expert-analytical methods of research (flow methods of expertise, expert assessments). Results. The proposed methodological approach to the implementation of educational projects in the format of double diplomas allows solving the problems of internationalization of higher education, forming systems thinking, skills in working with large amounts of data and modern application packages, making non-standard decisions in the economy among students of master’s programs based on modern learning technologies. The conceptual level of the proposed approach is formed by the concepts of student-centered learning, building an individual learning trajectory for undergraduate students. The process-methodological level of the proposed approach allows the introduction of modern innovative network technologies for the implementation of the content of the disciplines of the economic mathematical cycle and the cycle of information technology, the adaptation of national educational systems to international standards and the formation of a competitive educational environment. Conclusions. The priority areas for the implementation of international educational projects are: transferring international programs to the status of network programs implemented with leading universities (including the development of double diploma programs); bringing international programs to European quality standards and requirements of international accreditation agencies; passing the international joint professional and public accreditation of international educational programs.
The Application of Waterfall Model in The Design of Zakat Management Information System at DKM Baiturrahman

The process of receiving zakat is very sensitive because zakat receipts are included in the category of financial management that needs to be accounted for in reporting. DKM Baiturrahman Mosque is one of the institutions that carry out the process of receiving zakat, where the process of receiving zakat is carried out and managed using a manual or conventional system, reporting recapitulation still uses the manual method so that data recording still has several weaknesses such as vulnerability to data loss, data search that takes time quite a long time and poor data accuracy. Therefore, it is necessary to build an information system for zakat management by using a good information technology. This application is called SIPEZAK (Sistem Informasi Pengelolaan Zakat). The development of this website-based zakat management application is built using the waterfall model approach. With this information system, it is hoped that it will help the process of receiving zakat, distributing and reporting zakat funds to be more effective and efficient
A Study of Fine Needle Aspiration Cytology of Breast Lump With The Use of National Health Service Breast Screening Program (NHSBSP) - Study Of 500 Cases

Background: FNAC is rapid, simple, safe, cost effective and good screening procedure with high sensitivity and specificity. Clinically, the diseases of breast mainly present as palpable breast lump, pain and nipple discharge. Most of the breast lumps are benign but pose a significant anxiety to the patient, which can be allayed by giving assurance that, usually it is benign. So it is necessary to distinguish benign from malignant conditions&#x0D; Methods: The present study on was carried out during June 2017 to May 2019. We categorized the patients into C1 (Inadequate), C2 (Benign), C3 (Atypia, probably benign), C4 (Suspicious of malignancy) and C5 (Malignant) category according to NHSBSP and correlated histopathologically whenever available.&#x0D; Result: Total 500 cases examined. Majority of the cases were in the 3rd decade of the life. Benign category comprised of 368 cases (73.6%), followed by malignant disease 61 (12.2%). Fibroadenoma was most common comprising of 230 cases (62.3%) among benign cases. Out of 230 cases of Fibroadenoma, most (89) cases were in 21-30 years age group, followed by 11-20 years age group. Breast carcinoma was most common (31.1%) in 41-50 years age group and 61-70 (27.9%) years age group followed by 51-60 (19.7%) years age group.&#x0D; Conclusion: FNAC of breast lesions is advantageous for both clinicians and patients because of its immediate results, economy, and accuracy. FNAC has got significant diagnostic value in differentiating benign from malignant lesions providing us valuable information for planning of subsequent therapeutic management and avoiding unwanted surgeries.
Effective Preprocessing of Medical Images using Denoising Techniques

Since the last few decades, image denoising is one of the most widely concentrated areas of research in the domain of image processing. A wide variety of denoising algorithms have been explored to date, but the problem of noise prevention in Magnetic Resonance Images is still a great barrier to the diagnosis and treatment of certain diseases. This paper mainly focuses on the study and analysis of different Denoising algorithms, the type of noise handled, and their efficiency. Preprocessing of medical images is considered one of the important steps that can enhance the accuracy in the prediction of various diseases. The presence of noise and other artifacts are believed to degrade the prediction accuracy which is the important metric that directs physicians to prolong further in providing clinical guidance to the patients. Most of the algorithms perform denoising in the complex domain. Deep learning-based Denoising algorithms are found to produce more promising results than traditional ones. However, the number of training samples and the training time are some limitations worth mentioning. Magnetic Resonance Images are sources of input for medical diagnosis of a variety of diseases. On removal of noise, these images can go a long way in the early diagnosis of numerous fatal diseases and can save lives. The predominant objective of this summary is to direct the researchers to choose prompt denoising techniques appropriate for their applications despite the available limitations in the same. This review is comprehended with the main aim of suggesting effective image denoising approaches that can go a long way in enhancing the quality of biomedical images.
Motivating Literacy and Literary Skills in Students for Remediation: An Innovative and Foregrounding Approach

Purpose: Innovative And Foregrounding Approach to Motivating Literacy and Literary Skills (IFAMLSIS) provides a summary of the writer's foreground approach to motivating students on the core aspects of reading, writing and literary skills spectacled from an innovative skills program. The module, an action research, was developed as part of remediation program for students who lacked the proficiency in English language in the Department of English of the Federal College of Education Kano, Nigeria. The focus of this review is on the fundamental components of reading, writing, and literary abilities as seen through the lens of a novel Foregrounding Approach. It has been developed with purposeful activities that are meant to ground students in the skill of reading and enjoying literary texts, which in turn, are imagined, would change them into competent writers.&#x0D; Methodology: The module's operation is based on the collaborative "Think-Pair-Share" model, which allows students to work or navigate through assignments independently at first, then in pairs, groups, or as a class, as well as share ideas and cooperate on tasks and projects.&#x0D; Findings: The researcher reveals the several modules created for remediation for students with deficiencies in English language. Each part includes thorough explanations and summaries of the contents of each unit. Also included are suggestions for the implementation of the program as well as its advantages, particularly for ESL students.&#x0D; Contribution to theory: Although the module's implementation is still in development, it is anticipated that the results will be favorable. The module will be improved using the results of its pilot testing. The government, the commercial sector, and other stakeholders in higher education are urged to contribute financially to the publication, mass manufacturing, and distribution of the module throughout Nigerian colleges of education and elsewhere.
Pseudo-Relevance Feedback Method Based on the Topic Relevance Model

In the field of information retrieval, most pseudo-relevance feedback models select candidate terms from the top <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>k</mi> </math> documents returned by the first-pass retrieval, but they cannot identify the reliability of these documents. This paper proposed a new approach to obtain feedback information more comprehensively by constructing four corresponding models. Firstly, the algorithm incorporated topic-based relevance information into the relevance model RM3 and constructed a topic-based relevance model, denoted as TopRM3, with two corresponding variants. TopRM3 estimated the reliability of a feedback document in language modeling when executing pseudo-relevance feedback from both term and topic-based perspectives. Secondly, the algorithm introduced topic-based relevance information into Rocchio’s model and constructed the corresponding model, denoted as TopRoc, with two corresponding variants. Experimental results on the five TREC collections show that the proposed TopRM3 and TopRoc are effective and generally superior to the state-of-the-art pseudo-relevance feedback models with optimal parameter settings in terms of mean average precision.