Reviewerly/exHarmony huggingface dataset:
Cross-Scene Sign Language Gesture Recognition Based on Frequency-Modulated Continuous Wave Radar

This paper uses millimeter-wave radar to recognize gestures in four different scene domains. The four scene domains are the experimental environment, the experimental location, the experimental direction, and the experimental personnel. The experiments are carried out in four scene domains, using part of the data of a scene domain as the training set for training. The remaining data is used as a validation set to validate the training results. Furthermore, the gesture recognition results of known scenes can be extended to unknown stages after obtaining the original gesture data in different scene domains. Then, three kinds of hand gesture features independent of the scene domain are extracted: range-time spectrum, range-doppler spectrum, and range-angle spectrum. Then, they are fused to represent a complete and comprehensive gesture action. Then, the gesture is trained and recognized using the three-dimensional convolutional neural network (CNN) model. Experimental results show that the three-dimensional CNN can fuse different gesture feature sets. The average recognition rate of the fused gesture features in the same scene domain is 87%, and the average recognition rate in the unknown scene domain is 83.1%, which verifies the feasibility of gesture recognition across scene domains.
Saliency Guidance and Expansion Suppression on PuzzleCAM for Weakly Supervised Semantic Segmentation

The semantic segmentation model usually provides pixel-wise category prediction for images. However, a massive amount of pixel-wise annotation images is required for model training, which is time-consuming and labor-intensive. An image-level categorical annotation is recently popular and attempted to overcome the above issue in this work. This is also termed weakly supervised semantic segmentation, and the general framework aims to generate pseudo masks with class activation mapping. This can be learned through classification tasks that focus on explicit features. Some major issues in these approaches are as follows: (1) Excessive attention on the specific area; (2) for some objects, the detected range is beyond the boundary, and (3) the smooth areas or minor color gradients along the object are difficult to categorize. All these problems are comprehensively addressed in this work, mainly to overcome the importance of overly focusing on significant features. The suppression expansion module is used to diminish the centralized features and to expand the attention view. Moreover, to tackle the misclassification problem, the saliency-guided module is adopted to assist in learning regional information. It limits the object area effectively while simultaneously resolving the challenge of internal color smoothing. Experimental results show that the pseudo masks generated by the proposed network can achieve 76.0%, 73.3%, and 73.5% in mIoU with the PASCAL VOC 2012 train, validation, and test set, respectively, and outperform the state-of-the-art methods.
Data and knowledge co-driving for cancer subtype classification on multi-scale histopathological slides

Artificial intelligence-enabled histopathological data analysis has become a valuable assistant to the pathologist. However, existing models lack representation and inference abilities compared with those of pathologists, especially in cancer subtype diagnosis, which is unconvincing in clinical practice. For instance, pathologists typically observe the lesions of a slide from global to local, and then can give a diagnosis based on their knowledge and experience. In this paper, we propose a Data and Knowledge Co-driving (D&K) model to replicate the process of cancer subtype classification on a histopathological slide like a pathologist. Specifically, in the data-driven module, the bagging mechanism in ensemble learning is leveraged to integrate the histological features from various bags extracted by the embedding representation unit. Furthermore, a knowledge-driven module is established based on the Gestalt principle in psychology to build the three-dimensional (3D) expert knowledge space and map histological features into this space for metric. Then, the diagnosis can be made according to the Euclidean distance between them. Extensive experimental results on both public and in-house datasets demonstrate that the D&K model has a high performance and credible results compared with the state-of-the-art methods for diagnosing histopathological subtypes. Code: https://github.com/Dennis-YB/Data-and-Knowledge-Co-driving-for-Cancer-Subtypes-Classification.
FACTORS EFFECTING PURCHASE INTENTION IN BLOCKCHAIN AND NFT (NON-FUNGIBLE TOKEN) TECHNOLOGIES

Digitalization began to find a place in every industry faster than expected with COVID-19. Although terms such as Blockchain, NFT, Web 3.0, and Metaverse, which entered our lives with digitalization, are still very new, they will create a paradigm shift from standard business models. Blockchain technologies, especially NFTs, are creating a new opportunity for brands and creative industries as the frontrunner of digitalization with the innovative approach that is driven by scarcity, a new medium to connect with fans, and decentralized distribution power. This paper aims examine the effects of perceived value -with its predecessors scarcity, ownership &amp; uniqueness- and trust in blockchain on the purchase intention of NFTs, considering the technology adoption. In this regard, data was collected via questionnaire from NFT owners with different nations in Discord channels using convenience sampling technique. SEM was performed as statistical analysis, and the results indicated technology adoption is a significant variable on trust in blockchain, which has a direct effect on purchase intention. On the hand, even though scarcity and ownership were positively associated with perceived value, this construct had no statistically significant effect on purchase intention.
Time Series Analysis and Prediction on Bitcoin

Bitcoin is the most famous digital currency in the world and has become an investment asset. Prediction is one of the important matters in the investment market. In the economic field, there are different studies on the reasons for the price change of Bitcoin and how to predict the price trend of Bitcoin or how Bitcoin studies the market. Therefore, for Bitcoin, predicting the trend of Bitcoin price can effectively help Bitcoin investors. Data from www. Coingecko, the price of bitcoin is sorted according to the time sequence. Using the time series model, the change of bitcoin price in a specific period which is from 28 April 2013 to 22 August 2022 is calculated to predict the future trend of bitcoin price. Data preprocessing includes attributes removal, stationary test, and differencing. In predicting the price of Bitcoin, the ARIMA method that can produce high accuracy in short-term prediction is adopted. Use prediction test AIC and Check the residuals to select the best prediction model among the candidate models. The results of model testing show that AIC of ARIMA (5,1,2) is the smallest among all candidate models, and the results of residual check also show that ARIMA (5,1,2) model is the best model for predicting four periods.
Education Leaders' Perception on the Effectiveness of Online Learning during the COVID -19 Crises in UAE Universities

Purpose: The current study aimed at exploring education leaders' perceptions of online learning in four universities in the United Arab Emirates' (UAE) during the COVID-19 pandemic crisis. The study explored two dimensions of educational institutions’ experiences during the pandemic: the effectiveness and educational impact of online learning.&#x0D; Design/Methodology/Approach: To achieve the aim of the study, a 34-items survey was developed. The validity and reliability of the survey were ensured. A total of 113 education leaders across five UAE universities were surveyed in order to know their views on the effectiveness of online teaching during COVID-19 lockdowns.&#x0D; Findings: The results of the study showed that despite the pressure, stress and uncertainty that accompanied the shift to online education as a result of the spread of coronavirus. University education leaders highly appreciated online teaching in both domains of the survey. However, the more experienced education leaders expressed positive views on the effectiveness of online learning during the COVID-19 pandemic. At the same time, there were no significant differences in perceptions among education leaders based on gender, academic rank or position.&#x0D; Conclusion: The current study explored education leaders’ views on online learning. Apparently, addressing differences among education leaders can help in providing more effective and sound learning experiences.&#x0D; Practical implications: The results of the study highlighted the crucial role of education leaders especially during times of crises and massive disruptions and the need to take into account the differences between their views. Therefore, the study suggests dealing with education leaders' differences carefully in order to deliver effective online learning.&#x0D; Contribution to literature: The study provides a different perspective on online learning as it captures education leaders' perspectives on online learning during the COVID-19 pandemic.
Employing cluster-based class decomposition approach to detect phishing websites using machine learning classifiers

Phishing is an attack by cybercriminals to obtain sensitive information such as account IDs, usernames, and passwords through the use of the anonymous structure of the Internet. Although software companies are launching new anti-phishing tools that use blacklists, heuristics, visual methods, and machine learning-based methods, these products cannot prevent all phishing attacks. This research offers an opportunity to increase accuracy in the detection of phishing sites. This study develops a model using machine learning algorithms, specifically the decision tree and the random forest, due to their outperforming the rest of the classifiers and being accredited by researchers in this field to achieve the highest accuracy. The study is based on two phases: the first phase is to measure the accuracy of classifiers on the dataset in the usual way before and after feature selection. The second phase uses the class decomposition approach and measures the accuracy of classifiers in the dataset before feature selection and after feature selection to detect phishing sites. The class decomposition approach is a technique to improve the performance of classifiers by distributing each class into clusters and renaming the examples of each cluster with a new class. This provides a specific metric that more accurately predicts the level of phishing. Testing on a dataset containing 11,055 instances, 4,898 phishing, and 6,157 legitimate, each instance has 30 features. It achieved the highest accuracy in the first phase through the random forest algorithm by 96.9% before feature selection, and after feature selection, it was by 97.1%. In the second phase, the highest accuracy of both the decision tree and random forest classifiers was achieved by 100% with the two and four classes after feature selection. While before feature selection, the random forest algorithm achieved 100% with only the two classes.
Drivers of firms’ sociability on social media: Evidence from an emerging country

The purpose of this paper is to investigate the drivers of firms’ sociability on social media (SM), an unregulated area, in an emerging country, namely, Kingdom of Saudi Arabia (KSA). The sample of this study is 345 non-financial listed firms on Tadawul stock exchange during 2016-2019. Data are collected from several sources such as annual reports, the official website of the sample companies. Other data are collected manually such as the presence of the CEO and the sampled companies on SM. Our findings show that firm size and leverage level are important firms’ characteristics that drive firms’ sociability on social media. The finding shows that CEO sociability on social media is a key CEOs’ characteristic that drives firms’ sociability on social media. Further analysis reports that there is a complementary effect between CEO’s sociability on social media and firm size in increasing firms’ sociability on SM. The findings also show that there is a complementary effect between CEO’s sociability on social media and firm leverage in increasing firms’ sociability on SM. This study contributes to the disclosure literature by providing empirical evidence of the drivers of firms’ sociability on SM, an unregulated area in KSA. It also complements the considerable literature on voluntary disclosure which ignores the use of SM platforms as a “new” voluntary type of reporting. The present study complements recent literature on the adoption of SM by providing evidence that the sociability of top leaders is a driver of firms’ sociability on SM.
Analisa dan Rancangan Sistem Informasi Pariwisata Pacitan dengan UML dan ERD

Pacitan Kota Seribu Goa dimana pacitan memiliki banyak goa, pantai dan tempat wisata sehingga pacitan terkenal akan keindahan pariwisatanya. Saat ini, Pacitan belum memiliki sistem yang mengelola informasi-informasi pariwisata di Pacitan sehingga pengunjung akan mengumpulkan informasi-informasi yang terdapat di blog – blog satu persatu selain itu banyak tempat wisata pacitan yang belum diketahui pengunjung yang sebenarnya dapat dijadikan alternatif kunjungan wisata sehingga diperlukan sistem informasi pariwisata pacitan. Sistem Informasi yang akan dibangun terlebih dahulu harus dilakukan analisa dan rancangan. Tujuan penelitian ini melakukan analisa dan rancangan sistem informasi pariwisata Pacitan. Penelitian dimulai dari mengumpulkan sumber referensi, mengumpulkan data, analisa dan merancang sistem. Penelitian ini menghasilkan analisa sistem informasi pariwisata pacitan berupa Use Case Diagram dan Activity Diagram serta rancangan sistem informasi(SI) pariwisata pacitan berupa Entity Relationship Diagram, Class Diagram dan rancangan untuk antar muka pengguna. Analisa dan Rancangan Sistem Informasi Pariwisata ini berguna ketika implementasi ke sistem informasi pariwisata pacitan.&#x0D; &#x0D; Abstract: Pacitan City of a Thousand Caves where Pacitan has many caves, beaches and tourist attractions so that Pacitan is famous for the beauty of its tourism. Currently, Pacitan does not yet have a system that manages tourism information in Pacitan so that visitors will collect information contained in blogs one by one besides that there are many Pacitan tourist attractions that are not yet known to visitors which can actually be used as an alternative to tourist visits so a system is needed. Pacitan tourism information, while in the early stages of building an information system, analysis and design must first be carried out. This study aims to analyze and design a tourism information system in Pacitan. Research starts from collecting reference sources, collecting data, analyzing and designing systems. The research uses the waterfall method, where in this study there are still three initial stages in the method, namely collecting user needs, analyzing and designing information systems. This study resulted in an analysis of the Pacitan tourism information system in the form of Use Case Diagrams and Activity Diagrams and the design of the Pacitan tourism information system (SI) in the form of Entity Relationship Diagrams, Class Diagrams and designs for user interfaces. Analysis and Design of this Tourism Information System can be used to be implemented into tourism information systems, especially tourism in Pacitan Regency.
Sistem Informasi Geografis Pemetaan Lokasi Stunting Di Desa Gereneng Timur Berbasis Web

Cases of malnutrition often occur every year, one of them is in Gereneng Timur Village. Because the community's understanding of stunting is still lacking and it is difficult to obtain information quickly, it is a weakness for local governments to be able to identify locations affected by malnutrition. Case handling has also not been effective because data collection is still being carried out manually by the Village Maternity Boarding School (POLINDES) or the village government. The results of this researcher designed web-based software using the waterfall model in the development process and utilized the Geographic Information System (GIS) in mapping stunting locations in East Gereneng Village which aims to display location points along with the number of toddlers affected by stunting and information on how parents prevent stunting from an early age accessed by the community and village government.
Paleoproterozoic crustal evolution and Mesoproterozoic overprint in the Roosevelt-Guariba Transpressive Belt, Juruena Terrain, Southeastern Amazonas, Brazil

The Juruena Terrain (Rondônia-Juruena Province) in the southeastern Amazonas State shows a NW-SE dextral shear zones system denominated Roosevelt-Guariba Transpressive Belt (RGTB).The RGTB was generated in response to intracontinental crustal reworking, juxtaposing Paleoproterozoic Juruena granitic-gneiss and coeval supracrustal associations.This granitic-gneiss basement shows high-K calcalkaline (Teodósia Suite) to transitional I-to A-type (Igarapé das Lontras Suite) signatures, produced in a continental magmatic arc setting around 1760-1740 Ma (zircon, titanite; U-Pb SHRIMP, LA-ICP-MS).New geochronological data about of the deformation and metamorphic rocks in the RGTB were obtained from migmatite paragneisses, amphibolites, S-type leucogranite (Quatro Cachoeiras Complex and Itamaraty Suite; zircon U-Pb SHRIMP) and protomylonites and mylonites (Igarapé das Lontras and Teodosia protoliths; amphibole-muscovite-sericite Ar-Ar step heating).The results suggest that Southeastern Amazonas (northern Juruena Terrain) was metamorphosed/deformed during 1530-1460 Ma under temperatures of 900 o C (zircon) to 580 o C (hornblende) and 420 o C (muscovite).The geochronological data show that the granite-gneiss basement (continental magmatic arc) and supracrustal rocks (foreland basin) were reworked by a regional event with an age interval of 1530 Ma (high grade and anatexis) to 1460 Ma (medium grade).Finally, the Juruena Terrain is affected by a younger low-T event (1300 Ma, muscovite Ar-Ar step heating) with wide NE-trending structures (e.g.Buiuçu Shear Zone).This 1.3 Ga event suggests a continental reactivation caused by the pericratonic deformation related to Candeias/ Sunsás Orogeny during the Columbia Supercontinent break-up, before the Rodinia Supercontinent assembly.In summary, the Juruena Terrain in the southeast Amazonas shows a complex metamorphic and structural intracontinental evolution, involving polycyclic events from late Orosirian to Ectasian in a convergent tectonic setting.Therefore, the Juruena Terrain shows similar accretionary histories with other orogens and has apparent long-lived connections with Laurentia (Yavapai Province) and Baltica (Transscandinavian Igneous Belt), forming the core of the Columbia Supercontinent.Paleoproterozoic crustal evolution and Mesoproterozoic overprint in the Roosevelt-Guariba Transpressive Belt, Juruena Terrain, Southeastern Amazonas, Brazil
Medical QA Oriented Multi-Task Learning Model for Question Intent Classification and Named Entity Recognition

Intent classification and named entity recognition of medical questions are two key subtasks of the natural language understanding module in the question answering system. Most existing methods usually treat medical queries intent classification and named entity recognition as two separate tasks, ignoring the close relationship between the two tasks. In order to optimize the effect of medical queries intent classification and named entity recognition tasks, a multi-task learning model based on ALBERT-BILSTM is proposed for intent classification and named entity recognition of Chinese online medical questions. The multi-task learning model in this paper makes use of encoder parameter sharing, which enables the model’s underlying network to take into account both named entity recognition and intent classification features. The model learns the shared information between the two tasks while maintaining its unique characteristics during the decoding phase. The ALBERT pre-training language model is used to obtain word vectors containing semantic information and the bidirectional LSTM network is used for training. A comparative experiment of different models was conducted on Chinese medical questions dataset. Experimental results show that the proposed multi-task learning method outperforms the benchmark method in terms of precision, recall and F1 value. Compared with the single-task model, the generalization ability of the model has been improved.
A Valid and Reliable Scale for Education 4.0 Competency Determination (E4CD)

The purpose of this study is to develop a valid and reliable “Education 4.0 Competency Determination” scale. The study was conducted in two sessions: In the first session, exploratory factor analysis was applied to 308 students, and then a confirmatory factor analysis was applied to another group and conducted with 172 students. As a result, a scale consisting of 21 items and three- factors was obtained. The internal consistency coefficient of the first factor, which is mastery of digital technology, was α = .925 using the Cronbach’s Alpha formula and .921 using the Guttman’s Split-half method; the internal consistency coefficient of the second factor, which is information management, was α = .880 and .884 according to Guttman’s Split-half method while the internal consistency coefficient of the third factor, which is active participation in the process, was α = .802 according to the Cronbach’s Alpha formula and .783 according to the Guttman’s Split-half method.
Adapting Scrum for Software Capstone Courses

Scrum is a widely-used framework in industry, so many schools apply it to their software engineering courses, particularly capstone courses. Due to the differences between students and industrial professionals, changing Scrum is necessary to fit capstone projects. In this paper, we suggest a decision-making process to assist instructors in developing a strategy to adapt Scrum for their course. This framework considers critical differences, such as student’s workloads and course schedules, and keeps the Agile principles and Scrum events. To evaluate the adapted Scrum, we investigated student’s learning experiences, satisfaction, and performance by quantitatively analyzing user story points and source codes and qualitatively studying instructor’s evaluations, student’s feedback, and Sprint Retrospective notes. Our two case studies about adapted Scrum showed that having daily stand-up meetings in every class was not helpful, student’s satisfaction positively correlated to the difficulty of the task they tackled, and the project provided good learning experiences.
Efficient Dataflow Modeling of Peripheral Encoding

Identifying a visual stimulus in the periphery can be substantially impaired by the presence of other, nearby stimuli -- a phenomenon called “crowding”. Existing crowding models require significant compute time and their structure does not support easy modification, making it difficult to conduct large scale perceptual validation experiments. We advance crowding models of peripheral vision using a more efficient dataflow computational strategy which also simplifies adding and removing features. We leverage this flexibility by adding Gaussian filters that reduce ringing and encoding “end stopped” features which were missing from previous models. We evaluate our model in the context of texture recognition and letter crowding tasks in the periphery. Our improved computational framework may simplify development and testing of more sophisticated, complete models in more robust and realistic settings relevant to human vision and computer graphics.