Reviewerly/exHarmony huggingface dataset:
Bathroom activities monitoring for older adults by a wrist-mounted accelerometer using a hybrid deep learning model

Monitoring activities of daily life (ADLs) allows to evaluate health conditions for older adults. However, there are still a limited number of studies on bathroom activities monitoring using a wrist-mounted accelerometer. To fill this gap, in this study, researchers collected data from 15 older adults wearing a wrist-mounted accelerometer. Six bathroom activities, i.e., dressing, undressing, brushing teeth, using toilet, washing face, and washing hands, were investigated. In total, 49.4-hour data for bathroom activities were collected. A hybrid convolutional neural network (CNN) is introduced for bathroom activity recognition. This hybrid CNN model is developed using both hand-crafted and CNN-based features as input. The proposed hybrid CNN model is compared to four machine learning models, i.e., Multilayer Perceptron (MLP), Support Vector Machines (SVM), K-nearest Neighbors (KNN), and Decision Trees (DT), and a conventional CNN model. Based on the classification results of leave-one-subject-out cross-validation (LOSO), the hybrid CNN model outperformed the other models. The hybrid CNN model is also tested based on a transfer learning method. As a calibration step based on LOSO, the transfer learning method additionally trains the model with an example of each activity from the test subject. The transfer learning method obtained better classification performance than LOSO. With transfer learning, the f1-score for using toilet was improved from 0.7784 to 0.8437. This study proposes a deep learning model fusing hand-crafted features and CNN-based features. Besides, the transfer learning method offers a way to build subject-dependent models to improve the classification performance.Clinical relevance –This provides a model that helps monitoring older adults' bathroom activities using a single wrist-mounted accelerometer.
High Spatial Resolution Remote Sensing Data Classification Method Based on Spectrum Sharing

Existing remote sensing data classification methods cannot achieve the sharing of remote sensing image spectrum, leading to poor fusion and classification of remote sensing data. Therefore, a high spatial resolution remote sensing data classification method based on spectrum sharing is proposed. A page frame recovery algorithm (PFRA) is introduced to allocate the wireless spectrum resources in low-frequency band, and a dynamic spectrum sharing mechanism is designed between the primary and secondary users of remote sensing images. Based on this, D-S evidence theory is used to fuse high spatial resolution remote sensing data and correct the pixel brightness of the fused multispectral image. The initial data are normalized, the feature of spectral image is extracted, the convolution neural network classification model is constructed, and the remote sensing image is segmented. Experimental results show that the proposed method takes shorter time and has higher accuracy for high spatial resolution image segmentation. High spatial resolution remote sensing data classification is more efficient, and the accuracy of data classification and remote sensing image fusion are more ideal.
Generation of a Synthetic Dataset for the Study of Fraud through Deep Learning Techniques

<p class='IJASEITAbtract'>Fraud is defined as any purposeful or deliberate act including cunning, deception, or other unfair means to deprive someone of property or money. Nowadays, fraud-related activities are growing at a dizzying rate, causing substantial economic losses every year. For an adequate analysis of this phenomenon, it is necessary to have data that evidences this behavior. Even so, given that these data are scarce and difficult to find, generating synthetic data for their study is a viable option. We designed two algorithms to generate text to create a synthetic data set that allows fraud analysis. These algorithms rely on the Fraud Triangle Theory proposed by Donald R. Cressey and use Recurrent Neural Network (RNN) and Long Short-Term Memory Networks (LSTM), respectively. The datasets generated were analyzed from the semantic point of view, giving a score about their readability and grammar consistency. The results obtained from this evaluation indicate that the data generation architecture proposed using the LSTM algorithm provides better performance in sentence readability (efficiency greater than 70%) than RNN (less than 40%). With LSTM, it was possible to synthesize a comprehensive data set related to the fraud triangle's vertices. This will make it easier to investigate fraudulent actions that are linked to human behavior. We will present a fraud predictor system based on machine learning techniques in the future.
Radiometric Identification of Signals by Matched Whitening Transform

Radiometric identification is the problem of attributing a signal to a specific source. In this work, a radiometric identification algorithm is developed using the whitening transformation. The approach stands out from the more established methods in that it works directly on the raw IQ data and hence is featureless. As such, the commonly used dimensionality reduction algorithms do not apply. The premise of the idea is that a data set is “most white” when projected on its own whitening matrix than on any other. In practice, transformed data are never strictly white since the training and the test data differ. The Förstner-Moonen measure that quantifies the similarity of covariance matrices is used to establish the degree of whiteness. The whitening transform that produces a data set with the minimum Förstner-Moonen distance to a white noise process is the source signal. The source is determined by the output of the mode function operated on the Majority Vote Classifier decisions. Using the Förstner-Moonen measure presents a different perspective compared to maximum likelihood and Euclidean distance metrics. The whitening transform is also contrasted with the more recent deep learning approaches that are still dependent on feature vectors with large dimensions and lengthy training phases. It is shown that the proposed method is simpler to implement, requires no features vectors, needs minimal training and because of its non-iterative structure is faster than existing approaches.
Connecting Patient-Centric Blockchains with Multilayer P2P Networks and Digital Platforms

Healthcare represents a data-sensitive industry, and blockchain validation is a disrupting innovation in the field. Medicine ecosystems are increasingly patient-centric, being nurtured by growing amounts of (big) data always in need of validation. Blockchains play a vital role in this process, enabling transactions among peer-to-peer (P2P) entities without the need for a trusted third party, adding value to certified data. Patients represent a primary stakeholder that is linked to others through an interactive network. Complementary layers, linked by replica nodes and/or connecting edges, are fitter than atomistic networks to represent the multisided blockchain applications. These networks of networks increasingly embed digital platforms that represent a virtual bridging stakeholder. Being blockchains a distributed ledger that shares data among a network of peers, they can be naturally linked to digital networks. And digitalization—with its IT features—makes the stakeholders’ interaction intrinsically fitter for blockchains. Digital scalability deployed by platforms and their ubiquitous and permanent 24/7 features enhance the value co-creation of stakeholders (patients with healthcare providers, as MedTech players) that participate in multiple blockchains. The synergistic interaction of eHealth digital platforms and multilayer networks creates a proactive ecosystem that leverages healthcare blockchains, fostering massive adoption in a sensitive industry where privacy concerns increasingly matter. Patient-centric blockchains emerge as a natural validating byproduct of these interactions and may strongly contribute to foster long-term healthcare sustainability. This is also due to the circumstance that many healthcare bottlenecks may be softened thanks to blockchain adoption. This chapter shows some which are possible win-win patterns of the networked P2P interactions through blockchains. The intrinsic scalability of the model makes it ideally ubiquitous, with potential applications beyond the sensitive healthcare industry.
Challenges in Implementing the CEFR in English Language Classrooms

The adoption of the Common European Framework of Reference for Languages (CEFR) in the English Language Education Roadmap 2015-2025 is a reform effort to enhance the standard of our students’ English language proficiency level. While it is a commendable initiative, its implementation in schools has received mixed reactions especially from teachers who are confronted with a number of challenges. Hence, this study was carried out to investigate the nature of challenges encountered by English language teachers in implementing the CEFR in their classrooms. Data for this study were collected from questionnaires gathered from 117 English language teachers in primary schools in Johor Bahru district. The findings revealed the challenges were related to teachers’ English language proficiency level, designing class activities, students’ participation in class, the textbooks used, teachers’ workload, and students’ proficiency level. The challenges were found to be inter-related and could be traced to lack of CEFR-related training, insufficient teaching and learning materials, constraint of time, and the policy which does not allow for students to be streamed based on their proficiency level. All this seemed to make it more difficult for the teachers to implement the CEFR successfully.
Investigation of Input Modalities Based on a Spatial Region Array for Hand-Gesture Interfaces

To improve the efficiency of computer input, extensive research has been conducted on hand movement in a spatial region. Most of it has focused on the technologies but not the users’ spatial controllability. To assess this, we analyze a users’ common operational area through partitioning, including a layered array of one dimension and a spatial region array of two dimensions. In addition, to determine the difference in spatial controllability between a sighted person and a visually impaired person, we designed two experiments: target selection under a visual and under a non-visual scenario. Furthermore, we explored two factors: the size and the position of the target. Results showed the following: the 5 × 5 target blocks, which were 60.8 mm × 48 mm, could be easily controlled by both the sighted and the visually impaired person; the sighted person could easily select the bottom-right area; however, for the visually impaired person, the easiest selected area was the upper right. Based on the results of the users’ spatial controllability, we propose two interaction techniques (non-visual selection and a spatial gesture recognition technique for surgery) and four spatial partitioning strategies for human-computer interaction designers, which can improve the users spatial controllability.
Finding the curved pathway of the large intestine for robot-aided colonoscopy

Abstract To assist the insertion of a robot-aided endoscope during colonoscopy, a measuring system is required so that the endoscope tip can align automatically and thus find the curved pathway of the large intestine. To achieve this, a selfexpanding nitinol wire basket is used to sense the contour of the intestine. As the wire basket touches the wall, it is deflected towards the center of the intestine. The relative position of the wire basket within the camera image is captured, which describes the desired direction to follow the organ. To identify the wire basket in the image, the original RGB image stream is converted into the HSV (hue, saturation, value) color space. Thus, a binary image can be created, in which only the neongreen color portion of the wire basket is visible as a cross. The Hough Transformation is used to search for straight lines in the binary image. Once two lines are found, the intersection point can be calculated and thus its position in the image. The evaluation of the execution time of the algorithm on a live stream was 45 ± 31 ms on average. The algorithm robustly recognizes the wire basket even if it was not visible to the human eye in the original RGB image due to deficient lighting.
THE EMPIRICAL REVIEW OF THE ORGANIZATIONAL, TECHNOLOGICAL, ENVIRONMENTAL AND HUMAN RESOURCES FACTORS ON THE EFFICIENCY OF THE DIGITAL TRANSFORMATION, OMAN

The Sultanate of Oman accepts and uses e-services among public services, which largely depend on the institutional process and implementation of e-government, sometimes with different preferences for the reality of digitalization. It is important and plays an important role in the economic, political and prosperity of the Middle East. Therefore, the country is focusing on technology that enables services and government-wide strategies. Regarding such issues, the main aim of the study is to examine the means available to examine the effectiveness of electronic services and digital transformation in the selected public sector in the Sultanate of Oman. The study used online empirical sources in the names of organizational, technology, environment and human resource factors, as well as the effectiveness of digital change in various online database sources such as Google Scholars, Springer Link, Wiley, Science Direct, JSTOR, Emerald Full text, Scopus and EBSCO HOST, etc. The review results show that organizational, technological, environmental and human factors play an important role in ensuring the effectiveness of the public sector in the digital transformation in the Sultanate of Oman. The study also found that the success of e-government in Oman depends on the willingness and intention of citizens to use e-government services. The country's government should consider developing vital IT projects, ignoring citizens' opinions, to make traditional services more efficient online. Understanding citizen adoption is an important element of E-Government, enabling it to provide policymakers and decision makers with a range of strategic management plans and actions to build and rapidly accelerate adoption of these services to increase citizen participation, also the adoption rate. A summary of the study's findings provides guidance for the future scope of empirical research to ensure the effectiveness of the public sector for digital transformation in the Sultanate of Oman and its regions.
Lessons Learned about FOLIO’s APIs

APIs (Application Programming Interface) provide the ability to do what needs to be done. The fact that FOLIO includes API as one of its building blocks makes it that much more attractive. When my library’s administration decided to switch from a legacy ILS (Integrated Library System) to a FOLIO LSP (Library Services Platform) the first thing that I looked at was the API. The lessons learned helped me to configure the system and massage the data from ILS output to FOLIO-friendly input. By building web applications and writing Perl scripts our staff is able to get the job done even when it is impossible to accomplish the task through the user interface (UI).
New trends in the encyclopedic practice shaped by the digital age (based on Scholarpedia and Scholarly Community Еncyclopedia)

The article describes the changes in the sphere of encyclopedia publishing (online encyclopedic practice) on the example of some notable Internet resources (Scholarpedia and Scholarly Community Encyclopedia) caused by the development of digital technologies. With the appearance of electronic encyclopedias in the late 20th century, the encyclopedic activity has innovated with the multimedia content and the speed of its search. However, today digital encyclopedic practice is moving from formal to qualitative changes associated with the improvement of encyclopedic content and its building. In particular, the model of creating the analyzed in this article the Scholarpedia and the Scholarly Community Encyclopedia shows new approaches to the organization of encyclopedic practice that are different from the traditional ones. It expands the opportunities for improving the quality of encyclopedias as well as reaching them to a new level.
Solution of Mixed-Integer Optimization Problems in Bioinformatics with Differential Evolution Method

The solution of the so-called mixed-integer optimization problem is an important challenge for modern life sciences. A wide range of methods has been developed for its solution, including metaheuristics approaches. Here, a modification is proposed of the differential evolution entirely parallel (DEEP) method introduced recently that was successfully applied to mixed-integer optimization problems. The triangulation recombination rule was implemented and the recombination coefficients were included in the evolution process in order to increase the robustness of the optimization. The deduplication step included in the procedure ensures the uniqueness of individual integer-valued parameters in the solution vectors. The developed algorithms were implemented in the DEEP software package and applied to three bioinformatic problems. The application of the method to the optimization of predictors set in the genomic selection model in wheat resulted in dimensionality reduction such that the phenotype can be predicted with acceptable accuracy using a selected subset of SNP markers. The method was also successfully used to optimize the training set of samples for such a genomic selection model. According to the obtained results, the developed algorithm was capable of constructing a non-linear phenomenological regression model of gene expression in developing a Drosophila eye with almost the same average accuracy but significantly less standard deviation than the linear models obtained earlier.
Distributed ledger technology - Addressing the challenges of assurance in accounting systems: A research note

Background: With the progressive development of blockchain technology, its potential influence on the accounting and auditing professions is of interest to academia and practitioners. As the technology gains acceptance in businesses such as banking, stock exchanges, insurance, law, government services, and e-voting, business leaders are beginning to recognise its potential to transform their organisations. Despite concerns about how this technology will marginalise the accounting and auditing profession, blockchain continues to lag behind in adoption and there is time for accountants and auditors to reflect on their current practice and update their knowledge and skills to maintain their relevance to the industry. Motivation: The literature has not fully examined the implications of distributed ledger technology and its implications for the accounting and auditing profession. The intent of this research note is to identify opportunities for research that are of significance to the application of distributed ledger technology to accounting and auditing. Research Question: To identify possibilities that exist in researching the adoption, implementation and application of a distributed ledger solution in the context of accounting and auditing. Framework: Based on the literature, the study proposes a framework for a blockchain model of a simplified triple-entry bookkeeping system using smart contracts to automate self-verification and replication of transactions in a public distributed ledger. Findings: Drawing on the framework the article develops a series of research questions that may significantly reduce barriers and challenges facing organizations that want to implement blockchain technology in their accounting systems. Contribution: Given the complex nature of blockchain, cross disciplinary research is proposed to bring together information technology, accounting, assurance, economics and psychology resulting in further understanding of the technology as it relates to, and influences the accounting and auditing profession. In doing so, the paper makes several contributions to the literature.
Special Issue on Multi-Robot Systems: Challenges, Trends, and Applications

Multi-Robot Systems (MRSs) have emerged as a suitable alternative to single robots to improve current and enable new missions [...]
Speech Recognizing Comparisons Between Web Speech API and FPT.AI API

Nowadays, people use speech recognition services for many purposes in their daily lives, such as learning foreign languages, communicating, etc. Therefore, they need to decide which ones to use. High accuracy and short processing time speech recognition service will help improve the work effectively as the time to re-check output results and the delay time between recognition tasks. For Vietnamese speech recognition, Web Speech API and FPT.AI API are popular. Web Speech API supports multiple languages, while FPT.AI API focuses on Vietnamese as FPT.AI’s products are developed exclusively for the Vietnamese market. In order to assist people in choosing a suitable Vietnamese speech recognition service, in this paper, the speech recognizing accuracy and processing time between Web Speech API and FPT.AI API has been compared. 307 audio files containing Vietnamese speeches which are obtained from FPT Open Speech Dataset were chosen to test the accuracy and the processing time of both APIs. For the accuracy test, FPT.AI API was 0.57% more precise than Web Speech API. However, in the processing time test, Web Speech API was 50.99% faster than FPT.AI API. For Web Speech API, it was mostly accurate to process 12–14-second-long audio files, while FPT.AI API did best when process 2–4-second-long audio files. The audio files with duration values between 2 and 8 seconds are optimal for both APIs to proceed with STT conversions.
Android Pre-installed Applications Effects On User’s Privacy

Vendors are allowed put any pre-installed applications on their devices that have open-source Android operating system. Even if these applications have risk on users&#x2019; privacy and security, number of studies that analyze them in that context inclusively is limited. In this study, a public data set of pre-installed applications to fulfill the gap in this field is introduced. Also, using these data set, effects of Tracking Software Development Kits that have been found in pre-installed application on user privacy are analyzed. Thus, exploration of pre-installed tracking ecosystem on systems is contributed, and relationship between these software and third parties is exposed. Moreover, with a user study, users&#x2019; knowledge and perceptions about pre-installed applications are measured and their awareness levels are revealed.
HR strategy is necessary to underline the interactive relationship between business strategy and Human resource management

The higher education sector faces considerable competition around the world. Accordingly, universities need to make more efforts to increase their competitive advantages. This study aimed to empirically investigate the effect of organizational innovation (OI), knowledge management (KM), and strategic human resource management (SHRM), with a dependent variable of sustainable competitive advantages (SCAs), at Jordanian Universities. For this aim, a specially designed questionnaire has been distributed to study a convenience sample of 400 academic and administrative staff at Jordanian private and public universities, to obtain the required quantitative data. The study’s hypotheses were verified by Baron and Kenny’s mediation regression approach using the software Statistical Package for the Social Sciences (SPSS). The results of the study demonstrate that there is a significant positive relationship between the following pairs of variables: KM and SCA; SHRM and SCA; SHRM and OI; KM and...