Reviewerly/exHarmony huggingface dataset:
A Position Weighted Information Based Word Embedding Model for Machine Translation

Deep learning technology promotes the development of neural network machine translation (NMT). End-to-End (E2E) has become the mainstream in NMT. It uses word vectors as the initial value of the input layer. The effect of word vector model directly affects the accuracy of E2E-NMT. Researchers have proposed many approaches to learn word representations and have achieved significant results. However, the drawbacks of these methods still limit the performance of E2E-NMT systems. This paper focuses on the word embedding technology and proposes the PW-CBOW word vector model which can present better semantic information. We apply these word vector models on IWSLT14 German-English, WMT14 English-German, WMT14 English-French corporas. The results evaluate the performance of the PW-CBOW model. In the latest E2E-NMT systems, the PW-CBOW word vector model can improve the performance.
Emergent Communication Pretraining for Few-Shot Machine Translation

While state-of-the-art models that rely upon massively multilingual pretrained encoders achieve sample efficiency in downstream applications, they still require abundant amounts of unlabelled text.Nevertheless, most of the world's languages lack such resources.Hence, we investigate a more radical form of unsupervised knowledge transfer in the absence of linguistic data.In particular, for the first time we pretrain neural networks via emergent communication from referential games.Our key assumption is that grounding communication on images-as a crude approximation of real-world environments-inductively biases the model towards learning natural languages.On the one hand, we show that this substantially benefits machine translation in few-shot settings.On the other hand, this also provides an extrinsic evaluation protocol to probe the properties of emergent languages ex vitro.Intuitively, the closer they are to natural languages, the higher the gains from pretraining on them should be.For instance, in this work we measure the influence of communication success and maximum sequence length on downstream performances.Finally, we introduce a customised adapter layer and annealing strategies for the regulariser of maximum-a-posteriori inference during fine-tuning.These turn out to be crucial to facilitate knowledge transfer and prevent catastrophic forgetting.Compared to a recurrent baseline, our method yields gains of 59.0%∼147.6% in BLEU score with only 500 NMT training instances and 65.1%∼196.7%with 1, 000 NMT training instances across four language pairs.These proofof-concept results reveal the potential of emergent communication pretraining for both natural language processing tasks in resource-poor settings and extrinsic evaluation of artificial languages.
Synergizing Facebook and YouTube for e-Teaching Grammar during Covid-19 Pandemic

Background:Due to the on-going global COVID-19 pandemic, instructors were propelled to move from face-to-face instruction or even blended learning to online-only instruction to be able to assist, help and e-teach their students in the context of remote distance education.Both actors moved beyond the physical bounds of educational institutions and thus found refuge in social media sanctuary.For this reason, I devised a webenhanced mode of e-teaching that stems from combining some of the main educational features of two prominent social network platforms namely, Facebook and YouTube.An open community of practice in an open Facebook group was created.It included more than 80 third year secondary school students belonging to six distinct specialties and 50 high school English teachers from different cities, with the aim of leveraging digital technology to explore the potential of these social-media-mediated pathways.To supplement and consolidate secondary students' understanding of grammar lessons, personalized YouTube videos were created by taking into account the interlinked prongs of lingual, social, and cultural perspectives in the creation of these multi-mediaformat pedagogical materials in a storytelling framework.
Applying a Hybrid Sequential Model to Chinese Sentence Correction

In recent years, Chinese has become one of the most popular languages globally. The demand for automatic Chinese sentence correction has gradually increased. This research can be adopted to Chinese language learning to reduce the cost of learning and feedback time, and help writers check for wrong words. The traditional way to do Chinese sentence correction is to check if the word exists in the predefined dictionary. However, this kind of method cannot deal with semantic error. As deep learning becomes popular, an artificial neural network can be applied to understand the sentence’s context to correct the semantic error. However, there are still many issues that need to be discussed. For example, the accuracy and the computation time required to correct a sentence are still lacking, so maybe it is still not the time to adopt the deep learning based Chinese sentence correction system to large-scale commercial applications. Our goal is to obtain a model with better accuracy and computation time. Combining recurrent neural network and Bidirectional Encoder Representations from Transformers (BERT), a recently popular model, known for its high performance and slow inference speed, we introduce a hybrid model which can be applied to Chinese sentence correction, improving the accuracy and also the inference speed. Among the results, BERT-GRU has obtained the highest BLEU Score in all experiments. The inference speed of the transformer-based original model can be improved by 1131% in beam search decoding in the 128-word experiment, and greedy decoding can also be improved by 452%. The longer the sequence, the larger the improvement.
Contrast-distorted image quality assessment based on curvelet domain features

Contrast is one of the most popular forms of distortion. Recently, the existing image quality assessment algorithms (IQAs) works focusing on distorted images by compression, noise and blurring. Reduced-reference image quality metric for contrast-changed images (RIQMC) and no reference-image quality assessment (NR-IQA) for contrast-distorted images (NR-IQA-CDI) have been created for CDI. NR-IQA-CDI showed poor performance in two out of three image databases, where the pearson correlation coefficient (PLCC) were only 0.5739 and 0.7623 in TID2013 and CSIQ database, respectively. Spatial domain features are the basis of NR-IQA-CDI architecture. Therefore, in this paper, the spatial domain features are complementary with curvelet domain features, in order to take advantage of the potent properties of the curvelet in extracting information from images such as multiscale and multidirectional. The experimental outcome rely on K-fold cross validation (K ranged 2-10) and statistical test showed that the performance of NR-IQA-CDI rely on curvelet domain features (NR-IQA-CDI-CvT) significantly surpasses those which are rely on five spatial domain features.
Developing a Mobile Device Management (MDM) Security Metamodel for Bring Your Own Devices (BYOD) in Hospitals

With cybercrime on the rise, the healthcare environment has been listed as the top 5 of the most targeted industries for information security breaches. This is due to the current migration from physical to Electronic Health Records (EHR). The challenges of controlling the database costs also continue to escalate. As a result, measures such as Bring Your Own Device (BYOD) policies are commonly utilized to minimize costs and create convenience for hospital staff to use a device they are more comfortable with. However, BYOD can be used as a major entry point for gaining access to Health Information Systems (HIS) by cyber attackers/hackers despite the struggles of many hospitals to put in place effective mobile security policies. Several researches have been done to show on how to create effective mobile device BYOD strategies by using device management, data security, medical applications, information technology, education, policy, guidelines and a few others. But there is still a lack of literature about BYOD policy development in hospitals especially when it comes to Mobile Device Management (MDM), policy evaluation, and mobile device evaluation. To help address this issue, an MDM security metamodels has been proposed to help bridge this gap of knowledge between security professionals and shareholders within the healthcare environment. With awareness to the proposed solution, the elementary stage is to identify any existing MDM models that have been created for BYOD in healthcare and use the metamodel to represent some of the important existing concepts. Therefore, the context of this research paper aims to concentrate on improving existing BYOD security policies through the awareness of these existing MDM concepts that are represented through a metamodel syntax. This paper aims to discuss important MDM security concepts from various sources that have been used in healthcare, create a MDM security metamodel prototype called MDMSec ver 1.0 for the healthcare sector, using a selected metamodeling process, and lastly, to validate the prototype metamodel through two validation techniques.
Nature of Student Unrest in Secondary Schools in Kericho County Kenya

Student conflicts in secondary schools are one of the most common challenges faced in the 21st century in the modern world. Kenya has faced several incidents of student conflicts in Secondary schools mainly attributed to the cases of indiscipline and conflicts among students in the schools. In Kericho County, several incidences of student conflicts have been witnessed in the different schools, which have caused widespread material losses and other social impacts. The Specific objective was to examine the nature of Student Unrest in secondary schools in Kericho County. The study was underpinned by participative leadership theory and the functionalism theory. The study utilized the evaluation research design. The study was conducted in Kericho County. The study targetedthe following categories of respondents; students, teachers, school principals or head teachers, School boards of management, County director of education and Sub-county directors of education in Kericho county. Only 14% of the respondents agreed that there have been incidences of unrest in their schools within the last 3 years. However, this number is high considering the harm that the unrest causes. Manifestation of student unrest is usually through burning of dorms, fighting among students, boycotting classes, theft of school property, refusing punishment, bullying and striking against school administration. The causes of students include changes in government policies, poor administrative skills, congestion, inadequate and poor food quality, social media and political agitations.
HUMAN ACTIVITY RECOGNITION BASED ON SMARTPHONE SENSOR DATA USING CNN

Abstract. Human activity recognitions have been widely used nowadays by end users thanks to extensive usage of smartphones. Smartphones, by self-containing low-cost sensing technology, can track our daily activities for serving healthcare, sport, interactive AR/VR games and so on. However, smartphone technology is evolving and the techniques of using the data that smartphones go through are also improving. In this study, we used built-in sensing technologies (accelerometer and gyroscope) available in nearly every smartphone to detect the most common 5 daily activities of human by taking the data of these sensors and extract the features for a Convolutional Neural Network (CNN) model. We prepare a dataset and use TensorFlow to train the collected data from the sensors then filtered it to be processed. We also discuss the differences in CNN model accuracy with different optimizers. To demonstrate the model, we developed an android application that successfully predict an activity. We believe that after improving this application, it can be used for especially lonely old people to immediately warn authorities in case of any daily incidents.
Creating Synthetic Patients to Address Interoperability Issues: A Case Study with the Management of Breast Cancer Patients

Interoperability issues are common in biomedical informatics. Reusing data generated from a system in another system, or integrating an existing clinical decision support system (CDSS) in a new organization is a complex task due to recurrent problems of concept mapping and alignment. The GL-DSS of the DESIREE project is a guideline-based CDSS to support the management of breast cancer patients. The knowledge base is formalized as an ontology and decision rules. OncoDoc is another CDSS applied to breast cancer management. The knowledge base is structured as a decision tree. OncoDoc has been routinely used by the multidisciplinary tumor board physicians of the Tenon Hospital (Paris, France) for three years leading to the resolution of 1,861 exploitable decisions. Because we were lacking patient data to assess the DESIREE GL-DSS, we investigated the option of reusing OncoDoc patient data. Taking into account that we have two CDSSs with two formalisms to represent clinical practice guidelines and two knowledge representation models, we had to face semantic and structural interoperability issues. This paper reports how we created 10,681 synthetic patients to solve these issues and make OncoDoc data re-usable by the GL-DSS of DESIREE.
Development of an Attitude Scale of Mathematics and Science Teachers towards Mistake and Instant Feedback to the Mistake: A Validity and Reliability Study

The purpose of the research is to develop a valid and reliable attitude scale that can measure the attitudes of math and science teachers (315) and teacher candidates (105) towards mistakes and instant feedback. In the validity studies, the exploratory factor analysis was made with the SPS1S 8.0 package program after that the confirmatory factor analysis was made with Lisrel 8.8 software. To develop the scale; 1. Creation of Item Pool 2. Obtaining Expert Opinion, 3. Creation of Pre-Trial Form 4. Factor Analysis is made. According to factor analysis; Kaiser Meyer Olkin (KMO) rate; .808; Bartlett test result: 2148,354; Cronbach alpha reliability coefficient for the whole scale: .829. According to confirmatory factor analysis: Root Mean Square Error of Approximation (RMSEA) .022 (&lt;.05); p-Value for Test of Close Fit .00 (&lt;.05), standardized root mean square residual (SRMR) .014, Goodness of Fit Index (GFI) .75, Adjusted Goodness of Fit Index (AGFI) .69; Normed Fit Index (NFI) .91; Relative Fit Index (RFI) .78; Incremental Fix Index (IFI): .83; Parsimony Goodness of Fit Index (PGFI): .62; Degrees of Freedom: 760; Root Mean Square Residual (RMR): 2.07 and NonNormed Fit Index (NNFI): .88. According to research findings, attitude scale is valid and reliable so it can be used to determine math and science teachers and teacher candidates positive and negative attitudes toward mistake and giving instant feedback to mistake.
Distance education in the context of visuality: pro and contra

The paper analyzes the problem of implementing the achieve- ments of visual culture in distance education. The evolution of the dis- course of visual culture and its speci city in relation to visual studies, which perform special functions in cognition, language, education, art, science, religion, is shown. The main parameters of the relations between visual culture and distance learning as a natural result of the development of the information-digital age are determined. The e ciency of including visual presentations in the variety of their forms in distance learning is substantiated, it is proved that they carry a certain semantic load, make the subject material visible, generate various visual metaphors that form new images and visual schemes, and develop visual literacy.
Oral Malignancy Detection Using Color Features from Digital True Color Images

&lt;p class="0abstract"&gt;&lt;span lang="EN-US"&gt;One of the most prevalent forms of cancer worldwide is oral cancer which has a high rate of mortality. Diagnosis and treatment of oral premalignant lesions at an early stage reduces the death rate. The objective of this work is to detect malignancies by analyzing color features of digital true color oral images. A dataset of around 433 oral lesion images has been created that includes benign, premalignant and malignant lesions. The proposed method was experimented on this dataset. Different classifiers have been trained using various color features. The neural network classifier detects abnormalities with an accuracy of 94.82%. Results indicate that the color features have better potential in identifying benign and malignant oral lesions. &lt;/span&gt;&lt;/p&gt;
Students’ Perceptions of the Effectiveness of Using Smartphone Applications in Enhancing Vocabulary Acquisition

The normalization of mobile technology has given rise to mobile devices that are increasingly becoming effective learning platforms. This study explores Saudi learners&amp;rsquo; perceptions about the application and effectiveness of smartphone applications (apps) in enhancing their vocabulary acquisition. It also examines the factors that might affect their perceptions of smartphone apps&amp;#39; potential role in vocabulary building. An online questionnaire and the researcher&amp;rsquo;s observation were employed to elicit the data from 270 English majoring students at the University of Bisha, Saudi Arabia. SPSS and NVivo software programs were used to analyze the data. The overall findings revealed that respondents have positive perceptions of the effectiveness of smartphone apps in advancing vocabulary acquisition as they have a transformational role in providing them exposure to sufficient vocabulary input. It was also found that the two factors of familiarity with the apps&amp;#39; use and age affected respondents&amp;rsquo; perceptions of the apps effective role in vocabulary acquisition. Other factors of gender, possessing smartphones, college, educational level, frequency of using the smartphone apps, and the hours spent surfing the apps did not affect respondents&amp;rsquo; perceptions of the apps effective role in vocabulary acquisition. Hence, it is recommended that teachers and educational policymakers should encourage students to use their free time in accessing smartphone apps to enhance their vocabulary. Program designers should also consider users&amp;rsquo; learning needs.
Mandating the Use of Proximity Tracking Apps During Coronavirus Disease 2019: Ethical Justifications

The rise of the coronavirus disease 2019 (COVID-19) in a digital world has expectedly called upon technologies, such as wearables and mobile devices, to work in conjunction with public health interventions to tackle the pandemic. One significant example of this integration is the deployment of proximity tracking apps on smartphones to enhance traditional contact tracing methods. Many countries have adopted proximity tracking apps; however, there is a large degree of global differentiation in the voluntariness of the apps. Further, the concept of a mandatory policy—forcing individuals to use the apps—has been met with ethical concerns (e.g., privacy and liberty). While ethical considerations surrounding deployment have been put forth, such as by the World Health Organization, ethical justifications for a mandatory policy are lacking. Here, we use the Faden–Shebaya framework, which was formed to justify public health interventions, to determine if the compulsory use of proximity tracking apps is ethically appropriate. We show that while theoretically justified, due to the current state of proximity tracking applications and societal factors, it is difficult to defend a mandatory policy in practice.
Development and Evaluation of a Computer Game Combining Physical and Cognitive Activities for the Elderly

This study develops a cognitive computer game system for the motor-cognitive dual-task training of the elderly.This system simultaneously combines musical rhythm games with exercises for cognitive training, while the exercises are designed to correlatively combine movements with the concept of acupressure points.Incorporating the concept of acupressure points can motivate participants to complete the exercises.The system has the features of being improvable and expandable based on modular design.The system framework is divided into three parts: first, the motion sensing controller (MSC) can detect a trainee's slapping motions during cognitive training; a variety of installation methods are designed to flexibly coordinate with a user's conditions, slapping motions and acupressure points for application.Next, the rhythm game is a set of games combining images and musical rhythms.Games can be developed to correspond with different cognitive skills; in this study, relevant games have been developed for cognitive skills like processing speed, short-term memory, working memory, divided attention, and inhibitory function.Finally, the cognitive skill evaluation (CSE) uses the cognitive computer games to evaluate the user's relevant cognitive skills.Sixteen healthy elderly people aged 65 or older are recruited for evaluation of the various modes and functions of this system.After four weeks of interventional training twice a week, the subjects' cognitive skills such as short-term memory, divided attention, and inhibitory function improved significantly, and their overall cognitive function assessed by the Montreal Cognitive Assessment (MoCA) are also found to have improved.On the user's feedback questionnaire, their self-evaluation of physical conditions, the difficulty levels of the games, and their continued willingness to use the system all receive good appraisals.It can be seen that the rhythmic cognitive computer games in combination with exercises as developed in this study is helpful and feasible for the cognitive training of the elderly.
RMHSForest: Relative Mass and Half‐Space Tree Based Forest for Anomaly Detection

Anomaly detection refers to identify the true anomalies from a given data set. We present an ensemble anomaly detection method called Relative mass and half-space tree based forest (RMHSForest), which detect anomalies, including global and local anomalies, based on relative mass estimation and halfspace tree. Different from density or distance based measure, RMHSForest utilizes a novel relative mass estimation to improve the detection of local anomaly. Meanwhile, half-space tree based on augmented mass can estimate a mass distribution efficiently without density or distance calculations or clustering. Our empirical results show that RMHSForest outperforms the current popular anomaly detection algorithms in terms of AUC and processing time in the test data sets.
EXPLORING ITEMS AND DEVELOPING INSTRUMENT FOR MEASURING ORGANIZATIONAL PERFORMANCE AMONG SMALL MEDIUM ENTERPRISES IN JORDAN

This paper explored the reliability of an instrument to evaluate organizational performance (OP) of the SMEs sector in Jordan employing Exploratory Factor Analysis (EFA). The paper used EFA and is different from other studies with regard to Total Quality Management (TQM) practice as a mediator between Entrepreneurial characteristics and OP. We applied cross-sectional study design, and data collection was from a proportionate Stratified Sampling with 100 questionnaires distributed to managers or owners of Jordanian SMEs grouped by industry type (food industry, machinery/equipment, and paper manufacturing). We verified the consistency of the instrument which contained 10 items for each one of the constructs. The researchers employed Cronbach's Alpha to verify the internal reliability of the current instrument and confirmed that it was reliable. This paper provides a detailed explanation of the procedures for conducting the EFA on Organizational Performance. The outcomes of this study would benefit those involved in future research related to Organizational Performance.Keywords: Exploratory Factor Analysis, Entrepreneurial Characteristics, TQM Practices, Organizational PerformanceJEL Classifications: L2, L3DOI: https://doi.org/10.32479/irmm.10531