Reviewerly/exHarmony huggingface dataset:
Deep Belief Network for Predicting the Predisposition to Lung Cancer in TP53 Gene

Lung cancer, similar to other cancer types, results from genetic changes. However, it is considered as more threatening due to the spread of the smoking habit, a major risk factor of the disease. Scientists have been collecting and analyzing the biological data for a long time, in attempts to find methods to predict cancer before it occurs. Analysis of these data requires the use of artificial intelligence algorithms and neural network approaches. In this paper, one of the deep neural networks was used, that is the enhancer Deep Belief Network (DBN), which is constructed from two Restricted Boltzmann Machines (RBM). The visible nodes for the first RBM are 13 nodes and 8 nodes in each hidden layer for the two RBMs. The enhancer DBN was trained by Back Propagation Neural Network (BPNN), where the data sets were divided into 6 folds, each is split into three partitions representing the training, validation, and testing. It is worthy to note that the proposed enhancer DBN predicted lung cancer in an acceptable manner, with an average F-measure value of 0. 96 and an average Matthews Correlation Coefficient (MCC) value of 0. 47 for 6 folds.
Teacher Education for the Twenty-First Century (and a post-pandemic world)

Linguistics) publishes its special issue on Teacher Education for the twenty-first century within the context of the COVID-19 pandemics.In April 2020, the world is grieving the loss of normalcy, the vulnerability of life, and the fear of economic decline.Amidst a global crisis and closed schools and campuses, the world is facing the challenge of providing remote education for children, teenagers, and adults.2020 is a time that leads teacher educators to, once more, rethink the roles of teachers and teacher education for the twenty-first century.The wounds of humanity have been opened by the spread of a virus, exposing persistent inequalities based on race, gender, and social class.It is time to bolster our demands for social justice education, technology equity, and our commitment to human rights.Global populations are facing fear, anxiety, and grief.Uncertainties coexist with reflections upon meanings of a worldwide calamity.Ideological and political representations are tacit to conversations about capitalism, globalization, and even some xenophobic conspiracy theories.As a witness of this unfortunate historical moment, we are compelled to create meanings within the fields of applied linguistics and educational research.What lessons are there for us to learn?What challenges do teachers, learners, families, and communities are facing that research can help to overcome?This issue of RBLA is not about the pandemic or the horror it causes.It is about the future, as much as it is about the present.The joy and hope that Paulo Freire sees as a requirement for teaching is the primary
Virtual Reality as a Factor to Improve Productivity in Learning Processes

A 3D virtual interactive environment can follow the steps of those old second world war flying simulators and be used as a tool to train people in learning a practical process. This will result in an increase of productivity because people will be able of performing that practical process in less time thanks to the use of Virtual Reality as a technological learning tool. This work presents and statistical analysis of 100 students of the first semester of engineering trying to learn a simple and practical task using a 3D virtual interactive environment and the time that it took them to perform the same activity in real life. The results of this investigation prove statistically that there is nearly a 20% gain in the time that it took for the group that used virtual reality over those who did not use it.
An Analysis of Speech Acts Used by the Seventh-Grade Teacher of SMPN 2 Bangli in EFL Classroom Interaction

Speech act is an action performed via utterances. This study investigated the types and functions of speech acts used by the seventh-grade English teacher at SMPN 2 Bangli by adopting Searle’s theory. The data were obtained through classroom observation and semi-structured interview. The data were analysed qualitatively by using Miles and Huberman’s interactive model. It was found out that there were five types of speech acts used by the teacher, namely representative, directive, expressive, commissive, and declaration in which directive (47.52%) was dominantly used by the teacher, while the declaration was the one which was less produced (0.71%). Among the functions of directive act, commanding appeared the most (44.40%), while naming and declaring were in the same amount in declaration type (50%). The teacher’s directive acts proved that she played her role to manage the classroom instructional system, to counsel the students and their learning, to instruct and to assess. However, the teacher, who also acted as the homeroom teacher and was in authority during the English lesson, performed the declaration act to change some situations in the classroom.
The Implementation of Nonlinear Principal Component Analysis to Acquire the Demography of Latent Variable Data (A Study Case on Brawijaya University Students)

Nonlinear principal component analysis is used for data that has a mixed scale.This study uses a formative measurement model by combining metric and nonmetric data scales.The variable used in this study is the demographic variable.This study aims to obtain the principal component of the latent demographic variable and to identify the strongest indicators of demographic formers with mixed scales using samples of students of Brawijaya University based on predetermined indicators.The data used in this study are primary data with research instruments in the form of questionnaires distributed to research respondents, which are active students of Brawijaya University Malang.The used method is nonlinear principal component analysis.There are nine indicators specified in this study, namely gender, regional origin, father's occupation, mother's occupation, type of place of residence, father's last education, mother's last education, parents' income per month, and students' allowance per month.The result of this study shows that the latent demographic variable with samples of a student at Brawijaya University can be obtained by calculating its component scores.The nine indicators formed in PC1 or X 1 were able to store diversity or information by 19.49%, while the other 80.51% of diversity or other information was not saved in this PC.From these indicators, the strongest indicator in forming latent demographic variables with samples of a student of Brawijaya University is the origin of the region (I 2 ) and type of residence (I 5 ).
Supervisory Justice, Organizational Citizenship Behavior, and Innovative Behavior: The Mediating Role of Tacit Knowledge Sharing among Nurses

Aims: Using the perspective of social exchange theory, this study aims to provide insight on the relationship between supervisory justice, organizational citizenship behavior (OCB) and innovative behavior using mediation approach of tacit knowledge sharing.Research on the justice, OCB, and tacit knowledge sharing relationship has received minimal research attention although the role of justice and OCB is influencing the social exchange relationship quality between the employee and supervisor is well established.Method: Data were collected on a sample of 339 nurse dyads in 3 major disciplines in a Malaysian public teaching hospital.The data analyses were performed using Structural Equation Modelling (SEM) which confirmed using Partial Least Square (PLS 3.0).PLS is applied to test the research model as it has a less critical requirement of the sample size to validate.Findings: For the direct effect, the results provide support for the acceptance of all hypotheses because there are statistically significant relationships.For the indirect effect, following the mediation analysis procedure, it is concluded that tacit knowledge sharing partially mediates the relationship since both the direct and indirect effects are significant.Conclusion: This study provides a new perspective on the mediating effect of tacit knowledge sharing in predicting innovative behavior from individual factors (supervisory justice and OCB) among nurses.Contributions: From a theoretical perspective, this study contributes to the literature by introducing the mediating effect of tacit knowledge sharing on the relationships between individual factors and innovative behavior.From a practical perspective, this study provides empirical evidence on the proposed framework and provides an
Bangladesh: Income Inequality and Globalization

Global anxiety about the effect of globalization is increasing. Throughout recent years the influence of globalization on income allocation has been fiercely discussed. This research carries out a time series review to examine the effect of globalization on income inequality in Bangladesh between 1975 and 2018. Study results indicate that globalization variables – exports, imports, foreign aid, foreign direct investment (FDI), and remittance inflows – have a significant long-term impact on income inequality in Bangladesh. Long-term foreign aid and imports are improving, while exports, FDI, and remittance inflows are deteriorating income distribution in Bangladesh during the study period. Nevertheless, in the short term, exports, imports, FDI have little to no impact in the model and a change in foreign aid and remittances will have a significant conservative force attempting to resolve the system.
Corrections to “Unsupervised Anomaly Detection of Industrial Robots Using Sliding-Window Convolutional Variational Autoencoder”

Presents corrections to the above mentioned paper.
IMPROVING STUDENTS’ PUBLIC SPEAKING SKILLS THROUGH THE USE OF VIDEOTAPED FEEDBACK

This study aimed to improve students’ public speaking skills at the English Education Department (JTBI) of IAIN Batusangkar through the use of videotaped feedback. The research was a Classroom Action Research (CAR) whose participants were the fourth semester students. The data were collected through observation, test, and interviews and were analyzed quantitatively and qualitatively. The findings shows that the use of videotaped feedback improved the students’ public speaking skills in which the average score increased from 6.95714 (Mid Term Test) to 7.026 (Final Test). This implies that there was an increase in the ability of students from the level of need improvement to the satisfactory. Even in the aspects of Introduction, Body, Conclusion and Time, which were the focuses of this class action research, there were increases from 7.38, 7.1, 6,8, and 6.6 (UTS) to 8, 7.5, 7,3 and 7.2 (UAS) respectively. It also means that there were increases in students’ public speaking skills in all four aspects, which were from satisfactory (Introduction, Body), and need improvement (Conclusion, Time) to good (Introduction), and satisfactory (Body, Conclusion and Time). It is expected that the results of this study can be applied to other aspects of public speaking skills and other similar subjects.
A Framework for Semantic Text Clustering

Existing approaches for text clustering are either agglomerative, divisive or based on frequent itemsets.However, most of the suggested solutions do not take the semantic associations between words into account and documents are only regarded as bags of unrelated words.Indeed, traditional text clustering methods usually focus on the frequency of terms in documents to create connected homogenous clusters without considering associated semantic which will of course lead to inaccurate clustering results.Accordingly, this research aims to understand the meanings of text phrases in the process of clustering to make maximum usage and use of documents.The semantic web framework is filled with useful techniques enabling database use to be substantial.The goal is to exploit these techniques to the full usage of the Resource Description Framework (RDF) to represent textual data as triplets.To come up a more effective clustering method, we provide a semantic representation of the data in texts on which the clustering process would be based.On the other hand, this study opts to implement other techniques within the clustering process such as ontology representation to manipulate and extract meaningful information using RDF, RDF Schemas (RDFS), and Web Ontology Language (OWL).Since Text clustering is an indispensable task for better exploitation of documents, the use of documents may be more intelligently conducted while considering semantics in the process of text clustering to efficiently identify the more related groups in a document collection.To this end, the proposed framework combines multiple techniques to come up with an efficient approach combining machine learning tools with semantic web principles.The framework allows documents RDF representation, clustering, topic modeling, clusters summarizing, information retrieval based on RDF querying and Reasoning tools.It also highlights the advantages of using semantic web techniques in clustering, subject modeling and knowledge extraction based on processes of questioning, reasoning and inferencing.
Traumatism during mooring operations on vessels

Introduction: Mooring operations on modern vessels remain one of the least mechanized types of work.Mooring injuries are characterized by a particularly gross violation of soft tissues and bones. Materials and methods:The analysis of the treatment of 224 patients from members of crew who received occupational injuries while mooring the vessels while performing voyages in the northern water basin.When working on the material, analytical and statistical methods were used.The signifi cance of differences was calculated using Student's t-criteria for independent samples. Results:In the structure of occupational injuries of members of vessel`s crew of the Northern water basin, various injuries sustained during the mooring of vessels account for 10.1% of cases.In the Northern region, mooring injuries most often occur on transport river (46.7%) and sea (36.3%) ships, in which traffi c is intensive, loading and unloading can occur at various berths within the same port, which increases the frequency of mooring operations during short transitions and, accordingly, the probability of mooring injuries.Outpatient treatment due to mooring injuries received 50.8% of members of vessel`s crew.The second half of the mooring injuries required emergency hospitalization, long-term treatment in the surgical department and further rehabilitation at the outpatient stage.Discussion: Every tenth work-related injury sustained by of members of vessel`s crew of the Northern water basin occurs during mooring operations.Compliance with the mooring technology of the vessel, the improvement of the materials from which the mooring ends are made and the development of automated mooring complexes are the main areas of technological reduction of mooring injuries.Preventive measures for the prevention of mooring injuries should be continuous systemic in nature and should concern not only the technical improvement of mooring mechanisms, but also the constant training of members of vessel`s crew in safety techniques, fi rst aid in case of injuries and health education. Conclusions:1.In the general structure of occupational traumatism of members of vessel`s crew of the Northern water basin, various damages during the mooring of vessels account for 10.1% of cases.2. The risk of mooring injuries is greatest in the transport river and sea fl eets among sailors with production experience of up to 1 year of work in the specialty.3. Mooring injuries are characterized by the predominance of severe bruises, deep wounds and traumatic amputations of the phalanges of the fi ngers.4. The average period of incapacity for work with mooring injuries is 48.0 working days, the hospital bed for hospitalization in the surgical department -20.6.5. Returned to professional work after rehabilitation for mooring injuries, 94.0% of the victims of members of vessel`s crew.The initial exit of victims to the II group of disability was 1.3%; Group III -2.2%; 2.5% of mooring injuries were fatal.
Evaluating Human-Machine Translation with Attention Mechanisms for Industry 4.0 Environment SQL-Based Systems

The use of relational databases is increasingly present in the industry. Applications in medical, IoT and industry 4.0 are examples of this. Despite the large capacity and efficiency in storing and retrieving data, this type of database requires technical knowledge in specific query languages to access this information, which distances these types of application from the lay public. In this work, we propose an application of recent models in natural language processing that use neural networks, as well as attention mechanisms for the translation of natural language in English to SQL applied in an SQL database of a system to store data from sensors, focused on the concept of Industry 4.0. Paired examples of natural language phrases were generated with their corresponding SQL query to be used for training and validation. By training the neural network, we obtained a language model with an accuracy of approximately 99 % in the validation set.
Deep Neural Network--based Machine Translation System Combination

Deep neural networks (DNNs) have provably enhanced the state-of-the-art natural language process (NLP) with their capability of feature learning and representation. As one of the more challenging NLP tasks, neural machine translation (NMT) becomes a new approach to machine translation and generates much more fluent results compared to statistical machine translation (SMT). However, SMT is usually better than NMT in translation adequacy and word coverage. It is therefore a promising direction to combine the advantages of both NMT and SMT. In this article, we propose a deep neural network--based system combination framework leveraging both minimum Bayes-risk decoding and multi-source NMT, which take as input the N-best outputs of NMT and SMT systems and produce the final translation. In particular, we apply the proposed model to both RNN and self-attention networks with different segmentation granularity. We verify our approach empirically through a series of experiments on resource-rich Chinese⇒English and low-resource English⇒Vietnamese translation tasks. Experimental results demonstrate the effectiveness and universality of our proposed approach, which significantly outperforms the conventional system combination methods and the best individual system output.
RFID Controlled “GG” Pieces Ranking Detection with Watch-Dog Enable

This paper aims in developing a piece ranking detection of game of the generals board game that has low power consumption.Watch dog timer was used to build a low power board game that was applied in game of the generals.Experiments were performed to calculate the power savings, resulted in about 50 per cent power savings were achieved.The proponents concluded that, on the basis of identification and fair decision and operational standards, the system configuration consisted of introducing sleep modes to reduce the power usage of the proposed system.The proponent conducted a survey sample consists of a group of 30 people with varying level of strategies in playing the Game of the Generals whose age from 18 to 40 years old.Based on the statistical treatment applied in the datasets collected, the average time to spent to complete the game is 24.97 with a frequency of 28.And with a standard deviation (8.98).This resulted to a negative skew.For the number of times that the device turns into idle time, the average is 8.2 with a frequency of 11 and the 50th percentile of 10.The standard deviation of (5.28) from the normal of 8.2.The proponents concluded that the greater the number of times that the device turn into idle time or sleep mode the higher the power savings.
BlockVOTE : An Architecture of a Blockchain-based Electronic Voting System

Voting is an essential activity in the modern democracy. To facilitate the voting process, there are several attempts on proposing an electronic voting system such that, the voting and tallying processes can be done efficiently and the results would be accountable to the public. To date, however, an online electronic voting system has been rarely adopted in practice due to the possibility of having the voting result tampered through vote-rigging or cyber-attacking. In 2009, the blockchain algorithm was proposed by Satoshi Nakamoto. Blockchain is a technique for recording transactions between self-auditing ledgers in an open, distributed, permanent, and verifiable manner. Even though blockchain was originally designed for a financial applications, it is possible to apply blockchain to other domains, including in the implementation of an online decentralized-based electronic voting system. In this study, the architecture of a blockchain-based electronic voting system, named \textit{BlockVOTE}, is proposed. The architecture design and all related formal definitions are given. To validate the proposal, two BlockVOTE prototypes were implemented using two different blockchain application frameworks. The performance analysis of both versions of the prototypes are given. The analysis of both technical and management aspects on the possibility of adopting the proposed decentralized voting system in an actual voting scenario is also given at the end of this study.
Augmented-Reality-Based 3D Emotional Messenger for Dynamic User Communication with Smart Devices

With the development of Internet technologies, chat environments have migrated from PCs to mobile devices. Conversations have moved from phone calls and text messages to mobile messaging services or “messengers,” which has led to a significant surge in the use of mobile messengers such as Line and WhatsApp. However, because these messengers mainly use text as the communication medium, they have the inherent disadvantage of not effectively representing the user’s nonverbal expressions. In this context, we propose a new emotional communication messenger that improves upon the limitations of existing static expressions in current messenger applications. We develop a chat messenger based on augmented reality (AR) technology using smartglasses, which are a type of a wearable device. To this end, we select a server model that is suitable for AR, and we apply an effective emotional expression method based on 16 different basic emotions classified as per Russell’s model. In our app, these emotions can be expressed via emojis, animations, particle effects, and sound clips. Finally, we verify the efficacy of our messenger by conducting a user study to compare it with current 2D-based messenger services. Our messenger service can serve as a prototype for future AR-based messenger apps.
Implementation of Predictive Crime Analytics in Municipal Crime Management System in Calauan, Laguna, Philippines

Predictive crime analytics is a process of evaluating a dataset to discover veiled patterns that can be useful in forecasting crime occurrence.The application of a machine learning algorithm is widely used in developing artificial intelligence to integrate into computer systems.This project aims to develop a management system for crime records implementing predictive crime analytics in forecasting crime occurrences.Through predictive crime analytics and software development life cycle, a management information system that is capable of forecasting crime was successfully developed.A predictive model was designed using multinomial logistic regression, which acquires a totality accuracy of 86.60% and prediction confidence.It is also shown in this paper that regression is better than other classification algorithms in terms of predicting crime occurrence.Moreover, discussed in this research, among all the barangay in Calauan, Laguna.Dayap is the most vulnerable in different index crimes like rape, murder, robbery, theft, homicide, and illegal drugs.