Reviewerly/exHarmony huggingface dataset:
Teacher’s Professional Self-Awareness Within The Interactions With Students In Higher Education: Temporality and Relationality

Becoming and being a teacher in higher education is a long process of individual transformation. The research aims at highlighting the meaning of time and relations in regard to teachers’ professional self-awareness through their interactions with students in higher education. The research design was qualitative in which the phenomenological methodology according to Max van Manen version was applied. Findings revealed that temporality and relationality are social constructs that shape the teacher-student interactions in higher education as they are loaded with worldviews that guide their educational actions and and their relationships. Thus their subjectivities and life-world educational experiences-based worlds are built on temporalities. A higher education teacher’s professional self-awareness is a developmental process which requires from the person reflection on his/her own experiences. Teachers through interactions with students balance between expectations and requirements which encourage both sides to find ways of integrating creative methods into the teaching and learning processes. Through working with students, teachers step into the “unknown” and learn within togetherness. Being in togetherness brings bilateral interchange between teachers and students, which motivates both sides to be self-aware. These reciprocal interactions invite participants to grow and seek mutual interchange through different experiences and contexts.
Research on Automatic Question Answering of Generative Knowledge Graph Based on Pointer Network

Question-answering systems based on knowledge graphs are extremely challenging tasks in the field of natural language processing. Most of the existing Chinese Knowledge Base Question Answering(KBQA) can only return the knowledge stored in the knowledge base by extractive methods. Nevertheless, this processing does not conform to the reading habits and cannot solve the Out-of-vocabulary(OOV) problem. In this paper, a new generative question answering method based on knowledge graph is proposed, including three parts of knowledge vocabulary construction, data pre-processing, and answer generation. In the word list construction, BiLSTM-CRF is used to identify the entity in the source text, finding the triples contained in the entity, counting the word frequency, and constructing it. In the part of data pre-processing, a pre-trained language model BERT combining word frequency semantic features is adopted to obtain word vectors. In the answer generation part, one combination of a vocabulary constructed by the knowledge graph and a pointer generator network(PGN) is proposed to point to the corresponding entity for generating answer. The experimental results show that the proposed method can achieve superior performance on WebQA datasets than other methods.
Extending the R Language with a Scalable Matrix Summarization Operator

Analysts prefer simpler interpreted languages to program their computations. Prominent languages include R, Python, and Matlab. On the other hand, analysts aim to compute mathematical models as fast as possible, especially with large data sets. Data summarization remains a fundamental technique to accelerate machine learning computations. Based on this motivation, we propose a novel summarization mechanism computed via a single matrix multiplication in the statistical R language. We show our summarization benefits a large family of linear models, including Linear Regression, PCA, and Naive Bayes. We present a subsystem that enables exploiting summarization by detecting Gramian matrix products in R. We optimize the existing R source code by overriding the internal R matrix multiplication algorithm using ours. Our solution can be plugged into R and help solving where a similar matrix multiplication appears, much faster and without RAM limitations. Moreover, our solution can be benefited from the parallel processing ability of the summarization matrix. We present an experimental validation showing our subsystem incurs little overhead since it works on source code while providing much faster speeds compared to the R language built-in functions. To round up our comparisons, we also compare our subsystem with Spark in parallel machines. For our solution, we assume that data can be in the HDFS, disk, or already partitioned. Our solution triumphs Spark in most cases proving we can also compete in the big data space.
Epidemiology of falciparum malaria among residents of some rural and peri- urban communities in Ekiti State, Southwestern Nigeria

Malaria which is caused by parasites of the genus Plasmodium is a devastating parasitic disease of major public health challenge worldwide, particularly Nigeria. This study was carried out to investigate the epidemiology of falciparum malaria among residents of rural and peri-urban communities in Ekiti State, Southwestern Nigeria. Standard parasitological technique of microscopy was employed to determine and identify parasite prevalence and species. A questionnaire was used to collect subject's information such as age, sex, location, occupation and education. Out of the 300 individuals examined, a total of 283 (93.4%) individuals were infected with malaria parasite. Sex pattern of infection indicated that male had higher malaria prevalence of 95.0% compared to female with the prevalence of 93.3% (P>0.05). The age group 51 to 60 years had the highest malaria parasite prevalence of 100% while age group <10 years has the least malaria parasite prevalence of 86.0% (P>0.05). Similarly, a total mean malaria parasite density of 1455.90 parasite/μL of blood was recorded. The mean malaria parasite density does not significantly vary (P>0.05) among age and sex group. The age group >60 years recorded the highest mean parasite density of 2092.50 parasite/μL of blood while age group <10 has the least mean malaria parasite density of 1044. 42 parasite/μL of blood. In relation to sex, the highest mean malaria parasite density was found among the female (1461.80 parasite/μL of blood) compared to male (1450 parasite/ μL of blood). In the same vein, occupation as a socioeconomic risk factor play a major role with respect to malaria infection. The highest malaria prevalence of 113 (98.26%) was recorded among farmers while the least 34 (85%) was recorded among Civil servants (P<0.05). Thus, it is apparent that falciparum malaria is heavily prevalent in this study area and as such urgent management control measures and interventions should be made available and fully utilized.
Cross-heterogeneous Domain Authentication Scheme Based on Blockchain

With the rising popularity of the Internet and the development of big data technology, an increasing number of organizations are opting to cooperate across domains to maximize their benefits. Most organizations use public key infrastructure to ensure security in accessing their data and applications. However, with the continuous development of identity-based encryption (IBE) technology, small- and medium-sized enterprises are increasingly using IBE to deploy internal authentication systems. To solve the problems that arise when crossing heterogeneous authentication domains and to guarantee the security of the certification process, we propose using blockchain technology to establish a reliable cross-domain authentication scheme. Using the distributed and tamper-resistant characteristics of the blockchain, we design a cross-domain authentication model based on blockchain to guarantee the security of the heterogeneous authentication process and present a cross-domain authentication protocol based on blockchain. This model does not change the internal trust structure of each authentication domain and is highly scalable. Furthermore, on the premise of ensuring security, the process of verifying the signature of the root certificate in the traditional cross-domain authentication protocol is improved to verify the hash value of the root certificate, thereby improving the authentication efficiency. The developed prototype exhibits generality and simplicity compared to previous methods.
Image Captioning with Attention for Smart Local Tourism using EfficientNet

Abstract Smart systems have been massively developed to help humans in various tasks. Deep Learning technologies push even further in creating accurate assistant systems due to the explosion of data lakes. One of the smart system tasks is disseminating ‘users needed information’, which is crucial in the tourism sector to promote local tourism destinations. In this research, we design a local tourism specific image captioning model, which will later support the development of AI-powered systems that assist various users. The model is developed using a visual Attention mechanism and uses the state-of-the-art feature extractor architecture EfficientNet. A local tourism dataset is collected and used in the research and two different captions: captions that describe the image literally and captions that represent human logical responses when seeing the image. The two kinds of captions make the captioning model more humane when implemented in the assistance system. We compared two different models using EfficientNet architectures (BO and B4) with other well-known VGG16 and InceptionV3. The best BLEU scores we get are 73.39 and 24.51 for the training set and the validation set, respectively, using EfficientNetB0. The captioning result using the developed model shows that the model can produce logical caption for local tourism-related images.
Beyond the Pandemic: The Role of Haptics in Defining the New Normal

Discusses the role haptics technology plays in the COVID-19 pandemic. Haptics permeates our lives. During the COVID-19 pandemic, where social distancing continues to affect the way we interact with other people and the environment, haptic technology can play a pivotal role in bringing touch back into our everyday lives. Haptic technology is shifting from something useful to improve virtual and remote interactions into something vital for the physical and particularly mental wellbeing of the human species.
Data for Development: Shifting Research Methodologies for Covid-19

Successful and appropriate informal digital learning can help individuals and communities build sustainable and meaningful livelihoods, strengthen social cohesion and resilience, preserve and enhance cultural traditions and engage constructively and robustly with the wider world. Building digital learning that embodies participative and collaborative development and community ownership and control rests on the work of educators who understand these individuals and communities and their cultures, which may be very distant and different from global norms and the mainstream of their countries. These educators may, however, be reliant on research tools and techniques that are inappropriate or inadequate in these different settings and situations. This paper sets out a brief critique of these established tools and techniques as the prelude to reviewing a range of more innovative and eclectic ones drawn from a variety of disciplines. This is timely because COVID-19 has increased the barriers that separate educators from would-be learners whilst also increasing the education that these people and communities need.
ABC Technology - Artificial Intelligence, Blockchain Technology, Cloud Technology for Banking Sector

Banking sectors plays a critical role in modern society and enables a range of applications from infrastructure to social media. In this study, we discuss the current status and future directions as three emerging key technologies (Artificial Intelligence, Block chain, Cloud computing) will influence future need of banking sectors. Artificial intelligence in machines will replace to increase human capabilities. AI is the intelligent machine and software used to communicate with human for the purpose. Block chain provides data privacy and security.Cloud computing is a new technology. It allows client to access their personal files using internet access and using without any application. It enables sharing of resources to reduce execution cost and increase availability of service. Finally, we proposed a conceptual model to explore the influence of emerging paradigms and technologies on evolution of banking sectors.
Developing a Scalable and Accurate Job Recommendation System with Distributed Cluster System using Machine Learning Algorithm

The purpose of this research is to develop a job recommender system based on the Hadoop MapReduce framework to achieve scalability of the system when it processes big data. Also, a machine learning algorithm is implemented inside the job recommender to produce an accurate job recommendation. The project begins by collecting sample data to build an accurate job recommender system with a centralized program architecture. Then a job recommender with a distributed system program architecture is implemented using Hadoop MapReduce which then deployed to a Hadoop cluster. After the implementation, both systems are tested using a large number of applicants and job data, with the time required for the program to compute the data is recorded to be analyzed. Based on the experiments, we conclude that the recommender produces the most accurate result when the cosine similarity measure is used inside the algorithm. Also, the centralized job recommender system is able to process the data faster compared to the distributed cluster job recommender system. But as the size of the data grows, the centralized system eventually will lack the capacity to process the data, while the distributed cluster job recommender is able to scale according to the size of the data.
Emergent behaviour in a system of industrial plants detected via manifold learning

The efficiency behaviour of an industrial plant, part of a huge international structure of plants, is modelled as an emergent phenomenon in a complex adaptive system. The study is based on real in-service data obtained from an industrial production line monitoring system. Models of complex adaptive systems and some modern manifold learning methods are introduced in a unified formalism. The emergent behaviour is efficiently described in this setup.
Various variational approximations of quantum dynamics

We investigate variational principles for the approximation of quantum dynamics that apply for approximation manifolds that do not have complex linear tangent spaces. The first one, dating back to McLachlan [Mol. Phys. 8, 39–44 (1964)], minimizes the residuum of the time-dependent Schrödinger equation, while the second one, originating from the lecture notes of Kramer and Saraceno [Geometry of the Time-Dependent Variational Principle in Quantum Mechanics, Lecture Notes in Physics Vol. 140 (Springer, Berlin, 1981)], imposes the stationarity of an action functional. We characterize both principles in terms of metric and symplectic orthogonality conditions, consider their conservation properties, and derive an elementary a posteriori error estimate. As an application, we revisit the time-dependent Hartree approximation and frozen Gaussian wave packets.
Data-Filtering Methods for Self-Training of Automatic Speech Recognition Systems

Self-training is a simple and efficient way of leveraging un-labeled speech data: (i) start with a seed system trained on transcribed speech; (ii) pass the unlabeled data through this seed system to automatically generate transcriptions; (iii) en-large the initial dataset with the self-labeled data and retrain the speech recognition system. However, in order not to pol-lute the augmented dataset with incorrect transcriptions, an important intermediary step is to select those parts of the self-labeled data that are accurate. Several approaches have been proposed in the community, but most of the works address only a single method. In contrast, in this paper we inspect three distinct classes of data-filtering for self-training, leveraging: (i) confidence scores, (ii) multiple ASR hypotheses and (iii) approximate transcriptions. We evaluate these approaches from two perspectives: quantity vs. quality of the selected data and improvement of the seed ASR by including this data. The proposed methodology achieves state-of-the-art results on Romanian speech, obtaining 25% relative improvement over prior work. Among the three methods, approximate transcriptions bring the highest performance gain, even if they yield the least quantity of data.
Cloud Data Privacy Preserving Model for Health Information Systems Based on Multi Factor Authentication

With cloud computing (CC) becoming popular in recent years, variety of institutions, organizations, businesses and individual users are creating interest. They are adopting the technology in order to take advantage of shared web applications, low infrastructure cost, utility and distributed computing, cluster computing as well as reliable IT architecture. In the area of health, Cloud Health Information Systems (CHIS) play a key role not only on the healthcare businesses but patients as well. On the patient side, CHIS aid in sharing of medical data and health information, timely access of critical patient information and coordination of clinical services. Patients, who continue to demand for instantaneous and quality healthcare services are now able to access the services from experts even when they are not necessarily in the same physical location. This is being aided by proliferation of telemedicine through hosted cloud architecture. From the business perspective, CC has helped to cut down operational expenses by way of cost-effective clinical information system infrastructure through the implementation of a distributed platform. The platform has therefore saved businesses millions of dollars that would have gone to infrastructural and human resource investment. Even with these immense opportunities, cloud computing uptake has been serious inhibited by the privacy and security concerns. Due to the sensitivity of personal health information, businesses and individuals are apprehensive when it comes to adopting the technology or releasing the data to the cloud. This study is a results discussion of an enhanced model for attainment of data privacy on the cloud through use of multi factor authentication.
Dialogue Concerning the Two Chief Computing Systems: Imagine yourself on a flight talking to an engineer about a scheme that straddles classical and quantum

Computer scientists and engineers have started down a road that could one day lead to a momentous transition: from deterministic computing systems, based on classical physics, to quantum computing systems, which exploit the weird and wacky probabilistic rules of quantum physics. Many commentators have pointed out that if engineers are able to fashion practical quantum computers, there will be a tectonic shift in the sort of computations that become possible ¶ But that's a big if. ¶ Quantum computers hold great theoretical promise, sure, but the hurdles that need to be overcome to build practical machines are enormous. Some skeptics have argued that the technical challenges are so immense that it's very unlikely that general-purpose quantum computers will become available anytime in the foreseeable future. Others, including the engineers now working very hard to build these machines at Google, IBM, Intel, and elsewhere, are more sanguine, anticipating that 5 or 10 more years of work may be enough to bring the first practical general-purpose quantum computers on line. Only time will tell. ≫
THE IMPACTS OF MENTIMETER-BASED ACTIVITIES ON EFL STUDENTS’ ENGAGEMENT IN INDONESIA

The effort to enhance students’ engagement in the EFL classroom is still becoming an issue. Fortunately, some studies have shown a positive correlation between technology utilization to elevate the students’ engagement. Based on that potential finding, this study aims at digging out the impact of Mentimeter, as one of the popular tools in this recent time, on EFL students’ engagement. It is a descriptive qualitative study. There are 70 respondents of the non-English students that were selected by using purposive sampling. The results of the study reveal that there are three most prominent students' positive perception towards the implementation of Mentimeter in the EFL classroom, namely practicality, anonymity, and freedom. Those three aspects foster the students to be engaged in the process of English learning. Then, most students perceive that no reason makes them dislike Mentimeter usage in the EFL classroom. The next finding depicts a significant impact of Mentimeter on the students’ engagement in English learning by giving opinions and discussion activities. It was proven with the percentage of students’ participation, reaching 82% and 91%, above the average of Mentimeter participants’ contribution.
A Lightweight Application for Reading Digital Measurement and Inputting Condition Assessment in Manufacturing Industry

There is a vast need for the use of digital display instruments in the manufacturing industry due to the simple operation and high precision. In addition to the numerical data acquisition, it is usually necessary to input additional text for the condition assessment as well. However, since most of these measure instruments do not provide any interfaces for users to access the values and it often lacks proper devices to input the text during the working process, these two tasks are highly human intensive under current conditions. In order to facilitate the smooth running of the work for operators, we propose a lightweight application which can be installed on smartphones or wearable devices using multidigit recognition and speech recognition techniques without changing too much of their workflow. The experimental results demonstrate that our approach can achieve high accuracy. Thus, the proposed solution can effectively resolve data input issues in the manufacturing sites, thereby reducing human labor, increasing productivity, and automating part of the process. Taking operators’ existing workflow into consideration for design can provide an application with an easy learning curve. Moreover, with the rapid and economical approach, companies can financially benefit from the development of this low-cost application, especially for small- and medium-sized enterprises.