Reviewerly/exHarmony huggingface dataset:
Preparation of Future Teachers for Use of ICT in Primary School

The article addresses the problem of preparing future teachers for the use of ICT in primary school lessons. The authors characterize the functionality of information and communication technologies, focus on modern computer aids that can be used in the education and upbringing of younger students and highlight some aspects of preparing future teachers to use ICT in primary school education. It is established that ICT in primary school lessons is today a powerful didactic tool that involves children in active work, develops their cognitive interest, promotes better learning and enhances learning effectiveness. Learning needs to be polysensory and varied in order to meet students' information needs. In this context, information training of future elementary school teachers, which activates their cognitive and creative potential, generates the knowledge and skills needed for future professional activity, is of great importance. In fact, it is about improving the effectiveness of teacher education through the introduction of information and communication technologies, since they change the way scientific information is presented, provide individualization of learning, creating new forms of interaction between teacher and student in the process of solving various cognitive problems. Accordingly, the potential of educational disciplines and pedagogical practices at the Mukachevo State University is aimed at training competitive specialists ready to quickly process a large amount of educational information and original solutions to professional tasks by mastering modern ICT tools.
What eye movements reveal about strategy encoding of words in younger and older adults

The main goal of this study was to explore the organizational strategies used by younger and older adults when encoding words, using eye-tracking. Participants had to learn a set of organizable words and then a set of non-organizable words, each presented on a single display. Participants were then asked to recall the words of each set in the order in which they came to their mind. Hence, the participants' encoding strategies revealed by eye-tracking could be directly related to their subsequent memory performance. The results confirmed the detrimental impact of aging on memory and the weaker use of organizational strategies by older adults during the recall phase. The eye-tracking data showed that when they encode the words, older adults do not look at them for as long as younger adults, probably because of slower eye movements. They also revealed that compared to younger adults, older adults were much less able to adapt their word scanning strategy according to whether the words to encode were organizable or not. Finally, the relationships that were found between the recall scores and the eye-tracking data suggest that the eye movement pattern at learning can predict how people will recall the words.
Collaborative Multi-Expert Active Learning for Mobile Health Monitoring: Architecture, Algorithms, and Evaluation

Mobile health monitoring plays a central role in the future of cyber physical systems (CPS) for healthcare applications. Such monitoring systems need to process user data accurately. Unlike in other human-centered CPS, in healthcare CPS, the user functions in multiple roles all at the same time: as an operator, an actuator, the physical environment and, most importantly, the target that needs to be monitored in the process. Therefore, mobile health CPS devices face highly dynamic settings generally, and accuracy of the machine learning models the devices employ may drop dramatically every time a change in setting happens. Novel learning architecture that specifically address challenges associated with dynamic environments are therefore needed. Using active learning and transfer learning as organizing principles, we propose a collaborative multiple-expert architecture and accompanying algorithms for the design of machine learning models that autonomously adapt to a new configuration, context, or user need. Specifically, our architecture and its constituent algorithms are designed to manage heterogeneous knowledge sources or experts with varying levels of confidence and type while minimizing adaptation cost. Additionally, our framework incorporates a mechanism for collaboration among experts to enrich their knowledge, which in turn decreases both cost and uncertainty of data labeling in future steps. We evaluate the efficacy of the architecture using two publicly available human activity datasets. We attain activity recognition accuracy of over 85 % (for the first dataset) and 92 % (for the second dataset) by labeling only 15 % of unlabeled data.
Light Field Image Compression Using Multi-branch Spatial Transformer Networks Based View Synthesis

The recent years have witnessed the widespread of light field imaging in interactive and immersive visual applications. To record the directional information of the light rays, larger storage space is required by light field images compared with conventional 2D images. Hence, the efficient compression of light field image is highly desired for further applications. In this paper, we propose a novel light field image compression scheme using multi-branch spatial transformer networks based view synthesis. Firstly, a sparse subset of views are selected and are rearranged into a pseudo sequence to be encoded by a video codec at encoder. Then the other unselected views are synthesized based on the similarity between neighboring views with our proposed method at decoder. To better characterize the non-linear relationship between the sub-views, a multi-branch spatial transformer networks (MSTN) is designed to adaptively learn the affine transformations between the neighboring views, which are used to warp the input views to generate accurate approximation of the target views. Moreover, to better obtain the final view by the generated approximation views, the Wasserstein generative adversarial networks(WGAN) is applied with the improved training. Experimental results show the superior compression performance of our scheme compared with the state-of-the-art methods.
A mixed methods examination of knowledge brokers and their use of theoretical frameworks and evaluative practices

Knowledge brokering is a knowledge translation approach that includes making connections between researchers and decision-makers to facilitate the latter's use of evidence in health promotion and the provision of healthcare. Despite knowledge brokering being well-established in Canada, many knowledge gaps exist, including understanding what theoretical frameworks have been developed and which evaluative practices knowledge brokers (KBs) use.This study used a mixed methods design to examine how KBs in Canada (1) use frameworks, models and theories in their practice and (2) how they evaluate knowledge brokering interventions. We gathered interview and survey data from KB practitioners to better understand their perspectives on effective practices. Our analysis focused on understanding the theoretical frameworks used by KBs.This study demonstrates that KBs in Canada tend not to rely on theories or models that are specific to knowledge brokering. Rather, study participants/respondents draw on (sometimes multiple) theories and models that are fundamental to the broader field of knowledge translation - in particular, the Knowledge to Action model and the Promoting Action Research in Health Sciences framework. In evaluating the impact of their own knowledge brokering practice, participants/respondents use a wide variety of mechanisms. Evaluation was often seen as less important than supporting knowledge users and/or paying clients in accessing and utilising evidence.Knowledge brokering as a form of knowledge translation continues to expand, but the impact on its targeted knowledge users has yet to be clearly established. The quality of engagement between KBs and their clients might increase - the knowledge brokering can be more impactful - if KBs made efforts to describe, understand and evaluate their activities using theories or models specific to KB.
Hierarchical-Matching-Based Online and Real-Time Multi-Object Tracking with Deep Appearance Features

Based on tracking-by-detection, we propose a hierarchical-matching-based online and real-time multi-object tracking approach with deep appearance features, which can effectively reduce the false positives (FP) in tracking. For the purpose of increasing the accuracy rate of data association, we define the trajectory confidence using its position information, appearance information, and the information of historical relevant detections, after which we can classify the trajectories into different levels. In order to obtain discriminative appearance features, we developed a deep convolutional neural network to extract the appearance features of objects and trained it on a large-scale pedestrian re-identification dataset. Last but not least, we used the proposed diverse and hierarchical matching strategy to associate detection and trajectory sets. Experimental results on the MOT benchmark dataset show that our proposed approach performs well against other online methods, especially for the metrics of FP and frames per second (FPS).
Domain Independent Automatic Labeling system for Large-scale Social Data using Lexicon and Web-based Augmentation

Recently, with the large-scale adoption of social media, people have begun to express their opinion on these sites in the form of reviews. Potential consumers often forced to wade through huge amount of reviews to make informed decision. Sentiment analysis has become rapid and effective way to automatically gauge consumers’ opinion. However, such analysis often requires tedious process of manual tagging of large training examples or manually building a lexicon for the purpose of classifying reviews as positive or negative. In this paper, we present a method to automate the tedious process of labeling large textual data in an unsupervised, domain independent and scalable manner. The proposed method combines the lexicon-based and Web-based Point Wise Mutual Information (PMI) statistics to find the Semantic Orientation (SO) of opinion expressed in a review. Based on proposed methods a system called Domain Independent Automatic Labeling System (DIALS) has been implemented, which takes collection of text from any domain as input and generates fully labeled dataset in an unsupervised and scalable manner. The result generated can be used to track and summarize online discussion and/or use to train any classifier in the next stage of development. The effectiveness of system is tested by comparing it with baseline machine learning and lexicon-based methods. Experiments on multi-domains dataset has shown that proposed method consistently shown improved recall and accuracy as compared to baseline machine learning and lexicon-based methods.
From Local to Global Semantic Clone Detection

Clone detection detects similar code fragments (refer to as clone code) in software products. It can help with software optimization and maintenance. Code clone detection can be divided into textual, lexical, syntactic and semantic levels. The existing technologies have achieved many good results in the first three levels, but no significant results have been obtained in semantic clone detection. In this paper, we propose a novel semantic level clone detection approach. We use the control flow graph (CFG) as an intermediate representation of the program method, combining the classical dynamic time warping (DTW) algorithm in the field of speech recognition with two deep neural network models (bidirectional RNN autoencoder and graph convolutional network (GCN)) to detect semantic level clone from local to global. We experimented with a dataset consisting of five large-scale real-world systems and a code corpus containing a large number of programming problems. The experimental results show that our approach can achieve good results in detecting both local and global semantic clone.
A Text Similarity Measurement Based on Semantic Fingerprint of Characteristic Phrases

Text similarity measurements are the basis for measuring the degree of matching between two or more texts. Traditional large-scale similarity detection methods based on a digital fingerprint have the advantage of high detection speed, which are only suitable for accurate detection. We propose a method of Chinese text similarity measurement based on feature phrase semantics. Natural language processing (NLP) technology is used to pre-process text and extract the keywords by the Term frequency-Inverse document frequency (TF-IDF) model and further screen out the feature words. We get the exact meaning of a word and semantic similarities between words and a HowNet semantic dictionary. We substitute concepts to get the feature phrases and generate a semantic fingerprint and calculate similarity. The experimental results indicate that the method proposed is superior in similarity detection in terms of its accuracy rate, recall rate, and F-value to the traditional and digital fingerprinting method.
Client-Driven Personalized Trailer Framework Using Thumbnail Containers

Movie trailers are prepared using a one-size-fits-all framework. These days, however, streaming platforms seek to overcome this problem and provide personalized trailers via the investigation of centralized server-side solutions. This can be achieved by analyzing personal user data, and can lead to two major issues: privacy violation and enormous demand in computational resources. This paper proposes an innovative, low-power, client-driven method to facilitate the personalized trailer generation process. It tackles the complex process of detecting personalized actions in real-time from lightweight thumbnail containers. The HTTP live streaming (HLS) server and client are locally configured to validate the proposed method. The system is designed to support a wide range of client hardware with different computational capabilities and has the flexibility to adapt to network conditions. To test the effectiveness of this method, twenty-five broadcast movies, specifically in the western and sports genres, are evaluated. To the best of our knowledge, this is the first-ever client-driven framework that uses thumbnail containers as input to facilitate the trailer generation process.
Reliability analysis of shell truss structure by hybrid Monte Carlo method

The paper presents an example of reliability analysis of shell structures susceptible to stability loss from the condition of node snapping.In the reliability analysis of the structure, uncertain parameters of the task are represented by uncorrelated random variables.The approach used in the paper is an extension of the idea, which assumes the use of Neural Networks (NNs) in Monte Carlo (MC) simulations to analyze the reliability of the structure.For this purpose, it was necessary to build a simple hybrid system formed with the two independent sequentially working Finite Element Method (FEM) and Neural Networks applications.
War against Coronavirus (COVID – 19) in Tanzania: Designing a Low Cost Automatic Water Tap

In December 2019, an outbreak of severe acute respiratory syndrome coronavirus 2 (SARSCoV-2) infection occurred in Wuhan, Hubei Province, China and spread across China and beyond.On February 12, 2020, WHO officially named the disease caused by the novel coronavirus as Coronavirus Disease 2019 (COVID-19).On January 30, 2020, WHO declared COVID-19 as the sixth public health emergency of international concern.One of the major preventions is frequent hand washing.An attempt has been made to design a low-cost automatic water tap as a measure to fight the spread of Coronavirus (Covid-19).The designed system uses simple, easy to get and low cost instruments.The system does not require a user to touch the water tap.It uses microcontroller to implement intelligence.The solar panel is used as a source of power.The system design uses green solution based automatic water tap.The system will be used in gatherings to avoid Coronavirus spreading.Such gathering places include hospitals, markets,
Batik pattern recognition using convolutional neural network

Batik is one of Indonesia's cultures that is well-known worldwide. Batik is a fabric that is painted using canting and liquid wax so that it forms patterns of high artistic value. In this study, we applied the convolutional neural network (CNN) to identify six batik patterns, namely Banji, Ceplok, Kawung, Mega Mendung, Parang, and Sekar Jagad. 994 images from the 6 categories were collected and then divided into training and test data with a ratio of 8:2. Image augmentation was also done to provide variations in training data as well as to prevent overfitting. Experimental results on the test data showed that CNN produced an excellent performance as indicated by accuracy of 94% and top-2 accuracy of 99% which was obtained using the DenseNet network architecture.
Development on quantum metrology with quantum Fisher information

Quantum metrology is one of the hot topics in ultra-cold atoms physics. It is now well established that with the help of entanglement, the measurement sensitivity can be greatly improved with respect to the current generation of interferometers that are using classical sources of particles. Recently, Quantum Fisher information plays an important role in this field. In this paper, a brief introduction on Quantum metrology is presented highlighting the role of the Quantum Fisher information. And then a brief review on the recent developments for i) criteria of multi-particle entanglement and its experimental generation; ii) linear and non-linear atomic interferometers; iii) the effective statistical methods for the analysis of the experimental data.
Analysis K-Nearest Neighbor Algorithm for Improving Prediction Student Graduation Time

Higher education plays a major role in improving the quality of education in Indonesia. The BAN-PT institution established by the government has a standard of higher education accreditation and study program accreditation. With the 4.0-based accreditation instrument, it encourages university leaders to improve the quality and quality of their education. One indicator that determines the accreditation of study programs is the timely graduation of students. This study uses the K-Nearest Neighbor algorithm to predict student graduation times. Students' GPA at the time of the seventh semester will be used as training data, and data of students who graduate are used as sample data. K-Nearest Neighbor works in accordance with the given sample data. The results of prediction testing on 60 data for students of 2015-2016, obtained the highest level of accuracy of 98.5% can be achieved when k = 3. Prediction results depend on the pattern of data entered, the more samples and training data used, the calculation of the K-Nearest Neighbor algorithm is also more accurate.
Open Domain Dialogue Generation with Latent Images

We consider grounding open domain dialogues with images. Existing work assumes that both an image and a textual context are available, but image-grounded dialogues by nature are more difficult to obtain than textual dialogues. Thus, we propose learning a response generation model with both image-grounded dialogues and textual dialogues by assuming that the visual scene information at the time of a conversation can be represented by an image, and trying to recover the latent images of the textual dialogues through text-to-image generation techniques. The likelihood of the two types of dialogues is then formulated by a response generator and an image reconstructor that are learned within a conditional variational auto-encoding framework. Empirical studies are conducted in both image-grounded conversation and text-based conversation. In the first scenario, image-grounded dialogues, especially under a low-resource setting, can be effectively augmented by textual dialogues with latent images; while in the second scenario, latent images can enrich the content of responses and at the same time keep them relevant to contexts.
CloudDLP: Transparent and Scalable Data Sanitization for Browser-Based Cloud Storage

Browser-based cloud storage services are still broadly used in enterprises for online sharing and collaboration. However, sensitive information in images or documents may be easily leaked outside trusted enterprise on-premises due to such cloud services. Existing solutions to prevent data leakage in cloud storage services either limit many functionalities of cloud applications or are difficult to be scaled to various cloud applications. In this paper, we propose CloudDLP, a transparent and scalable approach for enterprises to automatically sanitize sensitive data in images and documents with various browser-based cloud applications. CloudDLP is deployed as an internet gateway within the premises of an enterprise using JavaScript injecting techniques and deep learning methods to sanitize sensitive premise data. It neither compromises the user experience nor significantly affects application functionalities in browser-based cloud storage services. We have evaluated CloudDLP with a number of real-world cloud applications. Our experimental results show that it can achieve automatic data sanitization with cloud storage services while preserving most functionalities of cloud applications.
PREDICTION AND OPTIMIZATION OF A DESULPHURIZATION SYSTEM USING CMAC NEURAL NETWORK AND GENETIC ALGORITHM

In this paper, taking desulphurizing ratio and economic cost as two objectives, a ten-input two-output prediction model was structured and validated for desulphurization system. Cerebellar model articulation controller (CMAC) neural network and genetic algorithm (GA) were used for model building and optimization of cost respectively. In the model building process, the grey relation entropy analysis and uniform design method were used to screen the input variables and study the model parameters separately. Traditional regression analysis and proposed location number analysis method were adopted to analyze output errors of experiment group and predict the results of test group. Results show that regression analyses keep high fit degree with experiment group results while the fitting accuracies for test group are quite different. As for location number analysis, a power function between output errors and location numbers was fitted well with the data of experiment group and test group for SO2. Prediction model was initialized by location number analysis method. Model was validated and cost optimization case was performed with GA subsequently. The result shows that the optimal cost obtained from GA could be reduced by more than 30% compared with original optimal operating parameters under same constraints.
Impact of Agricultural Programs on Youth Engagement in Agribusiness in Nigeria: A Case Study

Using the case of Fadama Graduate Unemployed Youth and Women Support (FGUYS) program, this study assessed the impact of agricultural programs on youth engagement in agribusiness in Nigeria. A total of 977 respondents comprising of 455 participants of the program and 522 non-participants were sampled across three states in Nigeria. Data were analysed using Descriptive and Endogenous Switching Probit Regression (ESPR) Model. The result showed that participation in the program was influenced by age, years of formal education, perception of agricultural programs and type of employment. Furthermore, the results showed a positive impact of the program on youths&amp;rsquo; likelihood to engage in agribusiness. The study recommends the need to invest more in agricultural programs such as the case study since it has desirable economic outcome for young people. Also, there is a need to improve the general outlook of agriculture such that it becomes more attractive to young people.