Reviewerly/exHarmony huggingface dataset:
Supporting Healthcare Integration of Refugees Exploiting Reusable Learning Objects: The ASPIRE Framework

The significant increase of refugees’ migration has demonstrated an overwhelming need for their access and integration to healthcare systems. E-learning, especially in the form of experiential digital learning, has been highlighted as an important ally in supporting refugees’ health needs. This paper aims to discuss the design and development process of Reusable Learning Objects (RLOs) to enhance digital health applications for refugees and integrate them to healthcare systems. The participatory approach based on the “ASPIRE” framework was followed. Using the “ASPIRE” process, 9 stakeholders were involved in the first phase of the development process, where a co-creation storyboarding workshop was held in Greece by the Medical Physics Laboratory of the Aristotle University of Thessaloniki. Essential qualitative and quantitative feedback was collected from stakeholders through an online questionnaire administered in Google Forms. Overall, enlightening ideas were expressed and all stakeholders commented positively on the value and efficacy of the workshop.
Masked Implementation of Format Preserving Encryption on Low-End AVR Microcontrollers and High-End ARM Processors

Format-Preserving Encryption (FPE) for Internet of Things (IoT) enables the data encryption while preserving the format and length of original data. With these advantages, FPE can be utilized in many IoT applications. However, FPE requires complicated computations and these are high overheads on IoT embedded devices. In this paper, we proposed an efficient implementation of Format-preserving Encryption Algorithm (FEA), which is the Korean standard of FPE, and the first-order masked implementation of FEA on both low-end (i.e., AVR microcontroller) and high-end (i.e., ARM processor) IoT devices. Firstly, we show the vulnerability of FEA when it comes to the Correlation Power Analysis (CPA) approach. Afterward, we propose an efficient implementation method and the masking technique for both low-end IoT device and high-end IoT device. The proposed method is secure against power analysis attacks but the performance degradation of masked measure is only 2.53∼3.77% than the naïve FEA implementation.
Target Tracking Algorithm Based on Adaptive Scale Detection Learning

In this paper, to better solve the problem of low tracking accuracy caused by the sudden change of target scale, we design and propose an adaptive scale mutation tracking algorithm using a deep learning network to detect the target first and then track it using the kernel correlation filtering method and verify the effectiveness of the model through experiments. The improvement point of this paper is to change the traditional kernel correlation filtering algorithm to detect and track at the same time and to combine deep learning with traditional kernel correlation filtering tracking to apply in the process of target tracking; the addition of deep learning network not only can learn more accurate feature representation but also can more effectively cope with the low resolution of video sequences, so that the algorithm in the case of scale mutation achieves more accurate target tracking in the case of scale mutation. To verify the effectiveness of this method in the case of scale mutation, four evaluation criteria, namely, average accuracy, cross-ratio accuracy, temporal robustness, and spatial robustness, are combined to demonstrate the effectiveness of the algorithm in the case of scale mutation. The experimental results verify that the joint detection strategy plays a good role in correcting the tracking drift caused by the subsequent abrupt change of the target scale and the effectiveness of the adaptive template update strategy. By adaptively changing the number of interval frames of neural network redetection to improve the tracking performance, the tracking speed is improved after the fusion of correlation filtering and neural network, and the combination of both is promoted for better application in target tracking tasks.
Quantitative and Qualitative Comparison of 2D and 3D Projection Techniques for High-Dimensional Data

Projections are well-known techniques that help the visual exploration of high-dimensional data by creating depictions thereof in a low-dimensional space. While projections that target the 2D space have been studied in detail both quantitatively and qualitatively, 3D projections are far less well understood, with authors arguing both for and against the added-value of a third visual dimension. We fill this gap by first presenting a quantitative study that compares 2D and 3D projections along a rich selection of datasets, projection techniques, and quality metrics. To refine these insights, we conduct a qualitative study that compares the preference of users in exploring high-dimensional data using 2D vs. 3D projections, both without and with visual explanations. Our quantitative and qualitative findings indicate that, in general, 3D projections bring only limited added-value atop of the one provided by their 2D counterparts. However, certain 3D projection techniques can show more structure than their 2D counterparts, and can stimulate users to further exploration. All our datasets, source code, and measurements are made public for ease of replication and extension.
The Role of Social Networks in the Organization of the Educational Process and Learning

&lt;p class="Paragraph"&gt;&lt;span lang="EN-GB"&gt;The objectives of the study are to conduct a survey among students and teachers of Russian universities, which will determine their general attitude to the use of social networks in education; to identify the functions of social networks from the perspective of respondents; based on the results of both stages of the study, to determine the role of social networks in the educational process. Students and teachers of four Russian universities (MGIMO University, Kuban State Technological University, North-Eastern Federal University, Russian State Social University) took part in the survey. A total of 500 students and 100 teachers were interviewed. The results of the first part of the study showed that the attitude of students and teachers towards the use of social networks in education is generally positive. Both groups of respondents noted the advantages of introducing social networks into the educational process and also stated their willingness to increase their use. When using social networks in education, students can fill in special forms to indicate social network accounts to be subsequently used. Also, the administration of group chats can be taken under the control of the university for the purpose of a more structured exchange of information/educational materials.&lt;/span&gt;&lt;strong&gt;&lt;/strong&gt;&lt;/p&gt;
Atypical corpus-based tools to the rescue How a writing generator can help translators adapt to the demands of the market

Corpus studies have become an undisputed aid for the evolution of translation, transferring knowledge from the academia to develop tools that have invaluably helped the profession. Nevertheless, the demands of the market require translators to improve their efficiency in order to adapt to its hectic pace. The aim of this paper is to present a possible solution through the use of corpus-based tools that are usually neglected in translation: writing aids. First, the reality of the translation market will be studied to understand the current context and translators’ needs. Then, we will analyze some of the existing tools derived from corpus studies available for translators, both the most and less usual. Finally, we will focus on a booming sector of the market, that of oenology, to exemplify how one of the less typical tools, the writing generator, may be helpful for translators in terms of cost, time, and quality given the current demands.
The Study of Handwriting Recognition Algorithms Based on Neural Networks

Handwriting Identifies basic graph-like problems and has a high real-world value in areas such as cloud accounting, finance, and postal administration.Due to the unrestricted problem of handwritten numbers when writing, it is relatively difficult to achieve rapid and effective recognition.With the emergence of deep learning-related algorithms and the rapid development of computer hardware technology, image classification methods based on Convolutional Neural Network (CNN) have gradually become a research hotspot.Because the convolutional network has a strong letter numbering ability and network generalization ability, the recognition rate can often exceed the traditional graph classing method.Therefore, the study of hand-written word recognition should be implemented using CNN through the network.Handwriting Word Recognition is the key technique for self-identification.Therefore, summarizing and analyzing the existing handwritten digit recognition algorithms, two handwritten digit recognition algorithms based on Convolutional Neural Network (CNN) are proposed.To improve the recognition performance of the CNN model, this article proposes a handwriting recognition algorithm based on the change to CNN.To extract the image feature information more fully, this paper proposes a handwriting recognition algorithm based on feature fusion and SVM.First, using the modified CNN model and the Gabor filter that introduces curvature systems, extract the CNN and Gabor characteristics of the character image; Second, the characteristics of its progress are fused to obtain more effective new features; Finally, the fusion feature is entered into the SVM classifier into the line number of words to recognize.The results of the experiment show that the algorithm can effectively improve the recognition effect of handwritten
X-METRA-ADA: Cross-lingual Meta-Transfer learning Adaptation to Natural Language Understanding and Question Answering

Multilingual models, such as M-BERT and XLM-R, have gained increasing popularity, due to their zero-shot cross-lingual transfer learning capabilities.However, their generalization ability is still inconsistent for typologically diverse languages and across different benchmarks.Recently, meta-learning has garnered attention as a promising technique for enhancing transfer learning under low-resource scenarios: particularly for crosslingual transfer in Natural Language Understanding (NLU).In this work, we propose X-METRA-ADA, a cross-lingual MEta-TRAnsfer learning ADAptation approach for NLU.Our approach adapts MAML, an optimization-based meta-learning approach, to learn to adapt to new languages.We extensively evaluate our framework on two challenging cross-lingual NLU tasks: multilingual task-oriented dialog and typologically diverse question answering.We show that our approach outperforms naive fine-tuning, reaching competitive performance on both tasks for most languages.Our analysis reveals that X-METRA-ADA can leverage limited data for faster adaptation.
Moses and the Character-Based Random Babbling Baseline: CoAStaL at AmericasNLP 2021 Shared Task

We evaluated a range of neural machine translation techniques developed specifically for low-resource scenarios.Unsuccessfully.In the end, we submitted two runs: (i) a standard phrase-based model, and (ii) a random babbling baseline using character trigrams.We found that it was surprisingly hard to beat (i), in spite of this model being, in theory, a bad fit for polysynthetic languages; and more interestingly, that (ii) was better than several of the submitted systems, highlighting how difficult low-resource machine translation for polysynthetic languages is.
Understanding Technical and Scientific Translation: A Genre-based Approach

In the recent past, 30 or 40 years ago, the terms technical and scientific were used interchangeably when discussing about communication, texts, terms, vocabulary or writing. Due mainly to this reason, the lines separating technical and scientific translation became increasingly blurred and little or no distinction was made between the two terms; as such, technical and scientific translation was regarded as one type of LSP translation. Using genre knowledge, in this article we show that technical translation and scientific translation can and should be treated separately, as two distinct types of LSP translation, for both educational and research purposes. We present two genre-based models for setting-up the technical and scientific curricula, aimed at familiarising students with these inter-related, yet different types of LSP translation, and which can also be used as starting points for research in these two fields.
String Diagrammatic Electrical Circuit Theory

We develop a comprehensive string diagrammatic treatment of electrical circuits.Building on previous, limited case studies, we introduce controlled sources and meters as elements, and the impedance calculus, a powerful toolbox for diagrammatic reasoning on circuit diagrams.We demonstrate the power of our approach by giving idiomatic proofs of several textbook results, including the superposition theorem and Thévenin's theorem.
Efficient Recovery of a Shared Secret via Cooperation: Applications to SDMM and PIR

This work considers the problem of privately outsourcing the computation of a matrix product over a finite field <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathbb {F}}_{q}$ </tex-math></inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> helper servers. These servers are considered to be honest but curious, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , they behave according to the protocol but will try to deduce information about the user’s data. Furthermore, any set of up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$X$ </tex-math></inline-formula> servers is allowed to share their data. Previous works considered this collusion a hindrance and the download cost of the schemes increases with growing <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$X$ </tex-math></inline-formula> . We propose to utilize such linkage between servers to the user’s advantage by allowing servers to cooperate in the computational task. This leads to a significant gain in the download cost for the proposed schemes. The gain naturally comes at the cost of increased communication load between the servers. Hence, the proposed cooperative schemes can be understood as outsourcing both computational cost and communication cost. Both information–theoretically secure and computationally secure schemes are considered, showing that allowing information leakage that is computationally hard to utilize will lead to further gains. The proposed server cooperation is then exemplified for specific secure distributed matrix multiplication (SDMM) schemes and linear private information retrieval (PIR). Similar ideas naturally apply to many other use cases as well, but not necessarily always with lowered costs.
Relation Classification with Cognitive Attention Supervision

Many current language models such as BERT utilize attention mechanisms to transform sequence representations.We ask whether we can influence BERT's attention with human reading patterns by using eye-tracking and brain imaging data.We fine-tune BERT for relation extraction with auxiliary attention supervision in which BERT's attention weights are supervised by cognitive data.Through a variety of metrics we find that this attention supervision can be used to increase similarity between model attention distributions over sequences and the cognitive data without significantly affecting classification performance while making unique errors from the baseline.In particular, models with cognitive attention supervision more often correctly classified samples misclassified by the baseline.
Transformer-based Screenplay Summarization Using Augmented Learning Representation with Dialogue Information

Screenplay summarization is the task of extracting informative scenes from a screenplay.The screenplay contains turning point (TP) events that change the story direction and thus define the story structure decisively.Accordingly, this task can be defined as the TP identification task.We suggest using dialogue information, one attribute of screenplays, motivated by previous work that discovered that TPs have a relation with dialogues appearing in screenplays.To teach a model this characteristic, we add a dialogue feature to the input embedding.Moreover, in an attempt to improve the model architecture of previous studies, we replace LSTM with Transformer.We observed that the model can better identify TPs in a screenplay by using dialogue information and that a model adopting Transformer outperforms LSTM-based models.
A Study For Intelligent Campus Service using Zigbee Based on Wsn

We propose an integrated management system model for the establishment of a smart campus, such as a digital library system and a smart card system capable of providing various services using zigbee, which is currently prominent. The integrated management system can provide a higher quality education environment through transparent and efficient administrative management and one-stop service provision, away from the labor-intensive system of educational institutions, and users can provide personalized and knowledgeable intelligent education services that meet their needs and environments.
KPQA: A Metric for Generative Question Answering Using Keyphrase Weights

In the automatic evaluation of generative question answering (GenQA) systems, it is difficult to assess the correctness of generated answers due to the free-form of the answer.Especially, widely used n-gram similarity metrics often fail to discriminate the incorrect answers since they equally consider all of the tokens.To alleviate this problem, we propose KPQAmetric, a new metric for evaluating the correctness of GenQA.Specifically, our new metric assigns different weights to each token via keyphrase prediction, thereby judging whether a generated answer sentence captures the key meaning of the reference answer.To evaluate our metric, we create high-quality human judgments of correctness on two GenQA datasets.Using our human-evaluation datasets, we show that our proposed metric has a significantly higher correlation with human judgments than existing metrics.
Exploring The Correlation between Cyber Security Awareness, Protection Measures and the State of Victimhood: The Case Study of Ambo University’s Academic Staffs

The advancement of information communication technology has triggered a revolution in using the Internet for legitimate educational purposes on university campuses. Therefore, the Internet has changed the way of human communication and contributed to the development of mankind. On the other hand it is regrettable that its revolution has helped malicious users to exploit it for the malign purpose to commit a cyberspace crime that has in turn negatively affected fellow users who were preyed on by cyber predators. This work aimed to examine the awareness of cybersecurity, the measures taken to protect against cyberattacks and the state of victimization among professors at Ambo University. Thus, the present study comes up with the following findings. First, the result shows that the respondents’ cybersecurity awareness was significantly influenced by cyber-crime victimization, fields of study, and protection measures. Second, the current study also depicts that the respondents’ protection measures were connected to and influenced by cyber-crime victimization, education level, and cyber-security awareness. Finally, the study’s findings show that being a cyber-crime victim has been linked to predictors’ variables: protection measures and the level of cybersecurity awareness.
Learning about Word Vector Representations and Deep Learning through Implementing Word2vec

Word vector representations are an essential part of an NLP curriculum.Here, we describe a homework that has students implement a popular method for learning word vectors, word2vec.Students implement the core parts of the method, including text preprocessing, negative sampling, and gradient descent.Starter code provides guidance and handles basic operations, which allows students to focus on the conceptually challenging aspects.After generating their vectors, students evaluate them using qualitative and quantitative tests.
A Simple Approach for Handling Out-of-Vocabulary Identifiers in Deep Learning for Source Code

There is an emerging interest in the application of natural language processing models to source code processing tasks.One of the major problems in applying deep learning to software engineering is that source code often contains a lot of rare identifiers, resulting in huge vocabularies.We propose a simple, yet effective method, based on identifier anonymization, to handle out-of-vocabulary (OOV) identifiers.Our method can be treated as a preprocessing step and, therefore, allows for easy implementation.We show that the proposed OOV anonymization method significantly improves the performance of the Transformer in two code processing tasks: code completion and bug fixing.
Development of Students’ Research Activity During Studying at Higher Education Institutions

The study is devoted to the development of activities for the development of research activities of students in the learning process in universities. The authors analysed the essence of students' research activities, identified the main tasks of students' research activities and formed a hypothesis that the development of students' research activities in the process of studying at a university will be useful and will be reflected in the dynamics of their creative self-development if it is carried out within the framework of the research development program as a joint activity of a teacher and a student. The authors proposed a model for the development of research activities of students in the learning process, and empirically tested its effect, which confirmed the hypothesis. However, during the experimental phase, some limiting factors were identified.
Towards a Model-Theoretic View of Narratives

In this paper, we propose the beginnings of a formal framework for modeling narrative qua narrative.Our framework affords the ability to discuss key qualities of stories and their communication, including the flow of information from a Narrator to a Reader, the evolution of a Reader's story model over time, and Reader uncertainty.We demonstrate its applicability to computational narratology by giving explicit algorithms for measuring the accuracy with which information was conveyed to the Reader, along with two novel measurements of story coherence.