Reviewerly/exHarmony huggingface dataset:
Multimodality of the transmission of philological knowledge during classroom activities using ICT

The article deals with multimodal learning strategies aimed at transferring philological knowledge with the help of information and communications technology (ICT). Using ICT makes it possible to apply visual association and create spatial images that express important object properties defined clearly by the means of dynamic visualization (animation). The authors note that using ICT in the sphere is associated with several methodological difficulties, since it requires a philologist-educator who possess a wide range of competences that go beyond humanitarian knowledge. The optimum software that allows creating audio-visual construct (interactive poster, computer animation, tag cloud, cognitive map etc.) is offered for studying process. The paper presents the results of sociological research on the readiness of students of philological specialties in pedagogical universities along with the teachers in different educational units for implementation of multimodal strategies of teaching using ICT. The authors proved the need to introduce an integrative course “Use of ICT in the study of philological disciplines”.
Time Series Wave Forecasting with Transformer Model, Case Study in Pelabuhan Ratu, Indonesia

Wave height prediction is vital in various fields of work and activities at sea, one of which is for ship navigation and operations. These activities depend on current ocean wave data and predictions for the future. This paper uses the Transformer model to predict ocean wave heights accurately. Although it is a new model, it effectively measures wave height. This study used six years and seven months of data divided into six years and seven months as training data and testing data, respectively. We used testing data to forecast the next three days, seven days, 14 days, and 30 days. As wave data, we use the reanalysis ERA5 data from ECMWF at Pelabuhan Ratu, Indonesia, obtained from ERA-5 as a case study. We compare the forecasting results to get the highest accuracy using Transformer and Long Short-Term Memory (LSTM). Our results show that the LSTM model has the best accuracy than the Transformer model. The highest accuracy of sea wave height forecasting for the LSTM model is 0.034 for MSE, 0.186 for RMSE, and 0.996 for R, and the best accuracy of sea wave height forecasting for the Transformer model is 0.118 for MSE, 0.186 for RMSE, and 0.992 for R.
Object-Based Perspective Transformation Data Augmentation for Object Detection

Perspective transformation can mimic the perspective phenomenon in captured images, while few researches focused on utilizing perspective transformation to augment image data. To well exploit its potential in data augmentation, a novel object-based perspective transformation data augmentation framework is proposed in this paper. First, this framework automatically cuts out objects from images based on bounding boxes provided by corresponding annotations. Next, a simulated random perspective transformation is performed on only those cut objects rather than the whole images. The final step consists of the combination of transformed objects and backgrounds, together with calculation of new annotation. Our framework can generate new images by mimicking the perspective phenomenon caused by different orientations of objects and can also introduce new backgrounds at the same time, hence the further augmentation towards insufficient or imbalanced image data without requirements of additional manual manipulations. Experiments are carried out on the Pascal VOC2007 datasets and the results show the effectiveness of our framework on insufficient or imbalanced datasets.
Development of a Surgical Image Object Display System Using AR Device and Evaluation of Its Depth Perception

In endoscopic surgery, which has been in increasing demand in recent years, endoscopic images are displayed in two dimensions, so the depth of the image is determined by the surgeon’s anatomical knowledge and experience, as well as by palpation with forceps. Therefore, complex surgeries are difficult and time-consuming, and there are reportedly problems with surgical safety and certainty. In order to intuitively recognize the distance between surgical instruments and organs, an endoscopic surgical support system should be developed that provides surgical images that allow real-time observation of the distance between surgical instruments and organs. The purpose of this study is to develop a prototype of an endoscopic surgical support system that provides an optimal field of view and prevents damage to the affected part due to contact between the surgical instruments and the living tissue by intuitively recognizing the distance between the instruments and the organ in real time, and to evaluate depth perception to confirm the effectiveness of the developed system.
Fast algorithm for decoding phase images with arbitrary step shift

A numerical algorithm for fast search of the initial phase shift on phase images with arbitrary stepwise shifts is proposed. The algorithm is based on finding the minimum deviation of the model function from the measurement results. The interval search method used directly in the proposed approach made it possible to significantly reduce the computational complexity of the algorithm. The paper presents the results of measuring a three-dimensional profile by phase triangulation methods using the proposed algorithm. The obtained results confirm the efficiency and high practical value of the proposed phase image decoding algorithm.
Towards Third-Person Visual Imitation Learning Using Generative Adversarial Networks

Imitation Learning plays a key role during our development since it allows us to learn from more expert agents. This cognitive ability implies the remapping of seen actions in our perspective. However, in the field of robotics the perspective mismatch between demonstrator and imitator is usually neglected under the assumption that the imitator has access to the explicit joints configuration of the demonstrator or that they both share the same perspective of the environment. Focusing on the perspective translation problem, in this paper we propose a generative approach that shifts the perspective of actions from third person to first person by using RGB videos. In addition to the first person view of the action our model generates an embedded representation of it. This numerical description is autonomously learnt following a time-consistent pattern and without the need of human supervision. In the experimental evaluation, we show that it is possible to exploit these two information to infer robot control during the imitation phase. Additionally, after training on synthetic data, we tested our model in a real scenario.
Documenting Architectural Styles Using CIDOC CRM

Documenting cultural heritage information is of major importance and significance nowadays in the emerging world of digital libraries, computing and information studies across the globe. Architecture and architectural styles fall within this big effort, as architecture is strictly connected with human societies and heritage. The CIDOC CRM is a well-established and continuously emerging reference model aiming to represent cultural heritage information. This paper connects the dots and offers for the first time a model for documenting architectural styles via CIDOC CRM and its extensions. In this paper, we discuss the notion of architectural style and the previous work on documenting architecture. Via classes and properties of CIDOC CRM and its extensions, we model the historical context of architectural styles/periods, we represent specific elements that characterize architectural styles using Gothic Architecture as an example, and we document various and complex spatial and temporal relationships and influences among architectural styles and periods, such sub-styles of given architectural styles, regional variations among architectural styles, and architectural influences from one architectural style to another.
Like Attracts Like: Personalized Federated Learning in Decentralized Edge Computing

The emerging Personalized Federated Learning (PFL) methods aim to produce personalized models for different users, so as to keep track of their individualized requirements in Edge Computing (EC). The centralized PFL methods may suffer from the communication bottleneck and single point of failure. As an alternative solution, the decentralized PFL (DPFL) methods are performed in a Peer-to-Peer (P2P) manner, and collaboratively train personalized models by model aggregation among congenial devices. However, these DPFL methods may incur high communication cost and low resource utilization induced by large-scale models. Herein, we take the communication constraint and heterogeneity into consideration and propose to realize communication-efficient DPFL with adaptive model pruning and neighbor selection. We theoretically analyze the convergence of the proposed DPFL method, and study the impacts of both model pruning and neighbor selection on training performance. Furthermore, we propose an efficient algorithm that combines model pruning and neighbor selection to achieve a trade-off between model quality and communication cost. Extensive simulation and testbed experiments on real-world datasets are conducted. The experimental results demonstrate that the proposed algorithm can improve the test accuracy by at most 13% and save the traffic consumption by 45.4% on average compared with the existing PFL methods.
Classifying Complaint Reports Using RNN and Handling Imbalanced Dataset

A complaint report contains citizen's complaint about problems in their area. The text of this report needs to be classified into specific categories to make it easier for the government to follow up on the information. This study uses a dataset from Jakarta Smart City (JSC), namely Cepat Respon Masyarakat. Currently, JSC still uses annotators to classify report texts manually. This of course takes quite a long time. So, automatic classification needs to be done. We use two Recurrent Neural Network (RNN) methods: Bidirectional Long Short-Term Memory (Bi-LSTM) and Gated Recurrent Unit (GRU). Furthermore, Word2Vec and FastTexts are used for word embedding. Another problem with the category classification is the existence of an imbalanced dataset, so it is necessary to carry out special handling using Synthetic Minority Over-Sampling Technique (SMOTE) and Class Weight. The best model was obtained from the classification experiments through a combination of FastText, GRU method, and SMOTE with an evaluation results accuracy of 0.78 and f1-score macro of 0.52.
On the role of visualization in teaching mathematics (using an example of the concept of function)

In the teaching community, there is an ongoing discussion about the difference between visual and analytical thinking and the issues of psychological-pedagogical argumentation of the concept of visual teaching mathematics. Following the discussion, this paper is aimed to identify visual images understanding. The research objective is to identify the role of images in the formation of mathematical concepts (using an example of the concept of function). The novelty of the work is that an image is considered not as a result but as a condition for mathematical concept understanding. The authors used theoretical and experimental methods of study (the analysis of psychological and pedagogical sources, testing, and survey). To achieve the goal, special examinational questions were developed. The authors identified the role of influence of using images and concepts initially existing in the student’s mind on the efficiency of understanding the essence of a mathematical concept. The study revealed that students who have the skills of both visual and analytical thinking demonstrate the best results of understanding mathematical concepts. The research shows that visual and analytical types of thinking not always comply with each other, therefore, methodologically verified teaching mathematical concepts and relations should be proportionally balanced. The prevalence of any type of thinking by the students is individual. The essential prerequisite for successful application of visual teaching methods is to take into account the initial images, ideas, and associations of students in teaching. Visualization is an important method of learning mathematics; however, understanding mathematical concepts does not suppose isomorphism between understanding of a concept and its image.
A Model for Smart Banking in Mauritius

Potentialities offered by Internet-of-Things (IoT) have facilitated the development of several applications in various domains such as smart environment (home, office, plant), social, enterprise, utilities, smart transportation and logistics, healthcare, insurance industry, recycling, agriculture, and breeding among others. The IoT is also making a breakthrough in the banking sector, which is one of the major pillars in any economy for a country. There are different facets that banks have embraced with the evolution of time such as retail banking, investment banking, corporate banking, international banking, and non-banking facilities. The application of IoT in banking sector is creating new opportunities such as personalized services to the customers, better monitoring, efficiency, communication, automation, and better control for the bank, thus enhancing the service quality in the banking sector. Nowadays, most of the banking transactions can be completed with the use of a mobile device. The interconnectedness with IoT helps to capture useful information, thus improving the tracking of each transaction in a more efficient way and minimizing risks for both the bank and the customers. IoT is increasing the confines for innovation in the banking sector, redesigning the customer experience. This chapter describes several smart banking systems and frameworks and proposes a model for smart banking. The model provides an overview of how devices and applications can connect while catering for better communication, data integration, sharing and analysis of data. Finally, the chapter presents use cases showing the application of the proposed model in Mauritius.
A Multi-population Schema Designed for Biased Random-Key Genetic Algorithms on Continuous Optimisation Problems

In Evolutionary Algorithms, population diversity is a determinant factor for the quality of the final solutions. Due to diverse problem characteristics, many techniques face difficulties and converge prematurely in local optima. The maintenance of diversity allows the algorithm to explore the search space and efficiently achieve better results. Parallel models are well-known techniques to maintain population diversity; however, design choices lead to different characteristics for the optimization process. For instance, the migration policy on the Island model can control how fast the algorithm converges. This work proposes a new migration policy designed for the Biased Random-Key Genetic Algorithm (BRKGA). Also, the proposal is compared with two traditional strategies and evaluates its performance in continuous search spaces. The results show that the proposal can improve the BRKGA optimization capability with suitable parameters.
Functional extreme learning machine for regression and classification

Although Extreme Learning Machine (ELM) can learn thousands of times faster than traditional slow gradient algorithms for training neural networks, ELM fitting accuracy is limited. This paper develops Functional Extreme Learning Machine (FELM), which is a novel regression and classifier. It takes functional neurons as the basic computing units and uses functional equation-solving theory to guide the modeling process of functional extreme learning machines. The functional neuron function of FELM is not fixed, and its learning process refers to the process of estimating or adjusting the coefficients. It follows the spirit of extreme learning and solves the generalized inverse of the hidden layer neuron output matrix through the principle of minimum error, without iterating to obtain the optimal hidden layer coefficients. To verify the performance of the proposed FELM, it is compared with ELM, OP-ELM, SVM and LSSVM on several synthetic datasets, XOR problem, benchmark regression and classification datasets. The experimental results show that although the proposed FELM has the same learning speed as ELM, its generalization performance and stability are better than ELM.
A Comparative Analysis of Security Features and Concerns in NoSQL Databases

Current developments in cloud computing and also distributed internet applications have produced the demand to store huge quantities of data in dispersed databases that offer high availability and scalability. Over the last few years, increasingly more firms are adopting various sorts of non-relational data sources, frequently described as NoSQL data sources. By definition, NoSQL systems are not relational and also do not provide full SQL capabilities. Moreover, unlike relational databases, it uses consistency and security in exchange for performance and scalability. Security issues end up being more important as a growing number of sensitive data is kept in NoSQL databases. This article, therefore, examines the security features in four of the most preferred NoSQL databases; Cassandra, MongoDB, Redis and Neo4j, one from each category of databases. Unlike in most survey literature on NoSQL security features, that comparisons made excluded the graph database Neo4j, it is inclusive in this work, which makes this survey unique from others. Also, this will give awareness to developers and database administrators and also help them in choosing the best platform for deploying NoSQL databases.
Evaluation of containerized cloud platform for education and research

The paper deals with the automated and container-ized deployment of the Cloud Computing platform, which is most of the time large distributed system consisting of many servers. In this environment, manual deployment by system administrators is not scalable and introduces issues by repetitive tasks on many different servers. In several chapters the paper introduces the StarlingX platform, its architecture, the terminology used in documentation and several deployment types. Last but not least, our test deployment on multiple available servers available for our department is described.
A Hybrid Cryptographic Data Security System Utilizing Fuzzy Vault Key

Security is the main concern in the areas of medical, industrial, military and other fields. Including cryptography and steganography, there are various technologies available today for ensuring the trustworthy transmission of digital data. It is very important to generate strong multi-layered systems that can withstand the rapid increase in computing tool capability which may endanger security. In this paper, we have introduced a hybrid data security system that uses the Advanced Encryption Standard (AES) algorithm, the Rivest-Shamir-Adleman (RSA) algorithm, Discrete Cosine Transform (DCT) steganography, and the fuzzy vault key generation system for text encryption, embedding text into images, and then encrypting those images. We have employed these techniques to get privileged by their effective features, which have added an enhanced security level to this system. For encrypted image, the resulting Number of Changing Pixel rate (NPCR) value is 99.47 percent and the Unified Averaged Changed Intensity (UACI) value is 33.21 percent that are both surprisingly closer to the optimum value.
Complexity in the adaptation of aspect-oriented software Development

The software products are an integral part of the modern era and this encourages the developers to explore new techniques for the development. As the software products are becoming more complex, this demands a new technique to deal with the complexity. Aspect-Oriented Software Development is an example of a technique that deals with modern complex systems. As the structure of the new technique is also complex for the new developers, they want to change their way of development according to the new demands and there is no model for the new developers. This paper purposed a model using the basic phases of software development. This model educates and encourages the new developers to adopt the new software development technique. The Paper also discusses the common tools and techniques regarding the different phases of aspect-oriented software development (AOSD) to deal with the complexity of the system more efficiently.
Emotion-Sentence-DistilBERT: A Sentence-BERT-Based Distillation Model for Text Emotion Classification

Text emotion classification is a hot research area in natural language processing, aiming to classify the human emotion into positive and negative categories based on words. As a well-established and widely used neural model, BERT has achieved many state-of-the-art results in various natural language processing tasks, including text emotion classification. However, the embedding of sentences from BERT has been proved to be insufficient in semantic representation, which we believe is especially crucial for building the emotion classification models. Another issue about employing BERT in text emotion classification is the model complexity, which has limited its training and application in natural human-computer interaction for emotional robotics. In this paper, we propose a novel Emotion-Sentence-DistilBERT (ESDBERT) model, which explores the rich emotional representation in sentences via a Siamese Network based Sentence-BERT module and further reduces the model complexity through a Knowledge Distillation process. Experimental results suggest that the proposed model can learn a rich emotional representation and to render a promising accuracy for text emotion classification compared with the undistilled BERT-based models.
A Combination of BERT and Transformer for Vietnamese Spelling Correction

Recently, many studies have shown the efficiency of using Bidirectional Encoder Representations from Transformers (BERT) in various Natural Language Processing (NLP) tasks. Specifically, English spelling correction task that uses Encoder-Decoder architecture and takes advantage of BERT has achieved state-of-the-art result. However, to our knowledge, there is no implementation in Vietnamese yet. Therefore, in this study, a combination of Transformer architecture (state-of-the-art for Encoder-Decoder model) and BERT was proposed to deal with Vietnamese spelling correction. The experiment results have shown that our model outperforms other approaches as well as the Google Docs Spell Checking tool, achieves an 86.24 BLEU score on this task.
Implementing FAIR Workflows along the research lifecycle

DataCite is a leading non-profit organization that provides DOIs for research datasets and other research outputs – an active participant in the research community that promotes data sharing through different community-building efforts and outreach activities. DataCite is the coordinator leading the “Implementing FAIR Workflows” project that, along with ChronosHub, Dryad, CEDAR, California Digital Library, and Australia Research Data Commons as project partners, aim to develop an exemplar FAIR and Open Research workflow based on the reality of the research lifecycle. ChronosHub, as one of the project partners, acts as an intermediary to facilitate the information flow between the different stakeholders in the research ecosystem. By applying the FAIR principles from the inception of the research project, all the different entities of research information get related to each other, which in the case of Chronoshub, refers to the point of submission of a manuscript to the publisher. This paper discusses how the stakeholders in the research ecosystem can contribute with collaborative actions to build bridges and remove the huge amounts of administrative work that currently is needed for making research FAIR, specifically the data collection, structuring, and quality assurance.