Reviewerly/exHarmony huggingface dataset:
Rationale for choosing the model and tool for developing an e-learning course

Abstract The emergence and discussion of the reasonable choice of methodological bases for the creation and subsequent application of electronic training courses within the framework of a separate study is not accidental. Analysis of the theory and practice of the use of information technologies in education showed that, along with the discussion of informatization of education, including adult education (androgogy), the use of computer-based training programs to improve the quality of teaching in various disciplines, studies of pedagogical capabilities of electronic training courses, principles of development and technology for creating electronic educational materials remain insufficiently studied. Properly grounded methodological and technological components of the process of creating such resources will ensure high quality of the final product, as well as reduction of time and financial costs. The article presents a generalized description of the basic models and methodologies for the development of electronic educational materials, namely: ADDIE (Analysis, Design, Development, Implementation, Evaluation); SAM (Successive Approximation Model); SMART (Specific, Measurable, Attainable, Relevant, Time-bound); ALD (Agile Learning Design). The authors pay special attention to the ALD (Agile Learning Design) model, describing its basic principles. The structural analysis of ALD with the help of the IDEF0 functional model allowed showing the logical interconnection of all the stages of creating an e-learning course, to demonstrate its iterative nature. The study also addresses the issue of the reasonable choice of tools for developing e-learning courses. The choice was made on the basis of T. Saati’s method of analyzing hierarchies. The selected alternatives were Adobe Captivate, Articulate Storyline and iSpringSuite as software solutions used for the professional development of e-learning materials. The choice was made on the basis of such criteria as maintenance, implementation, functionality, price, experience of usage. As a result of the testing with the MPRIORITY decision-making system, the Articulate Storyline software platform was selected as the tool for the implementation of the subsequent e-learning course project. This software product has significant functionality if compared to the other alternatives: low cost and availability of development. Further work on the creation of an e-course is sure to be carried out with its use.
Enabling FAIR Clinical Data Standards with Linked Data

This article reports on our efforts to support FAIR Clinical Data Standards with Semantic Web technologies, including the challenge of bridging the gap for non-technical users.
Digital quantum simulation of hadronization in Yang–Mills theory

A quantum algorithm of SU(N) Yang-Mills theory is formulated in terms of quantum circuits. It can nonperturbatively calculate the Dyson series and scattering amplitudes with polynomial complexity. The gauge fields in the interaction picture are discretized on the same footing with the lattice fermions in momentum space to avoid the fermion doubling and the gauge symmetry breaking problems. Applying the algorithm to the quantum simulation of quantum chromodynamics, the quark and gluon's wave functions evolved from the initial states by the interactions can be observed and the information from wave functions can be extracted at any discrete time. This may help us understand the natures of the hadronization which has been an outstanding question of significant implication on high energy phenomenological studies.
Signing information in the quantum era

Signatures are primarily used as a mark of authenticity, to demonstrate that the sender of a message is who they claim to be. In the current digital age, signatures underpin trust in the vast majority of information that we exchange, particularly on public networks such as the internet. However, schemes for signing digital information, which are based on assumptions of computational complexity, are facing challenges from advances in mathematics, the capability of computers, and the advent of the quantum era. Here, the authors present a review of digital signature schemes, looking at their origins and where they are under threat. Next, the authors introduce post-quantum digital schemes, which are being developed with the specific intent of mitigating against threats from quantum algorithms while still relying on digital processes and infrastructure. Finally, the authors review schemes for signing information carried on quantum channels, which promise provable security metrics. Signatures were invented as a practical means of authenticating communications, and it is important that the practicality of novel signature schemes is considered carefully, which is kept as a common theme of interest throughout this review.
The use of Kalman Filter in Control the Balancing Robot

In this paper describes a project of the gimbal which allows stabilizing devices such a video recorder, sport camera or smartphone during recording video clips. Function of this kind of stabilization including photography and robotics was presented in the introduction. As well as, this chapter includes purpose and scope of the each step of a design process. The next chapter is devoted to discussion of existing commercial solutions and specifying their advantages and disadvantages. Constructional features, that were used in the design of the mechanical part, were specified in the predesign phase based on the carried out review. The third chapter is devoted to the full process of designing devices. The first step was to create a mechanical structure in the CAD program, diagnostics of possible constructional errors and then realising a real mechanical system. Next, the author discusses elements of the electrical system and mutual connection and communication. The next part of the thesis is devoted to description of the way of data reading in the used sensors and the way of their filtration. The rest of the chapter is devoted to the control of the mechanical system. Starting from a linearity testing, through a mathematical model identification and at the end carrying out of control of the system both by a simulation and on physical device. The next chapter is devoted to testing control influence on device stabilization quality and correlating the data.
A Variational Joint Segmentation and Registration Framework for Multimodal Images

Image segmentation and registration are closely related image processing techniques and often required as simultaneous tasks. In this work, we introduce an optimization-based approach to a joint registration and segmentation model for multimodal images deformation. The model combines an active contour variational term with a mutual information smoothing fitting term and solves in this way the difficulties of simultaneously performed segmentation and registration models for multimodal images. This combination takes into account the image structure boundaries and the movement of the objects, leading in this way to a robust dynamic scheme that links the object boundaries information that changes over time. Comparison of our model with state of art shows that our method leads to more consistent registrations and accurate results.
Experimental Implementation of a Photonic Neural Network with a 1550nm-VCSEL subject to Optical Injection and Delayed Optical Feedback

We investigate experimentally the performance of an ultrafast photonic reservoir computer based on an off-the-shelf 1550nm-VCSEL subject to optical injection and delayed optical feedback, creating an artificial network of hundreds of GHz-speed neurons. We also demonstrate successful operation in different complex processing tasks.
Classful Object Generation Using Transposed Convolutional Network Fine-Tuned with a Classifier

On-demand generation of object that belongs to a specific class is of great importance in various AI applications. We show it is possible to generate on demand a object that belongs to a particular class from a single low-volume vector by using Tansposed Convolutional Network (TCN). TCN is an up-sampling method that can learn from experiences to optimally adjust its parameters to retain relevant information contained in those experiences. The network is first pre-trained using supervised learning to set its parameters in a good region and then fine-tuned with a trained classifier. The low volume input vector to the network is made up of two fixed-sized segments. The first segment employs one-hot scheme to indicate class identity of the object, while the second segment is composed of elements with their values randomly selected. The purpose of this second segment is to inject randomness in the object generation process and hopefully to capture some interesting features (such as style) in the training data. The contribution of this work is two-fold: 1) show that complexed, high dimensional, classiful object can be learned and generated on demand from a short low dimensional vector; 2) propose a fine tuning procedure to fine-tune the generator with a trained classifier to improve the quality of object generated. As a proof of concept, a generator network is built and trained to produce images of any hand-written digit on demand.
The Automated Technology for Converting Drawing and Design Paper-Based Documentation into Electronic 3D Model of an Object

We propose the original automated information technology for transformation of paper archives of drawing and design documentation into electronic 3D model of an object for CALS and BIM ideologies of designing and manufacturing of an object. The technology assumes the full automated cycle of processing of projective kinds from initial paper drawings to electronic frame 3D model of an object. Original models of representation and algorithms of solving the given problem are proposed.
Sparse Lifting of Dense Vectors: A Unified Approach to Word and Sentence Representations

As the first step in automated natural language processing, representing words and sentences is of central importance and has attracted significant research attention. Despite the successful results that have been achieved in the recent distributional dense and sparse vector representations, such vectors face nontrivial challenge in both memory and computational requirement in practical applications. In this paper, we designed a novel representation model that projects dense vectors into a higher dimensional space and favors a highly sparse and binary representation of vectors, while trying to maintain pairwise inner products between original vectors as much as possible. Our model can be relaxed as a symmetric non-negative matrix factorization problem which admits a fast yet effective solution. In a series of empirical evaluations, the proposed model reported consistent improvement in both accuracy and running speed in downstream applications and exhibited high potential in practical applications.
On deep learning approach in remote sensing data forgery detection

Digital image forgery is a known issue due to the increasing availability of technologies and software that make it easy to create distorted images. In order to counter such attacks, several approaches have been developed to detect fakes. Of particular importance are the methods for detecting distortions of a particular type of digital image - Earth remote sensing data, which can be used to ensure the safety of protected areas, monitor the state of the environment, etc. This article proposes a new scheme based on neural networks and deep learning, which is based on the use of the new convolutional neural network (CNN) architecture to improve the quality of detection of the most common type of attack on digital images - embedding duplicates. As part of the proposed architecture, additional preprocessing layers are used to improve detection quality. This approach also demonstrates invariance to distortions introduced into the duplicated region. Experiments show that the proposed solution exceeds the known copy-move detection algorithms - the metric value F1 reaches 0.77. At the same time the proposed deep learning approach shows high quality for the splicing detection task.
Construction of Audit Internal Control System Based on Online Big Data Mining and Decentralized Model

Construction of the audit internal control system based on the online big data mining and decentralized model is done in this paper. How to integrate the novel technologies to internal control is the attracting task. IT audit is built on the information system and is independent of the information system itself. Application of the IT audit in enterprises can provide a guarantee for the security of the information system that can give an objective evaluation of the investment. This paper integrates the online big data mining and decentralized model to construct an efficient system. Association discovery is also called a data link. It uses similarity functions, such as the Euclidean distance, edit distance, cosine distance, Jeckard function, etc., to establish association relationships between data entities. These parameters are considered for comprehensive analysis.
Sentiment-based sub-event segmentation and key photo selection

The number of people collecting photos has surged owing to social media and cloud services in recent years. A typical approach to summarize a photo collection is dividing it into events and selecting key photos from each event. Despite the fact that a certain event comprises several sub-events, few studies have proposed sub-event segmentation. We propose the sentiment analysis-based photo summarization (SAPS) method, which automatically summarizes personal photo collections by utilizing metadata and visual sentiment features. For this purpose, we first cluster events using metadata of photos and then calculate the novelty scores to determine the sub-event boundaries. Next, we summarize the photo collections using a ranking algorithm that measures sentiment, emotion, and aesthetics. We evaluate the proposed method by applying it to the photo collections of six participants consisting of 5,480 photos in total. We observe that our sub-event segmentation based on sentiment features outperforms the existing baseline methods. Furthermore, the proposed method is also more effective in finding sub-event boundaries and key photos, because it focuses on detailed sentiment features instead of general content features.
Collaborating with faculty on data awareness: A case study

McGill University Library is undertaking a collaboration between three librarians and various faculty members from the Desautels Faculty of Management. The project seeks to better understand what data is available for business and finance, where that data is stored across the institution, and how its access can be better promoted and shared among the Faculty. The need to address data coverage and access was originally proposed as a project for the Faculty’s committee on Teaching and Learning Resources. The project team is made up of two Business Librarians, a Data Librarian, an Associate Professor, and a Senior Faculty Lecturer. Progress on this project has been ongoing, though significant delays were caused by the COVID-19 pandemic and the resulting closure of the McGill University campus.
Research of Paper Recommendation System Based on Citation Network Model

In view of the increasing number of existing papers, this paper is a study of paper recommendation system. The data set used in this paper is the DBLP citation network in AMiner. First of all, we build a three layers citation network graph model. In this model, we integrate the citation relationship, paper’s feature information, co-authorship relationship and research field information into this model. Secondly, we proposed the algorithm PAFRWR. This algorithm combines three layers citation network graph mode with RWR. And, the search vector is constructed by word2vec model. Finally, in this experiment, using Recall@N and NDCG@N as evaluation metric. Then the restart probability of PAFRWR is determined by experiments. And the most effective search vector is determined by comparison. The Recall@N and NDCG@N of PAFRWR are higher than PageRank, LDA and Link-PLSA-LDA through the experiment. So the recommendation model and algorithm in this paper are more accurate and effective.
Training NER Models: Knowledge Graphs in the Loop

Motivated by the need of annotated data for training named entity recognition models, in this work we present our experiments on a distantly supervised approach using domain specific vocabularies and raw texts in the same domain. In the experiments we use MeSH vocabulary and a random sample of PubMed articles to automatically create an annotated corpus and train a named entity recognition model capable to identify diseases in raw text. We evaluate method against the manually curated CoNLL-2003 corpus and the NCBI-disease corpus.
Recommender E-Learning platform using sentiment analysis aggregation

The ubiquity and the fast growth of online resources has made it more and more difficult to try to respect the differences between learners in terms of cognitive ability and knowledge structure. This is even clearer with recommendation algorithms that use traditional collaborative filtering as they struggle through identifying more helpful, user friendly and easy learning resources. On top of that, the incoherent recommended content and the compound and nonlinear data on online learning users cannot be effectively handled, thus making the recommendations less efficient. To increase the level of efficiency of learning resource recommendations, this paper introduces a two steps efficient resource recommendation model. this model is based on unsupervised deep learning machine to identify learning styles and users' clusters, and a sentiment analyzer bonus system, based on user experience, to improve or decrease recommender items list classification. The model integrates also teachers to incite them to enhance the quality and the success rate of appropriate selected items. The elaboration of such a model requires the use of a considerable quantity of data learners' features, course content and assessment attributes. Furthermore, this model needs to incorporate learner interactions features. These are the requirements to build Learner features vector as input for the first step and Learner-Content ratings vector to choose the more efficient learning resource to recommend.
Secure information flow connections

Denning's lattice model provided secure information flow analyses with an intuitive mathematical foundation: the lattice ordering determines permitted flows. We propose a connection-based extension of this framework that permits two autonomous organisations, each employing possibly quite different security lattices, to exchange information while maintaining security of information flow as well as their autonomy in formulating and maintaining security policies. Our prescriptive framework is based on the rigorous mathematical framework of Lagois connections proposed by Melton, together with a simple type system and operational model for transferring object data between the two domains. The merit of this formulation is that it is simple, minimal, adaptable and intuitive. We show that our framework is semantically sound, by proving that the connections proposed preserve standard correctness notions such as noninterference. We then illustrate via examples how Lagois theory also provides a robust framework and methodology for negotiating and maintaining secure agreements on information flow between autonomous organisations, even when either or both organisations change their security lattices. Composition and decomposition properties indicate support for a modular approach to secure flow frameworks in complex organisations. Finally, a natural and conservative extension of the Decentralised Labels Model of Myers et al. shows the applicability of the framework — a Lagois connection between the hierarchies of principals in two organisations naturally induces a Lagois connection between the corresponding security label lattices, thus extending the security guarantees ensured by the decentralised model to encompass bidirectional interorganisational flows.
Research on point-cloud collection and 3D model reconstruction

Point-cloud collection is used to collect 3D surface features from the object. 3D reconstruction can form a visual 3D model based on point-cloud. They are the important parts of 3D surface measurement. 3D surface measurement can effectively help enterprises shorten the design cycle, improve product quality, save labor costs, and improve the competitiveness of enterprises. Optical image measurement is a branch of 3D surface measurement. Because optical image measurement has the advantages of non-contact, high speed, high degree of automation and good flexibility, it has been researched and applied widely. Image processing and calibration technology are often used in optical image measurement. Image processing can extract valuable information from images, and calibration technology is necessary for mathematical model. Multi-line structured light has been widely used in the measurement.
Reply to discussion on ‘A thermal maturity map based on vitrinite reflectance of British coals’, <i>Journal of the Geological Society, London</i> , 176, 1136–1142, https://doi.org/10.1144/jgs2019-055

We welcome the comments of Smith and Rippon (2021) about our map of vitrinite reflectance data derived from Carboniferous coal in Britain. The essence of their contribution is that our map is based upon a limited database (103 samples). We are well aware that data is available for other localities, at a range of depths within mines, and in some cases with details of statistical accuracy. Smith and Rippon conclude that the data available is too extensive to make it possible to produce a summary map. That is possibly why, despite the heritage of coal studies in Britain, such a map has not been published. However, the scale at which we have resolved variations in the reflectance values, simplified further in Figure 1, allows realization of the tremendous educational potential in the data. Our purpose was to produce a map, and we have employed a data base appropriate to do so, rather than assemble a national data base, which is the responsibility of others.
Radar Emitter Signal Sorting Method Based on Density Clustering Algorithm of Signal Aliasing Degree Judgment

In modern warfare, possessing advanced electronic warfare technology and equipment can occupy a dominant position in the war and greatly improve the victory rate. Radar emitter signal recognition is a key technology in electronic countermeasures. How to quickly and accurately recognize radar emitter signals is an urgent problem to be solved in electronic countermeasures. In this paper, A radar emitter signal sorting method is proposed to solve the problem of low success rate of radar emitter signal sorting in complex electromagnetic environment, the radar emitter signal sorting can be completed quickly and efficiently by judging the degree of radar emitter signal aliasing, reorganizing the sample sequence, and adopting fast density clustering sorting to process the carrier frequency and pulse width two sorting characteristics. It has certain practical value in the field of radar emitter signal sorting.