Reviewerly/exHarmony huggingface dataset:
On Improving Widyantoro’s Dynamic Programming for Forward-Backward Combination Method

In the previous works, Widyantoro et. al. have proposed a dynamic programming (DP) for forward-backward combination method on the problem of finding optimal path; yet, Widyantoro apparently neglected the two major points of the dynamic programming literature: (1) the useful of stage-wise procedure; and (2) the dynamic formulation of recurrence relation. The purpose of this paper is to present a new forward-backward DP formulation that exploits Widyantoro’s sample problem efficiently. Our DP exploits the graph structure of Widyantoro’s type for reducing time computation. We are also dedicated to improve the problem complexity while the posed problem looks trivial. This paper tries to educate the reader towards the artful use of dynamic programming.
Traditional Authentication

The paradigm for user authentication has not greatly changed since computers came to market; many systems still rely on passwords. This chapter firstly introduces the concept of authentication, and then introduces the main categories of authentication mechanisms used today. The specific authentication mechanisms are then individually introduced and critically discussed. Then, a summary of user perceptions toward these type of authentication mechanisms is provided. Finally, the contents of the chapter are summarised.
Development of the method for embedding information into raster images using Lagrange interpolation and Bezier curves

The article is devoted to the actual direction – steganography of digital images using interpolation. The review of embedding messages methods in raster files is carried out, the steganographic method of embedding in bitmap images using Lagrange interpolation and the Bezier curve formula for five points is developed. In this paper, the protocol of the developed BCES stegosystem is considered in detail, special cases and limitations of the method are analyzed, the result of RS analysis is obtained showing the stability of the BSEC method.
Neural Network for Classification of Student Performance in Exams

Education is a crucial factor influencing the progress of a nation. The future of a nation largely rests in the hands of educated youth, particularly highly educated individuals such as university students. The quality of education for students is commonly assessed based on their examination scores, with higher scores indicating better educational quality. However, students may not always achieve high examination scores due to various external factors, including parental educational background, access to lunch, and participation in additional classes or tutorials. This study aims to compare the accuracy of classification methods, namely Decision Tree, K-NN, SVM, Neural Network, and Random Forest, using optimization algorithms for Feature Selection and Feature Weighting. The research findings indicate that the application of the Neural Network classification with the Evolutionary optimization algorithm for Feature Selection outperforms the results obtained through Feature Weighting optimization in terms of accuracy.
Sentiment Orientation Analysis of Micro-lecture Comments Based on MacBERT-BiGRU Model

Addressing issues such as low classification accuracy and lack of text feature extraction capabilities in traditional text classification models, this paper proposes a model structure based on MacBERT and Bidirectional Gated Recurrent Unit (BiGRU). The dynamic word vectors obtained from the pre-trained MacBERT model are input into the BiGRU model to extract bidirectional context relations, rendering more accurate text classification results. The final accuracy reached 92.82%, precision reached 90.12%, recall rate reached 89.49%, and the comprehensive evaluation value FI reached 89.79%. By comparing the experimental results with other models, it was found that the MacBERT-BiGRU model performs well in Chinese text classification tasks.
3D Object Detection Algorithm Based on Multi-Feature Fusion from the Perspective of BEV

Environment perception is a key direction in the field of autonomous driving research. However, due to the complex and constantly changing traffic environment, traditional localization and recognition methods suffer from insufficient accuracy in target position identification and significant deviations in target labeling. Therefore, this paper proposes a three-dimensional object detection algorithm based on multi-feature fusion under the bird's-eye view (BEV) perspective. Firstly, the camera view features are converted to BEV features through a feature mapping module, and voxel-based features are extracted from point clouds. Then, by using tracking information from historical frames, point cloud features and image features are fused over multiple frames. Data augmentation is performed using image-flipping methods, and the information extracted from multiple perspectives of the same target is combined to improve the network's ability to accurately detect correct targets and reduce occurrences of false alarms and missed detection. Experimental results on the publicly available nuScenes dataset demonstrate that compared to other methods, this algorithm achieves a certain level of improvement in the detection accuracy of categories such as pedestrians, cars, and buses, thereby proving its effectiveness.
Fuzzy clustering in high-dimensional spaces: Visualization and performance metrics

In general, most of the clustering algorithms deal with high-dimensional data. Therefore, the resulting clusters are high-dimensional geometrical objects, which are difficult to analyse, visualize and interpret. Principal component analysis (PCA) is a widely used classical technique for unsupervised dimensional reduction of datasets which extracts smaller uncorrelated data from the original high dimensional data space. The methodology of classical PCA is based on orthogonal projection defined in convex vector space. Thus, a norm between two projected vectors is unavoidably smaller than the norm between any two objects before implementation of PCA. Due to this, in some cases when the PCA cannot capture the data structure, its implementation does not necessarily confirm the real similarity of data in the higher dimensional space making the results unacceptable. Furthermore, when applied with fuzzy clustering algorithms, it demonstrates its weakness in capturing the data structure what affects the PCA mapping quality. In this research we propose a new fuzzy PCA algorithm (FuzPCA) combining FCM and PCA which builds not only fuzzy similarity measures based on the membership functions calculated by FCM but also a measure corresponding to the rate of dissimilarities between the clustering structures of the dataset objects and their dimensions. We formulated the respective new fuzzy covariance matrix which is processed by the PCA during subsequent iterations. FuzPCA demonstrates higher clustering discriminatory ability and visualization accuracy than regular FCM when applied to high-dimensional datasets, in cases when clustersâ sizes or densities are different as well as in discovering small clusters. The function integrated with FuzPCA Silhouette provides preliminary information and visualization allowing faster choice of number of clusters which is additionally refined after implementation of fuzzy clustering validation metrics. The projected 2- and 3-D topologies of FuzPCA are more reliable, significantly improve the visualization results and demonstrate high mapping quality.
Modeling objects movement when collising with obstacles in PascalABC.NET language in schoolchildren's project activities

To solve the methodological problem of expanding the content of school informatics textbooks in terms of practical tasks, assignments for project work can be used, which are completed by students individually or in groups. Carrying out project work that involves the application of knowledge from other subject areas contributes to the development of meta-subject skills in students. The use of group work as the main form of performing practical tasks is aimed at achieving personal learning results. The project-activity approach helps to achieve the planned learning outcomes in the section of the informatics course under consideration. The article discusses assignments in the "Beginnings of programming" section of the VIII grade informatics course, which can be offered for students' project activities to create graphical and information models and when conducting a computer experiment. Based on the implementation of gradually more complex examples, students are introduced to project-type tasks — modeling the movement of objects when collising with obstacles. The software implementation of the models is carried out in the PascalABC.NET language.
Utilisation of Mobile Telephony as an EAS (Extension and Advisory Services ) Tool - A Study among Livestock based Women Self-Help Groups

Dairying provides self-employment opportunities to rural women in India. However, due to the lack of unique approaches to meet the information needs of women dairy farmers, dairying was not found profitable. Mobile phones as one of the communication channels can address the information and knowledge gap of rural women farmers. In this regard, a study was carried out about the mobile utility pattern of women farmers in livestock-based Womeh Self-Help Groups, their accessibility and affordability to mobile phones. The mobile utility pattern indicated that most of the respondents own mobile or had access through their family members and were using mobile for more than five years. SMS (Short Message Service) was the mostly used feature and were in the habit of checking SMS daily. Voice SMS was preferred over text SMS and evening time was the most preferred time for receiving SMS. Accessibility to mobile phone was observed by all the rural women in the study area, however, only a few were aware that scientific information can be received through mobiles. Most of them felt that it was good to pay for mobile extension advisory services which may improve the productivity of their farms an encouraging phenomenon towards the utilisation of digital tools for extension and advisory services.
Preferences of streaming platforms users among the youth audience

The purposes of this article are the following: to identify online cinemas that are most common among the audience studying or working at the RUDN University and to compare the results of the conducted work with the results of identical materials. The main tasks are to be named: studying the main proposals among streaming services and online cinemas in Russia; studying the findings of researches on the same topic; compiling a survey to collect primary research information; conducting surveys; summarising the achievements and formulating conclusions. During the study, students of different years aged 18 to 24 were interviewed. They formed a particular list and sequence of popular online cinemas, somewhat different from the research of the GfK agency. According to the survey results, the most attractive streaming services are Kinopoisk HD, Netflix and Ivi (formerly – ivi). In this case, their clear sequence reflects the popularity of use of platforms among students.
Generative Byte-Level Models for Restoring Spaces, Punctuation, and Capitalization in Multiple Languages

Restoration of textual features such as spaces, punctuation, and capitalization is a fundamental NLP task that has applications in post-processing of automatic speech recognition (ASR) outputs, hashtags on social media, and other types of unprocessed or noisy data. In this work, we build upon the token-free approach adopted in previous work on this topic by proposing a generative method that leverages pre-trained byte-level transformer models. The newly proposed models are shown to outperform previously proposed models across a range of languages—English, Japanese, and Gujarati—from distinct language families and possessing distinct linguistic features. Overall F-score for the English dataset is 96%, showing that our new models outperform not only previous token-free models (F-score 90%) but also a high-performing pipeline that employs word embeddings (F-score 95%). Furthermore, the newly proposed model is found to be more effective than previous models at restoring mid-token features, correctly reflecting 66% of tokens containing mid-token features in the English dataset compared to 47% in previous work.
Improved Algorithm for Classification of Fetal Congenital Heart Disease Based on Loss Function

In order to solve the existing problems of high difficulty in classifying fetal congenital heart disease and poor generalization. First, we processed existing echocardiography videos into image sets, combined Faster R-CNN and the attention mechanism to build a model for classifying cardiac end-systolic images, and used the model to expand the data set. Secondly, the loss function is improved and adjustment factors and label weights are introduced to improve the classification effect of congenital heart disease. Then achieve the classification of fetal congenital heart disease images in fetal echocardiography. Experimental results show that the algorithm achieves good results in fetal heart disease classification tasks. Setting up multiple sets of experiments also proves that expanding the data set and introducing label weights can effectively improve the accuracy of the model.
Data Security Enhancement in Cloud Computing by Proposing A DKE Encryption Protocol

This study uses cryptography to tackle the important problem of data security in cloud computing. Two keys are used in the Dual Key Encryption (DKE) method for both encryption and decryption. DKE employs a public key for the first encryption round before cloud upload and a user-only private key for the second round of encryption. Decryption operates oppositely. When tested and simulated with different file sizes on a CloudAnalyst simulator, DKE operates faster and more efficiently than cryptographic methods such as Triple DES (3DES), AES, RSA, and DES. Thanks to developments in information technology, cloud computing has made several services available online. Data security assurance is still very difficult to achieve, nevertheless. In this context, cryptography is essential and has led to the creation of the novel DKE technology. This technology is not only far more efficient than well-known cryptographic techniques such as 3DES, but it also improves data security.
Using 3D-modeling to improve the quality of bachelors’ training in the field of socio-cultural activities

Problem statement. The implementation of 3D modeling elements in the in-demand specialists’ training of the socio-cultural sphere is in line with the recommendations of UNESCO and the priorities of Russia’s state cultural policy. The study aimed at substantiating the effectiveness of the three-dimensional computer graphics usage for specialists’ training to improve the quality of their education. Methodology . Theoretical and methodological analysis and generalization of fundamental scientific works on the research problem, processing of test results, and three-dimensional models of cultural objects are applied. The pedagogical experiment involved 49 students of the direction of training 51.03.03 Socio-cultural Activities (level - bachelor’s degree) of Orel State Institute of Culture. Pearson’s χ2 test was used as a statistical processing method. Results . The features of the use of three-dimensional computer graphics in the training of specialists in the socio-cultural sphere to improve the quality of their training are determined: activation of information communication; support for solving economic and engineering problems as components of socio-cultural activities; automation of a large number of calculations, etc. Statistically significant differences in qualitative changes in the pedagogical system were revealed. Conclusion . The use of three-dimensional computer graphics in the training of specialists in the socio-cultural sphere will help to increase the level of their professional training while providing a set of conditions: a combination of design, educational and research activities; orientation to the performance of labor functions; application of fundamental theoretical information on technologies for organizing leisure activities in real socio-cultural activities, etc.
A Direct ASP Encoding for Declare

Answer Set Programming (ASP), a well-known declarative programming paradigm, has recently found practical application in Process Mining, particularly in tasks involving declarative specifications of business processes. Declare is the most popular declarative process modeling language. It provides a way to model processes by sets of constraints, expressed in Linear Temporal Logic over Finite Traces (LTL $$_\text {f}$$ ), that valid traces must satisfy. Existing ASP-based solutions encode a Declare constraint by the corresponding LTL $$_\text {f}$$ formula or its equivalent automaton, derived using well-established techniques. In this paper, we propose a novel encoding for Declare constraints, which models their semantics directly as ASP rules, without resorting to intermediate representations. We evaluate the effectiveness of the novel approach on two Process Mining tasks by comparing it to alternative ASP encodings and a Python library for Declare .
Continuous Authentication

This chapter introduces continuous biometric authentication. Initially the concept is introduced and the key components are discussed. The device types that continuous biometric authentication systems have been designed for and the sensing capabilities available on these devices are then described. Next, the datasets that these systems can be constructed with are noted. The user profiles and the way that features, classification, and biometric fusion may be used are then discussed. Following this, the evaluation techniques and approaches for system assessment are described. Lastly, an overview of user perceptions, threats, and possible use cases for continuous biometric authentication systems are provided.
The Influences of AI and Emerging Technologies on the Imagery Stock Industry as Exemplified by the Visual China Group

We review the technology-driven attribution of business models at different stages of the imagery stock industry and their intrinsic regularities. We take Visual China Group’s (VCG) business development over the past two decades as a timeline. This paper analyses the phenomenon by focusing on the emerging technology family represented by AI technology. It explores the theoretical origins, future outlook, and risk prediction based on experience. We try to induce the development of the industry from representative cases, on the one hand, highlighting the opportunities for efficiency leapfrogging due to the contribution of emerging technologies to productivity and, on the other hand, rethinking the challenges of business ethics, business models and governance mechanisms. This research proposed to implement research on digital governance mechanisms based on blockchain technology. It explored the production relations of the emerging media industry, which is based on the “governance of technology by technology”.
VuLASTE: Long Sequence Model with Abstract Syntax Tree Embedding for Vulnerability Detection

In this paper, we present a model named VuLASTE, treating vulnerability detection as a specialized text classification task. To address the vocabulary explosion problem, VuLASTE utilizes a byte-level BPE algorithm from natural language processing. Within VuLASTE, we introduce a novel AST path embedding to represent source code nesting information. Additionally, we employ a combination of global and dilated window attention from Longformer to extract long sequence semantics from source code. To tackle the issue of data imbalance, a common challenge in vulnerability detection datasets, we employ focal loss as a loss function. This ensures that the model prioritizes poorly classified cases during training. To evaluate our model's performance on real-world source code, we construct a cross-language and multi-repository vulnerability dataset from the Github Security Advisory Database. VuLASTE achieves top 50, top 100, top 200, and top 500 hits of 29, 51, 86, and 228, respectively, surpassing state-of-the-art researches.
Scaling Up Paraphrase Generation Datasets with Machine Translation and Semantic Similarity Filtering

Paraphrase generation is an important natural language processing task that can be used in other tasks such as machine translation, question answering, and semantic parsing. The problem of data scarcity remains the main obstacle in the way of developing models for this task, especially for low-resource languages. In this chapter, we introduce large paraphrase corpora and a new methodology for filtering non-relevant sentence pairs. We augmented some datasets using a model, we fine-tuned on a dataset created using our method, and we obtained satisfactory results asserting our data collection and filtering method’s effectiveness.
The impact of virtual reality technologies on the health of employees

The article is a review of research on the impact of virtual reality on the functional state and human health. The relevance of the work is due to the growing interest in the use of virtual reality technologies in industry, education, medicine and other fields. At the same time, it is known that more than 80% of users have various side effects.&#x0D; The authors have analyzed more than 60 foreign literary sources in order to study the peculiarities of the negative effects of being in a virtual environment on human organisms.&#x0D; An analysis of scientific publications has shown that most of them are devoted to the study of cyber sickness, although researchers note other negative symptoms and effects caused by virtual reality: visual and muscle fatigue, acute stress and mental fatigue. We identified three groups of factors influencing the development of side effects: individual, hardware and software factors, and found that the characteristics of the visual display have the greatest impact on the user's condition.&#x0D; The disadvantage of most studies is the use of the questionnaire method, as well as the assessment of the impact of short-term work in extended environments on the human body.&#x0D; The issues discussed in this article form the directions for further research in the field of human interaction and the virtual environment.
Hierarchical Multi-task Learning with Articulatory Attributes for Cross-Lingual Phoneme Recognition

This chapter proposes a novel hierarchical multi-task architecture for multilingual phoneme recognition in unseen, low-resource languages. We optimize articulatory attribute and phoneme classifiers jointly and provide attribute probability distributions as an additional input to the phoneme classifier. To improve this hierarchical connection, we propose removing blank logits of the Connectionist Temporal Classification to compute more consistent attribute probability distributions. As the acoustic model, we use a hybrid convolution-transformer architecture. The models were trained and evaluated on a subset of 24 languages from the Mozilla Common Voice corpus. Regular or unmodified hierarchical multi-task learning did not improve phoneme error rates (PERs) compared to a phoneme-only baseline in our experiments. In contrast, the evaluation shows that the proposed blank removal method outperforms the baseline by 3.16 percentage points (pp.) PER. The evaluation of zero-shot cross-lingual transfer on a dataset with 95 languages shows negative transfer effects between attribute and phoneme classifiers in regular and unmodified hierarchical multi-task learning. However, using the novel blank removal approach, the hierarchical multi-task model achieved an absolute PER improvement of 2.69 pp. over the baseline.