Reviewerly/exHarmony huggingface dataset:
IMPLEMENTATION OF VIRTUAL AND AUGMENTED REALITY TECHNOLOGIES, GAME SIMULATORS FOR FUTURE SPECIALISTS’ TRAINING

The article presents the outcomes of the research of virtual and augmented reality technologies percularities and the possibility of their implementation in the educational process. The main reasons and technologies of virtual reality in the field of information technologies are determined. The analysis of virtual reality programs has been carried out. To determine the relevance of the implementation of virtual, augmented reality and game simulator technologies into the educational process the survey was created by the authors and conducted remotely among the population of Khmelnytskyi region on the Google Forms. The results of the conducted survey suggest the answers to a number of questions, including whether our society is ready to implement virtual, augmented reality and game simulators into the educational process of future specialists’ training, as the field of virtual reality research continues to expand relentlessly, the integration of VR (Virtual Reality) with various areas of human life continues. In addition to the survey in Google Forms, a survey was also conducted to collect information about currently existing virtual environments, programs and game simulators that can be added to educational components or integrated into other educational systems not only in the field of education (Minecraft, Cooking Simulator, PC Building Simulator 1-2, Google Arts &amp; Culture). It is quite possible to integrate these simulators under the educational components of certain specialized subject areas. The conducted survey makes it possible to assume that users are already ready not only for the integration of virtual environments in the game sphere, but also for the full involvement of virtual technologies and game environments in the application of educational components. VR (Virtual Reality) and AR (Augmented Reality) technologies are gaining popularity in cultural, educational, gaming, business environments and more. The disturbed issues are studied in education, which encourages a deeper insight into the essence of the processes that take place under the influence of the latest information and communication developments.
Fraud Diamond: Four Elements of Financial Report Fraud Detection – Study on Coal Producers

The aim of this research is to examine the impact of fraud diamonds on the identification of fraudulent financial statements. Fraud diamonds, a framework used to detect fraudulent financial statements, considers various factors such as pressure (measured by ROA), opportunity (measured by Ineffective monitoring (IND)), rationalization (measured by auditor turnover (AUDCHANGE)), and capability (measured by the change of directors (DIRCHANGE)). The research sample comprised of 9 companies selected through purposive sampling. Logistic regression analysis was employed as the analytical method, with rationalization and capability serving as the independent variables, assessed through a binary variable with values 0 and 1. The findings indicate that pressure significantly influences the detection of fraudulent financial statements. Specifically, higher ROA values correspond to a lower likelihood of fraudulent financial statements. Opportunity also has an impact on the detection of fraudulent financial statements, as an increase in ineffective monitoring levels leads to a higher detection rate of financial statement fraud. On the other hand, rationalization does not affect the identification of fraudulent financial statements, suggesting that auditor turnover rate does not play a role in detection. Similarly, capability, represented by the replacement of the board of directors, does not impact the detection of fraudulent financial statements in companies.
The Watermark Imaging System: Revealing the Internal Structure of Historical Papers

This paper introduces the Watermark Imaging System (WImSy) which can be used to photograph, document, and study sheets of paper. The WImSy provides surface images, raking light images, and transmitted light images of the paper, all in perfect alignment. We develop algorithms that exploit this alignment by combining several images together in a process that mimics both the “surface image removal” technique and the method of “high dynamic range” photographs. An improved optimization criterion and an automatic parameter selection procedure streamline the process and make it practical for art historians and conservators to extract the relevant information to study watermarks. The effectiveness of the method is demonstrated in several experiments on images taken with the WImSy at the Metropolitan Museum of Art in New York and at the Getty Museum in Los Angeles, and the results are compared with manually optimized images.
Sentiment Analysis of Comment Data Based on BERT-ETextCNN-ELSTM

With the rapid popularity and continuous development of social networks, users’ communication and interaction through platforms such as microblogs and forums have become more and more frequent. The comment data on these platforms reflect users’ opinions and sentiment tendencies, and sentiment analysis of comment data has become one of the hot spots and difficulties in current research. In this paper, we propose a BERT-ETextCNN-ELSTM (Bidirectional Encoder Representations from Transformers–Enhanced Convolution Neural Networks–Enhanced Long Short-Term Memory) model for sentiment analysis. The model takes text after word embedding and BERT encoder processing and feeds it to an optimized CNN layer for convolutional operations in order to extract local features of the text. The features from the CNN layer are then fed into the LSTM layer for time-series modeling to capture long-term dependencies in the text. The experimental results proved that compared with TextCNN (Convolution Neural Networks), LSTM (Long Short-Term Memory), TextCNN-LSTM (Convolution Neural Networks–Long Short-Term Memory), and BiLSTM-ATT (Bidirectional Long Short-Term Memory Network–Attention), the model proposed in this paper was more effective in sentiment analysis. In the experimental data, the model reached a maximum of 0.89, 0.88, and 0.86 in terms of accuracy, F1 value, and macro-average F1 value, respectively, on both datasets, proving that the model proposed in this paper was more effective in sentiment analysis of comment data. The proposed model achieved better performance in the review sentiment analysis task and significantly outperformed the other comparable models.
A Deep Echo State Network-Based Novel Signal Processing Approach for Underwater Wireless Optical Communication System with PAM and OFDM Signals

Underwater wireless optical communication (UWOC) plays key role in the underwater wireless sensor networks (UWSNs), which have been widely employed for both scientific and commercial applications. UWOC offers high transmission data rates, high security, and low latency communication between nodes in UWSNs. However, significant absorption and scattering loss in underwater channels, due to ocean water conditions, can introduce highly non-linear distortion in the received signals, which can severely deteriorate communication quality. Consequently, addressing the challenge of processing UWOC signals with low optical signal-to-noise ratios (OSNRs) is critical for UWOC systems. Increasing the transmitting optical power and investigating more advanced signal processing technologies to recover transmitted symbols are two primary approaches to improve system tolerance in noisy UWOC signal channels. In this paper, we propose and demonstrate the application of deep echo state networks (DeepESNs) for channel equalization in high-speed UWOC systems to enhance system performance with both PAM and QPSK-OFDM modulations. Our experimental results demonstrate the effectiveness of DeepESNs in UWOC systems, achieving error-free underwater transmission over 40.5 m with data rates up to 167 Mbps. Moreover, we compare the performance of DeepESNs to conventional echo state networks and provide suggestions on the configuration of a DeepESN for UWOC signals.
A Deep-Learning-Based Approach to Keystroke-Injection Payload Generation

Investigation and detection of cybercrimes has been in the spotlight of cybersecurity research for as long as the topic has existed. Modern methods are required to keep up with the pace of the technology and toolset used to facilitate these crimes. Keystroke-injection attacks have been an issue due to the limitations of hardware and software up until recently. This paper presents comprehensive research on keystroke-injection payload generation that proposes the use of deep learning to bypass the security of keystroke-based authentication systems focusing on both fixed-text and free-text scenarios. In addition, it specifies the potential risks associated with keystroke-injection attacks. To ensure the legitimacy of the investigation, a model is proposed and implemented within this context. The results of the implemented implant model inside the keyboard indicate that deep learning can significantly improve the accuracy of keystroke dynamics recognition as well as help to generate effective payload from a locally collected dataset. The results demonstrate favorable accuracy rates, with reported performance of 93–96% for fixed-text scenarios and 75–92% for free-text. Accuracy across different text scenarios was achieved using a small dataset collected with the proposed implant model. This dataset enabled the generation of synthetic keystrokes directly within a low-computation-power device. This approach offers efficient and almost real-time keystroke replication. The results obtained show that the proposed model is sufficient not only to bypass the fixed-text keystroke dynamics system, but also to remotely control the victim’s device at the appropriate time. However, such a method poses high security risks when deploying adaptive keystroke injection with impersonated payload in real-world scenarios.
Generalized Spoof Detection and Incremental Algorithm Recognition for Voice Spoofing

Highly deceptive deepfake technologies have caused much controversy, e.g., artificial intelligence-based software can automatically generate nude photos and deepfake images of anyone. This brings considerable threats to both individuals and society. In addition to video and image forgery, audio forgery poses many hazards but lacks sufficient attention. Furthermore, existing works have only focused on voice spoof detection, neglecting the identification of spoof algorithms. It is of great value to recognize the algorithm for synthesizing spoofing voices in traceability. This study presents a system combining voice spoof detection and algorithm recognition. In contrast, the generalizability of the spoof detection model is discussed from the perspective of embedding space and decision boundaries to face the voice spoofing attacks generated by spoof algorithms that are not available in the training set. This study presents a method for voice spoof algorithms recognition based on incremental learning, taking into account data flow scenarios where new spoof algorithms keep appearing in reality. Our experimental results on the LA dataset of ASVspoof show that our system can improve the generalization of spoof detection and identify new voice spoof algorithms without catastrophic forgetting.
An Adaptive Remote Sensing Image-Matching Network Based on Cross Attention and Deformable Convolution

There are significant background changes and complex spatial correspondences between multi-modal remote sensing images, and it is difficult for existing methods to extract common features between images effectively, leading to poor matching results. In order to improve the matching effect, features with high robustness are extracted; this paper proposes a multi-temporal remote sensing matching algorithm CMRM (CNN multi-modal remote sensing matching) based on deformable convolution and cross-attention. First, based on the VGG16 backbone network, Deformable VGG16 (DeVgg) is constructed by introducing deformable convolutions to adapt to significant geometric distortions in remote sensing images of different shapes and scales; second, the features extracted from DeVgg are input to the cross-attention module to better capture the spatial correspondence of images with background changes; and finally, the key points and corresponding descriptors are extracted from the output feature map. In the feature matching stage, in order to solve the problem of poor matching quality of feature points, BFMatcher is used for rough registration, and then the RANSAC algorithm with adaptive threshold is used for constraint. The proposed algorithm in this paper performs well on the public dataset HPatches, with MMA values of 0.672, 0.710, and 0.785 when the threshold is selected as 3–5. The results show that compared to existing methods, our method improves the matching accuracy of multi-modal remote sensing images.
Using Event Data to Build Predictive Engine Failure Models

Diesel engine failures are one reason for delays and breakdowns on the UK rail network, resulting in significant fines and related financial penalties for a train operating company. Preventing such failures is the ultimate goal, but forecasting or predicting future failures before they occur would be highly desirable. In this study, we take real world Diesel Multiple Unit sensor data, recorded in the form of event data, and repurpose it for the remote condition monitoring of critical diesel engine operations. A methodology based on windowing of data is proposed that demonstrates the effective processing of event data for predictive modelling. This study specifically looks at predicting engine failures, and through this methodology, models trained on the processed data resulted in accuracies of 88%. Explainable AI methods are then utilised to provide feature importance explanations for the model’s performance. This information helps the end user understand specifically which sensor data from the larger dataset is most relevant for predicting engine failures. The work presented is useful to the railway industry, but more specifically to train operator companies who ideally want to foresee failures before they occur to avoid significant financial costs. The methodology proposed is applicable for the predictive maintenance of many systems, not just railway diesel engines.
The Linguistic Structure of Haiku/Senryu and Its Evolution Over Time

Although computational linguistic methods have been applied to spoken and written text, they have only recently been used to study poetry. Here, we examine the linguistic components of contest-winning haiku and compare them to a control sample of poems published in an industry-standard journal. We look also at differences between haiku and senryu and what stylistic trends may have affected these poems over time. The results show that winning haiku are shorter than those in a representative journal. Journal poems also have fewer pronouns and more adjectives and nouns. All of the poems show a decrease in words over a decade-long time span. This trend may reflect changes in stylistic writing conventions. The length of such poems probably also relates to innate working memory span limitations. Haiku that limit the number of images and ideas within this limit likely facilitate cognitive processing and increase aesthetic appeal.
Lean Six Sigma: Application of the Methodology in Data Processing for Cancer Registry

Since 2020, the Catania-Messina-Enna Cancer Registry (CR) has operated a transformational and incremental program while also applying a Lean Six Sigma methodology (LSS) to optimize the processes and reduce waste. Each project aimed to raise the performance of the CR while also providing the opportunity for human resources to express their talent. In this context, a machine learning project was developed to reduce the time spent on raw free-text histopathological reports that contain relevant information for cancer evaluation. The ability to extract meaningful information from histopathology reports is really important because reports provide crucial insights into the morphology and topography of cancer, enabling operators to validate oncology cases with the utmost diagnostic precision. However, the CR faced a significant challenge due to the extensive volume of written natural language reports, where only a small fraction contains pertinent information for cancer evaluation. In this paper, we describe how we applied the LSS method, the observed difficulties, and the benefits achieved by adopting a machine learning algorithm as a strategic solution in the Improve phase.
DEVELOPMENT OF A SECURE LOGGING AND MANAGEMENT SYSTEM FOR PENETRATION TESTING

The sphere of information security in Kazakhstan affects an increasing number of industries every year, and penetration testing is also gaining popularity, as it is one of the key methods for assessing the security and risks of a company. This article is devoted to the research and development of a web application to provide full control over the penetration testing process: monitoring the implementation of tasks and projects, reporting on all processes, dividing tasks between employees. The management system automatically selects recommendations for eliminating vulnerabilities and generates reports on penetration testing. As a classification algorithm, a decision tree is used. Differentiation of users by access levels, structured data storage, automatic recording of test results, generation of reports and selection of recommendations for eliminating vulnerabilities make the web application more perfect and convenient compared to similar systems. The importance of this study lies in the simplification of the implementation of penetration testing and the development of this service in Kazakhstan, which will improve the level of information security in enterprises of all industries.
Sentiment Analysis on Online Videos by Time-Sync Comments

Video highlights are welcomed by audiences, and are composed of interesting or meaningful shots, such as funny shots. However, video shots of highlights are currently edited manually by video editors, which is inconvenient and consumes an enormous amount of time. A way to help video editors locate video highlights more efficiently is essential. Since interesting or meaningful highlights in videos usually imply strong sentiments, a sentiment analysis model is proposed to automatically recognize sentiments of video highlights by time-sync comments. As the comments are synchronized with video playback time, the model detects sentiment information in time series of user comments. Moreover, in the model, a sentimental intensity calculation method is designed to compute sentiments of shots quantitatively. The experiments show that our approach improves the F1 score by 12.8% and overlapped number by 8.0% compared with the best existing method in extracting sentiments of highlights and obtaining sentimental intensities, which provides assistance for video editors in editing video highlights efficiently.
Hybrid Enhancement-based prototypical networks for few-shot relation classification

Few-shot relation classification is to recognize the semantic relation between an entity pair with very few samples. Prototypical network has proven to be a simple yet effective few-shot learning method for relation extraction. However, under the condition of data scarcity, the relation prototypes we achieve are usually biased compared to the real ones computed from all samples within a relation class. To alleviate this issue, we propose hybrid enhancement-based prototypical networks. In particular, our model contains three main enhancement modules: 1) a query-guided prototype enhancement module using rich interactive information between the support instances and the query instance as guidance to obtain more accurate prototype representations; 2) a query enhancement module to diminish the distribution gap between the query set and the support set; 3) a support enhancement module adopting a pseudo-label strategy to expand the scale of available data. On basis of these modules, we further design a novel prototype attention fusion mechanism to fuse information and compute discriminative relation prototypes for classification. In this way, we hope to obtain unbiased representations closer to our expected prototypes by improving the available data scale and data utilization efficiency. Extensive experimental results on the widely-used FewRel dataset demonstrate the superiority of our proposed model.
The Solar Energy Potential of Greece for Flat-Plate Solar Panels Mounted on Double-Axis Systems

The aim of the present work is to investigate the efficiency of flat-plate solar panels in Greece for delivering solar energy. In this study, the solar panels are mounted on a two-axis tracker, which follows the daily path of the sun. In this context, the annual energy sums are estimated on such surfaces from hourly solar horizontal radiation values at forty-three locations, covering all of Greece. The solar horizontal radiation values are embedded in the typical meteorological years of the sites obtained from the PVGIS tool. All calculations use near-real surface-albedo values for the sites, and isotropic and anisotropic models are used to estimate the diffuse-inclined radiation. The analysis provides non-linear regression expressions for the energy sums as a function of time (month, season). The annual energy sums are found to vary between 2247 kWhm−2 and 2878 kWhm−2 under all-sky conditions with the anisotropic transposition model. Finally, maps of Greece showing the distribution of the annual and seasonal solar energy sums under all- and clear-sky conditions are derived for the first time, and these maps constitute the main innovation of this work.
Customer Sentiment Recognition in Conversation Based on Contextual Semantic and Affective Interaction Information

In the e-commerce environment, conversations between customers and businesses contain a multitude of useful information about customer sentiment. By mining that information, customer sentiment can be validly identified, which is helpful in accurately identifying customer needs and improving customer satisfaction. Contextual semantics information and inter-speaker affective interaction information are two key factors for identifying customers’ sentiments in conversation. Unfortunately, none of the existing approaches consider the two factors simultaneously. In this paper, we propose a conversational sentiment analysis method based on contextual semantic and affective interaction information. The proposed approach uses different bidirectional gated recurrent unit (BiGRU) combined with attention mechanisms to encode the contextual semantic information of different types of conversational texts. For modeling affective interactions, we use directed graph structures to portray the affective interactions between speakers and encode them with affective interaction features using graph convolutional neural networks (GCN). Finally, the two features are afused to recognize customer sentiment. The experimental results on the JDDC dataset show that our model can more accurately recognize customer sentiment than other baseline models in customer service conversation.