Reviewerly/exHarmony huggingface dataset:
Proper Generalized Decomposition stereocorrelation to measure kinematic fields for high speed impact on laminated glass

Stereocorrelation (SC) is a powerful tool for measuring 3D surface deformations in mechanical tests. However, it may suffer from convergence issues for large displacements or speckle changes. A global space–time formulation is proposed herein using Proper Generalized Decomposition (PGD) as a regularization strategy. The entire image sequence is considered as a whole, and the kinematics is sought as the sum of modes, each of which is a function of space multiplied by a function of time (smoothed over high frequencies if needed). A discriminative benchmark case is chosen to validate this approach: an impact test on glass laminated with PVB layers. This case is difficult for many reasons, particularly because of severe contrast changes due to light reflections occurring at several places. It is shown that the new formulation outperforms classical approaches in robustness and measurement accuracy and provides results within a much shorter processing time.
A Syntactic Analysis of the Sentence Structure in Motivational Quotes using Tree Diagram for English Learning

Syntax is a branch of linguistics that explains sentence structure. This study aims to discover what types of sentences are used in motivational quotes, which frequently emerge in motivational quotes, and describe sentence structure utilizing a tree diagram. This research employs the descriptive qualitative technique. The study involved multiple processes in data collection, including; reading the novel's text, choosing sentences of motivational quotes contained in the novel Atomic Habits, and writing down all the sentences of motivational quotes selected for analysis. The data were analyzed using Noam Chomsky’s theory “Transformational Generative Grammar”. The result of the study shows that the motivational quotes from the novel Atomic Habits have all types of sentences. In addition, the researcher also found the types of sentences that frequently emerge in motivational quotes from the novel, namely 22 simple sentences, 2 compound sentences, five complex sentences, and one compound-complex sentence. So, the dominant motivational quote is a simple sentence. This study has various types of sentences in motivational quotes that can be used to help English learners and improve writing skills in a text or essay according to grammatical rules.
EXPERIENCE OF USING THE E-LEARNING SYSTEM IN MARTIAL LAW CONDITIONS

The large-scale aggression of the Russian Federation against Ukraine in February 2022 became a powerful challenge for the national education system. After its beginning, a significant number of students and teachers were forced to continue their studies in extreme conditions of military threat, forced emigration, temporary occupation in the absence of opportunities for a full-fledged organization of the educational process. Distance learning, which made it possible to communicate and use all available technical means to perform tasks, played a key role in providing education in these conditions. Studying the experience of using the distance learning system in higher educational institutions in the conditions of martial law, which continues in Ukraine today, is one of the urgent scientific and practical tasks. The purpose of the article is to generalize the experience of organizing distance learning in critical conditions (martial law, emergency, and others). To achieve the goal, the method of comparative analysis of the intensity of use of the distance learning system of distance learning at the "KROK" University in the conditions of martial law in 2022-2023 was used. The moodle system integrated with Microsoft 365 was considered as distance learning software. As a result of the analysis, it was established that the main reasons for the negative impact on the educational process and the decrease in the active use of the distance learning system were not technical problems, but organizational ones. At the heart of these problems lies the objective impossibility of rapid adaptation of training to circumstances that have changed critically on a nationwide scale. Based on the analysis of experience, the period of using the distance learning system was divided by time into two phases - critical and stable functioning. Differences in the use of the distance learning system during the 2021 coronavirus pandemic and the introduction of martial law in 2022-2023 are also identified.
Decision Procedures for Sequence Theories

Abstract Sequence theories are an extension of theories of strings with an infinite alphabet of letters, together with a corresponding alphabet theory (e.g. linear integer arithmetic). Sequences are natural abstractions of extendable arrays, which permit a wealth of operations including append, map, split, and concatenation. In spite of the growing amount of tool support for theories of sequences by leading SMT-solvers, little is known about the decidability of sequence theories, which is in stark contrast to the state of the theories of strings. We show that the decidable theory of strings with concatenation and regular constraints can be extended to the world of sequences over an alphabet theory that forms a Boolean algebra, while preserving decidability. In particular, decidability holds when regular constraints are interpreted as parametric automata (which extend both symbolic automata and variable automata), but fails when interpreted as register automata (even over the alphabet theory of equality). When length constraints are added, the problem is Turing-equivalent to word equations with length (and regular) constraints. Similar investigations are conducted in the presence of symbolic transducers, which naturally model sequence functions like map, split, filter, etc . We have developed a new sequence solver, SeCo , based on parametric automata, and show its efficacy on two classes of benchmarks: (i) invariant checking on array-manipulating programs and parameterized systems, and (ii) benchmarks on symbolic register automata.
A scoping review on multimodal deep learning in biomedical images and texts

Computer-assisted diagnostic and prognostic systems of the future should be capable of simultaneously processing multimodal data. Multimodal deep learning (MDL), which involves the integration of multiple sources of data, such as images and text, has the potential to revolutionize the analysis and interpretation of biomedical data. However, it only caught researchers' attention recently. To this end, there is a critical need to conduct a systematic review on this topic, identify the limitations of current work, and explore future directions. In this scoping review, we aim to provide a comprehensive overview of the current state of the field and identify key concepts, types of studies, and research gaps with a focus on biomedical images and texts joint learning, mainly because these two were the most commonly available data types in MDL research. This study reviewed the current uses of multimodal deep learning on five tasks: (1) Report generation, (2) Visual question answering, (3) Cross-modal retrieval, (4) Computer-aided diagnosis, and (5) Semantic segmentation. Our results highlight the diverse applications and potential of MDL and suggest directions for future research in the field. We hope our review will facilitate the collaboration of natural language processing (NLP) and medical imaging communities and support the next generation of decision-making and computer-assisted diagnostic system development.
Automatically detecting activities of daily living from in-home sensors as indicators of routine behaviour in an older population

The NEX project has developed an integrated Internet of Things (IoT) system coupled with data analytics to offer unobtrusive health and wellness monitoring supporting older adults living independently at home. Monitoring involves visualising a set of automatically detected activities of daily living (ADLs) for each participant. ADL detection allows the incorporation of additional participants whose ADLs are detected without system re-training.Following a user needs and requirements study involving 426 participants, a pilot trial and a friendly trial of the deployment, an action research cycle (ARC) trial was completed. This involved 23 participants over a 10-week period each with ∼20 IoT sensors in their homes. During the ARC trial, participants took part in two data-informed briefings which presented visualisations of their own in-home activities. The briefings also gathered training data on the accuracy of detected activities. Association rule mining was used on the combination of data from sensors and participant feedback to improve the automatic ADL detection.Association rule mining was used to detect a range of ADLs for each participant independently of others and then used to detect ADLs across participants using a single set of rules for each ADL. This allows additional participants to be added without the necessity of them providing training data.Additional participants can be added to the NEX system without the necessity to re-train the system for automatic detection of their ADLs.
The Role of Digital Transformation in Building a Competitive Economy: a Case Study of Azerbaijan

"The purpose of this study is to examine indicators of digital transformation in enterprises of Azerbaijan by analysing statistical data and forming recommendations for improving this process, considering regional features. In the course of the research, the following scientific methods were used: economic-statistical analysis, rating comparison analysis, deduction and induction, graphical methods. As a result of the study, the main advantages and disadvantages of the digital transformation process are identified. The place of Azerbaijan in the Digital Index rating is determined, the dynamics of the share of informationcommunication technologies in the gross domestic product (GDP) of the country is presented, and an analysis of the ease of establishing and conducting business in the country is conducted an economicstatistical analysis of data from Azerbaijani enterprises regarding the implementation of digital transformation on them was conducted. The following indicators were analysed: the main indicators of the use of information-communication technologies by enterprises, and the distribution of enterprises by the purposes of using the Internet. A number of recommendations were formulated regarding improving the process of digital transformation of enterprises in Azerbaijan."
Utilising state of the art eye tracking equipment to improve outcomes for maritime watchkeeper's on nocturnal navigational watches

The International Regulations for Preventing Collisions at Sea (IRPCS) require a ship's Officer of the Watch (OOW) to maintain a proper lookout at all times. This includes looking out of the window as well as monitoring information via Multi-Function Displays (MFDs) that are found on the modern-day ship's bridge. These displays lead the OOW to spend time interrogating their various menus and functions to seek required information, thus distracting them from their primary task of maintaining a lookout. This paper identifies the function of the human eye in performing the lookout function during the hours of darkness. State of the art Eye Tracking Devices (ETDs) are utilised to collect eye movement data in both real-world and simulator-based ship bridges, and this is used to identify the impact that MFDs have at night in reducing the eye's effectiveness in the watchkeeping task. A novel scanning pattern is presented that can be adopted by OOWs to make the best use of the eye's physiology, improving lookout effectiveness at night and reducing the distraction caused by MFDs. This paper aims to assist OOWs in making better use of their eyes, enabling them to maintain optimum lookout performance during the hours of darkness.
Pursuing Listeners’ Perceptual Response in Audio-Visual Interactions – Headphones vs Loudspeakers: A Case Study

This study investigates listeners' perceptual responses in audio-visual interactions concerning binaural spatial audio.Audio stimuli are coupled with or without visual cues to the listeners.The subjective test participants are tasked to indicate the direction of the incoming sound while listening to the audio stimulus via loudspeakers or headphones with the head-related transfer function (HRTF) plugin.First, the methodology assumptions and the experimental setup are described to the participants.Then, the results are presented and analysed using statistical methods.The results indicate that the headphone trials showed much higher perceptual ambiguity for the listeners than when the sound is delivered via loudspeakers.The influence of the visual modality dominates the audio-visual evaluation when loudspeaker playback is employed.Moreover, when the visual stimulus is present, the headphone playback pattern of behavior is not always in response to the loudspeaker playback.
Multi-Configuration Analysis of DenseNet Architecture for Whole Slide Image Scoring of ER-IHC

Nuclei classification is a mandatory process to obtain scoring information for whole slide images. In immunohistochemistry (IHC) staining specifically for estrogen-receptor (ER) biomarker, an Allred score based on the proportion and intensity of cancer nuclear staining is widely used in histopathology practice to predict response to hormonal treatment. This manually exhaustive process can be accelerated with the help of computational intelligence. In this article, we present a thorough analysis of 37 whole slide images of breast cancer cases with over 2.8 million segmented nuclei. ER-stained nuclei were classified into negative, weak, moderate and strong intensities using DenseNet deep learning architecture, contributing to Allred scoring. Seven different models and configurations were exhaustively analysed in six tests to obtain the scoring reaching best concordance of 56.8% and 81.1% with the pathologist’s manual score and suggested hormonal treatment. We also discussed in detail the causes that lead to the non-concordances. This study follows the pathologists’ workflow in obtaining the Allred score but is fully automated. It provides a basis for the development of more complex deep learning models, particularly for nuclei classification and achieving accurate scoring of ER-IHC stained whole slide images.
Standardized motion detection and real time heart rate monitoring of aerobics training based on convolution neural network

In order to make the teaching and training of aerobics more standardized, it is necessary to use scientific means to detect and monitor the movement standardization in teaching and training and the change of human heart rate in the training process, but at present, there are some difficulties in both detection and monitoring, Therefore, this paper proposes to use the advantages of convolutional neural network to solve the current aerobics teaching problems of motion detection and heart rate monitoring. In the process of operation, the complete aerobics video needs to be divided into several different images, the standardized action image background needs to be eliminated, and then the visual error caused by the difficult action image needs to be corrected. On the premise of image processing, the convolutional neural network is used to pre train the image, and the skeleton map of the human body is constructed in the computer. In the process of practical operation, the use of convolutional neural network for heart rate monitoring has many advantages. It can not only save the time of contact with the human body, but also integrate various information of the time dimension, reducing a lot of computing steps, saving a lot of computing resources for practical work, and promoting the improvement of system output signal quality to a certain extent. The result of the experiment also proves that the convolutional neural network can improve the accuracy of students' movement detection and heart rate change monitoring in aerobics teaching and training.
Research on the local path planning of an orchard mowing robot based on an elliptic repulsion scope boundary constraint potential field method

In orchard scenes, the complex terrain environment will affect the operational safety of mowing robots. For this reason, this paper proposes an improved local path planning algorithm for an artificial potential field, which introduces the scope of an elliptic repulsion potential field as the boundary potential field. The potential field function adopts an improved variable polynomial and adds a distance factor, which effectively solves the problems of unreachable targets and local minima. In addition, the scope of the repulsion potential field is changed to an ellipse, and a fruit tree boundary potential field is added, which effectively reduces the environmental potential field complexity, enables the robot to avoid obstacles in advance without crossing the fruit tree boundary, and improves the safety of the robot when working independently. The path length planned by the improved algorithm is 6.78% shorter than that of the traditional artificial potential method, The experimental results show that the path planned using the improved algorithm is shorter, smoother and has good obstacle avoidance ability.
Task-driven cleaning and pruning of noisy knowledge graph

Many knowledge graphs, especially those that are collaboratively or automatically generated, are prone to noise and cross-domain entries, which can impede domain-specific applications. Existing methods for pruning inaccurate or out-of-domain information from knowledge graphs often rely on topological graph-pruning strategies. However, these approaches have two major drawbacks: they may discard logical structure and semantic information, and they allow multiple inheritance. To address these limitations, this study introduces KGPruning, which is a novel approach that can effectively clean and prune noisy knowledge graphs by guiding tasks with a given set of concepts and automatically generating a domain-specific taxonomy. Specifically, KGPruning employs a graph hierarchy inference method that is based on the Agony model to precisely identify and eliminate noisy entries while striving to preserve the underlying hierarchy of semantic relations as much as possible. Furthermore, to establish a tree-structured taxonomy, KGPruning integrates semantic relations and structural characteristics to effectively eliminate out-of-domain information and multiple inheritance. Through extensive experimental evaluations conducted on open benchmark datasets as well as large-scale real-world problems, the superior performance of KGPruning over state-of-the-art methods is demonstrated on the task of pruning noisy knowledge graphs.
Employee Productivity: Exploring the Multidimensional Nature with Acculturation, Open Innovation, Social Media Networking and Employee Vitality in the Indian Banking Sector: An Analytical Approach

Purpose: The multi-dimensional nature of ‘employee productivity’ as a research construct needs extensive exploration, especially in view of the job-resources framework and in terms of stakeholder and accountability perspectives. The research study explored the topic with “individual” perceptions in exploring the pattern and tendency of the impact of acculturation and other organizational resources and protocols on the “perceived employee productivity”. Theoretical framework: The theoretical implications of the research lie in the empirical exploration of the influences across employees, workplace-provided job resources, and contextual aspects that seem to shape the impetus for employee productivity. Design/Methodology/Approach: The study delved into the aspects of ‘acculturation’, ‘open innovation’, ‘social media networking’, ‘employee vitality’, and ‘employee productivity’ for measuring the phenomenon in the Indian banking sector. The factors were operationalized with Likert scaling instruments and extractive factor analysis and structural equation modeling were leveraged. Findings: The study observed a positive significant relationship among acculturation, open innovation, social media networking, employee vitality, and employee productivity in the Indian banking sector. Research, Practical &amp; Social implications: The research provides evidence that the organizational provision of job resources is a driving force behind the attainment of effective and sustainable employee productivity in the banking service sector. The research outcomes support and vindicate the prior research on the role of job resources in coping with workplace-derived uncertainties and exhibiting workplace productivity. Originality/Value: The research outcomes illustrate the set of implications that possess meaning for core managerial application. The research has achieved the operationalization of employee productivity based on organizational support in the form of job resources.
Dynamic spectrum access for Internet-of-Things with hierarchical federated deep reinforcement learning

In Internet-of-Things (IoT), blooming IoT terminals along with growing demand for broadband services, such as video stream and virtual reality (VR) applications, have led the enhancement of spectrum efficiency to the research focus in IoT. The technology of dynamic spectrum access or sharing is commonly utilized to address this challenge. With the development of artificial intelligence (AI), there has been an increasing amount of research on dynamic spectrum access based on machine learning models, such as Q-learning, deep reinforcement learning and federated learning. When using federated learning to secure user privacy in dynamic spectrum access, several key challenges such as the lack of personalized model selection and parameter updates, and the difficulty of meeting the needs of different devices with a single global model are still under-investigated. In this paper, the heterogeneity of IoT users is considered and a hierarchical and personalized federated deep reinforcement learning approach is proposed to fulfill dynamic spectrum access. First, IoT users are trained locally by using deep reinforcement learning to obtain optimal rewards and spectrum access rate. Second, considering that users have their personalized characteristics, a client-edge-cloud federated learning framework is proposed to accelerate the model convergence and reduce communication overhead between cloud servers and clients. Accordingly, user parameters are divided into personalized and basic parameters. Basic parameters are individually uploaded for global model training, meanwhile personalized parameters are retained to guarantee the applicability of the model. Lastly, the proposed framework is used for dynamic spectrum access, thereby improving the efficiency of spectrum access and decrease the bandwidth occupied by exploiting the global optimization capabilities for federated learning. Only basic parameters are uploaded to substantially protect user’s privacy. Simulation results show that the proposed framework can improve user communication performances and the accuracy of spectrum access. The average convergency time for IoT users can be reduced by about 40% compared with common federated-learning-oriented dynamic spectrum access.