Reviewerly/exHarmony huggingface dataset:
Scientific and Methodological Principles of Information and Analytical Support of the Digital Transformation of Education and Pedagogics Under Martial Law Conditions (to the All Ukrainian Science Festival)

The collection of materials (summaries of reports) of the round table "Scientific and methodological principles of informational and analytical support of the digital transformation of education and pedagogy in the conditions of martial law", dedicated to the All-Ukrainian Science Festival, held on May 17, 2023 in a mixed mode, contains the summaries of the participants' reports and the main organizational event documents. The topics of the publications are placed in sections according to the directions specified in the program. The round table has a transdisciplinary character, i.e. it is integrative, it unites scientists, practicing educators, librarians, degree holders and higher education holders from different regions of Ukraine from various educational institutions and scientific institutions of the National Academy of Sciences of Ukraine: the State Scientific and Pedagogical Library of Ukraine named after V. O. Sukhomlynskyi, State Institution of Higher Education "University of Management of Education of National Academy of Sciences of Ukraine"; Institute of Pedagogical Education and Adult Education named after Ivan Zyazyun National Academy of Sciences of Ukraine; Institute of Professional Education of the National Academy of Sciences of Ukraine. Current problems of the development of processes of digital transformation of education, pedagogy and psychology are highlighted in scientific works. The abstracts of the reports reveal the main ideas, intermediate and final results of the scientific research of the participants of the round table, which will contribute to the active exchange of experience and the application of the positive results of the presented research in the national education system of Ukraine. The collection focuses on the intermediate results of the planned applied scientific research "Information and analytical support of the digital transformation of education and pedagogy: domestic and foreign experience" (2023-2025), which are based on the ideas and findings of modern researchers who, in emergency situations, force majeure circumstances turbulent scientific and educational space (post-pandemic consequences, martial law, total migration, social turbulence, etc.) The materials of the round table can be useful for those obtaining scientific education and scientific degrees and scientific titles, scientists, scientific workers and teachers of educational institutions of various levels of organization, those obtaining higher education and all those who are interested in the issues of digital processes in the scientific and educational space. The authors of the reports are responsible for the violation of the norms of academic integrity and ethical requirements for the presentation of the results of scientific research, for the relevance and reliability of the content and information provided in the theses of the reports based on the current legislation.
Memory-Augmented Autoencoder based Continuous Authentication on Smartphones with Conditional Transformer GANs

Over the last years, sensor-based continuous authentication on mobile devices has achieved great success on personal information protection. These proposed mechanisms, however, require both legal and illegal users' data for authentication model training, which takes time and is impractical. In this paper, we present MAuGANs, a lightweight and practical Memory-Augmented Autoencoder-based continuous Authentication system on smartphones with conditional transformer Generative Adversarial Networks (GANs), where the conditional transformer GANs (CTGANs) are used for data augmentation and the memory-augmented autoencoder (MAu) is utilized to identify users. Specifically, MAuGANs exploits the smartphone built-in accelerometer and gyroscope sensors to implicitly collect users' behavioral patterns. With the normalized legitimate user's sensor data, MAuGANs uses a CTGAN composed of a conditional transformer-based generator and a conditional transformer-based discriminator to create additional training data for the MAu. Then, the MAu is trained on the augmented legitimate user's data. The trained MAu reconstructs the current user data and then calculates the reconstruction error between the reconstructed data and current user data. To carry out user authentication, MAuGANs compares the reconstruction error with a predefined authentication threshold. We evaluate the performance of MAuGANs on our dataset, where our extensive experiments demonstrate that MAuGANs reaches the best authentication performance, when comparing with the representative state-of-the-art methods, by 0.33% EER and 99.65% accuracy on 10 unseen users.
Entity-Graph Enhanced Cross-Modal Pretraining for Instance-level Product Retrieval

Our goal in this research is to study a more realistic environment in which we can conduct weakly-supervised multi-modal instance-level product retrieval for fine-grained product categories. We first contribute the Product1M datasets, and define two real practical instance-level retrieval tasks to enable the evaluations on the price comparison and personalized recommendations. For both instance-level tasks, how to accurately pinpoint the product target mentioned in the visual-linguistic data and effectively decrease the influence of irrelevant contents is quite challenging. To address this, we exploit to train a more effective cross-modal pertaining model which is adaptively capable of incorporating key concept information from the multi-modal data, by using an entity graph whose node and edge respectively denote the entity and the similarity relation between entities. Specifically, a novel Entity-Graph Enhanced Cross-Modal Pretraining (EGE-CMP) model is proposed for instance-level commodity retrieval, that explicitly injects entity knowledge in both node-based and subgraph-based ways into the multi-modal networks via a self-supervised hybrid-stream transformer, which could reduce the confusion between different object contents, thereby effectively guiding the network to focus on entities with real semantic. Experimental results well verify the efficacy and generalizability of our EGE-CMP, outperforming several SOTA cross-modal baselines like CLIP [1], UNITER [2] and CAPTURE [3].
Adapted AZNN methods for time-varying and static matrix problems

We present adapted Zhang neural networks (AZNN) in which the parameter settings for the exponential decay constant $\eta$ and the length of the start-up phase of basic ZNN are adapted to the problem at hand. Specifically, we study experiments with AZNN for time-varying square matrix factorizations as a product of time-varying symmetric matrices and for the time-varying matrix square roots problem. Differing from generally used small $\eta$ values and minimal start-up length phases in ZNN, we adapt the basic ZNN method to work with large or even gigantic $\eta$ settings and arbitrary length start-ups using Euler's low accuracy finite difference formula. These adaptations improve the speed of AZNN's convergence and lower its solution error bounds for our chosen problems significantly to near machine constant or even lower levels. Parameter-varying AZNN also allows us to find full rank symmetrizers of static matrices reliably, for example, for the Kahan and Frank matrices and for matrices with highly ill-conditioned eigenvalues and complicated Jordan structures of dimensions from $n = 2$ on up. This helps in cases where full rank static matrix symmetrizers have never been successfully computed before.
Geomatics Methods and Soft Computing Techniques for the Management of Public Transport and Distribution of Medical Goods

Initially used exclusively for military scopes, RPAS (Remotely Piloted Aircraft Systems) have become increasingly common and versatile thanks to continuous technological innovations and today they find applications in many other fields. Two of the most interesting applications of drones concern the transport of goods and the monitoring/control of crowding in public transport for the management of the LPT (Local Public Transport). In the first case, drones can be used to deliver goods in different areas with the advantage of saving costs and delivery times; in the second case, drones can be used to monitor and control the flow of people inside public transport, to detect any situations of overcrowding and take prompt action to ensure the safety and comfort of passengers. This research aims to propose an innovative automatic management system for a group of RPAS used for the transport of essential medicines and for monitoring the crowding of people in relation to the use of Local Public Transport. This was possible through the creation of an experimental system for recharging drone batteries and above all the implementation of different soft computing algorithms and tools such as YOLO (You Look Only Once), SORT, and MAC address. All the information obtained in real-time and continuously updated, are transmitted to an Open GIS studied and programmed by us.
Thermal to digital image correlation image to image translation with CycleGAN and Pix2Pix

This paper explores the use of image-to-image translation techniques for translating thermography into digital image correlation data. A Pix2Pix model and a CycleGAN model were trained on a dataset of paired and unpaired images to translate between the two image domains. The results show that while the CycleGAN model exhibited some challenges in correlating images obtained by different optical systems, the Pix2Pix model demonstrates promising potential. The trained Pix2Pix model were also validated on unseen data, providing confidence for future research in this area.
Thermal entanglement in two-atom Tavis–Cummings model with taking into account the dipole-dipole interaction

Background. Interest in the study of entangled states of systems of natural and artificial atoms (qubits) interacting with selected modes of microwave resonators is associated with their use as logic elements of quantum computers. At the same time, the most important task of the physics of quantum computing is the choice of the most effective mechanisms for manipulating and controlling the entangled states of qubits in such devices. Aim. The dynamics of the entanglement of two dipole-coupled superconducting Josephson qubits induced by a thermal noise of a coplanar resonator is studied for various initial states of the qubits. Methods. Based on the exact solution of the quantum Liouville equation for the whole density matrix of the system under consideration, the time behavior of the qubit entanglement parameter (negativity) is found for chaotic thermal, pure separable, and entangled initial states of qubits. Results. It is shown that the entanglement of qubits induced by the thermal noise of the resonator is possible for both the chaotic thermal states and separable states of qubits, except the case when both qubits are excited. It has also been found that, for small values of the dipole–dipole interaction parameter, taking this interaction into account leads to an increase in the degree of entanglement. For values of the dipole-dipole interaction parameter greater than some limit value, the opposite effect takes place. It is found that for entangled initial states of qubits, the inclusion of direct interaction has a small effect on the entanglement dynamics. It is shown that the initial coherence of qubit states can lead to a significant increase in the degree of their entanglement in the presence of a dipole–dipole interaction. Conclusion. The dipole-dipole interaction can be used as an effective mechanism for qubits entanglement manipulation and controlling.
Ukrainian libraries in the time of war: Losses and challenges

The Russian-Ukrainian War has had a significant impact on Ukrainian libraries, particularly in the Eastern regions of Ukraine that have been directly affected by the conflict. The Russian aggression has caused significant damage to library infrastructure, collections, and services, and has disrupted the overall functioning of the library system in Ukraine. In this paper, we provide an overview of the situation with the Ukrainian libraries during the Russian-Ukrainian War, as well as a review of the risks Ukrainian librarians face on a daily basis.
Automaton-Guided Curriculum Generation for Reinforcement Learning Agents

Despite advances in Reinforcement Learning, many sequential decision making tasks remain prohibitively expensive and impractical to learn. Recently, approaches that automatically generate reward functions from logical task specifications have been proposed to mitigate this issue; however, they scale poorly on long-horizon tasks (i.e., tasks where the agent needs to perform a series of correct actions to reach the goal state, considering future transitions while choosing an action). Employing a curriculum (a sequence of increasingly complex tasks) further improves the learning speed of the agent by sequencing intermediate tasks suited to the learning capacity of the agent. However, generating curricula from the logical specification still remains an unsolved problem. To this end, we propose AGCL, Automaton-guided Curriculum Learning, a novel method for automatically generating curricula for the target task in the form of Directed Acyclic Graphs (DAGs). AGCL encodes the specification in the form of a deterministic finite automaton (DFA), and then uses the DFA along with the Object-Oriented MDP (OOMDP) representation to generate a curriculum as a DAG, where the vertices correspond to tasks, and edges correspond to the direction of knowledge transfer. Experiments in gridworld and physics-based simulated robotics domains show that the curricula produced by AGCL achieve improved time-to-threshold performance on a complex sequential decision-making problem relative to state-of-the-art curriculum learning (e.g, teacher-student, self-play) and automaton-guided reinforcement learning baselines (e.g, Q-Learning for Reward Machines). Further, we demonstrate that AGCL performs well even in the presence of noise in the task's OOMDP description, and also when distractor objects are present that are not modeled in the logical specification of the tasks' objectives.
Introducing the Software Development Curve

The idea of a learning curve is fairly well-accepted, that one makes progress in gaining skills at some rate that can be “steep”, where it takes a long time to gain much proficiency, or “gentle”, where a fair amount of proficiency is gained relatively quickly. Here, I want to propose the parallel idea of a software development curve. For some types of software, there is a gentle software development curve, where it is easy to get started in developing it, leading to...
Software Supply-Chain Security: Issues and Countermeasures

Software application development involves various actors and organizations in what is called the software supply chain. We discuss how we can achieve strong resilience of the software supply chain to cyberthreats and then propose a holistic end-to-end security approach for the software supply chain.
Review on Vision Based Real Time Fingertip Detection Approaches

Computer vision is an emerging area with a huge number of applications. Identification of the fingertip is one of the major parts of those areas. Augmented reality and virtual reality are the most recent technological advancements that use fingertip identification. The interaction between computers and humans can be performed easily by this technique. Virtual reality, robotics, smart gaming are the main application domains of these fingertip detection techniques. Gesture recognition is one of the most fascinating fields of fingertip detection. Gestures are the easiest and productive methods of communication with regard to collaboration with the computer. This analysis examines the different studies done in the field of fingertip identification. A survey is also included using various methods and measures. A few difficulties and examination bearings are additionally featured. Various researchers utilize fingertip recognition in HCI frameworks with numerous applications like identifying persons, intelligent homes, and so forth. A correlation chart of various experts is an additional result mentioned in this paper.
PHAM-YOLO: A Parallel Hybrid Attention Mechanism Network for Defect Detection of Meter in Substation

Accurate detection and timely treatment of component defects in substations is an important measure to ensure the safe operation of power systems. In this study, taking substation meters as an example, a dataset of common meter defects, such as a fuzzy or damaged dial on the meter and broken meter housing, is constructed from the images of manual inspection in power systems. There are several challenges involved in accurately detecting defects in substation meter images, such as the complex background, different meter sizes and large differences in the shapes of meter defects. Therefore, this paper proposes the PHAM-YOLO (Parallel Hybrid Attention Mechanism You Only Look Once) network for automatic detection of substation meter defects. In order to make the network pay attention to the key areas against the complex background of the meter defect images and the differences between different defect features, a Parallel Hybrid Attention Mechanism (PHAM) module is designed and added to the backbone of YOLOv5. PHAM integration of local and non-local correlation information can highlight these differences while remaining focused on the meter defect features. To improve the expressive ability of the feature map, a Spatial Pyramid Pooling Fast (SPPF) module is introduced, which pools the input feature map using a continuous fixed convolution kernel, fusing the feature maps of different receptive fields. Bounding box regression (BBR) is the key way to determine object positioning performance in defect detection. EIOU (Efficient Intersection over Union) is, therefore, introduced as a boundary loss function to solve the ambiguity of the CIOU (Complete Intersection Over Union) loss function, making the BBR regression more accurate. The experimental results show that the Average Precision Mean (mAP), Precision (P) and Recall (R) of the proposed PHAM-YOLO network in the dataset are 78.3%, 78.3%, and 79.9%, respectively, with mAP being improved by 2.7% compared to the original model and higher than SSD, Fast R-CNN, etc.
Designing pair of nonlinear components of a block cipher over quaternion integers

&lt;abstract&gt; &lt;p&gt;In the field of cryptography, block ciphers are widely used to provide confidentiality and integrity of data. One of the key components of a block cipher is its nonlinear substitution function. In this paper, we propose a new design methodology for the nonlinear substitution function of a block cipher, based on the use of Quaternion integers (QI). Quaternions are an extension of complex numbers that allow for more complex arithmetic operations, which can enhance the security of the cipher. We demonstrate the effectiveness of our proposed design by implementing it in a block cipher and conducting extensive security analysis. Quaternion integers give pair of substitution boxes (S-boxes) after fixing parameters but other structures give only one S-box after fixing parameters. Our results show that the proposed design provides superior security compared to existing designs, two making on a promising approach for future cryptographic applications.&lt;/p&gt; &lt;/abstract&gt;
Applications of picture fuzzy filters: performance evaluation of an employee using clustering algorithm

&lt;abstract&gt;&lt;p&gt;This article defines the concepts of picture fuzzy filter, picture fuzzy grill, picture fuzzy section, picture fuzzy base, picture fuzzy subbase, picture fuzzy ultrafilter, as well as their fundamental features. Characteristics of the aforementioned concepts are addressed, and equivalence between the picture fuzzy filter and picture fuzzy grills is established. Real-world examples are offered to demonstrate the advantages of picture fuzzy filters in the classification of sets using a clustering technique. Illustration is provided to show the advantages of picture fuzzy sets and the results are compared with intuitionistic fuzzy sets. Clustering technique is applied to the picture fuzzy filter collection reduces the computational process which helps the decision makers to classify the sets with fewer iterations.&lt;/p&gt;&lt;/abstract&gt;
An Analytical Approach for Twitter Sarcasm Detection Using LSTM and RNN

In the originality of sarcasm, its intended sense of the tweet written by the author is opposite to the literal meaning. Because of its intricate nature, it is much difficult, similarly without proper context human is not able to identify sarcasm in any verbal or textual sentence. In the recent time peoples on social media platform like Twitter, Facebook, WhatsApp succeed in recognize sarcasm despite interacting with strangers across the world. Sarcasm detection in Hindi Language text is the challenging task in the NLP due to the richness of morphology and low availability of resources. Present paper will analysis and calculate the performance of recurrent and convolution neural network model for detecting sarcasm in tweets by analysing the current state art of the performance and qualitative analysis of network functionality. In the proposed work sarcasm detection is done by binary classifier. In the present paper evaluation over 3 models 2 Recurrent Neural Networks (RNNs), 1 with LSTM-cells and 1 with GRU cells and also a CNN. For the analysis of network functionality, classification result will compare with resultant BoW models, jumbling the word content in tweets and understand the network decision. Experimental results shows that network prediction precisely based on the occurrence of word’s in the tweets and the performance based on F1 score reaches up to 85% using RNN and CNN models. Suggested model performs in a well way in comparison of Bag of Words BoW model and ensures the feasibility of neural network in sarcasm detection in tweets.
Optimization of Tilt Angle and Maximization of Solar Radiation for Fixed and Tracking Surfaces: A Case Study for Gaziantep, Turkey

Solar systems have become a feasible primary energy source in the last decade as their costs and performance have improved. The amount of solar radiation collected by any solar surface is heavily impacted by its tilt angle and orientation, in addition to the manufacturing features and technology utilized. Thus, studies on the determination of optimal tilt angles and maximization of solar radiation in solar systems are the focus of attention of researchers recently. So, in this study, utilizing NASA’s climatic and latitude data and the performance of fixed-axis, one-axis, and two-axis solar tracking systems is investigated to enable maximum solar radiation in Gaziantep. The yearly optimal tilt angle for Gaziantep is calculated at 28°. It is observed that the optimal tilt angles during the year varied between 1° and 61°, the one-axis tracking system harvests approximately 31% higher solar radiation, and two-axis tracking system harvests approximately 35% than the fixed-axis system in Gaziantep.