Reviewerly/exHarmony huggingface dataset:
Readability of Reading Texts as Authentic Materials Issued by ChatGPT: A Systemic Functional Perspective

Selecting the appropriate texts as the authentic material for English teaching, particularly at the university level, matched with students’ mastery level is still challenging. This study attempts to investigate the readability level of reading texts through the framework of Systemic Functional Linguistics (SFL) issued by ChatGPT, focused on whether the complexity level of the texts matches the standard level of the Common European Framework of Reference (CEFR) by proposed British Council. Through the qualitative design with content analysis, this study examines eighteen texts classified following CEFR levels issued by ChatGPT. It analyzes their lexical density, grammatical intricacy, and lexical variation to know the complexity of the texts. The analysis revealed that the complexity level of the texts issued by ChatGPT has not thoroughly followed the standard level of the texts used by the British Council on the CEFR level, indicating that several texts are inappropriate with their complexity levels. It is found that the complexity level of the texts issued by ChatGPT is determined by the length of the texts that increase from the basic level to the proficient level seen from the number of words in each text, not by their lexical density, grammatical intricacy, and lexical variation indexes. The study provides implications for the lecturer to carefully select the text used as teaching material issued by ChatGPT by relying on the lexico-grammatical analysis of the text to help students achieve the comprehension stage.
Method «Matrix of Values (Model of Nazarenko-Matveeva T.M.)» in the Implementation of the Federal Working Program of Education

The article considers the new author's method "Matrix of values (Nazarenko-Matveeva T.M. Model)" as a planning and predictive resource of the quality of educational activity and a tool for designing and implementing the federal work program of education. The variants of the matrix of values and the features of its use in the implementation of the education program in educational organizations of the general education system are presented.
Heroic and Patriotic Education Employees of the Russian Federation Ats During a Special Military Operation

The relevance. Through the prism of topical issues, the author of the article analyzes the content and specifics of the heroic-patriotic education of law enforcement officers.&#x0D; The problem statement: the ongoing events in Ukraine show that the principles of patriotism should become a modern ideology that is able to preserve the Russian Federation as a multinational state, where every citizen, regardless of ethnicity, feels like a full-fledged free person who is proud of his country.&#x0D; The purpose of the study: a constructive analysis of the heroic-patriotic education of police officers of the Russian Federation during the period of the special military operation in Ukraine.&#x0D; The research methods: dialectics, hermeneutics, synergetics, philosophical conceptology.&#x0D; The results and key conclusions: the organization of educational work with personnel is the most important component of daily management activities, through which it has a direct impact on the effectiveness of solving operational tasks, strengthening the rule of law and service discipline, and improving the moral and psychological state of personnel.
On the Theory of Modernization of Digital Education, Which Forms the Plurality of Identities and the Intellect of Students

The article deals with the problem of developing a theory of modernization of digital education, which forms a plurality of identities and the intelligence of students. The authors proceed from the fact that the functioning of modern societies in various states is complicated by the rapidly growing diversity in the world and the corresponding change in communication technologies and industrial relations, in connection with which the relevance of managing the socio-cultural life of people and society as a whole has increased. Therefore, identification research is of great importance today. The modern multiplicity of identification properties of people is associated with the existence of two opposite process-es in the information society: assimilation and multiculturalism. The authors of the article believe that in the system of modernization education of young people it is necessary to introduce such categories as "multiple identity" (multiple identity) and the plural-ity of skills, abilities and abilities (multiple intelligences), in view of the fact that students create themselves, describe, including their skills, potential and preferences. The authors came to the conclusion that educational organizations should independently develop their own policy of social and educational assimilation of students to the universal process of education, where there are "examples" of norms and values that allow students to identify themselves as "worthy" of certain expectations on the part of teachers, teachers and educational administration. institutions. This policy should also include methods of their social integration through informational impact and interaction of teachers with students, allowing them to maintain certain rules of interactive communication. The theory of modernization learning includes both communication theory and learning theory. The versatility of modern distance education is the construction of web communities, where dialectical and democratic pedagogy is recreated, combining various models and styles of learning (the concepts of behaviorism, cognitivism and constructivism are known in the education system).
Justification of Plants Water Demand in Conditions of Drip Irrigation

In the current work, a comprehensive methodology for the soil and climatic conditions of the Republic of Armenia has been developed to justify the water requirement/demand of plants in the case of drip irrigation. The proposed methodology enables to calculate the values of the individual components of the drip irrigation regime, and in practical terms, it was used to determine the water requirement of perennial plantations in the soil and climatic conditions of the Armavir and Kotayk marzes. Based on the obtained results, the crops drip irrigation norms/rates were mapped in the GIS environment. They can serve as a background for practical planning and justification of the drip irrigation water demand for various crops and contribute to the development of introduction rates of water-saving technologies in this field.
Study on adsorption capacity of corn stalks in decreasing the turbidity of Babura river water with batch operation

The adsorbent is a solid substance that can absorb certain components from the liquid phase. Most of the adsorbents are porous materials and the adsorption process takes place, especially in the porous wall or certain location inside that particle. The main objective of this research is to study the influence of the adsorbent’s surface area based on shape variation from corn stalk with adsorption ability to reduce the water river’s turbidity, the influence of time with adsorption capacity in reducing river water’s turbidity, the influence of adsorbent with water river and adsorption capacity, and defining adsorption kinetics from corn stalks. The shape of corn stalks that have been used such as spherical, half of spherical shape, and quarter of spherical shape. The volume of the sample is 250 mL. The measurement time is 300 minutes. The mass variations that have been used are 10 g, 15 g, and 20 g. The variation of sample takeover in the morning, afternoon, and evening. The measurement of adsorbent adsorption capacity in term of decreasing the turbidity of Babura River’s water is done by inputting the adsorbent into the sample, then the measurement of the water’s turbidity proceed by using a turbidity meter. The analysis result for the influence of the adsorbent’s surface area in decreasing Babura River’s water in the shape’s variation from spherical, half of spherical shape, and quarter of spherical shape is obtained in quarter of spherical shape. The highest amount of adsorbent in decreasing the turbidity of Babura River’s water is 20 g. Adsorption kinetics that has been used in the measurement of decreasing Babura River’s water turbidity is second order kinetics
Analysis and automated classification of images of blood cells to diagnose acute lymphoblastic leukemia

Analysis of white blood cells from blood can help to detect Acute Lymphoblastic Leukemia, a potentially fatal blood cancer if left untreated. The morphological analysis of blood cells images is typically performed manually by an expert; however, this method has numerous drawbacks, including slow analysis, low precision, and the results depend on the operator’s skill. We have developed and present here an automated method for the identification and classification of white blood cells using microscopic images of peripheral blood smears. Once the image has been obtained, we propose describing it using brightness, contrast, and micro-contour orientation histograms. Each of these descriptions provides a coding of the image, which in turn provides n parameters. The extracted characteristics are presented to an encoder’s input. The encoder generates a high-dimensional binary output vector, which is presented to the input of the neural classifier. This paper presents the performance of one classifier, the Random Threshold Classifier. The classifier’s output is the recognized class, which is either a healthy cell or an Acute Lymphoblastic Leukemia-affected cell. As shown below, the proposed neural Random Threshold Classifier achieved a recognition rate of 98.3 % when the data has partitioned on 80 % training set and 20 % testing set for. Our system of image recognition is evaluated using the public dataset of peripheral blood samples from Acute Lymphoblastic Leukemia Image Database. It is important to mention that our system could be implemented as a computational tool for detection of other diseases, where blood cells undergo alterations, such as Covid-19
Robust Blind Image Watermarking Using Coefficient Differences of Medium Frequency between Inter-Blocks

The existing discrete cosine transform (DCT) differential quantization robust watermarking has poor robustness against JPEG compression, cropping, and combined attacks. To improve such issues, a pair of adjacent block coefficients are selected to reduce the offset and improve the robustness of the watermarking. Firstly, at adjacent positions of neighboring blocks, the differences of medium frequency coefficients are calculated, and then the differences are used to divide regions. Experimental results show that this method is more robust to various attacks than the existing DCT differential quantization robust watermarking. The accuracy of watermark extraction under a JPEG compression attack increased by 2%, while the error rates of watermark extraction under a cropping attack and a combination attack decreased by 4.4% and 9%.
Blueprint Separable Subsampling and Aggregate Feature Conformer-Based End-to-End Neural Diarization

At present, a prevalent approach to speaker diarization is clustering based on speaker embeddings. However, this method encounters two primary issues. Firstly, it cannot directly minimize the diarization error during the training process; secondly, the majority of clustering-based methods struggle to handle speaker overlap in audio. A viable approach for addressing these issues involves adopting end-to-end speaker diarization (EEND). Nevertheless, training this EEND system generally requires lengthy audio inputs, which must be downsampled to allow efficient model processing. In this study, we develop a novel downsampling layer using blueprint separable convolution (BSConv) instead of depthwise separable convolution (DSC) as the foundational convolutional unit, which effectively preserves information from the original audio. Furthermore, we incorporate multi-scale feature aggregation (MFA) into the encoder structure to combine the features extracted by each conformer block to the output layer, consequently enhancing the expressiveness of the model’s feature extraction. Lastly, we employ the conformer as the backbone network to incorporate the proposed enhancements, resulting in an EEND system named BSAC-EEND. We assess our suggested methodology on both simulated and real datasets. The experiment indicates that our proposed EEND system reduces diarization error rate (DER) by an average of 17.3% for two-speaker datasets and 12.8% for three-speaker datasets compared to the baseline.
Global Individual Interaction Network Based on Consistency for Group Activity Recognition

Modeling the interactions among individuals in a group is essential for group activity recognition (GAR). Various graph neural networks (GNNs) are regarded as popular modeling methods for GAR, as they can characterize the interaction among individuals at a low computational cost. The performance of the current GNN-based modeling methods is affected by two factors. Firstly, their local receptive field in the mapping layer limits their ability to characterize the global interactions among individuals in spatial–temporal dimensions. Secondly, GNN-based GAR methods do not have an efficient mechanism to use global activity consistency and individual action consistency. In this paper, we argue that the global interactions among individuals, as well as the constraints of global activity and individual action consistencies, are critical to group activity recognition. We propose new convolutional operations to capture the interactions among individuals from a global perspective. We use contrastive learning to maximize the global activity consistency and individual action consistency for more efficient recognition. Comprehensive experiments show that our method achieved better GAR performance than the state-of-the-art methods on two popular GAR benchmark datasets.
Graph U-Shaped Network with Mapping-Aware Local Enhancement for Single-Frame 3D Human Pose Estimation

The development of 2D-to-3D approaches for 3D monocular single-frame human pose estimation faces challenges related to noisy input and failure to capture long-range joint correlations, leading to unreasonable predictions. To this end, we propose a straightforward, but effective U-shaped network called the mapping-aware U-shaped graph convolutional network (M-UGCN) for single-frame applications. This network applies skeletal pooling/unpooling operations to expand the limited convolutional receptive field. For noisy inputs, as local nodes have direct access to the subtle discrepancies between poses, we define an additional mapping-aware local-enhancement mechanism to focus on local node interactions across multiple scales. We evaluated our proposed method on the benchmark datasets Human3.6M and MPI-INF-3DHP, and the experimental results demonstrated the robustness of the M-UGCN against noisy inputs. Notably, the average error in the proposed method was found to be 4.1% lower when compared to state-of-the-art methods adopting similar multi-scale learning approaches.
Digital instinct—A keyword for making sense of students' digital practice and digital literacy

The paper introduces the student‐coined term digital instinct which describes students' disposition to resorting to digital technology for solving problems and doing school assignments. Taking cues from the term digital instinct , the paper describes a student perspective on digital literacy emerging from 100 lived experience descriptions and interviews with 37 Danish upper secondary students. The findings show that students' digital practice is characterised by experience‐based, intuitive and familiar use of technologies. Most notably, students employ digital technologies as cognitive partners that help them carry on with assignments that they initially did not understand, but that they were able to complete with the help of the computer. The study examines the nature of this partnership through the words of the students and identifies how technologies expand student agency but fall short of a reflective use of digital technologies. Recognising the strengths and weaknesses of students' digital practices may inform the concept of digital literacy and encourage teachers to acknowledge the digital instinct as a steppingstone to foster students' reflective use of digital technologies. Practitioner notes What is already known about this topic Students have inadequate command of digital literacies as described in curricular terminology. Students have positive as well as negative perceptions of the value and usefulness of digital technologies in school. Students both over‐ and underestimate their own digital literacies. What this paper adds Students have a fundamental utilitarian conception of digital technologies that either make schoolwork easier or more difficult, and they do not articulate that their use of digital technologies provides them with digital literacies. Students' conception of a digital instinct describes an intuitive and familiar, albeit unreflected, use of technologies where students employ experience‐based and self‐taught methods for using digital technologies. The digital instinct accounts for a feeling of agency among students, manifested in a widespread confidence that they can do assignments and solve problems when they make use of their computer. Implications for practice and/or policy Curricular terminologies struggle to capture what students can and cannot do with technology, and how much they understand the underlying technology. Teachers can involve and build on students' experience‐based digital practices as a starting point for developing digital literacy among students—also as an entry to a curricular perspective. Teachers should acknowledge students' conception of a ‘digital instinct’ as an important disposition in its own right.
Foreign language lessons in an institution of higher education as an important factor in the formation of studentsʼ leadership competence

The article examines the specifics of leadership competence formation during the teaching of a foreign language in a higher educational institution. The urgency of the problem stated in the article is due to the need to train a new generation of leading specialists with high foreign language proficiency and preparedness for effective activity in a dynamic external environment. The purpose of the research is to study the specifics of leadership competence formation in the process of teaching a foreign language in higher educational institutions. The object of this study is the process of teaching a foreign language in a higher educational institution, while the subject is the need to develop leadership competence among students. The study paid special attention to the analysis of the concept of “leadership competence.” The methodological approach in this study is a qualitative combination of theoretical research methods. Analysis, synthesis, interpretation, generalization, and abstraction were used to determine theoretical approaches to explaining the concept of “leadership competence” and revealing its content. Comparative-historical, problem-comparative, and structural methods were applied to analyze the specifics of the process of forming leadership competence using active learning methods during the teaching of a foreign language in a higher educational institution. The main results of this scientific research substantiate the effectiveness of using active methods of learning a foreign language for the formation of studentsʼ leadership competence in higher educational institutions. The results and conclusions of this study are of practical value for foreign language teachers in vocational pre-university education and higher education institutions, as well as for teachers, methodologists, and other pedagogical workers who are involved in the study of student leadership and the formation of leadership competence in the younger generation at the current stage.
Defect Detection in Carbon Fiber-Reinforced Plate by Imaging of Mechanical Nonlinearity-Induced Sideband Vibrations

Laser Doppler scanning vibrometry is used for imaging spectral vibration components in a carbon fiber-reinforced composite plate that contains a sub-surface delamination defect caused by hammer impact. The images reveal sideband generation at the location of the defect, reflecting mechanical nonlinearity-induced mixing between a high amplitude, low-frequency vibration that modulates the stress–strain behavior near the defect and a low amplitude, high-frequency probe vibration. In this work, a multifrequency probe is used to tackle the problem that the mixing coefficients are, in practice, frequency dependent. Based on the measured sideband amplitudes, a study is presented on the expected feasibility of detecting defects by a full field imaging scheme based on a photorefractive interferometer that is configured as a vibrometer acting as a bandpass filter around a sideband frequency of interest.
MSEN: A Multi-Scale Evolutionary Network for Modeling the Evolution of Temporal Knowledge Graphs

Temporal knowledge graphs play an increasingly prominent role in scenarios such as social networks, finance, and smart cities. As such, research on temporal knowledge graphs continues to deepen. In particular, research on temporal knowledge graph reasoning holds great significance, as it can provide abundant knowledge for downstream tasks such as question answering and recommendation systems. Current reasoning research focuses primarily on interpolation and extrapolation. Extrapolation research aims to predict the likelihood of events occurring in future timestamps. Historical events are crucial for predicting future events. However, existing models struggle to fully capture the evolutionary characteristics of historical knowledge graphs. This paper proposes a multi-scale evolutionary network (MSEN) model that leverages Hierarchical Transfer aware Graph Neural Network (HT-GNN) in a local memory encoder to aggregate rich structural semantics from each timestamp’s knowledge graph. It also utilizes Time Related Graph Neural Network (TR-GNN) in a global memory encoder to model temporal-semantic dependencies of entities across the global knowledge graph, mining global evolutionary patterns. The model integrates information from both encoders to generate entity embeddings for predicting future events. The proposed MSEN model demonstrates strong performance compared to several baselines on typical benchmark datasets. Results show MSEN achieves the highest prediction accuracy.
Efficient Task-Driven Video Data Privacy Protection for Smart Camera Surveillance System

As one of the most commonly used AIoT sensors, smart cameras and their supporting services, namely cloud video surveillance (CVS) systems have brought great convenience to people’s lives. Recent CVS providers use different machine learning (ML) techniques to improve their services (regarded as tasks) based on the uploaded video. However, uploading data to the CVS providers may cause severe privacy issues. Existing works that remove privacy information could not achieve a high trade-off between data usability and privacy because the importance of information varies with the task. In addition, it is challenging to design a real-time privacy protection mechanism, especially in resource-constraint smart cameras. In this work, we design a task-driven and efficient video privacy protection mechanism for a better trade-off between privacy and data usability. We use Class Activation Mapping to protect privacy while preserving data usability. To improve the efficiency, we utilize the motion vector and residual matrix produced during video codec. Our work outperforms the ROI-based methods in data protection while preserving data usability. The attack accuracy drops 70%, while the task accuracy is comparable to those without protection (within ± 4%). The average protection frame rate of the High Definition video can exceed 16 fps+ even on a CPU.