Reviewerly/exHarmony huggingface dataset:
ENHANCING INTERNATIONAL COMMUNICATION THROUGH MODERN APPROACHES TO TEACHING VISUAL ART IN KINDERGARTEN AND JUNIOR HIGH SCHOOL: EXPLORING LANDSCAPE THEMES AS EXAMPLES

This article examines the methodology of organizing classes on the updated program in kindergarten and junior high school, focusing on enhancing international communication through modern approaches to teaching visual art. The research specifically explores landscape themes as examples, aiming to stimulate creativity and promote understanding of different cultures and traditions. In recent years, innovative technology has emerged as an essential factor in improving the overall quality of education. The integration of these technologies into the teaching and learning process allows for the development of competitiveness among students, equipping them with all necessary skills to enter the global market. The study seeks to investigate the effectiveness of utilizing modern technology in teaching visual art, particularly in the context of landscape themes, and how it can foster cross-cultural understanding and communication. The article presents a comprehensive overview of the current state of visual art education in Kazakhstan, identifying key issues and challenges faced by educators and students alike. Additionally, it offers a thorough analysis of various style directions, techniques, and approaches used in teaching landscape themes in kindergarten and junior high school. The research encompasses both traditional and contemporary methods, highlighting their respective strengths and weaknesses and drawing on best practices from around the world to inform the development of a new, culturally inclusive curriculum. The presented research highlights the importance of childhood creativity and its impact on the development of cognitive, social, and emotional skills. The study emphasizes the need for educators to create an environment that encourages self-expression, experimentation, and critical thinking. By focusing on landscape themes, pupils can explore the cultural, historical, and geographical aspects of various regions and countries, broadening their understanding of the world and fostering empathy and appreciation for diversity. The article provides recommendations for implementing modern approaches to teaching visual art in kindergarten and junior high school. They include the adoption of interdisciplinary methods, collaboration between educators and artists, the integration of technology, and the establishment of international exchange programs. Additionally, the study highlights the significance of embracing innovation, technology, and cross-cultural understanding, the study hopes to pave the way for a more inclusive and globally connected education system that nurtures creativity and fosters international communication.
Towards privacy-preserving digital marketing: an integrated framework for user modeling using deep learning on a data monetization platform

Abstract This paper presents a novel approach to privacy-preserving user modeling for digital marketing campaigns using deep learning techniques on a data monetization platform, which enables users to maintain control over their personal data while allowing marketers to identify suitable target audiences for their campaigns. The system comprises of several stages, starting with the use of representation learning on hyperbolic space to capture the latent user interests across multiple data sources with hierarchical structures. Next, Generative Adversarial Networks are employed to generate synthetic user interests from these embeddings. To ensure the privacy of user data, a Federated Learning technique is implemented for decentralized user modeling training, without sharing data with marketers. Lastly, a targeting strategy based on recommendation system is constructed to leverage the learned user interests for identifying the optimal target audience for digital marketing campaigns. Overall, the proposed approach provides a comprehensive solution for privacy-preserving user modeling for digital marketing.
Transfer learning using infrared and optical full motion video data for gender classification

This work is a review and extension of our ongoing research in human recognition analysis using multimodality motion sensor data. We review our work on hand crafted feature engineering for motion capture skeleton (MoCap) data, from the Air Force Research Lab for human gender followed by depth scan based skeleton extraction using LIDAR data from the Army Night Vision Lab for person identification. We then build on these works to demonstrate a transfer learning sensor fusion approach for using the larger MoCap and smaller LIDAR data for gender classification.
Real-time robotic hand control using human gesture recognition

Currently, there are many options for controlling robotic devices. Human-machine interaction is a key component of the control infrastructure. The most common solution is mobile devices or embedded touch screens, as well as next generation virtual reality devices. In human-machine interaction, most input devices are controlled manually, which is not always convenient, and sometimes even impossible. One option is gesture control, which has become increasingly common in the last few years. This artificial cognitive “sensory perception” or ability is a communication channel between a human and a machine. This article presents a two-steps approach to real-time control robotic devices. The first step is the hand recognition method base on palm detection (SSD Detector) and hand landmark models. After a palm detection, the hand landmark model performs fine localization of the key points of the 3-D coordinate of the hand inside the detected areas of the hand through regression and direct coordinate prediction. The model learns a consistent internal representation of the hand posture and is resistant to even partially visible hands and self-occlusions. The second human gesture recognition step is based on obtaining the coordinates of the hand, the distance from the camera to the hand in space, raised, lowered fingers and other indicators that allow you to accurately determine the shown gesture. In terms of gesture recognition accuracy, the proposed real-time system is better than the state-of-the-art methods.
Constructing RDF from the legal texts

We present a methodology of modeling RDF for implementing Semantic Web that enables semantic search by taking the copyright law as an example. The RDF, which is a kind of a data format, plays an important role among the various components that make up Semantic Web. However, it is not easy to construct RDFs automatically for a number of reasons. In our semi-automatic process, we first conduct a morphological analysis for the input texts, segment the texts, and parse them using the dependency parser. Next, we convert the output into RDFs of a triple form. Lastly, we semi-automatically modify the RDFs that contain anaphora, conditional clauses, and meta information. By combining such RDFs with various knowledge systems such as WordNet and ontology, we can finally implement Semantic Web capable of semantic search.
Three-dimensional measurement of defects on optical surface by transient interference technique

Surface defects on optical components may strongly modulate the incident light field. The local intensity increases significantly, and even exceeds the Laser Induced Damage Threshold (LIDT) of optics. The operation of high-power laser systems is threatened seriously. A three-dimensional topography measurement technique for defects on large-aperture element surface based on the principle of transient interferometry is proposed. Based on the location measurement results of surface defect by the fast-scanning method, the transient interferometry system can focus on the measurement of regions including surface defects. The sub-aperture stitching algorithm is utilized to acquire the complete morphology of surface defects. The algorithm of defect depth extraction from 3D topography is proposed. Moreover, surface defect’s depth and width are statistically analyzed preliminarily, which lays a foundation for the manufacturing process improvement and evaluation for laser system applications. Surface defect and contaminant are distinguished effectively by the three-dimensional topography measurement system for surface defect of large-aperture optics. The established technique can effectively compensate the shortcomings of existing methods, such as microscopic scattering dark-field imaging.
PREDICTING MODULUS OF RUPTURE OF HEAT-TREATED WOODS BY ARTIFICIAL NEURAL NETWORK COMBINED WITH GENETIC ALGORITHM

A total of 104 spruce and Larix gmelinii wood samples were employed to generate a reliable ANN-based model for estimating the Moment of Rupture (MOR) of heat-treated woods.Seventy percent of the samples were used for training, while the remaining thirty percent were used for testing phases of the data set.The Feed Forward network with five topologies was used, including heat treatment at various temperatures, durations, and relative humidity as input parameters.The weights of the artificial neural network are optimized by employing a genetic algorithm.The model's accuracy is assessed by comparing results with the particle swarm optimization-based neural network model.The study concluded that the genetic algorithm-based ANN model performed better by yielding results with reduced error.
Medtrust - A Decentralized Model for Medical Crowdfunding Based on Smart Contract Using Etherium

Abstract: Trust and Transparency is a great challenge that the current world encounter when coming with systems that really matters the day to day life. When it comes to financial systems, the requirement becomes stronger. The paper suggests the implementation of a medical crowdfunding system incorporating trust and transparency with blockchain. With decentralized approach. The paper explains implementation of a Crowdfunding system based on blockchain using smart contract. The Paper explains the architecture, implementation and Test cases that been used and a study of the how the model solves the issue of trust. The system is been tested and deployed with hardhat framework over sepolia Test Network.
EFL students' perceptions of Talking Stick in English speaking class

The study aimed to investigate the English as a foreign language (EFL) students’ opinions on the use of Talking Stick in helping to improve their English speaking ability at a high school in Aceh Province, Indonesia. The study employed a quantitative method. The sample of the study was 15 first year students of a public senior high school in Banda Aceh, Aceh, Indonesia. The study selected the sample by using the purposive sampling, as the students have had Talking Stick technique in their English speaking class. To collect the data, the study disseminated questionnaires to the students. The results showed that the students had positive responses towards the use of Talking Stick in learning English speaking. The study recommends that such a technique can be applied more in other English classes in Indonesia.
Application of Statistical Methods to Support Automation of Pricing in Business

This paper presents the possibility of using statistical modeling to automate the process of dynamic pricing management with revenue control and adaptation to current legislation in this area. In addition, it is proposed the use of integration of Python tools with the ERP and BI systems popularly used in the industry. This integration in order to facilitates decision-making in this area for the business without the end of programming. The proposed solutions support the CEO enterprises make decisions regarding pricing strategies and optimize their revenues.
µFuncCache: A User-Side Lightweight Cache System for Public FaaS Platforms

Building cloud-native applications based on public “Function as a Service” (FaaS) platforms has become an attractive way to improve business roll-out speed and elasticity, as well as reduce cloud usage costs. Applications based on FaaS are usually designed with multiple different cloud functions based on their functionality, and there will be call relationships between cloud functions. At the same time, each cloud function may depend on other services provided by cloud providers, such as object storage services, database services, and file storage services. When there is a call relationship between cloud functions, or between cloud functions and other services, a certain delay will occur, and the delay will increase with the length of the call chain, thereby affecting the quality of application services and user experience. Therefore, we introduce μFuncCache, a user-side lightweight caching mechanism to speed up data access for public FaaS services, fully utilizing the container delay destruction mechanism and over-booked memory commonly found in public FaaS platforms, to reduce function call latency without the need to perceive and modify the internal architecture of public clouds. Experiments in different application scenarios have shown that μFuncCache can effectively improve the performance of FaaS applications by consuming only a small amount of additional resources, while achieving a maximum reduction of 97% in latency.
IMPROVING ENGLISH READING COMPREHENSION SKILLS THROUGH THE SQ4R METHOD

This research aims at obtaining data on improving English reading comprehension skill in Grade III, SD Negeri 066654. The research subjects were the students of Grade III in the second semester of 2021/2022 academic year. The research model used Kemmis and Mc. Taggard model covering planning, implementation, observation, and reflection. The data of reading comprehension evaluation showed that in the first cycle 55.5 % of the students gained &gt;75 and in the second cycle 83.33 % of them gained &gt;75. While based on the monitoring data, the activities of the teacher and the students were 55 % in the first meeting of the cycle I and increased to 85 % in the first meeting of the cycle II. Therefore, SQ4R method can improve English reading comprehension skill of the Grade III students in SD Negeri 066654.
A Post-Quantum Privacy-Enhancing Blockchain-Based Transaction Framework with Access Control

Protecting the transaction from address-based tracking is one of the core issues in blockchain privacypreservation. In this paper, we propose a transaction framework through which the trader of a transactionorganization transacts on the blockchain public chain with privacy-enhancing; meanwhile, the manager gets access to the trader's transaction with access control based on cryptography. In the proposedframework, the hash-based one-time address is utilized to protect transactions from unauthorized tracking; furthermore, the hash-based one-time signature is creatively being used twice to verify and track thetransactions safely in the semi-honest model; through access control, the authorized managers can obtaintransaction information within their authorities. Compared with the standard Bitcoin transaction system, the proposed system achieves privacy-enhancing and post-quantum security.
Automatic Indexing of Digital Objects Through Learning from User Data

Digital data objects increasingly take the form of a non-textual nature, and the effective retrieval of these objects using their intrinsic contents largely depends on the underlying indexing mechanism.Since current multimedia objects are created with ever-increasing speed and ease, they often form the bulk of the data contents in large data repositories.In this study, we provide an effective automatic indexing mechanism based on learning reinforcement by systematically exploiting the big data obtained from different user interactions.Such human interaction with the search system is able to encode the human intelligence in assessing the relevance of a data object against user retrieval intentions and expectations.By methodically exploiting the big data and learning from such interactions, we establish an automatic indexing mechanism that allows multimedia data objects to be gradually indexed in the normal course of their usage.The proposed method is especially efficient for the search of multimedia data objects such as music, photographs and movies, where the use of straightforward string-matching algorithms are not applicable.The method also permits the index to respond to change in relation to user feedback, which at the same time avoids the system landing in a local optimum.Through the use of the proposed method, the accuracy of searching and retrieval of multimedia objects and documents may be significantly enhanced.
Regularization, Semi-supervision, and Supervision for a Plausible Attention-Based Explanation

Attention mechanism is contributing to the majority of recent advances in machine learning for natural language processing. Additionally, it results in an attention map that shows the proportional influence of each input in its decision. Empirical studies postulate that attention maps can be provided as an explanation for model output. However, it is still questionable to ask whether this explanation helps regular people to understand and accept the model output (the plausibility of the explanation). Recent studies show that attention weights in RNN encoders are hardly plausible because they spread on input tokens. We thus propose three additional constraints to the learning objective function to improve the plausibility of the attention map: regularization to increase the attention weight sparsity, semi-supervision to supervise the map by a heuristic and supervision by human annotation. Results show that all techniques can improve the attention map plausibility at some level. We also observe that specific instructions for human annotation might have a negative effect on classification performance. Beyond the attention map, results on text classification tasks also show that the contextualization layer plays a crucial role in finding the right space for finding plausible tokens, no matter how constraints bring the gain.
Psychoeducational Learning Enhancement through Extracurricular Theatre Rehearsal: Case Study of EFL Students at the Higher School of Teachers (ENS Assia Djebar Constantine) 

This article tackles the psychoeducational learning milieu where the researcher, lecturer of literature, dealt with challenges EFL students are facing in class and how through extra-curricular activities she could manage to incite them on a theater stage and break the ice between them and literature. The aim of the article is to discuss the difficulties of EFL students in grasping literature lectures and to present an attempt from the part of the researcher as a lecturer to improve the students’ learning process and performances through theatre rehearsal. The paper describes mainly an experience with my students from the ENSC Assia Djebar and demonstrate the psychological effects as art on stage tantalized their inner child and enhanced notably their educational performance. The descriptive analysis of these effects is based on two questionnaires that unveil how the inner child was emerging through rehearsals and how art was helping students transcend their fears and shyness. Observation also served deeply to witness the progress of the students as art on stage cured their inner child after its disinhibition. The outcome of the theatrical experience shows that theatre is a real ice breaker in classes as it copes with the inner child of students that cherished role plays when young. Art heals the inner child of the students and this enhances educational performance in class.&#x0D;
The Use of Usability Testing in Usability Testing the SIKULI Website Based on ISO 9241-11

The daily use of websites aims to facilitate humans in their activities. The creation of a website must pay attention to factors related to user usability. Usability on a website must be considered because so that users who use the website feel comfortable in using the website and easily obtain the desired information. One of the websites supporting lecture activities is SIKULI UMRI. SIKULI is a lecture support system in the form of an e-learning website, but there are still several errors that cause discomfort to its users. Services or features available on SIKULI include home pages, course schedules, course pages, authorization details, course outcomes and descriptions, meetings, and grade data. This research aims to see and analyze usability based on ISO 9241-11. The three aspects of Usability measurement according to IS0 9241-11, namely Effectiveness, Efficiency, and Satisfaction are latent variables so that they cannot be calculated directly so that they require analysis that can connect these variables. Data collection was carried out by distributing research questionnaires to SIKULI website users and continued by testing the validity and reliability of the data. The results of this study indicate that SIKULI has been said to be quite good with the value of the Usability variable R-Square relationship of 66.8%.
An application of deep features on image quality assessment

Quality assessment of digital images plays an important role in modeling, implementation and optimization of image and video processing applications. One of the most popular methods in image quality assessment (IQA) is feature based IQA techniques. These feature based image quality assessment (IQA) techniques, which consist of feature extraction and feature pooling phases, extracts features from the images in order to generate objective scores. Various hand-crafted features have been used in the feature extraction phase of the feature based IQA methods. In this work, instead of implementing a hand-crafted feature extraction scheme, automatic feature extraction is utilized by using a pre-trained deep neural network (DNN) inference structure. Feature pooling, which provides mapping between the proposed features and the subjective scores, is carried out by utilizing a fully-connected layer at the end of the network architecture. Experimental results show that the proposed technique obtains promising results for the IQA problem by making use of the generalization capability of deep learning architectures.
Are security commit messages informative? Not enough!

The fast distribution and deployment of security patches are important to protect users against cyberattacks. These fixes can be detected automatically by patch management triage systems. However, previous work has shown that automating the task is not easy, in some cases, because of poor documentation or lack of information in security fixes. For many years, standard practices in the security community have steered engineers to provide cryptic commit messages (i.e., patch software vulnerabilities silently) to avoid potential attacks and reputation damage. However, not providing enough documentation on vulnerability fixes can hinder trust between vendors and users. Current efforts in the security community aim to increase the level of transparency during patch and disclosing times to help build trust in the development community and make patch management processes faster. In this paper, we evaluate how informative security commit messages (i.e., messages attached to security fixes) are and how different levels of information can affect different tasks in automated patch triage systems. We observed that security engineers, in general, do not provide enough detail to enable the three automated triage systems at the same time. In addition, results show that security commit messages need to be more informative—56.7% of the messages analyzed were documented poorly. Best practices to write informative and well-structured security commit messages (such as SECOM) should become a standard practice in the security community.