Reviewerly/exHarmony huggingface dataset:
¡EUREKA! DESIGN OF FLOATING LUMINARIES ON WATER: A LEARNING CHALLENGE IN ENGINEERING

The learning space in the 21st century, cannot be limited to a closed enviroment within a classroom. It is important to push the limits of both, creativity and innovation on the mind of the apprentice. Mixing apparently dissimilar topics, awakening intrinsic motivation and following a design methodology are the keys to achieve an efficient learning. Challenge based on learning, it is an integrative pedagogical approach that combines experience, cognition, and behavior. This approach, takes advantage of students' interest in finding practical meaning to education, while developing key skills competencies in a world mediated by rapid technological advance and sustainability. On this paper, a pedagogical strategy is exposed to impact first-year students in the application of a design methodology and the development of competencies in graphic expression, manufacturing, critical thinking, and tolerance to frustration. These skills, were obtained from the challenge of designing, materializing and testing a floating luminaire that works from solar energy. These prototypes, were manufactured with sustainable materials and processes, considering as an aesthetic/formal referent the work of Ersnt Haeckel and as a physical principle, Archimedes' law to achieve buoyancy and stability on water. The satisfying outcome of this learning experience, consisted on 56 luminaries that enlightened and floated over the water mirror of the cultural center of the XXXX´s university library, Luis Echavarría Villegas.
Internet-Induced?: A Re-examination of Sources of Writing Errors in Educated Nigerian English

This study examines writing errors in the written discourse of Nigerian university-educated users of English (sophisticated variety). The aim is to determine the extent to which non-standard usages attested in the writing of Educated Nigerians are manifestations of the impact of internet language. The population of this study comprised three hundred (300) respondents who are university educated users of the English language. They were selected by a stratified random sampling technique from six universities in South- South Nigeria. Usage features considered distinctively internet features which occurred in the studied texts were identified and coded using the principles of Content Analysis. The analysis shows that internet language features like Alphanumeric features, initialisms respelling, shortening, and symbols occurred in varying frequencies in the subjects’ productions. Moreover, the analysis shows that these internet language features are not necessarily induced by the electronic medium alone, but by other factors like inconsistencies in the linguistic system as well as the inability of English language users to discern the requirements of specific writing contexts.
An Overview of Algorithms for Solving Vehicle Routing Problems in the Quantum-Classical Cloud

Introduction. The hope of solving the problem of the avalanche-like growth of requirements for computing power, essential for solving complex routing problems and other problems of combinatorial optimization, relies on the latest quantum computers, in the development of which governments and corporations invest multi-billion investments. The article examines modern routing algorithms and performs their analysis and verification, if the authors of the algorithm provided appropriate test programs. The purpose of the article is to review the current state of development in the field of development of routing algorithms for hybrid quantum-classical clouds, analyze them and propose a classification of algorithms. Results. Modern quantum computers (QCs) make it possible to find approximate solutions to some of mathematical problems faster than classical computers. The inaccuracy of the solutions obtained by the QC is a consequence of physical and technological limitations: calculation errors are caused by thermal noise, a small number of computational elements - qubits and connections between them, which requires the decomposition of the problem and the use of heuristic algorithms. The analysis of approaches to the solution of optimization problems on QC allows us to single out: quantum response and variational search of eigenvalues based on quantum logic gates as the general directions of development of the vast majority of algorithms for solving routing problems. The considered algorithms reduce the vehicle routing problem to a quadratic unconstrained binary optimization problem, which is isomorphic to the Hamilton-Ising model. In this form, the problem is suitable for embedding in QC, which finds an approximate solution that has the best statistical reliability or corresponds to the quantum state with the lowest energy. As a separate class, vehicle routing algorithms for classical computers that use quantum computing to accelerate problem solving can be distinguished. For example, neural networks that calculate weighting factors using QC or an ant algorithm that calculates a pheromone trail in a hybrid cloud. It should be mentioned the quantum-inspired algorithms, which are based on software tools for the simulation of QC and the corresponding libraries and allow creating an effective class of algorithms for solving problems of vehicle routing. Conclusions. Combining hardware quantum annealing with a number of software tools for calculating optimization problems for classical computers in a hybrid quantum-classical cloud service allows to obtain advantages in speed and accuracy of some types of complex optimization problems of a commercial scale, in particular, routing vehicles, which is already bringing substantial profits to a number of corporations. Keywords: vehicle routing problem, quantum computer, annealing, combinatorial optimization, traveling salesman problem, clustering, qubit.
Development and Implementation Effects of Home Economics Community Housing Classes for Improving Empathy Ability of Middle School Students

The purpose of this study is to develop and apply a teaching-learning plan of apartment house class to improve middle school students' empathy for home economics education and to verify its effect. The study was conducted in five stages: analysis, design, development, implementation, and evaluation using the ADDIE model. In the analysis phase, the study set learning objectives after analyzing how community housing is dealt with in the 2015 revised home economics curriculum and 12 current technology and home economics textbooks. In the design and development stage, in order to evaluate the validity of the experts and to improve the empathy ability, the study goal design, the composition of the learning elements, the development of the learning materials, and the pre and post-questionnaire for the students were developed. In the implementation stage, the empathic ability evaluation was carried out before and after the beginning of the first class by applying the teaching-learning process plan of the 8th class. In the evaluation stage, we examined whether the teaching-learning process developed in this study has a significant effect on empathy ability by evaluating the pre-post difference of empathy ability. As a result of examining the results of the pre - and post - evaluation of empathy ability for the results of this study, both cognitive and communicative factors were improved, and the apartment house class had a significant effect on the improvement of empathy ability.
Self-awareness in the formation of the future music teacher’s scientific research culture

The purpose of the article is to highlight the functional characteristics of future music teacher’s selfawareness and worldview orientations as determinants of his scientific and research culture formation. Methods and methodology. The ideas of H.S.Kostyuka, S.D.Maksimenko, S.L.Rubinstein regarding the understanding of the ontology of mental phenomena, generalized by the principle of the unity of consciousness and activity, the development of professionally important qualities of the individual in the systemogenesis of activity; as well as the position of higher art education in relation to cultural, personality-oriented, systemic approaches as a methodological basis in revealing the peculiarities of the future teacher-musician’s self-awareness have acquired methodological significance for the selected issue. The scientific novelty of the article consists in illumination of the purpose of the professional worldview activity and in the context of the teacher-musician scientific research culture, the revealing of the essence of teachermusician self-awareness, the formation of which is manifested as support for its internal consistency and self-identity, the construction of conscious research and pedagogy; self-knowledge and search for the meaning of life through selfimprovement, self-understanding, self-education by means of research in the field of art education. The results. The theoretical-methodological aspects of the problem of future teachers-musicians’ scientificresearch culture formation are revealed, and in particular, issues regarding their self-awareness, semantic-forming and professional worldview activities. The scientific research culture of the future teacher-musician is defined as a dynamic, integrative quality of the personality, manifested in the ability to synthesize analytical-synthetic, inductive-deductive mental activity and emotional-figurative comprehension of musical art, the embodiment of scientific, pedagogical and artistic. application of scientific knowledge in pedagogical activity as a scientific-theoretical (explanatory) and constructive-technological (transformative) function. It is noted that the teacher’s self-awareness ensures the tension of his intellect, worldview intentions, social determinism of scientific research in musical and pedagogical education. It is emphasized that a particular importance of the researcher’s semantic-forming activity is acquired by the worldview, which is characterized by spiritual and aesthetic content and orientation, which is manifested as a valuable attitude to socio-cultural, educational and pedagogical processes through the prism of personal worldview. It was concluded that the result of the formation of future teacher-musician’s scientific and research culture is formed qualitative and instrumental properties of self-awareness, which ensure its functioning.
Various Cyber - Attack on Encryption Algorithm

Abstract: With the rapid advancement of technology across various fields, the significance of security has grown exponentially. As information is transmitted over networks or stored in devices, the question of data security has become a critical aspect of technology. To address this concern, data encryption is employed, wherein the data is encoded in a secure manner. However, the pressing question remains: can data truly be safeguarded against all cyber-attacks? In order to encrypt data, different algorithms are utilized, each with its own unique characteristics and level of security. The purpose of this paper is to delve into the impact of various types of attacks on these encryption algorithms. By examining the vulnerabilities and weaknesses of different encryption methods, we aim to gain a comprehensive understanding of the effectiveness of these algorithms when subjected to cyber-attacks.
Counterexamples for Noise Models of Stochastic Gradients

Stochastic Gradient Descent (SGD) is a widely used, foundational algorithm in data science and machine learning. As a result, analyses of SGD abound making use of a variety of assumptions, especially on the noise behavior of the stochastic gradients. While recent works have achieved a high-degree of generality on assumptions about the noise behavior of the stochastic gradients, it is unclear that such generality is necessary. In this work, we construct a simple example that shows that less general assumptions will be violated, while the most general assumptions will hold.
DIGITAL ART IN THE TRAINING OF DESIGN AND TECHNOLOGY SPECIALISTS

In the introduction of the article, the main trends of the modern world economy and their influence on the training of Ukrainian specialists in design and technology are clarified. The need for the synthesis of information technologies with art and scientific design education is emphasized. In the analysis of the latest researches and publications, provisions regarding the development of new forms and methods of training specialists in design and technology are singled out; development of scientific and artistic synthesis "science-art", mastering of digital art. A comparative analysis of the practical experience of design education in Great Britain and Ukrainian design education was carried out. The experience of using digital art for visual presentation of analytical computer graphics, which is necessary for digital illustration, is presented. Specific methods of research in the field of design education are characterized, corresponding to the modernized descriptors of the international and national framework of qualifications. It is recommended that the new descriptors of the National Framework of Qualifications be applied taking into account the STEAM approach to professional design education, where the abbreviation MM stands for design management and design marketing. The comments indicate insufficient digital socialization of design and technology specialists and little use of digital art in the training of design and technology specialists. The conditions for the transformation of the field of design in the cybertopia paradigm are formulated.Key words: digital art, design and technology, virtual environment, design education, science art.
Privacy by Design Solution for Robust Fall Detection

The majority of falls leading to death occur among the elderly population. The use of fall detection technology can help to ensure quick help for fall victims by automatically informing caretakers. Our fall detection method is based on depth data and has a high level of reliability in detecting falls while maintaining a low false alarm rate. The technology has been deployed in over 1,200 installations, indicating user acceptance and technological maturity. We follow a privacy by design approach by using range maps for the analysis instead of RGB images and process all the data in the sensor. The literature review shows that real-world fall detection evaluation is scarce, and if available, is conducted with a limited amount of participants. To our knowledge, our depth image based fall detection method has achieved the largest field evaluation up to date, with more than 100,000 events manually annotated and an evaluation on a dataset with 2.2 million events. We additionally present an 8-months study with more than 120,000 alarms analysed, provoked by 214 sensors located in 16 care facilities in Austria. We learned that on average 2.3 times more falls happen than are documented. Consequently, the system helps to detect falls that are otherwise overseen. The presented solution has the potential to make a significant impact in reducing the risk of accidental falls.
Vocal Behavior Acquisition with a Toy Operating by Sound Detection

We investigated a speech training support system targeting students in special needs education classes who are engaged in training to acquire a binary relationship where their vocalizations elicit reactions from others. Previously, there was a challenge in maintaining interest and achieving learning effectiveness when teachers intervened to encourage vocalizations using teaching aids such as picture books. To address this, we designed and integrated an electronic circuit with a movable toy that captures the interest of the supported students. The circuit includes a switch that turns on and activates a secondary circuit only when vocalizations are detected. In this paper, we report on the training using the developed speech support system and validate its functionality.
Nudge to Promote Employees' Information Security Compliance Behavior: A Field Study

This field study performed an experiment to observe practical effects of a nudge on facilitating employees' security compliance in one company's department. We examined if the nudges speeded up the employees' manual implication of applying the security patch to all their devices, which constituted a security compliance behavior in the experimental environment. Each employee was provided with one of three types of nudges informing the state of others: informing about the progress of general employees with a similar number of devices, informing about the progress of one's working team members, and providing information regarding both. As a result, providing information regarding both uniformly accelerated their patching behaviors although providing only team information severely delayed these behaviors. This study indicates the potential of a nudge as a security management intervention and showcases its effective design.
VR Facial Expression Tracking Using Locally Linear Embedding

Immersive virtual reality (VR) experiences have enabled the seamlessly transition of users to a completely new scene, which creates an authentic sense of presence within the virtual environment. By using VR headsets or head-mounted displays (HMD), users can further interact with the virtual surroundings and engage into a variety of activities that can deliver realistic and lifelike feeling. In order to perform better VR experience, the high-quality virtual facial animation tracking is indispensable, which aims to detect the real facial expression of users wearing HMD and reenact the detected facial expression onto virtual avatars to simulate the same facial animation in virtual environment. However, it is labor intensive to obtain and annotate the real facial expression as it requires Facial Action Coding System (FACS) experts to annotate each facial AU intensity. In addition, the mounted cameras embedded in the HMD can only capture partial facial images due to the occlusion of the HMD. Therefore, synthetic facial data are widely used for training and the performance greatly depends on the quality of generated data. In this paper, we focus on estimating facial expression in terms of facial action unit (AU) intensity. To obtain higher quality of synthetic facial data, we developed a model with domain adversarial training to generate domain invariant features representations and then utilized local neighbors in high-dimension feature space to perform facial AU intensity estimation on real people data. We evaluate our method on self-collected synthetic avatar data with labels and real facial expression data without labels. The results show that our method can obtain better tracking performance both quantitatively and qualitatively.
Secure Digital Identity using Non-Fungible Tokens

Digital identity management is a significant aspect of online security, with traditional methods presenting several challenges. The introduction of blockchain technology provides a promising solution to traditional digital identity challenges. The observed drawback of Blockchain based identity is scalability and interoperability. To overcome these problems, this research study proposes an NFT-based digital identity management system that prioritizes self-sovereign identity by leveraging the properties of NFTs, such as its novelty and immutability. This research study provides a detailed description of the system's workflow. The proposed system is built on the strengths of existing Blockchain based digital identity management systems while addressing their limitations. Overall, the proposed system provides an alternate approach to solve the challenges of traditional digital identity management.
360° Security in Cameras Using Linear Binary Pattern Histogram

This research study proposes a novel approach for enhancing the security of surveillance cameras by using a combination of panoramic cameras and linear binary pattern (LBP) histograms. The proposed method can detect and track intruders in a 360° field of view, which is achieved by stitching together multiple images captured by the panoramic camera. The LBP histogram is then used to extract features from the stitched image, which are fed into a support vector machine (SVM) classifier for intruder detection. The proposed method is compared to several state-of-the-art methods, and experimental results show that the proposed method outperforms them in terms of accuracy and detection rate. Furthermore, the proposed method is shown to be robust to lighting changes and occlusions, making it suitable for real-world applications and the approach is then evaluated on a dataset of 360° images captured by cameras and achieves high accuracy in detecting abnormal events such as intrusion and theft. The results demonstrate the effectiveness of the proposed approach in enhancing the security of cameras and can be useful in various applications such as surveillance systems, smart homes, and malls.
Dual-branch multi-scale densely connected network for image splicing detection and localization

In the last two decades, numerous methods have been proposed for image tampering forensics, especially when deep learning is leveraged as a powerful feature extraction technique. However, existing deep learning-based methods are not efficient to jointly explore robust representations of tampering traces from both low-level forensics patterns and high-level forensics semantics. Besides, they become unreliable for spliced regions with unknown scales in the evaluation stage. To overcome the above-mentioned limitations, a novel Dual-branch Multi-scale Densely Connected Network (DMDC-Net) is proposed in this work for image splicing detection. DMDC-Net is constructed based on an encoder-decoder framework, which equips a multi-scale dense (MD) module for feature reuse and pixel-wise feature fusion (PFF) module to leverage forensics clues from different data components adaptively. In DMDC-Net, the given input image will be fed into two individual subnets to obtain deep representation maps from RGB and high-frequency components, which contains both visual and noise inconsistencies for further analysis. Besides, the MD module is applied in Encoder of each subnet to make the model jointly learn low-level forensics patterns and high-level semantic representations, which incorporates local and global features by a multi-scale manner of dense connections. The outputs of each subnet are fused in the PFF module to take advantage of their complementarity by trainable weights and get the final detection score map. Experimental results demonstrate the effectiveness of our method and the superior performance than other state-of-the-art methods in CASIA v2.0 and Columbia dataset for different scenarios, such as post-processing operations.
Analysis for Microservice Architecture Application Quality Model and Testing Method

In recent years, microservice architectures have benefited from low coupling and high cohesion characteristics, allowing flexible on-demand deployment of complex applications and simplifying the difficulty of developing and maintaining large software systems. However, there is limited research on quality models and testing methods for microservice architectures, which leads to the problem that microservice architecture applications are difficult to test and evaluate. This paper analyzes the main problems when microservice architecture applications face quality measurement and software testing, further improves and refines the quality model of microservice architecture applications on the basis of the existing ISO software product quality model, and introduces testing methods for microservice architecture applications, which can provide reference for the construction of microservice architecture applications.