Reviewerly/exHarmony huggingface dataset:
Crossing the Great Divide of Software Engineering

The Great Divide, the continental divide of the Americas, is a water divide extending from the Bering Sea to the southern tip of South America, the Strait of Magellan. The Great Divide separates the watersheds that drain into the Pacific Ocean from those river systems that drain into the Atlantic and Arctic Oceans. While there are many other examples of geological and geographical divides, the Great Divide stands out by its magnitude and higher peaks compared to other divides and its spanning multiple countries and continents. The divide does not necessarily define any different characteristics on either of its sides; it is simply a water passage dividing the Americas into two. To hiking enthusiasts, hiking the Great Divide is defined by its magnitude, typically easily taking up to five months. Due to its clear visualization power, the Great Divide has been a source of inspiration for metaphors in movies, albums, books, and topics when describing any conundrum with two sides.
3D-Based Facial Emotion Recognition using Depthwise Separable Convolution

Facial images play a significant role in expression prediction. The 3D features of facial expression provides significant information. In the area of facial emotion recognition, the 3D geometry and 2D texture helps to improve the recognition rate. A lot of research works had achieved state-of-the-art results using handcrafted and deep convolutional neural networks containing many trainable parameters which require high computing power. In this paper, we employ two kinds of convolutions i.e., regular or normal convolution on the 2D texture image and separable convolution on the 3D depth map images. We run experiments with our proposed network on the BU-3DFER database. The proposed model was trained from scratch to adjust the weights and biases of the learnable layers on various image features and achieved state-of-the-art accuracy of 81.81% on the 2D texture image, 79.10% recognition accuracy on the 3D depth map, and 83.01% for combined 2D and 3D features.
Understanding components of embodiment: Evidence from the mirror box illusion

Past studies have examined embodiment in the rubber hand illusion, using principal components analysis (PCA) to identify factors from questionnaire responses during synchronous and asynchronous stroking. To better understand the phenomenology of embodiment, we used PCA in the mirror box illusion to examine performance across conditions that varied in movement synchrony to examine multisensory integration and movement type to vary the amount of multisensory congruence. We found three dissociable components in all conditions: embodiment, deafference and attentiveness. We also examined how these embodiment ratings varied across the four conditions. As hypothesized, embodiment ratings were highest for synchronous movement, with feelings of deafference highest for asynchronous movement. Furthermore, there was a movement by timing interaction, such that sliding resulted in greater differences in synchronous versus asynchronous ratings than tapping. These results suggest that embodiment or deafference can be changed as a function of the amount of multisensory congruence.
Development and Implementation of the «Smart Greenhouse» Project in the Educational Process of an Educational Institution

The article reveals the use of smart technologies in the educational process of educational institutions, discusses the development and implementation of the project “smart greenhouse”. The technology of developing a “smart greenhouse” is shown, which consists of studying the capabilities of a “smart greenhouse” from the standpoint of resource conservation, studying the functions of the components of a “smart greenhouse” regarding the conditions necessary for seed germination and plant cultivation, developing software for controlling the greenhouse microclimate, creating an experimental sample of a “Smart greenhouse” with specified conditions in full size. The application of a smart greenhouse is described within the framework of various subjects, sections, topics and various types of scheduled and extracurricular activities. It is important to develop and implement research projects using an experimental sample of a “smart greenhouse” in the educational process of an educational institution.
Damage Detection in Composites by AI and High-Order Modelling Surface-Strain-Displacement Analysis

In the recent years, machine learning algorithms have been widely employed for structural health monitoring applications. As an example, Artificial Neural Networks (ANN) could be useful in giving a precise and complete mapping of damage distribution in a structure, including low-intensity or localized defects, which could be difficult to detect via traditional testing techniques. In this domain, Convolutional Neural Network (CNN) are employed in this work along with one-dimensional refined models based on the Carrera Unified formulation (CUF) for surface strain/displacement based damage detection in composite laminates. A layer-wise kinematic is adopted, while an isotropic damage formulation is implemented. In detail, CUF-based finite element models have been exploited in combination with Monte Carlo simulations for the creation of a dataset of damage scenarios used for the training of the CNN. Therefore, the latter is fed with images of the strain or displacement field in a region of particular interest for each sample, which are subjected to the same boundary conditions. The trained CNN, given the strain/displacement mapping of an unknown structure, is therefore able to detect and classify all the damages within the structure, solving the so-called inverse problem.
Is Data Science and Blockchain a Perfect Match?

Blockchain technology can help with IoT, digital transaction records, and supply chain management. Enterprises over the world are focusing more on the innovative work of blockchain technology. Blockchain innovation permits the end-client to do exchanges with each other without the association of the middle man. Any benefits from cash to motion pictures can be moved, put away, executed, oversaw, and traded with no intermediaries. Data science, as we know it, is the science to extract valuable insights and information from both structured and unstructured data to solve real-world problems. With a lot of advantages and challenges, blockchain and data science can end up being an amazing blend to oversee information amount with quality productively. In this paper, we are investigating the new aspect of mixing data science and blockchain.
Effect of Data Compression on Cipher Text Aiming Secure and Improved Data Storage

Cryptography is a technique of protecting the data from an unauthorized access using encryption process. Encryption converts the plain text into cipher text which is in non-readable form. Past studies suggest that the size of cipher text is the major concern which hinders users from adopting encryption methods to secure the data. This work involves an experimental study on exploring the effect of data compression on an amino acid form of cipher text using dictionary-based methods as well as entropy coding methods without compromising the security. Compression ratio is measured for different file sizes. The results show that 47% storage savings is achieved using entropy coding method and 60% using dictionary-based coding method. Owing to this, storage efficiency is also doubled. The advantage of this method is thus convinced and provides an improved data storage.
Remote Presence: Live Holograms for a Social Classroom

Existing communication technologies have displayed a lack of affordances in supporting social-emotional connections, which is of particular interest in educational settings. We are therefore developing a live sensory immersive 3D video technology, built on a prior developed platform. Pilot trials in a Finnish school have yielded promising findings. We continue to advance the state-of-the-art platform in parallel with regards to 3D capture quality and data compression algorithms. Current developments entail joint investigations and evaluations of affordances to support emotional, social, motivational, and achievement impacts with learners and teachers from a Namibian and Finnish school. Participants can experience ”remote presence” wearing the hololens 2 while others are live streamed from another country captured by two cameras.
Robust Feature Extraction and Recognition Model for Automatic Speech Recognition System on News Report Dataset

Information processing has become ubiquitous. The process of deriving speech from transcription is known as automatic speech recognition systems. In recent days, most of the real-time applications such as home computer systems, mobile telephones, and various public and private telephony services have been deployed with automatic speech recognition (ASR) systems. Inspired by commercial speech recognition technologies, the study on automatic speech recognition (ASR) systems has developed an immense interest among the researchers. This paper is an enhancement of convolution neural networks (CNNs) via a robust feature extraction model and intelligent recognition systems. First, the news report dataset is collected from a public repository. The collected dataset is subjective to different noises that are preprocessed by min–max normalization. The normalization technique linearly transforms the data into an understandable form. Then, the best sequence of words, corresponding to the audio based on the acoustic and language model, undergoes feature extraction using Mel-frequency Cepstral Coefficients (MFCCs). The transformed features are then fed into convolutional neural networks. Hidden layers perform limited iterations to get robust recognition systems. Experimental results have proved better accuracy of 96.17% than existing ANN.
Biometric-Based Authentication in Online Banking

The use of technology has become the integral part of human life. Most importantly the introduction of Internet has made the lives of people easy and due to its cost effectiveness; usage of Internet by people has been increased. The Internet has made the people to move toward the online mode of transaction. Banks recommend their customers to use Internet banking facility and assure it as a safest mode of transaction but it is associated with huge risk. The continuous rise in online banking brings several security issues and increased cost of implementing higher security systems for banks and customers. Present online banking technology works on 2-Factor authentication mode, i.e., it works on both transaction level and authentication level. The main problem associated with 2-Factor authentication is that there may be risk associated, like cyberattack (SIM swapping fraud), etc., so we have proposed a biometric authentication technique which makes transaction more secure.
Real-Time Deepfake System for Live Streaming

This paper proposes a real-time deepfake framework to assist users use deep forgery to conduct live streaming, further to protect privacy and increase interesting by selecting different reference faces to create a non-existent fake face. Nowadays, because of the demand for live broadcast functions such as selling goods, playing games, and auctions, the opportunities for anchor exposure are increasing, which leads live streamers pay more attention to their privacy protection. Meanwhile, the traditional technology of deepfake is more likely to infring on the portrait rights of others, so our framework supports users to select different face features for facial tampering to avoid infringement. In our framework, through feature extractor, heatmap transformer, heatmap regression and face blending, face reenactment could be confirmed effectively. Users can enrich the personal face feature database by uploading different photos, and then select the desired picture for tampering on this basis, and finally real-time tampering live broadcast is achieved. Moreover, our framework is a closed loop self-adaptation system as it allows users to update the database themselves to extend face feature data and improve conversion efficiency.
The role and place of the lecture in “Flipped Classroom” technology

The Flipped Classroom technology is an active, student-oriented form of learning that is used for improve the quality of classroom lessons. This technology attracts the attention of teachers of various disciplines for many reasons, such as a significant reduction in classroom hours, the ability to build an individual learning trajectory, the development of self-education skills, continuous learning, etc. The Flipped Classroom technology has become widespread all over the world, but in our country it is not used often, mainly in the teaching of humanities. That’s why we want to share our experience of using this technology in the study of mathematics at a technical University, as well as to consider the advantages and disadvantages of this technology, the conditions for its application and compare the effectiveness of the Flipped Classroom technology with traditional training. In addition, the advantages of the “inverted class” technology are shown in terms of optimizing lectures, of motivating students to study the subject, and focusing on the development of students’ abilities to independently master knowledge. The article presents the experience of implementing this technology in a technical university, which lasts for 5 years. The results obtained allowed us to conclude about the effective organization of lectures in mathematics with the use of the flipped classroom technology and its promising application in preparing students for classes in mathematics.
Linguistic-based Approach for Recognizing Implicit Language in Hate Speech: Exploratory Insights

Language, in all its forms, is one of the most comprehensive ways to characterize human societies. By means of the analysis of regular components, either at phonetic, morphological, syntactic or semantic level, human language provides valuable information that can be translated into knowledge in order to represent behavioral patterns. For instance, in web texts, such as the ones posted on Twitter or Facebook, it is quite frequent to find linguistic expressions, such as the following one: “Don’t come here. If you are afraid for your life and you have no place to go, don’t pick this country.” This text could denote an explicit description of the current immigration phenomenon and, likewise, it could connote an implicit content of mockery, aggressiveness, or even hate. Both interpretations are possible, but only one of them is more likely according to the author profiling. This fact stresses out the underlying problem that it is faced in this investigation: Many of our utterances entail two communicative dimensions. The explicit dimension (literal use of language), and the implicit dimension (figurative use of language). Both dimensions are supposed to communicate information thought consciously. In this respect, the most challenging issue for this approach relies on the recognition of the correct communicative dimension profiled by the author in a web text. In this context, this article focuses on analyzing textual information, mainly extracted from Twitter, in order to set a computational framework to differentiate between explicit and implicit language. In particular, we are interested in recognizing figurative uses regarding irony and sarcasm, in order to apply the findings to better understand and prevent social problems related to hate speech.
Blockchain technology for secure COVID-19 pandemic data handling

In recent times, COVID-19 pandemic data collected via the Internet of Medical Things-enabled channels can be transmitted seamlessly and processed remotely by medical practitioners globally. However, the confidentiality and integrity of these channels pose serious security concerns. The transmission of medical information, which comprises highly sensitive personal data, must meet stringent security requirements. In order to achieve this, blockchain has been deployed to enhance the security, confidentially, trust, and integrity of sensitive data being transmitted over insecure channels as a result of its cryptographic characteristics. Blockchain is regarded as a protected and distributed structure of data, which enhances security, simplifies the tracking, and analyses the stored medical information independently. Additionally, blockchain allows the transmission and exchange of messages between two parties in a network configuration, independent of sole trusted authority. Blockchain technology is verifiably safe against an attacker who mismanages the scheme and compromises the central controller. Furthermore, blockchain technology has been deployed to handle distributed medical data in standard medical laboratories. The decentralized and immutability features of blockchain have facilitated the rapid development of the beyond 5G wireless services for health-related data sharing and processing. However, the adoption of blockchain technology in medical data handling is still in its infancy, and the need for a rigorous study in this domain cannot be overemphasized. Toward this end, this chapter first highlights how blockchain technology has developed in recent times and how it is applied in the medical data-handling space. Further to this, the chapter examines the potential benefits, key challenges, and prospects in blockchain technology. Additionally, the chapter highlights research efforts in blockchain in healthcare, especially as it relates to the dreaded COVID-19 pandemic. The chapter also discusses the application of blockchain technology in healthcare data-handling practices that can build trust with automated tracking of integrity and responsible credential verification. Finally, practical COVID-19 pandemic data were analyzed and presented to motivate the chapter further.
FreqCAM: Frequent Class Activation Map for Weakly Supervised Object Localization

Class Activation Map (CAM) is a commonly used solution for weakly supervised tasks. However, most of the existing CAM-based methods have one crucial problem, that is, only small object parts instead of full object regions can be located. In this paper, we find that the co-occurrence between the feature maps of different channels might provide more clues for object locations. Therefore, we propose a simple yet effective method, called Frequent Class Activation Map (FreqCAM), which exploits element-wise frequency information from the last convolutional layers as an attention filter to generate object regions. Our FreqCAM can filter the background noise and obtain more accurate fine-grained object localization information robustly. Furthermore, our approach is a post-hoc method of a trained classification model, and thus can be used to improve the performance of existing methods without modification. Experiments on the standard dataset CUB-200-2011 show that our proposed method achieves a significant increase in localization performance compared to the original existing state-of-the-art methods without any architectural changes or re-training.
A qualitative examination of parents' views on the applicability of the project-based learning approach in science courses

This study was conducted to investigate the role of the parents of students in the science and technology lessons of the project-based teaching approach. The research was carried out through a case study, one of the qualitative research methods. The data of the study were obtained from semi-structured interviews with 24 students' parents. As a result of the research, it was understood that the parents of the students saw the project as a study aimed at increasing the students' hand skills, thinking skills, and productivity to prepare something new. As a result, it was seen that after the project assignment was given by the teachers, the parents were involved in the process to help their children complete the projects. At the end of the process of doing the project assignments, it was concluded that the parents received informal training, both in one-on-one interviews with the parents who helped their children and in the observations made during the project exhibitions at the school. This situation was important in terms of the informal education of parents. It was also determined that the parents helped their children during the setup and presentation of the project and that they wanted to complete it when wrong or incomplete explanations were made. It was observed that as the contribution of the parents increased during the completion of the projects, the role of the students in the projects decreased and they did not take responsibility. It was concluded that the students thought of family aids as a tool to achieve higher academic success than the project assignments, and therefore the homework deviated from its purpose.
University education models as a leadership factor: The example of a Research University model

The article defines the concept of “Leadership university education model”. Taking the research model of university education as an example, the article explains how the leadership research model has arisen and why this model is a leadership model. It is explained why there is no understanding of the importance of university leadership models in the former Soviet countries (Russia and Belarus). The conclusion is made about the predominance of an eclectic mixture of liberal, research and professionally oriented models of university education in modern university education.
Awareness and Readiness of Nigerian Polytechnic Students towards Adopting Artificial Intelligence in Libraries

This survey research investigated the awareness and readiness for Artificial Intelligence (AI) among polytechnic students in Nigeria. Five Polytechnics were purposively selected for the study. The study population comprised of three hundred and twenty (320) final year students registered as members in the selected polytechnic libraries. A questionnaire was used for data collection. Purposive sampling was used to select one hundred (100) respondents from each of the polytechnics. Findings revealed that the students were aware of the existence of Artificial Intelligence usage in library operations and gained awareness during library orientation programmes. The students admitted that they needed to have basic computer skills to be relevant in this era. The major anticipated constraint in the use of AI is unstable power supply. The study recommends the need for polytechnic library managements to harness alternative sources of power supply, and the need to include practical ICT training in the curriculum.