Reviewerly/exHarmony huggingface dataset:
Teachers’ Training Needs for Digital Competences

This study investigates teachers’ training needs in the area of digital competences in education. It aims at revealing the degree of training needs for these teachers regarding Teacher Digital Competences (TDC). It also seeks to examine if the degree of teachers’ training needs regarding digital education competences differ according to educational stage (Cycle1, Cycle2). The study targets a sample of (701) Female teachers at Cycle7 (n: 58) and Cycle 2 (n: 49) of Basic Education schools using a questionnaire for this purpose. The study results show that averages of the teachers’ responses about the degree of their training needs in different digital competences ranged between (2.89-1.80), with an evaluation degree between large and medium. Moreover, the results indicated a big need in six areas of digital competences. The results also indicate that there are no statistically significant differences at the significance level (α≤ 0.05) in the training needs for the domains of digital competences between the teachers of (Cycle 1) and (Cycle 2) of basic education. Based on the findings, some main recommendations for practice and suggestions for further research are included.
Design and Implementation of a Chatbot System Integrated with Facial Expression Recognition

Different from those who desires to talk, those “empty-nest youth” who do not take the initiative to talk about their emotions still want someone to be with them, and interacting with them, give them some care, although they cannot talk about their emotions. In order to pay attention to the mental health status of this group of people and meet their inner demands, this paper first uses facial expression recognition technology to analyze users' emotions in real time. Then employs chatbot technology to intervene the user's mood through automated responses. In the process of realizing the chatbot function, MongoDB is introduced to store the sayings of chatbots with different emotions, and seven chatbots with different emotions are divided. In addition, MongoDB is used to store the scores of users' emotions, which can achieve the function of recording users' emotions. Finally, Django framework is used to realize the page of the web side, which can call all the functions of the system and achieve the effect of different interventions according to the user's emotions.
Theories of Computational Complexity

This chapter is devoted to the issues of algebraic and analytical computational complexity of problem solving. The main focus is on computer technology for computational processes, which is covered for various computational models.An important reserve for computational optimization is the construction and use of optimal in accuracy and speed computational algorithms. This allows you to solve the problem with the least error and can transfer the problem from the class of unsolvable problems to the class of solvable ones.The choice of information solution operator is also a reserve for computational optimization. Successful choice of the information operator makes it possible to reduce the Chebyshev radius of the uncertainty region of the approximate solution of the problem.These approaches are illustrated by the problem of approximate calculation of integrals from fast-oscillating functions in both one-dimensional and multidimensional cases.
The application of improved imbalanced learning based on fuzzy C-means clustering (FCM) and SMOTE

Data classification is one of the most applicable branches of pattern recognition and data mining science. Its wide range of application can be easily seen in everyday life. In the last few years, major changes have been occurred in data classification technology. Since the field of technology application has been increased, the size of information has been increased as well. Data classification has become difficult due to the unlimited size and imbalanced nature of the data. Data classification with imbalanced class distribution has caused a significant defect in the performance of standard classification learning algorithms, which assume that the data class distribution is relatively balanced. This paper presents a simple and effective sampling method based on Fuzzy C-means Clustering (FCM) and SMOTE (Synthetic Minority Oversampling Technique) that prevent noise generation and effectively resolve imbalance between classes. The evaluation of experiments shows that the proposed technique effectively reduces noise production. The obtained results for the accuracy of the proposed method indicate that it has been improved by an average of two percent compared to the base paper in different datasets.
Enhance Egocentric Grasp Recognition Based Flex Sensor Under Low Illumination

Egocentric recognition is exciting computer vision research by acquiring images and video from the first-person overview. However, an image becomes noisy and dark under low illumination conditions, making subsequent hand detection tasks difficult. Thus, image enhancement is necessary to make buried detail more visible. This article addresses the challenge of egocentric hand grasp recognition in low light conditions by utilizing the flex sensor and image enhancement algorithm based on adaptive gamma correction with weighting distribution. Initially, a flex sensor is installed to the thumb for object manipulation. The thumb placement that holds in a different position on the object of each grasp affects the voltage changing of the flex sensor circuit. The average voltages are used to configure the weighting parameter to improve images in the image enhancement stage. Moreover, the contrast and gamma function are used to adjust varies the low light condition. These grasp images are then separated to be training and testing with pre-trained deep neural networks as the feature extractor in YOLOv2 detection network for the grasp recognition system. The proposed of using a flex sensor significantly improves the grasp recognition rate in low light conditions.
Research on Crowd Counting and Density Estimation Algorithms Based on Deep Learning

Thanks to the rapid development of computer vision technology, methods based on deep learning have gradually replaced counting methods based on traditional machine learning, and substantial progress has been made in counting accuracy and real-time detection. Firstly, the research background and application fields of target counting are introduced. Secondly, according to the classification of model tasks, the deep learning hotspot models are classified into three categories, and the crowd density estimation algorithms based on multi-scale strategies, multi-stage models and attention mechanisms, and multi-feature fusion are introduced from different perspectives. An introduction to the three algorithm models. Finally, it summarizes the shortcomings of the current target counting model, and looks forward to the future research directions.
Tibetan-Chinese Cross-Lingual Sentiment Classification Based on Adversarial Network

The high-quality sentiment classification data between different languages is quite uneven. As a low-resource language, the lack of annotated corpus seriously hinders the research of text sentiment classification in Tibetan. This paper uses the Chinese-Tibetan bilingual word vector to map the two languages to the same shared space, uses the language adversarial network to learn the joint features of Chinese and Tibetan, shares the emotional knowledge of Chinese and Tibetan, and builds a Tibetan-Chinese cross-lingual emotional classification based on the adversarial network. The model (Ti-Ch CLSC) achieves a better sentiment classification effect in a small number of Tibetan sentiment annotation sets. In order to verify the effectiveness of the model in this paper, the model is compared with several mainstream Tibetan sentiment classification methods. Experiments show that Ti-Ch CLSC significantly improves the effect of Tibetan text sentiment classification.
Bridging the Gap between Voice Coaches and Sound Designers in the Theatre

This article distinguishes a lack of crossover between voice coaches and sound designers and seeks to bridge the gap between the two, as both roles have a direct effect on the sonic product of a theatrical production. The article provides contextual exposition regarding the shift to microphone usage in the theatre and offers a literature review identifying an absence of discipline overlap within both vocal pedagogy and theatrical sound design texts, making reference to directorial literature as well. Using participatory action research and exploratory case study, the article examines when the researcher spent one month coaching a production of Sophie Treadwell’s Machinal at a university in the United States, while working closely with the production’s sound designer. The case study’s analysis includes three proposed solutions to the issues identified and suggestions for bridging the practical gap between voice coaches and sound designers.
g-EoN: A Non-salient Approach Towards Encryption Using Gamma Function $$\varGamma \Big (-\frac{\alpha }{\beta }\Big ) = -\frac{\beta }{\alpha }\, \varGamma \Big (\frac{\beta -\alpha }{\beta }\Big )$$

Cryptography is the art of presenting secret information under code obfuscation. This can be achieved by various algorithms which convert human readable text into non-legible text. This paper presents a cryptosystem that adopts the Gamma function and tabular modeling to carry out both the encryption and decryption using two common keys namely Primary Common Key (PCK) and Secondary Common Key (SCK). The encryption outputs two ciphers when a message P and PCK is given as input. The value Key VK is generated by Value Key Generator (VKG). Cipher \(C_1\) uses modular arithmetic followed by Gamma function and Cipher \(C_2\) uses Gamma function in Gamma Cryptor Module (GCM). The result of VKG is given to GCM. During decryption, it requires both the ciphers \(C_1 C_2\) and a Value key VK for generating the plaintext back.
OSINT Explorer: A Tool Recommender Framework for OSINT Sources

The Internet has had a profound impact on our daily lives since its inception. It has become a determining element in how we interact and do business, particularly in terms of our ability to access information, jobs, our ability to stay connected, our company’s chances of survival, our ability to thrive in the workplace, and education, etc. There are several everyday problems for which the Internet provides resources, such as software and hardware solutions that we may rely on in times of crisis. The abundance of software and computational services offered by the Internet has introduced new challenges for what was previously unknown, i.e., within the plethora of resources available, users cannot work out which tool to use to solve the problem. The collection and review of freely accessible material, often from online sources that are freely accessible to the general public, is referred to as open-source intelligence (OSINT). With the plethora of OSINT tools available, it has become difficult for users to choose the best tool for the given problem. This article presents a framework for identifying OSINT tools that are most appropriate for solving given problems. The proposed framework is user-friendly and provides tools based on MIME types or advanced search features. The framework has been evaluated by subject experts and has shown to be an invaluable resource for end-user tool recommendations.
Dynamic Lexical Framework to Evaluate the Evolution of Emotions in Twitter

Human emotions and sentiments are dynamic by nature. Nowadays, social networks have become a key resource for human communication and a faithful representation of this dynamism. This fact poses major challenges to those systems addressing sentiment analysis. Therefore, having systems capable of inferring this dynamism has become a key issue. In this paper we introduce Emoweb 2.0, a prototype for dynamic sentiment analysis of Twitter data. A well-known lexicon is taken as starting basis and new words are appended by an unsupervised learning algorithm governing the process. Sentiment values of new words are calculated and dynamically updated depending on the trends detected. Tweet sentiment scores are also computed during the process. A visualization module is included to observe word sentiment fluctuations over time. The experiment performed is based on the ongoing COVID-19 pandemic showing promising results.
Cyber Security in Nigeria: Appraising Cybercrime, the Existing Legal Framework, the Challenges and the Way Forward

“Cyber security risks pose some of the most serious economic and national security challenges of the 21st century”-President Barrack ObamaCybercrime has continuously posed a global threat to human existence and survival. Particularly, the spate at which the Nigerian cyberspace is subjected to daily attacks from unscrupulous minds and criminals is alarming. Cybercrime has taken its dangerous tolls on individuals, businesses, institutions, governments and the economy. If the issue of cybercrime is not urgently addressed, it can wreak a greater havoc on the nation, than corruption and poverty would do. In light of this, this paper appraises the status of cyber security and cybercrimes in Nigeria; the extant legal framework set up to combat cybercrimes in Nigeria; the challenges faced in curbing cybercrimes; and the way forward – pathways to attaining a cyber secure Nigeria.
Video Stabilization for Drone Surveillance System

Now-a-days, drones are very commonly used in various civil applications like disaster management, traffic monitoring, crowd monitoring, infrastructure inspection and search and rescue. Drones have different types of sensors, among which cameras are normally used in these type of real time scenarios. The low flying small drones commonly have fixed cameras. The video/images from these cameras are not smooth due to the propeller vibrations and environmental effects. These unstable videos need to be smoothened without adding extra payloads. This will improve the quality of the videos and will help in further data/image processing. In this paper, we propose an optical flow-based motion estimation and further smoothening of drone videos to improve the Inter Frame Transformation Fidelity (ITF) when compared to the existing feature extraction methods.
Sound Recording, Processing and Installation for Pedagogical Purposes

One of the current problems facing modern academic training of students in pedagogical programs is the formation of digital competencies in the field of music. The skills for sound recording, processing and editing with the means of technology are extremely useful effects with various projections in pedagogical theory and practice. The article describes a study of students' attitudes towards the implementation of a team task related to the development of an audio product for pedagogical purposes
Exploration of University English Teaching Models Under the Background of Smart City

Smart cities have become a hot area of research now. As China develops, the development of Smart city is very important in China’s new urbanization construction. It is a new idea and new way of urban development, involving work, life, learning, and many other areas. The development of Smart City is based on talents. Traditional university talent training model has been very difficult to meet the demand for talent in smart cities, and the transformation of college the change of English teaching model has become a definite trend in the context of Smart City development. In the atmosphere of the construction of Smart City, the article explores the current situation and problems of English teaching in higher education. Based on the meaning of Smart City and the demand for talents, it explores new models of English teaching in higher education, and puts forward new ideas for talent training, with a view of training talents in colleges that can effectively help the development of Smart City construction.
Applying Byte-Shuffling to CLEFIA-Type Structure

Generalized Feistel Network (GFN) is widely used in block ciphers. CLEFIA is one of the GFN type-2 block ciphers. CLEFIA employs Diffusion Switching Mechanism (DSM) in its diffusion layer. DSM improves CLEFIA's security by increasing its number of active S-boxes, which is an indicator of security against differential and linear cryptanalyses. However, two matrices in DSM increase implementational cost. In this paper, we pursue the research question whether it is possible to achieve the same security as original CLEFIA with only one matrix without overhead in hardware. Our idea to answer the research question is applying byte-shuffling technique to CLEFIA. Byte-shuffling is an operation to shuffle 8-bit bytes. On the other hand, traditional GFN ciphers rotate 32-bit or larger words in their permutation layer. Since implementation of byte-shuffling is considered as cost-free in hardware, it adds no overhead in comparison with word rotation. Byte-shuffling has numerous shuffle patterns whereas word rotation has a few patterns. In addition, security property varies among the shuffle patterns. So, we have to find the optimal shuffle pattern(s) on the way to pursue the research question. Although one way to find the optimal shuffle pattern is evaluating all possible shuffle patterns, it is impractical to evaluate them since the evaluation needs much time and computation. We utilize even-odd byte-shuffling technique to narrow the number of shuffle patterns to be searched. Among numerous shuffle patterns, we found 168 shuffle patterns as the optimal shuffle patterns. They achieved full diffusion in 5 rounds. This is the same security as original CLEFIA. They achieved enough security against differential and linear cryptanalyses at 13th and 14th round, respectively, by active S-box evaluations. It is just one and two rounds longer than original CLEFIA. However, it is three and two rounds earlier than CLEFIA without DSM.
Stereo Vision Based Obstacle Avoidance Local-Planner for Autonomous Golf Cart

Perception and planning are key factors in deter-mining the safety of an autonomous vehicle. This paper proposes an approach for obstacle avoidance local planner by utilizing a stereo camera. Obstacles are detected by acquiring bounding boxes from an object detection architecture, i.e. the Faster R-CNN model. The boxes are then matched with depth data from the stereo camera in order to generate object points. In addition, a conformal state lattice is applied for the local planner to generate paths based on the object points. The proposed perception and planning system is implemented experimentally in an autonomous golf cart. Experimental results show that the system is able to detect and avoid static obstacles satisfactorily. Moreover, the autonomous golf cart is able to reach the desired final point with an error of less than 1 m and avoids an object with an average distance of 2 m.
Propositional Logic

Propositional logicConnectives/operatorsof propositional logic hasLogicpropositional a long history going back in time to ancient Greece to the times of Parmenides and Plato who raised the problem of truth and falsity but the cornerstone for it was laid by Aristotle.
Robust Method to Convert HIRAGANA Sequences into Japanese Text

We apply an attention-based sequence-to-sequence model for the Japanese HIRAGANA-KANJI conversion task in spontaneous speech transcripts. Experimental results indicate that short HIRAGANA sequences containing speech-specific errors can be converted into error-free HIRAGANA-KANJI mixed Japanese text.
Identification module of the algorithms for calculating estimates

In this work, we propose a new algorithm for forming the systems of support sets on based partial precedents of given training set. The main idea of this algorithm is the selection of best features for object classification in the condition of increasing the number of features in the training set.
Enabling Inference and Training of Deep Learning Models for AI Applications on IoT Edge Devices

IoT edge devices sense and process data to support real-time decision-making in latency-sensitive and mission-critical applications such as autonomous driving, industry automation, safety compliance, and security-threat monitoring. Running AI at edge brings the ability to make intelligent real-time decisions on the device. Moreover, on-device AI is vital to preserving data privacy. Hence, edge AI is an active topic for research and engineering at the major technology corporations, numerous start-ups, and academia. Deep learning neural network models have made tremendous improvements in prediction accuracies tending to surpass human intelligence for several tasks. Typically, these models are large-sized and hence, not suitable for resource-constrained edge devices and real-time inference. It is also challenging to train deep learning models on the edge device because they require large amounts of data and compute resources to train the model. We present the active ongoing research in optimizing deep learning models for inference at the edge using connection pruning, model quantization, and knowledge distillation. Then, we describe techniques to train/retrain the deep learning models at the resource-constrained edge device using new learning paradigms such as federated learning, weight imprinting, and training smaller models on fewer data.
Deep Learning Approaches to Identify Sukabumi Potentials Through Images on Instagram

Sukabumi Regency is one of the largest regencies on the island of Java. With a large area and a fairly dense population, it creates its own problems, such as in managing the potential of places and communities. The purpose of this research is to explore the potential of Sukabumi Regency through Instagram social media with #sukabumiupdate. Data collection is done by taking pictures from social media Instagram, the data taken is 6,970 images. Each data that has been collected is divided into 4 (four) class categories based on the type of image, namely Tourism class, culture class, culinary class, and handicrafts. Then the data is classified using a deep learning approach with three methods, namely CNN, VGG16, and VGG19. These three models are very good at image processing. From the results of data processing through the CNN approach, the accuracy value is up to 91%, then the VGG16 approach has an accuracy value of 99%, and finally, through the VGG19 approach, the accuracy is 95%. So it can be ascertained that from the three models of the deep learning approach the best accuracy value is VGG16.
Latino o cirillico? Le proposte di un alfabeto misto nelle visioni di unità jugoslava del periodo interbellico

This paper discusses the context of script choice (Latin and Cyrillic) in the Kingdom of Yugoslavia in the late 1920s and early 1930s, focusing on bialphabetism and biliteracy as official policies of the country. I place the topic in the framework of Latinization in the interwar period and examine three texts by Yugoslav authors that propose a ‘hybrid’ writing system containing the characters of both alphabets as a solution to digraphia. It then explores some reactions to such proposals, including the one of Aleksander Belić. The article is based on the analysis of previously unknown sources found in the Matica Srpska Library in Novi Sad.