Reviewerly/exHarmony huggingface dataset:
Fine-Tuned Self-supervised Speech Representations for Language Diarization in Multilingual Code-Switched Speech

Annotating a multilingual code-switched corpus is a painstaking process requiring specialist linguistic expertise. This is partly due to the large number of language combinations that may appear within and across utterances, which might require several annotators with different linguistic expertise to consider an utterance sequentially. This is time-consuming and costly. It would be useful if the spoken languages in an utterance and the boundaries thereof were known before annotation commences, to allow segments to be assigned to the relevant language experts in parallel. To address this, we investigate the development of a continuous multilingual language diarizer using fine-tuned speech representations extracted from a large pre-trained self-supervised architecture (WavLM). We experiment with a code-switched corpus consisting of five South African languages (isiZulu, isiXhosa, Setswana, Sesotho and English) and show substantial diarization error rate improvements for language families, language groups, and individual languages over baseline systems.
Emerging Appropriate Technologies And Innovations For Higher Education In Malawi

Malawi is currently facing a crisis of annual “Higher Education Enrolment”. Malawi’s tertiary gross enrolment rate is 0.4 percent as compared to 8.2% of Sub-Saharan countries average enrolment. According to the UNDP Human Development Report of 2014, the Malawi enrolment rate of 0.2% is the lowest in Africa in terms of quality and relevance […]
Psychological conditions for the development of the communicative culture of students

The article examines the issues of the development of professional communicative culture among pedagogical university students. The importance of introducing active teaching methods in the learning process is stressed. The goal of the study is to describe and analyze the experience in the development of students’ communicative culture relevant for the system of higher pedagogical education. The contradictions between the growing demands on professional mastery and business communication skills and the insufficient qualification of teachers constitute the basis of the problem of the study: what are the psychological conditions for the development of students’ communicative culture in the process of professional training at university? The authors conduct an empirical study using qualitative and quantitative data analysis methods and emphasize the role of elective courses, programs, and specialized disciplines as a psychological condition for the development of students’ communicative culture.
GAN-generated Fake Face Image Detection using Opponent Color Local Binary Pattern and Deep Learning Technique

Advancements in AI techniques like Generative Adversarial Network (GAN) facilitate the creation of realistic-looking fake face images and these images are used to create fake profiles on various social media platforms. In this work, we develop deep learning-based binary classification models to distinguish GAN-generated fake face images from camera-captured real face images. The classification models are developed by fine-tuning three lightweight state-of-the-art pre-trained Convolutional Neural Networks (CNNs) - GoogLeNet, ResNet-18, and MobileNet-v2 -using the transfer learning approach. In this method, instead of RGB images, joint color texture feature maps of the images obtained using Opponent Color-Local Binary Pattern (OC-LBP) are used as input to the CNN. For the experimental analysis, we use datasets that contain fake face images generated by Progressive Growing GAN (PGGAN) and Style-based GAN (StyleGAN2), and camera-captured real face images from CelebFaces Attributes- High Quality (CelebA-HQ) and Flickr Faces High Quality (FFHQ) datasets. The proposed method shows remarkable performance in terms of test accuracy, generalization capability, and robustness against JPEG compression. Also, the method exhibits excellent performance when compared with state-of-the-art methods.
Attention-Aware Feature Distillation for Object Detection in Decompressed Images

Recently, many object detection methods based on deep learning have achieved remarkable performance. Most of them are mainly trained on high-quality images. Due to the distribution difference between the high-quality training data and the data encountered during deployments, the performance of the detection model decreases rapidly on corrupted images (e.g., the decompressed images). In this paper, we propose an attention-aware feature distillation method to improve the performance of the detector on decompressed images. Conventional methods used $$l_2$$ -norm as distillation loss that treats every region in feature maps equally. However, the features of different regions may have different importance for object detection. Therefore, we propose to learn an attention weight for feature distillation so that important regions in feature maps can be restored better. Extensive experimental results show that our attention-aware feature distillation method can lead to significant improvements in object detection for decompressed images.
Natural Selection Simulator using Machine Learning

Neural Networks and Machine Learning can be used in areas such as video games and simulators. In video games, the behavior of enemy AI can be made less repetitive by using machine learning. In this paper, using Unity Game Engine, a Natural Selection Simulator is created. The agent is given a neural network and acts as an organism whose aim is to collect a food pill that spawns on a platform (a simple plane). The agent themselves are spawned on the edges of the platform, while the food pills are spawned in the middle. A variation of the NEAT Algorithm is used to train the agent’s movement first. The movement includes leaving the edge of the platform, collecting the food pill, and going back to the edge. Once the movement has been trained, a simple genetic algorithm is used to evolve the agent’s speed in order to mimic how the traits of an organism evolve in nature based on its environment.
Design of stable Unknown Input Estimator using Artificial Bee Colony Algorithm

We propose an improved design method for Unknown-Input Estimator (UIE) using Artificial Bee Colony (ABC) Algorithm. The cost function used in the previous method had a problem that the evaluation criterion was indirect. In this paper, we propose a new cost function using the closed-loop transfer function from the disturbance to the control target output.
Mobile Learning Application Applied for Enhancing Employee's Surveillance Prevention and Control of COVID-19 in Electronics Industry

The COVID-19 epidemic has rapidly spread in the electronics industry, resulting in a lack of labor in the production of products for export. Because employees were infected with COVID-19 and had to stop being detained according to government policies. Reasons for the rapid spread are born from employees' not having a proper understanding of the care and protecting themselves from COVID-19. Therefore, it is necessary to develop a training model in accordance with the current situation to increase the understanding of COVID-19 for employees. In this paper, we propose a training style through the application of mobile learning, accompanying the illustrated cartoons, to strengthen the surveillance, prevention, and control behaviors of COVID-19 among employees in the electronics industry. The samples in this study were five hundred and twelve employees in the electronics industry, factory 8. we have developed a mobile learning training model where employees scan QR-CODE and register for training. assessment and measurement of learning achievement before training and after training of employees in the electronics industry, employee behavior observation records and assesses satisfaction with the training style through the application of mobile learning, accompanying the illustrated cartoons, to strengthen the surveillance, prevention, and control behaviors of COVID-19 among employees in the electronics industry. An academic achievement after learning of employees is higher than an academic achievement before training. Statistically significant at the 0.01 level. After training, employees have behaviors of surveillance, prevention, and control of COVID-19 in the electronics industry than before training. Statistically significant at the 0.05 level. The employees who are trained by the application of mobile learning, accompanying the illustrated cartoons, are very satisfied.
Application of NLP Technology in the Information Extraction of Bridge Management and Maintenance Documents

&lt;p&gt;Bridge management and maintenance document is the accumulation of authoritative technical data to record the history of bridge operation, current technical status and management and maintenance process, which contains substantial information to support bridge maintenance decision. With more and more extensive application of bridge health monitoring in civil engineering industry, bridge management and maintenance documents become more common and quantitative. Therefore, a large number of these reports need to be manually read and analyzed to obtain effective information, which will waste a lot of effort. In order to improve this situation, this paper develops a frequency state analyzer using LSTM neural network to classify these documents automatically, and improve the efficiency of bridge management and maintenance work.&lt;/p&gt;
Research on the Network Management System of Students' Art Works based on DB2 Database

DB2 is a distributed database solution from IBM with excellent parallelism, certified to ISO standards at the highest certification level, capable of running on all major platforms, distributing database and data loads across servers, and best suited to massive data processing. The DB2 database is used to store and manage a large number of artworks produced by art-related majors during their studies, to facilitate teachers' review and students' learning from each other, and to transform these works into priority educational resources. This paper analyses the DB2 database product components and carries out the conceptual logic design and logical structure design of the database based on DB2, reducing storage space usage, increasing access speed and laying the foundation for software development.
The Study of English Critical Reading Abilities with Video Sharing on Social Media Platform and Mind Mapping of English Teaching Program Students

English critical reading is an essential ability for higher education in the digital age because university students, especially English Teaching Program students, have to search for large amounts of academic information or research papers on the internet. They need to analyze the information they gain from reading, and they have to analyze the data to organize the events or to compare the data and search for reasons to support the data they get. In addition, the understanding of idioms, grammar, and sentence structure is important for them to learn. Therefore, learning management supporting students to learn anywhere and anytime is the use of video, which is currently available on social media platforms, both on websites and on applications. Videos consisted of some content in terms of multimedia, including visual and audio, that helps students learn better. Furthermore, mind mapping could help readers present the information they get from their reading through critical reading. Mind mapping is a suitable tool for university students to analyze and summarize each topic that is related to each other. Moreover, it could encourage students to analyze reading. The aims of this research were 1) to examine English critical reading ability and 2) to compare the learning achievement of English Teaching Program students before and after using video sharing on social media platforms and mind mapping. The sample of this research was 25 English Teaching Program students in the Faculty of Education, Phranakhon Si Ayutthaya Rajabhat University. The statistics used in the research were Means and Standard Deviation (S.D.). The results of this research found that the abilities in English critical reading were at a good level and their learning achievement was significantly higher than before.
Infighting in the Crypto Bros Family

Satoshi created a mechanism for decentralized exchange that yields a decreasing miner reward structure and prophesized that transaction fees will outswamp mining fees by 2040 or 2044, unless a fork is agreed upon that advances this date. The bitcoin mining industry remains resolute in maintaining the lucrative Proof of Work rewards and hence electrical consumption and environmental degradation nonetheless.
Distributed sensors and neural network driven building earthquake resistance mechanism

&lt;abstract&gt; &lt;p&gt;The anti-seismic support and hanger are firmly connected to the building structure and are anti-seismic support equipment with seismic force as the main load. Real-time and accurate acquisition of the service status of the seismic support and hanger to check and judge whether the seismic support and hanger are in a normal working state is of great significance for practical engineering applications. In this paper, based on distributed sensor technology, a set of intelligent monitoring systems for seismic support and hanger of buildings is established. The sensing equipment installed on the seismic support and hanger senses the signal, and then the data collection, storage and processing are used to accurately judge the seismic support and hanger. Service performance status. To effectively fuse multi-source data in distributed sensor environment, an improved method based on wavelet and neural network data fusion is proposed. Compared with the existing methods, the experimental results show that the proposed method has good robustness. Besides, it has better performance in building seismic multi-source monitoring data fusion and is less affected by the data overlap ratio.&lt;/p&gt; &lt;/abstract&gt;
Natural Language Processing for Scientific Paper Evaluation: Comparing Human and Machine Judgements

A huge number of papers have been published about COVID-19. So much it’s overwhelming. Many papers appear on preprint servers such as arXiv before publication. Researchers and clinicians can get ahead of the curve by making use of these preprint papers, but how to tell what is worth reading? Could there be an automated recommendation mechanism? In this paper we address the question by experimenting with SPECTER document-level vector embedding which establishes the representations by incorporating state-of-the-art Transformer models, such as SciBERT, a BERT variant tailored to scientific text. Meanwhile, the dataset we choose to apply SPECTER embedding is the CORD-19 dataset.
Application of Big Data Information Processing Technology in the Improvement of Learning Self-efficacy in English Teaching

Learning self-efficacy affects students’ learning attitude and learning effectiveness. Therefore, teachers should use big data technology to give students more care and help to effectively improve their English learning autonomy and enhance their confidence in English learning. Based on this, this article attempts to explore how to use big data technology to cultivate students’ learning self-efficacy in English learning.
MANAGERIAL IMPROVEMENT OF EDUCATIONAL PROGRAMS OF HIGHER EDUCATION BASED ON DATA

The article reveals the importance of the processes of digital transformation of universities and the development of digitalization of management decisions based on data. The authors rely on the experience of leading universities of the Russian Federation and the competencies acquired by a team of employees of the Federal State Autonomous Educational Institution of Higher Education “Dostoevsky Omsk State University” in the process of participation in the “Program for the development of anti-crisis leaders and teams of the digital economy CLICK”, implemented as part of the federal project “Personnel for the Digital Economy”. The result of the work of the team within the framework of this acceleration and educational program was the project “Managerial Improvement of Educational Programs of Higher Education Based on Data”, the purpose of which was to improve the quality and speed of decision-making in the field of educational process management in an educational institution.
Python Collections: Dictionaries and Sets

AbstractIn the last chapter you were introduced to the concept of a list. As you saw, a Python list contains an ordered collection of items. In this chapter we will introduce two new data types, which also hold collections of items. The first is called a dictionary, and the second a set.
PROBLEM OF THE FORMATION OF FUTURE PEDAGOGUES’ READINESS FOR THE INNOVATIVE ACTIVITY

The problem of formation of readiness of future teachers for innovative activity is considered on the example of the course “Innovative activity in the subject area “Russian language””. Based on the totality of the described and analyzed author’s interpretations of the concept of “innovation in education”, a research position has been formed regarding innovation as an important element of maintaining education in a state of dynamic development adequate to the requirements of modern society, as a phenomenon of collective or individual professional creative activity of a teacher.The article states that the training of future teachers should be based, first of all, on student-centered teaching methods with a student as the basis of the learning process, while the teacher is a leader who helps and directs the educational activity of the student. One of these methods, which makes it possible to activate the studentʼs mental activity, is the method of problem-based learning.Readiness criteria are formed in the article based on the reviewed relevant studies, which makes it possible to develop training methods for future specialists. The stages of problem-based learning method, which should be applied to improve the effectiveness of education seekers, are considered, as well as the conditions for the successful application of the problem-based learning method to form the professional competence of the future specialist.&#x0D; Keywords: innovations in education; readiness for innovative activity; innovative activity; future educator; learning methodology; problem-based learning.
Multi-path Back-propagation Method for Neural Network Verification

PDF HTML XML Export Cite reminder Multi-path Back-propagation Method for Neural Network Verification DOI: 10.21655/ijsi.1673-7288.00281 Author: Affiliation: Clc Number: Fund Project: Article | Figures | Metrics | Reference | Related | Cited by | Materials | Comments Abstract:Symbolic propagation methods based on linear abstraction play a significant role in neural network verification. This paper proposes the notion of multi-path back-propagation for such methods. Existing methods are viewed as using only a single back-propagation path to calculate the upper and lower bounds of each node in a given neural network, so they are specific instances under the proposed notion. Leveraging multiple back-propagation paths effectively improves the accuracy of this kind of methods. For evaluation, the proposed multi-path back-propagation method is quantitatively compared with the state-of-the-art tool DeepPoly on benchmarks ACAS Xu, MNIST, and CIFAR10. The experiment results show that the proposed method achieves significant accuracy improvement while introducing only a low extra time cost. In addition, the multi-path back-propagation method is compared with the Optimized LiRPA, a tool based on global optimization, on the dataset MNIST. The results show that the proposed method still has an accuracy advantage. Reference Related Cited by
Applying Recurrent Structures to Tabular Data

Chapter 4 demonstrated the application of convolutional neural networks both to images and signals (their “natural” data domain), as well as to tabular data through clever tricks – soft ordering, DeepInsight, and IGTD. This chapter will pursue a similar path of exploration: exploring the application of recurrent networks, traditionally applied to sequences like text and signals, to tabular data.
Modelling for Resilient Navigation via Collaboration

The modelling of observations and errors is the foundation of resilient navigation fusion. The relative observations that can be introduced into collaborative resilient fusion vary with the available relative measurement sensors. Therefore, it is necessary to perform modelling in accordance with the constraint properties of different relative observations of navigation parameters. This chapter discusses the modelling of collaborative observations as well as the corresponding error covariance, presenting approaches based on range measurements, range difference measurements, bearing-only measurements, and vector-of-sight measurements. In these analyses, modelling is carried out for both the hierarchical and parallel navigation structures of an aerial swarm.
Tendencies and challenges from the perspective of personal development

In this article we aim to reflect on current challenges in education and trends in the teaching of school subjects, including personal development. The trends of educational policy in the last decades in the Republic of Moldova have generated development, updating of the national curriculum. Intensifying the focus of the curriculum on the student by diversifying the offers of disciplines: proposing the curricular area „Counseling and personal development” with the study discipline Personal development is one of the list of innovative approaches to the educational process. In this context, it becomes necessary to identify some theoretical premises for students’ learning in the context of personal development. Modern learning theories, which have been the basis for curricular updating in school subjects, offer us some learning models for the formation of tomorrow’s personality and landmarks in the personal development of students. For this purpose, we aimed to reflect on some current trends and challenges in education from the perspective of the student’s personal development.
Pre-Processing Techniques for Movie Review Sentiment Analysis: A Comparative Study for Best Feature Set Determination

Sentiment analysis is considered the process to extract the overall expression, opinions, or feelings from reviews about something such as products, services, or movies. Pre-processing is considered a crucial phase in sentiment analysis for text mining because it allows us to analyze the reviews according to their intended meaning by removing all of the appendages which are the words that do not affect the semantics of sentences. And therefore, the number of features will decrease and thus accuracy will increase. Accordingly, we have decided to evaluate our experiment in identifying the best influencing technique of pre-processing for several features by making a comparison between the features and by combining them together to reach the best result based on the feature number for each pre-processing technique and classification accuracy. this comparison was done by using three algorithms for classification SVM, NB, and DT after applying tools for feature selection and feature extraction with three techniques for tokenization. We concluded that there are some of these techniques that have a negative effect like lemmatization and the part of them is not due to any difference, other, which a little part, have an effect