Reviewerly/exHarmony huggingface dataset:
On training spiking neural networks by means of a novel quantum inspired machine learning method

In spite of the high potential shown by spiking neural networks (e.g., temporal patterns), training them remains an open and complex problem [1]. In practice, while in theory these networks are computationally as powerful as mainstream artificial neural networks [2], they have not reached the same accuracy levels yet. The major reason for such situation seems to be represented by the lack of adequate training algorithms for deep spiking neural networks, since spike signals are not differentiable, i.e. no direct way to compute a gradient is provided. Recently a novel training method, based on the (digital) simulation of certain quantum systems, has been suggested. It has already shown interesting advantages, among which the fact that no gradient is required to be computed. In this work, we apply this approach to the problem of training spiking neural networks and we show that this recent training method is capable of training deep and complex spiking neural networks on the MNIST data set.
Deep Learning Approaches for English-Marathi Code-Switched Detection

During a conversation, speakers in multilingual societies frequently switch between two or more spoken languages. A linguistic action known as "code-switching" particularly alters or merges two or more languages. The development of software or tools for detecting code-switching has received very little attention. This paper proposes a Deep Learning based methods for detecting code-switched English-Marathi data. These suggested methods can be applied to various applications, including phone call merging, Intelligent AI assistants, Intelligent travelling systems to assist travellers in navigation and reservations, call centres to handle customer service issues, etc. To create a system for code switch detection, our study demonstrates a detailed analysis of extracting several audio features such as the Mel-Spectrogram, Mel-frequency Cepstral Coefficient (MFCC), and Perceptual Linear Predictive coefficients (PLP). Our team's English-Marathi code-switched dataset served as the testing ground for our methodologies. Our model's accuracy was 92.99%, with 40 MFCC coefficients having energy coefficient serving as the zeroth coefficient.
Overview of Face Recognition Methodologies: A Literature Review

Computer vision and pattern recognition technologies have made a significant advancement in the recent past. A key application of this is face recognition. Its applications resonate in many fields. Various methodologies have been developed for this, and it is essential to compare, contrast and review them on the basis of various factors and aspects to find the optimal methodology. The journey of face recognition technology has progressed rapidly from traditional methods to deep learning applications. Yet, it is necessary to survey all these methods and critically and comprehensively evaluate them, as a majority of them are still limited to variation constraints such as lighting, expressions and orientation and other practical complexities. The objective of this paper is to examine, evaluate and illustrate the strengths and limitations of a range of papers by delving into various distinct yet comparable specifics of each methodology of implementing face recognition systems. The motive is to also culminate the essence of these methodologies to facilitate an insightful perspective and overview of the methodologies in a concise manner.
Exploring the Effectiveness of Mobile Learning Technologies in Enhancing Student Engagement and Learning Outcomes

This study aimed to investigate the impact of mobile learning technologies on student engagement and learning outcomes in higher education. A pre-test/post-test control group design was employed to compare the engagement and learning outcomes of students who used a mobile learning platform with those who did not. The study was conducted with undergraduate students at a large public university. Results showed that the intervention group reported a more positive experience with the platform, used it more frequently, found it more useful, and were more satisfied with it than the control group. Additionally, the intervention group outperformed the control group in terms of both course grades and standardized test scores. The findings suggest that mobile learning technologies can positively impact student engagement and learning outcomes in higher education. Future research could explore ways to optimize the platform further and investigate the scalability and sustainability of implementing mobile learning technologies in higher education.
The Significance of Applying Logical Knowledge in Addressing Viral Culture on Social Media

The rapid advancement of information technology has revolutionized human interactions through the emergence of various social media platforms like Facebook, Twitter, Instagram, TikTok, and others. Although initially intended for convenience and benefit, social media has now become associated with negative cultural trends in society. The enthusiastic adoption of social media has led to the rapid spread of viral culture, resulting in the dissemination of fake information, defamation, and uncontrolled outbursts on these platforms. The rapid propagation of viral culture highlights the need for a deep understanding of logical knowledge, which plays a crucial role in shaping human thought processes. Consequently, this study aims to examine the significance of logical knowledge in today's society, which faces the overwhelming inundation of viral issues on social media. Employing qualitative research methods, including documentary data collection and content analysis, the study concludes that logical knowledge holds great importance in addressing viral culture on social media. Additionally, it provides a systematic guide to human reasoning, aids in discerning between truth and falsehood, and cultivates critical thinking skills. These findings hold particular relevance in fostering a Madani society in Malaysia
Analysis of the Sentiment in the Evaluation Texts of University Students by Means of the Concept of Flexible Management

With the development of information technology (IT) and the popularity of the Internet, it is easier to obtain college students’ teaching evaluation text data. However, it is still challenging to deeply understand and effectively analyze these texts. Based on the flexible management concept in universities, this study aimed to understand and analyze the teaching evaluation texts of college students more accurately, thereby improving the teaching quality. The implicit features of those texts were recognized first in this study, which aimed to supplement and improve the implicit features neglected by existing sentiment analysis methods. A sentiment analysis method based on Bidirectional Encoder Representations from Transformers (BERT) was adopted to explore the deep semantic information of texts. It was done by using deep learning technology and to improve the accuracy of sentiment analysis. This in turn provides more valuable reference information for university teaching management and further promotes the practical application of the flexible management concept in university teaching management.
About the Role of Digital Technologies for the Personality-oriented Self-development of Students in the Field of Modern Education

The purpose of this research is to get the views of students on the development of personality-oriented personal development with digital technologies in the field of modern education. The phenomenology design, which is suitable for the nature of the research, was used among the qualitative research designs. The study group for the research consists of 33 university students studying at various universities in Kazakhstan. A semi-structured interview form developed by the researchers was used as a data collection tool in the study. As a result of the research, the majority of the students stated that they did not receive training with digital technologies related to their personality-oriented personal development. On the other hand, the majority of the students stated that they were willing to receive training with digital technologies related to their personality-oriented personal development. The students participating in the research offered suggestions for the content, form, and duration of the training programs related to the development of their personality-oriented personal development with digital technologies. Suggestions for the content of the training include training in digital technologies for personal development, communication skills, time management training, effective working techniques training, stress management training, speed reading memory techniques training, art training, and innovation and creativity training.
Analysis and Advice on Employment Forecasts for Graduates Taking into Account the Characteristics of Employment Mobility

In the context of accelerating globalization, the emerging knowledge economy and rapid technological development have brought unprecedented opportunities and challenges to the employment choices of graduates. The employment mobility of graduates has been constantly increasing, and their employment decisions are no longer limited to majors or regions. Instead, graduates can consider various factors more comprehensively, including industrial and urban development, compensation and benefits, etc. This study attempted to understand the characteristics of graduate employment mobility from a more macro perspective, by deeply analyzing the spatial correlation measure and the city-industry integration effect. Then, detailed research and prediction were conducted on the mobility characteristics based on the dynamic decomposition prediction mechanism, which provided not only a more scientific and accurate theoretical basis for graduate employment prediction but also scientific decision-making references for policy makers and educational institutions when formulating relevant policies and educational training plans. The research results contribute to promoting close city-industry integration and have important reference value for promoting urban economic development and optimizing urban industrial structure.
Research on the Construction of Industrial Catalysis Course Under the Background of Specialty-Innovation Integration

“Specialty-innovation integration” is a positive response of higher education to economic and social development needs, and it is also an inherent requirement for the reform and development of higher education. This paper discusses the construction of an “industrial catalysis” curriculum system under the background of specialty-innovation integration. Overall, it raises students’ comprehensive quality from three aspects, including teaching content, teaching method, and the concept of specialty-innovation integration, aiming to cultivate students as the innovative talents required by the development of the era for national and regional development.
Traffic Classification of Software-Defined Networks Using Machine Learning

The classification of network traffic has been a primary topic in the growing field of networking engineering. It is a foremost task for the Internet Service Providers (ISPs) to have knowledge about the types of network applications that are flowing into a network. The network operators or the ISPs need to manage the overall performance of a network. To accomplish these requirements, a learning-based traffic classification is proposed in this paper. Two machine learning (ML) algorithms Logistic Regression (supervised) and K-means clustering (unsupervised) were used to classify DNS, Telnet, Ping and voice traffic flows with Distributed Internet Traffic Generator (D-ITG). The accuracy is 78.89% indicating that the Logistic Regression models perform better when compared to K-means clustering.
Devanagari Handwritten Character Recognition Using Deep Learning and Neural Network

Manually processing handwritten documents is a very difficult task due to different people having different handwriting styles and a lack of currently present language dictionaries to interpret documents. There are millions of people who are disconnected from fundamental technologies due to the language gap. The majority of people don’t know English. Recognizing character becomes more important for automation in various fields. The paper proposes a methodology for the recognition of handwritten English and Hindi characters. In this paper, for character recognition, we apply deep learning technology with convolutional neural network (CNN).
Antecedents and Consequences of ICT Adoption in MSME

The Fourth Industrial Revolution stimulated by digital transformation hasimproved the way we live in this world, do our jobs and communicate with each other. The businesses have started to use ICT extensively to improve operations.In fact,Van et al. (2003) findings revealed that investments in technologywere a significant factor in United States better productivity growth than that of Europe in late 1990s.Thus, it becomes imperative for developing nations to devoteresources and adopt ICT in order to increase productivity and beat competition. The present research is an attempt to understand the factors that determine the adoption of ICT by MSMEs that enable them to perform better inemerging economies like India. The present study uses Technology Organisation Environment framework to elicit factors that determine ICT adoption intentions in MSMEs. The results reveal the relative advantage, compatibility of technology and management support have a strong impact on ICT adoption intention of MSMEs. Also complexity of technology has a significant inverse (negative) impact on ICT adoption intention while as decentralised decision making has no impact on ICT adoption intention. Lastly the firms that have high ICT adoption intention tend to have better firm performance.
Physics-Driven Deep Learning for Computer Aided Diagnostics Tasks in Medical Imaging

Deep Neutral Networks (DNNs) were initially proposed towards the midst of the 20th century, motivated by the neural structure and mechanism of the human brain. Thanks to major ad-vancements in computational resources, during the past decade DNN-based systems have demonstrated unprecedented performance in terms of accuracy and speed. However, recent work has shown that such models may not be sufficiently robust during the inference process. Furthermore, due to the data-driven learning nature of DNNs, designing interpretable and gen-eralizable networks is a major challenge, especially in critical applications, such as medical Computer Aided Diagnostics (CAD) and other medical imaging tasks, including classification, regression and reconstruction. Within this context, a line of physics-driven approaches for deep learning has recently emerged, aimed at improving the stability and generalization capacity of DNNs for medical imaging applications. In this paper, we review recent work focused on phys-ics-driven or prior-information learning for a variety of imaging modalities and medical applica-tions. We discuss how the inclusion of domain-related knowledge into the learning process and networks’ design supports their stability and generalization capability. In addition, we highlight current and future challenges within this scope.
FORMATION AND DEVELOPMENT OF THE PROFESSIONAL THINKING IN FUTURE GRAPHIC DESIGNERS WITH THE USE OF TIPS/TRIZ METHODS

Purpose: the present article is highlighting the experience of the Theory of Inventive Problems Solving use for the formation and development of professional thinking in students studying graphic design at the “Ion Creanga” State Pedagogical University of Chisinau. &#x0D; Меthodology. The research used the methods of contradictions solving TIPS/TRIZ, as well as the semiotic, hermeneutic, systemic, structural, morphological and interdisciplinary approaches. The authors also used the the Theory of critical thinking (Ch. Temple, D. Steel, K. Meredith) and the Project method. &#x0D; Results. The article describes the experience and results of the implementation of the curriculum "Fundamentals of artistic literacy using TRIZ methods" for students of graphic design. The methods of teaching the basics of artistic literacy through the prism of TRIZ methods are analyzed and systematized, stimulating the development of critical and creative thinking of future graphic designers. The application of TRIZ methods had a positive impact on the process of formation and development of students' professional artistic thinking.&#x0D; Scientific novelty. The scientific novelty consists in the fact that the authors used the TRIZ methods structure for the study and understanding of the modern visual language for the creation of design objects, and afterwards for the formation and development of the professional artistic thinking of the graphic design students. &#x0D; Practical significance. The practical significance of the research consists in the elaboration of the author course “The Fundamentals of the Artistic Literacy with the Use of TRIZ Methods” for the graphic design students from the “Ion Creanga” State Pedagogical University of Chisinau.
Adapt the Parameters of RBF Networks Using Grammatical Evolution

RBF networks are used in a variety of real-world applications such as medical data or signal processing problems. The success of these parametric models lies in the successful adaptation of their parameters using efficient computational techniques. In the current work, a method of adjusting the parameters of these networks using Grammatical Evolution is presented. Grammatical Evolution will be used to successfully discover the most promising range of parameter values and then the training of the parameter set will be achieved using a Genetic Algorithm. The new method was applied to a wide range of data fitting and classification problems, and the results were more than promising.
Improving Autonomous Vehicle Reasoning with Non-Monotonic&#x0D; Logic: Advancing Safety and Performance in Complex Environments

The software design of autonomous vehicles (AVs) incorporates artificial intelligence (AI) characteristics to enhance their safety and overall driving performance. Central to vehicle’s operation is the ability to reason effectively in complex and uncertain environments. However, traditional logical systems, such as monotonic logic, often struggle to handle the inherent uncertainties and exceptions encountered in real-world scenarios. This paper proposes the utilization of non-monotonic logic in order to enhance the reasoning capabilities of autonomous vehicles. By incorporating non-monotonic reasoning, vehicles can navigate intricate traffic scenarios, make plausible inferences, and adapt their decisions when faced with conflicting information. This research aims to provide a comprehensive review of non-monotonic logic&amp;#039;s application in autonomous vehicles, highlighting its advantages over traditional logical systems and its potential impact on safety and performance. Additionally, through this research, we seek to contribute to the advancement of autonomous driving technology by enhancing the reasoning capabilities of vehicles in various scenarios, such as car- following related to critical safety events. The personalized cognitive agent is proposed in driving behavior to consider particularly in their assumptions of homogeneous drivers. The personalized cognitive agent is incorporating heterogeneous driving behaviors, based on individual user preferences, characteristics, and needs. Driving behavior is a complex interplay of various factors, encompassing both human and external elements. Human factors, including age, experience, and gender, contribute significantly to how individuals navigate the roads. These factors influence decisions, reactions, and risk-taking tendencies on the part of drivers. Additionally, external factors such as weather conditions further compound this intricate dynamic, requiring drivers to adapt their behavior to the prevailing environment. The goal of a personalized cognitive agent is to provide tailored and customized experiences to cognitive vehicles, taking into account the unique requirements and individual preferences of occupants inside autonomous vehicles.
"Improvement of management mechanisms in monitoring the quality of personnel training"

This article discusses the monitoring of the quality of education in higher educational institutions, improving the effectiveness of teaching and the quality of education, as well as the mechanisms of education management.
Small group dynamics in problem-based learning

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Determination of Packet Data Transmission Parameters when Working in the Meter Range

The article considers the possibility of organizing packet TCP/IP exchange in the channel of the frequency range from 30 to 300 MHz, as a basis for implementing a secure VPN connection between two subscribers within one of the communication network modes. The VPN connection, when considered, is a TCP/IP packet transmission, the only difference is that a new special header is attached to the original packet, as well as to the cryptographic encryption of the original packet. Hence, VPN tunnel can be represented as a TCP/IP connection. TCP/IP protocol stacks are oriented to support high-speed interfaces operating at high frequencies (for example, Ethernet/Wi-Fi, speeds from 100 Gb/s, 2.4 GHz frequency). The main problem of such a packet implementation at relatively low frequencies (30-300 MHz) is the sufficient instability of mobile MV radio communication (communication in the meter wave range) at a given frequency range and low transmission rate (up to 32 Kb/s), as a result of which there may be a high probability of data bit errors, loss of information packets and huge time delays in the exchange. The Reno model is used as a model for evaluating packet transmission [rec. ITU-T Y.1541, RFC2001] and such regulated indicators [rec. ITU-T 1.1540, ITU-T Y.1541], as: channel bandwidth, signal-to-noise ratio (SNR), transmission delay (IPTD), transmission delay variation (IPDV), transmission error rate (IPER), packet loss rate (IPLR, SER), maximum packet transport unit (MTU). The simulation of a low-speed communication line with the characteristics: length up to 5 km, transmission speed from 300 bits/s to 32,000 bits/s (V.21 - V.34bis modem protocols), the sizes of transmitted packets is 72, 576 and 1500 bytes (threshold values of the maximum unit of packet transportation), transmitter power 2 W, the signal-code construction is a 4-FSK signal with Reed-Muller encoding RM(30, 14). Based on the analysis of the data obtained, three categories of channels were identified: 'bad' - low signal-to-noise ratio (2-5 dB), high packet loss (above 10%) and long transmission delay (above 400 ms); 'medium' - SNR from 5 to 10 dB, packet loss less than 1%, the delay varies from 400 to 800 ms; 'good' is a channel passing through all regulated TCP/IP parameters, the OSH is more than 10 dB, the error probability is less than 0.1%, the delay fits into the regulated 400 ms.