Reviewerly/exHarmony huggingface dataset:
Management of academic library services in the 21st century digital dispensation

Academic library services have undergone significant change since the development of ICT. Thus, managing academic library service in the digital age requires a strategic approach that incorporates technology, user engagement, and adaptability to meet the evolving needs of students, faculty, and researchers. This study therefore, explores the role of ICT in the modern management of academic library services. This study specifically centers on identifying the challenges of managing academic libraries in digital environment and suggests strategies for effective management of academic libraries in digital era. The desk research approach and brainstorming with professional colleagues were combined to elicit the facts for the study and adopted content analysis of various literatures relating to the subject was used to draw inferences. The empathy of the paper is on how academic libraries can leverage ICT to enhance various services, including lending, circulation, and cataloguing. Additionally, the study highlights the potential of social media platforms to address the technological needs of library users. Various aspects of library services, such as Library Management, e-Libraries, online cataloguing, and social media services, digitization, Online Public Access Catalogue (OPAC) were examined in terms of how ICT has impacted them. Furthermore, the paper discusses the challenges associated with managing library services in the 21st century. To address these challenges, the study suggests incorporating ICT skills into library curricula and increasing funding for ICT infrastructure. Ultimately, the paper emphasized the necessity of embracing and integrating technology to meet the evolving needs of library users.
Classification of Python Language Modules

The Python programming language contains a large number of internal and external modules. In the process of teaching Python or developing a program in that language, it is important to identify the modules available and the areas to which they belong, in other words, to classify the modules into classes. This article deals with the problem of classifying the internal and external modules of the Python programming language. In classifying, the author used a reliable source as well as personal experience and analysis. The article also provides information on the benefits of using DSLs.
A Comparative Analysis of ISLRS Using CNN and ViT

Indian Sign Language Recognition System (ISLRS) aims at recognizing and interpreting the hand gestures and movements in Indian Sign Language (ISL), in order to facilitate smooth communication between the hearing-impaired individuals and the normal population. This research aims at comparing ISLR System using a custom convolutional neural network (CNN) architecture as well as Vision Transformer (ViT). From the ISL alphabet dataset consisting of 36 classes, 26 classes corresponding to the English alphabets are considered in this analysis. The analysis showed that for the dataset, ViT outperforms CNN in terms of performance metrics considered.
Blockchain and digital governance: Decentralization of decision making policy

Abstract Blockchain is gaining popularity as a potential solution for the decentralized management of public information. A number of interesting solutions have been proposed by independent developers around the world to promote blockchain as a promising platform to potentially accelerate democratic processes and achieve certain public values in reforming digital governance. This could be reflected in facilitating direct citizen participation in digital politics, promoting greater civic cooperation and engagement in political discussions, boosting the role of civic commentary and even collaborative decision‐making in e‐government. In this regard, the article critically elaborates on the potential of this technology to promote political dimensions of public sector reforms in a post‐positivist manner, resorting to the analysis of concrete e‐participation cases in a number of public areas where the potential has already been realized and the opinions of professional software developers who are currently engaged in building various blockchain‐driven data management solutions in the e‐government area. This paper identifies, debates, and draws on what benefits and risks public policy can yield from the implementation of blockchain governance.
Enhancing Healthcare Efficacy Through IoT-Edge Fusion: A Novel Approach for Smart Health Monitoring and Diagnosis

This paper presents an innovative framework that leverages cutting-edge technologies to revolutionize healthcare systems, focusing on data security, privacy, and efficient medical diagnosis. Our approach integrates distributed ledger technology (DLT), artificial intelligence (AI), and edge computing to create a robust and dependable medical ecosystem. In our proposed system, patients’ health data is securely managed using a combination of elliptic curve cryptography-based identity-based cryptosystems and edge nodes, ensuring both privacy and integrity. These edge nodes, designed for low-power and short-range communication, play a pivotal role in in-vivo data collection and monitoring within the human body. The DLT model at the core of our framework utilizes peer-to-peer networks, enabling seamless information exchange while eliminating the need for centralized servers. We emphasize public edge DLTs, such as Ethereum, to ensure accessibility and data ownership for all stakeholders. Furthermore, our system incorporates a hybrid machine learning model for early detection and prediction of security threats, enhancing overall system efficiency. Our findings demonstrate a remarkable 99.7% accuracy in classification using this approach. In conclusion, this framework’s multidisciplinary approach bridges the gap between healthcare, edge computing, and DLT, promising real-time data processing, enhanced security, and privacy preservation. With the rise of the Internet of Things, this innovation holds the potential to transform the future of healthcare technology.
Simulation of quantum optics by coherent state decomposition

We introduce a framework for simulating quantum optics by decomposing the system into a finite rank (number of terms) superposition of coherent states. This allows us to define a resource theory, where linear optical operations are “free” (i.e., do not increase the rank), and the simulation complexity for an m -mode system scales quadratically in m , in stark contrast to the Hilbert space dimension. We outline this approach explicitly in the Fock basis, relevant in particular for Boson sampling, where the simulation time (space) complexity for computing output amplitudes, to arbitrary accuracy, scales as O ( m 2 2 n ) [ O ( m 2 n )] for n photons distributed among m modes. We additionally demonstrate that linear optical simulations with the n photons initially in the same mode scales efficiently, as O ( m 2 n ). This paradigm provides a practical notion of “non-classicality,” i.e., the classical resources required for simulation. Moreover, by making connections to the stellar rank formalism, we show this comes from two independent contributions, the number of single-photon additions and the amount of squeezing.
Improved Bald Eagle Search Optimization With Deep Learning-Based Cervical Cancer Detection and Classification

Cervical cancer (CC) is the fourth most popular cancer affecting women worldwide. Mortality and incidence rates can be consistently enhancing, particularly in emerging countries, because of the lack of screening services, lack of awareness, and restricted qualified experts. CC has screened utilizing human papillomavirus (HPV) test, Papanicolaou (Pap) test, histopathology test, and visual inspection after application of acetic acid (VIA). Intra- and Inter-observer variability can take place in the manual analysis method, resulting in misdiagnosis. Previous studies have exploited either deep learning (DL) or machine learning (ML) approaches, the preceding one could not be efficient as it needs segmentation and attaining hand-crafted features that utilize critical stage. Artificial Intelligence (AI) based computer-aided diagnoses (CAD) methods are generally explored for identifying CC for enhancing the standard testing method. This manuscript offers an Improved Bald Eagle Search Optimization with Deep Learning based Cervical Cancer Detection and Classification (IBESODL-CCDC) algorithm. The drive of the IBESODL-CCDC algorithm lies in the automated classification and detection of CC. In the presented IBESODL-CCDC technique, a contrast enhancement process takes place to enhance the image qualities. In addition, the IBESODL-CCDC technique utilizes a modified LeNet model for the feature extraction model. For CC detection, the IBESODL-CCDC technique applies an attention-based long short-term memory (ALSTM) network. A wide-ranging experiment was applied to validate the greater outcome of the IBESODL-CCDC technique. The experimental values highlight the remarkable performance of the IBESODL-CCDC algorithm with other recent systems.
Modeling of the COVID-19 Epidemic in the Russian Regions Based on Deep Learning

The neural network of COVID-19 5 days forecasting in Russian Federation region based on epidemic and social data from 2020 to 2023 is constructed and analyzed. The structure of neural network consists in recurrent and full-connected layers. In addition to training the neural network, its hyperparameters were optimized, such as the optimal number of neurons in each layer, regularization parameters, and optimizer parameters. It is shown that the mean squared error on the test period from 07.2022 to 05.2023 is approximately 5% for new diagnosed of COVID-19 and hospitalized ones in Moscow, Saint Petersburg and Novosibirsk region. The proposed approach makes it possible to refine mathematical models in epidemiology.
Underwater Image Super-Resolution using Generative Adversarial Network-based Model

Single image super-resolution (SISR) models are able to enhance the visual quality of underwater images and contribute to a better understanding of underwater environments. The integration of these models in Autonomous Underwater Vehicles (AUVs) can improve their performance in vision-based tasks. Real-ESRGAN is a powerful model that has shown remarkable performance among SISR models. In this paper, we optimize the Real-ESRGAN model for underwater image super-resolution. To optimize and evaluate the performance of the model, we use the USR-248 dataset. The proposed model generates images that demonstrate a higher level of visual quality than the outputs of the Real-ESRGAN model.
Density Estimation Helps Adversarial Robustness

Adversarial attacks pose a threat to deep learning models, as they involve subtle disturbances that are imperceptible to human vision. In this paper, a classification network is introduced that also includes a density estimation head modeled using the decoder of a variational autoencoder. Incorporating the loss of the variational autoencoder during the training of the classifier aids in achieving a robust latent variable. The experimental findings show that the suggested model successfully defends against various gradient-based adversarial attacks, including FGSM, R-FGSM, MI-FGSM, and PGD, in both scenarios involving white-box and black-box contexts.
Analysis and New Approaches to Solving the Problems of Integration of E-Science and E-Education Based on the Challenges of Industry 4.0

Industry 4.0 relies on the introduction of digital technologies for collecting, processing, and analyzing data, providing useful information for digital decisions. This work studies the problems of integration of e-science and e-education based on Industry 4.0 solutions. It analyzes the current state of affairs, identifies the problems and provides conceptual directions for their solution, thereby creating conditions for developing new, more effective, and innovative methods of management, research, and training. In general, the proposed conceptual approach to the integration of e-education and e-science involves combining technologies, methods and approaches taking into account social, economic, and cultural aspects of the issue.
Application of Deep Learning Technologies in Serious Games

Text classification is one of the important and classic problems. A number of studies have been conducted in this area. As in any field, classification plays an important role in serious games. However, a limited amount of work has been done on Graph Neural Networks. This work deals with the application of Graph Neural Network in serious games. We build a graph network consisting of text and their connections and implement the classification process by applying the Text Graph Neural Network. Our results suggest that GNNs may significantly improve text classification performance in serious games by capturing semantic and syntactic relationships between text units. This gives players a more personalized and engaging gaming experience, and ultimately better learning outcomes.
Development of Security Mechanisms in Cloud Based SCADA Systems

Moving SCADA (Supervisory Control and Data Acquisition) applications used to monitor critical infrastructure to the cloud can reduce costs, rise scalability and make it more beneficial for organizations (users) in terms of technical support. While cost reduction and efficiency gains are key to business, security is one of the most important issues. The article analyzes the threats and vulnerabilities that can interfere with the security of cloud SCADA systems. Several cybersecurity mechanisms have been developed to secure cloud SCADA systems.
Analysis of vibration causes of large aperture wafer under vertical support

The vibration of wafers can significantly impact the accuracy and efficiency of wafer inspection, especially when the wafer is placed vertically. And the larger the wafer size, the thinner the thickness, the more obvious its vibration. To improve the efficiency and accuracy of wafer inspection, and also to make wafer inspection more adaptable to the complex inspection environment and reduce the cost of inspection, this study through a large number of experiments to find out the main factors that lead to vibration of the vertically placed wafers, such as temperature changes, mechanical vibration, environmental disturbance, airflow disturbance, and fluid-solid coupling. The main method is to analyze the vibration law of the wafer through the vibration of the interference fringes produced when the wafer is affected by different disturbing factors during the interferometry process. After effectively controlling the above interference factors in the wafer inspection process, the practical data of wafer surface shape can be measured continuously and stably.
The environmental cost of cryptocurrency: Assessing carbon emissions from bitcoin mining in China

This study estimates the environmental impacts of Bitcoin mining. Employing a top-down measurement approach, this paper assesses the carbon footprint of Bitcoin mining in China from 2017 to 2021. The findings reveal that mining activities during this period contributed to a total of 77.84 million tons of carbon dioxide emissions in China. By utilizing data at the provincial level, we find that the seasonal migration of Bitcoin mining pools will lead to regional power demand shocks in China. Additionally, this study predicts future carbon emissions from Bitcoin mining in China, projecting cumulative carbon dioxide emissions of 76.40 million tons and 722.18 million tons by 2030 and 2060 respectively, in the absence of any policy interventions. Based on these findings, this paper posits that governments worldwide should make efforts to restrict the carbon emissions from Bitcoin mining and opt for environmentally friendly technological methods to fundamentally alleviate Bitcoin's reliance on energy. The implication for central banks is that carbon emission should be taken into consideration when designing the central bank digital currencies (CBDCs).
Enhancing Cryptocurrency Price Forecasting by Integrating Machine Learning with Social Media and Market Data

Since the advent of Bitcoin, the cryptocurrency landscape has seen the emergence of several virtual currencies that have quickly established their presence in the global market. The dynamics of this market, influenced by a multitude of factors that are difficult to predict, pose a challenge to fully comprehend its underlying insights. This paper proposes a methodology for suggesting when it is appropriate to buy or sell cryptocurrencies, in order to maximize profits. Starting from large sets of market and social media data, our methodology combines different statistical, text analytics, and deep learning techniques to support a recommendation trading algorithm. In particular, we exploit additional information such as correlation between social media posts and price fluctuations, causal connection among prices, and the sentiment of social media users regarding cryptocurrencies. Several experiments were carried out on historical data to assess the effectiveness of the trading algorithm, achieving an overall average gain of 194% without transaction fees and 117% when considering fees. In particular, among the different types of cryptocurrencies considered (i.e., high capitalization, solid projects, and meme coins), the trading algorithm has proven to be very effective in predicting the price trends of influential meme coins, yielding considerably higher profits compared to other cryptocurrency types.
Javanese (Indonesia) indigenous education in a children’s literature novel at Vorstenlanden in 1937

Studies on indigenous education have become popular in recent years. In Indonesia, indigenous education studies are carried out to rediscover a distinctive educational identity. This study aims to describe the indigenous education of children in Java during the Dutch colonial period through a study of children’s literary novels in Vorstenlanden in 1937. This is a qualitative study. The novel under study is titled Bocah Mangkunegaran which is a children’s literary novel by a well-known author at that time, namely Jasawidagda. This novel was chosen because of its journalistic style which combines historical facts with fiction. This novel records events in the colonial era from the point of view of indigenous children who are educated with indigenous education. The results of the study show that indigenous education is one of the efforts of the Javanese people to resist colonialism, western education models, and priyayi social class. Indigenous education for Javanese children emphasizes feeling, soul and character, not just logic and reason. Indigenous education covers: 1) Economic education for non-priyayi people; 2) Character education and schooling for Javanese children; 3) Education on Javanese religiosity (kejawen), mythology, and puppets; 4) Regional education; and 5) Scouting education (padvinder).
How Behavioral, Photographic, and Interactional Realism Influence the Sense of Co-Presence in VR. An Investigation with Psychophysiological Measurement

Feeling of co-presence in VR depends on the realism of virtual agents. Our study explores how three dimensions of realism—visual appearance, behavior, and interactability—affect co-presence and Orienting Response (OR), measured using heart rate (HR) and skin conductance response (SCR). Moreover, we test whether HR and SCR can be used as measures of psychological concepts that describe virtual interactions like co-presence. Fourty-five participants passively viewed virtual characters while their HR and SCR were recorded. Afterwards participants assessed the experience of interacting with the virtual agents. The interactability of the virtual characters increased co-presence, and so did heightened appearance realism, but only when the level of behavioral realism was high. High visual and behavioral realism led to increase in SCR while visual realism alone evoked deeper HR deceleration. Nonetheless, neither SCR nor HR correlated with any psychological concepts that describe virtual interactions. In conclusion, realism can increase both the co-presence and magnitude of the OR, yet physiological indices can not reliably gauge the experience of interactions with virtual characters.
Interaction Design and Research for User Interfaces of Central Bank Digital Currency: An Empirical Study

There is already a consensus among central banks to accelerate the development of Central Bank Digital Currencies (CBDC). To facilitate a smooth money transfer process, the interactive experience of the CBDC user interface design is very important. However, much of the current research has focused on the technical feasibility of the CBDCs, and no scientific research has yet attempted to design an effective CBDC user interface. In this study, a series of CBDC user interfaces were designed to help study the interaction process of CBDCs in a simulated payment transaction context with a focus on the user interface and interactive experience of the CBDCs. The experiment in this study was a 2 (graphical metaphor) × 3 (gesture type) × 2 (visual feedback) mixed factorial design in which a total of 12 CBDC prototypes were created and evaluated based on users' task performance and a series of subjective evaluations. The results of our empirical study revealed that graphical metaphor user interface design can improve the interface usability and subjective evaluation of CBDCs, and may also trigger the user's sense of experience with traditional banknotes. Specifically, the use of animated cues for currency movement on the CBDC user interface may help evoke the user's experience of using cash. In addition, we found that the wave type gesture may cause users to spend more time completing the CBDC operational tasks. Moreover, the animated cue of money moving may have faster performance and a higher degree of rationality. The above-mentioned results may provide an important design reference for CBDC developers to create easy-to-use CBDC services.
Cybersecurity Challenges and Solutions in the Fintech Mobile App Ecosystem

The rapid growth of the fintech industry, driven by the proliferation of mobile applications, has revolutionized financial services, providing unprecedented convenience to users. However, this innovation comes with inherent cybersecurity challenges that demand rigorous attention. This study delves into the complex and ever-evolving landscape of cybersecurity within the fintech mobile app ecosystem, aiming to identify challenges and present viable solutions. Cybersecurity threats in the fintech mobile app ecosystem encompass a broad spectrum, including data breaches, malware attacks, phishing schemes, and identity theft. As fintech apps handle sensitive financial data and transactions, they are prime targets for malicious actors seeking financial gain. To address these threats, this research examines current cybersecurity strategies and emerging technologies, such as advanced encryption, biometric authentication, and AI-driven anomaly detection. Furthermore, regulatory frameworks and industry standards play a crucial role in shaping cybersecurity practices within fintech. This study assesses the impact of compliance requirements on fintech companies and their ability to protect user data. Real-world case studies and incident analyses provide valuable insights into the consequences of cybersecurity breaches in this sector. Ultimately, this research aims to contribute to a comprehensive understanding of the multifaceted cybersecurity challenges faced by the fintech mobile app ecosystem and offers practical recommendations for fintech firms, regulators, and cybersecurity professionals to enhance security measures. Strengthening the security foundation is paramount to sustaining user trust, fostering continued innovation, and securing the future of mobile fintech.