Reviewerly/exHarmony huggingface dataset:
Preferences towards artificial intelligence in Ecuadorian university professors

The general objective of this research was to analyze the preferences towards Artificial Intelligence (AI) in university professors in Ecuador, particularly those who teach the subject of Microeconomics. The methodology used was quantitative, descriptive, field-typed, and non-experimental, based on documentary research. The population consisted of 25 teachers from three state universities in the city of Guayaquil, Ecuador. The data collection technique was the survey and the instrument was a questionnaire of closed questions, which was applied through the Google Forms platform. The results found indicate that a large majority know about the use of AI in higher education; they also consider that the benefits that AI brings to education are to improve teaching, a large percentage indicated not to use AIs to teach Microeconomics, a small number of teachers who use AI, do it for data analysis through Machine Learning, the fact of the little use of this technology among teachers is mainly due to the absence of university policies on the use of artificial intelligence. Among the conclusions, the formulation of educational policies oriented to the implementation of artificial intelligence in Ecuadorian universities, the adaptation of infrastructure, and the investment of resources for this purpose are considered essential, as well as the training of teaching staff to take full advantage of this technology that is already a reality in many national and international contexts.
ICT LITERACY OF PRE-SERVICE SPECIAL EDUCATION TEACHERS IN OPEN DISTANCE LEARNING (ODL) MODE

This paper aims to investigate the ICT literacy of pre-service special education teachers in ODL mode educator and total of 247 out of 370 ODL B.Ed. Special Education trainees of the Tamil Nadu Open University, India have participated in the study. Data was collected through online survey. Descriptive statistical analysis such as percentage analysis, Mean, Median, Standard deviation, T-test are used for data analysis. The study reveals that around fty percent of the sample has average level on their Information and communication technology literacy. On the other hand, it is observed that the low and high levels are equally shared the remaining fty percent of the sample. This could be interpreted that nearly one fourth of the trainees will have difculty in utilizing even the basic ICT tools including e-resources in their classrooms. It is imperative to provide periodical ICT trainings to ODL special education teacher trainees for better results in their classroom engagement with ICT tools to cater to the needs of diverse learners.
Quantum Theory in Finite Dimension Cannot Explain Every General Process with Finite Memory

Abstract Arguably, the largest class of stochastic processes generated by means of a finite memory consists of those that are sequences of observations produced by sequential measurements in a suitable generalized probabilistic theory (GPT). These are constructed from a finite-dimensional memory evolving under a set of possible linear maps, and with probabilities of outcomes determined by linear functions of the memory state. Examples of such models are given by classical hidden Markov processes, where the memory state is a probability distribution, and at each step it evolves according to a non-negative matrix, and hidden quantum Markov processes, where the memory is a finite-dimensional quantum system, and at each step it evolves according to a completely positive map. Here we show that the set of processes admitting a finite-dimensional explanation do not need to be explainable in terms of either classical probability or quantum mechanics. To wit, we exhibit families of processes that have a finite-dimensional explanation, defined manifestly by the dynamics of an explicitly given GPT, but that do not admit a quantum, and therefore not even classical, explanation in finite dimension. Furthermore, we present a family of quantum processes on qubits and qutrits that do not admit a classical finite-dimensional realization, which includes examples introduced earlier by Fox, Rubin, Dharmadikari and Nadkarni as functions of infinite-dimensional Markov chains, and lower bound the size of the memory of a classical model realizing a noisy version of the qubit processes.
Reviewing advancements in privacy-enhancing technologies for big data analytics in an era of increased surveillance

In the contemporary landscape of big data analytics, privacy concerns loom large, exacerbated by escalating surveillance measures. This review delves into the advancements of privacy-enhancing technologies (PETs) amidst this era of heightened scrutiny. The review explores the evolving landscape of PETs, highlighting their pivotal role in safeguarding individual privacy while enabling meaningful data analysis. Firstly, the review elucidates the escalating surveillance environment, characterized by ubiquitous data collection practices and the proliferation of sophisticated monitoring mechanisms. Against this backdrop, the imperative for robust privacy solutions becomes evident. Subsequently, the review navigates through the array of PETs, encompassing differential privacy, homomorphic encryption, secure multi-party computation, and federated learning, among others. Each technology is scrutinized for its efficacy in mitigating privacy risks without compromising analytical utility. Furthermore, the review delineates notable applications of PETs across diverse domains, including healthcare, finance, and social media. Case studies exemplify how PETs facilitate data sharing and collaborative analysis while preserving confidentiality and compliance with regulatory frameworks. Moreover, the review examines the challenges hindering the widespread adoption of PETs, such as computational overhead, interoperability issues, and regulatory ambiguities. Strategies for overcoming these hurdles are elucidated, encompassing advancements in algorithmic efficiency, standardization efforts, and policy advocacy. The review underscores the pivotal role of PETs in reconciling the imperatives of data analytics with the imperatives of privacy protection amidst escalating surveillance. It emphasizes the necessity for interdisciplinary collaboration among researchers, policymakers, and industry stakeholders to foster the development and deployment of effective PET solutions, thereby ensuring a harmonious balance between data utility and individual privacy rights in the digital age.
Credit Card Fraud Detection using Machine Learning (Logistic Regression)

Abstract: Credit Card Fraud Detection (CCFD) is a crucial application in machine learning. It aims for safeguarding financial transaction. Identifying fraud transaction and real transactions and telling that is it fraud or real. Traditional methods for credit card fraud detection rely on basic statistical techniques .credit card remains a significant challenge in the financial industry .It rely on machine learning algorithms .Fraud detection very useful for financial institution and consumers worldwide .particularly logistic regression, has emerged as a potent tool in identifying fraudulent transactions. It will consider the before amount ,after amount ,time, pin numbers based on that it will detect fraud transactions .This paper explores the application of logistic regression in credit card fraud detection , focusing on its implementation ,challenges and effectiveness .This project works for finding fraud transactions. Logistic Regression ,have shown promising results in detecting fraudulent transactions.
Quantum Computing

Abstract: The revolutionary technology known as quantum computing has the potential to completely transform several industries, including material research, medicine development, optimization, and cryptography. Quantum computers use the ideas of quantum mechanics to harness the power of quantum bits, or qubits, in contrast to classical computers, which function using binary digits, or bits. These qubits, which exist in a superposition of states, allow quantum computers to operate at a computational rate never before possible, potentially tackling challenging issues that are beyond the capabilities of classical systems
Breast Cancer Detection Using Artificial Intelligence

Abstract: Breast cancer is one of the most common diseases faced by women and causes more deaths every day. It is considered one of the greatest diseases in medical history. Automated diagnostic systems not only assist diagnosticians but also provide reliable, efficient and fast services that reduce the likelihood of death. This paper proposes a model to manually classify breast cancer samples into benign and malignant subcategories using multiple algorithms to predict interpretation. It is generally considered as all data containing a set of 30 characteristics related to the nuclei found in digitized images of fine needle aspiration (FNA) of the breast. All acquired features are visualized and understood to gain insight for future diagnoses. Breast cancer prediction uses support vector distribution (SVC) in its ability to identify new, unrecognized data based on a set of features.
On the Analysis of Mobile Apps. Why is most of our Research on Android?

Over the past decade, my research has been dedicated to the analysis of mobile applications, with a primary focus on the Android platform. As we delve into the intricacies of mobile app quality and trustworthiness, it becomes imperative to question the prevailing bias in our research toward the Android platform. This keynote talk aims to shed light on the reasons behind this predominant focus and provide insights into the shift of my research paradigm towards the iOS platform.
Authorship attribution, idiolectal style, and online identity

Assistant ProfessorDepartment of English LanguageCollege of Language SciencesKing Saud UniversityRiyadh 11495 Kingdom of Saudi Arabia Awarding Institution: University of Leeds, UKDate of Award: 14 October 2022
Feasibility and Stability Analysis for Basic Measles Model Using Fuzzy Parameter

In this article, we investigate the behaviors of the measles viral pandemic using fuzzy susceptible-infectiousrecovered (SIR) model. To examine the effects of various compartment phases, we analyze disease-free equilibrium points along with basic reproduction number. The measles model is stated to be globally asymptotically stable at the disease-free equilibrium point. In order to mathematically simulate the measles, we use a first-order nonlinear differential equation. The numerical solution is computed using the Runge-Kutta method, and the model’s feasibility is also covered.
Modeling of Sports Training Simulation Based on Energy Harvesting in Wireless Sensor Networks

For patients with limb motor dysfunction, the effect of physical exercise is directly related to their future quality of life. This article combines the physical training plan of rehabilitation therapists with the training of rehabilitation robots, which can effectively improve the training performance of existing lower limb rehabilitation robots. Therefore, a teaching and training method and a wireless data acquisition system based on energy acquisition wireless network sensor are proposed. Based on wireless wearable technology, wireless network sensors, PCs and electronic devices are used to monitor the activity information of human walking and standing in real time, and the physical fitness is tested by means of mean, variance, and standard deviation. Through the analysis of rehabilitation health, this article consists of two parts: power module and physical exercise. Finally, experiments show that the accuracy of wireless network sensors based on SVM algorithm is the most accurate under physical training. It provides a good means for wireless body area network technology.
APRE: Annotation-Aware Prompt-Tuning for Relation Extraction

Abstract Prompt-tuning has been successfully applied to support classification tasks in natural language processing and has achieved promising performance. The main characteristic of prompt-tuning based classification is to verbalize class labels and predict masked tokens like a cloze-like task. It has the advantage to make use of knowledge in pre-trained language models (PLMs). Because prompt templates are manually designed, they are more prone to overfitting. Furthermore, traditional prompt templates are appended in the tail of an original sentence. They are far from some semantic units in a sentence. It is weak to decode semantic information of an input relevant to PLMs. To aggregate more semantic information from PLMs for masked token prediction, we propose an annotation-aware prompt-tuning model for relation extraction. In our method, entity type representations are used as entity annotations. They are implanted near the site of entities in a sentence for decoding semantic information of PLMs. It is effective to make full use of knowledge in PLMs for relation extraction. In the experiment section, our method is validated on the Chinese literature text and SemEval 2010 Task datasets and achieves 89.3% and 90.6% in terms of F1-score, respectively. It achieves the state-of-the-art performance on two public datasets. The result further demonstrates the effectiveness of our model to decode semantic information in PLMs.
The Big Five Factors of Personality Traits and Leadership Practices of Academic Department Chairs: A Predictive Study

This study investigated whether the big five factors of personality traits can predict academic department chairs’ leadership practices. The study had a predictive research design; the data were collected from 424 participants in the 2023 academic year, and the instruments of this study were two questionnaires. The study found that the big five factors of personality traits predict the leadership practices of academic department chairs. Two factors of personality traits, conscientiousness and openness to experience, were statistically significant and predicted the practices in modelling leadership. Four factors (agreeableness, conscientiousness, openness to experience, and extroversion) were statistically significant and predicted leadership practices in inspiring a shared vision. Three factors (neuroticism, extroversion, and conscientiousness) were statistically significant and predicted leadership practices in challenging processes. Two factors (conscientiousness and openness to experience) were statistically significant and predicted leadership practices enabling others to act. Three factors (conscientiousness, agreeableness, and openness to experience) were statistically significant and predicted leadership practices encouraging the heart. The study recommended that academic leaders be required to consider personality traits as an important dimension in selecting and assigning academic department chairs and other academic leaders at all levels at higher education institutions.
On Optimizing Ensemble Models using Column Generation

Abstract In recent years, an interest appeared in integrating various optimization algorithms in machine learning. We study the potential of ensemble learning in classification tasks and how to efficiently decompose the underlying optimization problem. Ensemble learning has become popular for machine learning applications and it is particularly interesting from an optimization perspective due to its resemblance to column generation. The challenge for learning is not only to obtain a good fit for the training data set, but also good generalization, such that the classifier is generally applicable. Deep networks have the drawback that they require a lot of computational effort to get to an accurate classification. Ensemble learning can combine various weak learners, which individually require less computational time. We consider binary classification problems studying a three-phase algorithm. After initializing a set of base learners refined by a bootstrapping approach, base learners are generated using the solution of an linear programming (LP) master problem and then solving a machine learning sub-problem regarding a reduced data set, which can be viewed as a so-called pricing problem. We theoretically show that the algorithm computes an optimal ensemble model in the convex hull of a given model space. The implementation of the algorithm is part of an ensemble learning framework called decolearn . Numerical experiments with CIFAR-10 data set show that the base learners are diverse and that both the training and generalization error are reduced after several iterations.
Application of the QDST algorithm for the Schrödinger particle simulation in the infinite potential well

Abstract This paper examines whether a quantum computer can efficiently simulate the time evolution of the Schrödinger particle in a one-dimensional infinite potential well. In order to solve the Schrödinger equation in the quantum register, an algorithm based on the Quantum Discrete Sine Transform (QDST) is applied. The paper compares the results obtained in this way with the results given by the previous method (based on the QFT algorithm).
Genealogical Data-Driven Visits of Historical Cemeteries

This paper describes the Integration of Archives and Cultural Places (IaCuP) project, which aims to integrate information about a historical cemetery, including its map and grave inventory, with genealogical and documentary knowledge extracted from relevant historical archives. The integrated data are accessible to cemetery visitors through an interactive mobile application, enabling them to navigate a graphical representation of the cemetery while exploring comprehensive visualizations of genealogical data. The basic idea stems from the desire to provide people with access to the rich context of cultural sites, which have often lost their original references over the centuries, making it challenging for individuals today to interpret the meanings embedded within them. The proposed approach leverages large language models (LLMs) to extract information from relevant documents and Web technologies to represent such information as interactive visualizations. As a practical case study, this paper focuses on the Jewish Cemetery in Pisa and the Historical Archives of the Jewish Community in Pisa, working on the genealogical tree of one of the most representative families resting in the cemetery.
Action Segmentation Through Self-Supervised Video Features and Positional-Encoded Embeddings

Action segmentation consists of temporally segmenting a video and labeling each segmented interval with a specific action label. In this work, we propose a novel action segmentation method that requires no initial video analysis and no annotated data. Our proposal involves extracting features from videos using several pre-trained deep-learning models, including spatiotemporal and self-supervised methods. Data is then transformed using a positional encoder, and finally, a clustering algorithm is applied, where each produced cluster presumably corresponds to a different single and distinguishable action. For self-supervised features, we explored DINO, and for spatiotemporal features, we investigated I3D and SlowFast methods. Moreover, two different clustering algorithms (FINCH and KMeans) were investigated, and we also explored how varying the length of video snippets that generate the feature vectors affected the quality of the segmentation. Experiments show that our method produces competitive results on the Breakfast and INRIA Instructional Videos dataset benchmarks. Our best result was produced using a composition of self-supervised features generated by DINO, FINCH clustering, and positional encoding.
Design and analysis of adaptive control systems: Adaptive control algorithms using machine learning

A growing field of research is the use of adaptive control algorithms with machine learning techniques like Q-learning and SARSA. With prospective applications in robotics, healthcare, and other fields, this interdisciplinary method strives to combine the stability and robustness of conventional control systems with the self-learning skills of reinforcement learning. Making sure that systems are stable and that learning occurs effectively is the main research problem. This paper demonstrates the stability and convergence of existing adaptive control algorithms when integrating with machine learning. There are mainly four primary methods of control algorithms: reinforcement learning, neutral network, support vector machine and deep learning. Reinforcement learning is the main focus of this paper. Data efficiency, robustness, and generalization are the main problems with reinforcement learning. Q-learning and SARSA (State, Action, Reward, State, Action) are two algorithms for reinforcement learning. The research will be done by analyzing these two algorithms based on existing material and the actual application of these two algorithms. SARSA is believed to be more safe as its on-policy methodology, and Q-learning is believed to be more proactive as its off-policy methodology.
Generative AI From Theory to Model

This book chapter provides a comprehensive overview of generative AI and its applications in computer vision. The introduction section elucidates the concept of generative AI and underscores its importance within the realm of artificial intelligence. The chapter also provides a deep dive into the various techniques used in generative AI, such as creative style transfer, forecasting subsequent video frames, enhancing image resolution, enabling interactive image generation, facilitating image-to-image translation, text-to-image synthesis, image inpainting, the generation of innovative animated characters, the construction of 3D models from image, the utilization of the variational autoencoder (VAE) and its various adaptations, the implementation of generative adversarial networks (GANs) and their diverse iterations, as well as the use of transformers and their manifold versions. The chapter also highlights the current limitations and potential future developments in the field.
Development effects of industrialization, labor force participation and electrification on productive capacity in Africa: A panel data investigation

In our study spanning 2010–2022 across 50 African economies, we investigated the effects of industrialization, labor force participation, and electrification on Africa’s productive capacity. We also examined how electrification moderates the effect of industrialization and labor force participation on productive capacity. We adopted the pooled OLS, the fixed effect estimator, and the dynamic system GMM estimation technique for our analysis. Our findings indicate industrialization, labor force participation, and electrification as potent channels for enhancing Africa's productive capacity. In tandem with Africa’s reality, we find that electrification isn’t moderating the positive impact of industrialization and labor force participation on productivity. Our findings also revealed human capital development, government health expenditure, and institutional quality as pivotal drivers of productive capacity in Africa, while factors like global economic policy uncertainty and natural resource endowment exhibit predominantly negative roles. These results underscore the need for African policymakers and leaders to foster an enabling environment that facilitates industrialization, increased labor force participation, and improved electricity access to promote economic performance and productivity.