Reviewerly/exHarmony huggingface dataset:
Improved AI-based tool for radar image denoising

A lot of applications are possible with AI: classification, to sort a lot of images in many different categories, denoising, to improve the quality of a signal or an image, and “generative”, to create a fake image which seems to be very real. Here, I propose to use an autoencoder to denoise radar images. In order to achieve this, I first propose to create an adequate dataset of 60,000 images to train the autoencoder.
Generative Modelling of the Ageing Heart with Cross-Sectional Imaging and Clinical Data

Cardiovascular disease, the leading cause of death globally, is an age-related disease. Understanding the morphological and functional changes of the heart during ageing is a key scientific question, the answer to which will help us define important risk factors of cardiovascular disease and monitor disease progression. In this work, we propose a novel conditional generative model to describe the changes of 3D anatomy of the heart during ageing. The proposed model is flexible and allows integration of multiple clinical factors (e.g. age, gender) into the generating process. We train the model on a large-scale cross-sectional dataset of cardiac anatomies and evaluate on both cross-sectional and longitudinal datasets. The model demonstrates excellent performance in predicting the longitudinal evolution of the ageing heart and modelling its data distribution. The codes are available at https://github.com/MengyunQ/AgeHeart.
Digital technologies in art education: problems, solutions

Digital technologies, which entered education at the beginning of the 21th century, are actively developing in connection with the prevailing conditions in society throughout the planet in 2020-2021. They have influenced both the education system in general and the artistic direction, especially in distance learning. The developments of scientists and teachers presented at the last congress of sociologists ROS and psychological and pedagogical concepts of humanistic, personality-oriented education contain a discussion of the existing digital space and proposals for the organization of digital education in higher education, including the artistic students' specialization. Based on this and the didactic concept of teaching, we continued the author's methods of optimizing the artistic activity of students in distance learning at a university, described in previous articles, reflecting its specificity, results of the application, and outlined prospects for this direction of creative activity that develops personality in modern conditions.
Open Learning Resource as Scientific Communication: a selected webliography

The pandemic not only changes the learning process, but also access to learning resources as a part of scientific communication. Before the pandemic, libraries as a learning resource centre could serve the user physically, but during the new normal era, many libraries provide online services. This requires the ability of library managers to be able to offer more learning resources for the academic community. This paper aims to describe various links and descriptions of web-shaped information sources that can be used as learning resources. The method used is a qualitative method by searching websites that contain the open learning resources. The results show that various websites have been found that not only provide information, but also various sources that can be used in the learning process with a very broad subject. All of the resources described are learning resources that are open and legal to use
Zooming with Babies: Troubling a Shared Present

This paper outlines how the specific constraints of virtual communication technology have stirred new thinking around what kind of research and what knowledge is produced with babies. During Zoom sessions, the 2–4-month-old babies were frequently present but out of shot, or glimpsed as a small limb or movement or sound on the other side of the screen. The babies’ bodies, movements, and sounds exceeded the boundaries of the screen. Through a posthuman lens, presence, time, and agency unravel the Zoom screen as an active participant that interferes with what can happen in a shared present in a liminal space.
A Dual Forward–Backward Algorithm to Solve Convex Model Predictive Control for Obstacle Avoidance in a Logistics Scenario

In recent years, the logistics sector expanded significantly, leading to the birth of smart warehouses. In this context, a key role is represented by autonomous mobile robots, whose main challenge is to find collision-free paths in their working environment in real-time. Model Predictive Control Algorithms combined with global path planners, such as the A* algorithm, show great potential in providing efficient navigation for collision avoidance problems. This paper proposes a Dual Forward–Backward Algorithm to find the solution to a Model Predictive Control problem in which the task of driving a mobile robotic platform into a bi-dimensional semi-structured environment is formulated in a convex optimisation framework.
Delicate Comparison of the Central and Non-Central Lyapunov Ratios with Applications to the Berry–Esseen Inequality for Compound Poisson Distributions

For each t∈(−1,1), the exact value of the least upper bound H(t)=sup{E|X|3/E|X−t|3} over all the non-degenerate distributions of the random variable X with a fixed normalized first-order moment EX1/EX12=t, and a finite third-order moment is obtained, yielding the exact value of the unconditional supremum M:=supL1(X)/L1(X−EX)=17+77/4, where L1(X)=E|X|3/(EX2)3/2 is the non-central Lyapunov ratio, and hence proving S. Shorgin’s (2001) conjecture on the exact value of M. As a corollary, an analog of the Berry–Esseen inequality for the Poisson random sums of independent identically distributed random variables X1,X2,… is proven in terms of the central Lyapunov ratio L1(X1−EX1) with the constant 0.3031·Ht(1−t2)3/2∈[0.3031,0.4517), t∈[0,1), which depends on the normalized first-moment t:=EX1/EX12 of random summands and being arbitrarily close to 0.3031 for small values of t, an almost 1.5 size improvement from the previously known one.
Research on Extended Target-Tracking Algorithms of Sea Surface Navigation Radar

To solve the problem of false tracks generated by breakdowns and clutter in point-target tracking in polar coordinates, a fusion tracking algorithm based on a converted measurement Kalman filter and random matrix expansion is proposed. The converted measurement Kalman filter (CMKF) transforms the polar coordinate data of the target at the current time into Cartesian coordinates without bias. Based on linear measurements and states, the position of the extended target and the group target was predicted and updated by using a random matrix, and its track was drawn by combining the nearest neighbors to realize the tracking of the size, shape and azimuth of the extended target. Compared with point-target tracking, the accuracy of extended multi-target tracking was increased by 45.8% based on data measured using NAVICO navigation radar aboard ships at sea. The experimental results showed that the improved method in this paper could effectively reduce the interference of clutter on target tracking and provide more information about the target motion features.
A Comparison of Machine Learning Algorithms to Predict Cervical Cancer on Imbalanced Data

Cervical cancer is a leading cause of death in women. The present research analyzes, explores, compares and identifies the best method for predicting cervical cancer by applying machine learning techniques. The data is from the University Hospital of Caracas, Venezuela where a selection of variables was made according to the literature in order to predict cervical cancer. Seven algorithms were applied: decision tree (DT), random forest (RF), logistic regression (LR), XGBoost (XG), Naive Bayes (NB), multilayer perceptron (MLP) and K-nearest neighbors (KNN). Furthermore, three imbalanced data techniques were applied: SMOTETomek, SMOTE, and ROS for Hinselmann, Schiller, Cytology and Biopsy as target variables. In addition, accuracy, precision, recall, f-score and AUC were used to evaluate the results. Random forest was the algorithm with the highest results in accuracy, precision and f-score, with 94.57%, 72.46% and 60.70% respectively. Logistic regression and Naive Bayes had the highest values for recall and AUC with 68.37% and 79.11% respectively.
Evaluation of the Kahoot Tool as an Applied Assessment in Physical Education Learning

Technology and gamification strategies have influenced and modified the teaching-learning methods through various educational tools that allow motivating, arousing interest and complementing the training of students, however, it is essential to analyze the use of these as new assessment tools to obtain formative results. Therefore, the proposed objective is to analyze the valuation of Kahoot as an evaluation tool in the subject of Physical Education, for which the perception of 77 high school students of the tenth year of EGB and their teacher has been evaluated; the intervention is carried out in 6 classes with different topics of study. The results were positive because they help to strengthen learning, memorize concepts, self-evaluate knowledge and generate great interest, in conclusion, they consider Kahoot as an effective tool for the evaluation of the teaching process of the subject.
The Homo Economicus in Cameroon

Abstract This chapter proposes a reading of the Cameroonian economy based on an economic agent’s view from below. Its intent is to cast complementary light on the conceptual, theoretical and empirical analyses constituting most of this work. It is therefore written from the point of view of the citizen who interacts daily with the institutions designing and implementing public policies, and small and medium-sized business that make up the economic fabric. The perspective adopted here is also long-term, briefly covering the economic and social approaches of the precolonial era as well as those of the colonial and post-independence eras. This analysis emphasizes the richness and the paradoxes of the Cameroonian economy, of its ruling elites, its private sector, and economic agents of various levels. The chapter emphasizes the need to improve the business environment in Cameroon, and to reform the education and training system.
Determinants of the Performance of the Education System

Abstract Education is a top priority for the Cameroonian government, as reflected in frequent institutional reforms implemented over time in this strategic sector to improve its performance. In this context, the objective of this study is to investigate the effect of institutions on primary and secondary school enrolment, controlling for other key variables. Data used are from the World Bank, the Center for Global Peace, and the Cameroonian government. A standard education production function is specified and estimated using the SURE method. The adopted methodology led to some interesting results. i) There is some heterogeneity in the effect of institutions on the performance of the education system. Indeed, institutional factors seem to have had a positive and significant effect on primary school enrolment and no effect on secondary school enrolment. ii) The same heterogeneity is observed in the effect of public spending on education as a share of GDP, which appears to be positive and significant on primary school enrolment and not on secondary school enrolment. iii) High level of GDP growth rate would not be a necessary and sufficient condition for improving the Cameroonian education system performance. Therefore, the Government should do more to reduce political instability, corruption and improve good governance to boost the performance of the education system. An effective health policy would also help to achieve the same goal.
The Cameroon Economy

Abstract Cameroon, which is often referred to as Africa in miniature, has a political economy that has been influenced significantly by various groups and institutions. Cameroon is a bilingual country, with English and French as its official languages, as well as a mixed common-law and civil-law system, all remnants of British and French colonialism. Christianity has also affected the worldview of Cameroon’s various ethnolinguistic groups and the way they have interacted with one another over the years. Missionaries helped create written forms of many indigenous languages (e.g., Duala and Mungaka) and facilitated the creation of knowledge in these languages. Migrants from neighboring countries enriched the country’s cultural mix and contributed significantly to economic development. Internal migrations, occasioned by population pressures and the search for opportunities for self-actualization and economic advancement, contributed to the growth of Cameroon’s urban centers. In addition to Christianity, Islam and Cameroon’s various traditional religions inculcated in the people values, such as probity and morality, which have been critical to the development of economic and financial institutions in the country. Many of Cameroon’s traditional religious require that their adherents to be devout, honest, faithful and spiritual, values that augur well for the development of a fully functioning and sustainable economy.
Analysis and Incidents in the Professional Practices of IT University Students in the Context of the COVID-19 Pandemic

The objective of this research is to analyze the level of satisfaction and effectiveness of pre-professional practices in university students, before and during the pandemic. The analyzed data was collected through surveys applied to 67 students. The data was analyzed with a descriptive approach, using tables to summarize the results, while in the analysis of the difference in means in the effectiveness and satisfaction scales, the ANOVA method was used, obtaining a $$p-value =0.1134$$ , the same as indicated that there is no variation in the means of the scales. For the correlation analysis, the Pearson coefficient was calculated, whose value indicated a strong correlation between the satisfaction scale of the place where the practice was carried out and the level of satisfaction (0.637). Finally, a web page model is proposed that is capable of better guiding the choice of the place where the students will carry out the pre-professional practices.
Demystifying EFL Teachers’ Experiences During the Pandemic: A Study of the Psychosocial Risks Resulting from COVID-19

This study aims to identify the main psychosocial risks that COVID - 19 has caused in Ecuadorian EFL teachers and determine the factors associated with developing these risks. This study employed a quantitative approach and a non-experimental cross-sectional design, with a sample of 980 teachers from different educational levels from Ecuador. The data analysis was done using Stata 16 statistical program and a multivariate binary logistic regression (LR). The results showed that teachers are emotionally drained, isolated, frustrated with teaching, and exhausted because of teaching during the pandemic, being the women the most affected. The main factors that increased the probability of suffering these psychosocial effects were extra activities beyond working hours, status in the teacher’s institutions, and gender.
Assessing Digital Competence Through Teacher Training in Early Education Teachers

Training programs on digital competences for teachers are often carried out without a thorough diagnosis, so it is difficult to accurately identify which digital competences need to be developed in teachers. Best-case scenario, questionnaires are proposed as the only diagnostic tool, and the answers are often based on the subjectivity of respondents which inevitably led to contradictions as revealed by the literature. In this study, a machine learning algorithm has been used to determine the teachers’ needs in terms of detecting which and how much they need to improve their digital competences. 181 teachers took a 4-week-virtual course containing two modules (Content Creation and School Management) to assess specific teacher competences. Results showed that the type of question used in the evaluations influences the academic performance of the participants. Data were analyzed based on 1) three types of questions (matching, multiple choice and true/false) and 2) scores obtained by the participants at the end of the course. Firstly, matching questions reflect a greater distribution in the results, which indicates that this type of question may be more challenging, followed by the multiple-choice questions. While true or false questions should not be included as they leave too much room for chance. Secondly, performance level reflects that demographic information (e.g. age, type of institution, and level at which they teach) is associated with final performance of digital competences. This type of analytics can provide a much more detailed overview in terms of detecting strengths and weaknesses in the development of digital competences of teachers.
A new model for investigating the effective factors in the development of modern clinical and health services in the time of COVID-19

Excessive information exposure and misleading information in controlling COVID-19 disease have affected the dimensions of people's lives and caused mental health problems. Healthcare effectiveness is a critical subject worldwide. Thus, engaging resources in the progress and use of e-health technologies are very crucial, especially in the COVID-19 era. Due to the sensitivity of this issue and the world health organization's attention to developing new clinical and health services in the pandemic, this article introduces and presents a new model. This new model investigates the factors influencing the development of modern clinical and health services during the pandemic era. Also, this research presents a new model to investigate the technological, management, environmental, social, and cultural factors and perceived usefulness and ease of use influencing the development of modern clinical and health services throughout COVID-19. The questionnaire survey technique is utilized for gathering data. The authors contacted about 300 potential respondents. To statistically analyze the questionnaires, Smart-PLS 3.2 software is used. The outcomes illustrate a great strength of the suggested model. The results demonstrate that technological, management, social, cultural, and environmental factors positively affect perceived usefulness and ease of use. They also show that perceived usefulness and ease of use are significant factors in developing modern clinical and health services.
MILP‐based security evaluation for AEGIS/Tiaoxin‐346/Rocca

Abstract In this paper, the security of Advanced Encryption Standard‐based authenticated encryption schemes, including AEGIS family, Tiaoxin‐346, and Rocca by mixed integer linear programming tools is examined. Specifically, for the initialisation phase of AEGIS, Tiaoxin‐346, and Rocca, the security against differential attacks and integral attacks is evaluated by estimating the lower bounds for the number of active S‐boxes and utilising division property, respectively. In addition to the estimations of initialisation phases, the security of the encryption phases of AEGIS, Tiaoxin‐346, and Rocca against distinguishing attacks on keystream is evaluated by exploiting integral properties. As a result, the authors show that the initialisation phases of AEGIS‐128/128L/256, Tiaoxin‐346, and Rocca are secure against differential attacks after 4/3/6, 5, and 6 rounds, respectively. Regarding integral attacks, the distinguisher is found on 6/6/7, 15, and 7 rounds in the initialisation phases of AEGIS‐128/128L/256, Tiaoxin‐346, and Rocca, respectively. Additionally, the integral distinguisher is presented on 2/2/4, 4, and 4 rounds in the encryption phases of AEGIS‐128/128L/256, Tiaoxin‐346, and Rocca, respectively. As far as it is known, this study’s results are the first distinguishing attacks on the keystream on AEGIS, Tiaoxin‐346, and Rocca without relying on weak keys.
Unearthing Financial Statement Fraud: Insights from News Coverage Analysis

The financial statement (FS) fraud detection framework proposed in this paper, PeerMeta, makes improvements in all three aspects of learning processes: the selection of research samples, feature set, and detection model. For the research samples, prior studies are based on FS fraud events that have already been disclosed and confirmed. This paper constructs a new measure, Financial Statement Fraud Propensity (FSFP), based on media coverage that can reflect unrevealed FS fraud behaviors. We show that the FSFP exhibits more timely trend movement, has more negative relationship with regulation intensity than the existing measures, and hence reflects FS fraud more effectively. For the feature set, we add innovatively peer factors based on the studies of peer effects in FS frauds. The peers are learned through text similarity analysis of business description in the financial reports and the CNM algorithm, which is found better in grouping companies than the existing industry classification standards. For the detection model, two meta-learning algorithms, stacked generalization and semi-supervised adaptive learning, are proposed and applied to aggregate the 18 popular classifiers to increase the integration and dynamic adjustment capabilities of the detection model. The superiority of proposed framework in financial statement (FS) fraud detection is demonstrated under several performance measures.
Exploration with ARWs

This chapter presents an overview of various exploration schemes with single and multi Aerial Robotic Workers (ARWs) and their applications in Search and Rescue, Environmental Monitoring, and planetary exploration missions, under the assumption that the map is partially known or completely unknown. The presented methods in the chapter are in line with the field deployment of the ARWs in subterranean and planetary exploration missions. The addressed questions will include the operating environment configuration and path planning methods for single and multi-robot exploration. The chapter will also briefly present two exploration strategies in terms of frontier and sampling-based exploration algorithms. More specifically, frontier-based and Rapidly Exploring Random Tree (RRT)-based exploration methodologies with results will be explained in detail.
Navigation for ARWs

This chapter presents an overview of various navigation schemes used for ARWs and their application areas. Navigation schemes, in general, answer the question of how to move from the current position to the desired and optimally plan the path between them, which is a necessary step for almost all applications for autonomous flight. This chapter will go over reactive navigation schemes, such as the potential fields and Model Predictive Control with integrated obstacle avoidance, as well as global path-planning methods, such as map-based iterative planners like D⁎, and planning for complete coverage of infrastructure to perform a visual inspection.
Perception driven guidance modules for aerial manipulation

Micro Aerial Vehicles (MAVs) have showcased their potential for enabling the next generation of inspection technologies as a sensor payload carrier operating at various levels of autonomy. Combining their ability to navigate in unstructured areas with the dexterity of robotic arms leads to a new era of Aerial Robotic Workers (ARWs) able to interact with the environment and as such address infrastructure maintenance tasks. The development of perception capabilities that guide the aerial platform at the desired regions of interest and perform the respective manipulation tasks is, among others, a fundamental step towards the envisioned goal. Thus, the aim of this Chapter is to discuss visual guidance systems to assist the aerial platform before applying any physical interaction.