Reviewerly/exHarmony huggingface dataset:
Enhancing Human Activity Recognition Performance in Small-Sample Wi-Fi Sensing Using Data Augmentation Methods

Activity recognition based on Wi-Fi channel state information(CSI) has exhibited promising outcomes. However, numerous prevailing CSI-based activity recognition systems heavily lean on substantial datasets for model training. In reality, procuring CSI data about human behavior demands significant resources. Consequently, the challenge emerges in enhancing Wi-Fi perception under circumstances of low sampling rates. This research endeavors to investigate avenues for augmenting the available dataset. Our findings substantiate that the predicament of limited samples in Wi-Fi environments can be effectively alleviated by extending CSI from a one-dimensional time series to a two-dimensional recurrence plot (RP), coupled with the integration of data enhancement methodologies commonly deployed in computer vision. The results highlight that combining RP transformations with the horizontal flip technique achieves an 86% recognition rate using only 5% of the training samples, and a recognition rate of approximately 96% using a mere 10% of the training samples. Moreover, an outstanding recognition accuracy of 99.5% is attained with only half of the training samples, surpassing the capabilities of existing solutions in this field. We will publish our code at https://github.com/shengjunzi/Enhancing-Human-Activity.
Improvement of students' cognitive activities through teaching the concept “time”at the university

The article highlights ways to improve the cognitive activity of students by revealing the national character of the language, the conceptual analysis of the literary text. It is proposed to study the language in a cognitive direction on the basis of the continuity of language and nation, artistic text, including folklore texts. The article explores historical songs in a cognitive aspect for the first time. At the same time, through various methods and teaching methods have been determined the ways to improve cognitive thinking, the ability to broaden one's horizons, and the ability to reasonably express one's opinion. Based onthe texts of historical songs, theoretical and practical tasks are developed and introduced. Practical tasks determine how the concept of “time”is reflected in historical songs. It is supposed to reveal the nature of the concept “time”in the Kazakh worldview. Various tasks have been prepared to develop students' ability for cognitive analysis of the language of a work of art. The article uses interactive teaching methods and techniques in the educational process.The article uses interactive teaching methods in the educational process: pair work, group work, decision tree, filling in the Fila table (fact, idea, logic, argument), etc. The methods used are aimed at developing students' cognitive activity. The article provides for the development of students' thinking through writing works for students, improving the ability to express their thoughts in a reasoned manner, mastering language communication through the integrity of thought, consciousness, language, and increasing the ability to comprehensively implement the cognitive process. As a result of the study, active, creative, and cognitive activities of students will be developed
Extraction of Solar Photovoltaic Panels Based on High-Resolution Remote Sensing Images

Accurately and efficiently determining the installation positions, distribution, and total area of solar photovoltaic panels over a large area is important for investments and applications in photovoltaics. High-resolution optical satellite remote sensing imagery enables rapid and accurate extraction of ground-level objects. This provides the data foundation for automated extraction of photovoltaic panels based on deep learning. This paper utilizes high-resolution remote sensing imagery of solar photovoltaic panels. It employs the DeepLabv3+ semantic segmentation algorithm with the global convolutional network (GCN) to achieve a larger receptive field and enhance the network's global feature extraction capabilities. This improves the segmentation accuracy of photovoltaic panels. Experimental results demonstrate that the improved DeepLabv3+ model can achieve high-precision automated extraction of solar photovoltaic panels in complex remote sensing backgrounds.
Impact of Head Teachers' Transformational Style on Students Achievements

This research was done to explore the head teachers’ transformational leadership style, to see its impact on students’ achievement. The descriptive quantitative and survey approach was applied to conduct this research. The data were attained from secondary school teachers about the transformational leadership style of their heads through a five-point questionnaire. The matriculation results of students from sampled schools were also taken to see the effect of the head's leadership style on students' achievements. The purposive sampling method was used to collect data. Eight representative schools were selected from the district Lahore cantonment area and fifteen teachers were selected from each school to collect data and analyse SPSS. The data is presented in frequency distribution tables. Descriptive and influential statistics were applied to infer from data. It revealed that school heads with transformational leadership styles and strict rules regulations clear goals and values had a high effect on students’ achievements.
Understanding visitors' metaverse and in-person tour intentions during the COVID-19 pandemic: A coping perspective

The COVID-19 pandemic has posed a challenge to the development of global tourism. Metaverse tour, as an emerging travel pattern that combines metaverse characteristics, can bring positive implications for the digital transformation of tourism. However, there are few studies that integrate time and space to analyze the public’s intentions of metaverse tour and in-person tour, calling for a comprehensive research framework. Based on coping theory, a mixed-methods approach was proposed to incorporate several constructs to investigate the factors that influence public participation in metaverse tour. We further analyze the difference of public’s willingness to travel in-metaverse versus in-person at different times. This study enriches our understanding of the popularity of metaverse tour and the reasons for it, and provides insights into the further development of tourism to facilitate changes in the metaverse-based tourism pattern.
THE IMPACT OF COVID-19 ON DIGITAL HEALTH AND CARE IN SAUDI ARABIA

The outbreak of the COVID-19 pandemic has had a profound impact various sectors around the world, including digital health and care systems. Saudi Arabia, like many other countries, has faced significant challenges in ensuring the continuity and effectiveness of its healthcare services while minimizing the risk of virus transmission. This essay explores the impact of COVID-19 on digital health and care in Saudi Arabia, focusing on the changes in healthcare delivery and utilization, the adoption of digital technologies, and the challenges and opportunities that have emerged. The findings show that COVID-19 has accelerated the adoption and integration of digital health technologies in Saudi Arabia, leading to improvements in care delivery, increased patient engagement, and enhanced healthcare access. However, challenges such as the digital divide, data privacy concerns, and the need for cybersecurity measures must be addressed to fully harness the potential of digital health and care in the country.
Studi Kinetika dan Penentuan Dosis Optimum Koagulan FeCl3 dalam Menurunkan Konsentrasi Cu2+ pada Larutan

This study aims to determine the efficiency and adsorption capacity of various concentrations of FeCl3 coagulants (100-300 mg/L) to reduce Cu2+ concentrations in wastewater as well as the kinetic parameters that affect the adsorption mechanism in the coagulation process when coagulant particles added to wastewater will stick to the surface of colloidal particles, which will then change the charge. Several adsorption kinetics models employed in this study include Pseudo-first order, Pseudo-second order, Elovich, and Intra-particle diffusion. The applicability of the models produced in this work was evaluated by optimizing the non-linear equations, which provide values that are more precise and in agreement with real situations when compared to the linear kinetic models. These characteristics are measured based on the value of the coefficient of correlation (R2), Sum Square Error (SSE), and Chi-Square (x2). The results showed that the highest efficiency value of the FeCl3 coagulant was 98.705%, with the value of the adsorption capacity increasing along with the concentration of the FeCl3 coagulant. The kinetic model created in this work has a very excellent fit in terms of experimental data values and prediction data.
Interactive Latent Diffusion Model

This paper introduces Interactive Latent Diffusion Model (IELDM), an encapsulation of a popular text-to-image diffusion model into an Evolutionary framework, allowing the users to steer the design of images toward their goals, alleviating the tedious trial-and-error process that such tools frequently require. The users can not only designate their favourite images, allowing the system to build a surrogate model based on their goals and move in the same directions, but also click on some specific parts of the images to either locally refine the image through dedicated mutation, or recombine images by choosing on each one some regions they like. Experiments validate the benefits of IELDM, especially in a situation where Latent Diffusion Model is challenged by complex input prompts.
Cooperative Co-Evolution for Ensembles of Nested Dichotomies for Multi-Class Classification

In multi-class classification, it can be beneficial to decompose a learning problem into several simpler problems. One such reduction technique is the use of so-called nested dichotomies, which recursively bisect the set of possible classes such that the resulting subsets can be arranged in the form of a binary tree, where each split defines a binary classification problem. Recently, a genetic algorithm for optimizing the structure of such nested dichotomies has achieved state-of-the-art results. Motivated by its success, we propose to extend this approach using a co-evolutionary scheme to optimize both the structure of nested dichotomies and their composition into ensembles through which they are evaluated. Furthermore, we present an experimental study showing this approach to yield ensembles of nested dichotomies at substantially lower cost and, in some cases, even with an improved generalization performance.
Evolutionary Mixed-Integer Optimization with Explicit Constraints

Determining the feasibility of a candidate solution to a constrained black-box optimization problem may either be similarly expensive as the process of determining its quality, or it may be much cheaper. Constraints that allow obtaining degrees of feasibility or constraint violation without incurring significant computational costs are referred to as explicit. We present an evolutionary algorithm for solving mixed-integer black-box optimization problems with explicit constraints. The algorithm wraps active-set evolution strategies, an algorithm for solving continuous black-box problems with explicit constraints, in a branching mechanism that allows enforcing integrality constraints. In computer experiments we demonstrate that the algorithm solves a set of mixed-integer problems with significantly fewer objective function evaluations than several algorithms that do not exploit the explicitness of the constraints.
On the Use of Second Order Neighbors to Escape from Local Optima

Designing efficient local search based algorithms requires to consider the specific properties of the problems. We introduce a simple and efficient strategy, the Extended Reach, that escapes from local optima obtained from a best improvement local search and apply it to the linear ordering problem (LOP), the traveling salesperson problem (TSP) and the quadratic assignment problem (QAP). This strategy is based on two landscape properties observed in the literature. First, it considers that a local optimum is usually located in the frontier of its own attraction basin, and thus, it is enough to inspect the second order neighbors to reach a (better) solution inside an attraction basin of a better local optimum. Second, taking into account that for the LOP and specific neighborhoods it is possible to discard solutions without the need of being evaluated, we extend this result to the TSP with the 2-opt neighborhood to avoid the unnecessary evaluation of solutions. Efficient ways of evaluating the second order neighbors are also presented, based on the cost differences, reducing significantly the computation cost. Experimental results on random and benchmark instances show that our strategy, indeed, escapes from local optima despite its simplicity.
Reducing Overparameterization of Symbolic Regression Models with Equality Saturation

Overparameterized models in regression analysis are often harder to interpret and can be harder to fit because of ill-conditioning. Genetic programming is prone to overparameterized models as it evolves the structure of the model without taking the location of parameters into account. One way to alleviate this is rewriting the expression and merging the redundant fitting parameters. In this paper we propose the use of equality saturation to alleviate overparameterization. We first notice that all the tested GP implementations suffer from overparameterization to different extents and then show that equality saturation together with a small set of rewriting rules is capable of reducing the number of fitting parameters to a minimum with a high probability. Compared to one of the few available alternatives, Sympy, it produces much better and consistent results. These results lead to different possible future investigations such as the simplification of expressions during the evolutionary process, and improvement of the interpretability of symbolic models.
Halt the h-index

Sometimes, bringing home a message requires a more visual approach. That’s why recently, we teamed up with a graphic designer to create an infographic on the &lt;em&gt; h &lt;/em&gt; -index – or rather, on the reasons why not to use the &lt;em&gt; h &lt;/em&gt; -index.
Practicing what we preach: Our journey toward open science

Today CWTS published its open science policy on its website. This policy, which formally came into effect on September 1 this year, is the outcome of a co-creation process in which many CWTS colleagues participated in a period of one and a half years.
Reflection on Teacher Feedback in Hybrid Teaching of Translation

In the context of the diversity and multi-modality of teaching feedback, the importance of teacher feedback has been impacted by online (computer) feedback and peer feedback, so it's necessary to highlight and give full play to the important role of teacher feedback. After analysis of the characteristics of hybrid translation teaching, the research asserts that all feedback should be given full play to their roles in translation teaching. Teachers are suggested focusing on students' necessities to offer re-feedback and deep feedback by highlighting key and difficult points, meanwhile, to enrich cultural literacy and provide value guidance as well as promoting students to actively perceive, understand and adopt feedback information are the core work of teacher’s feedback to improve translation teaching efficiently and effectively.
ENHANCING READING ABILITY USING EFFICIENT READING TECHNIQUES

In the realm of education, it's evident that imparting reading strategies holds the key to enhancing student comprehension. However, the shortfall lies in equipping educators with a robust foundation for teaching these strategies effectively. This study delves into the realm of action research. The present paper focuses on the efficient reading strategies like prediction, making connections, visualizing, inferential, questioning, and summarizing.
The Application of Inquiry-based Teaching Method in College Tennis Teaching

With the continuous development of physical education in colleges and universities, the reform of teaching methods has become one of the important ways to improve students' comprehensive literacy. This study aims to deeply explore the application of inquiry-based teaching method in college tennis teaching, and provide innovative teaching ideas for college tennis teaching through theoretical analysis and countermeasure suggestions. Firstly, the thesis reviews the limitations of traditional teaching method in tennis teaching, and introduces the basic concept of inquiry-based teaching method and its successful application in other disciplines. Secondly, through literature review and theoretical analysis, the potential advantages of inquiry-based teaching method in cultivating students' independent learning ability, teamwork spirit and problem-solving ability are explored. Finally, specific countermeasures for implementing inquiry-based pedagogy in college tennis teaching are proposed, including suggestions for instructional design, teacher training, and assessment mechanisms. Through this study, we expect to provide useful references for the reform and innovation of college tennis teaching.
Systematic Review of Artificial Intelligence Application in Higher Education

The role of Higher Education in any society is inevitable when it comes to harmonious personality development of the youth and nation-building. The promising higher education system is research-oriented and is open to newer dimensions of teaching-learning pedagogy. One of the recent advancements in this sector is the emergence of the application of artificial intelligence which has eventually led to the emergence of New possibilities and challenges as well. The Massification of Higher Education and the diversified nature of curriculum has no doubt increased the workload of the teacher and the nature of the job has also undergone drastic changes. With changing times the teaching-learning methods including testing , measuring and evaluative processes involved in it , have experienced lots of changes recently with the interventions of the latest technology in the process. It is surprising to see how the application of artificial intelligence has aptly equipped not only teachers and teachers but also the whole system involved in the higher education system with its latest techniques. Despite all this, the use and application of artificial intelligence in higher education scenarios is still unclear to some extent especially in a country like India with different kinds of limitations when it comes to the use of technology. This paper tries to analyze the advantages and disadvantages of artificial intelligence and its role in the higher education system and finally the role of teacher in this respect.
Penerapan Media Pop-Up Book Terhadap Hasil Belajar Siswa Mi Al-Abrar Kota Makassar Pada Materi Bangun Datar

The aim of this research is to determine the effect of applying pop-up book media on student learning outcomes in class IV material at MI Al-Abrar, Makassar City. This research uses a type of experimental research with a Quasi Experimental Design research design. The sample for this research was class IV A with 34 students as the experimental class and class IV B with 26 students as the control class. This research uses descriptive analysis and inferential statistical analysis. Learning outcomes can be seen from the experimental class posttest results, which have an average of 80.29, which is very different from the control class posttest results with an average of 64.42. The results of this research hypothesis obtained tcount &gt; ttable or 6.220 &gt; 2.00172 and the sig (2-tailed) value obtained was 0.000 then 0.000 &lt; 0.05 so it can be concluded that H0 is rejected and H1 is accepted. This means that there is an influence of the application of pop-up book media on student learning outcomes in material on flat figures for class IV students at MI Al-Abrar, Makassar City. Through the data obtained, it can be concluded that there is an influence of the application of pop-up book media on student learning outcomes in the material of flat shapes for class IV students at MI Al-Abrar, Makassar City. The influence of pop-up book media can also be seen from students' learning activities during the learning process. Observation results mean the percentage of student learning activities in the experimental class was 73%, while in the control class it was 49%. So it can be concluded that the activity of experimental class students in the learning process of applying pop-up book media is more active compared to control class students.
Breast cancer screening with class imbalance datasets of mammogram imaging

In Taiwan, breast cancer ranks as the fourth leading cause of death among women. Many studies have reported that early detection and treatment are crucial in reducing mortality rates. Mammogram imaging is a key screening tool for the early detection and management of breast cancer. However, the identification and interpretation of breast lesions in mammography is not a trivial task. In addition, the extremely biased ratio of positive and negative case numbers makes it more challenging when it comes to the development of the computer-aided diagnosis (CAD) systems. Nevertheless, if we resample the negative cases to make the training dataset class distribution even with the positive cases, the total number of cases may not be sufficient for training a deep learning model. To avoid such dilemma, our strategy is to guide the deep learning models to learn the correct features of lesions by the annotations of lesions, including masses and micro-calcifications, from the medical physician. Subsequently, the deep features from the SPPF layer in a detection model of YOLOv5m are extracted for both positive and negative cases. Finally, a classifier of Random Forests is trained based on the extracted feature for mammography screening. For our experiments, the source datasets were from the competition of Screening Mammography Breast Cancer Detection held by Radiological Society of North America (RSNA) in late 2022. And the performance of our proposed reached to 0.624, 0.77, 0.596, and 0.672 for Accuracy, Sensitivity, Precision, and F1-Score, respectively.
FCM-Enabled Approach for Investigating Interdependencies of BIM Performance Factors in the Sustainable Built Environment

In pursuit of a sustainable built environment, BIM plays a crucial role in the project's performance and has egressed as a powerful technology in the construction industry, impacting the outcome and the project delivery workflows. Numerous dynamic and interdependent factors influence BIM performance. However, Existing literature prominently focuses on exploring the influencing factors for BIM performance, ignoring the impact and strength of the interplay of these factors on one another, therefore offering an inadequate picture of optimizing BIM performance. The evolving nature and degree of complexity of construction projects necessitate the identification and comprehensive understanding of the interdependencies between factors contributing to BIM performance in the sustainable built environment. A Fuzzy Cognitive Map (FCM) is a modeling method that represents and analyses the interplay between the factors in a complex system. So, this study proposes an FCM-enabled approach to investigate the interdependencies of factors contributing to BIM performance and conduct what-if scenarios, including predictive analysis. The developed FCM model can help reveal the hidden cause-effect relationships among a complex system of BIM performance factors, enabling stakeholders to develop more informed strategies and proactively plan to optimize BIM Performance