Reviewerly/exHarmony huggingface dataset:
A study on Evolution of Facial Recognition Technology

Since the inception of Facial Recognition (1960s) researchers began experimenting with computer-based facial recognition algorithms, but they were incompetent due to the limited processing power of computers. Then researchers developed feature-based recognition systems in the 1980s, which identified certain facial characteristics, such as the space between the eyes or the nose’s form, etc. to create a unique facial signature, however, they were still limited in their accuracy. 3D facial recognition systems were introduced in 1990s, which used depth perception to create more accurate facial models. These systems were primarily used in security and surveillance applications. Machine learning algorithms in 2000s could learn to recognize faces more accurately over time because it uses large datasets to train themselves to recognize patterns in facial features. Deep learning algorithms of 2010s could recognize faces with even greater accuracy as they use neural networks to analyze facial features at multiple levels of abstraction, allowing them to identify complex patterns. Real-time facial recognition systems were also developed during this period to recognize faces in real-time video streams and therefore found applicable in security and marketing. Covid-19 Pandemic incorporated Facial recognition technology with facemask requiring additional considerations and adjustments in order to be effective in accurately identifying individuals who are wearing masks. This paper presents a study of evolution of Facial recognition technology as viable biometrics since its inception and how it got molded over time due to technological, legal and global interventions. At the end, we conclude this paper with promising directions for future research on this field.
RESEARCH PAPER ON ENHANCING BREAST CANCER PREDICTION THROUGH MACHINE LEARNING AND CROSS-VALIDATION TECHNIQUES: A COMPARATIVE ANALYSIS

Breast cancer is a significant health concern affecting millions of women worldwide.Early detection and accurate prediction of breast cancer play a crucial role in improving patient outcomes and reducing mortality rates.This Research presents a comparative analysis of machine learning algorithms and cross-validation techniques to enhance breast cancer prediction.The study utilizes a comprehensive dataset comprising clinical features of breast cancer patients.Multiple machines learning algorithms, including support vector machines, random forests, KNN, logistic regression, and decision trees are employed to develop predictive models.Furthermore, various cross-validation techniques, such as k-fold cross-validation, stratified k-fold cross-validation, and leave-p-out cross-validation, are implemented to assess the models' performance.The comparative analysis aims to evaluate the effectiveness of different machine learning algorithms and crossvalidation techniques in predicting breast cancer.Performance metrics such as accuracy, f1 score, precision, recall is employed to measure the predictive power of each model.The results of the study demonstrate the performance variations among different machine learning algorithms and cross-validation techniques.The findings provide valuable insights into the strengths and limitations of each approach, enabling healthcare professionals to make informed decisions in selecting the most suitable models for breast cancer prediction.In conclusion, this research contributes to the advancement of breast cancer prediction by providing a comparative analysis of machine learning algorithms and cross-validation techniques.The findings can guide future research in developing more accurate and reliable models for early detection and personalized treatment of breast cancer, ultimately leading to improved patient outcomes and reduced mortality rates.
The Research Value and Time Significance of the Red Oral History of Hainan Agricultural Reclamation in the Context of Media

Hainan Agricultural Reclamation is facing the development of tourism, which plays an important role in promoting the culture of Hainan Agricultural Reclamation.Therefore, in the context of financial media, this paper studies the research value of the Red Oral History of Hainan Agricultural Reclamation and its significance of the times.From the aspects of cultural and artistic value, social and historical value, inheritance and practical value, this paper analyzes the research value of Hainan Agricultural Reclamation Red Oral History.According to the characteristics of Hainan reclamation culture, the significance of its era is divided into supporting the frontier defense beliefs of reclamation soldiers, meeting the spiritual and cultural needs of the public, and inheriting the spirit of national unity.Through the above research, the aim is to inherit the Hainan reclamation culture through oral history and provide guarantee for the development of the reclamation culture.
Logical Mechanism and Practical Exploration of Integrating Divided Classroom Education Model into Physical Education Teaching in Colleges and Universities

With the change of times, the reform of higher education continues to develop in depth, and the importance of physical education courses in colleges and universities is also increasing.The reform of physical education teaching is also being carried out in full swing in various colleges and universities.Many new teaching methods have been applied to college physical education, and the classroom teaching model is an inevitable development trend of higher education reform.This paper uses research methods of literature and logical analysis to elaborate on the split classroom education model, analyzes the logical mechanism of the split classroom education model in college physical education classes, explores the practical application of the split classroom education model in college physical education classes, and proposes corresponding strategies, hoping to help college physical education teachers apply the split classroom teaching method more scientifically and reasonably to college physical education classes, Improve the teaching effect of college physical education, and promote the cultivation of students' comprehensive quality on this basis.
Research on Teaching Strategies of Deep Learning in High School Mathematics from the Perspective of Core literary

With the further deepening and development of China's new curriculum reform, cultivating students' core literacy is not only one of the major needs of education reform in China, but also an inevitable need to further cultivate students' professional quality.There is no doubt that high school mathematics, as an important subject of the main subject, occupies an important position in the high school curriculum.It is necessary to use the teaching method of "deep learning" to effectively improve students' mathematical literacy and enhance their core literacy.Higher requirements are put forward in "deep learning" for the classroom teaching of math teachers in high school.This paper takes the cultivation of students' core literacy as the starting point, according to the current situation of high school mathematics classroom teaching, and deeply explores the teaching strategies of the teaching method of "deep learning" of high school mathematics.
Factors Affecting Learners’ Performance in General Mathematics at Phinma-Coc

In schools throughout the world, Mathematics is regarded as the most significant subject. Unfortunately, because of the multiple barriers that keep them from fully participating, many students in the Philippines find it difficult. Students have a hard time when solving activities in General Mathematics. This study aimed to 1) determine the factors that affect learners’ performance, 2) determine levels of learner’s quarterly performance, and 3) determine the significant relationship between factors affecting learners’ performance and the learners’ performance in General Mathematics at PHINMA-Cagayan de Oro College, School Year 2022-2023. The data was gathered among nine hundred thirty-one (931) General Mathematics learners. In this study, descriptive research design, particularly the descriptive correlational method, was applied. This study made use of a descriptive research design. The questionnaire used was adopted from TIMSS (2007) and PANORAMAED (2015). The statistical tool used frequency, mean, standard deviation, and Pearson Correlation to get the relationship between the two variables. The study showed that the majority of the respondents got satisfactory. Among three (3) factors that can affect learners’ performance, teacher-related factor got the highest followed by family-related factor and learner-related factor respectively. There is a significant relationship between learners’ and teacher-related factors and learners’ performance in General Mathematics. It is concluded that the learners appreciated the teacher’s efforts and positive traits. Additionally, students acknowledged their hard work in describing the subject in General Mathematics in the best methods possible. It is recommended that teachers keep their distinctive qualities while also finding new approaches to conduct discussions about Mathematics. To prevent students from forgetting the material quickly and to foster an interest in the subject and excellent study habits, teachers should also offer techniques for addressing these problems.
Research on the Role of Tutor Education in the Apprenticeship Tutoring System

In order to deal with the problems of vocational education and policy orientation in the new era, on the basis of learning from the vocational education experience of Britain and Germany, an apprenticeship mentoring system with Chinese characteristics was developed.
Blockchain As a Force for Good

While the previous chapters focused on money, revolutionary promises of cryptoassets extend well beyond the monetary sector. In particular, the underlying blockchain technology can become a force for the greater good through countless use cases. Here is just a glimpse of possible applications and their likely positive impact.
Third Chain Classes

Third chains are chords that are built from a concatenation of minor or major thirds. A major triad 0, 4, 7 is a third chain, concatenating a major third 0, 4 with a minor third 4, 7. Third chains are important in harmony, and we shall demonstrate how they are applied to the management of harmonic contents of general chords. This approach is important for the harmonic analysis of musical compositions.
Local Composition and Gesture Classification

Local compositions and gestures are the basic structures in classical musical concept spaces. They are essentially subsets in modules, such as chords, motifs, and rhythms, or else digraph maps from a digraph to a topological space/category. They are local since they are not built as configurations of smaller subsets. We describe in detail the theorem of classification of local compositions, including its proof.
Microtonal Contrapuntal Theories

We shortly present the extension of counterpoint worlds to microtonal environments, an extension that would be very problematic, if at all possible, without the theory exposed above.
An Efficient Dual-Pooling Channel Attention Module for Weakly-Supervised Liver Lesion Classification on CT Images

The classification of liver lesions on computed tomography (CT) images is vital for early liver cancer diagnosis. To aid physicians, several methods have been proposed to perform that task automatically. These methods mostly use regions of interest (ROIs) that are manually or automatically selected from liver CT slices as training and testing data. However, the manual selection of ROIs requires a lot of time and effort from experienced physicians making the number of available fully labeled datasets limited. When it comes to automatic ROIs selection using segmentation methods, some incorrect mask predictions that might occur can affect the quality of the selected ROIs causing the model for the final task to be trained improperly. In this paper, we explore the potential of using the whole liver CT slice in combination with the lesion type label for weakly-supervised lesion classification. We exploit the ability of attention mechanisms to lead convolutional neural networks (CNNs) to focus on important regions in the input image. Then, we propose a lightweight attention module modified from the Efficient Channel Attention (Eca) module, called Efficient Dual-Pooling Channel Attention (EDPca). Our experimental results show that attention mechanisms can significantly boost the performance of CNNs for the task of weakly-supervised liver lesion classification, bringing the results closer to that of the supervised task. Besides, our proposed module achieves better classification results than the other channel attention ones. Moreover, the combination of it and the spatial attention module from CBAM module outperforms other methods.
Cryptoasset Taxonomies

Note this book’s recurrent use of the word cryptoasset rather than the more common cryptocurrency. This distinction is purposeful, as cryptoassets encompass many more assets than only those meant to be currencies.
Pedestrian Fall Detection Based on Improved YOLOv4 Algorithm

For the problem of pedestrian fall detection, this paper selects the YOLOv4 object detection algorithm and uses the Mobilenetv2 network to replace its backbone feature extraction network, CSPDarknet53. Then, introducing the idea of depthwise separable convolution into the enhanced feature extraction network PANet, using depthwise separable convolution to replace the normal convolution in PANet, enables further parameter reduction. Additionally, the focal loss was introduced into the loss function to control the weights of various types of samples in the loss. The improved algorithm and the original YOLOv4 algorithm are trained and tested under the same conditions and using the same dataset. The experiment result shows that the improved algorithm can reach 85.43 mAP and the number of parameters is significantly reduced compared with YOLOv4.
Research on Long-distance Vehicle Detection Based on YOLOv5 Improvement

Video-based object detection is an important research content to extract foreground objects from background images in intelligent video surveillance. Video-based object detection has developed rapidly in recent years. However, in practical applications, detecting small and medium-sized objects in video is still a challenging task because small and medium-sized objects occupy too few pixels and obtain limited information[1]. This paper adopts ResNet50 based on YOLOv5s' improved remote vehicle detection accuracy algorithm, and scales down DenseNet to maintain its core functions. The PAN-Net of the Neck layer is replaced with BiFPN, and a weighted bidirectional feature network pyramid (BiFPN) [2] is added to improve the feature fusion ability. By increasing the resolution of the feature map, features from different layers of Backbone can be aggregated to improve the overall detection performance. Through the comparison and analysis of experimental results, the improved algorithm has a more accurate recognition effect on small targets. How to accurately and quickly identify the position of vehicles in complex backgrounds to further ensure traffic safety is of great significance.
Connections between Linear Complementary Dual Codes, Permanents and Geometry

Linear codes with complementary duals, or LCD codes, have recently been applied to side-channel and fault injection attack-resistant cryptographic countermeasures. We explain that over characteristic two fields, they exist whenever the permanent of any generator matrix is non-zero. Alternatively, in the binary case, the matroid represented by the columns of the matrix has an odd number of bases. We explain how Grassmannian varieties as well as linear and quadratic complexes are connected with LCD codes. Accessing the classification of polarities, we relate the binary LCD codes of dimension k to the two kinds of symmetric non-singular binary matrices, to certain truncated Reed–Muller codes, and to the geometric codes of planes in finite projective space via the self-orthogonal codes of dimension k.
Cyberthreats and Healthcare

Introduction: Cyberattacks against healthcare have been growing at an alarming rate globally targeting the theft of clinical research intellectual property, personally identifiable information, and personal health information. Recent studies have also shown a concerning correlation between cyberattacks and patient morbidity and mortality rates. Many top security experts consider cyberattacks a top national security concern.This paper is a descriptive analysis of healthcare-related breaches in the United States in the past decade and an analysis of cybersecurity threats that are currently facing the industry. Method: Breach reports of unsecured protected health information affecting 500 or more individuals in the US are publicly accessible through the U.S. Department of Health and Human Services Office for Civil Rights portal. The database was downloaded and searched for all reported breaches occurring between January 1, 2011 - December 31, 2021. Breaches were subdivided by states, dates, location, entity type, and individuals affected. Results: Of the 3,822 PHI breaches recorded, 1,593 (41.7%) were hacking/IT related, 1,055 (27.6%) were listed as unknown, 819 (21.4%) were theft related, 194 (5.1%) were loss related, 97 (2.5%) were related to improper disposal and 64 (1.7%) were listed as “others.” Breaches occurred within the main categories as follows: network server (957 [25%]), email (877 [23%]), paper/films (665 [17%]), other (454 [12%]), laptop (341 [9%]), desktop (309 [8%]), and electronic medical records (220 [6%]). Conclusion: A total of 3,822 breaches affecting 283,335,803 people in the United States were recorded from January 1, 2011 to December 31, 2021. The most reported breaches were from healthcare providers with 2,827 (75.1%) events, followed by health plans (500 [13.1%]), business associates (480 [12.6%]) and healthcare clearinghouses (10 [0.3%]). 4 (0.1%) breaches were from unknown sources. This report may help healthcare providers understand the extent of the issue and mitigate some of the associated risks.
Mathematics Education for the Future: Evidence from Mathematics Education and Research Centre in a Rural University

South African rural mathematics education is confronted by poor learner performance in all phases of schooling. To address this, a Mathematics Education and Research Centre was established in a rural university. Numerous innovative and developmental activities designed for the advancement of teaching and learning of mathematics are conducted through the centre for pre-service and in-service teachers. Research was conducted on the effectiveness of these programs in the centre. The qualitative data from all the stakeholders indicated that the innovative activities created a positive impact and made mathematics more accessible. The successes of the different programs suggest that such innovative and developmental activities are needed to make mathematics more accessible, which in turn can address the poor learner performance.
THE EFFECT OF USING REFLECTIVE METHOD IN FOREIGN LANGUAGE TEACHING

The article reveals the essence of the concept of "self-organization" as one of the problems that interested many specialists in various fields. The problem of self-organization aroused not only theoretical, but also practical interest among researchers. In this regard, the article draws attention to the need for the formation of competencies of selforganization of students in the educational process of the university. Therefore, students should have professional competencies of self-organization in solving certain tasks in the future professional and pedagogical field. To do this, the student, as a future teacher, must have the competencies of self-organization, constantly improving their knowledge. The contents of these questions are described in detail in the article.
Effects of a Marriage and Family Life Course on the Attitude of College Students

본 연구의 목적은 대학생을 대상으로 결혼과 가족생활의 이해 및 준비를 위한 교양강좌를 개발한 후, 강좌운영의 효과를 살펴보는 것이었다. 이를 위하여 서울에 위치한 두 곳의 4년제 대학교에서 결혼과 가족에 대한 교양강좌를 운영하였다. 수강생들을 대상으로 강좌 시작 전과 후에 결혼관에 대한 구조화된 질문검사를 실시하여 결혼에 대한 긍정적 태도, 의구심과 두려움, 부정적 태도를 평가하고, 수강 후 결혼 및 가족 생활에 대한 견해를 포함한 개방형 질문조사를 실시하였다. 강좌 시작 전과 후 두 번의 질문검사에 모두 응답한 학생 163명의 자료를 분석하였다. 수집된 자료로 SPSS 25.0을 이용해 기술통계치를 산출하고, 혼합 분산분석과 사후검증을 실시하였다. 그 결과, 한 학기동안 본 교양강좌 수강 후, 수강 전에 비해 남녀 대학생들 모두 결혼에 대한 긍정적인 태도가 유의하게 높아졌다. 여자대학생들의 경우, 결혼에 대한 의구심 및 두려움도 유의하게 감소하였다. 다만, 결혼에 대한 부정적인 태도는 남녀대학생들 모두 크게 변화되지 않았다. 아울러, 본 강좌를 수강한 대학생들은 결혼과 가족생활에 대해 막연하고 모호했던 생각이 수업을 통해 구체화됨에 따라 미래의 결혼과 가족생활에 대한 자신감과 기대가 향상되었다고 보고하였다. 이러한 결과는 성인진입기에 해당하는 대학생들에게 교양강좌를 통해 결혼과 가족생활에 대해 보다 현실적 이해를 도모하고 관련 영역의 기술 및 역량을 강화시킬 수 있음을 시사한다. 이를 바탕으로 결혼과 가족 생활에 대한 체계적인 교육을 통해 인구문제에 선제적으로 대처하는 방안에 대해 논의하였다. The purpose of this study was to develop a marriage and family life course for college students and examine the effects of the course on their attitudes toward marriage and family life. The course was held as liberal arts education at two different universities in Seoul. Attitudes toward marriage were assessed using structured questionnaires before and after the course by self-report. The course satisfaction from students was also reported using unstructured questionnaires after the course. A sample of 163 undergraduate students participated in the study. Collected data were analyzed with descriptive statistics, mixed ANOVAs, and post-hoc tests using SPSS 25.0. Mixed ANOVAs indicated positive attitudes toward marriage were significantly increased after the course in both male and female groups. Also, doubt and fear of marriage were significantly decreased in the female group. However, negative attitudes toward marriage were not significantly different after the course. In addition, students responded to the open-ended questions with confidence and expectations regarding the prospective marriage and family life. These findings suggest that the marriage and family life course as a liberal arts education could improve competence and required skills for preparing for prospective marriage and family life of students in emerging adulthood. The development of a systematic curriculum dealing with current population problems in South Korea was discussed.
Keyword search over dynamic encrypted cloud data using symmetric key based verification

The field of cloud security has developed a technique called Verifiable Searchable Symmetric Encryption (VSSE), which allows users to recoup translated data from the cloud using keywords and corroborate the results' validity.One common demand in similar schemes is dynamic updates to cloud data.Being VSSE schemes supporting data dynamic update are grounded on asymmetric-crucial cryptography verification, which can be time-consuming, making the verification process a significant burden due to the vast quantum of cloud data.Thus, an effective system of achieving keyword search over dynamic translated cloud data with symmetric-crucial-grounded verification is a critical unsolved problem.To address this issue, experimenters have proposed a practical scheme in this paper, exploring VSSE with symmetric-crucial-grounded verification.They've designed a new cumulative Authentication Label (AAT) grounded on symmetric-crucial cryptography, enabling the generation of an authentication label for each keyword.This AAT's accumulation property allows for easy authentication label streamlining during dynamic operations on cloud data.Experimenters have designed a new secure indicator composed of a search table ST grounded on the orthogonal list and a verification list VL containing AATs to achieve effective data update.Thanks to the connectivity and inflexibility of ST, the proposed scheme's effectiveness can be significantly bettered.The proposed scheme is secure and effective, as demonstrated by the security analysis and performance evaluation results.