Reviewerly/exHarmony huggingface dataset:
Digital Versus Conventional Teaching of Surgical Pathology: A Comparative Study

To compare the digital method and the conventional method of teaching surgical pathology to medical students.A prospective case-control study was conducted on second-year students during the period of August 20, 2022, through January 15, 2023. Students, divided into two groups of 45 each, were taught surgical pathology via both conventional and digital methods. Four specimens and four slides were taught in total to the same set of students. A pre-test and a post-test were used to evaluate students' performance and the impact of the teaching method. The answers were analyzed using a paired t-test. In the end, students' responses were obtained regarding their views on a better method of teaching on a Likert scale.To study gross pathology, 50.7% of students were in favor of the digital method, and 21% were not in favor. For the microscopic examination of tissues, 56.92% of students were in favor of the digital method, and 15% were not in favor. There was a significant increase in post-test scores (12.54-9.79 = 2.75, p=0.007) when digital methods for teaching surgical pathology were applied.The Likert scale demonstrated that the digital method of teaching surgical pathology not only improved student performance but also resulted in a better understanding of the subject.
A Study on Innovative Strategies for Teaching English to College Students Based on Knowledge Mapping

Abstract This paper proposes a personalized teaching method based on knowledge mapping and the commonly used personalized learning methods. A web crawler is used to crawl English exercises data on the Internet and classify them according to the knowledge categories, establish the connection between topics according to their cosine similarity, and build an English knowledge graph using NEO4J. After creating the knowledge map, a select few nodes are categorized with labels that reflect the characteristics of various pupils, and the node categories are divided into two categories: mastered and unmastered, after which the whole knowledge map is labeled using a graph convolutional neural network to generate a personalized knowledge map. After testing, the model works best when the threshold value is increased to 0.8 when generating the English knowledge map. And the overall English score of the experimental class taught with this paper’s method improved by 5.27 points, among which the average scores of completion, writing, and reading increased by 2.58, 1.32, and 1.17 points, while the overall English level of the control class did not change significantly. As a result, the approach used in this research to create tailored learning routes based on personalized knowledge mapping has excellent applicability.
Video Deraining Mechanism by Preserving the Temporal Consistency and Intrinsic Properties of Rain Streaks

The problem of video deraining is one of the most focused research areas where several techniques are introduced to achieve higher visual quality in the results. The process of video deraining tries to eliminate the presence of rain streaks in the videos so that the overall quality of the video can be enhanced. The existing frameworks tried to accurately eliminate the rain streaks in videos, but there is still room for improvement to preserve the temporal consistencies and intrinsic properties of rain streaks. This work does the job with the combination of handcrafted and deep priors that deeply extract and identify the nature of rain streaks in different dimensions. The proposed work included three main steps: Prior extraction, Derain modelling and optimization. Four major priors are extracted from the frames where the gradient prior (GP) and sparse prior (SP) are extracted from the rain streaks, and the smooth temporal prior (STP) and deep prior (DP) are extracted from the clean video. The unidirectional total variation (UTV) is applied to extract the GP, and the L1 normalization method is followed to extract the SP and STP. The DP is then extracted from the clean frames using the residual gated recurrent deraining network (Res-GRRN) model based on deep learning. Derain modelling is carried out based on the extracted priors, and the stochastic alternating direction multiplier method (SADMM) algorithm is utilized to solve the optimization problem. The proposed approach is then implemented in python and evaluated using the real-world dataset. The overall PSNR achieved by the proposed approach is 39.193dB, which is more optimal than the existing methods.
Protecting Girls from Harassment and Fraudulent Calls: A Voice-to-Text Approach

The rise in harassment calls and fraud, particularly targeting girls, has resulted in adverse consequences including psychological distress and, in extreme cases, suicide. Furthermore, fraudulent calls urging individuals to click on malicious links have led to substantial financial losses. This study presents a comprehensive approach to address this challenge for the development of an innovative detection system. Additionally, we introduce a novel prototype that employs a voice-to-text approach to transcribe phone calls, utilizing Natural Language Processing (NLP) techniques as well as Machine Learning (ML) algorithms to identify harassment or fraud-related content. When a malicious call is detected, the system automatically alerts parents or guardians and the nearest police station to prevent tragic outcomes such as suicides among targeted girls and financial fraud. By focusing on both preventive measures and advanced detection, this integrated approach aims to promote a safer communication environment and a more inclusive society.
Perception and Acceptance of Medical Chatbot Among Undergraduates in Ekiti State University, Nigeria

This study explores the perceptions and acceptance of medical chatbots among undergraduate students at Ekiti State University, Ado-Ekiti, Nigeria. A medical chatbot is an artificially intelligent conversational agent that simulates human-like communication, catering to user inquiries and generating logical responses. These chatbots leverage natural language processing and machine learning to engage in dynamic interactions, retrieve relevant information, and adapt to new data. This research investigates two primary aspects: the perception of undergraduate students towards the use of medical chatbots and the level of acceptance of these chatbots among the same demographic. The study employs a descriptive cross-sectional survey design, involving a sample size of 300 undergraduate students, determined using Taro Yamane's method. The data collection process includes a semi-structured questionnaire, validated by experts in Tests and Measurement and Public Health. The collected data are analyzed using SPSS version 28. The findings reveal an equitable gender distribution among participants, with a slightly higher representation of females. Additionally, a substantial proportion of respondents fall within the 18-25 age bracket, with a significant presence of undergraduates below 18 years old. The study indicates positive perceptions of medical chatbots among undergraduate students, suggesting a favorable view towards their adoption. While the majority of participants exhibit acceptance of medical chatbots, there is skepticism about the precision and reliability of healthcare suggestions provided by these platforms. In conclusion, this study sheds light on the positive perceptions and acceptance of medical chatbots among undergraduate students in Ekiti State University. The findings suggest a potential for integrating this technology into healthcare, education, and research endeavors, while acknowledging the need for further investigation into the underlying factors influencing these perceptions. As the healthcare landscape evolves, chatbots can potentially offer valuable contributions to enhancing health services, especially in scenarios where in-person visits are unnecessary. However, continuous research is essential to ensure their accuracy, trustworthiness, and effectiveness across diverse demographic groups
Intelligent Operation and Inspection Software Based on Edge Computing Concept and Artificial Intelligence Technology

A new intelligent operation and inspection software based on the concept of edge computing and artificial intelligence technology is proposed for the physical and operational architecture of substation electrical equipment intelligent inspection, and a human-computer interaction interface is designed for the convenience of operation and maintenance personnel, which effectively avoids the problems of poor real-time and monitoring blind areas of off-line inspection and conventional energized inspection, and solves the defects of cloud computing and manual inspection. The key technologies included in the realization of intelligent operation and inspection software functions are: the design of physical and operational architecture, and the analysis of structured and unstructured charged inspection data. The intelligent operation and inspection software is stable and reliable, which can greatly improve the efficiency of inspection and the intelligence level of the substation.
Breast Cancer Detection by Extracting and Selecting Features Using Machine Learning

The cancer of the breast is a significant cause of female death worldwide, but especially in developing countries. For better results and higher survival rates, early diagnosis and screening are crucial. Machine learning (ML) methods can aid in the initialdiscovery and diagnosis of breast cancer by choosing the most informative elements from medical data and eliminating irrelevant ones. The approach of feature extraction involves taking unstructured data and extracting a representative set of characteristics that may be used to classify or forecast data. The aim is to decrease the dimensionality of the feature space while upholding or even refining the accuracy of the ML model. An artificial intelligence model is developed on the given features to categorize mammography images into benign and malignant groups. Different supervised learning techniques, including support vector machines, random forests, and artificial neural networks, are employed and contrasted in order to select the best-performing model. This research offers a comprehensive framework for utilizing machine learning methods to detect breast cancer. The technique demonstrates how it might assist radiologists in the early detection of breast cancer by effectively extracting and selecting critical characteristics that could improve patient outcomes and potentially save lives.
Online Reviews System using Aspect Based Sentimental Analysis &amp; Opinion Mining

Aspect extraction is the most critical and thoroughly researched process in SA (Sentiment Analysis) for conducting an accurate classification of feelings. Over the last decade, massive amounts of research have focused on identifying and removing elements. Products have centralized distribution channels, and certain apps may occasionally operate close to the most recent product to be created. Any e-commerce business enterprise must analyses user / customer feedback in order to provide better products and services to them. Because broad reviews frequently include remarks in a consolidated manner when a customer gives his thoughts on various product attributes within the same summary, it is difficult to determine the exact feeling. The key components of this software are included in their release, making it a valuable tool for management to improve the consistency of their own system's specifications. The goal was to categories the aspects of the target entities provided, as well as the feelings conveyed for each aspect. First, we are implementing a supervised classification framework that is tightly restricted and relies solely on training sets for knowledge. As a result, the key terms comes from associated at various elements of a thing within its entirety perform customer sentiment using certain elements. In contrast to current sentiment analysis approaches, synthetic and actual data set experiments yield positive results.
Distributed Infrastructure for an Academic Cloud

The various community infrastructure literature reveals the challenges in educational institutions to embrace cloud computing trends. Setting up an own data center in effect means a private cloud. If research on the open cloud services is available within the institution, then the rollout of such research products becomes an in-house implementation. Thus, even reducing the dependence on cloud vendors. Distribution of resources opens the channel for better communication within academic institutions. It also attracts opportunities to procure individual hardware with a bigger gain. Enormous spending and unaccounted credits fall into central budgets if not controlled in a structured manner. Also, increasing the overall data management cost as an institution needs a different perspective for its’ long-term benefits. The expenses allow branching the cloud management tasks either in a vendor’s private cloud or own Cloud if feasible. Bigdata does touch the academics to so much extent that such disparate de-central data management creates several pitfalls. The solution then suggested to have a controlled environment claimed as distributed computing. Infrastructure spending shoots up with a pay as you go model. We claim that a distributed infrastructure as an excellent opportunity in the computing when performed at the cost of trust of a private cloud. The open-source movements experiment the distributed clouds by promoting OpenStack swift.
UUV Target Tracking Method Based on High Order CGHF Algorithm

Abstract The delay in the signal detected by sonar due to the limited speed of sound, in unmanned underwater vehicle (UUV) bearing-only target tracking with signal propagation delay, Cubature Gauss-Helmert Filter (CGHF) is often used for target tracking. CGHF uses the third-order Spherical-Radial Cubature Rule (SRCR) to calculate the Gaussian weight integral. Because underwater target tracking is a highly nonlinear system, third-order SRCR has only third-order Taylor expansion term accuracy, the tracking error of CGHF algorithm is large. Aiming at the problem that third-order SRCR has only third-order Taylor expansion term accuracy, a CGHF algorithm based on high-order SRCR is proposed in this paper. In this paper, the state model of Gauss-Helmert model (GHM) is established, the state of the model includes the signal transmission time. The measurement model of underwater target tracking is also given. Based on the GHM model, a new high order CGHF (HOCGHF) is derived by using higher order cubature points. In this paper, a method of evaluating the tracking accuracy of underwater targets and setting the initial filter value is given. Two kinds of underwater target tracking scenarios with different nonlinear degrees are established. The simulation results show that HOCGHF algorithm can effectively track the target in two different underwater target tracking scenarios, and has higher tracking accuracy than the existing methods.
Critical issues in Leveraging Blockchain in Healthcare Sector

Blockchain innovation has brought various benefits to the healthcare sector. Utilizing blockchains in clinical contexts will reduce handling time since when a patient signs up for a review, the complete collected data will be accessible at once because of accessibility on the distributed ledger. Also, specialists will not need to stress over the patients giving them a legit clinical history, because of their capacity to progressively see the correct, credible, and quality source-recorded information. It eliminates any likely clinical history mistakes. Similarly, the patients will not need to stress over having a second assessment from another specialist, because of the straightforwardness of the information. Having patient records on a blockchain organization will prompt individuals to know and associate with various others, across the globe, with similar ailments as they have, which is not only valuable for their well-being, but also make the patients feel acknowledged, upheld, and have reinforced determination to battle the ailment. Patients will have total independence regarding their information, and they will choose who to impart the information to. In this paper, we present all the challenges and critical issues associated with implementing blockchains in the healthcare sector.
Optimal Node Embedding Dimension Selection Using Overall Entropy

Graph node embedding learning has gained significant attention with the advancement of graph neural networks (GNNs). The essential purpose of graph node embedding is down-scaling high-dimensional graph features to a lower-dimensional space while maximizing the retention of original structural information. This paper focuses on selecting the appropriate graph node embedding dimension for hidden layers, ensuring the effective representation of node information and preventing overfitting. We propose an algorithm based on the entropy minimization principle, called Minimum Overall Entropy (MOE), which combines graph node structural information and attribute information. We refer to one-dimensional and multi-dimensional structural entropy (MDSE) as a graph’s structural entropy. A novel algorithm combines graph Shannon entropy, MDSE, and prior knowledge for faster convergence of optimal MDSE. We introduce an inner product-based metric, attribute entropy, to quantify node characteristics and simplify its calculation. Extensive experiments on Cora, Citeseer, and Pubmed datasets reveal that MOE, requiring just one computation round, surpasses baseline GNNs.
Mutual Information Dropout: Mutual Information Can Be All You Need

Dropout is a powerful way for preventing model overfitting. However, it is inefficient due to it randomly ignoring some neurons. Although there are many ways on Dropout, they are still either inefficient on improving generalization ability or not effective enough. In this paper, we propose Mutual Information Dropout, which is an efficient Dropout based on dropping neurons with low mutual information. In Mutual Information Dropout, instead of randomly ignoring some neurons, we first evaluated the mutual information of neurons to dropout with mutual information below a certain threshold. In this way, Mutual Information Dropout can achieve effective improving generalization ability with evaluate neurons. Extensive experiments on Three datasets show that Mutual Information Dropout is much more efficient than many existing Dropout and can meanwhile achieve comparable or even better generalization ability.
Graph Autoencoder-Based Anomaly Detection for Chemical Mechanical Planarization

The chemical mechanical planarization is a critical step in semiconductor manufacturing process, and anomaly detection can help improve yields and reduce costs. However, the CMP process is typically a batch process, and the data has the three-way structure that cannot be directly modeled. Moreover, most machine learning methods analyze samples in isolation for anomaly detection, without considering the wafer-to-wafer correlation in the process. In this paper, we propose a graph autoencoder-based framework for anomaly detection in CMP process. We first extract statistical and stable features from process variables, then generate a wafer graph by processing time and similarity, finally use graph autoencoder for anomaly detection. Experimental results on the 2016 PHM challenge dataset compared with four popular algorithms show that the proposed method achieves highest AUC, Precision@K and Recall@K.
Research on the modernization of the spirit of marathon sports culture based on information dissemination mechanism

Abstract This paper first explores the mechanism of marathon information culture dissemination by first calculating the time required for each layer of receivers to obtain the information and further obtaining the upper bound of the time required for the information to travel throughout the dissemination. The maximum allowable number of transmissions is obtained according to the protocol, and the lower bound of the propagation time is further obtained to find the time range of information propagation. Then, the structure of marathon sports culture is studied, and the marathon sports culture is divided into surface, middle and bottom layers, and the marathon spirit is discussed in depth. Finally, the communication effect of marathon sports culture under new media is analyzed in terms of the ways of communication and the comparison of new media and traditional media content. In the comparison between new media and traditional media, for the content of sports material culture in terms of sports architecture or sports sculpture and sports logo or clothing introduction, traditional media accounted for 11.9% and 9.8%, while new media were 3% and 4% more than traditional media. Among the topics and effects, the top three average reads were the topics of event information, registration and recruitment and brand promotion, with 490,963, 91,563 and 773,243 reads, respectively. The rise of new media has promoted the spread and development of marathon sports culture.
The construction of a new theoretical system of university education development based on the principle of multi-optimization of fuzzy ICDS

Abstract The purpose of exploring the new theoretical system of college education development is to improve college students’ quality in all aspects. This paper constructs a new theoretical system of college education development based on the new theory of college education development--”three comprehensive education” theory. The fuzzy credibility measure and credibility distribution are used to explain the fuzzy processing theory, and the uncertainty index of the fuzzy processing model is optimized based on the cross-rhombus search algorithm to improve the search speed of the fuzzy processing model. Data experimental analysis is conducted for the fuzzy ICDS multi-optimization principle. Regarding search speed, the average search speed improvement of the algorithm in this paper relative to the CD algorithm saves about 1.37 to 4.69 points per block. It improves classification accuracy by 12.02% and 7.65% compared to the best fuzzy and GFCM algorithms, respectively. This shows that using the fuzzy ICDS multi-optimization principle can help analyze the effectiveness of the “three holistic education” theory in constructing the education development system and provide a reference for planning and implementing the new education development system.
The Smart Coverage Path Planner for Autonomous Drones Using TSP and Tree Selection

The problem of efficiently covering an area with a limited number of sensors or cameras is an important research topic in various fields such as surveillance, monitoring, and inspection. This problem can be posed as a Traveling Salesman Problem (TSP), where the optimal path needs to be found to visit all the coverage points and return to the starting point. However, the computational complexity of solving TSP increases exponentially with the number of coverage points, making it impractical for large-scale applications. This paper proposes a novel approach to solving the coverage problem as a TSP by incorporating tree selection algorithms to improve efficiency and accuracy. The proposed approach represents the study area as a graph, with each node representing a coverage point and the edges representing the distance between them. To reduce computational complexity, we use various tree selection algorithms to select the best possible starting point for the TSP algorithm. To evaluate the effectiveness of the proposed approach, we conduct simulations using different scenarios with varying numbers of coverage points. The simulation results demonstrate that the proposed approach significantly improves the quality of coverage while reducing computational complexity compared to traditional TSP algorithms. Moreover, the proposed approach can be applied in a wide range of real-world applications, such as surveillance, monitoring, or inspection, where efficient and accurate coverage of an area is essential.