Reviewerly/exHarmony huggingface dataset:
Weakly Supervised Video Re-localization Through Multi-agent-reinforced Switchable Network

The objective of video re-localization (VRL) is to localize a successive sequence of frames, namely, the target moment, from untrimmed reference videos that semantically correspond to a given query video. During training, the weakly supervised setting of VRL provides only coarse-grained video-level rather than frame-level annotations. For the weakly supervised VRL (WS-VRL) task, obtaining effective video feature representations that can be used to evaluate the relevance between videos and localizing the accurate temporal boundaries of the target moment remain challenging. In this paper, a novel multi-agent-reinforced switchable network (MARS) is proposed to address these challenges. MARS can adaptively guide video feature encoding and moment localization using multiple learned agents. Specifically, an agent-controlled switchable encoder is used to obtain effective video feature representations, and an agent-reinforced boundary localizer is used to determine accurate localized moments through progressive refinement. Furthermore, a relevance-oriented reward generator was designed to estimate the relevance of the localized moment to the query video and assign a reward to multiple agents. The effectiveness of the proposed MARS model was verified through extensive experiments on the ActivityNet-VRL dataset.
Audit model using controls from ISO/IEC 27002 for the information security of electronic voting based on IoT and Blockchain

In recent years, significant progress has been made in the integration of technologies in voting systems with the aim of improving the transparency of the process. This study proposes an audit model based on the ISO/IEC 27002 standard to evaluate electronic voting systems that make use of Blockchain technology and the Internet of Things (IoT), with the aim of improving security in voting processes. To validate this model, an evaluation of the security control was carried out by applying this international standard to a voting system that integrates Blockchain and IoT. In our analysis of 6 security controls applied to the reality of electronic voting, the system passed all established tests; Therefore, it can be concluded that the risk of compromising system information has been significantly mitigated. This research contributes to the advancement of the security of electronic voting systems and provides a solid foundation for future developments in this field. The combination of emerging technologies and international standards promotes trust, security and therefore mitigates any risk of compromising an electronic voting system by following the best practices of the international standard ISO/IEC 27002.
Meta-TFEN: A Multi-Modal Deep Learning Approach for Encrypted Malicious Traffic Detection

Malware poses a significant threat to internet security. Existing deep learning-based methods for malware traffic detection typically rely on single-modal features, overlooking the heterogeneity of encrypted traffic, thus limiting their detection performance. To address this limitation, this paper proposes a multi-modal deep learning approach called Meta-TFEN for detecting encrypted malicious traffic. The method utilizes TCN, Bi-GRU, and LSTM to extract multi-modal features including the payload of secure transport layer protocols, statistical features, and features of TLS encryption activities. It employs an fusion network to capture the dependencies between modalities and integrates discriminative features to enhance detection performance. Additionally, this paper uses a meta-learning framework for classification to enable rapid deployment of the model. The performance of Meta-TEEN evaluated on public datasets and its applicability in real-world environments is explored using real samples. The experimental results clearly demonstrate that the Meta-TEEN method surpasses other state-of-the-art methods in terms of accuracy.
The Role of College Students' Innovation and Entrepreneurship under the New Normal

With the development of big data technology and Internet technology, the "Internet Plus" plays an important role in various fields. It will become a new educational model to propel the development of the innovation and entrepreneurship education of college students, thus providing them with a clear direction of innovation and entrepreneurship, so that they can adapt to the changes of the new environment. At the same time, in the context of the new normal, "Internet Plus" can also bring new ideas to college students' entrepreneurial activities, which can enable them to provide an important contribution to the realization of innovation development goals in China, to cultivate more innovative talents. Each section is developed in a staggered way and connected through the Fengcheng river system to form a circular artery connecting the "Confucian worship" block, Old Street, Daohe Ancient Block and Jing'an Road Block and reaching Wanda Plaza. It aims to realize the perfect combination of tradition and modernity, create a "Confucian worship Tour - Fengcheng River cultural tourism scenic belt" with the characteristics of "Confucianism", "opera", "folk custom" and "business", and promote the establishment of national AAAAA level tourist attraction.
Analysis of the impact of using containerization techniques on application performance in Python

This article comprehensively evaluates the impact of two containerization environments, Docker and Podman, on the performance of Python applications. The paper characterizes the two tools and presents the differences in their architectures. The scope of the study covers three aspects. The first is a comparison of resource usage, such as CPU usage, RAM usage and execution time, during the calculation of the number π. The next step is to analyse the resource usage when sorting an ordered list. The final aspect of the research is a comparison of the start-up time of the container in both environments. The tests carried out allow the presence of a performance overhead in both containerization environments, with an average of 8%. In addition, it can be seen that there is better resource management in the case of the Podman tool and a more dynamic environment in the case of the Docker tool.
On Predefined-Time Projective Synchronization of Fuzzy Inertial Neural Networks

This paper explores the projective synchronization of fuzzy inertial neural networks within predefined-time. An effective control strategy is proposed by means of a time-dependent scaling function. Sufficient criteria are derived rigorously for implementing the projective synchronization of fuzzy inertial neural networks within predefined-time. Particularly, when the projective coefficient is chosen as 1 or −1, the criteria of predefined-time complete and anti-synchronization of fuzzy inertial neural networks as a corollary are also provided. A numerical example is given to demonstrate the correctness of results in this paper.
Big Data Resources for Digital Pathology

The Neural Engineering Data Consortium (NEDC), known for its open source data resources [1], has previously released the Breast Tissue subset of the Temple University Digital Pathology Corpus (TUDP), which consists of 3,505 partially annotated images [2]. This corpus is part of a much larger repository of over 100,000 images that will be released as part of NEDC’s digital pathology resources in 2023. In this abstract, we introduce our recently released corpus of 14,288 digital pathology images that were collected from Fox Chase Cancer Center’s (FCCC) Biosample Repository [3], and describe some changes being made to TUDP to organize these corpora in a unified framework.
Analyzing Factors Impacting Intention to Use AI-Powered Tools in the Education Field

This study investigates the various elements associated with and the consequences of Information Quality, Social Influences, Facilitating Conditions, Attitude Toward Using, Internet Experience, Self-Efficacy, and Intention to Use AI-powered educational tools. The inevitability of applying artificial intelligence (AI) in the education sector is evident due to the expanding AI market and the proliferation of AI-powered solutions. Numerous artificial intelligence (AI) tools have been integrated into education, shown by the utilization of GitHub Co-pilot and PyTorch, which facilitate the acquisition of coding skills. The research employed a Purposive sampling methodology for data collection and used Smart-PLS for data analysis. The data collection platform will utilize Google Forms, specifically in the Jabodetabek region of Indonesia, from April to July 2023 to ensure that respondents can contribute data remotely. The Structural Equation Modeling (SEM) data analysis employs SMART PLS as its primary analytical tool. The research model of this study comprises a total of nine variables. The variables under consideration encompass Information Quality, Social Influences, Perceived Usefulness, Perceived Ease of Use, Facilitating Condition, Internet Experience, Self-Efficacy, and Intention to Use. There are a total of nine hypotheses, with eight of them yielding significant results while one does not reach statistical significance.
Knowledge Graph Construction and Representation Method for Potato Diseases and Pests

Potato diseases and pests have a serious impact on the quality and yield of potatoes, and timely prevention and control of potato diseases and pests is essential. A rich knowledge reserve of potato diseases and pests is one of the most important prevention and control measures; however, valuable knowledge is buried in the massive data of potato diseases and pests, making it difficult for potato growers and managers to obtain and use it in a timely manner and to develop the potential of knowledge. Therefore, this paper explores the construction method of a knowledge graph for automatic knowledge extraction, which extracts the knowledge of potato diseases and pests scattered in heterogeneous data from multiple sources, organises it into a semantically related knowledge base, and provides potato growers with professional knowledge and timely guidance to effectively prevent and control potato diseases and pests. In this paper, a data corpus on potato diseases and pests, called PotatoRE, is first constructed. Then, a model of ALBert-BiLSTM-Self_Att-CRF is designed to extract knowledge from the corpus to form a triplet structure, which is imported into the Neo4j graph database for storage and visualisation. Furthermore, the performance of the model constructed in this paper is compared and verified using the datasets PotatoRE and People’s Daily. The results show that compared to the SOTA models of ALBert BiLSTM-CRF and ALBert BiGRU-CRF, the accuracy of our model has been improved by 2.92% and 3.12%, respectively, using PotatoRE. Compared to the Bert BiLSTM-CRF model on two datasets, our model not only improves the accuracy, recall, and F1 values, but also has a higher efficiency. The model in this paper solves the problem of the difficult recognition of nested entities. On this basis, through comparative experiments, the TransH model is used to effectively represent the constructed knowledge graph, which lays the foundation for achieving inference, extension, and automatic updating of the knowledge base. The achievements of the thesis have made certain contributions to the automatic construction of large-scale knowledge bases.
Research on Machine Learning with Algorithms and Development

Machine Learning, as one of the key technologies in the field of artificial intelligence, has made significant advancements in recent years. This study provides a relatively systematic introduction to machine learning. Firstly, it gives an overview of the historical development of machine learning, and then focuses on the analysis of classical algorithms in machine learning. Subsequently, it elucidates the latest research advancements in machine learning, aiming to comprehensively explore the applications of machine learning in various domains and discuss potential future directions.
Hydrothermal monazite and xenotime chemistry as genetic discriminators for intrusion-related and orogenic gold deposits: implications for an orogenic origin of the Pogo gold deposit, Alaska

Abstract Attempts to geochemically distinguish between metamorphic-hydrothermal systems that form orogenic gold deposits and both reduced and oxidized magmatic-hydrothermal systems using isotopes or metal associations have proven ambiguous, particularly for orogenic gold and reduced intrusion-related gold systems. The absence of conclusive geochemical discriminators and the overlap in geologic characteristics have led to gold deposit models being potentially incorrectly applied, which in turn negatively affect regional mineral exploration and mine planning. In this study, in situ electron microprobe geochemical analyses of hydrothermal monazite and xenotime crystals associated with different types of gold-bearing deposits are shown to be effective geochemical discriminators. There are notable differences in mineral chemistry such as rare earth element (REE) profiles, total light REE, Dy, Er, Pr, Y, Nd/Sm, and La/Sm that distinguish monazite precipitated from metamorphic-hydrothermal fluids that form orogenic gold deposits and those precipitated from magmatic-hydrothermal fluids that form both porphyry Cu-Mo-Au and reduced intrusion-related gold deposits. Notable differences in overall xenotime abundances and concentrations of heavy REEs, Ca, and Sc are distinctive between the different deposit classes for xenotime. The origin of the controversially classified Pogo gold deposit, Tintina gold province, Alaska, which has been characterized as both a reduced intrusion-related and an orogenic gold deposit, is tested based upon the noted chemical differences associated with these hydrothermal phosphates. The findings of this study have implications for exploration and mine development in the Tintina gold province and other areas that contain deposits that are controversially classified as either orogenic or as magmatic-hydrothermal gold deposits.
Research on the Characteristic Training Mode of Applied Talents with Industry and Education Integration and Collaborative Education

This paper takes the current social background of China as the starting point, and conducts in-depth research and discussion on the characteristic training mode of applied talents of industry-education integration and collaborative education. This paper advocates to improve the quality of application-oriented talents training by deepening school-enterprise cooperation, optimizing educational content and improving educational methods.
AI-Powered Segmentation of Invasive Carcinoma Regions in Breast Cancer Immunohistochemical Whole-Slide Images

Aims: The automation of quantitative evaluation for breast immunohistochemistry (IHC) plays a crucial role in reducing the workload of pathologists and enhancing the objectivity of diagnoses. However, current methods face challenges in achieving fully automated immunohistochemistry quantification due to the complexity of segmenting the tumor area into distinct ductal carcinoma in situ (DCIS) and invasive carcinoma (IC) regions. Moreover, the quantitative analysis of immunohistochemistry requires a specific focus on invasive carcinoma regions. Methods and Results: In this study, we propose an innovative approach to automatically identify invasive carcinoma regions in breast cancer immunohistochemistry whole-slide images (WSIs). Our method leverages a neural network that combines multi-scale morphological features with boundary features, enabling precise segmentation of invasive carcinoma regions without the need for additional H&amp;E and P63 staining slides. In addition, we introduced an advanced semi-supervised learning algorithm, allowing efficient training of the model using unlabeled data. To evaluate the effectiveness of our approach, we constructed a dataset consisting of 618 IHC-stained WSIs from 170 cases, including four types of staining (ER, PR, HER2, and Ki-67). Notably, the model demonstrated an impressive intersection over union (IoU) score exceeding 80% on the test set. Furthermore, to ascertain the practical utility of our model in IHC quantitative evaluation, we constructed a fully automated Ki-67 scoring system based on the model’s predictions. Comparative experiments convincingly demonstrated that our system exhibited high consistency with the scores given by experienced pathologists. Conclusions: Our developed model excels in accurately distinguishing between DCIS and invasive carcinoma regions in breast cancer immunohistochemistry WSIs. This method paves the way for a clinically available, fully automated immunohistochemistry quantitative scoring system.
A Novel Artificial Intelligence Techniques for Women Breast Cancer Classification Using Ultrasound Images

Background: Females benefit from ultrasound screening and diagnosis of breast cancer, and artificial intelligence has enabled the automatic identification of medical conditions on medical imaging. Methods: This study aimed to develop machine learning (ML) and deep learning (DL) models for the detection and classification of breast cancer in a breast ultrasound image (BUSI) and United States (US) ultrasound images datasets and to compare the models’ performance to previous studies. The ultrasound scans were collected from women between the ages of 25 and 75. The dataset contains 780 images with a resolution of 500 × 500 pixels. There were 133 normal images with no cancerous masses, 437 images with cancerous masses, and 210 images with benign masses among the 780 cancerous images in the BUSI dataset whiles the US ultrasound images includes 123 and 109 ultrasound images of malignant and benign breast tumors. Two traditional ML models, random forest (RF) and K-Nearest Neighbor (KNN), as well as a deep learning (DL) model using convolutional neural networks (CNN), were trained to classify breast masses as benign, malignant, or normal. Results: The CNN obtained an accuracy of 96.10%, the RF an accuracy of 61.46%, and the KNN an accuracy of 64.39% with the BUSI dataset. Standard evaluation measures were employed to assess the performance for benignancy, malignancy, and normality classification. Furthermore, the models’ area under the curve-receiver operating characteristics (AUC-ROC) are 0.99 by the CNN, 0.85 by the RF, and 0.65 by the KNN. Conclusions: The study’s findings revealed that DL surpasses conventional ML when it comes to training image datasets; hence, DL is suggested for breast cancer detection and classification. Furthermore, the resilience of the models used in this study overcomes data imbalance by allowing them to train both binary and multiclass datasets.
Effective Strategies to Motivate Primary and Secondary School Students to Learn Chinese

Chinese learning motivation is the internal motivation that directly promotes students' Chinese learning, and for primary and secondary school students, strengthening Chinese learning motivation plays an important role in the improvement of their performance and overall development. In order to address this often talked about issue, this paper starts from analysing the conceptual composition of Chinese learning motivation, collating and analysing and synthesizing the current empirical research, and arriving at the factors affecting students' Chinese learning motivation. Chinese learning motivation is mainly divided into internal factors including students' personal Chinese learning motivation and learning interest, and external factors including family, school and society. On this basis, this paper refers to the first-line research results on enhancing Chinese learning motivation at home and abroad, and combines the author's investigation and teaching practice experience to explore effective strategies to enhance students' Chinese learning motivation from internal factors including reasonable attribution, internal motivation for learning, self-regulation ability, and external factors including Chinese classroom teaching, the role of Chinese teachers' transformation and students' learning life.
A Usability Study of Mobile Augmented Reality-Based Learning Media for Heavy Equipment Safety in Indonesian Vocational School

Mining involves working in hazardous environments with various risks. The most common cause of mining accidents was mechanical failure, involving the use of heavy equipment. Thus, students who learn mining geology in vocational school must increase their knowledge of heavy equipment safety before they can work with it in the actual workplace. However, heavy equipment is very expensive and requires proper inspection and routine maintenance which needs high financial allocation for vocational schools. Thus, technology that can develop heavy equipment safety learning media reasonably affordable, interactive, and safe is needed. With the help of immersive technology, such as augmented reality (AR) will benefit students to attain important knowledge in heavy equipment safety. This study developed a learning module and a mobile AR application as learning media for heavy equipment safety and evaluated its validity and usability.
FORMATION OF PROFESSIONAL COMPETENCE OF A LECTURER IN AN INSTITUTION OF HIGHER EDUCATION UNDER INCLUSIVE CONDITIONS

The article describes the process of formation of professional competence of teachers in institutions of higher education in the process of professional activity in the conditions of inclusive education. Now, competence is interpreted as the ability or readiness to implement the acquired knowledge, skills, and abilities in real activity. Competence components are subject and operational knowledge, skills and abilities - ability and readiness to use them in activities and confidence in activities and responsibility for their results. In modern society, which is constantly undergoing changes, the high professionalism and professional competence of teaching specialists, teachers, has acquired an important importance, because the competitiveness of specialists is an urgent issue that is being prepared by a higher education institution. This directly applies to workers in the field of education with an inclusive population, because teachers must be competent in the scientific field of activity, because professional education has always been and continues to be aimed at the comprehensive development of the individual in order to prepare him for active and effective participation in public production with the greatest benefit for themselves and for society as a whole. In world educational practice, the concept of competence acts as a central, a kind of "nodal" concept, because the competence of an individual: combines the intellectual and practical components of education; the concept of competence contains the ideology of interpreting the content of education, which is formed from the result; the competence of the individual has an integrative nature, as it incorporates a number of homogeneous or closely related knowledge and experience belonging to broad spheres of culture and activity (informational, legal, etc.). Personal competence has a certain structure, the components of which are related to a person's ability to solve various problems in everyday, professional or social life. The structure of competence of a person includes: competence in the field of independent cognitive activity; in the sphere of civil and social activities; in the field of social and labor activity; competence in the household sphere; in the field of cultural and recreational activities. Among the knowledge and practical experience formed in the process of a person achieving a certain level of competence are the skills of self-education, critical thinking, independent work, self-organization and self-control, teamwork, the ability to predict the results and possible consequences of various solutions, establish cause-and-effect relationships connections, find, formulate and solve problems.