Reviewerly/exHarmony huggingface dataset:
End-to-End Learning for Weakly Supervised Video Anomaly Detection Using Absorbing Markov Chain

We propose a principled deep neural network framework with Absorbing Markov Chain (AMC) for weakly supervised anomaly detection in surveillance videos. Our model consists of both a weakly supervised binary classification network and a Graph Convolutional Network (GCN), which are jointly optimized by backpropagation. Unlike the previous works that employ AMC for label noise filtering in a post-processing step, the proposed framework migrates the component inside the GCN part of our model and realize an end-to-end learning network. The integration of the AMC module into our deep neural network model enables us to learn the associated parameters automatically, which is helpful to improve the quality of segment-wise label estimation via tightly-coupled processing between the main network and the AMC model. Our algorithm achieves outstanding performance compared to the state-of-the-art weakly supervised anomaly detection methods on UCF-Crime and ShanghaiTech dataset.
Metaverse and Its Regulation

The digital era has improved many aspects of our lives. There has never been greater access to information, nor a time when so much of the information available to us was free. Many marginalized groups and individuals now have large and unstoppable digital megaphones in their hands. Those who are physically far apart can feel closer to each other. Art has never been so easy to find, nor have so many artists paid for their work. Similarly, knowledge and expert opinions in fields ranging from medicine to law to education to aeronautics to finance are readily available through digital media (usually free).The metaverse is a shared virtual world accessed through physical means that users can access from any platform while connected to the internet, and where they can interact through their virtual avatars. It will likely be used for a multitude of purposes such as gaming (the primary use as of now), shopping and novel advertising, job training, education, working remotely, doctor’s appointments, travel, social activities, entertainment, buying and selling virtual land, trading crypto & NFT’s and selling real-world goods virtually. Access to the Metaverse can be via virtual reality, augmented reality, or a combination of both. Metaverse is a unique development in cyberspace. It uses Virtual Reality (VR) and Augmented Reality (AR) headsets to transpose users from physical space to virtual reality. A person sitting in Nigeria can roam the fields and hill tracts of Mongolia. People can weigh in on opinions about the Russia-Ukraine conflict, Taiwan strait crisis, and Israel-Palestine dispute amongst a host of other topics with people across the globe while interacting in the Plaza and people with common interests such as soccer fans can find a meeting place. Also, business executives can conduct conferences, attend meetings, and deliver presentations while sitting in the comfort of their homes. However, a host of important questions arise as to how to regulate this new frontier. The issues of concern regarding data collection (such as biometric data specifically), which might later be used by algorithms for targeted advertised campaigns make regulators wary of this new space. Moreover, there is the looming threat that with a lack of criminal code regulating activities in the metaverse, it might be circumvented by unscrupulous fraudulent actors for unlawful activities. These concerns are addressed in Section iii (a) of this report. Also, the question of regulating and underwriting financial transactions and contracts in the metaverse is something that is not entirely covered by existing laws and regulations. Lastly, the spread of fake news across the metaverse can result in disastrous consequences in the real world. The need to regulate the metaverse requires a robust legal architecture that caters to the ever-evolving needs of this new space; in particular regulating targeted advertising and marketing in this new domain
Human Pose Tracking and Juxtaposition (Hptj) Model: Pose Monitoring System Using Deep Learning

Human pose estimation is the process of continuously monitoring a person’s action and movement to track and monitor the activity of a person or an object. Human pose estimation is usually done by capturing the key points which describe the pose of a person. A guiding practicing framework that enables people to learn and exercise activities like yoga, fitness, dancing, etc., might be built using human posture recognition remotely and accurately without the help of a personal trainer. This work has proposed a framework to detect and recognize various yoga and exercise poses to help the individual practice the same correctly. A popular Blaze-pose model extracts key points from the student end and compares the same with the instructor pose. The extracted key points are fed to the HPJT model to compare the student pose with the instructor. The model will assess the correctness of the pose by comparing the extracted key points and give feedback to students if any corrections need to be made. The proposed model is trained with 40+ yoga and exercise poses, and evaluated the model’s performance with the mAP, and the model achieved an accuracy of 99.04%. The results proved that any person could use the proposed framework in real-time to practice exercise, yoga, dance, etc. At their respective location without the help of a physical instructor with precision and accuracy, leading to a healthy life.
Early Prediction of Electronics Engineering Licensure Examination Performance using Random Forest

The performance in the licensure examination of graduates has a direct influence on the different aspects of a higher educational institution. This study compared the accuracies of several classification algorithms to identify predictors for student’s professional certification success using an extensive data mining process. Based from the evaluation results, the Random Forest model got the highest cross-validated accuracy score of 92.70%. Permutation feature importance, a model inspection technique, was used to discover knowledge from data of 500 graduates of electronics engineering program of Southern Luzon State University from 2014 to 2019. Out of the 33 variables, verbal reasoning or reading comprehension skills is found to have a strong attribution to students’ licensure examination outcomes together with ratings from mathematical, professional and electrical circuit courses. Hence, the knowledge extracted from the data can serve as a basis for crafting policies, interventions and strategies to improve student’s performance in the electronics engineering licensure examinations.
A Practical Method to Eye-tracking on the Phone: Toolkit, Accuracy and Precision

While eye-tracking has become a core asset for many computing environments, mobile phones, as a prime computing device, are lacking a practical platform to conduct eye-tracking studies and develop gaze applications easily. In this work, we aim to tackle this issue by investigating a system concept that allows for the deployment of remote eye-trackers for mobile devices. We describe a toolkit that supports eye-tracking in mobile apps based on a simple phone, PC, and remote eye tracker setup. We evaluate our approach through a technical evaluation of accuracy and precision in various user contexts important for mobility (sitting, standing, walking, lying). Our results show that eye-trackers can be easily used with high accuracy, and how it is impacted through body posture and motions of the user. Our work paves the way for enabling easy-to-use eye-tracking studies on mobile devices.
A Survey of Sign Language Recognition Systems

Technology is now employed to solve practically all humanity's problems. The vast communication gap between the common people and the deaf community is one of the society's fundamental challenges. Computer-based Sign Language Recognition (SLR) systems that translate gestures into text/speech are now being developed by researchers all over the world. This paper attempts to summarize the current state-of-the-art methodologies used by various researchers in the field of SLR systems, right from collecting the data, image processing and using it to translate the gestures. Moreover, the steps followed to develop a SLR system are discussed, and some of the existing SLR applications are reviewed.
Architecture Optimization and Core Code De-Redundancy of Educational Management Information System in Higher Vocational Colleges Under the Background of Data Governance Algorithm

From the two aspects of data standardization and data cleaning, formulate data abnormality judgment rules, establish data abnormality processing methods, and conduct data governance on online monitoring data. Summarized and put forward the concepts and methods suitable for the reengineering of teaching management process in higher vocational colleges, and after the practical test of teaching management, integrated and optimized some core teaching management processes in the information environment, as well as the optimization of its structure and the redundancies of core codes. Briefly analyze. By clarifying the allocation of powers and responsibilities, management systems, governance objectives, and governance routes, etc., we will improve my country's government data governance system and provide effective organizational guarantees for government data governance.
Counselors Use Social Networks to Build Intelligent and Clustered Theoretical Analysis of Leading Topics

Based on the new media and Web technology, this article analyzes the use of social networks by college counselors to construct intelligent and clustering theories leading topics. The era of Web3.0 and mobile Internet has come, and the penetration of social networks into the growth environment of college students has become ubiquitous. Intelligent analysis and big data visualization technologies have carried out data collection, storage, and analysis on the situation of students in the era of big data, and finally formed an intelligent management model of big data. Firmly establish an open and inclusive way of thinking, effectively use the advantages of new media and self-media to prevent bad tendencies and problems in a timely manner, reconstruct the teacher-student interaction in the social network environment, enhance content attractiveness and ideological appeal, and seize online education and the initiative for comprehensive education work increased by 7.1%.
Detection of Facial Components Utilizing a Modified Version of the Viola-Jones Algorithm

One area that has received the most excellent attention from researchers in recent years is face recognition, which is often regarded as the primary motivating factor for developments in computer vision technology. This article discusses the importance of facial recognition technology and the many approaches developed Because the computer processes the data, many algorithms have been devised to identify faces. The face detection technique is examined and some of its more significant difficulties. This work describes a Viola-Jones algorithm to recognize faces in an image and find facial characteristics. An important part of the process is locating the different features of the face, such as the eyes, the nose, the mouth, and the face itself. This method is used to recognize and locate the many features of a human face shown in an image.
Multi-Area Complete Coverage with Fixed-Wing UAV Swarms Based on Modified Ant Colony Algorithm

As a typical application, multiple unmanned aerial vehicles (UAVs) are usually sent to search for possible targets in chosen areas. In this paper, the multi-area complete coverage problem using fixed-wing UAV swarms is investigated, where the areas can be of arbitrary shape and size. For each area, its longitude direction, corresponding to the optimal sweep direction, is firstly found by the diameter function method, and is then used to compute the sweep waypoints. Observing that coverage gap and redundancy exist, we propose a waypoint adjustment procedure to achieve gap filling and redundancy eliminating. The problem can then be formulated as a multi-vehicle routing problem, and a modified ant colony algorithm is presented to address it. Finally a simulation example shows that 10 UAVs can cover 5 areas in 682.8 seconds.
Development of Language Feature Analysis System for Mineral Engineering English Corpus Based on Web

Because the existing software has different versions of the research results, and the research methods have no continuity and coherence, it is convenient for professionals to conduct research on language characteristics by creating new functional processes. Using the technical characteristics of the database, compared with the existing analysis system, which only has the means of comparison between corpora and corpora, it has established a mineral science and technology English corpus. The system has added the function of uploading and comparing corpora, The system has added the function of corpus and uploaded corpus, as well as the comparative analysis between uploaded corpus and uploaded corpus, and expanded the application scope of the system. For a series of problems in the system design and implementation and problems that need to be improved and optimized, the system has also proposed problems that are not well solved in terms of function and technology, as well as preliminary solutions.
Construction of Intelligent Detection System for Dissemination of College Students' Entrepreneurship Management System based on Fuzzy Network Information Feedback Analysis Algorithm

Aiming at the problem that the fuzzy C-means clustering algorithm cannot be directly used for fuzzy clustering of incomplete data, this paper proposes a fuzzy clustering algorithm for incomplete data based on information feedback RBF network estimation. A multi-path based inference system based on fuzzy neural network is proposed. The planning algorithm and the related path risk avoidance mechanism are expounded. Then, from the aspects of organizational system construction, application of new technologies such as big data, resource integration, and mechanism construction, it proposes a strategy for building a smart employment and entrepreneurship service system for college students. Then, from the aspects of organizational system construction, application of new technologies such as big data, resource integration, and mechanism construction, it proposes a strategy for building a smart employment and entrepreneurship service system for college students.
Research on Entity Naming Algorithm for NLP Based on Multi-level Fusion Recurrent Neural Network

Natural language processing (NLP) entity naming is one of the important contents of NLP. Its function is to extract information with practical meaning from the text so the system can perform high-level analysis. Due to the polysemy of words in traditional language texts, the texts in long, difficult sentences and complex sentences are difficult to be recognized by machines and accurately, which brings certain troubles to the current program naming algorithm. Therefore, it is necessary to design a new NLP entity naming algorithm, through the deep learning algorithm analysis of the language text, fully integrate it so it can be parsed and named by the computer language. This paper first analyzes the problem of entity naming in NLP and introduces the neural network architecture and supporting attention mechanism. It can better recognize natural language named entities.
Wavelet Local Binary Pattern (WLBP): A Novel Local Descriptor in Harsh Light Variations

LBP and most of its variants performed well in moderate light changes. But under harsh light changes their performances are not effective. So there is a need of more productive descriptor in harsh light variations. With this note, the proposed work introduces a novel descriptor under harsh light variations so-called Wavelet LBP (WLBP). Initially input image is passed through wavelet decomposition (by using haar wavelet at level 1), which generates four sub-bands. First is approx element and other three are detail elements. Further LBP is deployed to all four sub-bands for feature extraction. It has been noticed during the experiments that histogram of the transformed image doesn't perform impressive in harsh light variations. In contrary, map feature beats the histograms feature comprehensively. Therefore the map LBP features from all four sub-bands are integrated into one framework called as WLBP. FLDA is utilized for the feature compaction and matching is achieved from SVMs. Results are done on YB and EYB datasets, whose images are severely deteriorated by light variations. The accuracy achieved by WLBP is well par from previous histogram based descriptors. WLBP also outperforms all literature methods.
Design of personalized recommendation algorithm for mobile intelligent book management system based on cloud computing technology

With the continuous maturity of network technology, digital technology has gradually penetrated into many aspects of real life. The concept of digital technology is consistent with the concept of library management. The use of digital technology can realize the connection of library management information value chain. Library management is cumbersome and complex, the number of borrowing continues to increase [1], including a large number of information data, there is an urgent need to develop an effective library information management system. At present, in today's society with the continuous development of economy and the continuous innovation of science and technology, the management procedure of library borrowing information cannot meet people's growing needs. There will also be problems such as improper information processing and less functions, which will eventually affect people's use emotion and borrowing service. Therefore, in order to give readers a more comfortable and convenient borrowing experience and more effectively improve the library workflow, it is particularly important to design and run a library borrowing information management system based on cloud computing. Therefore, this paper studies the design of book query system based on Java, in order to reduce the work intensity of library managers and provide convenience for readers to query books. Using the relevant algorithms of big data technology to classify and sort out book information, provide users with personalized recommendations and improve their use efficiency. It can also make it easier for administrators to manage books and user information and improve the utilization rate of book resources.
Personalized Intelligent Recommendation of Cultural Resources Based on User Preference Collaborative Filtering Algorithm

With the rapid growth of user information, more and more netizens hope to obtain more comprehensive and effective useful resources through the Internet. However, the traditional information retrieval system can not meet the requirements of its very large amount of data, especially complex and extremely valuable because of the low efficiency of algorithm, low accuracy and limited database resources. Therefore, this paper proposes a collaborative filtering algorithm based on user preference to study the personalized recommendation of ethnic cultural resources. Firstly, this paper introduces the concept, function and characteristics of ethnic cultural resources, and then studies the collaborative filtering algorithm based on users' preferences. On this basis, the personalized recommendation framework of ethnic cultural resources is studied, and the operation performance of this framework is tested. The final test results show that the personalized recommendation method of ethnic cultural resources based on user preference collaborative filtering algorithm is different from other similar applications. Using the same attribute set for the same purpose can obtain more similarity information. Therefore, it can be seen that the personalized recommendation method of ethnic cultural resources based on user preference collaborative filtering algorithm can meet the basic needs.
A path planning algorithm based on improved RRT* for UAVs

The path planning for UAVs in cluttered environments is one of the keys to ensuring flight missions. This paper considers a more radical strategy based on the informed rapidly exploring random tree star algorithm and presents an improved rapidly exploring random tree star algorithm. In the stage where the feasible path between the starting position and the target position is not searched, the random sampling range is reduced to a variable ellipse whose two focuses are the start and end positions, respectively. The ellipse's major axis depends on an adaptation factor that varies with the number of sample points. The improved rapidly exploring random tree star algorithm can sample in the ellipse at the initial stage of path planning to obtain a higher search efficiency and expand the search range when no feasible path is found for a long time. At the same time, the rapidly exploring random tree, rapidly exploring random tree star, and informed rapidly exploring random tree star algorithms are compared with the proposed improved rapidly exploring random tree star algorithm in simulation. The simulation results show that the improved rapidly exploring random tree star algorithm in this paper can achieve higher search efficiency.
Application Research of Monitoring Abnormal Behavior Analysis and Detection Method Based on GANomaly Network

With the wide application of video surveillance, video surveillance has achieved full coverage of various key places. For video surveillance, the amount of internal information is huge, and the degree of reduction of events is high. Traditionally, a real-time manual inspection can neither meet the full coverage of the time nor achieve the full range of space with low labor cost. The idea of using semi-supervised generative adversarial networks for abnormal behavior detection is proposed to deal with this problem. This paper focuses on this problem, starting from the structure and optimization of the auto-encoder, and studies the basic method of using the encoder for feature extraction. After that, the convolutional generative adversarial network was constructed, and GANomaly was further optimized to detect anomalies by comparing the feature differences between the original image and the abstract image to improve its anti-interference ability. Finally, this paper discusses the reconstruction method of the loss function and defines the loss functions of the adversarial training and discriminative models, respectively. The research in this paper is expected to effectively improve the effect of monitoring abnormal behavior detection and enhance the application value of video surveillance real-time detection.