Reviewerly/exHarmony huggingface dataset:
CNN-Based Crosswalk Pedestrian Situation Recognition System Using Mask-R-CNN and CDA

Researchers are studying CNN (convolutional neural networks) in various ways for image classification. Sometimes, they must classify two or more objects in an image into different situations according to their location. We developed a new learning method that colored objects from images and extracted them to distinguish the relationship between objects with different colors. We can apply this method in certain situations, such as pedestrians in a crosswalk. This paper presents a method for learning pedestrian situations on CNN using Mask R-CNN (Region-based CNN) and CDA (Crosswalk Detection Algorithm). With this method, we classified the location of the pedestrians into two situations: safety and danger. We organized the process of preprocessing and learning images into three stages. In Stage 1, we used Mask R-CNN to detect pedestrians. In Stage 2, we detected crosswalks with the CDA and placed colors on detected objects. In Stage 3, we combined crosswalks and pedestrian objects into one image and then, learned the image to CNN. We trained ResNet50 and Xception using images in the proposed method and evaluated the accuracy of the results. When tested experimentally, ResNet50 exhibited 96.7% accuracy and Xception showed 98.7% accuracy. We then created an image that simplified the situation with two colored boxes of crosswalks and pedestrians. We confirmed that the learned CNN with the images of colored boxes could classify the same test images applied in the previous experiment with 96% accuracy by ResNet50. This result indicates that the proposed system is suitable for classifying pedestrian safety and dangerous situations by accurately dividing the positions of the two objects.
MRET: Multi-resolution transformer for video quality assessment

No-reference video quality assessment (NR-VQA) for user generated content (UGC) is crucial for understanding and improving visual experience. Unlike video recognition tasks, VQA tasks are sensitive to changes in input resolution. Since large amounts of UGC videos nowadays are 720p or above, the fixed and relatively small input used in conventional NR-VQA methods results in missing high-frequency details for many videos. In this paper, we propose a novel Transformer-based NR-VQA framework that preserves the high-resolution quality information. With the multi-resolution input representation and a novel multi-resolution patch sampling mechanism, our method enables a comprehensive view of both the global video composition and local high-resolution details. The proposed approach can effectively aggregate quality information across different granularities in spatial and temporal dimensions, making the model robust to input resolution variations. Our method achieves state-of-the-art performance on large-scale UGC VQA datasets LSVQ and LSVQ-1080p, and on KoNViD-1k and LIVE-VQC without fine-tuning.
Exploring the usability and user experience of social media apps through a text mining approach

Abstract This study aims to evaluate the applicability of a text mining approach for extracting UUX-related issues from a dataset of user comments and not to evaluate the Instagram (IG) app. This study analyses textual data mined from reviews in English written by IG mobile application users. The article’s authors used text mining (based on the LDA algorithm) to identify the main UUX-related topics. Next, they mapped the identified topics with known theoretical constructs to place them in their nomological network relevant to the usability (the 5Es framework by Quesenbery) and UX (the Honeycomb model by Morville). Finally, to expand the study with an emotional diagnosis, sentiment analysis was performed on two levels: (i) for each recognised topic, and (ii) for the full dataset to uncover general insights into users’ emotions within all reviews. The case study of the IG app confirms the usefulness of user feedback data for software development and points out that the review data have the potential for the early detection of frustration and negative feelings introduced during the use of the application. Conducting conventional UUX evaluations with users is problematic since they are remotely located, and the user-generated content of a social app undergoes continuous and frequent changes. Thus, the consecutive stages of the proposed methodology, based on text mining algorithms, constitute a proposed framework for examining the user-perceived quality projection of applications from user feedback, and they are the main contribution of this article. The used approach can be valuable for helping developers, designers and researchers to reveal user problems and fulfil user satisfaction regarding UUX aspects for specific software features.
The problem of varying annotations to identify abusive language in social media content

Abstract With the increase of user-generated content on social media, the detection of abusive language has become crucial and is therefore reflected in several shared tasks that have been performed in recent years. The development of automatic detection systems is desirable, and the classification of abusive social media content can be solved with the help of machine learning. The basis for successful development of machine learning models is the availability of consistently labeled training data. But a diversity of terms and definitions of abusive language is a crucial barrier. In this work, we analyze a total of nine datasets—five English and four German datasets—designed for detecting abusive online content. We provide a detailed description of the datasets, that is, for which tasks the dataset was created, how the data were collected, and its annotation guidelines. Our analysis shows that there is no standard definition of abusive language, which often leads to inconsistent annotations. As a consequence, it is difficult to draw cross-domain conclusions, share datasets, or use models for other abusive social media language tasks. Furthermore, our manual inspection of a random sample of each dataset revealed controversial examples. We highlight challenges in data annotation by discussing those examples, and present common problems in the annotation process, such as contradictory annotations and missing context information. Finally, to complement our theoretical work, we conduct generalization experiments on three German datasets.
Transformation of the educational values of youth during the COVID-19 pandemic

Introduction . The coronavirus pandemic, which has spread widely around the world in early 2020, has seriously affected the education sector. It led to the almost complete paralysis of schools, colleges and universities. The process, which, only at first glance, seems temporary, in fact, causes serious fears of the scientific community with deep socio-economic consequences and a new round of the global educational crisis. The study of transformational processes taking place in the field of educational values of young people is an urgent problem today. Purpose setting . The article sets and solves such tasks as identifying positive and negative trends in the field of higher education caused by the COVID-19 pandemic, the main values of students, and ascertaining the degree of satisfaction with Kazakhstani higher education students. Important issues of discussion include such as the purpose and value of training. Methodology and methods of the study . When revealing the problems of transforming educational values, dialectical, structural-functional, and activity-based approaches were used as a methodology, as well as a set of sociological methods: a survey, information analysis, peer review, etc. in identifying the impact of a pandemic on the education system. The authors also turn to such methods as social analytics and problem-thematic interpretation of educational processes, which will reveal the sociocultural features of the transformation of worldview values and directions of educational policy. Results . The COVID-19 pandemic has shaken the very foundations of education: its ability to be. As studies around the world show, students are increasingly asking questions, is it worth studying? There is growing pessimism among young people about the future. In this regard, Kazakhstan conducted its own research that studies the mood of student youth, their well-being, value orientations and their assessment of the quality of education in a pandemic. In the course of the study, the result was obtained: the pandemic as a whole did not affect life prospects: the vast majority of the young people surveyed (86,6 %) have a clear life goal, more than 90 % have this life goal fully or partially coincides with their chosen profession. Conclusion . The study will complement the understanding of the educational values of students under the influence of the pandemic, globalization, digitalization, and other factors. Its results will be useful to teachers of higher educational institutions, philosophers of education and everyone who is interested in the problems of higher education.
On semi-automated extraction of causal networks from raw text

A rule-based framework, named SCANER (Semi-automated CAusal Network Extraction from Raw text) is presented. SCANER converts raw text into causal networks using a set of natural language processing rules. The raw textual data that contains useful information for informing future decisions and developing strategies in various critical domains must be converted into useful, understandable models. However, building such models manually from raw text is a labor-intensive and time-consuming process and needs to be automated. The automated extraction of causality from raw text, however, has its own set of challenges due to the linguistic complexity and ambiguity of the texts. It can also be argued that for complicated scenarios and critical domains subject matter experts cannot rely solely on such automatically generated models without sound reasoning to support them. This implies the need for a semi-automated human-in-the-loop approach. This research, therefore, is focused on combining the strengths of both manual and automated approaches and on developing a framework for a rule-based semi-automated approach to generate causal networks from raw text. SCANER is evaluated on three collections of raw text from political, food insecurity, and medical domains using F-Score as the evaluation metric. The ground truth for the causal links is generated after incorporating feedback from a group of three human judges. A comparative analysis with Eidos, an open-source causality extraction system, demonstrates the advantages of SCANER in generating dense and accurate causal networks from the raw text.
Methodological approaches to advanced training of a computer science teacher in the field of designing digital educational resources

Introduction . The article is devoted to the development and design of an interactive digital educational esource for teachers.Purpose setting. The author of the article aims to identify the current state of educational content, and the ability of teachers to design digital educational resources in the academic process. Methodology and methods of the study . The methodological basis of the research was the formation of digital competence of teachers, which is carried out in the process of professional development programs aimed at improving knowledge, skills and abilities in the development of digital educational resources. Results . The analysis of information is carried out, which shows that the level of digital competence of teachers, the degree of readiness and desire of teachers to use digital tools and services in the educational process suggest the need to develop issues of organizing effective online communication of participants in the educational process, productive feedback from students through digital technologies; forming a critical assessment of students» behavior and correcting their actions when working in a digital educational environment, monitoring students » independence in performing educational tasks; applying the results of analyzing the digital footprint of students to eliminate and correct educational shortcomings of individual students; using the potential of digital tools and services in organizing group work and project activities of students, communication and student involvement. Based on the analysis of the list of relevant digital competencies in the field of education, the author suggests the introduction into the educational process by teachers of the development and design of digital educational resources for the discipline they teach on the platform Moodle. Conclusions . The practical significance of the work is that the proposed methods can be used in the development of digital educational resources by teachers for their professional activities.
An effective revocable and traceable public auditing scheme for sensor-based urban cities

The various data collected by urban sensor devices need a huge storage space. The cloud’s powerful calculation ability provides decent performance support and reduces the overhead on local storage. There are many kinds of data collected by urban sensor devices, and the same kind of sensors often need to upload large numbers of files together, which are usually private, so secure cloud storage in group members is essential. However, the cloud may try to modify or hide data for its own benefit, which requires the detection of a third-party auditor. This paper puts forward a new public audit scheme. Some changes have been made to the way of generating the private key, the parameters of key generation come from the feature information set of the same sensor. When the two information sets are close enough, the correctness of the authenticator generated by this file can be verified. When the sensor is revoked due to damage or loss, this scheme will effectively revoke and regenerate the private key with less overhead, thus preventing the leakage of data privacy. This also avoids the huge overhead caused by re-downloading data to generate authenticators in traditional schemes. In addition, our scheme is effective in tracking malicious members and reducing the computing overhead of the group. Finally, the experimental results show the effectiveness and practicality of the proposed scheme.
Quantum architecture search via truly proximal policy optimization

Quantum Architecture Search (QAS) is a process of voluntarily designing quantum circuit architectures using intelligent algorithms. Recently, Kuo et al. (Quantum architecture search via deep reinforcement learning. arXiv preprint arXiv:2104.07715, 2021) proposed a deep reinforcement learning-based QAS (QAS-PPO) method, which used the Proximal Policy Optimization (PPO) algorithm to automatically generate the quantum circuit without any expert knowledge in physics. However, QAS-PPO can neither strictly limit the probability ratio between old and new policies nor enforce well-defined trust domain constraints, resulting in poor performance. In this paper, we present a new deep reinforcement learning-based QAS method, called Trust Region-based PPO with Rollback for QAS (QAS-TR-PPO-RB), to automatically build the quantum gates sequence from the density matrix only. Specifically, inspired by the research work of Wang, we employ an improved clipping function to implement the rollback behavior to limit the probability ratio between the new strategy and the old strategy. In addition, we use the triggering condition of the clipping based on the trust domain to optimize the policy by restricting the policy within the trust domain, which leads to guaranteed monotone improvement. Experiments on several multi-qubit circuits demonstrate that our presented method achieves better policy performance and lower algorithm running time than the original deep reinforcement learning-based QAS method.
Customer Review Analysis and Identifying Spam Reviews

Gathering customer feedback is essential for any organization, especially in the airline service sector. Surveys are one of the most common ways to collect customer feedback and measure customer satisfaction. However, creating surveys and managing survey responses can be a challenging and time-consuming task. This is where twitter come’s into picture, where everybody can share their opinion, this allows airlines to understand how customers feel about their services, identify areas of improvement, and make necessary changes to improve their services. By using this, airlines can gain valuable insights into customer preferences that can help them create more personalized experiences for their customers. Additionally, it can help airlines stay ahead of the competition by understanding what customers want and providing them with better services than their competitors. To achieve a better overall rating from their consumers, some businesses employ comment spam to downgrade the rankings of their competitor firms based on the categories of their items. Thus, one of the tasks to boost sentimental analysis's authenticity is to analysed these spammers patterns and identify them as genuine or fake. Thus, in this project we focus on the reviews which is given by the people on twitter for an airline company and for every individual review. We basically classify them into spam or not spam thus we will use different algorithms like Support Vector Machine,Naïve Bayes, Random Forest and choose the optimal one after comparison of each algorithm
Learning positive-negative rule-based fuzzy associative classifiers with a good trade-off between complexity and accuracy

Nowadays, the call for transparency in Artificial Intelligence models is growing due to the need to understand how decisions derived from the methods are made when they ultimately affect human life and health. Fuzzy Rule-Based Classification Systems have been used successfully as they are models that are easily understood by models themselves. However, complex search spaces hinder the learning process, and in most cases, lead to problems of complexity (coverage and specificity). This problem directly affects the intention to use them to enable the user to analyze and understand the model. Because of this, we propose a fuzzy associative classification method to learn classifiers with an improved trade-off between accuracy and complexity. This method learns the most appropriate granularity of each variable to generate a set of simple fuzzy association rules with a reduced number of associations that consider positive and negative dependencies to be able to classify an instance depending on the presence or absence of certain items. The proposal also chooses the most interesting rules based on several interesting measures and finally performs a genetic rule selection and adjustment to reach the most suitable context of the selected rule set. The quality of our proposal has been analyzed using 23 real-world datasets, comparing them with other proposals by applying statistical analysis. Moreover, the study carried out on a real biomedical research problem of childhood obesity shows the improved trade-off between the accuracy and complexity of the models generated by our proposal.
Computer Vision Based Virtual Mouse and Keyboard System

Gesture recognition is a technology that uses computer algorithms to interpret human gestures as commands for a computer or other device. A virtual mouse and keyboard project that uses gesture recognition would involve creating a system that can recognize specific hand or body movements and translate them into actions on the computer, such as moving the cursor or typing on a virtual keyboard. The abstraction for this project would involve developing the algorithms and programming necessary to detect and interpret the gestures, as well as designing the user interface and integrating the system with the computer's operating system
Image enhancement using convolution neural network due to aerosols suspended in environment

Images taken in hazy or fog weather have a variety of issues, including poor visibility, poor contrast, and colors alterations. The significant visibility degradation that comes with declining image quality has an impact on the performance of many vision-based tasks. Dehazing techniques have been designed to quickly and effectively address the issue of visual haze. This research presents a residual-based CNN-based dehazing method. From hazed inputs to clear images., we may directly calculate the nonlinear mapping density and precision. The results of our experiments show that the proposed strategy is effective, and our method outperforms competing strategies when both the subjective and objective visual qualities of dehazed images are taken into account.
A New Ensemble Learning Method for Multiple Fusion Weighted Evidential Reasoning Rule

Ensemble learning, as a kind of method to improve the generalization ability of classifiers, is often used to improve the model effect in the field of deep learning. However, the present ensemble learning methods mostly adopt voting fusion in combining strategies. This strategy has difficulty mining effective information from the classifiers and cannot effectively reflect the relationship between different classifiers. Ensemble learning based on the evidential inference rule (ER rule) can effectively excavate the internal relationships among different classifiers and has a certain interpretability. However, the ER rule depends on the weight distribution of different combination strategies, and the setting of the evidence weight will affect the accuracy and stability of the model. Therefore, this paper proposes a new ensemble learning method based on multiple fusion weighted evidential reasoning rules and constructs an ensemble learning framework for data fusion and decision mapping. This framework takes the evidence weight, confidence, and feature data of each classifier as input and the integration results as output. The weight of evidence was determined by multiple fusion weights of the entropy weight method and order relation method. Finally, the integrated learning process is set up by the ER algorithm. The method proposed in this paper is verified by multiple datasets. Experimental results show that the surface construction model has good performance, and the defects of single weighting instability are greatly improved under the premise of improving the integration effect.