Reviewerly/exHarmony huggingface dataset:
Expedited Online Learning With Spatial Side Information

The applicability of model-based online reinforcement learning algorithms is often limited by the amount of exploration required for learning the environment model to the desired level of accuracy. A promising approach to addressing this issue is to exploit side information, available either <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> or during the agent’s mission, for learning the unknown dynamics. Side information in our context refers to information in the form of bounds on the differences between transition probabilities at different states in the environment. We use this information as a measure of reusability of the direct experience gained by performing actions and observing the outcomes at different states. We propose a framework to integrate side information into existing model-based reinforcement learning algorithms by complementing the samples obtained directly at states with second-hand information obtained from other states with similar dynamics. Additionally, we propose an algorithm for synthesizing the optimal control strategy in unknown environments by using side information to effectively balance between exploration and exploitation. We prove that, with high probability, the proposed algorithm yields a near-optimal policy in the Bayesian sense, while also guaranteeing the safety of the agent during exploration. We obtain the near-optimal policy in time steps that are polynomial in terms of the parameters describing the model. We illustrate the utility of the proposed algorithms in a setting of a Mars rover, with data from onboard sensors and a companion aerial vehicle acting as the side information.
CREATION OF THE IMPLEMENTATIVE CONDITIONS OF INFORMATION TECHNOLOGIES IN HIGHER EDUCATION INSTITUTIONS

The paper highlights the problemsof the implementationof informationtechnologies into the professional activity of teachers in higher education institutions. Further integration of the country into the world economic and cultural space encourages the synchronization of the Ukrainian education with the global trends, taking into account IT development. The potential directions for the development of higher education digitalization have been identified. One of the main directions of higher education institution activity – modelling and expansion of information and communication educational environment is specified. The author outline the conditions of information and communication learning environment contributing to the increase the digital competence of teachers – IT knowledge, IT skills and readiness to use the achievements of informatiszation process in their professional activities. Basing on the analysis of the main IT development trends in the educational sphere, the authors have formulated proposals to improve the professional activity of teachers and to implement information technologies into the Ukrainian higher education institutions in compliance with the transformations currently taking place in the modern world.
Intellectualization of the Online Platform of Flexible Learning System by Predictive Agent Model Elements

The paper is devoted to modeling the system of user interaction with the online platform of a flexible training system, the purpose of which is to implement a recommendation system for training users’ modern professions using automation elements. The article considers agent-based modeling, which is a method of simulation modeling that examines the behavior of decentralized agents and how such behavior determines the behavior of the entire system as a whole. The scientific novelty lies in use of proprietary algorithms for automating training, as well as study of methods of user behavior on the online platform using a recommendation system. As a result of the work, curves of user behavior on the platform were obtained before and after application of the recommendation system algorithms.
Project Topic Recommendation by Analyzing User’s Interest Using Intelligent Conversational System

Finding one of the best projects to work on which actually are germane to the students and specifically those topics which are also trending is one of the most onerous tasks to achieve. Students spend most of their time researching what projects to work on to select the best out of the topics pertinent to them. The proposed system aims to solve this problem with the help of an intelligent conversational system that captures and analyzes the student’s interests and then recommends the project topics by displaying various research papers and other reasonably key websites. The system has a chatbot that precisely captures the user’s interests and derives the keywords based on the user information. The system then essentially uses Google Custom Search API which ranks the results. The final results are then displayed by incorporating the importance and weightage of the keywords and user likings extracted. Thus, the proposed model displays the results that align with the user interests and predilection thereby giving accurate results.
The Construction and Improvement of Digital Library Service

In recent years, with the development of 5G information technology, big data, artificial intelligence and other communication technologies, the types and forms of information are becoming more and more rich, the functions of traditional libraries obviously cannot meet these needs. Therefore, the digital library is more and more familiar to people. this paper introduces the concept and development of digital books, the characteristics and types of digital libraries, analyzes the present situationof the development of digital libraries, emphatically puts forward the countermeasures that university libraries should take in the face of the development and change of digital books and paper books, and makes bold predictions for the development of digital libraries in the future.
The Premise of Automatic Four-wheel Alignment: Intelligent Identification and Processing of Rim Targets

Abstract With high demand of automatic development of four-wheel alignment technology, this paper explores the core technology of automatic detection of four-wheel alignment instrument: intelligent identification and processing of rim target. According to the working process of 3D vision four-wheel aligner, starting from the target positioning, this paper proposes a method based on matching idea to realize the target recognition and positioning; Then the target area image is segmented to obtain the center coordinates on it, which provides a basis for the subsequent calculation of four-wheel positioning parameters. Simulation results demonstrate the effectiveness of our method.
Hybrid MCDM for Cloud Services: AHP(blocks) &amp; Entropy, TOPSIS &amp; MOORA (methodology Review and Advances)

Cloud services (CS) offer virtual, reliable, and flexible hardware and software resources as part of the distant Cloud Computing (CC) concept. The wide variety of products from different providers create the necessity for users to collect data and define criteria for comparing alternatives with reliable evaluation methods and techniques. This paper presents a combined approach based on Analytical Hierarchy Process(AHP), Information Entropy, and Multi-criteria decision making (MCDM) techniques. The set of evaluation criteria is decomposed and logically structured in a reasonable number of blocks which weights are computed more easily and effectively with a reduced number of AHP pair-comparisons. With the integration of Information Entropy, the weights of individual criteria within each block are defined objectively on basis of real primary data for quantitative and quantifiable parameters. This hybrid approach is applied in a case study based on QoS and QoE criteria with popular evaluation techniques like TOPSIS and MOORA.
A technique for optimum distribution of watermark in a document image

Watermarking is a very useful technique to ensure the integrity of a document image. But when too many bits are embedded into image, it reduces quality of the document. When limited number of watermark bits are used, these should not be concentrated in background or few parts of the image. As very few existing techniques address this issue, in this paper, we propose a watermark distribution scheme, which divides image into optimum number of segments and distributes watermark based on information content such that watermark is well spread across the document. After embedding, on average the proposed scheme achieves 31.79% more area with ensuring 38.59% better quality in terms of PSNR compared to state-of-the-art schemes.
Comparative study of 3D object detection frameworks based on LiDAR data and sensor fusion techniques

Abstract Estimating and understanding the surroundings of the vehicle precisely forms the basic and crucial step for the autonomous vehicle. The perception system plays a significant role in providing an accurate interpretation of a vehicle’s environment in real-time. Generally, the perception system involves various subsystems such as localization, obstacle (static and dynamic) detection, and avoidance, mapping systems, and others. For perceiving the environment, these vehicles will be equipped with various exteroceptive (both passive and active) sensors in particular cameras, Radars, LiDARs, and others. These systems are equipped with deep learning techniques that transform the huge amount of data from the sensors into semantic information on which the object detection and localization tasks are performed. For numerous driving tasks, to provide accurate results, the location and depth information of a particular object is necessary. 3D object detection methods, by utilizing the additional pose data from the sensors such as LiDARs, stereo cameras, provides information on the size and location of the object. Based on recent research, 3D object detection frameworks performing object detection and localization on LiDAR data and sensor fusion techniques show significant improvement in their performance. In this work, a comparative study of the effect of using LiDAR data for object detection frameworks and the performance improvement seen by using sensor fusion techniques are performed. Along with discussing various state-of-the-art methods in both the cases, performing experimental analysis, and providing future research directions.
Research on Safety Helmet Detection for Construction Site

In the complex construction environment, unknown danger is everywhere. As an important safety gear, safety helmets can effectively protect the head. After analyzing and summarizing the existing safety helmet detection methods, this paper proposes an improved lightweight safety helmet detection model based on YOLOv4. Firstly, the backbone network of YOLOv4 is replaced with its lightweight version CSPDarknet53-Tiny, the SPP structure was abandoned, and the PAN structure is replaced with FPN structure to improve the model detection speed. Secondly, in order to make up for the loss of detection accuracy, the attention mechanism model CBAM is introduced to improve detection accuracy. Finally, helmet detection experiment was carried out. The experimental results show that the detection speed of the lightweight model is up to 114.26FPS, and the detection accuracy of the helmet is 90%, which is suitable for deployment in actual construction scene.
College student learning styles and interests in times of pandemic

Learning styles are different methods that students use to learn, influenced by different factors that modify this learning, such as motivation, study methods, and the student's attitude, among others. This work focuses on the learning styles and interests of the university student, the main objective is to identify the factors that affect the learning style and interests of higher education students in times of pandemic. The methodology was integrated from the research instrument that generated information for the explanation of this issue, its focus being quantitative, in addition to the theoretical value based on the integration of theories. The research was carried out with a sample of 1,412 higher education students. The instrument was designed considering 160 variables, 10 signal variables and 150 variables of learning styles and interests divided into several subdimensions, the STATISTIC program was used for statistical treatments based on frequencies, percentages, comparison and factorial analysis. Among the main research results it was found that women attend college because they have an interest in learning and preparing to successfully achieve their goals, and not just because of a college degree, unlike men. In addition, students are clear that experience is essential to develop the knowledge acquired, having a real experience of what the world of work is, venturing into them and at the same time being learning. The intervention proposal is to promote in the universities that from the first semesters or semesters the student is allowed to have a real experience in the labor field about his career.
Election Result Prediction Using Sentiment Analysis

Social media today has become a very popular communication tool for users. Millions of users share their opinions on different aspects on daily basis. Sentiment Analysis determines the polarity and inclination towards any specific topic, idea or entity. Applications of such analysis can be seen during elections, movie promotions, and many other fields. In our project, we aim to predict the winning probability of any political party by using both labelled as well as unlabeled data. Labelled data can be collected by using polling method but the result may not provide better accuracy. Hence, it is necessary to fetch live data to predict the accurate election result. Twitter is a microblogging site which allows the users in posting quick and real-time updates about different activities or events as the spread of information and news is quick enough. With the help of hashtags, the needed data can be easily generated and put to use. We exploited the python library “Tweepy” for accessing the Twitter API and fetched live data from Twitter. 350 tweets for each political party are fetched by using keywords. Using “TextBlob” library of python, sentiments are applied to each tweet and depending upon more positive tweets for particular party, winning party is declared. Also, popular text classification algorithms like Na¨ıve Bayes, SVM and Random Forest are used to train model using labelled data. The accuracy of the predicted result is calculated and the result is declared Finally, result is represented in the form of bargraph for labelled data according to the number of voted for each political party and for unlabeled data using pie chart for each political party representing positive, negative and neutral sentiments.
Online Small Sample Learner Modeling and Curriculum Recommendation with Healthy Emotional Factors of College Students

Online education is a popular way for college students at present, and it is also a good compensation way to meet the special situation that traditional offline teaching cannot complete the teaching task. Traditional classroom teaching methods have been difficult to meet the learning requirements of contemporary college students, while online classroom has made up for the shortcomings of traditional classroom teaching to some extent because of its short class hours, prominent focus, and online mobile learning. First, this paper proposes an online SSL (small sample learner) model for college students to integrate healthy emotional factors. The characteristics of learners are divided into three categories: basic characteristics of learners, characteristics of behavioral factors, and characteristics of emotional factors, and the problem of solving mapping functions is transformed into the problem of solving kernel functions. Second, a novel curriculum recommendation model integrating healthy emotional factors is proposed, which fully considers the influence of user comments on similarity and transforms the similarity of users in the overall score of the project into the similarity of users in the emotional tendency of the special attributes of the project. Through the experimental evaluation, the accuracy and stability of the recommendation are greatly improved.
Blockchain-based Services for Sharing Education Records in Industrial IoT Environment

The path towards Industry 4.0 have pushed the workforce to continuously develop their skills to stay at the forefront of their careers and remain attractive to their employers. In this case, the electronic portfolio is designed as a promising tool for professionals and employers, in assessing and presenting guidance for further learning in the workplace environment. However, the implementation of electronic portfolios faces huge challenges, especially due to the lack of interoperability to collect and share competition evidence among multiple organizations and institutions. The growing trend of professional (learning) interoperability has brought other challenges that must be addressed in order to fully utilize the potential of the electronic product portfolio in the context of the fourth industry. This article proposes a framework taking the benefits of blockchain, decentralized storage systems, smart contracts and session management in achieving professional-centric interoperability while creating electronic portfolio and share evidences among multiple organizations without giving up the confidentiality, integrity and availability of shared evidence. The simulation of the proposed model for proof of concept on Ethereum proved its viability in real world practice. In expansion, the assessment of the proposed model proved its effectiveness in terms of cost, storage space and security as a possible solution in electronic portfolios implementation and assisting professionals to stay competitive in global labor market.
Estimation Of Battery State Of Health Based On Random Weight PSO-ELM

The estimation of the battery's state of health is instructive for the use and maintenance of the battery. The more accurate the estimated health status is, the higher the reliability of the system. Because the battery is influenced by many elements such as internal resistance, temperature and voltage, it is difficult to directly calculate its health status. Aiming at problems such as difficulty in measuring internal parameters and difficulty in mathematical modeling when estimating battery health status under operating conditions, this paper first predicts the health status of the battery through an extreme learning machine (ELM), and then uses random weighted particle swarm algorithm to optimize the ELM, and finally through the comparison of the prediction results of the extreme learning machine and the random weighted particle swarm optimization extreme learning machine, the validity of the battery health prediction used in this paper is verified.
Research on Aircraft Type Recognition from Remote Sensing Images in Complex Scenes

Aircraft targets in remote sensing images are important research objects. Accurately identifying different types of aircraft will bring the greatest value of intelligence into play. In this paper, we combine the YOLOv5 detection algorithm and the MMAL-Net classification algorithm to detect the aircraft target and identify the aircraft type in the remote sensing image. Firstly, we use the YOLOv5 algorithm to determine the area where the aircraft target is located in the complex scene. Then the MMAL-Net algorithm is used to classify the aircraft in the area at the fine-grained level, and the type of aircraft target is identified. Through the experiment, the method proposed in this paper can accurately detect the aircraft target and identify the aircraft type, and the accuracy is 73. 2%.
Shoeprint Image Retrieval Based on Dual Attention Light Hash Network

Aiming at the problem of the limited computing power of public security Internet of Things (IoT) mobile terminals, a new dual-attention light hash network is designed to realize the rapid retrieval of crime scene Investigation shoeprint images. First, a spatial attention (SA) module is constructed using deformable convolution to pay more attention to the significant regions of the shoeprint image; then, a channel attention (CA) module is constructed using a dimensionless local cross-channel interaction strategy to perform adaptive weighting of different channels of the feature mapping. Finally, a hash module is constructed using the slicing method to transform the real-valued features into a binary output as the final representation of the shoeprint image. The generated hash codes are constrained in the siamese network to enhance their distinguishing ability. We test our method on the CSFID and FID-300 datasets and we compare our method with other state-of-the-art methods in the shoeprint image retrieval domain. The experimental results show that our method can improve the image retrieval baseline by a large margin and better than other methods.
Averaging approaches for highly accurate image-based edge localization

We introduce an optical and a digital averaging technique that considerably improves edge localization performance. Especially for high quality images, the optical method achieves measurement uncertainties down to levels of millipixels. The approach uses an optical replication scheme based on a computer-generated hologram to reduce noise and discretization errors. The second method is based on a neural network denoising architecture and is especially suited for high levels of photon noise. Edge localization can be improved by up to 60% while preserving high lateral and temporal resolution. The methods are first tested using high quality images obtained by a scientific CMOS sensor imaging a razor blade mounted on a mechanical stage. Then, the laboratory results are tested for larger distances to validate the methods for building deformation measurements.
How to Solve the Liar Paradox Without Sacrificing Classical Logic

After seven chapters of seeing how important proposed solutions to the paradoxes can go wrong, several important things learned along the way are pulled together to propose a positive alternative (rooted in a non-standard way of parsing disquotationalism about truth called Content Inheritance Disquotationalism) that retains the full resources of both classical logic and a fully transparent truth predicate. A wide range of possible objections are considered and resolved. The CID-based solution is compared to its paracomplete and paraconsistent rivals on a crucial standard emphasized by Graham Priest—the Principle of Uniform Solution. Finally, several loose ends are tied up through a survey of objections to classical logic rooted in quantum weirdness, borderline applications of vague terms, and Edwin Mares’s claim that predicates can be not only “underdefined” (leading to truth-value gaps) but “overdefined” (leading to truth-value gluts). Some of these, particularly concerns about vagueness, are difficult philosophical problems not resolved by anything said here, but none add up to a good reason to abandon classical logic.