Reviewerly/exHarmony huggingface dataset:
Secure and Intelligent Video Surveillance using Unmanned Aerial Vehicles

In hard-to-reach areas, such as remote rural areas with limited infrastructure, a region with challenging terrain, or urban neighbourhoods that receive very little foot traffic or have a very small native population, the road and traffic conditions are often poorly recorded due to the lackluster Intelligent Transportation Systems in these areas. Nevertheless, using Unmanned Aerial Vehicles can be a viable solution to help survey and track conditions in these areas. In contrast to surveying, privacy is a very important issue and therefore in this paper, we present an Unmanned Aerial Vehicle equipped with a system that can help survey road conditions in remote areas without invading the privacy of the surrounding community. The system records and encrypts videos of the road and its conditions. Following the successful recording and encrypting of the video, the Unmanned Aerial Vehicle returns to the base station where it establishes secure communication with a base server and uploads the videos through an object detection application to report on the results of the surveyed area. The results were able to show the fundamental traffic anomalies from cars, trees, trucks, potholes, people, and animals with over 90% success rate for some of those objects compared to other pre-trained models.
V3Trans-Crowd: A Video-based Visual Transformer for Crowd Management Monitoring

Autonomously monitoring and analyzing the behavior of the crowd is an open research topic in the transportation field. The real-time identification, tracking, and prediction of the crowd behavior is primordial to ensure smooth crowd management operations in many public areas such as public transport stations and streets. First, the complexity brought by the interaction and fusion from individual to group that needs to be assessed and analyzed. Second, the classification of these actions which might be useful in identifying danger and avoiding any undesired consequences. In this paper, we propose a transformer-based crowd management monitoring framework called V3Trans-Crowd that captures information from video data and extracts meaningful output to categorize the behavior of the crowd. We provide an improved hierarchical transformer for multi-modal tasks. Inspired by 3D visual transformer, our proposed 3D visual model, V3Trans-Crowd, has been shown to achieve great performances in terms of accuracy compared to state-of-the-art methods, all tested on the standard Crowd-11 dataset.
Non-Gaussian Bayesian filtering by density parametrization using power moments

Non-Gaussian Bayesian filtering is a core problem in stochastic filtering. The difficulty of the problem lies in parameterizing the state estimates. However the existing methods are not able to treat it well. We propose to use power moments to obtain a parametrization. Unlike the existing parametric estimation methods, our proposed algorithm does not require prior knowledge about the state to be estimated, e.g. the number of modes and the feasible classes of function. Moreover, the proposed algorithm is not required to store massive parameters during filtering as the existing nonparametric Bayesian filters, e.g. the particle filter. The parameters of the proposed parametrization can also be determined by a convex optimization scheme with moments constraints, to which the solution is proved to exist and be unique. A necessary and sufficient condition for all the power moments of the density estimate to exist and be finite is provided. The errors of power moments are analyzed for the density estimate being either light-tailed or heavy-tailed. Error upper bounds of the density estimate for the one-step prediction are proposed. Simulation results on different types of density functions of the state are given, including the heavy-tailed densities, to validate the proposed algorithm.
Risk Assessment of Scientific and Technological Cooperation among RCEP Countries Based on Cloud Model

Accurate risk assessment of international scientific and technological (S&amp;T) cooperation is significant to international cooperation. In this paper, 5 risk dimensions with respect to the political, economic, social, cultural as well as technological of the regional comprehensive economic partnership countries (RCEP) are selected to establish a proper S&amp;T cooperation index system for risk assessment. To calculate the weight of each index, a cross-entropy combination weighting method is proposed based on the combination weighting method of game theory. Furthermore, the standard cloud model is constructed by using the golden ratio, and the risk of S&amp;T cooperation among the RCEP countries is analyzed by the cloud model. The results show that the combination weighting method proposed in this paper is effective, and its calculation is simpler than that of the game theory combination weighting method. Besides, compared with political, social, cultural, and technological indicators, economic indicators have a greater impact on S&amp;T cooperation risk. Furthermore, it is also obtained from the results that the risk of S&amp;T cooperation with China and the Philippines is at a lower to low level and medium to higher level, respectively, and the risk of S&amp;T cooperation with other countries is all at a lower to medium level.
Teacher and power: the evolution of professional requirements in the 1920–1930s

The study of the initial Soviet stage of the general education history is important both for understanding the transformative processes in the Soviet society as a whole, and for comparing the Soviet and post-Soviet societies. This issue is particularly topical due to the fact that, in accordance with the decree of the President of Russia, 2023 has been declared the Year of Teacher and Mentor. Having defined the impact of the state on the formation of priorities for professional activities in general education as the subject of research, the author highlights the educational and pedagogic course of work. An analysis of archival documents stored in the funds of the State Archive of Recent History of the Saratov Region was carried out. 1918 - 1941 Documents were studied within the framework of two historical periods. The first period started with the adoption of the "On a unified labor school" provision and ended in 1930 with return to stable programs and textbooks, the second period started in 1930 and ended with the beginning of World War II (1941). As a result of the study, it was found that loyalty to the content of educational programs in the twenties was replaced by increased control in the thirties. This period is characterized by the of teachers activities monitoring expansion that included student performance, the sanitary condition of schools, the school staff performance, assessment of the calligraphy rules observing by teachers when putting grades in students notebooks. Educational work remained a priority, since the curriculum in any discipline was associated with the communist education. The socialist competition in the school became a tool for controlling the professional activities of teachers. Educational institutions were involved in the socialist completion along with industrial enterprises. For schools the competition combined both vectors of teachers activities control and educational and pedagogic work in one tool. The widespread practice of ignoring the fulfillment of socialist competition conditions testifies to its visibility. The data, stored in the regional archive, on cases of conscious violation of the established work order, socialist competition imitation confirm that.
Multi-level dynamic error coding for face recognition with a contaminated single sample per person

Single sample per person (SSPP) has always been a significantly challenging and practical problem for face recognition owing to limited information and facial variations. Although the existing holistic and local methods have achieved great success in face recognition with SSPP, their performances suffer a serious degradation when SSPP is accompanied by a contaminated gallery database. In this situation, the gallery set is usually disturbed by facial variations such as illumination, expression, and disguise. To solve this problem, we propose a novel method called multi-level dynamic error coding. First, a multi-level pyramid structure is constructed for holistic and local sparse representation, where gallery dictionary patches are extracted to build a local gallery dictionary and a variation dictionary is built by extracting the generic dataset patches to depict potential facial variations. Second, we further propose a scheme of dynamic error coding by constructing an error function at different levels to reduce the negative impact of variations. At last, the corrected holistic and local errors are fused to perform the classification. Experimental results on various benchmark data sets have demonstrated that our method has strong generalization ability to dictionary and is more robust against facial variations under SSPP.
Technical optimism theoretical limits

This work reveals a part of the technical optimism theoretical limits. The aim of the study is to represent the technical optimism as a modification of a spontaneous value mindset and to reveal the main theoretical limits of it. In this regard, the main categories of the technical optimism are described as a phenomenon (engineering as the wheal a priori, assertion of the engineering orientation to obtain benefits, technical reality acceptance), the technology axiological neutrality is identified and substantiated. The essence of the technical singularity is revealed as one of the options for describing the limit of the scientific and technological progress, and as one of the main modern optimistic technology concepts. The basic aspects of the epistemic and existential modalities of the technical optimism are revealed, that allows to see the basis of the technical reality axiological thematization. The main research methods are idiographic and nomothetic methods. The study is novel in that it identifies the technical optimism modalities.
Complex image classification by feature inference

Image classification is a fundamental task in image processing. Despite the long time research, there are still many challenging problems to be solved. In this study, we introduce the problem of complex image classification. Images in realistic scenarios are complex and classifying samples directly are not always the right way even the performance is high. To address the issue, we propose a novel classification schema where classification is combined with image inpainting. There are two models including one inference network and one classification network in the proposed schema. The masked content that is specified as occlusions and interferences is inpainted by feature inference. The inference network inpaint the image with a mask and the network classifies the inferred image. We apply the proposed schema to existing classification models including AlexNet, GoogleNet, Inceptionv3, ResNet50, and EfficientNetb7 and specific datasets including ImageNet, PlantCLEF, and CUB-200. Despite the simplicity, experimental results show that it significantly improves the performance of complex classifications.
Combined knowledge transfer and adaptive coordinate systems approach for evolutionary bilevel optimization

Reducing the number of lower-level function evaluations (LFEs) is crucial to designing a bilevel evolutionary algorithm (BLEA) for bilevel optimization. Direct neighbor solution transfer (DST) is regularly used in BLEA because it can improve the lower-level search efficiency and reduce the number of LFEs. But this method still faces the challenge of misleading searches to make the algorithm converge to the wrong optimum. This paper proposes a bilevel improved multi-operator differential evolution algorithm (BL-IMODE) for bilevel optimization, which uses the knowledge transfer and adaptive coordinate systems approach. Specifically, this paper proposes a lower-level search knowledge transfer (LLS-KT) and a lower-level (LL) optimal solution distribution information transfer (LLO-DIT) mechanism to implement knowledge transfer. In LLS-KT, transferring LL solutions to the LL optimizer is similar to DST, but the LLS-KT transfers multiple solutions and thus has less possibility of misleading searches than DST. Moreover, the LLO-DIT transfers the LL optimal solution distribution information to the LL optimizer for generating promising offspring solutions. Since this method does not directly transfer LL solutions, it avoids misleading searches. Additionally, the adaptive coordinate systems (ACS) approach is used in two-level optimizers to suit different fitness landscapes and further reduce the number of LFEs. In ACS, three differential evolution mutation operators are produced in two coordinate systems. An adaptive strategy is used to select the suitable differential evolution mutation operator to generate promising offspring solutions in the Eigen coordinate or original coordinate system. Experimental studies on three test suites benchmark problems for comparison with seven state-of-the-art algorithms are conducted. The results show BL-IMODE has a significant advantage over the comparative algorithms. Finally, BL-IMODE is applied to tackle the inverse optimal control problem.
Modern Electronic Educational Technologies in the Process of E-Learning

The purpose of the study is to focus attention on the positive aspects of using E-learning in contrast to the traditional format in a modern university, as well as on the need for effective management of E-learning in order to improve the quality of education.&#x0D; Methodology. The following research methods were used to solve the assigned tasks: theoretical – analysis of scientific sources, pedagogical and teaching-methodical materials, comparison; empirical – generalization of pedagogical experience; a pedagogical experiment to check the effectiveness of organizing students' work using LMS Moodle platform.&#x0D; Results. This study focuses on the positive aspects of using E-learning. Introducing E-learning into the educational process provides new opportunities, provides everyone with the necessary knowledge and develops digital literacy skills. The study analyses the current state of е-learning in higher education and summarizes some theoretical issues of using LMS Moodle, both positive and negative. It offers practical suggestions for the qualitative integration of E-learning into the teaching and learning process based on practical and good management of the E-learning process. The case of Simon Kuznets Kharkiv National University of Economics is used, which implemented the distance learning platform, a platform called “Personal Learning Systems” (PLS). A management system for electronic learning at the university was developed based on the modelling method, the primary purpose of which is to use modern electronic educational technologies in the educational process, to prepare a methodological base for electronic educational resources, and to improve electronic educational technologies in the format of electronic learning. &#x0D; Conclusions. The E-learning management system “personal learning systems” (PLS) in the university is considered an information-management system whose primary purpose is the application of modern electronic educational technologies in the educational process, in the preparation of a methodological base of electronic educational resources, improvement of electronic educational technologies in a distance learning format.
Social Disadvantages of Ease of Transactions Using Blockchain

A distributed, immutable record like that provided by blockchain technology has the ability to greatly streamline and fortify financial transactions while also reducing associated risks. Blockchain transactions are simple, but this convenience comes at a social cost. The concentration of wealth in the hands of a few would be a potential drawback, as would be the possibility of a blockchain's centralization. The anonymity of blockchain transactions also makes them conducive to unlawful operations like money laundering and the selling of illicit items. Finally, the energy consumption essential to a blockchain network's operation raises environmental problems.
Automatic generation of creative text in Portuguese: an overview

Creativity is an inherently human skill, and thus one of the goals of Artificial Intelligence. Specifically, linguistic computational creativity deals with the autonomous generation of linguistically-creative artefacts. Here, we present four types of text that can be tackled in this scope-poetry, humour, riddles, and headlines-and overview computational systems developed for their generation in Portuguese. Adopted approaches are described and illustrated with generated examples, and the key role of underlying computational linguistic resources is highlighted. The future of such systems is further discussed together with the exploration of neural approaches for text generation. While overviewing such systems, we hope to disseminate the area among the community of the computational processing of the Portuguese language.
Sign Language Recognition Based on Hands Symbol's Classification

One of the most natural and ancient types of conversational language is sign language. The technology that converts sign language into writing for people who have difficulty communicating, such as those who have speech issues, hearing disabilities, or are deaf, is the subject of this study. This paper is based on a real-time method based on finger writing and neural networks for American sign language. An interesting field of vision study is the automatic recognition of human gestures from video images. The Research recommend employing a convolution neural network (CNN) method to recognize human hand motions from a photograph. The objective is to identify hand movements used in human work activities from a camera image. Hand placement and orientation are employed to collect the training and assessment data for CNN. The hand is first put through a filter, and once that has been done, it is put through a classification, which determines what class the hand movements belong to. Then, CNN is trained using the measured pictures.
Prediction of Pneumonia Patient Proportions Using Kriging and Inverse Distance Weighted Methods

Pneumonia is a respiratory tract disease caused by bacteria, viruses, or fungi. In Indonesia, pneumonia is one of the diseases with the second highest number of cases after malaria from 2007 to 2015. Mapping of regions with pneumonia prevalence needs to be done so that the government can pay more attention to areas with high pneumonia rates, and the public can be more aware of areas that are prone to pneumonia. Spatial methods that can be used to predict the distribution of pneumonia patients are ordinary kriging and inverse distance weighted. The research results showed that the best method chosen for predicting the proportion of pneumonia patients in Sumatra, Java, Kalimantan, and Sulawesi islands is the ordinary kriging method with its theoretical semivariogram model being exponential, and the obtained RMSE value is 0.0178. The average proportion of pneumonia patients in Sumatra Island is 0.01655, with the highest proportion value of 0.02504 in Simalungun District, North Sumatra. Then, the average proportion of pneumonia patients in Java Island is 0.01672, with the highest proportion value of 0.02443 in Garut District, West Java. The proportion of pneumonia patients in Kalimantan Island is 0.01657, with the highest proportion value of 0.01835 in Balikpapan District, East Kalimantan. The average proportion of pneumonia patients in Sulawesi Island is 0.0166, with the highest proportion value of 0.02397 in South Minahasa District, North Sulawesi.
Web App for Tracking Real-Time Performance of Cryptocurrencies

Cryptocurrency is money that exists in digital or virtual form. Cryptocurrencies do not have a central issuance or regulatory authority, but instead use a decentralized system to record transactions and issue new units. Cryptocurrencies are a secure and popular standardized way to transact anywhere on earth with relatively low fees. This article provides an overview of what cryptocurrency is and begins to answer some of the most common questions asked by newcomers. Nowadays, everyone wants to explore and invest in the crypto market due to its high volatility and higher returns. Cryptocurrencies are inherently volatile because their prices change rapidly. Therefore, we thought of creating a platform to track the performance of cryptocurrencies. The platform will track the performance of cryptocurrencies and provide information on changes in the value of cryptocurrencies Our results will provide users with the necessary information, including the real-time data of cryptocurrencies in visual form like Graphs or Charts, so that they can decide whether to invest in them. We also provide various tools to facilitate the decision-making process of investing in the crypto market.
The Use of Virtual Reality in Education for Sustainable Development

Environmental sustainability is one of the major global topics of the 21st century. In order to effectively include the concept of sustainability in the topic of environmental education, educators are responsible for incorporating the concept into the existing curriculum. However, it may not always be possible to conduct field studies based on environmental education due to economic, transport, and time constraints, while certain situations and conditions may be particularly difficult to replicate. One recent solution to this problem is the application of Virtual Reality technology, which has been shown in various recent studies. VR is a computer-generated three-dimensional environment that allows the user to perceive content more realistically. In the process of teaching sustainability issues such as climate change, waste management, food consumption, air and water pollution, and deforestation, the present study has revealed that cooperation between different fields of expertise, including science education and computer and instructional technologies, plays an important role.
Learning transferable non-negative feature representation for facial expression recognition

Conventional facial expression recognition (FER) methods often assume that the training and test procedures are carried out on a single database, without considering the cross-database problem due to the difference in ages, collecting devices, nationalities, etc. To tackle the above challenge, we propose a novel transferable non-negative feature representation (TNFR) method in this work. The core ideas of TNFR are two-fold. First, we utilize the basis matrix as a transformation matrix to reduce the distribution discrepancy between two databases. Second, we utilize the label information to improve the discrimination ability of the model. In addition, we replace the original label matrix with a block-label matrix to make the model more flexible. Experiments on four benchmark databases are provided to demonstrate the effectiveness of the proposed TNFR method in comparison with state-of-the-art methods.