Reviewerly/exHarmony huggingface dataset:
Didactic potential of educational VR films (on the example of the film “Electric power industry of Russia”)

The introduction of modern video technologies into pedagogical practice is one of the ways to increase students’ motivation to learn about the world. The article describes in detail the advantages, methodological purpose, and didactic potential of educational VR films on the example of the film in 360° format “Electric power industry of Russia”, created by a team of employees of The Academy of Public Policy Implementation and Professional Development of Educators of the Ministry of Education of the Russian Federation (The Academy of Ministry of Education of the Russian Federation) for free use in Russian educational organizations. The methodology of the study of educational VR films didactic potential presented in the article is based on the activity approach to learning, the principles of systematicity, and interdisciplinarity. The activity approach in the use of VR film is provided by the maximum effect of presence, which is experienced by the viewer, and independence in the choice of the subject of attention. The work of an active and proactive student is complemented by the activity of a teacher who can guide the student and choose objects from an educational VR film to set learning objectives, and formulate questions and tasks. The principles of systematicity and interdisciplinarity are provided by educational content created on the basis of the integration of knowledge from different subject areas, which allows teachers to use the didactic potential of the VR film in different disciplines and age groups. The proposed methodological solutions for the use of VR film are of practical value for the educational process in a modern school: they contribute to the formation of a holistic view of students about the achievements of Russian science and modern professions (in particular, in the electric power industry), the education of patriotism and pride in the Fatherland, the development of skills of independence and self-control in learning activities. The authors see the development of the project as one of the strategic directions in the field of digital transformation of education, aimed at improving the quality of education of modern schoolchildren.
A Data Analysis Privacy Regulation Compliance Scheme for Lakehouse

To meet the diverse data storage and analysis needs in the Internet of Things era, businesses embrace the lakehouse approach, a hybrid deployment of data lakes and data warehouses on a single platform. Data consumers leverage data mining techniques through open APIs to explore data's untapped potential. However, concerns arise regarding compliant data access and utilization. While privacy regulations like the General Data Protection Regulation (GDPR) offer conceptual guidance, their technical implementations remain vague. This paper proposes a privacy regulation compliance framework specific to lakehouse data analysis. By introducing a compliance verification layer between the analysis and processing layers, the scheme enables regulatory adherence. The utilization of Trusted Execution Environments (TEEs) guarantees verification of analysis requests, with blockchain serving as a storage medium for results. To mitigate unauthorized data analysis, we introduce a reputation-based punishment mechanism. Experimental results demonstrate the scheme's feasibility.
Online thermal field prediction for metal additive manufacturing of thin walls

Various data-driven modeling methods have been developed to predict the thermal field in metal additive manufacturing (AM). The generalization capability of these models has been shown with simulation, but rarely tested with online physical printing. Instead, this paper aims to study a practical issue in metal AM, i.e., how to predict the thermal field of yet-to-print parts online when only a few sensors are available. This work proposes an online thermal field prediction method using mapping and reconstruction, which could be integrated into a metal AM process for online performance control. Based on the similarity of temperature curves (curve segments of a temperature profile of one point), the thermal field mapping applies an artificial neural network to estimate the temperature curves of points on the yet-to-print layer from measured temperatures of certain points on the previously printed layer. With measured/predicted temperature profiles of several points on the same layer, the thermal field reconstruction proposes a reduced order model (ROM) to construct the temperature profiles of all points on the same layer, which could be used to build the temperature field of the entire layer. The training of ROM is performed with an extreme learning machine (ELM) for computational efficiency. Fifteen wire arc AM experiments and nine simulations are designed for thin walls with a fixed length and unidirectional printing of each layer. The test results indicate that the proposed prediction method could construct the thermal field of a yet-to-print layer within 0.1 s on a low-cost desktop computer (Intel Core i7-3770 CPU @ 3.40GHz processor, 24.0 GB RAM). Meanwhile, the method has acceptable generalization capability in most cases from lower layers to higher layers in the same simulation, as well as from one simulation to a new simulation on different AM process parameters. More importantly, after fine-tuning the proposed method with limited experimental data, the relative errors of all predicted temperature profiles on a new experiment are sufficiently small, which demonstrates the applicability and generalization of the proposed thermal field prediction method in online applications for metal AM.
Multi-objective UAV Trajectory Planning Based on Improved Wolf Pack Algorithm Improved Wolf Pack Algorithm

For multi-objective trajectory planning problem of an unmanned aerial vehicle(UAV) in complex environment, a problem model is constructed based on flight environment information, mission objectives, and flight performance constraints. A multi-objective optimization function is established, and an improved wolf pack algorithm is proposed for verification. Fixed wing aircraft cannot change its speed direction instantly. Based on the characteristics of the problem and the two-dimensional Dubins curve, it has effectively solved the three-dimensional shortest path. Compared with ant colony algorithms, the results shows the improved wolf pack algorithm can search the optimal track faster, and the optimized track can consider both the number of correction times and track length to achieve multi-objective optimization. It has better global optimization ability and faster rate of convergence, which proves its effectiveness in solving UAV track planning.
Research On the Solution of One Class of Fractional-Order NeuralNetworks with Time Delays and Partial Derivatives

In this paper, based on a class of Caputo-type fractional-order neural network model with time delay and partial differential, the lattice equation form of the model is constructed by combining the finite difference idea and the exponential time difference technique, and the discrete solution form of the model lattice equation is obtained by the Mittag-Leffler function. Through a series of assumptions, the existence and uniqueness of the discrete solution form of the lattice equation under the initial conditions are proved by using the Banach space compression theorem. Through this paper, some ideas and methods are provided for this kind of fractional equation model.Based on one class of Caputo-type fractional-order neural networks model with time delay and partial differential, this paper combines the finite difference idea and Mittag-Leffler function, and uses the exponential time difference technique to construct the lattice equation form of the model. The discrete approximation of the model is realized, and the discrete solution form of the lattice equation is obtained. Through a series of assumptions, the existence and uniqueness of the solution of the lattice equation under the initial conditions are proved by using the Banach space compression theorem. Through this paper, some ideas and methods are provided for this kind of fractional equation model.
How ground-truth label helps link prediction in heterogeneous graphs

Link prediction is one of the most essential tasks in data mining. A lot of studies have shown great progress in homogeneous graph. However, besides recommendation system and knowledge graph, little research solves the problem of link prediction in heterogeneous graphs. The main cause behind the failure of link prediction in heterogeneous graphs is that the way of connecting nodes is different from that in homogeneous graphs. In this article, we come up with a new model, Pro-SEAL, since it originates from SEAL. We notice that original labeling trick only takes distance into consideration, ignoring that different kinds of nodes with same distance may contribute to varying degrees. With this in mind, we design a novel labeling trick and make improvement in the final GNN learning structure. We take both ground-truth label and distance into account since they both matter when edges link. It expands framework of graph neural network(GNN) in link prediction and enables it to better adapt to heterogeneous graph for the first time. Extensive experiments on eight real-world datasets show that our model has obtained great results compared with some classic and advanced model.
Text Sentiment Classification Model based on Fusion of DualChannel Features of CNN and BiLSTM

Text sentiment analysis is an important task in natural language processing (NLP), which aims to determine people's emotional tendency towards a certain topic or event by analyzing the language and emotion in the text. Aiming at the traditional emotion classification model can't fully capture the semantic information implied in short text comments, a two-channel emotion classification model based on CNN and BiLSTMl is proposed.Dynamic allocation weights introduced since the attention mechanism, build fusion BiLSTM and CNN's dual channel neural network architecture, and extract the bureau of emotional characteristics and emotional characteristics as global pay attention to the input feature fusion layer, through your emotions full text feature fusion module integration characteristic information and emotional polarity to break..Compared to the experimental results show that the model of emotion classification performance of the optimum Transformer model, this model (CNN-BiLSTM-AFF) on a public data set senti_weibo_100k accuracy, F1 value, the recall rate of 1.034%, 1.265% and 1.045% respectively.
Onboard Trajectory Coordination of Multiple Unmanned Air Vehicles

The increasing diversity of autonomous air vehicles (AAVs) and their application has created an increased need for effective aircraft trajectory coordination within congested airspaces. Here, a method to coordinate a flight formation of multiple cooperative unmanned aerial vehicles (UAVs) using onboard command and control is introduced. The proposed system has been verified in simulation and validated in flight. The results show that it is possible to control a fleet of multi-rotor aircraft using onboard equipment. Areas of improvement to increase accuracy and reliability of the system are proposed.
Online Voting System Based on Face Recognition and QR Code Authentication

Even in this modern day of development, a significant portion of people are still unable to vote in a democracy to create a government. One reason for not voting is that the voters are not available in their constituency on the day of voting. Internet voting or online voting can enable people to cast their vote from wherever they are. In this work, an online voting systemOnline Voting System with multiple authentication processes is proposed which can allow people with Indian citizenship to vote online instead of visiting a polling place. Face recognitionFace Recognition and QR code authentication are the verification strategies suggested to ensure voters’ identity and to avoid proxying while voting. Haar cascade algorithm is used to detect human faces in the OpenCVOpenCV platform and deep learningDeep Learning techniques are used to recognize the face. The proposed system is encoded using Python language and is implemented on Raspberry PiRaspberry Pi 3 using a Pi camera to capture images such as faces and QR codes. For ease of interaction, two GUIs are developed: the first one for the authentication process and the second one for the voting process. Avoidance of proxy voting, multiple voting by a single voter, avoidance of visits to poll booths, and automatic counting are the major features of the proposed work.
Estimation of landscape evolution of the Samotlor oil-gas field

The Samotlor oil and gas field in Western Siberia, one of the largest in the world in terms of oil reserves, has been developed since 1969. Natural forest and marsh landscapes are experiencing the strongest anthropogenic pressure due to the creation of a dense network of cluster drilling sites, roads connecting them with pipelines, enterprises for primary oil refining, lines of communication. Oil production and transportation activities during the development of the Samotlor field are accompanied by pollution of the territory and degradation of vegetation, and a reduction in forest areas. To identify the dynamics of natural forest-bog landscapes, which manifests itself in a change in the ratio of forest and swamp areas, we used a comparison of a survey topographic map at a scale of 1:200 000 in 1980, showing the distribution of forests, as well as passable and impenetrable swamps, with modern high-resolution satellite images, according to which deciphered the boundaries of forests and various types of swamps-raised ridge-hollow, ridge-lake-hollow and lowland. Comparison of materials from different times was carried out on a 7.5×13.5 km site southeast of Lake Samotlor. A comparison of maps compiled in 1980 and 2019 shows that the area of forests here has almost tripled; Herbaceous-shrub communities with small islands of residual forests formed on the site of reduced forest tracts. Among the swamps, a reduction in the areas of lowland fen was revealed, their localization along the shores of lakes, with the expansion of the areas of raised bogs. In place of the impenetrable swamps of 1980, ridge-lake-hollow complexes of raised bogs were formed, with wide distribution of ridge-hollow. It has been shown that, along with the negative processes of anthropogenic deforestation, ecosystem pollution and vegetation degradation, during the development of the deposit there is an increase in the drainage of the territory, accompanied by a local increase in greenery, which is additionally promoted by the tendency to climate warming.
Malaria Parasite Detection and Outbreak Warning System Using Deep Learning

MalariaMalaria is an epidemic disease which causes by parasites that are spread through bites from infected Anopheles mosquitoes. Through early diagnosisDiagnosis and timely treatment, malariaMalaria death rates can be decreased and prevented. But the manual examination of blood smears by laboratory technicians is time-consuming and probable chances of human errors. Considering this need, in this paper, we are demonstrating a proposed system deployed over a WebApp could help laboratories to perform fast and accurate tests. For the malariaMalaria parasite detection in the blood cells, we proposed our custom CNN modelCustom CNN model and even implemented the dataset on the Transfer Learning model (VGG19) to compare the results. We got the highest accuracy of 97.74% for our CNN model which is the simplest of the current models as it prioritizes only important parameters, provides great computational efficiency, and is realistic for implementation. Additionally, a malariaMalaria outbreak warning system is proposed to warn the people in localities when there is availing risk based on climatic factors. A logistic regressionLogistic Regression model implemented by Gradient Descent Optimizer is deployed which predicts the malariaMalaria outbreak from the live weather forecastForecast inputs from the OpenWeather API and displays the probability of the outbreak to the users with one click.
FPGA Implementation of SLIM an Ultra-Lightweight Block Cipher for IoT Applications

Increased security of radio frequency identification (RFIDRadio Frequency Identification (RFID)) systems which are resource-constrained devices is in high demand these days. Systems which have access control, payment methods and transaction banking systems are all examples of high-security applications where RFIDRadio Frequency Identification (RFID) technology are used. The attacker tries to deceive RFIDsRadio Frequency Identification (RFID) in order to gain illegal entry to services by not paying for them or get around security measures by detection of secret password. One of the most difficult problems with RFIDRadio Frequency Identification (RFID) systems is ensuring effective security from such infringement activities. For RFIDRadio Frequency Identification (RFID) systems, lightweight cryptographic system gives security confidence. SLIM, a novel ultra-lightweight cryptographyLightweight Cryptography technique for RFIDRadio Frequency Identification (RFID) devices, is described in this article. As most commonly block ciphersBlock Cipher are used cryptographic system and provides highly strong security for IoT ecosystem, SLIM which is based on Feistel structure is a block cipherBlock Cipher of 32-bit size. The most difficult aspect of creating a lightweight block cipherBlock Cipher is balancing cost, performance and security. SLIM, alongside all block ciphersBlock Cipher that are symmetric, encrypts and decrypts using the same key. The suggested method performs well in both software and hardware contexts, has a small implementation footprint, a reasonable cost/security ratio for RFIDRadio Frequency Identification (RFID) devices, also is energy-saving. SLIM has shown good immunity to most successful differential and linear cryptanalysis assaults. The use of VLSI technology in the form of Xilinx software is also done in this paper. In this paper, SLIM has almost 4 times improvement in efficiency compared to other block ciphersBlock Cipher.
AR and IoT Integrated Machine Environment (AIIME)

Machine maintenance accounts for 15–40% of overall manufacturing costs. Moreover, industries have progressed from breakdown type of maintenance to most recent and modern, predictive maintenance (PdM)Predictive Maintenance (PdM) which heavily relies on data collection. This has paved the way for IoT to be adopted for PdMPolydimethylsiloxane (PDMS). Many manufacturers are coming up with machines with built-in sensorsSensors for parameter monitoring; this makes them expensive. However, many machines are already in use, and putting sensorsSensors for data collection and applying the Internet of ThingsInternet of Things to such machines is a huge task. The concept of modular sensorSensor systems is proposed in this article to address that. Also, with an increase in fast computing, augmented realityAugmented Reality is getting adopted for many applications. It has the potential to enhance reality and impact many fields. It is found that less work has been done on integrating the industrial Internet of ThingsInternet of Things with augmented realityAugmented Reality. The present work focuses on designing a modular sensorSensor system and displaying data over a machine's digital twinDigital Twin in AR. Along with that, virtual step-by-step instructional guidance in augmented realityAugmented Reality for maintenance is also adopted. This article proposes an idea of AR and IoT integrated machine environment (AIIME). AIIME is a complete machine environment where a digital twinDigital Twin is used to assist maintenance. Concepts and different aspects of AIIME are discussed in this article. The aim is to design a better machine environment and lay a foundation for AR industrial metaverse.
Development of Affordable Smart and Secure Multilayer Locker System for Domestic Applications

The security systemSecurity System or authorization plays an important role in preventing unauthorized users from accessing secured physical and logical locations. The most basic forms of security systemsSecurity System are standard door lock keys and computerized automated identification systems. The major purpose of this paper is to design and build the smart and intelligent multilayer security systemSecurity System for lockers. TOTP authentication, fingerprint authentication, and passcode/password authentication are all part of the smart locker system. To get access to the locker, two or more authenticators registered in the system must be present or must allow the access to unlock the locker using the system-linked mobile application. The authentication procedure begins when registered members launch the mobile application after entering the passcode/password, then pick which locker system they need to open or get access to, and then finally, the application will request fingerprint authentication to obtain the TOTP. Each user will go through the identical procedure to obtain their own TOTPs, by which they must enter into the locker system in the sequence which was assigned. Furthermore, various other security systemsSecurity System that may be incorporated into the system for a larger sector of security applications, such as security systemsSecurity System in large organizations. The goal of developing this system is to create a powerful smart intelligent multilayer locker security systemSecurity System that is extremely secure, low-cost, and remotely accessible.
Undergraduate students’ perceived mobile technology-learning barriers in their academic studies: a study in Greece

Abstract Mobile technology is used by undergraduate students for educational purposes, it supported students’ learning during the pandemic, while mobile learning may have potential benefits and barriers. This study investigates Greek undergraduate students’ perceptions on mobile technology-learning barriers in their academic studies. This topic is of international interest and still underexplored in Greece. An online questionnaire was completed by 212 students, and this study is descriptive-inferential. Students’ perceptions regarding major barriers, reveal both external (internet connectivity issues, high cost of mobile devices, outdated operating system) and internal (tutors’ negative attitudes and insufficient knowledge for mobile technology-learning integration) barriers. Other obstacles are associated with student concerns about distraction, and inadequate training opportunities to use mobile technology in their studies. Students’ confidence, attitudes and digital skills regarding mobile technology adoption/use are perceived as minor barriers (or no barriers). The findings have implications for students, university tutors and university stakeholders.
Privacy-Preserving k-Nearest Neighbor Classification over Malicious Participants in Outsourced Cloud Environments

In recent years, many companies have chosen to outsource data and other data computation tasks to cloud service providers to reduce costs and increase efficiency. However, there are risks of security and privacy breaches when users outsource data to a cloud environment. Many researchers have proposed schemes based on cryptographic primitives to address these risks under the assumption that the cloud is a semi-honest participant and query users are honest participants. However, in a real-world environment, users’ data privacy and security may be threatened by the presence of malicious participants. Therefore, a novel scheme based on secure multi-party computation is proposed when attackers gain control over both the cloud and a query user in the paper. We prove that our solution can satisfy our goals of security and privacy protection. In addition, our experimental results based on simulated data show feasibility and reliability.