Reviewerly/exHarmony huggingface dataset:
Informativeness in Twitter Textual Contents for Farmer-centric Plant Health Monitoring

Data mining in social media has been widely applied in different domains for monitoring and measuring social phenomena, such as opinion analysis towards popular events, sentiment analysis of a population, detecting early side effects of drugs, and earthquake detection. Social media attracts people to share information in open environments. Facing the newly forming technical lock-ins and the loss of local knowledge in agriculture in the era of digital transformation, the urge to re-establish a farmer-centric precision agriculture is urgent. The question is whether social media like Twitter can help farmers to share their observations towards the constitution of agricultural knowledge and monitoring tools. In this work, we develop several scenarios to collect tweets, then we applied different natural language processing techniques to measure their informativeness as a source for phytosanitary monitoring.
Pedagogy in the Сonstellation of Sciences: Methodological Dialogue and “Knowledge Transfer”

According to representatives of the exact sciences, as well as philosophers and methodologists of the humanities, the epistemological status of pedagogy today is quite low. There are no verified regulations for the empirical, theoretical and contextual substantiation of conclusions and methods for obtaining them; the voices of the theoreticians of pedagogical science about the structure and component composition of pedagogical theory sound polyphonic. The differences between theory and concept, long recognized in the science of science, do not find the right understanding and response in pedagogical works. Pedagogy, being an integral member of the community of human sciences, must today enter into an active and meaningful scientific dialogue with methodologists, science scholars, humanists, who represent a “strong” version of human knowledge. De­scribed by the famous Russian philosopher L.A. Mikeshina’s methodological dia­logues prompted us to take up the urgently required concretization of the author’s interesting conclusions and arguments in relation to the methodology of pedagogy which L.A. Mikeshina presents as transfer of terms (extrapolation). Based on the analysis of the cited book and many works of philosophers and teachers, we man­aged to build a triad of criteria for correct extrapolation of terms: a) address cor­rectness; correspondence of the pedagogical sound of the term transferred to ped­agogy to that which is accepted in donor science; b) the creative focus of the transferred term; c) detachment from repetition of the well-known method of noise stylistic reproduction. As one of the examples of the transfer of knowledge into pedagogy, the author considers the absorption by the science of education of the stylistics of related sciences based on scientific metaphors. The second part of the article is devoted to the theoretical construction of pedagogy. A methodologi­cal screen of a particular pedagogical theory is presented, white spots of the topic are revealed, the idea of theoretical constructions in pedagogy, reflected qualita­tively and quantitatively, is proposed. The connection between the problem of knowledge transfer (1) and the theoretical construction of pedagogy (2) is shown.
A Comparative Study of Hyperparameter Optimization Techniques for Deep Learning

Algorithms for deep learning (DL) have been widely employed in a variety of applications and fields. The hyperparameters of a deep learning model must be optimized to match different challenges. For deep learning models, choosing the optimum hyperparameter configuration has a direct influence on the model’s performance. It typically involves a thorough understanding of deep learning algorithms and their hyperparameter optimization (HPO) techniques. Although there are various automatic optimization approaches available, each has its own set of advantages and disadvantages when applied to different datasets and architectures. In this paper, we analyzed which algorithm takes the longest optimization time to optimize an architecture and whether the performance of HPO algorithms is consistent across different datasets and architectures. We selected VGG16 and ResNet50 architectures, CIFAR10 and Intel Image Classification Dataset, as well as Grid search (GS), Genetic algorithm (GA), Bayesian optimization (BO), Random search (RS), Hyperband (HB) and Particle swarm optimization (PSO) HPO algorithms for comparison. Due to the lack of pattern, it is challenging to determine which approach obtains the best performance on different datasets and architecture. The results show that all of the algorithms have similar results in terms of optimization time. This research is expected to aid DL users, developers, data analysts, and researchers in their attempts to use and adapt DL models utilizing appropriate HPO methodologies and frameworks. It will also help to better understand the challenges that currently exist in the HPO field, allowing future research into HPO and DL applications to move forward.
Early Prediction of Complex Business Processes Using Association Rule Based Mining

Complex business processes are challenging and hard to analyse. The objective here is to enhance delivery of processes in terms of improving quality of service and customer satisfaction. Therefore, an automated process prediction system is desirable to monitor and evaluate complex business processes and forecast process outcome during execution time. The analysis of such processes would help domain experts to make in-time decisions to improve the process. The in-time response greatly effects the quality of service and customer satisfaction. Therefore, in this paper, the early process prediction framework using Classification Based on Association rules (CBA) has been proposed to predict outcomes for such incomplete processes. The essential part of the proposed system is to extract association rules from the process data up to a certain point in time (i.e. the cut-off time) at which the prediction needs to be made; in an live process this would usually be the current time. The CBA algorithm generates rules with user specified support and confidence which are then utilised for early process prediction. The experimental results based on real business process data are presented for on-time and delayed processes. The proposed early process prediction system is evaluated using different metrics such as accuracy, precision, recall and the F-measure. Moreover, the proposed system is also compared with our prior published work in terms of accuracy, recall and F-measure. The analysis shows that the performance of proposed system outperforms schemes in the literature.
Deep Learning-Based Topic Categorization of Tamil Social Media Text Content

Nowadays, interpreting the topic of social media text content is an important task. It will take time when tried to do manually. AI with machine learning and deep learning approaches is used immensely for detecting the topic category. In the current work, Tamil text data from social media sources like YouTube, Facebook, Twitter are collected. The text content is categorized as entertainment, education, sports, politics, health, and others. The current work provides a comparative analysis of various machine learning and deep learning approaches for topic categorization on the collected data.
Training Deep Filters for Physical-Layer Frame Synchronization

In this paper we demonstrate the application of Fully Convolutional Neural Network (FCN) for Frame Synchronization (FS) in bursty single carrier transmissions, commonly used in wireless sensor networks and Internet of Things (IoT) applications. Our approach shows greatly improved performance compared to noncoherent correlation-based methods under carrier phase and frequency offsets, especially for shorter preambles. Using a fully convolutional architecture allows the training of a deep filter, which we believe is more suited to signal processing tasks than more commonly used deep learning architectures with fully connected layers. In terms of deployment within a wider communications system, it could be treated similarly to a typical signal processing filter, which means it can be deployed to inputs of arbitrary length. Additionally, because the proposed model is composed only of convolutional layers, the entire model benefits from the weight sharing property of convolutional filters, and results in a greatly reduced memory footprint compared to that of similar models containing fully connected layers.
Multi-view 3D Object Detection Based on Point Cloud Enhancement

Recent years, with a boundless growth in the field of autonomous driving, three-dimensional object detection technologies based on LiDAR and cameras have attracted extensively attentions. However, the LiDAR cannot obtain the complete point cloud information of the object, especially for the object that is heavily occluded. The existing multi-view detection schemes rarely process point cloud data. Thus, in this paper, on the basis of the two-stage detection network, a sampling method based on cylindrical space is proposed, combining the original point cloud and the output of the first region proposal network (RPN) to enhance the point cloud of the objects in the three-dimensional space. The second RPN will complete the accurate regression and classification of the three-dimensional bounding box by using enhanced point clouds. We solve the problem of object point cloud incomplete well. The proposed scheme is verified on the KITTI dataset. The verification results show that the method not only has good results in car three-dimensional and Bird’s Eye View (BEV) object detection, but also achieves the state of the art results in pedestrian detection, especially on heavily occluded objects.
The blockchain based access control scheme for the Internet of Things

The Internet of Things (IoT) is an information carrier based on the Internet and traditional telecommunications networks. Access control is an important part of the service architecture, which can effectively solve the security issues in IoT. Therefore, many solutions adopt the access control to solve some security problems. However, these existing solutions cannot easily deploy access control in the IoT. As a decentralized, non-tamperable, and highly credible distributed ledger technology, blockchain is in line with the current trend of network development. To improve the transparency, non-tampering of the access control process and traceability and auditability of access flow changes, we propose a blockchain-based access control scheme for the IoT, which uses a combination of on-chain and off-chain to reduce the storage pressure of the blockchain. On the one hand, we deploy policy decision points (PDP) and other components on the smart contract in the form of chain code to better judge and execute decision-making. On the other hand, we put policy administration points (PAP) in the off-chain organization to reduce the storage pressure of the blockchain and make better access flow transformation and traceability. Ultimately, we concluded through feasibility analysis that our solution can maintain high throughput in a large-scale transaction volume environment.
Data Hiding Technique for Color Images using Pixel Value Differencing and Chaotic Map

The huge advance in information technology and communication resulted in extreme usage of digital networks that makes information security playing an important role as never before. Steganography is the art of hiding secret message bits into different multimedia data using either spatial domain or frequency domain to provide security for the transferred information against unauthorized access. Most of the techniques that apply Pixel Value Differencing approach (PVD) depend on sequential embedding manner, which lacks security. In the proposed method, a complex chaotic map is used to randomly choose the coefficients pairs for embedding the secret message. First, the cover image is transformed using IWT. Then, the embedding process starts in the highest frequency band of IWT and continues to the next sub-bands. Adaptive embedding is performed according to intensity variation between pixel pairs using PVD and Least Significant Bit substitution (LSB). Non-sequential embedding performed by using chaotic map lets the method more secure. The experimental results show that the proposed technique achieves high PSNR with improved capacity compared to other techniques.
Design of Teaching System of Industrial Robots Using Mixed Reality燭echnology

Traditional teaching and learning about industrial robots uses abstract instructions, which are difficult for students to understand. Meanwhile, there are safety issues associated with the use of practical training equipment. To address these problems, this paper developed an instructional system based on mixed-reality (MR) technology for teaching about industrial robots. The Siasun T6A-series robots were taken as a case study, and the Microsoft MR device HoloLens-2 was used as the instructional platform. First, the parameters of the robots were analyzed based on their structural drawings. Then, the robot modules were decomposed, and 1:1 three-dimensional (3D) digital reproductions were created in Maya. Next, a library of digital models of the robot components was established, and a 3D spatial operation interface for the virtual instructional system was created in Unity. Subsequently, a C# code framework was established to satisfy the requirements of interactive functions and data transmission, and the data were saved in JSON format. In this way, a key technique that facilitates the understanding of spatial structures and a variety of human-machine interactions were realized. Finally, an instructional system based on HoloLens-2 was established for understanding the structures and principles of robots. The results showed that the instructional system developed in this study provides realistic 3D visualizations and a natural, efficient approach for human-machine interactions. This system could effectively improve the efficiency of knowledge transfer and the student’s motivation to learn.
The Results of All-Ukrainian Teachers’ Survey Regarding Functioning of Specialized Media on Educational Topics in Ukraine

The article presents the results of a representative survey of Ukrainian teachers on the level of their satisfaction with educational content and media coverage of education issues.The results of the survey, announced in the Communication Strategy 2019-2021 of the Ministry of Education and Science of Ukraine, show that all the specialized media we analyzed (“Osvita.ua”, “NUS”, “Osvitoria”, and “Pedpresa”) are in the top 10 Internet resources visited by teachers (according to the survey of Gemius Audience). We interviewed the teachers to determine the level of satis-faction of the representatives of secondary schools pedagogical staff with the content on educa-tional topics in highly specialized and widely specialized media, to comprehend the affective correlation between the content of educational topics on various resources, and also to deter-mine the opinions of key content consumers on educational topics regarding necessarity of crea-tion of new information resources in Ukraine. It is a representative survey: α=.886 (88.6%) with (N=384). A questionnaire and 8 questions in the form of a Likert scale were used for better data interpretation. The results of a nationwide teachers survey show they are more satisfied with coverage of education topics by narrowly specialized media (“Osvita.ua”, “Osvitoria”, “NUS”, “Pedpresa”, etc.) than by broader special-ized media. The teachers eveluate the opportunity for creation of new resources (sites) that would publish information and analytic content on education topics at 8.08 points out of 10.
Enriching Scholarly Knowledge with Context

Leveraging a GraphQL-based federated query service that integrates multiple scholarly communication infrastructures (specifically, DataCite, ORCID, ROR, OpenAIRE, Semantic Scholar, Wikidata and Altmetric), we develop a novel web widget based approach for the presentation of scholarly knowledge with rich contextual information. We implement the proposed approach in the Open Research Knowledge Graph (ORKG) and showcase it on three kinds of widgets. First, we devise a widget for the ORKG paper view that presents contextual information about related datasets, software, project information, topics, and metrics. Second, we extend the ORKG contributor profile view with contextual information including authored articles, developed software, linked projects, and research interests. Third, we advance ORKG comparison faceted search by introducing contextual facets (e.g. citations). As a result, the devised approach enables presenting ORKG scholarly knowledge flexibly enriched with contextual information sourced in a federated manner from numerous technologically heterogeneous scholarly communication infrastructures.
Leukocyte Subtyping Using Convolutional Neural Networks for Enhanced Disease Prediction

Deep learning shown its potential in a variety of medical applications and proved as a count on by people as a step ahead approach compared to traditional machine learning models. Moreover, the other implementations of these models such as the convolutional neural networks (CNNs) provide extensive applications in the field of medicine, which usually involves processing and analysis of a large dataset. This paper aims to create a CNN model which can solve the problem of white blood cell subtyping which is a daunting one in clinical processing of blood. The manual classification of white blood cells in laboratory is a time-consuming process which gives rise to the need for an automated process to perform the task. A CNN-based machine learning model is developed to classify the leukocytes into their proper subtypes by performing tests on a dataset of around twelve thousand images of leukocytes and their types, and a wide range of parameters is evaluated. This model can automatically classify the white blood cells to save manual labor, time and improve efficiency. Further, pretrained models like Inception-v3, VGGNet and AlexNet are used for the classification, and their performance is compared and analyzed.
Research on an improved fish recognition algorithm based on YOLOX

The key to the development of underwater resources is to detect underwater targets quickly and accurately in real time. However, due to the influence of light, the underwater image is easy to be distorted and the contrast is low and so on, which greatly affects the performance of the detection algorithm, In order to improve the detection accuracy of underwater targets, After a detailed analysis of the underwater detection target features, The attention mechanism ECA module was added to the YOLOX model, Real-ESRGAN was used to treat multiple target and fuzzy images in detection images, the accuracy improved about 10 percent, A high-precision target detection algorithm suitable for underwater fish was developed, The ideal detection result was achieved.
Realısatıon of Performance Optımısed 32-Bıt Vedıc Multıplıer

This paper demonstrates the improved adaptation of the Vedic Multiplier using the Vedic standards, which includes old sutras.In this paper, current and proposed model are examined.Verilog HDL is utilized to execute the improved adaptation of Vedic Multiplier.Streamlined proposed model can likewise be utilized to achieve higher-request bits duplication exercises up to 32 bits.Vedic Multiplier up to 32-bit, the reproduction results are examined.These outcomes showed that the streamlined Vedic multiplier changed the execution improvement measurements, for example, time delay, also in device use too.Alongside this, a correlation is made among existing and enhanced proposed model to recognize about the presentation improvement measurements.
ENVIRONMENTAL MONITORING OF ATMOSPHERIC PRECITATION (SNOW) IN THE BASTAK NATURE RESERVE

In the paper, the authors consider the results of atmospheric fallout study in the Bastak nature reserve over a 10-year period. They show that the atmospheric air in the protected area is not polluted by natural or by anthropogenic impurities. Even single bursts of elevated concentrations of elements did not exceed either their background levels or permissible values.
Privacy-Preserving Data Scheduling in Incentive-Driven Vehicular Network

The lightweight privacy-preserving algorithm in the vehicular networks (VNs) improves the reliability of data transmission for the vehicles. However, it is challenging for vehicles to execute resource-consuming algorithms while driving. In addition, the high-speed mobility of vehicles also brings data scheduling problems for vehicles and other equipment. To tackle the problems mentioned above, this article proposes a privacy-preserving data scheduling in an incentive-driven VN, which achieves efficient and secure data transmission based on an incentive mechanism among vehicles. The algorithm first balances the benefits between the source, forwarding, and destination nodes through a multidimensional incentive mechanism to ensure positive benefits. After the vehicles participate in the task under the action of the incentive mechanism, the key task will then be identified and completed. Finally, the data interference mechanism guarantees the security of data transmission between the vehicle and the edge server. The simulation experiment results show that the proposed algorithm is superior to other algorithms in revenue, satisfaction, and reliability.
Research and Design of Compact Equipment Using Phase_Shifting Deflectometry Apply in Optical Surface Measurement

Recently, aspherical optics are increasingly used, because aberrations of optical systems can be reduced, applied in many applications from compact optical devices such as camera lenses to large complex telescope systems. Fabricating and testing aspherical surfaces on very large or small optics presents many challenges because the radius of curvature varies across the surface. With the measurement providing a wide range of high-resolution areas, the deflectometry method can solve the problem of measuring the profiles of optical parts or free form curved surfaces. This paper researches and designs a surface measuring compact device for optics using the phase-shifting deflectometry method to achieve high accuracy and resolution. In addition, evaluating measurement results components to optics aberration is also covered in this research.
POS Tagger Model for South Indian Language Using a Deep Learning Approach

Computational Linguistics is necessary for understanding the language which brings human being with an insight into thinking and intelligence. It is an inspiring research concept in Natural Language Processing (NLP) domain. Parts of Speech (POS) labeling is a very crucial phase in NLP, since based on this, most of the other tasks like Syntax parsing, Semantic parsing, Sentiment analysis, Sentence level classification etc. are carried out. This paper presents a POS tagger model developed on Kannada texts which is one of the South Indian Languages by employing deep neural network methodology. The deep learning model adopted in this work has a combination of word embedding and Recurrent Neural Network (RNN) along with Long-Short memory (LSTM) techniques. The total size of the dataset used during this implementation is 10,000 annotated Kannada sentences (190,000 Kannada words) comprising of five different domains like Agriculture, Sports, Literature, Tourism and Science and Technology. Most of the sentences from the available dataset are compound and ranged up to 10–11 words. The dataset is taken from Technology Development for Indian Languages (TDIL) website. It has been divided into 8000 Kannada sentences as a training dataset and 2,000 Kannada sentences as a test dataset. The BIS (Bureau of Indian Standards) tagset is adopted for POS tags, in which we considered 27 prime POS tags. An average accuracy of POS tagging on an unseen dataset obtained from the trained POS tagger model is 81%. The results are extended with plotted graphs and shown in this paper.