Reviewerly/exHarmony huggingface dataset:
Incorporating Vehicle Detection Algorithms via Edge Computing on a Campus Digital Twin Model

A digital twin involves a realistic digital model, sensing of real-world parameters, depiction of those parameters in a digital environment, and simulation of the virtual system. While various methods exist to create digital models of geographic locations and their road networks, little work has been done to automatically track vehicles from video and generate virtual vehicles in the digital model. This paper presents one pipeline for performing vehicle detection and tracking on an edge device, and the results are visualized in a university campus digital model running on a workstation. Challenges stemming from camera lens effects, irregularities in the digital model, and object tracking uncertainties are discussed. The precise transformation of 2D vehicle detection to 3D representation is the main challenge addressed in this work. The approach presented focuses on calibrating different parts of the scene separately. The focus of future work will be to make this calibration process semi-automated.
Enhancing Industrial Cyber Security, Focusing on Formulating a Practical Strategy for Making Predictions through Machine Learning Tools in Cloud Computing Environment

Cloud computing has revolutionized how industries store, process, and access data. However, the increasing adoption of cloud technology has also raised concerns regarding data security. Machine learning (ML) is a promising technique to enhance cloud computing security. This paper focuses on utilizing ML techniques (Support Vector Machine, XGBoost, and Artificial Neural Networks) to progress cloud computing security in the industry. The selection of 11 important features for the ML study satisfies the study’s objectives. This study identifies gaps in utilizing ML techniques in cloud cyber security. Moreover, this study aims at developing a practical strategy for predicting the employment of machine learning in an Industrial Cloud environment regarding trust and privacy issues. The efficiency of the employed models is assessed by applying validation matrices of precision, accuracy, and recall values, as well as F1 scores, R.O.C. curves, and confusion matrices. The results demonstrated that the X.G.B. model outperformed, in terms of all the matrices, with an accuracy of 97.50%, a precision of 97.60%, a recall value of 97.60%, and an F1 score of 97.50%. This research highlights the potential of ML algorithms in enhancing cloud computing security for industries. It emphasizes the need for continued research and development to create more advanced and efficient security solutions for cloud computing.
Research on Vocational Education Personnel Training Under the Strategic Background of Guangdong-Hong Kong-Macao Greater Bay Area -- Taking Zhaoqing City as an Example

The economic development of the Guangdong-Hong Kong-Macao Greater Bay Area depends on the intellectual and talent support of vocational educators. Based on the analysis results of talent cultivation needs of the Greater Bay Area, on the basis of the strategic planning opportunities of the Greater Bay Area and the successful experience of vocational education at home and abroad, combined with the basic conditions of Zhaoqing city, this paper puts forward the strategy of building a vocational education talent base in Zhaoqing City, and puts forward the concrete realization path and safeguard measures such as encouraging enterprises to run universities.
Generalizable and efficient cross‐domain person re‐identification model using deep metric learning

Abstract Most of the successful person re‐ID models conduct supervised training and need a large number of training data. These models fail to generalise well on unseen unlabelled testing sets. The authors aim to learn a generalisable person re‐identification model. The model uses one labelled source dataset and one unlabelled target dataset during training and generalises well on the target testing set. To this end, after a feature extraction by the ResNext‐50 network, the authors optimise the model by three loss functions. (a) One loss function is designed to learn the features of the target domain by tuning the distances between target images. Therefore, the trained model will be more robust to overcome the intra‐domain variations in the target domain and generalises well on the target testing set. (b) One triplet loss is used which considers both source and target domains and makes the model learn the inter‐domain variations between source and target domain as well as the variations in the target domain. (c) Also, one loss function is for supervised learning on the labelled source domain. Extensive experiments on Market1501 and DukeMTMC re‐ID show that the model achieves a very competitive performance compared with state‐of‐the‐art models and also it requires an acceptable amount of GPU RAM compared to other successful models.
SEARCHING FOR WAYS TO IMPROVE THE FORCED DISTANCE LEARNING OF STUDENTS

First, the coronavirus pandemic, and then the war caused the search for ways to organize the educational process. Education is undoubtedly important for any country in the world, so it is necessary to organise the educational process at a high quality level under any conditions. Thanks to information technologies, it has become possible to study at a distance online. However, educators had to face a number of difficulties when implementing the educational process in a distance format. The purpose of this study is to analyze the problem of organizing distance learning in the conditions of the pandemic and russian aggression and to identify ways to improve forced distance learning in Ukraine. The main research methods that were used in writing the article are statistical, graphic, analysis and synthesis. Distance learning has become a challenge not only for students but also for teachers. There are problems in the process of working remotely during quarantine: technical competence of teachers and students; registration and communication procedures, meaningful structuring of information flow; remote schedule and workload optimisation; multitasking; personal space as a learning environment; electronic dean's office. In the article, the authors analyzed statistical data on the number of education seekers and educational institutions in which classes were held in various formats: distance, face-to-face, and mixed. Four teaching models are explored in the article: correspondence, tele-education, multimedia, virtual. Authors distinguish certain types of learning: traditional learning, synchronous learning, blended learning, and revel the essence of them. Authors analyzed platforms such as Moodle, Google Classroom, which can be used for study and communication, or an educational institution can use its own developed learning platform, such as MIX SumDU at Sumy State University. Analyzing the essence of distance education and e-learning, authors conclude that the idea is not new, as its elements are implemented in educational process. The development of distance education in Ukraine continues and it is enhanced by the development of the Internet and the improvement of distance learning methods.
Illumination Removal via Gaussian Difference L0 Norm Model for Facial Experssion Recognition

Face images in the logarithmic space can be considered as a sum of the texture component and lighting map component according to Lambert Reflection. However, it is still not easy to separate these two parts, because face contour boundaries and lighting change boundaries are difficult to distinguish. In order to enhance the separation quality of these to parts, this paper proposes an illumination standardization algorithm based on extreme L0 Gaussian difference regularization constraints, assuming that illumination is massively spread all over the image but illumination change boundaries are simple, regular, and sparse enough. The proposed algorithm uses an iterative L0 Gaussian difference smoothing method, which achieves a more accurate lighting map estimation by reserving the fewest boundaries. Thus, the texture component of the original image can be restored better by simply subtracting the lighting map estimated. The experiments in this paper are organized with two steps: the first step is to observe the quality of the original texture restoration, and the second step is to test the effectiveness of our algorithm for complex face classification tasks. We choose the facial expression classification in this step. The first step experimental results show that our proposed algorithm can effectively recover face image details from extremely dark or light regions. In the second step experiment, we use a CNN classifier to test the emotion classification accuracy, making a comparison of the proposed illumination removal algorithm and the state-of-the-art illumination removal algorithm as face image preprocessing methods. The experimental results show that our algorithm works best for facial expression classification at about 5 to 7 percent accuracy higher than other algorithms. Therefore, our algorithm is proven to provide effective lighting processing technical support for the complex face classification problems which require a high degree of preservation of facial texture. The contribution of this paper is, first, that this paper proposes an enhanced TV model with an L0 boundary constraint for illumination estimation. Second, the boundary response is formulated with the Gaussian difference, which strongly responds to illumination boundaries. Third, this paper emphasizes the necessity of reserving details for preprocessing face images.
Subsampling of 3D Pixel Blocks as a Video Compression Method for Analog Transmission

Hybrid digital–analog transmission of video signals enables flexibility in dividing video information into two parts to utilize the available bandwidth better. This study proposes a compression scheme to reduce the utilized bandwidth. The scheme uses different subsampling in three-dimensional (3D) blocks, where subsampling factors are selected to minimize reconstruction distortion. The study evaluates various methods for subsampling and reconstruction to find the best combination in terms of reconstruction quality and complexity. Results show that medium-quality reconstructions can be obtained for compression ratios of about 0.125–0.3 samples per pixel.
Low-Code Machine Learning Platforms: A Fastlane to Digitalization

In the context of developing machine learning models, until and unless we have the required data engineering and machine learning development competencies as well as the time to train and test different machine learning models and tune their hyperparameters, it is worth trying out the automatic machine learning features provided by several cloud-based and cloud-agnostic platforms. This paper explores the possibility of generating automatic machine learning models with low-code experience. We developed criteria to compare different machine learning platforms for generating automatic machine learning models and presenting their results. Thereafter, lessons learned by developing automatic machine learning models from a sample dataset across four different machine learning platforms were elucidated. We also interviewed machine learning experts to conceptualize their domain-specific problems that automatic machine learning platforms can address. Results showed that automatic machine learning platforms can provide a fast track for organizations seeking the digitalization of their businesses. Automatic machine learning platforms help produce results, especially for time-constrained projects where resources are lacking. The contribution of this paper is in the form of a lab experiment in which we demonstrate how low-code platforms can provide a viable option to many business cases and, henceforth, provide a lane that is faster than the usual hiring and training of already scarce data scientists and to analytics projects that suffer from overruns.
Enhancing Low-Pass Filtering Detection on Small Digital Images Using Hybrid Deep Learning

Detecting image manipulation is essential for investigating the processing history of digital images. In this paper, a novel scheme is proposed to detect the use of low-pass filters in image processing. A new convolutional neural network with a reasonable size was designed to identify three types of low-pass filters. The learning experiences of the three solvers were combined to enhance the detection ability of the proposed approach. Global pooling layers were employed to protect the information loss between the convolutional layers, and a new global variance pooling layer was introduced to improve detection accuracy. The extracted features from the convolutional neural network were mapped to the frequency domain to enrich the feature set. A leaky Rectified Linear Unit (ReLU) layer was discovered to perform better than the traditional ReLU layer. A tri-layered neural network classifier was employed to classify low-pass filters with various parameters into two, four, and ten classes. As detecting low-pass filtering is relatively easy on large-dimension images, the experimental environment was restricted to small images of 30 × 30 and 60 × 60 pixels. The proposed scheme achieved 80.12% and 90.65% detection accuracy on ten categories of images compressed with JPEG and a quality factor 75 on 30 × 30 and 60 × 60 images, respectively.
An Integrated Transformer with Collaborative Tokens Mining for Fine-Grained Recognition

Fine-grained recognition mainly classifies subclass images into hundreds of subcategorical labels by locating the discriminative regions (e.g., Cape May warbler or Magnolia warbler bird). Due to the high complexity and non-differentiation of region locations through the traditional backbone architecture, most existing approaches utilize multi-level reinforcement learning to distinguish the similar appearance among sub-categories. These methods explore incomplete information through only the intra-class informative regions in one image or the inter-class and intra-class relationship in pairwise images, leading to the tendency for overlapped region locations. Since the inter-class correlations and new backbone with complete contextual semantic information play important roles in distinguishing fine-grained classes, we propose a novel transformer with the collaborative token mining (TCTM) scheme by fully exploiting the relationships between inter-class and intra-class regions. The proposed TCTM scheme with a new transformer backbone consists of two modules that collaboratively explore the spatially aware tokens: the Pyramid Tokens Multiplication (PTM) module which exploits the integrated multi-stage inter-class and intra-class correlations from new transformer architecture and the Tokens Proposals Generation (TPG) module which captures two groups of top-four discriminative tokens. The two PTMs extract contrastive tokens for each image and learn to rank these tokens, assuming that those from the same class and the same module should have smaller distances. The TPGs further sort and update the candidate tokens from the extracted attention tokens by ranking their probabilities with ground truth subcategorical labels. Through the collaboration between the PTM and TPG, our TCTM scheme can take the integrated correlations into account and mine the discriminative tokens for final fine-grained classification. Extensive experiments on four popular benchmarks show that our proposed TCTM outperforms the state-of-the-art methods by a large margin.
Explaining image enhancement black-box methods through a path planning based algorithm

Abstract Nowadays, image-to-image translation methods, are the state of the art for the enhancement of natural images. Even if they usually show high performance in terms of accuracy, they often suffer from several limitations such as the generation of artifacts and the scalability to high resolutions. Moreover, their main drawback is the completely black-box approach that does not allow to provide the final user with any insight about the enhancement processes applied. In this paper we present a path planning algorithm which provides a step-by-step explanation of the output produced by state of the art enhancement methods. This algorithm, called eXIE, uses a variant of A $$^*$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow /> <mml:mo>∗</mml:mo> </mml:msup> </mml:math> to emulate the enhancement process of another method through the application of an equivalent sequence of enhancing operators. We applied eXIE to explain the output of several state-of-the-art models trained on the Five-K dataset, obtaining sequences of enhancing operators able to produce very similar results in terms of performance and overcoming the huge limitation of poor interpretability of the best performing algorithms.
An Overview of Ant Colony Optimization Algorithms for Dynamic Optimization Problems

Swarm intelligence is a relatively recent approach for solving optimization problems that usually adopts the social behavior of birds and animals. The most popular class of swarm intelligence is ant colony optimization (ACO), which simulates the behavior of ants in seeking and moving food. This chapter aim to briefly overview the important role of ant colony optimization methods in solving optimization problems in time-varying and dynamic environments. To this end, we describe concisely the dynamic optimization problems, challenges, methods, benchmarks, measures, and a brief review of methodologies designed using the ACO and its variants. Finally, a short bibliometric analysis is given for the ACO and its variants for solving dynamic optimization problems.
TECHNOLOGICAL ASPECTS OF ACCOUNTING AUTOMATION SYSTEM AS A DECISION SUPPORT SYSTEM

Nowadays all businesses need access to an extensive pool of information. Reporting, financial and accounting analysis, financial planning and budgeting are key factors for market-oriented corporate management and successful business. In the digital economy, strong competition, responsible entrepreneurship and corporate social responsibility the business feels a need for revolution and new decision sciences for complex systems that collect big data, analyze it and create reports that can be used by managers for decision-making. The present study aims to prove that the accounting automation system is an important tool for managing the economic activity of companies, because it mediates the interrelationships between managers/decision-makers, or subjects of management, economic activity and its results, or object of management, in support of Business intelligence, or BI, Data and Analysis, Power Platform, etc. The following research tasks have to be settled for reaching the above-mentioned goal: 1. to analyze the components of decision support systems; 2. to research the types of decision support systems; 3. to demonstrate the impact of the accounting automation system as a decision support system on the optimization and improvement of the working processes and making adequate timely management decisions; 4. to analyze the technological aspects of accounting automation system as a decision support system; 5. to research the challenges and solutions regarding ERP systems and SAP. In the course of the study shall be justified the thesis that the successful business is based on true, accurate and reliable information which is mostly generated by automated accounting system as a decision support system and contemporary information technologies. The results of the research are expressed in establishing the way to ensure and guarantee the unification, acceleration and optimization of the input-processing-output process, analysis, financial modelling, financial planning, etc. about decision support and respectively wealth management.
Real-Time Hardware Identification of Complex Dynamical Plant by Artificial Neural Network Based on Experimentally Processed Data by Smart Technologies

Artificial neural networks with different structures are used for identification of complex dynamic plant with distributed parameters. The plant is a high-temperature plasma in the spherical Globus-M2 tokamak. Experimental data from it were processed by plasma reconstruction code based on Picard iterations, namely, the Flux-Current Distribution Identification (FCDI) code. This represents smart technology employed to obtain distributed plasma parameters by minimizing the difference between measured and reconstructed signals. An artificial neural network was then applied to identify the data obtained by the FCDI code on the hardware as a real-time testbed realized on a Speedgoat computer. The aim of this repeated identification is to increase the operational response speed in real time in the closed-loop control system of the plasma shape.
SmartEDU: Accelerating Slide Deck Production with Natural Language Processing

Slide decks are a common medium for presenting a topic. To reduce the time required for their preparation, we present SmartEDU, a platform for drafting slides for a textual document, and the research that lead to its development. Drafts are Powerpoint files generated in three steps: pre-processing, for acquiring or discovering section titles; summarization, for compressing the contents of each section; slide composition, for organizing the summaries into slides. The resulting file may be further edited by the user. Several summarization methods were experimented in public datasets of presentations and in Wikipedia articles. Based on automatic evaluation measures and collected human opinions, we conclude that a Distillbart model is preferred to unsupervised summarization, especially when it comes to overall draft quality.
User interest modeling and collaborative filtering algorithms application in English personalized learning resource recommendation

In order to solve the problem that the current traditional English classroom teaching in large classes has higher teaching efficiency, but it is difficult to take care of every student, and it is difficult to meet the needs of students’ personalized learning, this paper combines user interest modeling and collaborative filtering algorithms to propose a knowledge-oriented learning resource recommendation method. Moreover, this paper proposes an algorithm based on user interest model. In addition, on the basis of obtaining the user’s historical interest model, this paper combines user behavior information to obtain the user interest model and calculates the similarity between the candidate items and the user, and makes TOP-N recommendation based on the similarity calculation result. Finally, this paper conducts experiments on the news dataset and compares the results with the benchmark algorithm to prove the effectiveness of the algorithm. The results show that this paper has achieved certain results in the research of combining multi-data source user interest modeling and recommendation and has contributed to the research of interest modeling.
Development of a short‐term solar irradiance forecasting using satellite image in combination with numerical weather prediction model

Abstract A short‐term solar irradiance forecasting method has been developed to provide highly accurate prediction, using satellite image prediction model in combination with numerical weather prediction model. This method was applied and evaluated using the solar irradiance observational site at the CRIEPI Akagi Testing Center (Gunma Prefecture, Japan). Based on historical forecasting and observation datasets at JMA observation sites in the computational domain, the best blending ratio between the satellite image prediction model and the numerical weather prediction model was selected and used. The developed method has shown high accuracy. Compared to satellite image forecasting, accuracy results averaged between three and 6 h of lead‐time showed improvements in the RMSE by 44% for the before‐sunrise case, 20% for the after‐sunrise case, and 8% for the noon to sunset case.