Reviewerly/exHarmony huggingface dataset:
Mining Educational Data to Improve Teachers’ Performance

Educational Data Mining (EDM) is a new paradigm aiming to mine and extract the knowledge necessary to optimize the effectiveness of the teaching process. With normal educational system work, it’s often unlikely to accomplish fine system optimisation due to the large amount of data being collected and tangled throughout the system. EDM resolves this problem by its capability to mine and explore these raw data and as a consequence of extracting knowledge. This paper describes several experiments on real educational data wherein the effectiveness of Data Mining is explained in the migration of the educational data into knowledge. The’s experiment goal at first was to identify important factors of teacher behaviors influencing student satisfaction. In addition to presenting experiences gained through the experiments, the paper aims to provide practical guidance on Data Mining solutions in a real application.
Digital competence: What makes nurses a ‘big gun’ in the future health industry

This letter aims to respond to the editorial's viewpoint by Aungsuroch, Y. and Gunawan, J. related to nurses' preparation in the industrial revolution 4.0. Without a doubt, the era of disruption creates massive changes in many sectors, including in the health industry, and nurses are demanded to adopt and adapt to the rapid development of information technology. Therefore, digital competence is highly emphasized as an essential skill for today's nurse generation in order to be the "big gun" of tomorrow.
Secure Decentralized IoT Service Platform Using Consortium Blockchain

Although many studies have been devoted to integrating blockchain into IoT device management, access control, data integrity, security, and privacy, blockchain-facilitated IoT communication is still much less studied. Blockchain has great potential in decentralizing and securing IoT communications. In this paper, we propose an innovative IoT service platform powered by the consortium blockchain technology. The proposed platform abstracts machine-to-machine (M2M) and human-to-machine (H2M) communications into services provided by IoT devices. Then, it materializes the data exchange of the IoT network through smart contracts and blockchain transactions. Additionally, we introduce the auxiliary storage layer to the proposed platform to address various off-chain data storage needs. Our proof-of-concept implementation was tested against various workloads and connection sizes under different block configurations to evaluate the platform’s transaction throughput, latency, and hardware utilization. The experimental results demonstrate that our solution can maintain high performance with a throughput of approximately 800 reads per second (RPS), 50–80 transactions per second (TPS), and a latency of 50 ms–2 s under light to moderate workloads. Our extensive evaluation of the performance impact of batch size, batch timeout, and connection size also provides valuable insights into the optimization of blockchain configuration for achieving high performance.
Research on Key Technologies of Super-Resolution Reconstruction of Medium and Long Wave Maritime Infrared Image

Complex illumination, solar flares and heavy smog on the sea surface have caused difficulties to accurately obtain high-quality imaging and multi-dimensional information of marine monitoring targets, such as oil spill, red tide and underwater vehicle wake. The principle of existing imaging mechanism is complex, and thus it is not practical to capture high-resolution infrared images efficiently. To combat this challenge by utilizing new infrared optical materials and single point diamond-turning technology, we designed and processed a simple, light and strong condensing ability medium and long wavelength infrared imaging optical system with large relative aperture, which can obtain high-quality infrared images. On top of this, with the training from a combination of infrared and visible light images, we also proposed a super-resolution network model, which is composed of a feature extraction layer, an information extraction block and a reconstruction block. The initial features of the input images are recognized in feature extraction layer. Next, to supply missing feature information and recover more details on infrared image extracted from a dense connection block, a feature mapping attention mechanism is introduced. Its main function is to transfer the important feature information of the visible light images in the information extraction block. Finally, the global feature information is integrated in the reconstruction block to reconstruct the high-resolution infrared image. We experimented our algorithm on both of the public Kaist datasets and self-collected datasets, and then compared it with several relevant algorithms. The results showed that our algorithm can significantly improve the reconstruction performance and reveal more detail information, and enhance the visual effect. Therefore, it brings excellent potential in dealing with the problem of low resolution of optical infrared imaging in complex marine environment.
Selection of Intelligent Rules for the Evolution of Elementary Cellular Automata for Image Encryption

The paper is devoted to the search for new approaches to the formation of key arrays for encryption of color images. Emphasis is placed on using the initial key sequence of the smallest length. In this case, the key is the initial state of an elementary cellular automaton for implementing evolution based on a given rule. The use of an evolutionary approach on cellular automata to the formation of large key arrays made it possible to achieve unpredictable image encryption based on a single rule of an elementary cellular automata. The task of the research is to search for the rules of elementary cellular automata, which, based on a small initial key bit sequence, allow one to form a reliable key array of large dimensions for encrypting the bit layers that make up the image. To solve this problem, an experiment was carried out, on the basis of which the search for the necessary rules and options for choosing the elements of each bit array was carried out to encrypt the bit layers of the image. To form each bit key array, different initial conditions were used for elementary cellular automata. It is shown that for different initial conditions and for the chosen rules, the encryption quality is preserved. The most reliable encryption is the use of two key arrays formed on the basis of the evolution of one rule for different initial conditions. As a result of the experiments, the rules were determined (rules 90, 105, 150 and XOR function based on the two previous steps of evolution), which can be used without additional rules. Each bit layer of the image is encrypted using different subarrays of each generated one key array of the same dimension. It has been established that the most effective for encryption is the rule 105 and the XOR function based on the two previous steps of evolution. The resulting histograms of the distribution of brightness for each color of the encrypted image confirm the high quality of encryption based on the proposed method.
Traffic Sign Detection Based on Lightweight Multiscale Feature Fusion Network

Traffic sign detection is a research hotspot in advanced assisted driving systems, given the complex background, light transformation, and scale changes of traffic sign targets, as well as the problems of slow result acquisition and low accuracy of existing detection methods. To solve the above problems, this paper proposes a traffic sign detection method based on a lightweight multiscale feature fusion network. Since a lightweight network model is simple and has fewer parameters, it can greatly improve the detection speed of a target. To learn more target features and improve the generalization ability of the model, a multiscale feature fusion method can be used to improve recognition accuracy during training. Firstly, MobileNetV3 was selected as the backbone network, a new spatial attention mechanism was introduced, and a spatial attention branch and a channel attention branch were constructed to obtain a mixed attention weight map. Secondly, a feature-interleaving module was constructed to convert the single-scale feature map of the specified layer into a multiscale feature fusion map to realize the combined encoding of high-level semantic information and low-level semantic information. Then, a feature extraction base network for lightweight multiscale feature fusion with an attention mechanism based on the above steps was constructed. Finally, a key-point detection network was constructed to output the location information, bias information, and category probability of the center points of traffic signs to achieve the detection and recognition of traffic signs. The model was trained, validated, and tested using TT100K datasets, and the detection accuracy of 36 common categories of traffic signs reached more than 85%, among which the detection accuracy of five categories exceeded 95%. The results showed that, compared with the traditional methods of Faster R-CNN, CornerNet, and CenterNet, traffic sign detection based on a lightweight multiscale feature fusion network had obvious advantages in the speed and accuracy of recognition, significantly improved the detection performance for small targets, and achieved a better real-time performance.
Live Feedback for Training Through Real-Time Data Visualizations: A Study with League of Legends

With competitive gaming on the rise, players have developed an increased interest in data-driven feedback of their in-game performance. In many cases, this feedback is presented visually post-play but live companions that provide real-time analytics are gaining more and more attention. However, the design of such systems poses different requirements which have been little explored from a scholarly point of view. In this paper, we report on a study of two visualizations - a physical and digital one - providing live feedback to League of Legends players designed based on a community survey (N= 175) and expert input. We further discuss results of a qualitative evaluation with 13 players and an expert, touch upon potential benefits and drawbacks of physical and digital implementations, and present derived design considerations and challenges for such real-time data visualizations, revolving around issues of information density, contextualization, representation, and a diverse range of user needs.
Coeffects for sharing and mutation

In type-and-coeffect systems , contexts are enriched by coeffects modeling how they are actually used, typically through annotations on single variables. Coeffects are computed bottom-up, combining, for each term, the coeffects of its subterms, through a fixed set of algebraic operators. We show that this principled approach can be adopted to track sharing in the imperative paradigm, that is, links among variables possibly introduced by the execution. This provides a significant example of non-structural coeffects, which cannot be computed by-variable, since the way a given variable is used can affect the coeffects of other variables. To illustrate the effectiveness of the approach, we enhance the type system tracking sharing to model a sophisticated set of features related to uniqueness and immutability. Thanks to the coeffect-based approach, we can express such features in a simple way and prove related properties with standard techniques.
Application of Data Mining for Slot Time Prediction at International Airports in Indonesia: J48 Algorithm

Abstract&#x0D; In aviation, the safety and smooth flow of flight traffic is a business core, where every flight traffic service is expected to avoid delays caused by aircraft movement either in the air or on land. Therefore, the time slot at the airport is essential for the accuracy of the movement of aircraft, both Departure and Arrival, and this is intended to avoid delays caused by the accumulation of queues of planes that will depart and planes that will land, with a large number of aircraft movements at the International Airport. Soekarno Hatta requires analysis with data mining techniques such as the J48 algorithm and Decision Tree, and Naïve Bayes.&#x0D; &#x0D;
Vocational education teachers’ soft-competences: Assessment methodology and correlation analysis

Introduction. The main problem of the article lies in the search for optimal and objective principles and methods for assessing vocational education teachers’ soft-competencies, as well as valid diagnostic tools. The purpose of the article is to evaluate the vocational education teachers’ soft-competencies and to identify correlations between them. Materials and Methods. The research methodology is based on the principles of a competency-based approach to education (D. C. McClelland, A.-K. Koenen, F Dochy, W. Lambrechts, etc.). To assess the teachers’ soft-competencies, a set of diagnostic tools was developed and used during the pilot study from March 21 to July 17, 2022 (n=132), consisting of the following components: a motivational questionnaire; a business game (includes self-presentation, solution of a universal case, group discussion); a set of tests (for intelligence, an individual psychological questionnaire, etc.). The study applied a methodological approach to assessing the vocational education teachers’ soft-competencies with the help of the Assessment Center, which involves complex diagnostics, including both standardized tests, self-reports, cases, and observation of real behavior. The analysis of the results obtained was carried out using the methods of statistical (descriptive statistics, measures of data scatter, normal distribution) and correlation analysis (nonparametric correlation coefficient r-Spearman) using the IBM SPSS Statistics 24 software. Results. The findings show that the most developed soft-competencies include the ability for self-organization and self-development, as well as the ability to communicate. The remaining competencies are at the basic level and are used only within the framework of solving familiar, standard problems. Statistically significant positive correlations were found between the ability for systemic and critical thinking and the ability to communicate; between the readiness to become a leader in the educational community and the ability to communicate. Statistically significant negative correlation was found between the willingness to accept personal responsibility and the ability to communicate. Conclusions. Conclusions are drawn that there is a risk associated with the human resources potential of teachers in the vocational education system: soft-competencies are at the threshold level of development, and even with the potential for self-development formed, it will take time, additional development programs and systematic work to support vocational education teachers.
K-nearest neighbor based facial emotion recognition using effective features

&lt;span lang="EN-US"&gt;In this paper, an experiment has been carried out based on a simple k-nearest neighbor (kNN) classifier to investigate the capabilities of three extracted facial features for the better recognition of facial emotions. The feature extraction techniques used are histogram of oriented gradient (HOG), Gabor, and local binary pattern (LBP). A comparison has been made using performance indices such as average recognition accuracy, overall recognition accuracy, precision, recall, kappa coefficient, and computation time. Two databases, i.e., Cohn-Kanade (CK+) and Japanese female facial expression (JAFFE) have been used here. Different training to testing data division ratios is explored to find out the best one from the performance point of view of the three extracted features, Gabor produced 94.8%, which is the best among all in terms of average accuracy though the computational time required is the highest. LBP showed 88.2% average accuracy with a computational time less than that of Gabor while HOG showed minimum average accuracy of 55.2% with the lowest computation time.&lt;/span&gt;
Defending Person Detection Against Adversarial Patch Attack by Using Universal Defensive Frame

Person detection has attracted great attention in the computer vision area and is an imperative element in human-centric computer vision. Although the predictive performances of person detection networks have been improved dramatically, they are vulnerable to adversarial patch attacks. Changing the pixels in a restricted region can easily fool the person detection network in safety-critical applications such as autonomous driving and security systems. Despite the necessity of countering adversarial patch attacks, very few efforts have been dedicated to defending person detection against adversarial patch attack. In this paper, we propose a novel defense strategy that defends against an adversarial patch attack by optimizing a defensive frame for person detection. The defensive frame alleviates the effect of the adversarial patch while maintaining person detection performance with clean person. The proposed defensive frame in the person detection is generated with a competitive learning algorithm which makes an iterative competition between detection threatening module and detection shielding module in person detection. Comprehensive experimental results demonstrate that the proposed method effectively defends person detection against adversarial patch attacks.
Mixed-Precision Implementation of the Density Matrix Renormalization Group

The mixed-precision optimization is an effective emerging technique for quantum chemistry methods to obtain better computational performance and maintain the chemical accuracy. Here, we developed a two-level mixed-precision implementation for the density matrix renormalization group (DMRG) method. This implementation is based on the idea that the DMRG process is an iterative process. Therefore, the first several iteration steps can be executed in single precision. A feasible single-precision DMRG may generate moderate accuracy, and when a few double-precision cleanup sweeps are added, the double-precision accuracy will be recovered. In the double-precision sweeps, we developed a mixed-precision diagonalization method that can run the most time-consuming step in single precision and maintain the double-precision accuracy. By combining these two mixed-precision schemes, we implemented our mixed-precision DMRG method. The benchmark result shows that our mixed-precision implementation achieved a good performance. A speed-up of up to 2.31 is achieved, and the accuracy is preserved within 0.01 kcal/mol.
Lightweight and anchor-free frame detection strategy based on improved CenterNet for multiscale ships in SAR images

Ship detection using synthetic aperture radar (SAR) images has important applications in military and civilian fields, but the different sizes of the ship downgrade the detection accuracy of multiscale ships. Aiming at the problem of the poor accuracy and low efficiency of multiscale ship detection in complex scenes, this paper proposes a lightweight and anchor-free frame detection strategy for multiscale ships in SAR images. First, to deal with the problems of limited training samples, different sizes, attitudes, and angles of the ships in SAR images, a data augmentation strategy suitable for SAR images is adopted to expand the training space, followed by multiscale training to enhance the model generalization ability for multiscale ship detection. Second, a lightweight and anchor-free ship detection model based on the improved CenterNet is proposed, which abandons the dense anchor frame generation and extracts the key point of the ships for detection and positioning. Compared with the anchor frame-based detection method, this proposed detection model does not need to use the post-processing method to remove redundant anchor frames, and can accurately locate the center point of the ships with a better detection performance. Third, to reduce the model size and simplify the model parameters, a more lightweight network design is adopted in combination with the characteristics of SAR images. Hence, a residual network (ResNet) with fewer convolutional layers is constructed as the backbone network, and the cross-stage partial network (CSPNet) and spatial pyramid pooling (SPP) network are designed as the bottleneck network. The shallow ResNet can fully extract the SAR image features and reduce the training overfitting, and CSPNet and SPP can effectively combine the low-level image features to obtain the high-level features, reducing the model computation while at the same time enhancing the feature extraction ability. Finally, the evaluation index of the common objects in the context dataset is introduced, which can provide higher-quality evaluation results for ship detection accuracy and provide comprehensive evaluation indicators for multiscale ship detection. Experimental results show that the proposed strategy has the advantages of high detection efficiency, strong detection ability, and good generalization performance, which can achieve real-time and high-precision detection of the multiscale ship in complex SAR images.
Gas Cylinder Detection Using Deep Learning Based YOLOv5 Object Detection Method

Detection and tracking of objects has critical importance in terms of speeding up the process and facilitating the work in many areas. Especially in the process of counting objects, which is difficult and time-consuming for experts. In this paper, a study was carried out to detect gas cylinders with different colors and shapes using the deep learning-based Yolov5 method. The process of counting cylinders in the stock area or in the filling facilities can be difficult for the specialist due to the different sizes, arrangement and large number of cylinders. Within the scope of the study, a data set containing different types of cylinders in gas filling facilities was created. When the obtained results are evaluated, it has been observed that the Yolov5 algorithm detects the gas cylinders with different color and shape properties with a high success rate of 96.16%. In addition to the detection success, it has been observed that the method is also successful in different objective detections such as precision, sensitivity and box intersection.
A Novel Objective Video Quality Assessment Metric For Cloud Gaming Applications

This study introduces a unique method for evaluating the Quality of Experience (QoE) of cloud gaming applications objectively for various network conditions, involving different packet latency and packet loss ratio (PLR) parameters. We use a combination of a perceptual hash (pHash) and a structural similarity index measure (SSIM) to detect the duplication of packets with greater accuracy than either pHash or SSIM alone. We discovered that increased PLR causes more frame duplication in cloud gaming applications, which lowers the end-user QoE. Using the NetEm emulator and the Parsec streaming platform in a controlled network environment, we evaluated our model on three distinct games based on content complexity, genre, and popularity in cloud gaming applications. We observed that different content complexity of games is impacted differently by varying network conditions; for example, slow-moving games are more affected by PLR and frame duplication. We also carried out a subjective QoE evaluation of cloud gaming with 22 volunteers. The results obtained from subjective testing were used to extend our study to compare the accuracy of our model compared with existing Full-Reference (FR) and No-Reference (NR) techniques. We discovered that our model surpasses the majority of the currently available solutions in terms of predicted accuracy, many of which were based on Video on Demand (VoD) applications. This demonstrates how important it is to create a QoE model, particularly for cloud gaming given its distinctive utilisation of network parameters.