Reviewerly/exHarmony huggingface dataset:
Training methods considering block partitioning for neural networks-based intra prediction

This paper presents methods of Neural Network (NN) training reflecting block partitioning for Matrix-based Intra Prediction (MIP)-based networks. A training method using a dataset considering coding block partitioning leads to a NN-based predictor that is more suitable for a legacy block-based video codec compared to a training method that does not consider block partitioning. In addition, training using block partitioning of actual video encoding allows better intra prediction than a training method considering block partitioning in the training process. The MIP-based intra-prediction networks are implemented in VVC by replacing the MIP to evaluate the proposed training methods. The experimental results show that the proposed training method considering block partitioning of actual encoding gives the coding gain of 0.19% Bjøntegaard Delta (BD)-rate on average compared to training without considering block partitioning.
On the completion of automatic football game commentary system with deep learning

Due to the recent development of machine learning, quality automatic football game commentary is getting possible. By now most of the effort of researchers has spent on key action spotting. This paper is on our strategy for filling in the gap between key actions in order to form a complete automatic football game commentary system. Novel deep learning and signal processing techniques have been used/created in this research work, and attractive demonstrations are available.
E-module in Learning Mathematics: An effort to Stimulate Learning Independence

The use of technology in education results in user-friendly, distinctive, valuable, and informative learning apps. MIT App Inventor was used in this development research to create a valid and interesting junior high school mathematics learning module. The ADDIE model was used to conduct this development research. To assess the product's feasibility, validation was performed by media and material experts. Product trials were conducted by analyzing students' answers to establish the product's attractiveness. This study's test subjects were 45 UIN Raden Intan Lampung students majoring in Mathematics Education in semester 1. The feasibility test results from media and material validators were 3.32 and 3.52 in the valid category. The attractiveness test received a score of 3.54 on the small-scale trial with the highly attractive and 3.6 on the large-scale trial with the same criteria. It is possible to conclude that the developed module is feasible.
GALG: Linking Addresses in Tracking Ecosystem Using Graph Autoencoder with Link Generation

Online tracking technology is a critical tool for user-centric platform practitioners to link users across multiple web pages and make detailed user profiles for the improvement of recommender systems like targeted advertising. Recently, due to the dynamic address allocation and security upgrade, mitigations indirectly make prior tracking techniques unreliable. To overcome the problem, traffic-based tracking techniques are proposed to link users’ dynamic addresses through similarity learning of user behaviors in their traffic interaction. However, prior work either provides poor similarity learning ability or is impractical when applied to a large scale. In this paper, we propose GALG, a graph-based artificial intelligence approach to link addresses for user tracking on TLS encrypted traffic. GALG uses the framework of graph autoencoder and adversarial training to learn the user embedding with semantics and distributions. Employing a new theory – link generation, GALG could link all the addresses of target users based on the knowledge of address-service links. When evaluated on real-world user datasets, GALG outperforms existing approaches in both performance and practicality.
Cybercopters Swarm: Immersive analytics for alerts classification based on periodic data

This paper assesses the usefulness of an interactive and navigable 3D environment to help decision-making in cybersecurity. Malware programs frequently emit periodic signals in network logs; however, normal periodical network activities, such as software updates and data collection activities, mask them. Thus, if automatic systems use periodicity to successfully detect malware, they also detect ordinary activities as suspicious ones and raise false positives. Hence, there is a need to provide tools to sort the alerts raised by such software. Data visualizations can make it easier to categorize these alerts, as proven by previous research. However, traditional visualization tools can struggle to display a large amount of data that needs to be treated in cybersecurity in a clear way. In response, this paper explores the use of Immersive Analytics to interact with complex dataset representations and collect cues for alert classification. We created a prototype that uses a helical representation to underline periodicity in the distribution of one variable of a dataset. We tested this prototype in an alert triage scenario and compared it with a state-of-the-art 2D visualization with regard to the visualization efficiency, usability, workload, and flow induced.
The Analysis of Rhetorical Devices in Two Tesla Promotional Videos

This study was conducted to discover the rhetorical devices Elon Musk used when delivering Tesla promotional videos, namely the Tesla Unveils Model 3 and Tesla Semi &amp; Roadster Unveil. This study also wants to examine the messages that are underscored by the use of rhetorical devices. In this study, the writer used Harris’ (2018) theory of rhetorical devices as the leading theory. The study was done by applying qualitative content analysis (QCA). The study showed that Elon Musk utilized climax, asyndeton, polysyndeton, sentential adverb, exemplum, amplification, anaphora, and hypophora in Tesla Unveils Model 3. Rhetorical devices such as climax, asyndeton, sentential adverb, exemplum, amplification, anaphora, and hypophora are found in Tesla Semi &amp; Roadster Unveil. However, the data regarding polysyndeton is non-existent in Tesla Semi &amp; Roadster Unveil. Each rhetorical device employed by Elon Musk helped underscore the messages in both videos. Ultimately, rhetorical devices affect the promotional video positively as they help amplify the meaning of the speech.
Red Blood Cells Classification with Sharpening Segmentation and Mask R-CNN

Identifying and measuring red blood cells (RBC) before prescribing treatment for blood-related disorders is crucial for appropriate diagnosis. A pathologist manually performs such a process under a light microscope, as is customary. Nevertheless, manual visual inspection is arduous and subjective, leading to variance in RBC identification and counting. This paper proposes classification using sharpening segmentation combined with the algorithm mask R-CNN to increase the accuracy of calculating the number of RBCs. Eventually, the RBCs were classified as overlapping or single RBCs using the mask R-CNN classifier algorithm. In this study, a combination of 3 preprocessing image methods, including sharpening with the wand library, clahe, and the Otsu threshold, was used. The classification with the sharpening method proposed in this study gives an average accuracy of 17.4% better than without sharpening. The suggested approach has been evaluated on images of RBC and exhibits a reliable and effective methodology for classifying single and overlapping RBC.
Application of Panoramic Dental X-Ray Images Denoising

Dental X-ray imaging helps dentists detect many problems such as caries, cysts and jaw structure problems. Clinical diagnosis and preventive examinations of dental structures play an important role by providing a comprehensive imaging evaluation with panoramic x-rays for dentists. However, researchers primarily use image processing methods to analyze and improve a dental X-ray image and increase its contribution to the diagnostic time. Image segmentation, classification, threshold-based analysis, artificial neural networks, frequency-based methods are the most widely used image processing techniques to analyze medical images and assist in the development of computer aided medical diagnosis systems. In this study, images were analyzed in terms of noise removal by using convolutional neural networks and binary and wavelet filters to improve the images that were distorted and lost their clarity as a result of noise caused by various reasons during shooting. The performances of these methods were compared and it was seen that successful results were obtained in different noise types by using convolutional neural networks.
Multi-Scale Channel Adaptive Time-Delay Neural Network and Balanced Fine-Tuning for Arabic Dialect Identification

The time-delay neural network (TDNN) can consider multiple frames of information simultaneously, making it particularly suitable for dialect identification. However, previous TDNN architectures have focused on only one aspect of either the temporal or channel information, lacking a unified optimization for both domains. We believe that extracting appropriate contextual information and enhancing channels are critical for dialect identification. Therefore, in this paper, we propose a novel approach that uses the ECAPA-TDNN from the speaker recognition domain as the backbone network and introduce a new multi-scale channel adaptive module (MSCA-Res2Block) to construct a multi-scale channel adaptive time-delay neural network (MSCA-TDNN). The MSCA-Res2Block is capable of extracting multi-scale features, thus further enlarging the receptive field of convolutional operations. We evaluated our proposed method on the ADI17 Arabic dialect dataset and employed a balanced fine-tuning strategy to address the issue of imbalanced dialect datasets, as well as Z-Score normalization to eliminate score distribution differences among different dialects. After experimental validation, our system achieved an average cost performance (Cavg) of 4.19% and a 94.28% accuracy rate. Compared to ECAPA-TDNN, our model showed a 22% relative improvement in Cavg. Furthermore, our model outperformed the state-of-the-art single-network model reported in the ADI17 competition. In comparison to the best-performing multi-network model hybrid system in the competition, our Cavg also exhibited an advantage.
INFORMATION AND MATERIAL SUPPORT TO ENVIRONMENTAL EDUCATION IN SLOVAKIA IN THE TIMES OF CRISIS

Appears in: INTED2023 Proceedings Publication year: 2023Page: 8763ISBN: 978-84-09-49026-4ISSN: 2340-1079doi: 10.21125/inted.2023.1955Conference name: 17th International Technology, Education and Development ConferenceDates: 6-8 March, 2023Location: Valencia, Spain
FISH FOR STUDY: AN EDUCATIONAL GAME TO PROVIDE MOTIVATION AND AVOID DISTRACTIONS FROM MOBILE DEVICES DURING LEARNING

FISH FOR STUDY: AN EDUCATIONAL GAME TO PROVIDE MOTIVATION AND AVOID DISTRACTIONS FROM MOBILE DEVICES DURING LEARNING
COMPLEMENTARY TRAINING FOR ENGINEERING STUDENTS IN ADDITIVE MANUFACTURING (3D PRINTING)

Appears in: INTED2023 Proceedings Publication year: 2023Pages: 8776-8784ISBN: 978-84-09-49026-4ISSN: 2340-1079doi: 10.21125/inted.2023.0655Conference name: 17th International Technology, Education and Development ConferenceDates: 6-8 March, 2023Location: Valencia, Spain
AUTO-GENERATION OF PROGRAMMING EXERCISES FOR EDUCATION OF MPI AND AVX PARALLEL PROGRAMMING

Appears in: INTED2023 Proceedings Publication year: 2023Pages: 841-849ISBN: 978-84-09-49026-4ISSN: 2340-1079doi: 10.21125/inted.2023.0261Conference name: 17th International Technology, Education and Development ConferenceDates: 6-8 March, 2023Location: Valencia, Spain
CONCEPTUALIZATION OF PEDAGOGICAL ENTREPRENEURSHIP: THEORY AND RESULTS OF QUALITATIVE RESEARCH

Appears in: INTED2023 Proceedings Publication year: 2023Pages: 2856-2864ISBN: 978-84-09-49026-4ISSN: 2340-1079doi: 10.21125/inted.2023.0788Conference name: 17th International Technology, Education and Development ConferenceDates: 6-8 March, 2023Location: Valencia, Spain
DISCOVERING THE OPPORTUNITIES FOR DEVELOPING CYBERSECURITY AWARENESS IN SECONDARY EDUCATION: A COMPARATIVE STUDY, WALES VS. SAUDI ARABIA

DISCOVERING THE OPPORTUNITIES FOR DEVELOPING CYBERSECURITY AWARENESS IN SECONDARY EDUCATION: A COMPARATIVE STUDY, WALES VS. SAUDI ARABIA
An Auxiliary Approach to Prediction of Binary Outcome with Bayesian Network Model: Exploration with Data for Recurrence of Breast Cancer

Introduction: Logistic regression is the classical statistical model that is incorporated to predict a binary outcome variable. These models have theoretical assumptions of independence of predictor variables and linearity of association with the outcome in the logarithmic scale. Alternative models developed in the machine learning context like Naïve Bayes model with similar assumptions and Bayesian Network (BN) model can be used for binary prediction. Aim: To compare the predictive performance of logistic regression, Naïve Bayes and BN model in predicting the recurrence of Breast cancer. Materials and Methods: The dataset was procured from UCI Machine Learning repository on recurrence of breast cancer. The study was done on retrospective data from December 2021 to July 2022. The sample size was boosted with the bootstrapping with logistic regression model. The dataset was split into training (70%) and testing (30%) dataset for internal validation. The effect estimates of the potential prognostic variables were estimated using multiple logistic regression model. Naïve Bayes and BN model was also learnt from the training dataset. The indices of predictive accuracy were estimated for the models in both training and testing dataset. Results: Degree of malignancy and side of affected breast were found to be significant predictors of recurrence of breast cancer. BN model had the least misclassification rate and the best sensitivity in comparison to other models in spite of imbalance in outcome variable. Conclusion: BN model performed the best in comparison to logistic regression model when the assumptions of logistic regression model were violated and there is imbalance in proportion of outcome.
Memory aware optimized Hadoop MapReduce model in cloud computing environment

&lt;p&gt;In the last decade, data analysis has become one of the popular tasks due to enormous growth in data every minute through different applications and instruments. MapReduce is the most popular programming model for data processing. Hadoop constitutes two basic models i.e., Hadoop file system (HDFS) and MapReduce, Hadoop is used for processing a huge amount of data whereas MapReduce is used for data processing. Hadoop MapReduce is one of the best platforms for processing huge data in an efficient manner such as processing web logs data. However, existing model This research work proposes memory aware optimized Hadoop MapReduce (MA-OHMR). MA-OHMR is developed considering memory as the constraint and prioritizes memory allocation and revocation in mapping, shuffling, and reducing, this further enhances the job of mapping and reducing. Optimal memory management and I/O operation are carried out to use the resource inefficiently manner. The model utilizes the global memory management to avoid garbage collection and MA-OHMR is optimized on the makespan front to reduce the same. MA-OHMR is evaluated considering two datasets i.e., simple workload of Wikipedia dataset and complex workload of sensor dataset considering makespan and cost as an evaluation parameter.&lt;/p&gt;
Predicting teachers’ use of digital technology

In recent years, the use of digital technology and its relationship to the ability known as digital competence has become a core concept in discussions about the skills that individuals should possess in a knowledge society. Teachers’ skills in using digital technology are becoming a critical component of developing practical pedagogical knowledge for practice and student learning enhancement. The purpose of this study is to predict the factors that influence secondary school teachers when it comes to using digital technology. This study will establish relationships between personal innovativeness, technology self-efficacy, attitude toward digital technology, and digital competence. In Pahang, Malaysia, a survey was conducted with 493 secondary school teachers from 50 public secondary schools of 11 districts. Proportionate stratified sampling, cluster random sampling, and simple random sampling were used to obtain samples. The findings have established that technology self-efficacy, attitude towards digital technology, and digital competence significantly affect how teachers use digital technology. These findings emphasize the importance of increasing teachers’ digital competence as well as numerous elements that influence teachers’ use of digital technology to fulfill the expectations of future qualified professions and thus prepare students for their future.
Discussion on cooperative governance and security storage technology of power data based on blockchain

The power grid business has the characteristics of wide coverage, diverse terminal equipment types, large numbers, and rich business scenarios. The blockchain technology itself has the characteristics of non-tampering and traceability, which is highly compatible with the opening of the power grid business and can be powerful supporting the digital transformation strategy of the power grid company. In order to ensure the authenticity and reliability of electric power data, data security and reliable storage technology was studied based on blockchain technology. On the data source side of the power system, a set of oracles mechanism for the grid data on-chain process was designed to achieve data reliability and consistent transmission. In addition, to prevent data damage or malicious tampering after power grid data on the blockchain, a hybrid data storage scheme based on shadow chain was proposed to realize data safe storage and improve value-added services such as data storage and sharing.
JSVulExplorer: a JavaScript vulnerability detection model based on transfer learning

Software vulnerabilities will make the system vulnerable to attack, affect the reliability of the software and cause information leakage, which will have a huge impact on enterprises or individuals. Vulnerabilities are inevitable in software development engineering. Therefore, relying on some methods or tools for continuous vulnerability analysis of code is the solution to minimize software vulnerabilities. We propose a neural network model, JSVulExplorer, for static vulnerability analysis of the dynamic programming language JavaScript. The JSVulExplorer focuses on feature enhancement of data. We use pre-training to learn the semantic similarity between code slices, utilize abstract syntax trees to generate path information, and design positional encoding to use the path information. Based on transfer learning, we combine the pre-trained model with path information to improve vulnerability detection performance. Experiments show that JSVulExplorer has significantly improved precision and recall compared to previous models. It is verified that the dynamic event-based programming language can also use the static analysis method for vulnerability detection.
Multi-hop question answering for SRLGRN augmented by textual relationship modelling

Multi-hop question answering aims to predict answers to questions and generate supporting facts for answers by reasoning over the content of multiple documents. The recently proposed Semantic Role Labeling Graph Reasoning Network (SRLGRN) has achieved excellent performance on multi-hop QA tasks. However, SRLGRN is lacking in modelling the textual relationships, which are import cues for reasoning. To this end, this paper proposes an enhanced SRLGRN multi-hop question answering approach by modelling textual relationships at different granularity (document relationships and sentence relationships). By modelling document relationships, a novel document filter based on document relationship threshold is designed for SRLGRN to dynamically select documents relevant to the question from multiple documents. By modelling sentence relationships, a sentence relationship-aware answer type prediction module is added to SRLGRN, which models sentences in documents as sentence graphs and then uses graph convolution network to predict answer type. The obtained answer type further guide the answer reasoning module of SRLGRN to obtain question answer with supporting facts. The experimental results show that the proposed scheme outperforms SRLGRN in terms of answer prediction and supporting fact prediction, with a 2% improvement in answer F1 metrics and a 3.1% improvement in joint F1 performance.
Dialog history management for end-to-end task-oriented dialog systems

End-to-end task-oriented dialogue systems rely heavily on an understanding of the dialog history. This often faces the challenge of inferring which dialog history information is critical to generating responses. In this paper, we address this challenge by leveraging a dialog history manager component that dynamically focuses on dialog history memory. It performs multiple add and forget operations by fusing an enhanced entity representation of dialog history and Knowledge Base (KB) information as queries, remembering entities relevant to responses and filtering out unimportant information. Experimental results on an open task-oriented dialogue dataset show that our model outperforms the baseline system in terms of effectiveness and produces contextually consistent responses.
Embedding diverse features in latent space for face attribute editing

Face attribute editing, one of the important research directions in face image synthesis and processing techniques, aims to photorealistic editing single or multiple attributes of face images on demand using editing and generation models. Most existing methods are based on generative adversarial networks, using target attribute vectors to control the editing region or Gaussian noise as conditional input to capture texture details. However, these cannot better control the consistency of attributes in irrelevant regions, while the generation of fidelity is also limited. In this paper, we propose a method that uses an optimized latent space to fuse the attribute feature maps into the latent space. At the same time, make full use of the conditional information for additional constraints. Then, in the image generation phase, we use a progressive architecture for controlled editing of face attributes at different granularities. At last, we also conducted an ablation study on the selected training scheme further to demonstrate the stability and accuracy of our chosen method. The experiments show that our proposed approach, using an end-to-end progressive image translation network architecture, obtained qualitative (FID) as well as quantitative (LPIPS) face image editing results.
Deductive Matrix Synthesis for Fault Simulation

Elements of logical vector computing are proposed, which are focused on fault simulation in digital devices. The concept of a logical vector is introduced for simulation of a single fault as addresses. For the first time the method of synthesis of deductive matrices on the logical vector of functionality is considered. The deductive matrix is used to generate tests for diagnosing a functional element and simulating faults to verify the quality of tests. A compact vector form for specifying graph structures is proposed. The advantages of fault simulation on logical vectors are shown, which lies in the high parallelism of processing faults as addresses. All models and methods are original, the validity of which is confirmed by numerous examples of the synthesis of the failure simulation matrix.