Reviewerly/exHarmony huggingface dataset:
FOLD-SE: An Efficient Rule-Based Machine Learning Algorithm with Scalable Explainability

We present FOLD-SE, an efficient, explainable machine learning algorithm for classification tasks given tabular data containing numerical and categorical values. The (explainable) model generated by FOLD-SE is represented as a set of default rules. FOLD-SE uses a novel heuristic called Magic Gini Impurity for literal selection that we have devised. FOLD-SE uses a refined data comparison operator and eliminates the long tail effect. Thanks to these innovations, explainability provided by FOLD-SE is scalable, meaning that regardless of the size of the dataset, the number of learned rules and learned literals stay quite small while good accuracy in classification is maintained. Additionally, the rule-set constituting the model that FOLD-SE generates does not change significantly if the training data is slightly varied. FOLD-SE is competitive with state-of-the-art traditional machine learning algorithms such as XGBoost and Multi-Layer Perceptrons (MLP) w.r.t. accuracy of prediction while being an order of magnitude faster. However, unlike XGBoost and MLP, FOLD-SE generates explainable models. The FOLD-SE algorithm outperforms prior rule-learning algorithms such as RIPPER in efficiency, performance, and scalability, especially for large datasets. FOLD-SE generates a far smaller number of rules than earlier algorithms that learn default rules.
SWOT Analysis of Parallel Processing APIs - CUDA, OpenCL, OpenMP and MPI and their Usage in Various Companies

Purpose: Parallel Processing is the technique of achieving High Performance Computing (HPC) with parallel execution of programs that are synchronized during the execution time. This research paper studied the companies which use Parallel Processing techniques in their projects and products along with the identification of major Application Program Interfaces (APIs) that are used to achieve parallelism. The major aim of this research work is to perform the SWOT analysis of Parallel Processing APIs to identify the importance of each one from the company perspective. Design/Methodology/Approach: The research method adopted to accomplish the SWOT Analysis of Parallel Processing APIs - CUDA, OpenCL, OpenMP and MPI and their Usage in Various Companies is qualitative and exploratory in nature. Systematic literature review of different companies that use Parallel Processing techniques to build and develop parallel programs is done during this research work. Findings/Results: Parallel Processing constructs can be used to solve various problems in the six major application domains as: - Computational Finance &amp; Business Economics, Artificial Intelligence, Machine Learning (ML), Data Science, Numerical Analysis and Design of Algorithms. Major Parallel Processing APIs used in companies are: - CUDA, OpenCL, OpenMP and MPI for implementing the problems with parallel execution. Foremost companies that use Parallel Processing APIs are studied and various applications, systems, models, and projects that are developed using Parallel Processing techniques are listed. SWOT Analysis is performed on all four Parallel Processing APIs and its SWOT(Strengths-Weaknesses-Opportunities-Threats) are identified. Originality/Value: Listing of SWOT Analysis (Strengths-Weaknesses-Opportunities-Threats) of Parallel Processing APIs - CUDA, OpenCL, OpenMP and MPI. Paper Type: Company Analysis research paper
Target Recognition Analysis Based on Deep Learning

With the in-depth development of big data and artificial intelligence technology, image recognition and detection technology have become an extremely important field of research in artificial intelligence technology. How to effectively identify targets in complex environments is currently a major research hotspot. This article utilizes deep learning technology for image recognition and detection, explains how to use deep learning for feature recognition, explores several neural network features and their advantages and disadvantages, and provides a detailed introduction to the important applications of convolutional neural networks in image recognition.
Cultural Education and Intercultural Competence in Korean Language Textbooks : Focusing on Textbooks Published in Hungary

Due to the increasing global presence of South Korea, the interest in Korean language and culture has increased exponentially in Hungary as well. Culture is recognized as an inseparable part of foreign language learning, and to nurture successful intercultural communicators, appropriate cultural instruction is necessary in language classrooms. Recognizing the central role of textbooks in language education, as they provide guidance and framework, while they also act as "vessels" for cultural content, this research focuses on the cultural content and the potential intercultural competence development in Korean language textbooks. This study analyzes beginner Korean language textbooks published in Hungary by Hungarian authors on the basis of their cultural content to assess the extent of potential culture learning through textbooks. Qualitative content analysis was carried out based on a set of evaluative questions focusing on weighing the "cultural load". The findings reveal that the two examined textbooks ("헝가리 학생을 위한 한국어 교재" and "흰학 1"), which are aimed for the Hungarian audience, are primarily focused on the correct use of grammars by frequently making comparisons with Hungarian grammatical structures. The textbooks are quite limited in their inclusion of cultural content, especially when compared to other widely used publications by Korean authors. The Hungarian textbooks offer little supplementary information necessary for the understanding of cultural implications, thus leaving students to decipher these implications by themselves or leaving the task to the teacher. The textbooks omit exercises where students have to make meaningful comparisons between their own Hungarian culture and Korean culture, which is an essential step in developing intercultural competence. The results imply that these textbooks – although they contain grammatical explanations in Hungarian, which is helpful for the Hungarian learners – might be less effective from the perspective of cultural education. As of yet, the current situation of Korean language education in Hungary remains academically quite unexplored, which prompts further research and dialogue.
ISES: Instance-level Semantic Enhanced Score Model for Image-Text Retrieval

Image-Text Retrieval (ITR) is an important measure of the performance of text and image mutual retrieval, by searching for semantically relevant information in the relevant modality and then matching the corresponding modality, the key difficulty lies in the problem of how to find the relevance of the semantic information between the interacting modalities. Previous work has only used pre-trained models to obtain features in terms of images and text to achieve matching. However, this type of approach lacks the pairing and embedding methods required to effectively match multimodal data. In addition, these efforts also lead to significant loss of fine-grained information within modalities. To alleviate these problems, we propose ISES: an Instantiated Semantic Enhanced Scoring model for cross-modal retrieval. Specifically, we have crafted two efficient Semantic Enhancement Score (SES) modules. One module (SES-1) is specialized to learn t5he semantic similarity between image-to-image and the other is specialized (SES-2) to learn the semantic similarity between text-to-text. In addition, we also introduce Instance loss into ITR to further optimize the image semantic enhancement information and text semantic enhancement information, with a view to obtaining better cross-modal retrieval performance. For instance, compared with the existing best method NAAF, the metric R@1 of our ISES on the MSCOCO testing set is improved by 2.60% and 1.21% on I2T retrieval and T2I retrieval.
Dynamic Domain ID Based on DDS for AIoT

A recent development in the field is the emergence of a novel Internet of Things (IoT) framework known as the Artificial Intelligence of Things (AIoT). Based on this framework, it can enable smart autonomous vehicles, unmanned aerial vehicles, and intelligent industrial production lines to effectively reduce costs and expand the scope of sensor perception by utilizing collaborative sensor networks and shared computing resources provided by edge computing. The sensor network formed by traditional internet has successfully achieved global unified wide area connectivity. However, for a certain control object, sensors that are far away from it do not need to participate in the control process, while local existing sensors fail due to the movement of the control object in a specific direction. So this requires a new type of network that can dynamically update the domain. To enhance the real-time responsiveness of regional communication involving controllers and controlled devices, it is essential for the network to establish dynamic local connections that adapt to the spatial location changes of cars, unmanned aerial vehicles, or production line logistics. As a response to this requirement, this paper introduces a novel edge computing distributed communication network and a dynamically updated domain identity architecture. Every endpoint node has the capability to autonomously form a local cluster based on its spatial location for signal transmission. By dynamically polling and accessing this cluster, it achieves close sharing of sensors and AI computing power, leading to a cost reduction in building a comprehensive intelligent control system compared to traditional single-machine AI systems. This holds significant potential for advancing the implementation of comprehensive AI projects.
Motion Vector Domain Video Steganography Maintaining the Statistical Characteristics of Skipped Macroblocks

Motion vector (MV) based video steganography methods achieve covert communication by embedding secret messages in MVs. The most crucial evaluation metric of steganography is the security against steganalysis. However, recently proposed H.264/AVC steganalysis methods based on Skipped macroblocks can effectively capture the statistical differences of the stego video before and after recompression calibration, thus posing challenges to steganography algorithms. In this paper, we propose a distortion function derivation scheme that can maintain the statistical characteristics of the Skipped macroblock of stego videos. Firstly, the designed distortion function upgrades the existing basic distortion function. Secondly, the designed distortion derivation scheme is mainly used to keep the two statistical properties, the motion vector prediction (MVP) and the partition status, unchanged for the Skipped macroblock before and after message embedding. Finally, the derivation method is applied to two typical basic distortion functions for experimental validation. The experimental results show that the proposed derived distortion function can increase the ability to resist the attack of Skipped-based steganalysis.
Mobile Robot Global Planning Based on Improved A* Algorithm Path Planning Research

For the problems with low planning efficiency, frequent path corners, and significant path bending angles in the routes planned by the standard A* method, we present an improved A* algorithm. Firstly, the valuation function is refined based on the traditional A* algorithm. An initial path is then planned with the introduction of a weight-based valuation function. Subsequently, a corner optimization strategy is implemented to optimize the original path. This leads to a new path with reduced path inflection points without increasing the cost. Finally, the moving average method is employed to smooth the planned path and achieve a more optimal robot movement path. Simulations were carried out to evaluate the traditional A* algorithm, the A* algorithm with weight introduction, the A* algorithm with weight introduction and inflection point optimization, and the improved A* algorithm. The simulation results demonstrate that the improved A* algorithm provides a shorter search time, fewer path inflection points, and better path smoothing.
A Path Planning Framework for Air-land Amphibious UAV with Fast Response and Fine Learning

Air-land amphibious unmanned aerial vehicle (ALAUAV), which integrate ground mobility and aerial flight capabilities, are versatile intelligent entities. Efficient global and precise local path planning is of paramount significance in ensuring the high-quality execution of their missions, as well as system safety and stability. However, current methodologies often focus on single aspects, resulting in complex path computations and inadequate precision. In this study, we propose a path planning framework based on the integration of multiple perspectives and reinforcement learning. In addressing known environments, we transform intricate three-dimensional (3D) terrains into two-dimensional (2D) multi-view representations and perform parallel computations, effectively resolving issues related to delayed responsiveness and reduced efficiency. Subsequently, for dynamic and unknown environments, we establish a deep reinforcement learning model to achieve local fine-grained path planning. To demonstrate the effectiveness of our approach, we conduct simulation experiments in both known and unknown environments. The experimental results unequivocally demonstrate the superior performance of our path planning method in the context of ALAUAV, laying a robust foundation for further expanding the application of this technology in complex scenarios.
Side-Channel Attacks Based on Deep Learning

The technological and economic competition between countries has resulted in the frantic development of industrial technology and financial technology. In recent years, many Internet-centric smart terminal devices have basically covered all aspects of life. However, people's privacy, corporate transaction documents, and state secrets are all threatened by intelligence. Its threat not only comes from the communication threat in the network, but the most hidden danger of the threat comes from the "disclosure" of side information in the cryptographic security chip. Through the template attack principle and strategy that has been proposed, aiming at the problem of low attack success rate in the template attack, PCA dimensionality reduction is performed on the data set extracted by CWLITE. Then use feature normalization processing and correlation analysis to extract the output data of the first round of AES S-boxes in the data set, input them into the built convolutional neural network and CNN-BILSTM network, and give Comparative Results.
Session-based Recommender based on Normalized Graph Neural Network with Margin Penalty

With the gradual development of e-commerce, Session-Based Recommendation (SBR) for anonymous users have gradually received more and more attention from researchers. Existing graph-based SBR systems lack the ability to distinguish sessions with the same last item and are sensitive to the long-tail distribution of items. To address this problem, a novel Session-based Recommender based on Normalized Graph neural network with Margin Penalty (SR-NGMP) is proposed. In SR-NGMP, a mandatory margin is introduced to ensure that sessions with the same last item difference between them, and L2 penalty is applied to alleviate the long-tailed distribution of items. The SR-NGMP is then experimented on two real-world datasets, and proved our SR-NGMP achieves a superior performance over state-of-the-art methods
ENHANCING EFL (ENGLISH AS FOREIGN LANGUAGE) STUDENT’S PUBLIC SPEAKING ABILITY THROUGH PRE-TEACHING VOCABULARY

The aim of this research is to investigate the effectiveness of pre-teaching vocabulary taught in the class by English lecturer towards the first semester students of Computer Informatics who are the users of English as Foreign Language (EFL students). The students are treated by pre-teaching vocabulary method in three stages they are introduction stage, content or body stage, and the conclusion stage. Pre- teaching vocabulary taught by the lecturer in every stage aimed to ease the students to comprehend the presentation step by step before doing English presentation in front of the class as the public speaking assignment. The data collection techniques in this research are observation and practice assessment. The result of this research shows that pre-teaching vocabulary method taught in the class for the first semester students significantly enhance the ability of student’s English public speaking in presentation assignment. Students are more confident enough to perform English presentation in front of the class because the topic assigned by the lecturer has been clearly explained and the students are easier to join words by words or phrase by phrase into a short presentation.
PERBANDINGAN ALGORITMA C4.5 DAN NAIVE BAYES UNTUK MEGUKUR MINAT PENJUALAN SEPATU

The Second Gangbrand shoe shop is one that sells second-hand shoes or what you could also call quality and original second-hand shoes that have certain brands at affordable prices that are cheaper than the original price. This research aims to measure the level of customer interest by comparing the C4.5 algorithm method and the Naive Bayes algorithm. The data source was obtained from second gangbrand stores which were taken based on customer interest. So it is necessary to carry out data analysis to classify customer interest data using the C4.5 and Naive Bayes algorithms to compare accuracy and precision which are the benchmarks in this research. Calculations in this research were carried out manually using Microsoft Excel according to the C4.5 and Naive Bayes algorithm calculation models and then evaluated using the Rapidminer 10.3 tool which was used to help determine accurate values. After conducting research testing, the C4.5 algorithm received an accuracy value of 60.00% and a precision of 50.00%, while the Naive Bayes algorithm received an accuracy value of 60% and a precision of 33.33%. So it can be concluded that the two algorithms have the same accurate accuracy value, but in terms of precision value the C4.5 algorithm is superior in determining customer interest recommendations. It is hoped that the results of this research can provide input and information for future researchers.
Prototyping first-person viewer positions for VR narratives with storyboards and pilot productions

In this article, we present two related approaches to scripting or prototyping VR narratives. We tested storyboarding and pilot production techniques with 40 students over two years, and they were tasked with creating documentary VR narratives characterized by embodied moods and experiences. The aim was to help students tailor a strong first-person viewer relationship with the virtual environment utilizing the ways the body can be centred in story worlds. In particular, the article deals with character positioning and point of view-writing in the first-person, drawing on insights from human–computer interaction (HCI) and postphenomenology. There is an ongoing exploration of new experimental approaches to story world production for immersive media, and prototyping processes from fields such as HCI are becoming increasingly relevant for screenwriters. We provide analyses of how storyboards and pilot productions can situate the user in relation to the virtual environment. Based on the analyses, we evaluate the use value of such prototyping for students and practitioners who need to make first-person viewer positions for VR narratives, and we compare our approach with similar pedagogical projects as represented in the literature.
Abitibi360: An example of the evolution of writing for 360-degree films

While virtual reality technologies have been developing since the 1960s in North America, they became broadly accessible for the public in 2016, with the launch of affordable virtual reality headsets in the global market. Amongst recent virtual reality works, 360-degree films are particularly popular. With this article, I study the evolution of writing for 360-degree films from 2016 to 2020 through the analysis of the 360-degree documentary series Abitibi360 created by Canadian filmmaker Serge Bordeleau, of which Season 1 was produced in 2017 and Season 2 followed in 2020. My goal is to determine how his writing processes changed over this time period and to highlight the various factors that influenced this change. To conduct this research, I present an analysis of creative documents produced by Bordeleau for the two seasons. This work will demonstrate how the author evolved to use virtual reality technologies to develop his own language, even though he continued to be influenced by his original medium: cinematographic documentary films. Bordeleau became more creative as he mastered the techniques of virtual reality production. In particular, he learned to guide the viewer’s attention to chosen points of interest within the 360-degree image by using several techniques, such as light, movement, colour and noise.
Deep Learning-based Compression Artifacts Reduction for JPEG Image Classification

Computer vision has been one of the main application areas for Deep Learning (DL) techniques. Image classification, in particular, has been widely culminating in systems increasingly capable of replacing human visual analysis. However, having high-quality images is one of the main requirements for high performance of DL-based classification, but often factors associated with the models application condition make this ideal scenario impossible. Lossy compression fits very well in these conditions because it is a type of coding technique widely used in image storage and transmission. In this paper, we propose compression artifact reduction (CAR) as a way to circumvent the degradation problem for lossy compression. To this end, we perform experiments using JPEG with very low levels of quality factor (QF) compressing the test dataset for classification. Then, using a DL-based CAR model, we restore this same dataset in order to investigate a possible improvement in the classifiers performance. The two evaluated datasets presented positive results: in Flowers-102 we reached an average 0.52% increase in accuracy for 10 QFs values, whereas for Cub-200 this value was more expressive, around 34.57%. Those findings reinforce that DL-based CAR may increase performance in classification models degraded by drastic levels of signal corruption.
Financial Sector Development and Economic Growth in Nigeria (1990-2020)

In order to establish whether there is a causal relationship between the financial sector development and economic growth during the sample period of 1990–2020, the article investigates the relationship between the development of the financial sector and economic growth in Nigeria.The Autoregressive Distributive Lag (ARDL) Model is used in this work to measure and a useful tool for determining whether there is long-term relationship between economic data series. The ARDL model can be used to predict and distinguish between short-term dynamics and long-term relationships. Tests like the long-run relationship are used to determine the data's stationarity characteristics and their long-term relationship &amp; short-run tests, bounds tests, heteroskedasticity tests, serial correlation, and co-integration tests were used. Financial intermediaries(FINIM), financial depth(FINDEPT), and market stock(MKTSTCK) all have a causal relationship with the real gross domestic product(RGDP), according to the study. Economic growth is significantly impacted by the growth of the financial industry. The public and private sectors must be informed about operations in the financial industry. The World Bank's World Development Indicators (2020) database, which covered the years 1990 to 2020, and the Central Bank of Nigeria's Statistical Bulletin were the sources for all data.