Reviewerly/exHarmony huggingface dataset:
Does the Manufacturing Industry Also Need Gamification? – A Gamification Design Practice for CNC Machine Tool Skill Training

In CNC (Computer Numerical Control) vocational colleges, the current traditional pedagogy has become conflicting with the learning preferences of the “Generation Z”, resulting in their low sense of competence, low learning autonomy, and insufficient recognition of industry identification. Thus, gamification design provides novel ideas for addressing these issues. Based on the theories of self-determination theory and motivational affordances, this study analyzes the sore points and study needs of students who are majoring in CNC technology at vocational colleges. Hence, according to the 12 dimensions of motivational affordances, the “needs-affordances-features” framework and the course content of CNC technology, a set of gamification design strategies for CNC machine tool skill training is proposed. Consequently, a smartphone-based interactive design practice is completed. Following this design practice, a user experience test is executed, and a positive result is obtained. Theoretically, this study explores the feasibility of modified gamification design processes that combine the motivational affordance model and the “needs-affordances-features” framework. Practically, the integration of gamification design into CNC machine tool skill training is not only a bold attempt to boost the learning motivation of students, but also a practical research foundation for the innovative development and transformation of CNC technology vocational education.
Penerapan Metode K-Means Clustering Dalam Pengembangan Strategi Promosi Berbasis Data Penerimaan Mahasiswa Baru (Studi Kasus :Universitas Nurul Huda)

The admission of new students is a key element in the success of a university. In its effort to enhance efficiency in admitting new students, Nurul Huda University proposes applying the K-Means Clustering method as a solution to manage candidate student data more effectively and intelligently. The data used in this research is derived from the Admission of New Students process for the academic years 2021/2022 and 2022/2023, totaling 1275 entries, which will then be processed using data mining techniques to generate analysis. The goal is to determine promotional strategies based on the origin of profiles of newly enrolled students. The method applied is clustering with the K-Means algorithm. After data processing, analysis is conducted using the Knowledge Discovery in Databases (KDD) technique, consisting of five stages: selection, preprocessing, transformation, data mining, and evaluation. The implementation in this research utilizes Rapidminer software, resulting in three data clusters: Cluster 1 with 345 entries, covering 27% of the total; Cluster 2 with 86 entries (7%); and Cluster 3 with 835 entries (66%). For promotion, the marketing team is deployed to districts dominant in the East OKU region and potential areas outside the East OKU District. They conduct direct visits to introduce Nurul University to students, distribute brochures, display pamphlets, and adapt strategies using a promotion mix strategy
Method of Named Entity Recognition for Chemical Industry Accidents Based on ALBERT-BiLSTM-CRF

In recent years, there have been frequent production safety accidents in the chemical industry, prompting the need for named entity recognition of chemical industry accidents. However, due to the scarcity of annotated corpus in the chemical industry field, this paper obtain 659 accident investigation reports from websites such as the Safety Management Website, and manually constructed a BIO annotated corpus. To process text data using the ALBERT model, semantic representation vector of the text were obtained, followed by BiLSTM network layers and CRF layers. After 30 iterations of training, experimental results showed that the ALBERT-BiLSTM-CRF model outperformed four other groups of models: BiLSTM, CRF, BiLSTM-CRF, and IDCNN-CRF. Recall, precision, and F1 score all increased as a result of using this model.
A Hybrid Initialization and Effective Reproduction-Based Evolutionary Algorithm for Tackling Bi-Objective Large-Scale Feature Selection in Classification

Evolutionary algorithms have been widely used for tackling multi-objective optimization problems, while feature selection in classification can also be seen as a discrete bi-objective optimization problem that pursues minimizing both the classification error and the number of selected features. However, traditional multi-objective evolutionary algorithms (MOEAs) can encounter setbacks when the dimensionality of features explodes to a large scale, i.e., the curse of dimensionality. Thus, in this paper, we focus on designing an adaptive MOEA framework for solving bi-objective feature selection, especially on large-scale datasets, by adopting hybrid initialization and effective reproduction (called HIER). The former attempts to improve the starting state of evolution by composing a hybrid initial population, while the latter tries to generate more effective offspring by modifying the whole reproduction process. Moreover, the statistical experiment results suggest that HIER generally performs the best on most of the 20 test datasets, compared with six state-of-the-art MOEAs, in terms of multiple metrics covering both optimization and classification performances. Then, the component contribution of HIER is also studied, suggesting that each of its essential components has a positive effect. Finally, the computational time complexity of HIER is also analyzed, suggesting that HIER is not time-consuming at all and shows promising computational efficiency.
Application of Recommendation Algorithms Based on Social Relationships and Behavioral Characteristics in Music Online Teaching

This research designed an improved collaborative filtering algorithm to be responsible for music recommendation tasks in the online music teaching platform. This algorithm integrates the user's social trust into the similarity calculation formula. Then, the algorithm uses behavioral feature data driven by preferences, music tags, and popularity as the basis for recommendation calculation. It adopts user data testing on an online music teaching platform. The results showed that when the number of recommended music was eight, the recommended recall rates of XCF, CTR, TSR, and UB-CF recommendation models reached their maximum, reaching 97.82%, 95.26%, 93.95%, and 88.72%, respectively. The AUC and average computational time of the ROC curves for XCF, CTR, TSR, and UB-CF recommended models are 0.7, 0.68, 0.64, 0.57, and 160ms, 136ms, 114ms, and 88ms, respectively. The experimental data shows that the recommendation accuracy of the music recommendation model designed in this study is significantly higher than that of traditional recommendation models.
Evaluating Complex Entity Knowledge Propagation for Knowledge Editing in LLMs

In today’s world, where information keeps growing rapidly and changing constantly, language models play a crucial role in making our lives easier across different fields. However, it is tough to keep these models updated with all the new data while making sure they stay accurate and relevant. To tackle this challenge, our study proposes an innovative approach to facilitate the propagation of complex entity knowledge within language models through extensive triplet representation. Using a specially curated dataset (CTR-KE) derived from reliable sources like Wikipedia and Wikidata, the research assesses the efficacy of editing methods in handling intricate relationships between entities across multiple tiers of information. By employing a comprehensive triplet representation strategy, the study aims to enrich contextual understanding while mitigating the risks associated with distorting or forgetting critical information. The study evaluates its proposed methodology using various evaluation metrics and four distinct editing methods across three diverse language models (GPT2-XL, GPT-J, and Llama-2-7b). The results indicate the superiority of mass-editing memory in a transformer (MEMIT) and in-context learning for knowledge editing (IKE) in efficiently executing multiple updates within the triplet representation framework. This research signifies a promising pathway for deeper exploration of data representation for knowledge editing within large language models, and improved understanding of contexts to facilitate continual learning.
DEEP-IMAGE-MATCHING: A TOOLBOX FOR MULTIVIEW IMAGE MATCHING OF COMPLEX SCENARIOS

Abstract. Finding corresponding points between images is a fundamental step in photogrammetry and computer vision tasks. Traditionally, image matching has relied on hand-crafted algorithms such as SIFT or ORB. However, these algorithms face challenges when dealing with multi-temporal images, varying radiometry and contents as well as significant viewpoint differences. Recently, the computer vision community has proposed several deep learning-based approaches that are trained for challenging illumination and wide viewing angle scenarios. However, they suffer from certain limitations, such as rotations, and they are not applicable to high resolution images due to computational constraints. In addition, they are not widely used by the photogrammetric community due to limited integration with standard photogrammetric software packages. To overcome these challenges, this paper introduces Deep-Image-Matching, an open-source toolbox designed to match images using different matching strategies, ranging from traditional hand-crafted to deep-learning methods (https://github.com/3DOM-FBK/deep-image-matching). The toolbox accommodates high-resolution datasets, e.g. data acquired with full-frame or aerial sensors, and addresses known rotation-related problems of the learned features. The toolbox provides image correspondences outcomes that are directly compatible with commercial and open-source software packages, such as COLMAP and openMVG, for a bundle adjustment. The paper includes also a series of cultural heritage case studies that present challenging conditions where traditional hand-crafted approaches typically fail.
HEALTHCARE PRACTICES AND DIGITAL TECHNOLOGIES AMONG PARENTS OF PUPILS ATTENDING FEDERAL COLLEGE OF EDUCATION (TECHNICAL) STAFF SCHOOL AKOKA, LAGOS.

The study examined healthcare practices and Digital Technologies among parents of pupils attending Federal College of Education (Technical) Staff School Akoka, Lagos. Four (4) research questions were raised and answered to guide the conduct of the study. The study adopted a descriptive survey research study of which population of the study comprised parents of pupils attending FCE (T) staff school Akoka, Lagos numbering five hundred (500). The sample comprised of two hundred (200) parents constituting forty percent (40%) of the population. Simple random sampling technique was used in the selection of pupils in the different classes to take the questionnaire home to their parents. It was concluded that healthcare practices and Digital Technologies among parents of pupils attending Federal College of Education (Technical) Staff School Akoka, Lagos is significant. It was also concluded that the parents shun all form of untrusted healthcare practices such as traditional medicine among others. Recommendations made among others is that Digital Technologies in health care practices should be made accessible to all and sundry.
Centralized Database Access: Transformer Framework and LLM/Chatbot Integration-Based Hybrid Model

This paper addresses the challenges associated with the centralized storage of educational materials in the context of a fragmented and disparate database. In response to the increasing demands of modern education, efficient and accessible retrieval of materials for educators and students is essential. This paper presents a hybrid model based on the transformer framework and utilizing an API for an existing large language model (LLM)/chatbot. This integration ensures precise responses drawn from a comprehensive educational materials database. The model architecture uses mathematically defined algorithms for precise functions that enable deep text processing through advanced word embedding methods. This approach improves accuracy in natural language processing and ensures both high efficiency and adaptability. Therefore, this paper not only provides a technical solution to a prevalent problem but also highlights the potential for the continued development and integration of emerging technologies in education. The aim is to create a more efficient, transparent, and accessible educational environment. The importance of this research lies in its ability to streamline material access, benefiting the global scientific community and contributing to the continuous advancement of educational technology.
Using Social Media as a Platform of Communication Between School Administration and the Local Communities to Promote Inclusive Education for Children with Special Needs

This study aimed to explore the level of using social media as a platform of communication between school administration and the local communities in Saudi Arabia schools from the parents' perspectives. The descriptive method was used as a methodology to achieve the study objectives. The study involved 265 parents of students from different academic stages and different cities in Saudi Arabia to take part in an online questionnaire of ten items, which was sent to the participants online. The results showed that the best practices of school administration in electronic communication included creating WhatsApp groups with parents for informal communication and employing Twitter and Instagram accounts as a media interface for the school. These results reveal that male parents think more slightly that school administration uses social media to communicate with the local community. The findings also indicated that the age of the parents did not affect their views because the school uses similar platforms and parents are familiar with using the social media websites regardless of their age.
Experimental and Numerical Study of the Effect of Cloud Cover on the Electrical Performance of Photovoltaic Plants

&lt;p&gt;The production of electricity from the sun mainly consists of transforming the light emitted by the sun into electrical energy through photovoltaic cells. This production varies proportionally with the light that illuminates the solar photovoltaic module. According to the literature, we noted that during the period of cloudy passage, the production of the solar modules could see a fall going from 5 to 60% according to the size of the photovoltaic fields. The present work consisted of studying the impact of cloudy passages on the electrical production of PV fields at four sites (Loumbila, Sandogo, Ouaga 2000 and IRSAT). To achieve this, a theoretical and experimental study was conducted. The simulation results were compared to the experimental results of two daily sunshine profiles (sunny and cloudy). The results show significant differences between the experimental and simulated data for the two sunshine profiles considered. The losses of electrical production generated by these cloudy passages vary from 27 to 32% and from 35 to 52%, respectively, for the simulated and experimental results. These differences were explained by the probabilistic character of the cloudy passages and by the fact that other major parameters (aerosols, dust deposits and ageing of the solar modules) were not considered. As perspectives, we recommend a continuation of the work by studying, in addition to the cloudy passages, the influence of aerosols and dust deposits on the PV fields realized by us.&lt;/p&gt;
Untrained and Unmatched: Fast and Accurate Zero-Training Classification for Tabular Engineering Data

Abstract In engineering design, navigating complex decision-making landscapes demands a thorough exploration of the design, performance, and constraint spaces, often impeded by resource-intensive simulations. Data-driven methods can mitigate this challenge by harnessing historical data to delineate feasible domains, accelerate optimization, or evaluate designs. However, the implementation of these methods usually demands machine-learning expertise and multiple trials to choose the right method and hyperparameters. This makes them less accessible for numerous engineering situations. Additionally, there is an inherent trade-off between training speed and accuracy, with faster methods sometimes compromising precision. In our paper, we demonstrate that a recently released general-purpose transformer-based classification model, TabPFN, is both fast and accurate. Notably, it requires no dataset-specific training to assess new tabular data. TabPFN is a Prior-Data Fitted Network, which undergoes a one-time offline training across a broad spectrum of synthetic datasets and performs in-context learning. We evaluated TabPFN's efficacy across eight engineering design classification problems, contrasting it with seven other algorithms, including a state-of-the-art AutoML method. For these classification challenges, TabPFN consistently outperforms in speed and accuracy. It is also the most data-efficient and provides the added advantage of being differentiable and giving uncertainty estimates. Our findings advocate for the potential of pre-trained models that learn from synthetic data and require no domain-specific tuning to make data-driven engineering design accessible to a broader community and open ways to efficient general-purpose models valid across applications.
Message Binary Matrix Reshaping and Rotating Left to Encrypt-Decrypt Secret Message

Secret messages are the most circulated via communication environments digital data, these messages may be secret, private, and the communication environment may unsafe, so they need protection from being hacked. In this paper research a simple method of message cryptography will be proposed, the method will use one round and simplified set of instruction to apply message encryption-decryption. The method will use a simple private key to determine the reshaping factor and the number of rotating left digits. The message binary matrix will be reshaped to a number of columns equal the reshaping factor, and each columns will be rotated left for a selected number of digits to apply message encryption-decryption. The reshaping factor will be tested to show the best choice of the reshaping factor, which will provide the best performance, the message length will be tested and some recommendations will be provided. The quality of the proposed method will tested by implementing various messages and examining the quality parameters values to prove that the proposed method will satisfy the quality requirements of good crypto system.
Proposed LSB4 of Data Steganography

The process of hiding confidential data is one of the important processes used to protect this data from intruders or protect it from the penetration process. The most popular methods used in data steganography are LSB and LSB2 methods. These methods are efficient methods of data hiding, they provide a good stego media quality and good speed parameters. They are capable of hiding short and long secret messages. To increase the capacity and to decrease both the hiding and extraction times LSB4 method will be introduced and used. The proposed method will be implemented using various covering images and various in length secret messages, the obtained results will be analyzed to prove the quality and the speed of the proposed method. The obtained results of the proposed LSB4 method will be compared with the results of LSB method to show the enhancements provided by the proposed method. The proposed LSB4 method will be supplied by a security issues to protect the hidden message, the secret message will be protected by using a special private key to apply bits’ substitution on the contents of characters’ binary matrix.
An Insight into the Consequences of Digitalization and Digital Technologies for Small and Medium Enterprises (SMEs) in Africa

IMF (2020) estimates that 20 million jobs must be created annually in SSA and Africa at large to accommodate the region's expanding labor force. However, digital technologies like email, the internet, and mobile money have a huge potential to generate wealth and jobs that African businesses still need to realize. Even though mobile phone technology has helped spread Internet-based innovations throughout the region, this process is slowed down by a vast Internet divide; businesses and people use these technologies less than they could. In 2015, Internet penetration rates in African nations were below 60% of the total population, with penetration rates as low as 5% in some nations including Niger, Sierra Leone, and Guinea-Bissau. Again in 2015, small African firms employed almost 80% of the labor force on the continent. But surveys by the World Bank between 2013 and 2018 show that less than 60% of SMEs used email for business, and less than 30% used websites for the same thing. In contrast, 90% of major businesses polled within the same time period acknowledged utilizing email and/or a website for conducting business. Since SMEs are currently the largest employers and wealth creators in the region, the poor dissemination and adoption of digital technology severely limit their ability to advance. More specifically, and unlike previous research-based analyses of the digitalization of African firms, the research article combines quantitative analysis and qualitative data to give readers a bird's-eye view of how digital technologies affect the performance of small and medium-sized enterprises (SMEs) in Africa and the opportunities for private sector growth that come with the ongoing digitalization of the economy.
Long‐range quantum energy teleportation and distribution on a hyperbolic quantum network

Abstract Teleporting energy to remote locations is new challenge for quantum information science and technology. Developing a method for transferring local energy in laboratory systems to remote locations will enable non‐trivial energy flows in quantum networks. From the perspective of quantum information engineering, we propose a method for distributing local energy to a large number of remote nodes using hyperbolic geometry. Hyperbolic networks are suitable for energy allocation in large quantum networks since the number of nodes grows exponentially. To realise long‐range quantum energy teleportation (QET), we propose a hybrid method of quantum state telepotation and QET. By transmitting local quantum information through quantum teleportation and performing conditional operations on that information, QET can theoretically be realized independent of geographical distance. The method we present will provide new insights into new applications of future large‐scale quantum networks and potential applications of quantum physics to information engineering.