Reviewerly/exHarmony huggingface dataset:
Cyber Security Analysis of Smart Buildings from a Cyber Security Architecture Point of View

Nowadays, the smart city concept is gaining ground worldwide, driven by the development of information technology and IoT devices.As a result, smart buildings are built to provide more efficient, environmentally friendly, comfortable and automated features.The industry of building automation dates back nearly a hundred years.However, with the development of information technology, the size of built-in IT devices is shrinking year by year, and the amount of information generated by individual sensors, IT devices and by the people working in these buildings is fast increasing.Consequently, IT systems in intelligent buildings need to be adequately designed considering several aspects.Cyber security is one of these aspects, and it is the subject of the present research.A poorly designed and protected IT system with many Internet of Things elements creates a massive exposure to cyberattacks and hackers.After all, these systems may include the entire network IT structure, the elevator control and access control systems, the building's HVAC system, or even the parking lot system.In summary, the examination of primary cyber security factors and standards should be considered when designing a smart building.
‘Treasure hunt’: Using loose parts media to develop social financial education model for early children

<h2>Abstract</h2> A globalized world demands every country's citizen to proceed to build a creative economy to adapt to the rapid changes that follow. Therefore, promoting social and financial education should be engaged with children early. However, a learning model available to awaken children's socio-financial capabilities is vividly rare, if not none. Furthermore, the Early Childhood Education Institution is the best place for children to experience social and financial education. This research aims to develop a social financial education model for early childhood. This study employed Research and Development (R&D) in the development process of the educational model. The data were collected through questionnaires and focus group discussions. The data analysis used descriptive quantitative and t-tests to explain the results of field studies, FGD, and trials and to measure the effectiveness of models during experimental and operational trials. In the analysis, the researchers found that the Model Script and Financial Social Education Guide for early childhood using loose parts media were very suitable. Further, the <i>t</i>-test result between the pretest and post-test was 0.924 (92.4%) at α = 0.05. In conclusion, the financial social education model using loose parts of media effectively develops children's social and financial abilities.
NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classification

Graph neural networks have been extensively studied for learning with inter-connected data. Despite this, recent evidence has revealed GNNs' deficiencies related to over-squashing, heterophily, handling long-range dependencies, edge incompleteness and particularly, the absence of graphs altogether. While a plausible solution is to learn new adaptive topology for message passing, issues concerning quadratic complexity hinder simultaneous guarantees for scalability and precision in large networks. In this paper, we introduce a novel all-pair message passing scheme for efficiently propagating node signals between arbitrary nodes, as an important building block for a pioneering Transformer-style network for node classification on large graphs, dubbed as \textsc{NodeFormer}. Specifically, the efficient computation is enabled by a kernerlized Gumbel-Softmax operator that reduces the algorithmic complexity to linearity w.r.t. node numbers for learning latent graph structures from large, potentially fully-connected graphs in a differentiable manner. We also provide accompanying theory as justification for our design. Extensive experiments demonstrate the promising efficacy of the method in various tasks including node classification on graphs (with up to 2M nodes) and graph-enhanced applications (e.g., image classification) where input graphs are missing.
A Survey of Some Density Based Clustering Techniques

Density Based Clustering are a type of Clustering methods using in data mining for extracting previously unknown patterns from data sets. There are a number of density based clustering methods such as DBSCAN, OPTICS, DENCLUE, VDBSCAN, DVBSCAN, DBCLASD and ST-DBSCAN. In this paper, a study of these methods is done along with their characteristics, advantages and disadvantages and most importantly, their applicability to different types of data sets to mine useful and appropriate patterns.
Artistic Robotic Arm: Drawing Portraits on Physical Canvas under 80 Seconds

In recent years, the field of robotic portrait drawing has garnered considerable interest, as evidenced by the growing number of researchers focusing on either the speed or quality of the output drawing. However, the pursuit of either speed or quality alone has resulted in a trade-off between the two objectives. Therefore, in this paper, we propose a new approach that combines both objectives by leveraging advanced machine learning techniques and a variable line width Chinese calligraphy pen. Our proposed system emulates the human drawing process, which entails planning the sketch and creating it on the canvas, thus providing a realistic and high-quality output. One of the main challenges in portrait drawing is preserving the facial features, such as the eyes, mouth, nose, and hair, which are crucial for capturing the essence of a person. To overcome this challenge, we employ CycleGAN, a powerful technique that retains important facial details while transferring the visualized sketch onto the canvas. Moreover, we introduce the Drawing Motion Generation and Robot Motion Control Modules to transfer the visualized sketch onto a physical canvas. These modules enable our system to create high-quality portraits within seconds, surpassing existing methods in terms of both time efficiency and detail quality. Our proposed system was evaluated through extensive real-life experiments and showcased at the RoboWorld 2022 exhibition. During the exhibition, our system drew portraits of more than 40 visitors, yielding a survey outcome with a satisfaction rate of 95%. This result indicates the effectiveness of our approach in creating high-quality portraits that are not only visually pleasing but also accurate.
Optimizing Neural Networks for Imbalanced Data

Imbalanced datasets pose pervasive challenges in numerous machine learning (ML) applications, notably in areas such as fraud detection, where fraudulent cases are vastly outnumbered by legitimate transactions. Conventional ML methods often grapple with such imbalances, resulting in models with suboptimal performance concerning the minority class. This study undertakes a thorough examination of strategies for optimizing supervised learning algorithms when confronted with imbalanced datasets, emphasizing resampling techniques. Initially, we explore multiple methodologies, encompassing Gaussian Naive Bayes, linear and quadratic discriminant analysis, K-nearest neighbors (K-NN), support vector machines (SVMs), decision trees, and multi-layer perceptron (MLP). We apply these on a four-class spiral dataset, a notoriously demanding non-linear classification problem, to gauge their effectiveness. Subsequently, we leverage the garnered insights for a real-world credit card fraud detection task on a public dataset, where we achieve a compelling accuracy of 99.937%. In this context, we compare and contrast the performances of undersampling, oversampling, and the synthetic minority oversampling technique (SMOTE). Our findings highlight the potency of resampling strategies in augmenting model performance on the minority class; in particular, oversampling techniques achieve the best performance, resulting in an accuracy of 99.928% with a significantly low number of false negatives (21/227,451).
Factors Associated with Juvenile Delinquency: A Case of Arusha Juvenile Remand Home in Arusha City-Tanzania

The assessment of factors related to juvenile delinquency was conducted at Arusha Remand Home in Arusha City. This study adopted a cross-sectional design and gathered both quantitative and qualitative data from various primary and secondary sources. A sample of 103 respondents was selected using a simple random sampling technique. Structured interviews using researcher-administered questionnaires were employed to collect primary data from the participants. In-depth interviews were conducted with selected juveniles to obtain insights into the delinquency in the city. Descriptive statistics and the Likelihood Ratio Chi-Square test were employed for quantitative data analysis, utilising IBM SPSS Statistics version 20. The findings indicated that theft offences accounted for a significant proportion of juvenile delinquency (45.6%), followed by abuse (29.1%) and vandalism (25.2%). The primary causes of delinquent behaviours were identified as poverty (67%), peer influence (28.2%), and family dysfunction (4.9%). Additionally, the study identified gender, household size, parenting style, and the causes of committing crimes as factors associated with juvenile delinquency. Based on these findings, it is recommended that the Arusha City Council, in collaboration with other stakeholders, develop targeted interventions and initiatives to address the specific challenges faced by juveniles. Government welfare officers in Arusha City should design educational programmes aimed at equipping families with the necessary knowledge and skills to create a nurturing environment for their children, thereby guiding juveniles onto a positive path. Implementing these recommendations aims to prevent and reduce juvenile delinquency and promote the overall well-being and positive development of young individuals in Arusha City
IMPROVING ROBUSTNESS OF DEEP NETWORKS USING CLUSTER-BASED ADVERSARIAL TRAINING

Deep learning models have been found to be susceptible to adversarial attacks, which limits their use in security-sensitive applications. One way to enhance the resilience of these models is through adversarial training, which involves training them with intentionally crafted adversarial examples. This study introduces the idea of clustering-based adversarial training technique, with preliminary results and motivations. In this approach, rather than using adversarial instances directly, they are first grouped using various clustering algorithms and criteria, creating a new structured space for model training. The method's performance is evaluated on the MNIST dataset against different adversarial attacks, such as FGSM and PGD, with an examination of the accuracy-robustness trade-off. The results show that cluster-based adversarial training could be used as a data augmentation method to enhance the generalization in both clean and adversarial domains.
EDUCATIONAL PERSPECTIVE OF INTELLECTUAL PROPERTY RIGHTS

With the lightning-fast pace of technological advancements, scientific as well as medical innovation that we are witnessing in the modern world, intellectual property has increasingly taken on the role of playing a vital role. In addition, shifts in the global economic environment have had an impact on the evolution of business models in which intellectual property plays a central role in determining value and potential for growth. To fulfil its international obligations outlined in the "World Trade Organization Agreement on Trade-Related Aspects of Intellectual Property Rights", the country has recently enacted a number of new laws intended to strengthen protections for "intellectual property rights (IPRs) (TRIPS)". This demand has resulted in an increase in the number of professionals who specialise in intellectual property. The purpose of this study is to broaden the professionals' understanding of intellectual property and to provide them with a more in-depth understanding of the subject matter. This will enable the professionals to practise, and procedure of the intellectual property that is protected through "patents, trademarks, copyrights, designs, and geographical indications". In addition to satisfying the prerequisites for a potential career path in this field, the purpose of this paper is to cultivate specialised education in the body of knowledge and the complexities of the various aspects of the subject.
DISCOURSE MARKERS IN ABSTRACTS OF INTERNATIONAL JOURNALS

This study investigated discourse markers used in international journal’ abstracts. This study was aimed at finding out: a) discourse markers used in abstracts of international journals, and b) the most dominant types of discourse markers used in international journal’ abstracts. The study employed six abstracts of international journals chosen randomly as the sample of the study. The study utilizes qualitative descriptive design. In data analysis, this study used Fraser’s theory (1999) that emphasized on four types, namely contrastive marker, elaborative marker, inferential marker, and temporal marker. In addition, the result shows that there are four types of discourse markers found in international journal’s abstracts. Elaborative marker are the most dominant types; marker and is the overuse in the abstracts. It occurred 56 times of the total six abstracts of international journals followed by 2 times for marker also, moreover, in addition, therefore, then, thus, so, or, then, 1 time for marker in addition, furthermore, for instance, beside, although, in contrast, in spite of, whereas, however, nonetheless, result indicate that, in conclusion, the result of, because, and finally. Moreover, the study found contrastive markers consistsed of although, in contrast, in spite of, whereas, however, and nonetheless. Elaborative markers consist of and, or, in addition, furthermore, for instance, beside, and moreover. Inferential markers consist of so, therefore, thus, then, result indicate that, the result of, in conclusion, and because. Temporal markers consist of finally. This study concludes that the most dominant types of discourse markers was elaborative markers due to the excessive use in the 6 abstracts. Keywords: abstract, discourse analysis, discourse markers, journal
LITERACY BASED TEACHING IN READING RECOUNT TEXT BY USING AUTHENTIC MATERIALS

This study deals with the development of authentic materials in teaching reading recount text in literacy-based teaching. This study was aimed at 1) observing how the teacher develops recount text by using authentic materials in the literacy-based teaching, and 2) figuring out the students’ responses on authentic materials in recount text developed in the literacy-based teaching. This study employed a qualitative method with the type of case study design. By using purposive sampling technique, this study involved 23 students of class VIII-D and the English teacher is the sample of this study. The research instruments used in this study were the classroom observation, interview, and questionnaires. This study used the triangulation to analyze the data. The result revealed that the teacher used only five of seven principles of literacy in developing authentic materials in teaching reading recount text. They are interpretation, collaboration, problem solving, reflection and self-reflection; and literacy involves language use. Furthermore, the use of authentic materials in reading recount text improved the students’ motivation and reading comprehension. Meanwhile, the use of authentic materials also helped them to overcome their difficulties in learning reading, improving students’ vocabulary, and enhancing the students’ attention in learning reading. It can be concluded that the use of authentic materials by using some principles of literacy has given positive effect to the student’s motivation and reading comprehension. Authentic material is one of media which can help and facilitate the students in a reading text. Keywords: Literacy based teaching, reading, recount text, and authentic materials.
An Otsu thresholding for images based on a nature-inspired optimization algorithm

Thresholding is a type of image segmentation, where the pixels change to make the image easier to analyze. In bi-level thresholding, the image in grayscale format is transformed into a binary format. The traditional methods for image thresholding may be inefficient in finding the best threshold and take longer computation time. Recently, metaheuristic swarm-based algorithms were applied for optimization in different applications to find optimal solutions with minimum computational time. The proposed work aims to optimize the fitness function obtained by the Otsu algorithm using a metaheuristic swarm-based algorithm called the bat algorithm. As a result, the optimal threshold value for bi-level images in cloud detection was obtained. Also, one of the trajectory-based algorithms called hill climbing was applied to optimize the fitness function taken from the Otsu algorithm. The HYTA dataset was used to evaluate the work, which was later confirmed through testing. The findings of experiments indicated that the developed algorithm is promising and the performance of the metaheuristic population-based algorithm is better than the trajectory-based algorithm in terms of efficiency and computational time for image thresholding.
Undergraduate students’ exploration of Instagram and TikTok in learning language skills contents: A leverage to digital literacy

The literacy needed to live efficiently and functionally in a knowledge-driven society goes beyond the ability to read, write and calculate to a more encompassing literacy such as digital literacy. Two social media platforms, Instagram and TikTok, lend themselves to digital literacies and practices needed to learn language skills and content with ease and fun. The study investigated undergraduate students’ knowledge of and competence in using Instagram and TikTok as learning spaces for learning language skills and content. The study also examined the influence of age and gender on undergraduate students’ competence to use Instagram and TikTok to learn language skills and content. Two major language departments in a university in Nigeria were involved in the study. Three hundred and fifty-two students constituted the sample for the study; 110 from Linguistics/Languages Department and 242 from English and Literary studies. Data were collected using a close-ended-self-constructed questionnaire and a checklist. The data were analysed using inferential statistics. The study revealed that undergraduate students possess adequate knowledge of Instagram and TikTok as learning spaces and are competent to harness the features of Instagram and TikTok to learn language skills and language content. The study also shows that age and gender influence undergraduate students’ competence in using Instagram and TikTok. The study recommended that undergraduate students should harness the potential of Instagram and TikTok on activities that will enhance their linguistic repertoire.
SOCIALIZATION OF THE APPLICATION OF GEOMEMBRANE TECHNOLOGY IN PEOPLE'S SALT PRODUCTION AND MARKETING STRATEGY IN KUALA CANGKOY VILLAGE FIELD DISTRICT NORTH ACEH REGENCY

Socialization of service is aimed at salt farmers in developing geomembrane technology for smallholder salt businesses in accordance with local resource conditions and capacities, potential and capacity for salt development and marketing, and the availability of technology to increase sales of salt based on information technology, as well as technical management capabilities in one management. The problem with salt farmers is mostly coastal communities which are identical with helplessness, poverty and backwardness, especially from the socio-economic aspect. Traditional production technology, small production scale, and limited marketing and promotion strategies cause the products they produce are still far from prosperous due to low income. The low income of salt farmers makes it difficult for salt farmers to implement innovations and increase the productivity of salt farmers in various regions. The objectives of implementing science and technology activities for the community (PKM) are, firstly, geomembrane technology to increase the resource capacity of salt farmers in an effective and efficient manner, secondly to increase sales marketing results of salt production, thirdly to increase sales significantly through information technology promotion media, fourthly to create shelters. Ergonomically salt in Kuala Cangkoy, Lapang District. The specifications of the socialization results from community service produced can achieve economies of scale and be able to have salt carried out in an integrated manner in the marketing management system. Have a strong bargaining position in the market, both the primary product market and its derivative products based on information technology technology. The last is being able to increase the income of partner members collectively from the results of storing salt because the place is built according to ergonomic principles and from a marketing perspective.
The Use Of ICT By Teachers In Implementing A Competency-Based Curriculum At Elementary School

Implementation of the curriculum in elementary school based on the use of ICT by educators. Nowadays, ICT has developed from time to time, this requires all educators to be able to use ICT. As educators, of course, they must be able to master technology, especially for the learning process for students. This ICT learning does not only exist in public schools, but also in elementary school By learning to use ICT, and educators who use it can also master it, learning will be easier for both educators themselves and students. The purpose of this research itself refers to how the use of ICT by educators in elementary school in order to implement the competency curriculum in elementary school. The research was conducted using in-depth interviews with educators at elementary school through the WhatsApp application with a qualitative method. The results of the research are that the use of ICT for educators is quite developed, even ICT is very helpful for educators in providing material that is easily understood by students. The use of ICT in elementary school is very beneficial for educators and for students, with ICT in learning it makes it very easy for students to understand the material provided, moreover students are very happy with things that attract their attention so that the material provided will be easier to understand. The limitations of this research are that the researcher did not make a report on what percentage of educators can use ICT, as well as the extent to which educators and researchers have only studied one elementary school. Therefore, it is hoped that future researchers can conduct better and more complete research
Improving Lead Time Forecasting and Anomaly Detection for Automotive Spare Parts with A Combined CNN-LSTM Approach

This paper presents a solution to a challenge faced in the supply chain management of a spare parts distributor with a dispersed global supply network and local distribution network in Morocco.The problem is a lack of accurate lead time information, leading to difficulties in meeting customer demand.The proposed solution is a framework using an LSTM (Long Short Term Memory) model for lead time forecasting and anomaly detection.The framework combines CNN (Convolution neural network) -Bidirectional LSTM model for forecasting and an LSTM autoencoder with One-Class Support Vector Machine for anomaly detection.The data was obtained from a legal ERP system of a major automotive distributor in Morocco.The results show that the framework is effective in overcoming the lead time information issue, and the relevance of the methods used has been verified via precise performance indicators, such as the RMSE (Root of the mean of the square of the errors) testifying to the accuracy of the results and also through comparison with other models.