Reviewerly/exHarmony huggingface dataset:
Performance Evaluation of Laboratory Researchers Based on Timely Data Driven

For the internal driving force of the pharmaceutical industry: Performance management of researchers is a key link in motivating team morale, improving personnel stability, and improving enterprise efficiency. This article introduces the key information fields designed for the daily experimental operation behavior of researchers in electronic experimental notebooks, including reaction starting materials, hazardous processes, operating steps, system time, reaction results, etc., and analyzes the role and significance of these data fields in researcher performance management. And it explores how to reflect data through the experiments of researchers, to identify the progress of research and development projects, to analyze key obstacles to project progress, and to provide necessary work guidance and business support to researchers in a timely manner. It also discusses how to use objective real-time data from experiments to set performance evaluation indicators for researchers (including achievement performance, workload, work efficiency, etc.), and then conducts objective and authentic performance evaluation for researchers. How to solve the problem of inability to compare the performance of researchers from different research and development departments by introducing departmental performance comparisons. Avoiding the common drawbacks of subjective evaluation and prioritizing results over processes in performance management, this approach aims to stimulate the individual potential of researchers and provide reference for improving the performance management and research efficiency of researchers in the pharmaceutical industry.
Relationship Between Adolescents’ Positive Risk Behaviors, Sensation Seeking and Self-esteem

Objective: To investigate prevalence of positive risk behaviors and the relationship between positive risk behaviors, sensation seeking and self-esteem of adolescent. Methods: Using convenience sampling we conducted a self-reported survey among 4911 adolescents during March to May in 2023. Results: The participation rate of extreme sports, stimulate adventure rides, watching thrilling movies and listening rock music were 18.3%、49.8%、67.1%、81.6% respectively. Sensation seeking was influencing factor of participation of extreme sports (<I>OR</I>=1.215, 95%<I>CI</I>=1.183-1.248), stimulate adventure rides (<I>OR</I>=1.150, 95%<I>CI</I>=1.126-1.175), watching thrilling movies (<I>OR</I>=1.163, 95%<I>CI</I>=1.136-1.191) and listening rock music (<I>OR</I>=1.131, 95%<I>CI</I>=1.099-1.164), high sensation seekers were more likely to participate these positive risk behaviors. Self-esteem was influencing factor of participation of extreme sports (<I>OR</I>=1.034, 95%<I>CI</I>=1.018-1.050) and stimulate adventure rides (<I>OR</I>=1.023, 95%<I>CI</I>=1.011-1.036), high self-esteem adolescents were more likely to participate the two positive risk behaviors. Conclusion: We should pay attention to the prevalence of positive risk behaviors, sensation seeking and self-esteem were influencing factors of adolescents’ positive risk behaviors.
Construction of a neural network for handwritten digits recognition based on TensorFlow library applying an error backpropagation algorithm

The object of this study is a neural network for recognizing handwritten digits based on the TensorFlow library using the backpropagation algorithm. The main problem addressed is the development of an effective model with high recognition accuracy. Working on such a task is important as it allows understanding how algorithms and models can effectively work with real data and helps improve machine learning techniques. It has been determined that after 20 training epochs, the loss function is 0.105, and the recognition accuracy is 0.976, comparable to human recognition capability. The classification report indicates that the model is effectively trained on training data and demonstrates high accuracy on test data, capable of generalizing information to new examples. Visualization of recognition results confirms that the model correctly recognizes even poorly written digits. The results can be explained by the peculiarities of the model architecture, optimal selection of hyperparameters, and successful use of the backpropagation algorithm, which was not explicitly specified during model training. TensorFlow provided a convenient toolkit for implementing the neural network and optimizing its parameters. As a result, the model has a fairly high accuracy in image recognition. A significant feature of the results is the high recognition accuracy achieved through the optimal model architecture, correct choice of hyperparameters, and effective use of the backpropagation algorithm. Unlike models built using Keras and convolutional layers, the research model quickly learns, which is important, and does not compromise on accuracy. This result was made possible by the above features of model construction. The results could be practically applied in the field of handwritten character recognition, especially in automated document classification systems, in banking recognition systems, and in other areas where the accuracy of handwritten character recognition is essential
Cost-Sensitive Boosting Algorithm for Classifying Underdeveloped Regions in Indonesia

Imbalanced classes are indicated by having more instances of some classes than others. The cost-sensitive boosting algorithm is a modification of the AdaBoost algorithm, which aims to solve the problem of imbalanced classes. In this study, we evaluate the cost-sensitive Boosting algorithm AdaC2 using Indonesia's underdeveloped region's data. This study confirms that the cost-sensitive boosting algorithm (AdaC2) performs better in classifying the instances in the minority classes than standard classifiers algorithms.
Digital Literacy in Basic Secondary School Students: A Gender Comparative Study

This study aimed to compare the self-perception of digital skills across genders among secondary school students in Peru, to identify potential disparities and advocate for equity-promoting strategies. A structured survey was administered to evaluate operational and formality skills, as well as those related to digital content, among a representative sample of students from both genders using a convenience non-probabilistic sampling method. Females reported higher competence in most digital skills, notably in areas such as online safety and technical problem solving, whereas males demonstrated similar or superior competence in basic navigation and educational technology skills. The implementation of inclusive technological literacy workshops, integration of collaborative research projects and information sharing, and encouragement of male engagement in digital content creation are recommended. These should be supported by equitable access to technological resources, to enhance the confidence and digital competence of both genders in Peruvian classrooms.
SLAH: A Siamese Network Vision Tracking Algorithm Using LSTM Adaptive Head

In the field of single object tracking, conventional methods often rely on correlation filters or visual image processing. However, these approaches typically focus solely on extracting target object features and consider only the position information in the current frame. They lack integration of contextual and spatial-temporal information, which can limit tracking accuracy.Our method takes inspiration from single object detection and tracking techniques. It combines efficient model-based object detection with the spatial position of the final target in the image. This approach offers significant advantages in reducing tracking drift (deviation from the target object) and improving tracking accuracy (predicting bounding boxes that closely follow the target).To extract spatial information, we utilize a three-layer ResNet and Convolutional Block Attention Module (CBAM) in conjunction with a siamese network. This combination effectively captures the spatial characteristics of the target object. Additionally, we employ an adaptive processing head with an internal structure of Long Short Term Memory (LSTM) to capture temporal information. By integrating spatial and temporal cues, our method achieves more robust and accurate tracking.Through comprehensive comparison and ablation experiments across multiple datasets, we have demonstrated notable improvements with our method. It tightly connects the predicted bounding box with the tracking target and effectively combines spatial and temporal information for precise object tracking.
YOLOv5-UOD: Underwater Object Detection Method Based on Improved YOLOv5

Underwater object detection presents a distinct and intricate challenge within the realm of computer vision. Although object detection methods have demonstrated exceptional performance on conventional datasets, the challenges associated with low visibility and color distortion in intricate underwater environments frequently lead to suboptimal image quality. Consequently, enhancing underwater object detection methods holds significant practical value. In this study, we take a pioneering approach by applying one of the most advanced object detection algorithms, YOLOv5, to underwater environments. We further tailor this approach with techniques designed specifically for underwater scenarios. Our improvements encompass the incorporation of the Swin Transformer as the foundational backbone network for YOLOv5, making the network particularly suitable for the challenges posed by underwater images featuring blurred objects. We enhance the process of feature extraction by harnessing multi-scale feature fusion, which strengthens the representation of object features in detection by integrating crossscale connections and applying context information weighting operations. This is achieved by constructing a novel feature pyramid by amalgamating the fused feature maps. Additionally, we replace the traditional Non-Maximum Suppression (NMS) with Soft-NMS, enhancing the network's capability to recognize occluded and overlapping underwater objects. The experimental outcomes provide empirical evidence supporting the efficacy of our improved network model for underwater object detection, achieving an impressive average precision (mAP) of 87.3%. Our model outperforms traditional object detection models and proves its applicability in complex underwater environments.
Industrial education 4.0: The role of human, technology, and data literacy

This study aims to confirm the effect of industrial education 4.0 on data, technology, and human literacy among postgraduate students in Indonesia. The research adopted covariance-based structural equation modeling by employing AMOS 25 to examine the relationship between variables. The model estimation was performed using confirmatory factor analysis as a standard model for measuring industrial education 4.0 and involved structural model analysis to confirm the hypotheses. The population in this research paper was 2.958 postgraduate students in 32 study programs, while the sample was 312 respondents, which was determined using the proportional random sampling technique. This study tested the three hypotheses proposed, showing that industrial education 4.0 has a positive and significant effect on data literacy, technological literacy, and human literacy among postgraduate students in Indonesia. This paper raises the need for developing industrial education 4.0 on these literacies.Keywords: data literacy; digital literacy; human literacy; industrial education 4.0
A New Class of Optimal Frequency Hopping Sequences with Applications to Secure Communication Waveforms

Frequency hopping (FH) is a spread spectrum technique used to protect against detection, interception, location, and jamming where the transmission frequency is changed in a seemingly random manner, only occupying a given frequency band for a very short amount of time. FH systems provide low probability of intercept (LPI) capabilities mainly by using large hop bandwidths. Using large portions of the electromagnetic spectrum is beneficial because it makes it potentially more difficult for a third party to monitor the entire bandwidth at once. A popular way to implement FH is by using specially designed pseudorandom sequences known only to the intended users. The pseudorandom sequences must be designed according to certain mathematical properties in order to guarantee that an attacker cannot easily learn the hopping sequence and defeat the protection. Prior research has revealed an interesting equivalence relation between mathematically optimal FH sequences and partitioned difference families in cyclic groups. Using this relationship, we provide a method that yields new families of optimal FH sequences, inequivalent to known ones. The resulting FH sequence families contain several members whose underlying pseudorandom sequences possess high linear span, thereby making them desirable for secure communications. Our research shows exponential growth of the linear span, which is a significant increase in security over the state of the art (SOA).
Optimizing Inventory with Frequent Pattern Growth Algorithm for Small and Medium Enterprises

The success of a business heavily relies on its ability to compete and adapt to the ever-changing market dynamics, especially in the fiercely competitive retail sector. Amidst intensifying competition, retail business owners must strategically manage product placement and inventory to enhance customer service and meet consumer demand, considering the challenges of finding items. Poor inventory management often results in stock shortages or excess. To address this, adopting suitable inventory management techniques is crucial, including techniques from data mining, such as association rule mining. This research employed the FP-Growth algorithm to identify patterns in product placement and purchases, utilizing a dataset from clothing store sales. Analyzing 140 transactions revealed 24 association rules, comprising rules with 2-itemsets and frequently appearing 3-itemset rules. The highest support value in the final association rules with 2-itemsets was 10% with a confidence level of 56%, and the highest support value in the 3-itemsets was 67% with the same confidence level. Additionally, three rules had a confidence level of 100%. Thus, the association rules generated by the FP-Growth frequent itemset algorithm can serve as valuable decision support for sales of goods in small and medium-sized retail businesses.
Towards Deep Learning Based Access Control using Hyperledger-Fabric Blockchain for the Internet of Things

The rapid expansion of Internet of Things (IoT) devices has resulted in significant progress and developments in various sectors, such as smart healthcare, self-driving vehicles, smart banking, smart home, Industry 4.0, etc. Traditional centralised access control methods are inadequate to deploy in decentralised IoT networks. Although the existing Blockchain-based access control approaches provide a better way of managing access permission for IoT systems, they are ineffective in addressing critical security gaps and preventing unauthorised access while detecting malicious anomalies. To address these two challenges in a single platform, in this paper, we propose a novel approach towards a deep-learning-based authorisation solution that is deployed within the Hyperledger-Fabric (HLF) private Blockchain. Our approach allows smart-contract to define attribute-based access control policies augmented with the Artificial Neural Network (ANN) model, which can effectively identify and isolate malicious anomalies and prevent unauthorised access from malicious devices. We run experiments to evaluate our platform in terms of security and performance, and our results show positive indicators essential for addressing security issues in decentralised IoT networks.
The Application of Numerical Measure Variations in K-Means Clustering for Grouping Data

The K-Means Clustering algorithm is commonly used by researchers in grouping data. The main problem in this study was that it has yet to be discovered how optimal the grouping with variations in distance calculations is in K-Means Clustering. The purpose of this research was to compare distance calculation methods with K-Means such as Euclidean Distance, Canberra Distance, Chebychev Distance, Cosine Similarity, Dynamic TimeWarping Distance, Jaccard Similarity, and Manhattan Distance to find out how optimal the distance calculation is in the K-Means method. The best distancecalculation was determined from the smallest Davies Bouldin Index value. This research aimed to find optimal clusters using the K-Means Clustering algorithm with seven distance calculations based on types of numerical measures. This research method compared distance calculation methods in the K-Means algorithm, such as Euclidean Distance, Canberra Distance, Chebychev Distance, Cosine Smilirity, Dynamic Time Warping Distance, Jaccard Smilirity and Manhattan Distance to find out how optimal the distance calculation is in the K-Means method. Determining the best distance calculation can be seen from the smallest Davies Bouldin Index value. The data used in this study was on cosmetic sales at Devi Cosmetics, consisting of cosmetics sales from January to April 2022 with 56 product items. The result of this study was a comparison of numerical measures in the K-Means Clustering algorithm. The optimal cluster was calculating the Euclidean distance with a total of 9 clusters with a DBI value of 0.224. In comparison, the best average DBI value was the calculation of the Euclidean Distance with an average DBI value of 0.265.
COMPARAÇÃO DE DESEMPENHO DO ALGORITMO DE DEUTSCH-JOZSA NAS LINGUAGENS QUÂNTICAS SILQ E QASM

Due to the importance given to information in the last few decades, a performance and processing advantage of information becomes relevant, something that can be found through quantum computing. The Deutsch-Jozsa Algorithm is the first example of a quantum algorithm that offers an exponential advantage against classical algorithms, whether in a local environment or through cloud simulators. Seeking to explore the advantages of quantum computation, the Deutsch-Jozsa Algorithm was implemented in two quantum programming languages, namely the high level language Silq, focused on the execution of the algorithm on a local environment through VSCode, which offers a cleaner and friendly sintaxe as well as quantum uncomputation, and also in OpenQASM, a low level language meant to interact with quantum circuits, used to better visualizethe Deutsch-Jozsa Algorithm. This paper aims to make the differences between high and low-level quantum languages clear, as well as incentivize the change to a new paradigm
Peningkatkan Keterampilan Penggunaan Aplikasi Pembelajaran Online Bagi Guru dan Siswa Siswa Sekolah Dasar

The development of information technology does not only play a role in the economic field, but also in the field of education. Today every teacher and school student at almost all levels of education is required to be technology literate, especially information technology because every job has been in contact with information technology. In addition to supporting educational activities, information technology literacy also supports teachers in solving administrative problems and for students to support the learning system. One of the software that can be used to support education and administrative issues is Microsoft Word, Microsoft Excel and Microsoft PowerPoint. Microsoft Word, Microsoft Excel and Microsoft PowerPoint software has many features that are still not familiar to some users, especially elementary school teachers, especially elementary school students. This is also experienced by the teachers of SD 17 Bengkulu City who are not familiar with the many tools provided in Microsoft Word, Microsoft Excel and Microsoft PowerPoint software. Some of the obstacles that are often encountered are the less than optimal use of the merge function in Word, the function of conditional formulas in Excel and animation and multimedia in PowerPoint. This is the background for the dedication team to provide training "Improving the Skills in Using Microsoft Office Software for Teachers and Students at SDN 17 Bengkulu City". This activity was held in the classroom of SD N 17 Bengkulu city with lecture methods and direct practice. The participants of this training were teachers and grade 6 students, all of whom were teachers and students of SDN 17 Bengkulu City
Implementation of UCD with a kanban board to build an interaction design for an animal recognition application

Studying types of animals is one way to expand knowledge about living creatures on earth. By introducing types of animals, especially children, it will help increase emotional intelligence, creativity and imagination of living creatures as a basis for understanding the diversity of creatures on earth. Every child certainly has different abilities and development, in general children will experience significant progress between the ages of 3 and 6 years, so at this age it is very appropriate to introduce types of animals packaged in interactive multimedia as a medium for learning. Providing knowledge about types of animals will expand knowledge about nature, build emotional intelligence and cognitive abilities which are important in their growth. In this research, an application for recognizing animals for children will be created that implements the User Centered Design (UCD) method with a kanban board, which is an alternative development approach that focuses on the needs and preferences of users at each stage of development, so that it is hoped that the application being developed will meet their desires and needs. user. The final result of this research is an animal recognition application, which implements the UCD method in UI/UX development and the Kanban framework in team work processes.
The Influence of Depression on Learning Motivation among Chinese High School Students

Mental health problems, like depression, have long been a subject that has attracted researchers from a diverse range of fields, including psychology and education. Depression could lead to worse academic performance and suicidal behaviours. In China’s background, high school students are in a critical stage of development and need to prepare for Gaokao. Keeping a high learning motivation is crucial. Previous research was focused on adults and university students and seldom looked at high school students. Also, the influence of depression on learning motivation is unknown. This research aims to find out the relationship between depression and learning motivation among Chinese high school students. The SDS and MAAT survey was used to gather data from high school students, and 171 students participated in the study. The SPSS was used to analyse data. Notably, results indicated a significant negative correlation between depression and learning motivation among Chinese high school students. Importantly, depression has a negative influence on learning motivation. Additionally, this research did not consider locations and other external effects on students. The results provided straightforward implications for schools, teachers, students and parents to prevent or intervene in students’ mental health problems to increase high school students' learning motivation.