Reviewerly/exHarmony huggingface dataset:
A REST API Based on Machine Learning to Predict Survival Using Categorical Features

Survival analysis is an area of statistics that deals with the study of the time it takes for an event of interest to occur, usually applying mainstream statistical techniques. The appearance of the Big Data phenomenon and the digitization of medical information lead to the generation of huge amounts of information that require optimal processing. For this, it is necessary to apply new algorithms, such as machine learning techniques. This article introduces a REST API based on machine learning to predict survival using categorical features.
MicroFaaS on OpenFaaS: An Embedded Platform for Running Cloud Functions

Function-as-a-Service (FaaS) platforms present a new cloud computing paradigm by enabling serverless deployment and execution of functions. MicroFaaS, a cost-effective and energy-efficient datacenter architecture, replaces x86-based rack servers with ARM-based single-board computers (SBCs). This paper focuses on enhancing MicroFaaS by incorporating OpenFaaS, a popular secure function building and deployment framework. Through extensive experimentation, MicroFaaS on OpenFaaS showcases improved energy efficiency, ease-of-use, and scalability over traditional cloud systems.
Semantic segmentation of lightweight unmanned aerial vehicles in sea scenes

The existing semantic segmentation algorithms are mainly applied to road scenes, while in the sea special situations such as the long-range strip shape of ships, blurred boundaries of the area to be segmented, and wind and wave disturbances can not be ignored. In addition, the computing power and data storage memory space of drone onboard systems are limited. Traditional semantic segmentation algorithms have certain limitations and can not meet the requirements of drone semantic segmentation for scenes in the sea: lightweight, real time and accuracy. This paper is based on the junior framework of DeepLabV3+ and proposes a lightweight unmanned aerial vehicle (UAV) semantic segmentation of sea scenes-light Multi-scale attention model(LSMA) which features parallel multi-scale attention feature fusion. The segmentation not only achieves the goal of real-time and accuracy of image processing but also ensures the overall lightweight of the model. Firstly, the optimized MobileNetV2 is used in the backbone network, greatly reducing the number of network parameters. Secondly, a lightweight channel and location attention mechanism are introduced to process the deep semantic features transmitted from the backbone network in parallel with the hollow convolutionn space pyramid module in order to improve the network’s ability to segment the boundary between the sea target and the sea surface with similar features. The experimental results show that compared to traditional neural networks, the proposed network can significantly reduce the number of network parameters and system overhead while ensuring high-precision image segmentation. This model on the updated Vocdevkit dataset achieves a MIoU of SO.73%, mPA of 90.44%, and accuracy of 94.68%. The processing time for a single 512x512 image is only 225ms, 43.75% higher than that of traditional networks.
3D reconstruction of single-image based on deep learning and fringe projection profilometry

Due to the complicated realization process, the traditional three-dimensional (3D) reconstruction method of structured light gradually fails to meet the needs of actual production and complex scenes. The combination of fringe projection profilometry and deep learning effectively improves the situation. Classical neural network models have gradually shown their unique advantages in the field of 3D reconstruction. Although the existing reconstruction methods have been improved in different aspects, they still have the problems of complex data set production and low reconstruction accuracy, so they are difficult to be applied to the actual 3D measurement. On this basis, a virtual 3D measurement simulation system based on fringe projection profiling is built to generate batch training data, simplifying the actual data collection process. And used the traditional fringe projection profilometry to rebuild the model as the subsequent ground truth, to verify the effectiveness of the virtual data set. In this paper, the phase information is taken as the target, a multi-scale feature fusion convolution neural network is used to transform a single fringe image into multiple single frequency phase shift images, then the single frequency phase shift images used as input to get the fringe order. In this way, 3D reconstruction of complex objects can be realized, which simplifies the complicated calculation process of traditional methods. After a large number of experiments, the proposed method is proved to be more accurate and efficient than the existing methods.
Data fusion of line structured light and photometric stereo point clouds based on wavelet transformation

Achieving high accuracy and fine-grained three-dimensional (3D) results has been an important research direction in visual measurement. Line Structured Light Sensor (LSLS) has the advantage of high accuracy but lacks clarity. Conversely, Photometric Stereo (PS) provides higher clarity but lower measurement accuracy. To address this issue, a novel method is proposed which fuses the point clouds from both LSLS and PS by use of two-dimensional wavelet transformation. The object is firstly measured by use of the LSLS and PS methods separately to obtain point cloud data. The surface points are then matched, and the measurement results are decomposed using wavelet transformation. The corresponding low-frequency and high-frequency information can be obtained. Subsequently, the low-frequency information from LSLS and the high-frequency information from PS, at suitable scales, are selected for reconstruction. Fused result through this process ensures high measurement accuracy and clarity. Experiments also validate the effectiveness of this method.
Strategies and Forms of Communication Writing The Novel "Cold Baby” By Witzlu on The Online Novel Digital Platform

This research explores the strategies and forms of communication employed in the creation of the novel "Cold Baby" by Witzlu on the online digital novel platform. Language plays a crucial role in human communication, and the written language is a significant medium for conveying ideas and information. Writing skills, particularly in literature, are essential but often lack adequate publication and promotional strategies, hindering the writer's impact. Effective novel writing strategies encompass understanding the target audience, selecting a unique genre, choosing a compelling plot, crafting strong characters, creating engaging conflicts, using narrative techniques, employing dialogue effectively, and varying language styles. In the digital age, writers have embraced online platforms for publishing and promoting their works, reaching a wide readership and contributing to the growth of Indonesian literature in digital form. The research aims to shed light on these strategies, inspiring aspiring writers to create quality digital literature and enrich Indonesia's literary heritage.
VULDAT: Automated Vulnerability Detection from Cyberattack Text

The existing literature on the connection between attacks and system vulnerabilities often relies on manual techniques. We have developed an approach, called VULDAT, to automatically identify software vulnerabilities and weaknesses from the text of an attack by leveraging the information contained in MITRE repositories and datasets that contain descriptions of attacks and information about attack methods, as well as code snippets that describe the related weaknesses of vulnerabilities. Thus, this research focuses on analyzing attack text descriptions and predicting their effects using natural language processing (NLP) and machine learning techniques. This can be helpful to quickly examine new attacks found in the real world and assist security experts in taking the appropriate actions.
Redundant node identification using Siamese neural network to enhance the availability in visual sensor network

Summary Many monitoring applications related to surveillance, tracking and multipurpose visual monitoring have taken into consideration the use of wireless visual sensor networks. When sensors are deployed over a monitored field that could potentially damage the monitoring capability and availability in the visual sensor network. In order to overcome these issues, faulty nodes are identified and replaced by redundant node based on Siamese network in this research. Initially, camera nodes are randomly deployed in the visual sensor network, and the data are received from the network through gateway. To identify the redundant nodes, initially, the frames are divided into equivalent time slot, and then, Siamese network is utilized to identify the redundant nodes in a network. Siamese neural network is type of convolutional neural network that is utilized to recognize the similar images in the network. After that, faulty nodes are identified based on some parameters such as entropy, energy, transmission delay and network coverage. If the average energy, entropy, transmission delay and network coverage are below the threshold value, then the node is identified as faulty node. Finally, replace the faulty node with redundant node to enhance the availability in the visual sensor network for critical monitoring applications. The simulation analysis shows that the developed approach takes 772 s to identify redundant node and take 27 s to identify fake nodes and the developed method is executed at 1308 s. Thus, this prediction model helps to improve the coverage quality of target‐based monitoring in order to achieve availability.
THE USE OF FLASHCARD GAME IN IMPROVING STUDENTS’ READING COMPREHENSION OF TENTH GRADE STUDENTS

This research concentrated in the use of flashcard game in improving students’ reading comprehension. The objective of this research were: (1) to find out students’ reading comprehension scores before taught using the flashcard game as learning media, (2) to find out students’ reading comprehension scores after taught using the flashcard game as learning media, (3) to discover the significant different of using flashcard game in improving students’ reading comprehension. This research used quantitative pre-experiment research design. This research used one group pre-test and post-test design. The procedure of data collection were test and treatment. This research used SPSS to analysis the data to know whether there was a significant difference on students’ reading comprehension before and after taught using flashcard game as the teaching media. The result showed that the students reading comprehension score before taught using the Flashcard Game is 60,27 and the students reading comprehension score after taught using the Flashcard Game is 86,25. The result of the hypothesis testing showed that the value of the t-test was greater than the value of the t-table (10,834 &gt; 1,689). It means H0 is rejected and H1 is accepted. The result of the research showed that the there was an improvement of the students’ reading comprehension by using the flashcard game as the teaching media for tenth grade students.
SBFT: A BFT consensus mechanism based on DQN algorithm for industrial Internet of Thing

With the development and widespread use of blockchain in recent years, many projects have introduced blockchain technology to solve the growing security issues of the Industrial Internet of Things(IIoT). However, due to the conflict between the operational performance and security of the blockchain system and the compatibility issues with a large number of IIoT devices running together, the mainstream blockchain system cannot be applied to IIoT scenarios. In order to solve these problems, this paper proposes SBFT (Speculative Byzantine Consensus Protocol), a flexible and scalable blockchain consensus mechanism for the Industrial Internet of Things. SBFT has a consensus process based on speculation, improving the throughput and consensus speed of blockchain systems and reducing communication overhead. In order to improve the compatibility and scalability of the blockchain system, we select some nodes to participate in the consensus, and these nodes have better performance in the network. Since multiple properties determine node performance, we abstract the node selection problem as a joint optimization problem and use Dueling Deep Q Learning (DQL) to solve it. Finally, we evaluate the performance of the scheme through simulation, and the simulation results prove the superiority of our scheme.
Improving the Science blog &amp;#8211; The Rogue Scholar service.

Some 13 years ago, I speculated about the longevity of the type of science communication then (and still now) represented by Blogs. I noted one new project called ArchivePress that was looking into providing solutions equivalent to what scientific journals have done for some 350 years of science communication. The link to ArchivePress no longer works, but details of the project can still be found here.
Meta-Trainer: An Augmented Reality Trainer for Home Fitness with Real-Time Feedback

Meta-Trainer allows people to train on their own, supervised by a virtual trainer that provides exercise samples and real-time feedback. The system relies on a set of smart glasses for Augmented Reality (AR), to enable users to interact with the virtual trainer in a hybrid environment. Given a target exercise, the virtual trainer demonstrates the movements to be reproduced. Differently from video-based applications, the user can move around the virtual trainer so as to look at the exercise from different angles for a more immersive and realistic experience. The AR glasses can also track head movements, which are then used to give feedback and track the training progress. This system offers an advancement compared to video-only trainers, not requiring the user to look at a screen while performing the exercise.
Price Prediction of Ethereum Using Blockchain Historical and Exchange Data by Supervised Machine Learning Algorithms

Predicting or forecasting crypto currency prices is now one of the most difficult tasks in crypto market trading due to its qualities and dynamic nature. The purpose of the present work is to analyse the exchange and blockchain data and develop a prediction model using machine learning. Ethereum (ETH) is one of the crypto currencies, and data has been taken from the price time series from January 1, 2017 to December 31, 2021, on a daily basis. The algorithm for gathering data has been trained and tested using a machine-learning algorithm. The adequacy of the developed machine learning models was validated using MAPE, RSME, MAE, and R2 scores. The developed model can predict future results with an accuracy of up to 85% for 7 days. Based on the findings, it is suggested that blockchain historical data and exchange data can be utilised as input characteristics in the development of a machine learning model to forecast Ethereum's future price.
INQUIRY TRAINING BASED E-MODULE DEVELOPMENT IN SCIENCE SUBJECTS TO IMPROVE HOTS LITERACY ABILITY OF CLASS IV SCHOOL STUDENTS BASE

The purpose of this study was to determine the feasibility and effectiveness of Inquiry Training-based E-Modules in science subjects to improve HOTS literacy skills for Class IV Elementary School students. This research is a type of research and development (Reasearch and Development) using the ADDIE model ( Analysis, Design, Development, Implementation, Evaluation ). The results showed that the E-Module based on Inquiry Training in science subjects to improve HOTS literacy skills was "Very Eligible" with details of the average score given by design experts 4, 4 in the "Very Good" category with a feasibility presentation of 88 % with “Very Eligible” category. The material expert validation results were 4.56 in the "Very Good" category and the feasibility presentation rate was 91% in the "Very Eligible" category. And based on the results of the pre-test and post-test there was an increase in presentation of 29.3 %. Initial pre-test results were 5 4.5 % and post-test results were 8 4 %. Therefore it can be concluded that Inquiry Training-based E-Modules in science subjects to improve HOTS literacy skills are appropriate and effective for use in the learning process
INCREASING STUDENT PARTICIPATION THROUGH LEARNING CONCEPT MAP OF EARLY CHILD EDUCATION STUDENTS

This research is Classroom Action Research (CAR). The research was conducted for 2 cycles with 3 meetings. Methods of data collection using written tests, observation sheets to observe the participation of Early Childhood Education students.The results showed that there was an increase in Student Participation Through Concept Map Learning for Early Childhood Education students. In cycle I it was 52.50% and in cycle II it was 75.83%. So, from cycle I to cycle II, students' reading comprehension ability increased by 23.33%. While the increase in student participation through learning concept maps for Early Childhood Education students in cycle I was 41.67% and in cycle II was 75%. So from cycle I to cycle II it increased 33.33%. Therefore it can be concluded that Concept Map learning can increase student participation in early childhood education.
Trusted Multimodal Socio-Cyber Sentiment Analysis Based on Disentangled Hierarchical Representation Learning

The rapid development of the digital age has led to a qualitative leap in social media. To meet the cognitive needs of users, social media platforms have been mining users’ private information and disseminating information through various means. However, these platforms lack effective management of information release and various forms of emotional expressions make public propaganda increasingly diverse and complex. Therefore, accurately identifying the relationships between multimodal data poses a challenge. An effective modal representation must consider both the consistency of multimodal data and the complementarity of single-modal data. However, existing methods focus on fusing different modal features into a unified feature representation, while neglecting to evaluate the reliability of prediction results. In this article, we disentangle the consistency and complementarity in the fused representation problem of multimodal data. We construct the modal private task (unique) by using the Dirichlet distribution and evidence theory to solve the uncertainty of each modal prediction. The model can output the uncertainty of prediction and learn complementary information through the fusion of decision layers. At the same time, we construct the modal common task using a low-rank tensor fusion model to learn consistent features. Finally, we compare the model with the current mainstream methods on three public datasets, and the experimental results show that the performance of our method reaches the level of current advanced algorithms.
Stereo matching method based on high-precision occlusion-recovering and discontinuity-preserving

Stereo matching is the core of machine vision, such as 3D reconstruction, posture perception and driverless technology in the future etc. Currently, there are still great difficulties in improving the matching accuracy of disparity discontinuity and occlusion regions. In this paper, we propose a new high-precision occlusion-recovering and discontinuity-preserving method for stereo matching. We focus on disparity refinement, and present an adaptability-based disparity reconstruction that achieves accurate occlusion-recovering by fusing color aberration, distance, and disparity value. For discontinuity-preserving, disparity-based secondary guided filtering is proposed, then introduce adaptive image enhancement and adaptive Canny edge detection to correct errors in discontinuous regions. The experiments on Middlebury standard dataset show that our algorithm has high overall accuracy, especially in the disparity discontinuity and occlusion regions. This novel method is more direct, efficient, and less sensitive to the accuracy of disparity estimation, and the live-action shooting shows the robustness of our algorithm. The method has the prospect of civil and engineering use, and presents scientific reference for high-precision target identification, target tracking, 3D measurement, etc.
Digital Transformation Of Subak Management In Bali Through GIS Implementation

Subak is a customary law society with socio-agrarian-religious characteristics, consisting of a group of farmers who manage the irrigation of rice fields or paddies. The agricultural irrigation system in Bali or Subak was officially recognized as one of the world's cultural heritages in 2012 by UNESCO. Many Subak data still rely on traditional recording systems, making it crucial to have a digital transformation using an information system platform capable of collecting data on Subak and providing related information about Subak in the Bali Province. To address this problem, a mobile-based Geographic Information System (GIS) that encompasses information about Subak is developed. The data collection method used in this research includes literature studies and questionnaires. The result obtained from this research is an Android mobile application tested using Black Box Testing. All tests were successfully met according to the testing criteria that have been established.&#x0D;
Predicting API Expertise: A Cross-Community Replication Using Zipf's Law

Experience and knowledge are essential and adequate conditions to assess developer expertise. In practice, while various mechanisms are used for assessing developer expertise, research has shown that existing mechanisms may not support the holistic evaluation of developer expertise or contain the complexity behind the development of that expertise (e.g., experience with individual APIs and programming concepts). Prior research has shown that developer contributions on platforms like GitHub and StackOverflow can provide personal insights into broader developer expertise. This paper explores the potential for providing more granular assessments of developer expertise based on their interactions in these online programming communities by empirically analyzing developer contributions on StackOverflow and GitHub related to Python APIs. We found that techniques from prior work scale to support the automated ranking of developers based on their experience with a given Python API. Our findings suggest that we can use syntax patterns in written language and code to detect and rank developers' low-level technical expertise accurately.