Reviewerly/exHarmony huggingface dataset:
Data Security Access Control Based on Blockchain and Smart Contracts

With the development of the Internet and edge computing technology, many industrial Internet applications have emerged, followed by a large amount of data in the Industrial Internet. How to securely store and share this data has become a hot topic in current research. Firstly, a blockchain and IPFS based on-chain and off-chain storage architecture is proposed, which detects data through machine learning algorithms and stores data without anomalies. Then, the roles of users were classified and various smart contracts were designed for registering users, deleting users, and managing roles and their permissions. Finally, an experimental platform was built through open-source software to verify the feasibility of the proposed scheme and realize the safe storage and sharing of normal data.
Cooperative search for moving targets with the ability to perceive and evade using multiple UAVs

This paper focuses on the problem of regional cooperative search using multiple unmanned aerial vehicles (UAVs) for targets that have the ability to perceive and evade. When UAVs search for moving targets in a mission area, the targets can perceive the positions and flight direction of UAVs within certain limits and take corresponding evasive actions, which makes the search more challenging than traditional search problems. To address this problem, we first define a detailed motion model for such targets and design various search information maps and their update methods to describe the environmental information based on the prediction of moving targets and the search results of UAVs. We then establish a multi-UAV search path planning optimization model based on the model predictive control, which includes various newly designed objective functions of search benefits and costs. We propose a priority-encoded improved genetic algorithm with a fine-adjustment mechanism to solve this model. The simulation results show that the proposed method can effectively improve the cooperative search efficiency, and more targets can be found at a much faster rate compared to traditional search methods.
Novel Delivery Model with the Combination of Iterative and Sequential Models

The paper proposes a novel delivery model for an e-Learning project with an adaptation of benefits of the both iterative and sequential models by eliminating drawbacks of the both models. The sequential model is a chronological delivery model with distinct phases of initiation, planning, executing, monitoring, controlling and closure suitable for projects where the requirements are well-defined and stable, and where changes to the requirements are unlikely. The Sequential model may not be suitable for projects where the requirements are not clear or likely to change, as changes to the requirements may require changes to previous phases. The Iterative model which emphasizes flexibility, collaboration, and customer satisfaction. The iterative model is well-suited, where the requirements are not well-defined and likely to change, as the iterative and incremental approach allows for flexibility and adaptability. The iterative model may not be suitable for projects where there are strict deadlines or where the requirements are very complex and cannot be easily broken down into smaller iterations. To address the drawbacks of both models, the paper proposes a unique blended model with the best of linear and iteration approaches with the collaboration of Delivery and Practice with POD teams, Sprint 0 and gets the delivery expeditiously. The proposed model is a fine blend of best of both models by eliminating shortfalls of the Iterative and Sequential models.
Spatial and linguistic mobility in the narrative of a transnational speaker between Peru and Italy

The aim of this paper is to explore the language-space relationship by analysing the way Latin American transnational speakers experience mobility processes and the way these shape their linguistic practices and identities. Namely, the focal point of this study is the way spatial and sociolinguistic bordering are constructed and conceived by analysing the narrative of a transnational woman who experienced back and forth migration between Peru and Italy with her family. I will analyse this aspect from a theoretical point of view in the first part of the paper, where I will refer to that part of the literature that, while studying the relationship between language and migration, addresses questions about how language ideologies, the centre-periphery dynamics, as well as the material conditions of diaspora influence language practices and the construction of social identity in contemporary societies. I will then explore these epistemological aspects through the voice of a Peruvian transnational woman relating the social and sociolinguistic cross-bordering she experienced with her family through her spatial mobility.
The Effect of Digital Journalism Training on Media Sub Domain PR FM and PR MN on Cognitive Improvement of the Editorial Crew

Handling hoaxes is the responsibility of journalism, and Digital Journalism must act as a beacon of accurate information in society. Ethical and reliable journalism fosters a healthy cycle of information dissemination. The rapid and widespread accessibility of information on social media inundates the public with unverified content. Mainstream media outlets have adopted strategies to maintain speed while upholding the quality of their coverage. One effective approach employed by online media is the media incubator strategy, where subdomains are developed under the main domain to swiftly gather information from various regions, including PR FM and Minds of the People Media Networks (PRMN). Consequently, the Community Service Program (PKM) with a focus on editorial targets in media subdomains at PR FM and PRMN becomes vital. This initiative involved providing training by technology practitioners, press professionals, and academics from Fikom Unisba. The training emphasized online and digital journalism, with a strong emphasis on journalistic ethics in news and feature writing, as well as utilizing technology for news reporting. The results of this activity demonstrated a significant improvement in the cognitive insight of the journalists, as evident from the difference in pre-test and post-test scores conducted before and after the training.&#x0D; Keywords: digital journalism, subdomain media, journalists
An ontology-based machine tool serving framework in industrial Internet environment

The number of machine tools in the industrial internet environment is large, and manufacturing process will generate a large amount of data. To realize the networked, service-based control and collaborative sharing of machine tools is an application trend in the industrial Internet. However, the problems including data heterogeneity, how to provide production services provide, and privacy protection need to be solved first. In this paper, we propose an ontology-based machine tool serving framework (OBMSF) to solve the above issues. First, an ontology and a data storage model are used to solve the data heterogeneity problem while protecting the data privacy in the industrial internet. Then, through semantic inference rules, a production service for the monitoring of abnormalities in the machine tool operation is provided. Finally, the privacy protection problem is solved by a privacy protection program that dynamically changes the privacy rules of the machine tools in the industrial Internet.
Main Canopy of Islamic Complex, Putrajaya, Malaysia – A Testimony to Excellent Performance of PTFE Membrane under Tropical Climate

This paper describes a landmark membrane structure where the surface appearance has been able to be maintained after years in service. The membrane structure is the main canopy structure located at the courtyard of Islamic Complex, Putrajaya, Malaysia. It is made using PTFE membrane which is known for its excellent durability. The construction process of the main canopy together with the mini canopy which consists of a group of four funnel-shape membrane structure made using PVDF membrane will be presented in this paper. The paper will also touch on the performance of PTFE membrane in humid tropical weather condition like Malaysia.
Topic Modeling for Skill Extraction from Job Postings

With an increase in the number of online job posts, it is becoming increasingly challenging for both job searchers and employers to navigate this large quantity of information. Therefore, it is crucial to use natural language processing techniques to analyze and draw inferences from these job postings. This study focuses on the most recent job postings in Turkiye for Computer Engineering and Management Information Systems. The objective is to extract skills for the job postings for job seekers wanting to apply for a new position. LSA is a statistical method for figuring out the underlying characteristics and meanings of sentences and words in natural language. Frequency analysis was also performed in addition to the LSA analyses with the goal of conducting a thorough examination of job postings. This study was conducted to determine and evaluate the skills that the sector actually needs. Thus, job seekers will have the chance to develop themselves in a more planned way. The findings indicate that the job postings for the two departments reflect various characteristics in terms of social and technical abilities. A higher requirement for social skills is thought to exist in the field of Management Information Systems rather than Computer Engineering. It has been discovered that employment involving data have been highly popular in recent years, and both departments often list opportunities involving data analysis.
Faktor Kerentanan Banjir Rob Kecamatan Medan Belawan Kota Medan

The rise in sea level has impacted the emergence of a flood disaster in the district of Belawan City Field. The flooding of these robs caused the sinking of settlements, industries and ports. The height of flooding that increases every year has an impact on the increased shortages caused and increased economic losses of communities working in vulnerable sectors. Therefore, vulnerability factors are necessary to determine effective adaptation directions. The research was carried out in the district of the Field of Belawan City of Medan where there are 6 (six) floods affected by the flood of rob: the Kelurahan Belawa I, Belawana II, Blawan Bahari, Balawan Pulau Sicanang and Bagan Deli. The study begins with the theoretical and empirical formulation related to vulnerability to flood areas followed by interviews with stakeholders aimed at finding issues more openly in which the interviewed parties are asked for opinions. The stakeholder used is the stakeholder who knows the phenomenon of the flooding of rob that occurs in the research area consists of 3 (three) groups representing the government and society. Factors affecting the level of vulnerability to flooding in the area are grouped into physical, social, economic and environmental vulnerability factors.
Real-time 3D shape measurement of dynamic scenes using fringe projection profilometry: lightweight NAS-optimized dual frequency deep learning approach

Achieving real-time and high-accuracy 3D reconstruction of dynamic scenes is a fundamental challenge in many fields, including online monitoring, augmented reality, and so on. On one hand, traditional methods, such as Fourier transform profilometry (FTP) and phase-shifting profilometry (PSP), are struggling to balance measuring efficiency and accuracy. On the other hand, deep learning-based approaches, which offer the potential for improved accuracy, are hindered by large parameter amounts and complex structures less amenable to real-time requirements. To solve this problem, we proposed a network architecture search (NAS)-based method for real-time processing and 3D measurement of dynamic scenes with rate equivalent to single-shot. A NAS-optimized lightweight neural network was designed for efficient phase demodulation, while an improved dual-frequency strategy was employed coordinately for flexible absolute phase unwrapping. The experiment results demonstrate that our method can effectively perform 3D reconstruction with a reconstruction speed of 58fps, and realize high-accuracy measurement of dynamic scenes based on deep learning for what we believe to be the first time with the average RMS error of about 0.08 mm.
Improved YOLOv3 algorithm for small object detection in remote sensing images

Aiming at the low detection performance of YOLOv3 algorithm in remote sensing image small target detection, an improved YOLOv3 remote sensing image small target detection algorithm is proposed. Firstly, the problem of insufficient feature extraction of small targets is solved by reconstructing the residual block of darket-53. Secondly, a shallow feature enhancement module is proposed, which expands the receptive field of the shallow feature map and enriches the small target feature information of the shallow network by using the multi-branch cavity convolution structure and attention mechanism. Finally, the EIOU loss function is used to replace the target location loss function in the original algorithm, which makes the model converge quickly and reduces the rate of missing detection. The experimental results show that the improved algorithm has a good detection effect on VisDrone dataset. mAP@0.5 saw a 3.3 percent increase compared to YOLOv3. This shows that the improved algorithm can detect small targets in remote sensing images more effectively.
Analysis of printed document identification based on Deep Learning

In this study, we investigate the effectiveness of ResNet, a deep neural network architecture, for a deep learning approach to address the problem of printed document identification. ResNet is known for its ability to handle the vanishing gradient problem and learn highly representative features. Multiple variations of ResNet have been applied, including ResNet50, ResNet101, and ResNet152, which provide the backbone architecture of our classification model and are trained on a comprehensive dataset of microscopic printed images containing some microscopic printing patterns from various source printers. We also incorporate Mix-up augmentation, a technique that generates virtual training samples by interpolating pairs of images and labels, to further enhance the performance and generalization capability of the model. The experimental results showed that ResNet101 and ResNet152 variants outperformed in accurately distinguishing printer sources based on microscopic printed patterns. We developed a mobile app to test the feasibility of our findings in practice. In conclusion, this study aims to lay the groundwork for creating a sufficiently pre-trained model with accurate performance of identification that can be deployed on mobile devices to detect the printed sources of documents.
Exploring MediaPipe optimization strategies for real-time sign language recognition

The present study meticulously investigates optimization strategies for real-time sign language recognition (SLR) employing the MediaPipe framework. We introduce an innovative multi-modal methodology, amalgamating four distinct Long Short-Term Memory (LSTM) models dedicated to processing skeletal coordinates ascertained from the MediaPipe framework. Rigorous evaluations were executed on esteemed sign language datasets. Empirical findings underscore that the multi-modal approach significantly elevates the accuracy of the SLR model while preserving its real-time capabilities. In comparative analyses with prevalent MediaPipe-based models, our multi-modal strategy consistently manifested superior performance metrics. A distinguishing characteristic of this approach is its inherent adaptability, facilitating modifications within the LSTM layers, rendering it apt for a myriad of challenges and data typologies. Integrating the MediaPipe framework with real-time SLR markedly amplifies recognition precision, signifying a pivotal advancement in the discipline.
Youtube tutorial videos on developing math competencies in high school students: A theoretical review

Education is one of the substantial factors for the development of a country, this will be achieved if the educational actors promote educational actions in order to raise the level of development of mathematical competences and consequently the improvement of the teaching-learning processes in the educational institutions; For this reason, from an innovative perspective, it is necessary to implement technological resources in the pedagogical work in order to strengthen the preparation of students; in this sense, technological tools such as YouTube video tutorials oriented to pedagogical practice require a reflective and coherent analysis that make it possible to enhance the mathematical competences of secondary education students. The research is a theoretical review of scientific literature, using the method of analysis and synthesis, exhaustive search of materials such as thesis reports, scientific articles, specialized literature, databases and information platforms related to the subject of study. The design was non-experimental, the documentary analysis technique was applied, resorting to information bases. The general objective was to analyze the strategy of YouTube video tutorials in the development of mathematical competences in high school students.
An augmented reality collaborative experiment: evaluation of effectiveness for train remote maintenance tasks.

Recently, the applications of augmented reality (AR) technology in train remote maintenance have attracted much attention from researchers. However, the train remote maintenance is a collaborative task for teams, and the transformation of collaborative methods brought about by AR has a significant impact on user’s performances in the task. In this work, an experiment was used to compare the performance differences between AR remote cooperation and traditional social software remote cooperation in train remote maintenance tasks. Social presence, collaborative availability and system availability affected by the two collaboration methods were analyzed. The results indicate that team members are more inclined to use AR device for remote collaboration in train remote maintenance tasks, and the social presence collaborative availability and system availability of the AR devices are better than traditional remote social methods. These results show that the AR technology has significant application advantages in remote train maintenance and provide a reference for the design of AR remote maintenance systems.
Implementation and Benchmarking of Kubernetes Horizontal Pod Autoscaling Method to Event-Driven Messaging System

The rapid development of technology is accompanied by a large number of demands and users. One of the solutions is to create reliable infrastructure that can support application performance. Kubernetes is a platform which offers one of the solutions by providing container application orchestration and enabling high availability and scalability through various automatic scaling mechanisms such as Horizontal Pod Autoscaler (HPA), which dynamically scales the number of pod resources without restarting the entire system. By default, Kubernetes only monitors built-in resource metrics such as CPU and memory usage for each host machine and pod. To use Custom Metrics, external software such as Prometheus can be employed to monitor metrics as needed. On the other hand, Kubernetes Event-driven Autoscaling (KEDA) software offers a solution to simplify and facilitate autoscaling by efficiently implementing event-based automatic scaling and achieving the scale-to-zero capability. In this paper, we investigate the performance of autoscaling through various experiments to understand the behavior of each custom metrics autoscaling solution.
Hand gesture recognition via deep data optimization and 3D reconstruction

Hand gesture recognition (HGR) are the most significant tasks for communicating with the real-world environment. Recently, gesture recognition has been extensively utilized in diverse domains, including but not limited to virtual reality, augmented reality, health diagnosis, and robot interaction. On the other hand, accurate techniques typically utilize various modalities generated from RGB input sequences, such as optical flow which acquires the motion data in the images and videos. However, this approach impacts real-time performance due to its demand of substantial computational resources. This study aims to introduce a robust and effective approach to hand gesture recognition. We utilize two publicly available benchmark datasets. Initially, we performed preprocessing steps, including denoising, foreground extraction, and hand detection via associated component techniques. Next, hand segmentation is done to detect landmarks. Further, we utilized three multi-fused features, including geometric features, 3D point modeling and reconstruction, and angular point features. Finally, grey wolf optimization served useful features of artificial neural networks for hand gesture recognition. The experimental results have shown that the proposed HGR achieved significant recognition of 89.92% and 89.76% over IPN hand and Jester datasets, respectively.
FSTT: Flow-Guided Spatial Temporal Transformer for Deep Video Inpainting

Video inpainting aims to complete the missing regions with content that is consistent both spatially and temporally. How to effectively utilize the spatio-temporal information in videos is critical for video inpainting. Recent advances in video inpainting methods combine both optical flow and transformers to capture spatio-temporal information. However, these methods fail to fully explore the potential of optical flow within the transformer. Furthermore, the designed transformer block cannot effectively integrate spatio-temporal information across frames. To address the above problems, we propose a novel video inpainting model, named Flow-Guided Spatial Temporal Transformer (FSTT), which effectively establishes correspondences between missing regions and valid regions in both spatial and temporal dimensions under the guidance of completed optical flow. Specifically, a Flow-Guided Fusion Feed-Forward module is developed to enhance features with the assistance of optical flow, mitigating the inaccuracies caused by hole pixels when performing MHSA. Additionally, a decomposed spatio-temporal MHSA module is proposed to effectively capture spatio-temporal dependencies in videos. To improve the efficiency of the model, a Global–Local Temporal MHSA module is further designed based on the window partition strategy. Extensive quantitative and qualitative experiments on the DAVIS and YouTube-VOS datasets demonstrate the superiority of our proposed method.