Reviewerly/exHarmony huggingface dataset:
Secured Workflow Scheduling Techniques in Cloud: A Survey

Scheduling a scientific workflow in a cloud computing environment is a critical issue that is widely discussed in the literature, and since security is one of the most important user concerns, researchers were constantly looking for the most efficient techniques to solve the problem while taking into account the security and privacy of data and tasks. Thus we introduce in this paper, a literature review, taxonomy and comprehensive analysis of the research works that investigate the mentioned problem. Additionally, we highlight and discuss limitations, gaps and problem aspects that are not sufficiently investigated in the literature, as well as future research challenges.
Automatic Driving Vehicle Path Planning Based on Deep Convolution Neural Network

Autonomous vehicles need to keep a certain safe distance from obstacles. Aiming at the problem of over fitting phenomenon in obstacle recognition, which leads to the reduction of path search performance, a path planning method of autonomous vehicle based on deep convolution neural network is proposed. The two-dimensional environment model is used to describe the surrounding environment of the vehicle, establish the automatic driving structured road, and generate the virtual local target points. The deep convolution neural network is used to identify obstacles. The convolution layer maps the input data to the feature space, and the full connection layer maps the feature space to the sample label space through linear transformation, that is, the final output sample category, to complete the detection of virtual target points. Use the information of the target point to search the path towards the target, remove the obstacle node through the collision check function, judge whether the expansion node has reached the target area, and realize the path planning. The simulation results show that the average search time, the average number of search nodes and the number of successful searches of the automatic driving vehicle path planning method based on deep convolution neural network proposed in this paper are better than those based on genetic algorithm and SVM. The design method has significantly improved the search speed and efficiency.
Human motion abnormal posture monitoring method based on Intelligent Bracelet

In order to overcome the problem of low classification accuracy and monitoring accuracy of sample data in traditional abnormal posture monitoring methods, a new human motion abnormal posture monitoring method based on Intelligent bracelet is proposed in this paper. Firstly, the structure of smart Bracelet sensor is analyzed, and Gabor transform method is used to collect human motion data. Secondly, based on the data acquisition results, the appearance model of abnormal posture monitoring of human motion is constructed, and the output result of the model is the abnormal posture monitoring result of human motion. Finally, a comparative verification experiment is carried out. The experimental results show that compared with the traditional monitoring methods, the sample data classification accuracy of this method is higher, and it can accurately monitor the abnormal posture of human movement.
Design of personalized tourism route recommendation system based on knowledge graph

With the development of tourism, the multiple values of recommendation system are reflected. When too many people are online, the system response time is long. To solve these problems, a personalized tourism route recommendation system based on knowledge graph is designed. Hardware part: through digital power supply to obtain analog power, ensure the full range of voltage consistent, connected to the relevant components; Software part: build user interest model, classify different neighbor node structure, use knowledge graph to optimize the scenic spot entity alignment process, obtain candidate site set, comprehensively calculate scenic spot recommendation score, design personalized tourism route recommendation system software storage function. Test results: The average response time of the personalized tourism route recommendation system in this paper and the other two systems were 11.260ms, 17.715ms and 18.129ms respectively. The results show that the application effect of personalized tourism route recommendation system is better after making full use of knowledge graph.
Exploration and Application of the Peri-anesthesia Nursing Management Mode of the Five-Sphere Integrated Plan

Objectives: The objective of this research was to test the application of peri-anesthesia nursing management (anesthesia preparation room, operating room, recovery room, post anesthesia visit, and anesthesia general management) on hospital services. Methods: We used a quantitative research design to collect data from 311 nurses, with structural equation modeling used for data analysis. Results: The application of the peri- anesthesia nursing management mode of the five-sphere integrated plan impacted hospital services for patients. Conclusion: The practice of the five-sphere integrated mode not only highlighted the professional characteristics of anesthesia nursing, but also ensured the quality of medical treatment of patients during peri-anesthesia, and provided new ideas and references for nursing management of other specialties.
Pengembangan Bahan Ajar Matematika Berbasis Game Untuk Meningkatkan Minat dan Motivasi Belajar Siswa Dikelas Awal

The purpose of developing game-based mathematics teaching materials in the early grades is to increase student motivation and interest in learning and to be able to help students think logically in solving problems. And improve students' ability to apply difficult mathematical concepts. Because there are so many educators who focus more on learning mathematics that use learning resources from textbooks that contain material and formulas that do not explain real mathematics learning. thus causing boredom and boredom, it is necessary to apply the development of game-based mathematics teaching materials. Where there are several game development methods that need to be applied in the classroom: math duel and ice breaking. So that by developing this game method, students' interest and motivation in learning will be more enthusiastic and there will be no more boredom in the classroom. As well as increasing students' self-confidence in dealing with mathematical tasks or problems and increasing students' ability to work in teams or socialize with classmates.
Forschung: Vakuum in Forschung und Praxis 4/2023

Vakuum in Forschung und PraxisVolume 35, Issue 4 p. 12-20 Magazin Forschung: Vakuum in Forschung und Praxis 4/2023 First published: 15 August 2023 https://doi.org/10.1002/vipr.202370403AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat Volume35, Issue4August/September 2023Pages 12-20 RelatedInformation
Pembuatan Sistem Kebakaran Ruang Server Berbasis Web pada DISKOMINFO Kota Makassar

Development of Web-Based Fire Alarm System in Server Room at Diskominfo Makassar City. Diskominfo Makassar City is an institution that carries out some of the regional government affairs of Makassar City in the field of communication and informatics. Makassar City Diskominfo has a server room to handle services in the communications and informatics sector for sub-districts and villages in Makassar City. Making a web-based fire alarm is intended as a form of meeting server room security standards in terms of fire threats that can occur at any time and helping to supervise or monitor the server room. The working method or procedure for making this tool goes through the stages of analysis, design, implementation, and testing. The main components needed consist of a KY026 fire sensor, MQ2 sensor, ESP8266 microcontroller, and Laravel 7. This fire alarm tool utilizes Internet of Things (IoT) technology so that the tool can be connected to web applications. This tool detects indications of fire based on the flame wave indicator and carbon monoxide gas through the KY026 flame sensor and MQ2 sensor. Users of this tool will receive email notifications if there is a fire in the server room. It is easy for users to monitor server rooms by accessing web applications built using Laravel 7. This website is still local and can be accessed via a search engine with the address 192.168.43.244. This tool is expected to minimize damage and accelerate the handling of fire incidents in the server room. Testing of this tool was carried out on two components, namely the KY026 fire sensor and the MQ2 sensor. Based on the results of the tests that have been carried out, both components are able to detect fire indicators at certain distances. This is proven by the user receiving a fire notification when the component detects a wave of fire or carbon monoxide gas and the user can monitor the condition of the server room in real time.
The Mobile Application in Learning for People with Special Hearing Capabilities

The insertion of Augmented Reality (AR) and Virtual Reality (VR) in mobile applications has been a very indisputable fact in recent times, much more if this is dedicated in the educational field of people with special abilities including hearing, which requires a differentiated treatment with this resource to take advantage of their visual difficulty. Therefore, it is transcendental to obtain information to know the existing relationship within the teaching-learning process. The objective of the research was the development of a mobile application to strengthen the learning of students with special auditory abilities of the Specialized Educational Unit Dr. Camilo Gallegos Domínguez. The project has a quantitative approach, which helped us to analyze the data and a qualitative approach to know the degree of assimilation shown by the students about the mobile application. We used the Technological Acceptance Model (TAM) and Design to know the perception of the students by means of a survey as an instrument. The results showed a high degree of satisfaction towards the use of AR and VR in the mobile application as a learning method.
How to Teach Programming to Beginners in a Playful Way?

The teaching of informatics in most European countries begins at an early school age in the first grade of primary schools. The main topic and requirement of children's IT education is the development of IT literacy and digital culture, especially algorithmic and programming thinking to solve everyday problems of life in the 21st century. In different countries, a variety of educational tools and training programs are used in primary school computer science classes to develop the algorithmic and programming thinking of younger schoolchildren, and to develop their digital literacy as well. In addition, educational institutions have their platforms and websites or are engaged online in educational environments recommended by the leadership of schools and relevant ministries. Not all teachers who teach informatics are qualified and well prepared to use teaching aids and platforms of the new generation – that is, they lack the necessary digital literacy. Computer science teachers take appropriate training courses, and some of them are engaged in self-development and self-education in this field. The situation in teaching computer science in primary schools is complicated. Not only are there a lack of qualified teachers, but the schools do not even have the necessary technical and technological equipment. There is a lack of (electronic) teaching aids, high-quality and fast Internet connection, methodological guidance for teachers and access to teaching materials is insufficient. Often, if the school is well equipped with hardware and software, these resources are not used effectively. The biggest problems are in the area of ​​teaching programming. Many teachers involved in teaching this subject do not know how to program themselves. This article provides an overview of tools and possibilities for teaching programming in elementary school. Emphasis is placed on electronic support for teaching and learning, on various possibilities for teaching programming, on electronic and virtual tools, emulators and visualized simulation models, computer games that can be used to acquire basic knowledge and skills from programming, in a playful and entertaining way, both in school and out-of-school environments. Among the tools described are also several own applications that were developed at the authors’ university. Our experience proves that programmable games and robot kits can be adequately replaced with emulators and simulators for these devices. Another significant advantage of these is that they can also be used in distance learning. Programming in a simulation environment is just as fun as programming a real robot. The program compiled for robot control can also be tested with a visualized simulation experiment in the virtual space.
A Subword-Centric Character Level Representation for Named Entity Recognition

Named Entity Recognition (NER) is a fundamental task for many applications in natural language processing (NLP). Previous deep learning models for NER task mainly used word embeddings to capture word similarity, but fail to encode orthographic features in entities. Extending word embedding with character-level information can encode such information without relying on external resources. However, research on character-level models is still in a fairly early stage in the task of NER: current work simply uses standard CNN or RNN to capture character information. The starting point of our paper is that certain subwords (character n-gram) are useful for NER. Inspired by the success of attention mechanism widely used in NLP, we propose an attention-based convolution neural network to build a character-aware word representation to allow our model to automatically learn such subwords. We evaluate our proposed model using two NER corpora and find that our model can achieve state-of-the-art performance without relying on external resources. By visualizing the attention layer, we find that typical subwords in entities can be learned effectively in the proposed model.
A Bidirectional Encoder Representations from Transformers and CNN Based Prediction Model on Competitive Products

In this paper, the topic deals with Automatic Sales Leads Finding. The authors intended to find and classify the potential customers of a group of companies that have similar products. The authors propose a novel model and measurements to explore and find the potential customers of each brand. The model is based on bidirectional encoder representations from transformers and convolutional neural networks to classify potential customers. Eventually, two popular brands Apple and Samsung are selected to evaluate the model. Public tweets of these brands collected are designed in this paper with final accuracy above 75%.
ADAGIO, a BDI Music Recommender Telegram Chatbot

Within the digital market, music is the product that has generated the most revenue in recent years. One of the technologies that help make this happen are recommendation systems, which are used in most music distribution platforms. These tools are developed with the goal of gaining more users, neglecting the need to further improve recommendation and communication with the user. In this paper, we report a) the design of a Belief-Desire-Intention architecture for the implementation of an agent integrated with a messaging platform, which allows focusing on the user’s needs about music recommendation while it does remain as an independent system (without being part of any streaming service); and b) the successful integration of two platforms: Python AgentSpeak and Telegram.
Open-Domain Question Answering with Topic Clustering

The contents available online have enormously increased in the past years and still rapidly increasing each day. Even the best search engines struggle to find the right content and have difficulty to locate exact information to answer user queries. Information is dispersed within several documents across the internet making it hard for traditional search engines to locate it. The information retrieval (IR) problem is addressed through document retrieval in the first instance and current models do not look at the relevant context while retrieving the documents. This research proposes a new approach for IR through a combination of K-means clustering with the Random forest classifier to obtain a limited set of context which when used to retrieve relevant documents using the term frequency-inverse document frequency (TF-IDF) similarity gives better performance than the state-of-the-art models. The performance achieved is around 77% for the document retrieval part and an overall performance of 71% for the whole system using the pre-trained RoBERTa models.
Real Time Detection Of Waste Type Using Single Shot Multibox Detector

The lack of human initiative to manage their own wastes is one of many reasons why waste management in residential area is not optimal. A system to detect waste type in real time is a necessity to support the waste management process to be faster and optimal. This research propose the waste type detection systems using 2 types of Single Shot Multibox Detector models, SSD300 and SSD512. Both models were compared based on the accuracy and speed of detection on TACO dataset dan Waste Classification Data. SSD512 achieves a better accuracy of 0.63 mAP compared to the accuracy of SSD300, which is 0.57 mAP. Both models can also be said to be real time, with the SSD300's detection speed being faster at 51 fps compared to the SSD512's detection speed at 28 fps.
Cascaded 3D Object Segmentation with Volumetric Propagation Network

Deep convolutional neural networks have been widely adopted for automatic object segmentation of volumetric medical image as they are powerful tools for learning visual representations from images. However, it is still time-consuming to train and test the segmentation networks on the high dimensional volumetric data. In this paper, we design an efficient coarse-to-fine deep learning framework that can not only accelerate the segmentation process but also improve the accuracy. This is achieved by (a) training a segmentation network with down-sampled volumetric images, and (b) restoring the fine details of object via a refinement network. Specifically, we propose a novel deep learning building block, Volumetric Propagation Network (VPN), that can preserve the structure of objects via modeling the global pairwise relations between voxels. The module can be flexibly embedded into any type of convolutional networks for volumetric data processing. To illustrate its efficiency, the proposed approach is validated with the challenging task of segmenting ascending aorta from Computed Tomography Angiography (CTA) images. Our experiments show that the proposed cascaded network architecture outperforms state-of-the-art volumetric segmentation networks while being an order of magnitude faster.
Visual Question Answering in Bangla to assist individuals with visual impairments extract information about objects and their spatial relationships in images

With millions of speakers worldwide, Bangla is one of the most spoken languages in the world and as a result, a large number of people rely on Bangla as their primary medium of communication. Among them, a lot of individuals have visual impairments including but not limited to central vision loss, peripheral vision loss, and blurry vision. This poses several challenges to these individuals - one of them being extracting information from images. While techniques such as image captioning have been proposed to address this issue, visual question answering (VQA) offers a much in-depth and robust way to understand an image. However, there has been a paucity of research into developing a VQA system in Bangla to assist individuals with visual impairments. To reduce this gap, we have introduced a VQA system in Bangla designed to assist visually impaired individuals. VQA is a multifaceted problem, and in this paper we focus on finding the spatial relationships between objects. We have broken down this problem into three sub-tasks: object detection, object counting, and finally relative positioning for the detected objects. The system takes in questions from the user, understands which sub-task to perform and then returns the answer. We have have leveraged several pre-trained models such as Bangla-BERT, EfficientDet-D7, InceptionResNetV2, and MiDas v2.1. The major aspects of this paper are the introduction of a procedurally generated dataset to train models to identify what action to perform based on the prompt of the user and using image segmentations to identify the relative spatial position between objects in all three spatial dimensions.
Technological Pedagogical and Content Knowledge: Tai Chi teachers in higher education

The aim of the study was to examine the TPACK levels of Tai Chi teachers in higher education. 218 Tai Chi teachers in higher education institutions in Henan Province, China voluntarily responded to an online questionnaire. SPSS 24.0 software was used for data analysis. The findings showed significant differences in Tai Chi teachers’ TPACK levels and its components based on the different demographic variables. The implication of this study is for policymakers to draw up relevant policies that can enhance the weaker areas of TPACK components among Tai Chi teachers.
Automatic Conversion Algorithm and Dissemination Network of Realistic Style of Cultural Heritage Documentaries Fused with Heterogeneous Image Fusion

Starting from the basic principle of information fusion, this paper discusses the research status of heterogeneous image fusion structure, feature-level fusion algorithm and decision-level fusion algorithm in detail.The creation of non-fiction gives the audience the highest prestige, and the real thing has great power, which subtly leaves a thought in the heart of the audience.From the theoretical perspective of "cultural space" and "third space", taking intangible cultural heritage documentaries as the object, it analyzes the reconstruction of intangible cultural heritage cultural space in the context of new media and the expansion of intangible cultural heritage-mediated survival, and the template matching of similarity The method realizes the automatic registration of visible light and infrared images containing airport scenes.
Research on the Application of Network Resources in College Mathematics Classrooms

The idea of "integrating education into entertainment" has been proposed for a long time, but its application effect is not satisfactory.The emergence of information technology provides new ideas for the implementation of "integrating education into entertainment".By utilizing commonly used digital devices and network resources, "integrating education into entertainment" can be achieved.Propose strategies to improve teaching quality by utilizing online course resources, that is, breaking through teaching priorities and difficulties, enhancing teachers' modern information technology knowledge and skills, enriching teaching methods, strengthening course teaching design, and achieving significant teaching reform results, improving students' learning efficiency, and enhancing teachers' teaching level and professional abilities.