Reviewerly/exHarmony huggingface dataset:
Jaringan Syaraf Tiruan Mendeteksi Penyakit Pneumonia Infeksi Saluran Pernafasan Akut Dengan Algoritma Backpropagation

Acute Respiratory Infections (ARI) is a health problem that often affects children and adults. For adults, acute respiratory infections are mild or common, but in children under five, this disease is a threat that can cause death. One of the causes of death due to acute respiratory infections is the incorrect diagnosis. This study aims to determine the level of accuracy and optimal neural network architecture in detecting ARI using the backpropagation method. The backpropagation method is a pattern recognition technique that is able to provide decision results based on trained data. This research was implemented using MATLAB software with several forms of network architecture. Symptoms of ARI that were used as input for detection of the disease consisted of 13 variables targeting non-pneumonia and pneumonia ARDs. Based on the research results, the architecture with the best configuration consists of 13 input layer neurons, 20 hidden layer neurons and 2 output layer neurons with a binary sigmoid activation function (logsig), a learning rate value of 0.5, an error tolerance value of 0.001, a maximum of epoch of 216 and MSE 0.000997. Artificial neural networks with the backpropagation method used for weight adjustment can respond to training data and testing data well, marked by the resulting network accuracy 100% in accordance with the desired target.
Using Apache Spark Streaming and Kafka to Perform Face Recognition on Live Video Streams of Pedestrians

Through the use of Apache Spark Streaming, Kafka, and OpenCV on a distributed platform, we propose a method for recognising pedestrians in real time.This method intends to recognise motion and faces by matching new photos to a database of previously identified images.Apache Spark Streaming and Kafka have been utilised as real time analysis frameworks, which deliver event based decision making on Hadoop's distributed environment, because video processing and analysis from multiple resources is time consuming when using the Cloud or even any single highly configured machine.If real-time event analysis can be performed continuously, a choice may be made either immediately thereafter or simultaneously with the occurrence of the event in question.Hadoop is the foundation of all real-time analysis distributed solutions, so even processing massive amounts of films in parallel is no longer a bottleneck.Whenever continuous monitoring and decision making is involved with a large data set, this event based analysis can be put to use.This includes, but is not limited to, the monitoring of border areas of countries by cameras and drones, road traffic monitoring, the life science domain, airlines, logo recognition, and more
Drug Market Analysis Strategy Based on the Background of the National Unified Market

Building a unified national market is the basic support and inherent requirement of building a new development pattern.This paper selects the indicators that have an impact on the unification of the national market and divides them.Therefore, we have established a suitable evaluation system.Secondly, based on the above index system, data are collected and clustered, and the curve fitted by grey prediction analysis predicts the trend for the next four years, indicating that China's drug market has a certain room for growth.Finally, put forward effective and reasonable suggestions to the relevant departments.
Regional Difference Assessment of Comprehensive Benefit of Marine Resources Development and Utilization Based on Web GIS Technology

As the most cutting-edge technology of GIS in recent years, Web GIS (WG) can collect, store, process, analyze and visualize heterogeneous, multi-source, and massive spatial geographic data.Therefore, the introduction of WG technology into MIM to build an information system for massive, multi-source, and heterogeneous MIM can improve the management efficiency of marine resources (MR) for the government and enterprises, and realize the precision, precision, and management of MR.Scientific planning and management are of great significance.The main purpose of this paper is to study the development and utilization of MR based on WG technology.This paper focuses on the analysis of the comprehensive benefits (CB) of marine resource utilization and the evaluation of differences in different regions.This paper analyzes the evaluation principles, and uses the corresponding calculation to compare the different benefits of different regions in my country.The final experiment shows that the CB index of MR is 1.89, 1.68, 1.56 in the high-level area of CB of MR.These areas have large MR, and they are at the forefront of the country in terms of marine aquaculture output value and marine fishing output value per capita marine fishery production value, and the income level of fishermen is also relatively high.The development of MR is a new profitable situation, and the fishery ecological environment has gradually improved.MR The overall level of CBs is high.
How to rapid prototype a blockchain-based iot network

The Internet of things (IoT) devices’ standard network architecture is based on custom connections to a cloud service. The manufacturer of the IoT devices themselves usually manages the cloud. This centralization introduces a single point of failure of the system. By introducing the use of a blockchain, the entire network architecture becomes decentralized and this makes the network resilient to network failures. In this paper, we describe how to rapid prototype a Blockchain-based IoT network using wide-available technologies as Raspberry Pi and Ethereum.
E-Learning in faculties of engineering during COVID-19 pandemic in Jordan

Abstract : The recent pandemic COVID-19 has forced almost all governments in the glob to take stringent actions to counteract the spread of this new deadly various. In Jordan, the government introduced the Marshal Law and enforced lockdown of all activities in both of private and public sectors in the country including schools, universities and social gatherings. As a result, demand on the Internet increased sharply: working from home, on-line teaching and learning and on-line orders for different supplies. Such sudden and unexpected dramatic changes increased the load on the fragile existing network and available E-learning systems. In this study, online teaching process, in-house solutions for virtual coaching, in the faculties of engineering, was evaluated and analyzed, with focus on students' points of view. To achieve such goal, a structured questionnaire was distributed to engineering students from different levels and streams. Statistical analysis was conducted to determine the level of satisfaction ofstudents and testify how far the E-learning process was successful as compared with conventional classroom learning. It was concluded that students were not comfortable with this first experience related to online teaching, especially when it comes to specialized technical and practical courses as well as labs. Moreover, they faced serious problems in following up online lectures. This could be attributed to the fact that this is the 1st experience on distance learning and weakness in existing facilities and lack of awareness among students as well as staff members. Keywords : Distance Learning, Online Teaching, COVID-19 Pandemic, Engineering Education.
Geometric measurement of film-cooling holes using co-axial light-field imaging

In a high thrust-weight-ratio aero-engine, turbine blades are exposed to extremely high temperature and pressure which sets higher demands in the blade cooling technology. The mainstream types of cooling include internal airflow cooling, film cooling, and thermal barrier coating. Cooling efficiency is substantially subject to the machining accuracy of blades, more specifically the film-cooling holes on them. A coordinate measuring machine(CMM) is extensively used in the measurement and inspection of turbine blades which enables measuring blade patterns and building geometric models. However, the probes utilized by a CMM are similar in scale to that of a film-cooling hole, making it almost impossible for a CMM to measure film-cooling holes. The probes are prone to damage and the measurement accuracy cannot be guaranteed. This paper proposed a non-contact measuring technique to cope with the inspection of turbine blades. The technique is based on light-field imaging. A light-field camera with a high magnification lens is applied to caperture images of film-cooling holes and their neighborhood which are then inputted to a 3D reconstruction algorithm. A point cloud is derived by the algorithm and fundamental parameters are extracted. The whole inspection requires only one platform and is able to measure shaped film-cooling holes.
Working Posture Classification Based on Wearable-embedded Sensors and Application to Work Detection

To evaluate the work contents of a care worker, this paper proposes a novel lower limb posture estimation method, and a motion recognition method for the automation of care records. In our approach, wearable-embedded sensors consisting of inertial sensors and insole-type plantar pressure sensors were used to recognize the posture of the whole body during care tasks. The upper body posture was calculated from triaxial accelerations, and posture classification of lower limbs can be reached with characteristics extracted from plantar pressure distribution. To achieve accurate posture recognition considering unexpected posture in training phase and compound postures combining multiple postures, a new posture estimation method combining normal and complementary Gaussian mixture network (NACGMN) and posture-fitness function was developed. Using the time-series data of posture information, our proposed work estimation manner utilizing hidden semi-Markov models can recognize performed care works based on the frequency distribution of state transitions. In experiments, we measured and recognized transfer assistance works between a bed and a wheelchair in our laboratory and an actual care facility, and the results demonstrated the effectiveness of the proposed system.
The Phenomenon of Imagination in Contemporary Education. Competence Essential for the Development of Children Supported by the Creative Visualisation Technique

The rapidly changing world forces the necessary changes in education consisting of the development of teaching strategies that prepare the student for unpredictable, non-standard, and fluid situations. The author discusses the problem of supporting competence essential for the development of school-age children and their functioning in such a world. The article may be of interest to early school education teachers, but not only. The strategy of education using imagination, which covers the method of designing educational opportunities, refers in the conducted research to competence triad, i.e. independence-innovativeness-cooperativeness. To support the said competence, the author constructed a technique of creative visualisation and developed an educational programme for two parallel groups based on the method of designing educational opportunities, and in one of the groups, an innovative factor in the form of creative visualisation techniques was implemented. In the research, the qualitative and quantitative style was used implementing participant observation, humanistic interpretation (works of art and statements) and tests for creative thinking (drawing and verbal). The qualitative analysis consisted of description and interpretation concerning the analysed set of competence with the use of their manifestation. The quantitative analysis aimed at answering the question about a significant increase in competence required the use of a method based on the t-Student test. The results of the qualitative and quantitative analyses showed the beneficial impact of creative visualisation in the area of the competence in question.
Benchmarking Monte Carlo simulations for simple quantum systems in epistemically-restricted phase-space representation

We develop an unconventional Monte Carlo simulation within the phase-space formulation of quantum mechanics taking into account a global-nonseparable random variable whose strength is epistemically restricted on the order of Planck’s constant. Compared to the ordinary variational Monte simulation, we find that energy expectation values of simple quantum systems such like harmonic oscillators can be calculated by our method with a reasonable accuracy and competitive calculation time at a large sampling N of random numbers (e.g., at N > 105 samples). We also discuss the comparison of simulations performed in different computing resources.
MOBILE APPLICATION FOR ADJUSTING AIR-BLAST SPRAYERS IN COFFEE PLANTATION

Mobile application development advances, particularly for smartphones and tablets, have allowed farmers to make decisions more assertively in their agrobusiness management. This article addresses the development and evaluation of an app aimed at people who deal with the pesticide application technology in coffee farming, more specifically, adjustment and calibration of sprayers. This mobile app provides the main data necessary for a correct calibration of air-blast sprayers to apply pesticides in coffee planting. Its functionalities include calculation of the application rate for each situation (L ha-1) based on data obtained in the field, such as canopy volume. The app, called SprayCafé, was developed for the Android platform using the Java programming language in the integrated development environment Android Studio. After the development, the application was evaluated, based on a questionnaire answered by 139 users, who ranked the following requirements: ease of use, loading time, adequacy of screen resolution, data relevance, sequence of information, and applicability, among others. The system proved to be simple and robust; it was thus assessed as adequate to the field and to be of great value for coffee planting, especially because it allows safer and more adequate pesticide application. The graphical user interface is interactive and easy to use.
Artificial neural networks are used in a Dark Having to learn Biometric smart card in contrast to the Integrated Method

Deep Learning based face recognition attendance system using novel robust 4-layer Convolutional Neural Network architecture is proposed for the face recognition problem, with a solution that is capable of handling facial images in comparison with Holistic Matching Method (HMM) classifier to enhance the efficiency of the system. The algorithms have been implemented and tested over a dataset which consists of 20 records as a dataset of 10 samples each with threshold value of 0.05% and confidence interval 95%. and the pretest power is 80 % with an error correction of 0.05. The Novel Robust 4-Layer CNN classifier gives 95.2% accuracy in the classification of face detection while the HMM gives 86.2% accuracy. There is a significant difference between the two groups p < 0.05. Conclusion:Novel robust 4-layer Convolutional Neural Network (CNN) architecture gives significantly better classification in terms of accuracy and precision compared to that of Holistic Matching Method method (HMM).
Suspicious Activity Detection Using Convolution Neural Network

Video Surveillance plays a significant role in today's world.Various technologies have been utilized to implement the safety of life and property by installing high quality CCTV cameras.It is impossible to manually monitor each and every moment activity.Furthermore, in practical scenario the most unpredictable one is human behaviour and it is very difficult to find whether it is suspicious or normal.In this work the notion of Convolution Neural Network is used to detect suspicious or normal activity in an environment, and a system is proposed that sends an alert message to the corresponding authority, in case of predicting a suspicious activity.
DATA-ENABLED CRYPTOCURRENCY MARKET ANALYSIS AND VISUALIZATION PLATFORM

The cryptocurrency industry has evolved rapidly in recent years, and it is increasingly popular as a convenient tool to complement the traditional stock and futures exchanges.Accurate market research enables traders to make more informed decisions and benefit from their investments.Our objective is to introduce a web platform for aggregating various types of cryptocurrency data, both on-and off-chain.Its novelty lies in offering a visual representation of market data analysis, which is driven by multi-modal data fusion and representation techniques, as well as artificial intelligence.We propose a full-stack framework that consists of a front-end web application for user interaction and visualization, and a backend server for data fetching, preprocessing, and analysis.In our implementation, we used data from the cryptocurrency market, on-chain statistics, and textual data from social media, to create a deep-learning-based market trend model.For market prediction, our data analysis module processed high-frequency vocabulary extracted from social media, sentiment analysis of social media content, historical price trend, and historical hash rates.Investors and market analysts can benefit from our platform by directly observing the dynamic of multi-modal cryptocurrency data and easily exploring market trends, generated by our market prediction model delivered by a front-end application.The complete implementation can be found in our publicly available GitHub link upon request.
PENGEMBANGAN BAHAN AJAR BERBASIS EBOOK PADA PELAJARAN TIK UNTUK SISWA KELAS VII MTs HIDAYATUSSIBYAN NW SANGKERANG

Abstract: This research is a research on the development of ebook-based teaching materials for Information and Communication Technology (ICT) subjects for grade VII MTs students. Hidayatussibyan NW Sangkerang. The purpose of this study is to help students provide eBook- based teaching materials that are relevant, practical and easy to use for students. This development research method carried out 5 stages of testing, namely: 1) Content Test, 2) Media Test, 3) Design Test, 4) Small Group Test, and 5) Field Test. Based on the results of data analysis, the development of teaching materials based on ICT Subjects for Class VIII uses the Dick &amp; Carey development model whose development process only reaches the ninth stage of development. The results of data analysis concluded that the Development of eBook-Based Teaching Materials for ICT subjects using offline multimedia had a Good (Eligible) qualification. Keywords: e-Book-Based Teaching Materials Abstrak: Penelitian ini adalah penelitian pengembangan bahan ajar berbasis ebook mata pelajaran Teknologi Infromasi dan Komunikasi (TIK) bagi siswa kelas VII MTs. Hidayatussibyan NW Sangkerang. Tujuan penelitian ini adalah untuk memabntu siswa menyediakan bahan ajar berbasis eBook yang relevan, praktis dan mudah digunakan bagi siswa. Metode penelitian pengembangan ini melakukan 5 tahapan uji coba, yaitu: 1) Uji Isi, 2) Uji Media, 3) Uji Desain, 4) Uji Kelompok Kecil, dan 5) Uji Lapangan. Berdasarkan hasil analisis data bahwa pengembangan bahan ajar berbasis eBook Mata Pelajaran TIK Kelas VIII ini menggunakan model pengembangan Dick &amp; Carey yang proses pengembangannya hanya sampai pada tahap kesembilan tahap pengembangan. Hasil analisis data menyimpulkan bahwa Pengembangan Bahan Ajar Berbasis eBook Mata pelajaran TIK menggunakan multimedia offline memiliki kualifikasi Baik (Layak). Kata Kunci : Bahan Ajar Berbasis e-Book
Sentiment analysis data by ShopeePayLater Twitter’s opinion using naïve Bayes classifier

The Covid-19 pandemic effect on online shopping transactions rather than offline in order to prevent the spread of the Covid-19 virus. The paylater service help Indonesian become more easier to shop by taking out a loan. Shopee is one of the marketplaces that has a paylater service, namely Shopee PayLater, which is one of the payment methods on the Shopee platform that allows Shopee users to shop and only pay at a later date when it is due. Every new ShopeePayLater user want to know how the previous user responded as a form of testimony after using this feature via Twitter in the form of tweets. So it is necessary to conduct a sentiment analysis to find out public opinion with the existence of ShopeepayLater using the Naïve Bayes Classifier method so that it can provide useful information for Shopee and the public regarding ShopeePayLater services. Calculation of classification accuracy is carried out using G-Mean and AUC, because the sentiment of the tweets data is included in the imbalancedata category resulting in a G-Mean value of 64,79% and AUC of 66,12%.
Preface: 7th International Conference on Mathematics – Pure, Applied and Computation

First Page
Lattice-based lightweight cryptosystem

The lattice-based cryptographic constructions are emerging as a major contender of post quantum cryptography. The power-of-2 cyclotomic rings are widely used in lattice-based solutions but these rings have limitations in choice and are scarce. In the proposed work, prime cyclotomic rings are considered for investigation as they enjoy the same security and benefits as power-of-2 rings. In today's world of resource constraint devices, cryptographic constructions are expected to be simpler and lightweight, compared to conventional cryptography. The major concern in the deployment of lattice-based constructions is the size of the payloads. The byte modulus, which is an adaption of the LAC scheme, is used to overcome this problem. Our results show that the proposed scheme is more compact but reasonably secure and relatively efficient in comparison to other popular solutions such as LAC. Further, to withstand decryption failure, the popular BCH and Turbo code are used as error correction with the proposed scheme. The performance of BCH and Turbo code along with prime cyclotomic rings with or without byte modulus is studied and reported.
Il video a 360° come supporto didattico. Un caso di studio nell’insegnamento di Analisi Sensoriale degli Alimenti

The chapter presents a case study based on the design, development, and testing of an interactive 360° video applied to the teaching of Sensory Analysis of Food at the University of Florence. The case study describes the design of 360° video storyboards and the creation of the video by the use of 360° cameras for reproducing a sensory laboratory environment. Video enrichment with Vivista, an educational software allowing lecturers to embed interaction points (e.g. quizzes, texts, images) within the 360° videos is also described. Different immersive modalities for the use of the videos with students in different contexts are described and discussed. The chapter concludes with a reflection on the opportunities that 360° videos may provide to university students for practice and experience.
Road Vehicle Detection Based on Feature Fusion Between Frames

With the rapid economic development, motor vehicles are becoming more popular, and artificial intelligence applications on the road are emerging in endlessly. In current road vehicle detection algorithms, most of them use single-frame image information intercepted from video sequences for vehicle detection. This method does not take into account that the difference between frames in the video sequence is mainly the motion background information. Aiming at this design limitation, this paper proposes a target detection method based on IFFF (Inter-Frame Feature Fusion). In the input part of the model, in addition to adding the picture of the current frame, the feature map output of the previous frame will be added to enrich the information of the current frame and improve the detection performance of the current frame. At the same time, a spatial pyramid pooling structure is added to the network to further integrate local and global features to improve the ability to detect vehicles. Experiments show that the method proposed in this paper can improve the detection effect of vehicles in road scenes. Compared with the original CenterNet detection network, the mAP index is improved by 4.3%.