Reviewerly/exHarmony huggingface dataset:
Effect of interactive media, make a match and lecture method on learning virus achievement

The study investigates the effect of interactive media, make a match, and lecture method on learning achievement of virus. The research method used was quasi-experimental (quasi-experimental) with the pretest-posttest non-equivalent control group design. This research was conducted from September 2019 to November 2019 in High School A and High School T with a duration of learning ± 3 hours of study.ANOVA test results on the average value of N-Gain showed the same results as the post-test. N-Gain in ICT learning media class, make a match learning model class and lecture learning method classes respectively amounted to 58.6 ± 13.6, 40.8 ± 17.6 and 40.5 ± 14.3. However, based on linear regression analysis, student learning outcomes are influenced by student perception factors and students' visual learning styles (P &lt;0.05). The successful use of interactive ICT learning media is influenced by student perception factors and student visual learning style factors.
Using Task Models to Understand the Intersection of Numeracy Skills and Technical Competence With Medical Device Design

Abstract Task models are used in many different ways throughout the design and development of interactive systems. When the interactive systems are safety critical, task models can play an important role in ensuring system behaviours are consistent with user requirements, which may help to prevent errors. While task models can be used to describe a user’s goals and the steps required to achieve that goal, to understand where user errors may occur we also need to consider the users’ understanding of how to perform a task and how this relates to the system they are using. Our focus is on the use of medical devices such as syringe drivers and infusion pumps for intravenous medication, which forms a major part of hospital inpatient care throughout the world. While we might rely on software engineering and human factors techniques to ensure correctness of such devices, their use by medical personnel in practice includes other factors that are equally important. These include training medical personnel in the use of medical devices. Also numeracy education for medical staff to ensure that they are able to set up and perform the necessary calculations to convert prescribed medication into the appropriate values and measures for their delivery mechanisms. We have developed an approach that aims to bring together concepts of technology design (both functional correctness and usability concerns), numeracy and medication delivery competency. In order to do so we use task models as a common language that enables us to consider these different domains in a single way. We find that the ability to describe the two domains within a single process allows us to compare models of knowledge, tasks and use of devices, which can elicit potential mismatches and problems.
Roll Padding and WaveNet for Multivariate Time Series in Human Activity Recognition

Padding is a process used for the border treatment of data before the convolution operation in Convolutional Neural Networks. This study proposes a new type of padding designated by roll padding, which is conceived for multivariate time series analysis when using convolutional layers. The Human Activity Recognition raw time distributed dataset is used to train, test and compare four Deep Learning architectures: Long Short-Term Memory, Convolutional Neural Networks with and without roll padding, and WaveNet with roll padding. Two main findings are obtained: on the one hand, the inclusion of roll padding improves the accuracy of the basic standard Convolutional Neural Network and, on the other hand, WaveNet extended with roll padding provides the best performance result.
Identification of Currency via Image Recognition

The primary objective of this report is to identify currency with such a centralized system that is compact, and also which is highly portable. In this review, the characteristics of the idealized currency will be processed and analyzed in mat files, and so these stored features will be matched to the sample currency. Perceive whether something is real or false. With this structure, it's easy to illustrate this concept everywhere, at any moment. We have used the MATLAB toolbox for data recognition the rendering of the image is a way to enhance the image's pictorial details for the sake of computer or hardware vision. Currency recognition has significant problems, such as steganographic recognition, currency note resolution, dirty notes, etc. This approach is working. First, to classify the nationality to use certain predefined rules. Areas of significance, and then derive the denomination value. Using features such as scale, color, or script on a note, based on how distinctive the notes are in the same region. We listed 20 of the most traded currencies, as well as their denomination.
Efficient Image Processing Technique for Authentication of Indian Paper Currency

Now a days due to the development in color printing technology the rate of counterfeit notes production and distribution is increasing. This is a massive problem, faced by almost all the countries. It affects the economy, sine it compromises the security of the real economy. Such counterfeit currencies are used to fuel nefarious motives, usually involving terrorist activities. According to the research, developing countries like India have been impacted by this very negatively. Even after the steps taken in 2016 to remove the counterfeits, by executing the demonetization of 500 and 1000 rupees bank notes in India the counterfeits of the new notes have begun circulating. This is due to the highly advanced technology adopted by the counterfeiters which makes the tracking of these counterfeit notes hard. This has become a very critical issue and the negative impact due to the counterfeit currency keeps rising. The only one solution for this problem for a common man is to detect the fake currency, by using the fake currency detector machine. These machines are used in banks and large scale business, but for a small business or for a common man these machines are not affordable. This paper gives the complete methodology of fake note detector machine, which is affordable even for a common man. By implementing the applications of image processing techniques we can find out whether the currency notes are fake or not. Image processing technique consists of a number of operations that can be performed on an image, some of which include image segmentation, edge detection, gray scale conversion etc. The proposed system will have advantages like simplicity, reliability and costs less.
Experience of organizing a unified information space of an educational organization during a pandemic

The article discusses the capabilities of the 1C:Education platform for creating a unified information environment for educational organization during a pandemic and for distance learning. A comparative analysis of the capabilities of various Russian platforms for distance learning is carried out. The experience of the school’s work in a distance mode using the 1C:Education platform is summarized.Attention is focused on the use in the educational space of the school of methodological, organizational and technological means of support of pedagogical activity in a distance mode, developed on the basis of solutions of the 1C company.The views on the organization of electronic distance learning and support of the system of continuous educational process using the 1C:Education platform are presented.The distance learning platform 1C:Education can be used by educational organizations as a basis for designing, monitoring and modernizing their own educational environments, in which priority is given to the development of a socially adapted, competitive personality with intellectual potential, able to apply knowledge in practical activities for the benefit of society based on cultural, moral and civic values.
An Intelligent Teaching Test Paper Generation System Based on Ant Colony Hybrid Genetic Algorithms

In this paper, the problem of intelligent test paper generation is discussed, and the advantages and disadvantages of current commonly used intelligent test paper generation algorithms are analyzed. By comparing genetic algorithm with ant colony algorithm, it is found that the two algorithms have complementary advantages and they can be integrated organically. On this basis, an intelligent test paper generating strategy based on hybrid genetic algorithm and ant colony algorithm is proposed. The experiments show that the convergence speed of hybrid test paper generating strategy is faster than that of single genetic algorithm and ant colony algorithm. Compared with single genetic algorithm and ant colony algorithm, the efficiency and success of hybrid test paper generating strategy are greatly improved, which has better practicability.
Study on Efficient DRLSE-Oriented Edge-Based Medical Image Segmentation of Cardiac Images

Today, medical image-based processing is very much used in recent research in medical domain very effectively. So, this technical approach helps the medical expertise person to take faster decision regarding earlier diagnosis and faster decision regarding operation and next stages treatments. Level set approach is a crucial method that is used domain as computer vision. So, the normal level set approach is more error prone and creates several irregularities for the evolution in the stages of stabilities of algorithms. Here, the potential function is built and create a distance regularized term for making solution of initiating level set function in periodical way. A energy function is created by combining internal and external energy function. The main purpose of this task is medical image-based detection. The DRLSE-based segmentation approach reduces the re-initialization problems and can minimize the errors. The problems of further initialization can hamper the accuracy or rate of performance. This approach can efficiently able to initialize the level set value which is flexible to changing the time steps in a difference scheme for elimination of amount of iterations. The approach provides more than 85% overall accuracy and sensitivity scores for proper segmentation of the cardiac images in medical imaging domain. This approach can effectively segment the area of contour region to the boundary with varying number of iterations with lesser error rate.
Generating instances with performance differences for more than just two algorithms

In recent years, Evolutionary Algorithms (EAs) have frequently been adopted to evolve instances for optimization problems that pose difficulties for one algorithm while being rather easy for a competitor and vice versa. Typically, this is achieved by either minimizing or maximizing the performance difference or ratio which serves as the fitness function. Repeating this process is useful to gain insights into strengths/weaknesses of certain algorithms or to build a set of instances with strong performance differences as a foundation for automatic per-instance algorithm selection or configuration.
Research on Practice Evaluation System of Higher Vocational Preschool Education Based on Artificial Intelligence

To improve the quality of practical teaching and promote the synchronization of practical teaching and practice, the theory of artificial intelligence network is introduced into the evaluation of practical teaching quality, and the relevant mathematical model is established. Firstly, a perfect practical teaching evaluation index system is provided, and the related index system and quality standard are established. Then, when the convergence speed of BP algorithm training network is slow, genetic algorithm is used to optimize the operation parameters of the network, the optimization results are taken as the initial value of BP algorithm, and BP algorithm is used to train the network until the required accuracy is reached. Finally, by using data cleaning, network training and rationalization test, a more reasonable result is obtained, which provides a new idea for teaching quality evaluation.
A Travel Assistant Application Based on Android Baidu Map

It is very necessary to develop applications running on mobile phones because Mobile Internet and smart phones are playing an increasingly important role in tourism. Although Android Baidu map provides basic map data, the services for Guilin tourism are still limited. To solve this problem, a travel assistant application (APP) for Guilin based on Android Baidu Map and Java language is designed and developed. The APP uses SQLite Embedded Database to store tourism information, and uses Android Baidu Map SDK package to obtain the data in Baidu Map database as tourism map information, which will be loaded into the APP for interface display. The design of request permission, SQLite database, APP page layout and specific functions are carried out for the App. Combined with the Service1, Service2 and Service3 of Android Baidu Map SDK, the APP specific functions such as map display, map positioning, geocoding, Point of Interest (POI) retrieval, route planning, navigation and route POI information are implemented to help users obtain more convenient and quick tourism information services.
Sparse representation algorithm for fusion error

Abstract In recent years, face recognition technology has been widely used in security, finance, media data retrieval and other fields due to its convenience, concealment, and stability, and has become a popular research field in various scientific research institutions. Among them, face recognition under complex conditions under different lighting, different expressions, and partial occlusion has become a research hotspot. Sparse representation algorithm, as the mainstream algorithm to solve the problem of face recognition under complex conditions, although it improves the recognition accuracy compared with other algorithms, there is still room for improvement. Aiming at the partial occlusion problem in face recognition, this paper conducted many experiments under different training samples through the sparse representation algorithm of fusion error. The experimental results show that the algorithm improves the recognition rate and provides a new solution for the problem of face recognition under partial occlusion.
Efficient inter-image relation graph neural network hashing for scalable image retrieval

Unsupervised deep hashing is a promising technique for large-scale image retrieval, as it equips powerful deep neural networks and has advantage on label independence. However, the unsupervised deep hashing process needs to train a large amount of deep neural network parameters, which is hard to optimize when no labeled training samples are provided. How to maintain the well scalability of unsupervised hashing while exploiting the advantage of deep neural network is an interesting but challenging problem to investigate. With the motivation, in this paper, we propose a simple but effective Inter-image Relation Graph Neural Network Hashing (IRGNNH) method. Different from all existing complex models, we discover the latent inter-image semantic relations without any manual labels and exploit them further to assist the unsupervised deep hashing process. Specifically, we first parse the images to extract latent involved semantics. Then, relation graph convolutional network is constructed to model the inter-image semantic relations and visual similarity, which generates representation vectors for image relations and contents. Finally, adversarial learning is performed to seamlessly embed the constructed relations into the image hash learning process, and improve the discriminative capability of the hash codes. Experiments demonstrate that our method significantly outperforms the state-of-the-art unsupervised deep hashing methods on both retrieval accuracy and efficiency.
An Automated Approach to Accelerate DNNs on Edge Devices

Deployment of Deep Neural Networks (DNNs) on edge devices can significantly increase the utility of DNNs for a variety of applications. However executing DNN models on the edge device is still a major challenge, as heavy computation and memory bandwidth requirements of such models limit their adoption. Employing a highly optimized code for DNN model execution can easily enable many more use-cases than currently possible. However, current strategies are still based on manual optimization for efficient resource utilization. This is not only cumbersome but also requires a high level of expert intervention in the rapidly changing DNN Model landscape. In this work, we provide an automated way of optimizing Convolutional Neural Network (CNN) models using Deep Reinforcement Learning (DRL) algorithm. The experiments with our DRL technique demonstrate 1.85×, 1.58×, 1.64× speedup in execution time for MobileNetV1, MobileNetV2 and Efficientnet-lite0 CNN models respectively on Mobile CPU devices.
Automatic Segmentation of Colon Using Multilevel Morphology and Thesholding

The segmentation of colon may faces problems such as presence of diaphragm, air packets and other extra colonic components such as bones, lungs, stomach and small bowels. The proposed method uses morphological based Otsu thresholding method for the accurate segmentation of colon. Before starting the segmentation process, diaphragm present at the bottom of the abdomen is removed by using erosion followed by dilation. In segmentation process, the air packets and some non-colon parts are completely removed by implementing the Otsu thresholding. Then the other non-colon components such as small bowels are removed by morphological operations. The effectiveness of this segmentation scheme is compared with the existing methods and is analyzed. This method could obtain an average of accuracy of 99.05%, specificity of 99.70% and sensitivity of 87% in comparison with other existing methods.
Android-based Indonesian sign language model using robot hand

Abstract Sign language is one of the most effective ways to communicate with deaf people. However, not everyone is able to use sign language. Therefore, in this study, a sign language display system was created using a hand robot that was moved through an Android-based application on a smartphone to overcome this problem. The design and development of the system begin from the manufacture of hardware consisting of one hand robot with 11 servo motors as the actuators, the Arduino Mega 2560 microcontroller as the main controller and a Bluetooth module for wireless communication between hand robot and smartphone. Then software development is carried out on the hand robot controller and android based application on a smartphone. Furthermore, system testing is carried out by validating the results of the letters and numbers shown by hand robot with gestures in the Indonesian Language Signalling System Dictionary (SIBI). The results show that the hand robot has been able to display letters and numbers according to the instructions given by an Android-based application on a smartphone with an 80% accuracy rate.
PowerHC: non linear normalization of distances for advanced nearest neighbor classification

In this paper we investigate the exploitation of non linear scaling of distances for advanced nearest neighbor classification. Starting from the recently found relation between the Hypersphere Classifier (HC) [1] and the Adaptive Nearest Neighbor rule (ANN) [2], here we propose PowerHC, an improved version of HC in which distances are normalized using a non linear mapping; non linear scaling of data, whose usefulness for feature spaces has been already assessed, has been hardly investigated for distances. A thorough experimental evaluation, involving 24 datasets and a challenging real world scenario of seismic signal classification, confirms the suitability of the proposed approach.
Power Sum Polynomials in a Discrete Tomography Perspective

For a point of the projective space \(\text {PG}(n,q)\), its Rédei factor is the linear polynomial in \(n+1\) variables, whose coefficients are the point coordinates. The power sum polynomial of a subset S of \(\text {PG}(n,q)\) is the sum of the \((q-1)\)-th powers of the Rédei factors of the points of S. The fact that many subsets may share the same power sum polynomial offers a natural connection to discrete tomography. In this paper we deal with the two-dimensional case and show that the notion of ghost, whose employment enables to find all solutions of the tomographic problem, can be rephrased in the finite geometry context, where subsets with null power sum polynomial are called ghosts as well. In the latter case, one can add ghosts still preserving the power sum polynomial by means of the multiset sum (modulo the field characteristic). We prove some general results on ghosts in \(\text {PG}(2,q)\) and compute their number in case q is a prime.
Survey of File Carving Techniques

File carving is a method that enables researchers to collect data from unallocated memory space. File carving is a technique used to retrieve data other than data recovery technique and data restoration technique. In general, carving is used to retrieve data with the absence of metadata that point to the content. There are various styles of file carving techniques used for types of files, including PDF, JPEG, XML and etc. []. This paper focuses mainly on the different types of carving techniques commonly used based on particular circumstances. The key contribution of this paper is the study and analysis of current existing file carving techniques for file carving in order to define their functionality. These techniques are also classified according to the carving method styles used and the fragmentation issues as well as the type of a file system since these techniques rely on the content and the file structure of the particular file to be carved. This paper is divided into two sections, the first section is the traditional carving approach and the second section is the advanced carving approach. This paper will be a guide for researchers searching for carving techniques in order to choose the appropriate carving algorithm and to continue improving it in the future.
An Intelligent Life Jacket System Based on OneNET

In order to solve the problem of single function and low intelligence of traditional life jacket, an intelligent water life jacket system principle prototype is designed and developed. The system includes intelligent life jacket, OneNET Cloud Service Platform and mobile app. With MCU as the main control and water sensor as the falling water monitoring equipment, GPS real-time positioning information is uploaded to OneNET and mobile app through GPRS wireless, so as to realize the water safety monitoring and management of small and medium-sized waters. The simulation and field test show that the system can stably realize the functions of falling water detection, positioning, alarming and uploading. The working time of the whole process of the system is limited within 10 seconds, and the GPS precision is controlled within 10 meters. This project is expected to provide technical reference for the development of life-saving equipment and the safety management of small and medium waters, which has certain practical value.