Reviewerly/exHarmony huggingface dataset:
Exploring the Potential of the Outdoors with Digital Technology in Teacher Education

This paper refers to a study that aims to understand the perceptions of pre-service teachers about the use of digital technology in outdoor mathematics. We followed a qualitative, interpretative approach and collected data through participant observation, questionnaires and photographic records. The participants were forty-eight pre-service teachers, that were enrolled in a Didactics of Mathematics unit course, and they used the MathCityMap app to do a math trail in the city of Viana do Castelo. Results show that they valued the experience, having the possibility to solve realistic problems, developing cooperative work, critical thinking and establishing mathematical connections. They found the app to be user friendly and motivating, mentioning its contribution for students’ engagement through active learning, spatial orientation, autonomy and being more interactive than the paper version. As for limitations, the participants highlighted the possible lack of access to Wi-Fi; the fact that students of younger ages normally do not have smartphones; and, in terms of the tasks, the limitation of the answer formats to either a value or multiple choice.
The Urgency of Policy Formulation: Dynamics of Covid-19 Handling and Big Data

Abstract This paper attempts to discuss the role of big data to formulate policies in responding to social and economic threats due to Covid-19 pandemic in Indonesia. The use of big data as a primary source is based on the dynamic of spreading cases in other countries that were suddenly flared. Those primary resources are online or open data such as population, social economic condition and infection case. In related indicators, population is a big portrait of the countries, thus we observed the number of new infection per day, month and how it impacts the economy. The research problem is how big data is used to formulate policy during the Covid-19 time. We use Decision Theory under Uncertainty to explain three indicators that influence decision-making under Covid-19. This research applied Machine Learning R and SIR Model to obtain the latest data. The argument is that big data can be used as a base resource for the policy formulation and decision-making. In this case, base resource refers to first input in the policy formulation process as well as input and output information in the decision-making process.
COMPETENCES OF A SPECIAL TEACHER IN INCLUSIVE EDUCATION

The article analyzes foreign and domestic scientific approaches to determining the professional competence of a special teacher for work in an inclusive education. In the context of the active spread of inclusive education in the country, it seems important to modernize their training for work in inclusive schools. The authors prove the need to improve the competencies of special teachers in the context of an educational inclusive process. The development of inclusively oriented professional and personal competencies among teachers largely depends on the success of the inclusion of children with special educational needs in the general educational process. Based on the study of the opinions of practitioners in inclusive organizations of special teachers, it is argued that inclusive education of children with special educational needs requires a change in approaches to training special teachers, modernization of the content and directions of their training.
Cluster Analysis Using Improved Bio-inspired Fuzzy C-Means Algorithm

Optimization is a vital constituent of machine learning as it brings about efficiency of the analysis process. An optimizer is used to minimize an objective function, which in turn leverages efficiency of any given model. Many metaheuristic algorithms have been applied on many models for optimization purposes. The mostly favoured metaheuristic algorithms are the population-based ones as they initialize a population of candidate solutions, rather than a single-solution strategy. This paper takes off from the original research made on fuzzy C-means (FCM), where the weighting component (m) was set at 2. Setting the weighting component at 2 may not ideally work well for all applications. Then, the point of this examination is to optimize the weighting component m using bio-inspired algorithms that are population-based in nature. The idea is to compare performance of conventional FCM with improved version of FCM by augmenting it with different optimization techniques. Three comparatively new and popular bio-inspired optimization algorithms such as moth-flame Optimizer (MFO), gray wolf optimizer (GWO) and whale optimization algorithm (WOA) are selected to optimize tunable parameters of conventional FCM. For experimentation purpose, five different data sets namely seeds, iris, glass identification, mall customer segmentation and BuddyMove are selected. The experimental results show considerable improvements in the overall performance of conventional FCM as a consequence of parameter optimization. A new approach for optimizing FCM parameters using bio-inspired algorithms is presented.
Yolo-tiny-MSC: A tiny neural network for object detection

Abstract Object detection is an important and active area of computer vision. However, the expensive computational cost and memory requirement are big challenges for deploying object detection networks in resource constraints embedded devices. To address this problem, we designed the multi-stage cascade module to reduce the computational cost of object detection networks. The proposed structure is used to build a compact and efficient object detection neural network. Experiments show that our multi-stage cascade module could significantly reduce the computational and parameter budgets meanwhile achieve outstanding object detection accuracy.
Research and application of visualization technology for massive land spatial data based on vector tiles

In the face of ever-growing and complex massive land spatial data, the traditional methods of vector data cartographic generalization and raster tiles technology are increasingly difficult to meet the visualization requirements such as seamless data browsing and efficient real-time rendering. Based on the new data model of vector tiles, this paper develops a visualization technology of land spatial data, which can achieve visual lossless automatic synthesis of spatial features, parallel production of vector tiles, and efficient rendering and service publishing of data. The practice shows that the vector tiles technology can better meet the needs of massive multi-scale land spatial data visualization, and provides technical support for natural resource management and data application analysis.
Guitars 4 Vets: MKE nonprofit's website crashes after ACM spotlight

A Milwaukee-based nonprofit took center stage at the Academy of Country Music Awards, with the spotlight so big it caused the Guitars for Vets website to crash. Guitars for Vets: Milwaukee nonprofits website crashes after ACM spotlight
Heterogeneous Image Template Matching Based on Region Proposal Network

Abstract Heterogeneous template matching is important to disaster relief. The method of traditional heterogeneous template matching in real-time performance is so weak and usually has a lower accuracy than deep learning method. A heterogeneous template matching method based on region proposal network is proposed in this paper. Specifically, deep features between the optical images and SAR images are extracted by using residual network, and MRPN, a suitable region proposal network for matching work, is proposed to complete precise positioning of the template region. Finally, the channel fusion is performed on the matching results to get a better result and confidence. It is shown that the real-time performance and the accuracy performance have been greatly improved, which is reaching 97%.
Detection of Point Outliers in Meteorological Data (Case Study: Ulaanbaatar, Mongolia)

Outliers could have an impact on misspecification of models, the bias in estimating of parameters, incorrect results, and poor forecasts. Outliers in weather data arise due to human error and instrument error. Outliers can be categorized into three categories: point outliers, collective outliers, and contextual outliers. In this paper, we propose a point outlier detection method based on a moving median filter. The method consists of three key steps. They are autocorrelation, moving median filter, and threshold determination. The autocorrelation step gives the information of similarity between immediate neighbors. The result of the autocorrelation step defines window width of the median filter. The output of the median filter gives candidate outliers and exact outliers detected based on the threshold values. Out analysis of the results demonstrate in this paper is successful and effective in detecting outliers in meteorological data.
Smart Face Locker Using OpenCV and Arduino with Mail Transfer

The mechanism of detecting face is called face detection, it can be performed with various open platforms available over the internet. Face recognition is in limelight nowadays in the world of researchers. Face recognition is the process by which a smart and intelligent system recognizes the detailing of the face. This paper describes a new algorithm and system prepared to detect and recognize faces which decreases human intervention and is advanced in the technological aspects of life. The paper focuses on the minimal deflection of the confidence in the detected face and is moreover successful enough in displaying the results as stated. The system is developed on open source free platform, i.e., OpenCV which is capable to detect faces and is fully functional on the ground level. This face recognition system could be introduced in our attendance system, lockers, intruder system, and many more. The approach uses Arduino as its hardware support along with SMTP mail service which sends mail to the user. Results will prove the effectiveness of our system on the basis of a quantitative measure called confidence.
Forming Information Competency in Primary School Students in Project Activity

The study aims to provide theoretical and experimental substantiation of possibilities of forming primary school students’ information competency in project activity. The article presents the concept “primary school students’ information competency”. It is ascertained that project activity increases effectiveness of information competency forming in primary school students if suitable multimedia technologies are used. The study is novel in that it develops content of formation stages of information competency in primary school students in project activity with the use of multimedia technologies. As a result, use of project activity in organisation of educational process in primary school makes it possible to form in a holistic manner the selected components (value-motivational, cognitive, technical-technological, communicative, reflexive) of students’ information competency, which are necessary for effective addressing of educational objectives in primary school.
Analysis of Control Strategies for Data Capture System in a High-performance DAC

A given data capture system featuring a delay-locked loop for clock recovery is analyzed. Several control strategies for this system were proposed and the system performance under these strategies was estimated. The estimations were confirmed by simulation and the most optimal control strategy was chosen.
Smart Parking System Using MobileNet and Single Shot Detection Algorithm and IoT

In today's world, automobiles are essential part of our day to day lives and people who drive regularly can certainly say that parking is the most difficult and exhausting part of driving. Finding a parking spot in big cities is another strenuous assignment. As this is the era of remarkable technologies, this paper proposes a smart parking system with the help of IoT (Internet of Things) and artificial neural network to ease off the fatigue of the drivers.
Product Classification in E-Commerce Sites

Given a predefined catalog hierarchy where all the categories are defined, the task by which the catalog path of every product is automatically predicted is known as product classification. This paper explores some of the methods of product classification. With the help of supervised learning, we have presented various machine learning models to classify products into a set of known categories. With the information such as name and description of the product being provided, this model can accurately place it under a designated category. Machine learning models that have been deployed include decision trees, support vector machine (SVM), random forest, logistic regression, and Naïve Bayes, with logistic regression giving the highest accuracy of 91.55%. The proposed implementation has huge potential to be able to slowly but substantially increase the automation of the categorization of products.
An Improved Firefly Algorithm for Software Defect Prediction

Software defect prediction (SDP) plays an important role to help software development, where various advanced intelligence algorithms but no firefly algorithm (FA) are used to improve the prediction accuracy within a project or across projects. Current FA faces the problem of many unnecessary movements which reduces its efficiency of searching for an optimal solution. Therefore, an improved multiple swarms with different strategies firefly algorithm is proposed, named MSFA. The key principle of MSFA is to divide the swarm into three groups, where each group plays a different role to balance exploration and exploitation. Experimental studies were tested on CEC 2013 and SDP. The test results on CEC 2013 prove that MSFA achieves a high balance between the exploration and the exploitation. The test results conducted on SDP show that MSFA has a higher prediction accuracy, but much less computation cost compared with other FA variants.
Benign–Malignant Mass Characterization Based on Multi-gradient Quinary Patterns

This paper introduces a new descriptor, multi-gradient quinary pattern (M-GQP), for the categorization of breast masses as malignant or benign. The proposed attributes measure local information via local quinary pattern (LQP) based on a five-level encoding scheme from the gradient images obtained using Sobel operator having eight distinct masks at different directions. The gradient magnitude and angle image features provide better consistency and stability in high texture regions like edges and micro-information in different orientations, respectively. The assessment is performed using the mammographic images of the mini-MIAS dataset. A group of salient discriminators are opted via stepwise logistic regression technique, and a cross-validation method with tenfold is leveraged along with Fishers linear discriminant analysis as a classifier to avoid any bias. An Az value of 0.97 with an accuracy of 90.26% is achieved as the best outcome which is further compared with some of the competing methods in the literature.
Medical Data Compression for Lossless Data Transmission and Archival

Medical applications create an enormous amount of data. Medical data transmission via networks necessitates a huge bandwidth rate. Also, digital medical data necessitate enormous storage and archive. With the evolution of the Internet and multimedia designs, medical data is required to be transmitted in a rapid manner. One of the practical solutions to this issue is medical data compression. Data compression (DC) and transmission is important in the medical field. DC is used to transmit a large amount of data for minimizing the cost. DC is introduced to minimize the image for focusing on the removal of redundant data. DC is classified into two categories, namely, lossy and lossless techniques. DC is designed to reduce storage, bandwidth, and time consumed for transmission. Coding is utilized to remove unwanted data. The different DC algorithm is used to enhance the compression rate. Some of the medical data compression techniques are outlined to lessen data redundancy via specialized data coding and, as a result, can significantly minimize the constructive amount of medical data. In other words, medical data compression involves the procedure of encoding medical data in such a manner that less storage is essential to archive them over a network. The contemporary prototype of medical data compression is split into two stages, namely, designing and entropy coding. Selecting the appropriate prototype is paramount due to the reason that the more consistencies we identify, the more are the probabilities to minimize the series scope. Next, based on the understanding acquired via designing, unwanted data are eliminated by applying coding. Here, encoding is performed to eliminate dispensable data. As several DC techniques have been progressed, a requirement comes to light to assess the techniques, and an endeavor is made to review and classify different DC techniques based on three classifications, namely, coding schemes, data quality specifications, and application appropriateness. Some of the coding schemes for lossless data compression, to name a few, are run-length encoding, Huffman encoding, and LZW encoding. Also with the expeditious rise in high-speed data acquisition, bandwidth acquisition and storage have become the focal restrictions concerning DC techniques. We observed that it is impracticable to outline an exclusive lossless compression technique for different data types without a certain understanding of the series. It is also unfeasible to develop a disparate lossless compression algorithm for every potential series. The intelligent alternative is to devise comprehensive DC and to utilize such an algorithm to the series that can be handled, with a higher amount of precision. Some of the analyzed algorithms are component analysis, partial matching, state-space transitions, and tree sequence.
Retrieval of Trademark Images by Using Local Shape and SURF Feature Fusion

Trademark images are the copyright of any individual or commercial organizations. To protect the copyright, retrieval of trademark image is required which retrieves similar registered trademarks. This paper proposed a method which extract the shape features using Local Zernike Moments, orientation and description using SURF to retrieve the similar trademark images to protect the copyright. Then, a weighted similarity is computed by using the weighted average of Zernike moments and SURF to fuse. Euclidean distance is applied to retrieve the more similar images. Experiments have been carried out on proposed dataset and found that proposed approach works better and improves the accuracy.
Optimization of workpiece detection model based on channel pruning and fixed-point quantization

As the development of deep learning, the accuracy and speed of object detection tasks are constantly improved, and more and more neural network models meet the needs of object detection. Many network models provide a new possibility for workpiece detection in factory workshops. However, the existing object detection model has the problems of large parameters, slow running speed and high memory consumption, which makes it difficult to be applied to the actual workshop production scene. In order to solve the above problems, based on the research of network model compression and acceleration algorithm, this paper optimizes the classical mobile Mobilenet-yolov3 network by quantification and pruning, and makes experimental analysis and comparison with existing result. The experimental results show that using the same workpiece data set, the new model can get the same effect in F1 score and accuracy rate as the original model, and also slightly improve the recall rate. Compared with the original model, the computational complexity and model size and the Flops (forward inference speed) are greatly reduced.
Application and Research of Computer Big Data based on Structure in Internet Learning

Abstract With the development of science and technology, information data has penetrated into various industries. Through data mining, analysis and application of computer, the development trend of society can be effectively predicted, and the correct development strategy can be formulated. In the field of education, computer big data also plays an important role in supporting the implementation of the concept of lifelong learning. However, in teaching practice, how to play an optimal role in computer big data has become the focus of teaching exploration [1]. Based on this, the following research on the application of computer big data in Internet learning aims to enhance the positive role of computer big data in the field of education and improve learning efficiency.
firstGlimpse: Learning How to Learn Through Observation via Memory Modeling with Reinforcement Learning

While there are many systems extant that take well-known models and refine how to learn with them, there are very few that deal with the initial stages of raw model formation. How do we, as human beings, learn? Can we teach machines to ‘learn to learn’? In this paper, the authors introduce just such a system. firstGlimpse is a hierarchical memory-modeling technique that can learn from scratch via observation. firstGlimpse uses reinforcement learning to develop its models and reinforce the models that are most viable, most likely, etc. This hierarchical approach to memory modeling develops, through observation, basic symbols that comprise the elements of the learned material. These basic symbols are combined into words, these words combined into phrases, phrases into sentences, and so on, creating a modeling system for general learning that can be built from the ground up. As a new symbol is observed, it is recorded. Likewise, that symbol’s connections with other symbols is also recorded in another layer of the memory. Next, these connected symbols are viewed as words, and the words are observed as being connected to other words. In this manner, phrases are added. This continues upwards to higher levels of the modeling hierarchy. This paper explains the process of firstGlimpse’s reinforcement learning by using the game of Set as a simple example. We will show, starting from scratch, that it can learn to play by observing humans playing the game and recording its observations within its hierarchical memory. The experiments will show that this method is successful both in observing the links of the memory graph and in the results of successfully playing the game.
Generative Adversarial Network Performance in Low-Dimensional Settings

A generative adversarial network (GAN) is an artificial neural network with a distinctive training architecture, designed to createexamples that faithfully reproduce a target distribution. GANs have recently had particular success in applications involvinghigh-dimensional distributions in areas such as image processing. Little work has been reported for low dimensions, where properties of GANs may be better identified and understood. We studied GAN performance in simulated low-dimensional settings, allowing us totransparently assess effects of target distribution complexity and training data sample size on GAN performance in a simpleexperiment. This experiment revealed two important forms of GAN error, tail underfilling and bridge bias, where the latter is analogousto the tunneling observed in high-dimensional GANs.
Development and Research of Quantum Transformation Models

The article discusses the theory of quantum image processing and quantum image manipulation (fast geometric transformations, including the flip, orthogonal rotations and limited geometric transformations). In this work we have investigated the area of quantum image processing and have tested the FPQI model. Models (developed in the work and others) were investigated by explicitly modeling quantum behavior on a large quantum system. A basic scheme for encoding 2×2 image has been created to gain a clearer understanding of the mechanics of quantum image models.