Reviewerly/exHarmony huggingface dataset:
Classification Method of Aerobics Course Online Teaching Resources Based on Artificial Intelligence Technology

In light of the current uneven distribution of aerobics course online resources, this paper presents research on the classification method of aerobics course online teaching resources based on artificial intelligence technology, constructs the display education resource management system based on artificial intelligence technology, realizes the classification of teaching information characteristics, and constructs the classification and evaluation algorithm of aerobiology course online resources. The online teaching resource classification of aerobics course achieves its design goal. Finally, the experiment shows that the artificial intelligence-based online teaching resource classification method for aerobics course is highly practicable and fully meets the research requirements.
JomWowNFC: Why NFC must be used for Merchant &amp; Consumer?

As technologies advance, contactless and cashless payment becomes more popular in the world. Concurrently, the Research and Development in NFC payment rise progressively. Due to the time constraint, the researcher could not collect as much information and resources to produce an ideal research report and also lacks actual interactive actions of the person with NFC Payment. This study has proposed to determine the importance of using NFC Payment between Merchant and Consumer&#x0027;s sides. From the data collected from the survey and interview results, the majority of the respondents are aware and keep their intentions on NFC Payment. According to the opinion feedback from the respondents, they strongly agree that NFC Payment could speed up the transaction time if both merchant and consumer are using NFC to do the business transaction. Therefore, the sample of NFC payment mobile application had been developed to conveniences all the NFC users. Without doing the development of the NFC Payment, it will affect the technological advances of the e-payment services.
Robust Feature Matching via Local Consensus

Feature matching is the foundation and key task of remote sensing image registration, which is to establish a reliable point corresponding relationship between the feature points of two images. In this article, a simple and effective local consensus method for rigid and nonrigid feature matching is proposed and applied to solve the problem of high outliers ratio caused by nonrigid transformation, nonlinear radiation difference, and speckle noise in the remote sensing image registration task. We first establish the putative feature correspondences according to the similarity between local descriptors and then use local consensus constraints (including neighborhood consensus and motion vector consensus) to remove outliers. The specific steps are given as follows. First, we use the neighborhood consensus constraint of feature points to carry out preliminary filtering to remove outliers with obvious errors and retain a large number of inliers, so as to obtain a clean reliable set. Then, the reliable set space is grided into several nonoverlapping cells, and the estimated motion vector is calculated for each cell. By taking the comprehensive deviation between the ordinary motion vectors and estimated motion vectors, we transform the matching problem into a mathematical optimization model and derive a closed-form solution with linear time and linear space complexities. In this way, our method can also significantly increase the speed of operation without sacrificing accuracy. A large number of feature matching experiments on remote sensing prove that our method is superior to existing methods and also has good results in the general scene.
Worth-Focused Design, Book 2: Approaches, Contextx, and Case Studies

This book introduces the concept of worth for design teams, relates it to experiences and outcomes, and describes how to focus on worth when researching and expressing design opportunities for generou
Perception system in Autonomous Vehicle: A study on contemporary and forthcoming technologies for object detection in autonomous vehicles

The most significant part in the design and operation of a self-driving vehicle is the detection of objects in its operating environment. Object detection becomes more challenging during adverse weather conditions like rain, fog, snow etc. The performance of the contemporary methods and technologies for object detection is significantly compromised in adverse weather conditions. This study presents a comprehensive review of the up-to-date technologies of object detection in different weather conditions. A comparison on various sensors and existing sensor-fusion techniques along with image pre-processing systems are looked upon in detail. Moreover, the Deep learning (DL) systems are given utmost stress as it stands close to human intelligence, when it comes to object detection, adaptation and decision making. Ultimately, the upshot of the review is encapsulated and possible research directions are put forward towards the development of a fully functional perception system irrespective of the operating environment.
Improving Clustering Method Performance Using K-Means, Mini Batch K-Means, BIRCH and Spectral

The most pressing problem of the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> -Nearest Neighbor (KNN) classification method is voting technology, which will lead to poor accuracy of some randomly distributed complex data sets. To overcome the weakness of KNN, we added a step before the KNN classification phase. We developed a new schema for grouping data sets, making the number of clusters greater than the number of data classes. In addition, the committee selects each cluster so that it does not use voting techniques such as standard KNN methods. This study uses two sequential methods, namely the clustering method and the KNN method. Clustering methods can be used to group records into multiple clusters to select commissions from these clusters. Five clustering methods were tested: K-Means, K-Means with Principal Component Analysis (PCA), Mini Batch K-Means, Spectral and Balanced Iterative Reduction and Clustering using Hierarchies (BIRCH). All tested clustering methods are based on the cluster type of the center of gravity. According to the result, the BIRCH method has the lowest error rate among the five clustering methods (2.13), and K-Means has the largest clusters (156.63).
Research on Fall Detection Algorithm for the Elderly Living Alone Based on YOLO

The 21st century is an era of rapid population aging, and accidental falls have gradually become the number one killer threatening the health of the elderly. Nowadays, the rapid development of computer vision provides a new solution for elderly fall detection. However, traditional machine learning methods have disadvantages such as cumbersome detection steps, poor real-time performance, bloated model deployment, and poor robustness in complex scenarios. This paper uses the YOLO series of algorithms to automatically extract features, and complete the end-to-end prediction of the target frame and category at one time. In this paper, two modified YOLOV4 and YOLOV5S networks are used as fall detection models. The network is classified and trained on a self-made fall data set, and tested in real scenarios to compare the performance indicators of the two models. Experiments show that the use of YOLOV5S can basically achieve real-time and accurate end-to-end detection and the model is lightweight and easy to deploy, and has good robustness in complex environments.
Zooming in on embodied social sensemaking: Mapping the design space in the context of videoconferencing

Embodied sensemaking theory is thoroughly social: we make sense of the world in interaction with others. Various designs have been proposed to scaffold 'embodied social sensemaking'. Yet the details of such scaffolding need elaboration. For example, what happens in remote settings, when bodies cannot engage directly? This paper investigates, through design, how interactive artifacts scaffold embodied social sensemaking. The aim is to establish clearer links between theory and design. Twenty-four embodied interaction prototypes were created to support remote videoconferencing by design students who were also, necessarily, active videoconferencing users during a COVID-19 lockdown. A reflective ordering process revealed a design space made up of six phenomenal aspects and three basic types of scaffolding. The analysis adds further details to existing design frameworks for embodied sensemaking. The paper provides guidance to designers who aim to create meaningful and theoretically grounded embodied augmentations of remote social interaction technologies.
GRA-PIN: A Graphical and PIN-Based Hybrid Authentication Approach for Smart Devices

In many smart devices and numerous digital applications, authentication mechanisms are widely used to validate the legitimacy of users' identification. As a result of the increased use of mobile devices, most people tend to save sensitive and secret information over such devices. Personal Identification Number (PIN)-based and alphanumeric passwords are simple to remember, but at the same time, they are vulnerable to hackers. Being difficult to guess and more user-friendly, graphical passwords have grown in popularity as an alternative to all such textual passwords. This paper describes an innovative, hybrid, and much more robust user authentication approach, named GRA-PIN (GRAphical and PIN-based), which combines the merits of both graphical and pin-based techniques. The feature of simple arithmetic operations (addition and subtraction) is incorporated in the proposed scheme, through which random passwords are generated for each login attempt. In the study, we have conducted a comparative study between the GRA-PIN scheme with existing PIN-based and pattern-based (swipe-based) authentications approaches using the standard Software Usability Scale (SUS). The usability score of GRA-PIN was analyzed to be as high as 94%, indicating that it is more reliable and user friendly. Furthermore, the security of the proposed scheme was challenged through an experiment wherein three different attackers, having a complete understanding of the proposed scheme, attempted to crack the technique via shoulder surfing, guessing, and camera attack, but they were unsuccessful.
Sign Language Translator of Dzongkha Alphabets Using Arduino

Language that is visually communicated is called sign language. The sign language is not a single language but a collection of languages that are not really related to each other, but use signs which mean something according to their standards. A vocally challenged individual uses these languages but is not understandable unless a normal person learns the standard. Therefore, a sign language translating system is required to understand the signs. This paper develops such a system by the use of an assembly of flex sensors, a gyroscope, an accelerometer and contact sensors which senses bend, orientation and contact. Every sign has its meaning and the design delivers it in the form of an audio which makes it clear to the audience what the particular sign means. The paper focuses on signs of Dzongkha alphabets which is a native language in Bhutan.
Modulation Recognition Analysis Based on Neural Networks and Improved Model

With the emergence and development of computer technology, software technology and other technologies, there are a number of new solutions to the problem of modulation recognition. Modulation recognition is a key step in digital signal processing. The traditional recognition method based on likelihood ratio decision theory has gradually been replaced by the recognition method based on neural network. In this paper, for the recognition of modulation signals, in the PyTorch framework, Convolutional Neural Networks and Residual Neural Network are used to identify modulation signals and test the accuracy. In addition, the advantages and disadvantages of the two models for modulation signal recognition performance are compared, and the model is improved at the same time. In this paper, linear layers with different parameters are added to the Convolutional Neural Networks to observe the impact of its recognition rate and loss function, and a new improvement direction for the recognition of modulation signal datasets is proposed.
Emerging Technologies Serving Cytopathology

Cytopathology became a popular since George Papanicolaou proposed the famous test Pap 60 years ago. Today cytopathology laboratories use the microscope as primary diagnostic device; however modern laboratories host numerous modalities for molecular tests and exchange data via networks; additionally, there are imaging systems producing pictures and virtual slides at enormous sizes and volume. The latest technological developments for cloud computing, big data and mobile devices has changed the way enterprises, institutions and people use computerized systems. In this chapter are explored potential applications of these technologies in the cytopathology laboratory including: data storage, laboratory information systems, population screening programs, quality control and assurance, education and proficiency testing, e-learning, tele-consultation, primary diagnosis and research. The impact of their adoption on the daily workflow is highlighted, possible shortcomings especially for security and privacy issues are identified and future research directions are presented.
Relationship between elemental content determined

Context Studies comparing the elemental contents obtained via portable X-ray fluorescence (pXRF) spectrometry under different scanning conditions and traditional time-consuming concentrated acid-digestion-based methods are rare in tropical soils. Aims (1) To compare the contents of Al, Ca, Cu, Fe, K, Mn, Zn, Cr, Ni, Ti, V and Zr obtained by pXRF with the results of the USEPA 3051a method; and (2) to evaluate the impacts of different conditions on the pXRF results. Methods Soil samples were analysed with pXRF under five conditions: field (F), post-field (PF), air-dried fine earth (ADFE, &lt;2 mm), macerated (M), and macerated and sieved (MS, &lt;150 μm). Linear regressions were adjusted between the USEPA 3051a and pXRF results for the five conditions. Coefficient of determination (R2), root-mean-square error (RMSE), and residual prediction deviation (RPD) were used as validation parameters for the models. Key results The different scanning conditions provided contrasting results for the total elemental contents via pXRF. The validation generated good models, as indicated by the condition, R2, and RPD values, and the results were, respectively: Ca (M; 0.88; 3.00), Cu (F; 0.91; 3.29), Fe (ADFE; 0.94; 4.14), Mn (F; 0.85; 2.65), Cr (ADFE; 0.86; 2.77), and Ni (ADFE and M; 0.74 for both; 2.10 and 2.08). Conclusions PXRF can accurately determine the contents of Ca, Cu, Fe, Mn, Cr and Ni in tropical soils compared with the 3051a method. Implications Results of the USEPA 3051a method can be accurately predicted via pXRF and regression models, reducing time, cost and work required.
Dynamic Two Hand Gesture Recognition using CNN-LSTM based networks

Millions of speech-hearing disabled persons routinely use various signs like hand shapes, movement of hands, lip, and facial expressions to communicate. Indian Sign Language(ISL) is a language that has a large vocabulary of words, which changes from region to region. Generally, there is no dataset publicly available on sequential gestures for ISL. Therefore, the authors have presented the dynamic hand gesture dataset having 33 categories, including months of the year, days of the week, and those used in day-today life. This dataset is collected with a technique called burst shots. To enable speedy evaluation, a smaller subset of the dataset is used with 12 classes represents the months of the year. It is pretty complex because of its signs, and each category contains an average of 5 to 6 gestures per class. The proposed model is designed to work on low-power embedded hardware and this paper also discusses the workflow for the deployment of the particular neural network on embedded hardware. Furthermore, the proposed model is compared with different sequential architectures to find the most suited model for dynamic hand gesture recognition.
Running up the hill – long-term streaming video pilots: process, analysis and outcomes

Purpose Academic libraries are struggling to support the growing demand for streaming video. The purpose of this paper is to detail the experience of running three long-term pilots with different streaming video platforms, including processes involved, lessons learned and next steps. Design/methodology/approach This paper uses a mixed methods approach, combining analysis of usage data with case study observations. Findings The length of the pilots allowed for deep understanding of the needs of this academic library’s community’s engagement with streaming video in the classroom, and confirmed anecdotal information that availability of multiple platforms supports diverse needs which led to continuing access to all platforms, operationalized to be managed within existing processes. Using usage data and feedback from a task force led to decisions to continue with all three platforms that were piloted. Research limitations/implications While this research describes the experience at one academic library, the information may be generalizable enough that other libraries may use it for their streaming video collection development decisions. Originality/value Long-term pilot studies for streaming video platforms can be challenging for many libraries to undertake. With a modest initial financial commitment, the library was able to explore how the community might use streaming video. Through analysis of usage data, the library was able to see when, where and what was being used and could make better informed decisions about where to concentrate future funds for streaming video support.
VecMetaPy: A vectorized framework for metaheuristic optimization in Python

This work aims to accelerate metaheuristic optimization algorithms and their experimental environment using the vectorization feature in the NumPy library in Python. It is built upon EvoloPy, a framework previously introduced by Faris et al. (2016)[41] for metaheuristic optimization in Python. We have vectorized every aspect of this framework including function evaluation, random number sampling, strategy selection, and performing the update equations. We compare the wall-clock time of our vectorized framework for a few algorithms with the original implementation in EvoloPy. The results demonstrate the substantial improvement in the algorithm’s execution time, even to three orders of magnitude in cases like a 180-dimensional search space. The codes can be found at https://github.com/BaratiLab/VecMetaPy.
Removing the impulse noise from images based on fuzzy cellular automata by using a two-phase innovative method

In this paper, a novel method is proposed to restore digital images corrupted by impulse noise. The proposed method in this paper consists of two phases for detecting impulse noise and image restoration. Corrupted pixels in the first phase of the proposed method are identified in two steps. In the first step, the corrupted pixels are identified by minimum values and the average Moore neighborhood pixels of the central pixel. In the second step of this phase, the pixels identified in the previous step as an uncorrupted pixel, are examined whether they are uncorrupted or corrupted once again for further accuracy by a new algorithm. The new algorithm uses cellular automata to identify corrupted pixels by measuring the harmonic and arithmetic means values of four different states of the position of five pixels in the neighborhood of the central pixel. In the second phase of the proposed method, the corrupted pixels are restored using the cosine similarity of the maximization of four different positions of the five pixels neighborhood to the central pixel using fuzzy cellular. The proposed algorithm is evaluated using image enhancement factor (IEF), Structural Similarity Index (SSIM), and Peak Signal to Noise Ratio (PSNR). The proposed method has been tested on 580 images at 15–90% noise density and the qualitative and quantitative results show that the proposed filter is robust enough at different noise levels and meaningful image details such as edges compared to other methods are well maintained.
Strawctures: A Modular Electronic Construction Kit for Human-Scale Interactive Structures

Tools shape the way we think and make. This paper presents Strawctures, a toolkit for prototyping human-scale interactive artifacts. Strawctures consists of eight different functional modules including electronic components (touch and light for input, vibration, buzzer, and LEDs for output, two logic modules, and one extension) and connectors to explore form and function simultaneously. By simplifying prototyping processes of interactive artifacts in a real size, Strawctures provide a means to bring electronics to early stages in designers' exploration, especially for those who are not trained nor confident in electronics. We demonstrate three application examples including a scarecrow (garden decoration), a pendant lighting (furniture), and a house (large-scale construction toy) to share the unique creative affordances of the kit as a tool for designers (Figure 1).
Enhancing Twin Delayed Deep Deterministic Policy Gradient with Cross-Entropy Method

Hybridizations of Deep Reinforcement Learning (DRL) and Evolution Computation (EC) methods have recently showed considerable successes in a variety of high dimensional physical control tasks. These hybrid frameworks offer more robust mechanisms of exploration and exploitation in the policy network parameter search space when stabilizing gradient-based updates of DRL algorithms with population-based operations adopted from EC methods. In this paper, we propose a novel hybrid framework that effectively combines the efficiency of DRL updates and the stability of EC populations. We experiment with integrating the Twin Delayed Deep Deterministic Policy Gradient (TD3) and the Cross-Entropy Method (CEM). The resulting EC-enhanced TD3 algorithm (eTD3) are compared with the baseline algorithm TD3 and a state-of-the-art evolutionary reinforcement learning (ERL) method, CEM-TD3. Experimental results on five MuJoCo continuous control benchmark environments confirm the efficacy of our approach. The source code of the paper is available at https://github.com/ELO-Lab/eTD3.
Research on UAV Multi-Object Tracking Based on Deep Learning

UAV is widely used in civil or military fields due to its advantages of flexibility, compact and lightness, and it can replace human beings to explore unknown regions or perform various dangerous tasks, such as terrain survey, border patrol, intelligent transportation, power grid detection and disaster detection. Multi-object tracking is an important field in computer vision that has been studied for a long time. In recent years, with the rapid development of UAV technology, object tracking based on UAV has become a research hotspot. In this paper, the application of deep learning in UAV object tracking is studied based on the improved tracking-by-detection multi-object tracking neural network. The processed public data set is used to train the backbone network based on CSPDarknet53 as the detector while the dataset of cars is used to train a pretraining apparent feature vector based on deep learning. Besides, the Kalman filter is used to extract object motion information and update the prediction. Finally, tracking results are obtained by Hungarian matching algorithm. Several groups of experiments on the UAV123 data set show that the trained multi-object tracking algorithm on UAV platform can track the object stably under rapid object movement, fast turning of object motion, multi-object motion, object scale changes and other conditions.