Reviewerly/exHarmony huggingface dataset:
Advanced Electrical Measurements Technologies

Many combinations of numbers had mystical implications in ancient times, prompting many mathematicians and historians to believe that the ancients often embraced intricacy for its own sake [...]
Analysis of Animation Peripheral Design Ability Based on Artificial Intelligence

With the development of communication technology and hardware technology, only continuous innovation in animation production can meet the development needs of the times. Animation has recently received substantial interest in special education. New forms of representation, including animation, multimedia, and virtual reality, are some of the key developments that education technology has made possible. Animation software is an influential mass media, communication, and education platform. Progressive improvements in technical methods along with the expansion of knowledge and skills have resulted in an increase in variety as well as the speed of production of new products for animation. In this study, we propose an Advanced Animation Teaching Model (AATM) based on Design-Oriented Learning Approach (DOLA). Design-based learning is the incorporation of design projects in classrooms so that students can acquire creative solutions and learn curriculum content by participating in actual, cross-curricular challenges. This model keeps pace with the times to meet the needs of the modern market. It combines introduction to animation, color composition, color psychology, composition basis, spatial perspective, animation scene design, and visual art design-related theories to form the theoretical basis for the major. The results showed the trends of animation used in education and the strengths of animation to improve the learning of students and enhance the thinking ability of the students.
A Comparative Study on Adversarial Attacks and Defense Mechanisms

Deep Neural Networks (DNNs) have exemplified exceptional success in solving various complicated tasks that were difficult to solve in the past using conventional machine learning methods. Deep learning has become an inevitable part of several applications in the present scenarios. However., the latest works have found that the DNNs are unfortified against the prevailing adversarial attacks. The addition of imperceptible perturbations to the inputs causes the neural networks to fail and predict incorrect outputs. In practice., adversarial attacks create a significant challenge to the success of deep learning as they aim to deteriorate the performance of the classifiers by fooling the deep learning algorithms. This paper provides a comprehensive comparative study on the common adversarial attacks and countermeasures against them and also analyzes their behavior on standard datasets such as MNIST and CIFAR10 and also on a custom dataset that spans over 1000 images consisting of 5 classes. To mitigate the adversarial effects on deep learning models., we provide solutions against the conventional adversarial attacks that reduce 70% accuracy. It results in making the deep learning models more resilient against adversaries.
Trimmed scores regression for <i>k</i>-means clustering data with high-missing ratio

Data sets with missing values bring great challenges to k-means clustering (KMC). At present, most studies focus on KMC data with low missing ratio while few studies on KMC data with high missing ratio. The current imputation methods have the following problems when dealing with the KMC data: (1) the error between imputation value and original true value is large, which leads to the poor imputation precision; (2) the imputation results have a great influence on the clustering results, which reduce the accuracies of the clustering results. We propose a novel imputation method, to deal with the problems, called as trimmed scores regression (TSR), which obtains an imputation estimator from a regression equation with a trimmed score matrix, and a novel cluster with k-means method. Compared with other imputation methods in numerical analysis, the TSR method exhibits better performance.
Fuzzy Applications in Demonstration of Chinese Teaching Research Based on Machine Learning

In order to improve the effect of Chinese teaching, this paper combines machine learning to conduct Chinese teaching research, constructs an intelligent Chinese teaching system, and conducts online monitoring of the Chinese teaching process. Moreover, this paper uses the general histogram matching algorithm to transfer the color style of the source image to the original image. The image’s local color information will be lost as a consequence of this procedure, according to the processing findings. This paper proposes a multi-directional cutting algorithm to partition the image, then uses the local linear embedding algorithm to realize the color style transfer method, and builds an intelligent Chinese teaching system based on machine learning to achieve a more realistic and natural color style transfer effect. The experimental research shows that the Chinese teaching method based on machine learning proposed in this paper can effectively improve the quality of Chinese teaching.
Research on Coding and Enhancement Algorithm for Very Low-Rate Speech Compression of Beidou Short Message

In recent years, with the development of broadband communication technology, the bandwidth of voice communication is no longer a problem in the field of wired communication, but in the field of wireless communication, bandwidth is a precious resource. In the case of strict transmission bit rate constraints, low-rate speech coding is of great significance. As a low-rate speech coding algorithm, the MELP algorithm has improved the LPC-10 coding scheme, and introduced mixed excitation, aperiodic pulse, Fourier series, pulse discrete filtering and adaptive spectral filtering. A relatively natural voice quality is achieved at a rate of s. The calculation load of the algorithm is small, and it has strong toughness to severe background noise, and can be transplanted into DSP system very well. This paper firstly introduces the current situation of low-rate speech coding and compares the advantages and disadvantages of several main speech coding algorithms. On this basis, the research is carried out, some formulas are deduced theoretically, and the simulation analysis is carried out under MATLAB7.0. In this paper, the MELP algorithm is improved so that its voice quality can quickly meet the needs of the future large-scale communication system development.
Telkom University Slogan Analysis on YouTube Using Naïve Bayes

YouTube is often used in public and private universities as branding for first-year students. YouTube facilitates users to interact by giving likes or dislikes, adding viewers to the video, and responding to videos through comment pages that can analyze by public feedback for branding. In doing branding, many alumni and college students discuss Telkom University as the best private university in content uploaded on YouTube. That can trigger the public to give positive, negative, or neutral comments to Telkom University. In this research, sentiment analysis focuses on the scientific context of branding the slogan "Number 1 Best Private University" to find out the perspectives and opinions of the public that can be used as evaluation material for the university to improve its reputation. Dataset takes from user opinions on YouTube regarding content that discusses Telkom University's branding slogan using the Term Frequency–Inverse Document Frequency (TF-IDF) feature extraction and Naïve Bayes as a classification. The final results of this research show that the ratio of 90:10 normalized then combined with the unigram-bigram token and Naïve Bayes with alpha 0.6 brings out the best performance, with an average accuracy of 85.27%, the precision of 91.41%, recall of 62.45%, and the F1-Score of 64.78%.
A single pulse envelope extraction algorithm based on a pulse model

Pulse compression signal is widely used in the field of radar ranging to solve the contradiction between range resolution and operating range. Vector signal analyzer is an important tool for measuring signal modulation quality. The commonly used single-pulse envelope extraction method of neural network has high algorithm complexity and is not suitable for application in the instrument software platform. In this paper, a single pulse envelope extraction method based on a pulse model is proposed. Firstly, the top power, base power, and overall pulse amplitude of the pulse compression signal are determined by using a pulse histogram; Secondly, the ON modulation state, the OFF modulation state, droop, overshoot, and ripple of the pulse model are estimated based on the pulse basic model theory, and the pulse model with waveform distortion is further extracted; Finally, according to the actual pulse model, the single pulse envelope is determined and characterized. The simulation results prove that the root means the square relative error of the parameter estimation results in this paper is less than 1%, which meets the parameter accuracy standard of the vector signal analyzer.
Eye Tracking and Emotion Recognition Using Multiple Spatial-Temporal Networks

E-commerce products need to be measured by reader responses as a more objective evaluation. Some of them are through emotion expression identification or eye-tracking. Using these two variables from video capture provides a more thorough evaluation of the response to interest and emotion. This study proposes a spatial-temporal multi-networks method using Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) from video for 60 seconds. The results showed that two classes of emotional expression and four directions of eye-tracking gave better accuracy, namely 95.83%, compared to three classes of emotion and four directions of eye-tracking, which was 91.67%. Experiments also show that using CNN-LSTM significantly increased accuracy, while the weight correction technique does not have much effect. The evaluated F1 score shows the consistency of the proposed model.
What Affects User Satisfaction of Payroll Information Systems?

With digital developments that continue to occur, it can affect several parts of the institution or company by changing the system from manual to online. With this progress, government institutions have also begun to implement a computerized system and payroll is one of the sections that is affected by it. For example, payroll which is usually given in person, can now be sent via bank transfer. This study was conducted to analyze the quality of information, system quality, and information system security on user satisfaction in the payroll section of a government institution, namely, the Center for Financial Transaction Reports and Analysis. This study will use data that has been processed in two ways. The first is one of the functions of Microsoft Excel, namely data analysis and the second uses SEM PLS analysis to test the three pre-determined hypotheses. The results of hypothesis testing indicate that the quality of information and information system security affect user satisfaction significantly, while system quality does not substantially affect user satisfaction. The limitation of this research is the limited number of employees in the payroll section of the Financial Transaction Reports and Analysis Center. Suggestions for further research are to use a more general section that has more employees.
AdaBoost Algorithm for Marketplace Product Similarity Detection

The marketplace is a platform that has a duty as an intermediary between sellers who want to sell and buyers who want to buy a product with an online transaction process. So a marketplace website only acts as a third party in handling product transactions in terms of ordering products and several online payment methods provided by the marketplace. It can be seen in several marketplaces such as Shopee, Lazada, Tokopedia, and so on. Of course, they have a lot of products, for example, clothing, staple foods, electronic devices, and many others. With so many products in a marketplace, of course, many products look the same but users or buyers often or don't even know that one product and several other products are the same. In this study, the author uses a product similarity dataset and uses the AdaBoost algorithm to get high classification results. In the dataset used, to classify, the author uses product titles and images which will later be used to distinguish one product from another. For the classification results using the AdaBoost algorithm, an accuracy of 91.81% is obtained, with the accuracy of the score, which means that the model developed by the author has a very good performance in detecting product similarities based on product titles and images in a marketplace.
A Full-Scale Feature Extraction Network for Semantic Segmentation of Remote Sensing Images

Semantic segmentation of high-resolution remote sensing images is an important and challenging task. Aiming at the difficulty of accurate segmentation of confusing objects in high-resolution remote sensing images, we propose a full-scale feature extraction network (FSFN et). The proposed architecture follows the encoder-decoder paradigm. The encoder adopts ResNeXt50 to fully extract features, and inputs the four-scale output of the encoder to the global feature extraction module (GFM) as part of the decoder input. The decoder uses group convolution residual module combined with attention mechanism (GRM) to refine features, and employs dense connection (DC) to strengthen feature propagation. In addition, the third and fourth decoding blocks are added with deep supervision (DS) to speed up the convergence of the network. We evaluate our proposed architecture on the Vaihingen and Potsdam datasets provided by the International Society for Photogrammetry and Remote Sensing (ISPRS). The experimental results show that the proposed approach can significantly improve the segmentation performance, reaching 89.8% and 89.3% overall accuracy (OA) on the two datasets, respectively.
Sellybot: Conversational Recommender System Based on Functional Requirements

Recently, high-tech products are very fast in issuing new types. For example, smartphones have various brands and types with different specifications. This condition triggers doubts among the public to buy the product due to limited knowledge about the technical specifications that suit their needs. Therefore, it is necessary to develop a recommender system based on product functional requirements. In our prior work, a Conversational Recommender System (CRS) has been developed to recommend smartphones based on high-level requirements (product functional requirements) by combining Navigation by Asking (NBA) and Navigation by Proposing (NBP). Thus, users who are unfamiliar with the technical features of the product can express their needs more easily. However, the system uses a dialog form, so users are still less flexible in expressing their needs. In this study, we further develop this research by building Sellybot, a CRS that uses natural language in its interactions with users. We built Sellybot using the RASA framework. Evaluation is done by observing the accuracy and user satisfaction. The evaluation results show that the system has an accuracy of 84.8% and for the questionnaire, it is found that 80.3% of users choose Sellybot, where users feel more flexible in using the system, and get a better experience.
Feature Enhancement Method for Text Information Generation of Remote Sensing Images

The redundancy of irrelevant information brings great inconvenience to the interpretation of remote sensing images. Image captioning can efficiently extract effective information and eliminate irrelevant data to the greatest extent. However, inadequate use of visual features is a common problem in the current research on remote sensing image captioning. To solve this problem, we propose two visual feature enhancement methods for remote sensing image captioning, which are the combination of multi-scale features and the use of remote sensing images pre-training weights. Experiments are carried out using different language models, and the results prove the effectiveness of our proposed feature enhancement methods.
Determination of the relationship between object distance and optimal defocus level for fringe projection systems

In fringe projection measurement, the suitable defocus of the projector can improve the measurement accuracy. This paper presents a method to determine the optimal defocus level in different object distances. In each object distance, this method adjusts the focal length of the projector through some special patterns, and the plane plate is measured by binocular fringe projection measurement system in different defocus levels. Then, the fitting plane error in different defocus levels is polynomial fitted, and the optimal defocus degrees are calculated on the fitting curve. Finally, the corresponding relationship between the object distance and the optimal defocus level is obtained by spline interpolation fitting. Experimental results show that this method can be successfully applied to fringe projection measurement with any object distance, and the measurement accuracy is improved by more than 10%.
Automatic Edge Segmentation of Surface Antenna for 3D Surface Detection

With the continuous development of radio application systems such as communication, broadcasting, radar, guidance and radio astronomy, people have an increasing demand for surface antennas, one of the important development trajectories. For the high-performance surface antenna, especially a large aperture surface antenna, its three-dimensional shape is helpful for advanced and scientific design. For the 3D reconstruction technology based on Pixel2Mesh, it is necessary to automatically segment the parabolic antenna. In this paper, deep learning technology is used to realize the semantic segmentation of surface antennas. The experimental results show that the designed SAUNet3+ network has excellent performance, and the Dice index can reach 81.39%. The automatic edge segmentation method of surface antenna provides a theoretical and experimental basis for the three-dimensional surface detection task of surface antenna.
A Moving Ferromagnetic Target Localization Method Based on YOLO-V3

Moving ferromagnetic target localization and tracking offers a wide spectrum of uses in the military and medical areas. We present an automatic localization method based on YOLO- V3 to achieve fast recognition and precise localization of moving ferromagnetic targets. On the basis of ensuring efficiency and accuracy, this method has a strong anti-interference ability. To begin, the darknet-53 network extracts features from magnetic anomaly pictures taken at any time, and magnetic target feature maps are constructed at various scales to predict magnetic target positions. The boundary frame with varying sizes is then constructed automatically based on the real position of each magnetic target as the geometric center of the boundary frame. Finally, interactive datasets are created using COMSOL and MATLAB, and the proposed approach is compared to the Nara localization method in a variety of conditions, including single magnetic source, double magnetic source, noise, and noise free. The proposed method's recognition accuracy is over 95%, the average positioning error is around 1.5cm, and the positioning time is about 0.031s, according to experimental data. In terms of accuracy and robustness, the proposed method outperforms the Nara localization method, and it can also be used to monitor and localize ferromagnetic objects in real time, even when the data is noisy.
Real-time Waste Detection Algorithm Based on Optimized PicoDet

Object detection technology has been widely used in the field of garbage classification, but embedded devices cannot withstand the computational load of most current object detection models, which brings great challenges to the development of garbage detection technology. In order to realize the real-time garbage detection in low computing power environment, a real-time garbage detection method based on improved PicoDet is proposed. The original CSP- PAN structure is replaced by BiFPN, and higher-level features are obtained through residual connection and weighted bidirectional feature fusion, which reduces the computational cost of the model while ensuring improved accuracy. The CIoU Loss is used as the Reg loss to improve the positioning accuracy and convergence speed of the model. Comparative experiments are carried out on self-built datasets. The experimental results show that the model has an accuracy rate of 97.30% and a speed of 98FPS, which is higher than the test results of other models, and the detection speed and accuracy rate are significantly improved.
Design and Analysis of a Gravitic Tether-Based Grappler for Aerial Payload Deployment

In this paper, we propose a novel mechanism for attaching payloads to pole-like structures, titled GRASPER (GRavity ASsisted Payload EntangleR). Inspired by the tangling-based weapon ’bolas’, the ’pendulum catch’ experiment and driven by SWaP (size, weight and power) constraints imposed on aerial platforms, we developed GRASPER as an alternative to arm-based grippers for load transportation and attachment tasks specific to pole-like structures. It works with two-bodies; a counterweight and a payload that are released onto a pole at height. Being a passive attachment system, it is lightweight, simple to use, surprisingly robust and functions remotely at a distance; a plus for rotorcraft endurance and safety. To study GRASPER, we first define a model accounting for the influence of gravity, rotational dynamics and the Capstan effect on a two-body system released from rest. The time-evolution of the system is simulated numerically, then compared against real-world experiments across variables such as mass, string length and counterweight types, with design implications for GRASPER derived thereafter. Finally, we demonstrate its feasibility of implementation on a typical quadrotor for aerial payload deployment applications.
Multi-robot Path Planning Algorithm in Dense Environments Using Particular Collision-free Traffic Rules

Scheduling collision-free paths for a large number of robots in dense environments with high efficiency is achieved in this work. We propose an algorithm, OMPP (One-way Multi-robot Path Planning), using a new topological skeleton representation of the dense environment by introducing the particular collision-free traffic rules. We propose the integer programming technique based on the topological skeleton graph to tackle the multi-robot path planning optimization problem using distance metrics. We realize two practical achievements in solving multi-robot path planning problems in dense environments: collision-free robotic path generation and an efficient solving process. We have performed numerous simulations. According to the extensive simulation data, our algorithm suggests a higher overall performance in dense environments than the existing representative algorithms.