Reviewerly/exHarmony huggingface dataset:
The Prediction Model of IPIX Radar Sea Clutter Based on SSA-ELM Neural Network

Abstract The prerequisite for improving radar detection capability is to reduction of sea clutter from interfering with the target, and the accurate prediction of sea clutter is an essential prerequisite for effective suppression. To achieve an accurate prediction of sea clutter, a model for sea clutter prediction combination of the salp’s swarm algorithm, prediction using extreme learning machine after performing parameter search, which improves the prediction performance of ELM and backpropagation (BP) neural network. The convergence speed and accuracy are improved, and the overall prediction The IPIX radar sea clutter prediction reaches an accuracy of more than 99% and the prediction results of this model are better than that when only using ELM or BP neural network prediction.
Design and Realization of Mobile Robot Driven by Omnidirectional Wheels

Abstract Omnidirectional mobile robot refers to a robot technology that can move in any direction on the ground. Aiming at the problems of complex control, low driving efficiency, and poor carrying capacity of ordinary omnidirectional mobile robots, an omnidirectional wheel-driven mobile robot was proposed. The robot can achieve traverse characteristic motion with zero gyration radius without changing its attitude. Through the kinematic analysis of the mobile robot equipped with this wheelset, the omnidirectional motion function of efficient driving is verified. Prototype experiments show that the robot can effectively improve space utilization and intelligence in scenarios with limited space.
Video Sequence Image Matching Algorithm Based on Adaptive Gamma Correction

Abstract Aiming at the low matching accuracy and poor robustness in video sequence image matching, a video sequence image matching algorithm based on Adaptive Gamma Correction is proposed in the paper. Firstly, Adaptive Gamma Correction is used to enhance the contrast of video sequence images. Then feature extraction and description are realized by the ORB algorithm. The RANSAC algorithm is optimized in matching by improving the interior point ratio after Brute Force Match. The experimental results show that the algorithm proposed improves the correct matching rate by 19.51%, 12.53%, and 4.87% respectively compared with the SIFT, ORB, and ORB+RANSAC algorithms without increasing the running time. Meanwhile, the algorithm in this paper has good robustness in video sequence image matching.
Improving the performance of Uyghur speech recognition based on Factorized Time-Delay Neural Network

Abstract Speech recognition systems have low accuracy in recognizing the Uyghur language, a low-resource language, due to its strong language specificity and few public training datasets. Given this problem, considering the characteristics of Uyghur, we use morpheme units to build a language model and use mixture data augmentation methods to expand the training data. A 9-layer TDNN-F is applied, which can effectively utilize contextual information. An optimal 9.88% WER (Word Error Rate) is achieved in experiments on the open-source dataset THUYVG-20. Compared to the baseline system of this dataset, the WER is reduced by 6.7%, which significantly improves the accuracy of the Uyghur speech recognition, and provides a reference in other low-resource languages for speech recognization.
Risk Level Assessment and CO Prediction of Underground Mines for Poisoning and Asphyxiation Accidents

To effectively prevent the occurrence of poisoning and asphyxiation accidents in underground mines, this paper establishes an evaluation index system for the factors influencing accidents, constructs a combined assignment model to solve the problem of low accuracy of assignment results caused by a single algorithm, predicts the CO concentration after blasting because CO poisoning is the main cause of accidents, explores the accuracy of different time series prediction methods, and projects the required ventilation after blasting to ensure the safe operation of personnel. Firstly, starting from “man-machine-environment-management”, social factors are introduced to build an evaluation index system. Secondly, three combinatorial allocation models were compared, namely rough set theory–G1 method (RS-G1), entropy method–G1 method (Entropy-G1), and CRITIC method–G1 method (CRITIC-G1). The best model was selected and the allocation rating model was constructed in combination with the cloud model, and the mine risk level was evaluated by using the model. Thirdly, the GM(1,1) model, the quadratic exponential smoothing method, and the ARIMA model were compared by calculating posterior differences and errors, and the method with the highest accuracy was selected for predicting CO concentration. The results show that the inclusion of social assessment indexes in the assessment index system makes the consideration of assessment indexes more comprehensive. The RS-G1 combined assignment model achieved higher accuracy than other combined assignment models, and the GM(1,1) model had the highest accuracy and the best prediction effect. The results of the study can help provide targeted prevention and management measures for poisoning and asphyxiation accidents in underground mines.
3D human pose estimation based on negative exponential reduction Gaussian kernel

Abstract Human pose estimation has become an important research direction in the field of motion recognition. 3D human pose estimation adds depth information to 2D pose estimation, which is more widely used. In this paper, the weight of each voxel is calculated in the 3D discrete space by projecting the joint point heatmap to directly estimate the 3D human pose. To improve the accuracy of 3D human pose estimation, the Gaussian kernel of heatmap with the variable size is reduced by a negative exponent in the process of training. The dilated convolution of a small convolution kernel is used to replace the large convolution kernel to solve the problem of large computation overhead when detecting key points in discrete 3D space. Experimental results show that this method is effective and can accurately estimate the 3D pose in multi view images.
Regularized K-means Clustering for Multi-View Data

Abstract Clustering is a common problem in machine learning. Multi-view data can better express the integrity of data because most of the data currently processed is single-view data. Therefore, the problem of clustering analysis of multi-view data has attracted a lot of attention, and how to improve the accuracy of clustering analysis of multi-view data has become a hot topic. We propose the regularized K-means (RKM) algorithm to deal with multi-view data, based on the K-means algorithm, add regularization terms, which can avoid data overfitting. Through numerical analysis, the RKM significantly improves clustering performance to the comparison methods.
Encrypted Traffic Classification Based on a Convolutional Neural Network

Abstract To resolve the issues of low accuracy, weak universality, and easy invasion of privacy in traditional encryption traffic classification methods, an encryption traffic classification method based on a convolutional neural network is offered. Firstly, according to the packet size and time message of the net traffic, the original traffic is transformed into a two-dimensional picture to avoid relying on the packet payload to violate privacy, and then the model is embedded. The Inception module performs feature fusion to improve the classification accuracy. Finally, the average pooling layer and the convolution layer are used to replace the fully connected layer, increasing the calculation speed and avoiding overfitting. Experimental results show that the algorithm achieves an accuracy of more than 95% for application traffic classification tasks.
Improved Object Detection Algorithm Based on Faster RCNN

Abstract This paper studies the target detection algorithm based on Faster R-CNN. Aiming at the insufficient regression accuracy of the target prediction box, an improved target detection algorithm based on Faster R-CNN is proposed. Firstly, ResNet 50 residual network is selected and the feature pyramid (FPN)is introduced to improve the ability of target detection. Secondly, GIOU is introduced to optimize the anchor frame to improve the positioning problem of the target candidate frame. Finally, a bilinear interpolated ROI Alian is used to replace the original ROI pooling, which avoids the pixel error caused by two quantization operations. The data set Pascal VOC 2012 is used for training and testing, and it is verified that the proposed algorithm improves the mAP by 5.4% compared with the original algorithm.
PRRT: An Intelligent Planning Algorithm with Memory

Abstract This paper proposes a practically executable path planning method, namely, Pheromones-RRT(PRRT), for robots with a large joint range in a complex environment. To inter-activate with the real world, the point cloud is utilized as the scene information, while for sampling, the pheromones approach is designed to describe the pheromone content carried by each sampling point. During the sampling process, random sampling nodes are performed with a probability of ε, or those nodes with the highest pheromone content in the current sampling tree are selected with a probability of 1-ε and sampled in their neighborhood. To avoid the local minimum problem, the concept of pheromone volatile factor (PVF) is proposed, while in the expansion, double trees are also generated by PRRT in both cartesian and configuration spaces to improve the speed of the algorithm. The pheromone accumulation enables PRRT to certain learning abilities, reducing the randomness of the sampling process. Simulation results show that the proposed method can effectively plan an optimal obstacle avoidance path with satisfactory performances as compared with the RRT-Connect.
Reflective Teacher Education in the Digital Age

360°(degree) videos inherit interesting potentials for teaching competence development. Reflecting on personal teaching performance from multiple perspectives potentially increases the depth in situated reflection and provides new learning insights for pre-service teachers. In this light, this chapter describes a case study conducted at the University of St. Gallen in Switzerland, where 360° videos were used for reflection on personal performance of pre-service teachers in order to clarify, whether they add value to reflective observations compared to classical video recordings. Furthermore, the chapter aims to explore future developments of 360° video reflection by using artificial intelligence (AI)-based applications to create an added, pedagogical value. Key findings are that (I) 360° videos can effectively enhance reflection processes in teacher education when combined with feedback and situated learning processes in a learning design, (II) 360° videos offer advantages to follow teacher-student-interactions and to reflect upon personal teaching performance from multiple perspectives, and (III) the implementation of an intelligent tutoring system (ITS) by means of supervised learning and by using natural language processing (NLP) potentially leads to an improved learning setting for teaching competence development of pre-service teachers.
Toward Robust and Efficient Low-Light Image Enhancement: Progressive Attentive Retinex Architecture Search

In recent years, learning-based low-light image enhancement methods have shown excellent performance, but the heuristic design adopted by most methods requires high engineering skills for developers, causing expensive inference costs that are unfriendly to the hardware platform. To handle this issue, we propose to automatically discover an efficient architecture, called progressive attentive Retinex network (PAR-Net). We define a new attentive Retinex framework by introducing the attention mechanism to strengthen structural representation. A multi-level search space containing micro-level on the operation and macro-level on the cell is established to realize meticulous construction. To endow the searched architecture with the hardware-aware property, we develop a latency-constrained progressive search strategy that successfully improves the model capability by explicitly expressing the intrinsic relationship between different models defined in the attentive Retinex framework. Extensive quantitative and qualitative experimental results fully justify the superiority of our proposed approach against other state-of-the-art methods. A series of analytical evaluations is performed to illustrate the validity of our proposed algorithm.
A Joint Network Based CNN for Yoga Pose Classification and Scoring

Comparing to traditional rehabilitation, rehabilitation at home becomes a need during pandemic. The technique brought up in this paper allows patients and yoga fans exercise at home with low cost and comfort while can also evaluate their postures. Previous works focus either on classifying poses or scoring on the sameness between the two input branches of patients’ poses and normative poses, but they ignore the combination of them in one single network. In this study, a residual block based Siamese CNN network with classification and scoring modules is proposed, aiming at providing accurate pose matching scores and classify pose types on yoga postures simultaneously. The Siamese network takes two inputs of learner’s pose and standard pose, which are preprocessed skeleton images by OpenPose. With the addition of residual block on the first convolutional module, back propagation is facilitated, which boosts up the process of updating parameters and optimization. The model calculates total loss by summing up cosine embedding loss and cross entropy loss in which the weight parameter lambda could be modified based on need. As for the scoring module, cosine similarity is used to calculate pose resemblance on batch level. The improvement in model performance is obvious when comparing the loss and accuracy between the Siamese network with residual block and VGG-16. Experimental results indicate that the residual block based Siamese network achieves competitive performance compared to the VGG-16 and can provide scoring feedback to learner’s yoga poses.
Discovering Significant Sequential Patterns in Data Stream by an Efficient Two-Phase Procedure

One essential topic of mining sequential patterns in the data stream is to optimize the time-space computations. However, more importantly, it should pay more attention to the significance of mining results as a large portion of them just response to the user-defined constraints purely by accident and they may have no statistical significance. In this paper, we propose FSSPDS, an efficient two-phase algorithm to discover the significant sequential patterns (SSPs) in the data stream with typical sliding windows, which has never been considered in existing problems. First, for generating SSPs candidates with high-quality, FSSPDS takes testable support and pattern length constraints into account and insignificant patterns were removed timely by a pattern-growth method. In the second phase, appropriate permutation testing is used to test the significance of the SSPs candidates. Exact permutation <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>p</mi> </math> values are obtained in a novel combination way based on unconditional Barnard’s test statistic which better reflects the process of data generations and collections. Experimental evaluations show that FSSPDS allows the discovery of SSPs in the data stream and rivals the state-of-the-art approaches efficiently under the control of family-wise error rate (FWER), especially for time efficiency, which was approximately an order of magnitude higher.
Sign language recognition based on depth image processing

Deaf and mute people mainly rely on sign language to communicate, but they still face many problems in their daily life. The interaction between humans and computers has developed extensively in recent years, the flexible and easy-to-understand characteristics of sign language make it suitable for application in the technology of computer vision. This paper mainly introduces sign language recognition from the current research status, application of deep image processing technology, collection of sign language datasets and the final results. After analysis, this paper finally established a recognition model with the wrist joint as the center point to complete the extraction of hand features. This paper input a large number of pictures to train the model, and the final recognition rate reached 91.3%. This paper can widely apply this recognition technology in computer-aided teaching, TV program bilingual broadcasting, virtual human research, medical research, game entertainment and many other aspects.
A Deep Learning based IoT Framework for Assistive Healthcare using Gesture Based Interface

Around the world, the number of senior citizens is increasing and shall continue to increase, and it is expected to be around 20 percent by 2050. Realizing its importance, the United Nations has identified Health and Wellness as one of the Sustainable Development Goals (SDG). The unfortunate pandemic situation due to the COVID-19 outbreak opened up new challenges for contact-less interactions and control of devices for ensuring the well being of citizens. In this paper, our main aim is to develop an intelligent framework based on a gesture-based interface that will help the senior citizens and physically challenged people interact and control different devices using only gestures. We focus on dynamic gesture recognition using a deep learning-based Convolutional Neural Network (CNN) model. The proposed system records continuous real-time data streams from non-invasive wearable sensors. This real-time continuous data stream is fragmented into data segments that are most likely to contain meaningful gesture data frames using the Adaptive Threshold Setting algorithm. The segmented data frames are provided as input to the CNN model to train, test, validate, and then classify it into predefined clusters, which are gestures. We have used the MPU6050 Inertial Measurement Unit based sensor model for collecting the data of the hand/ finger movement. The popular and widely used ESP8266 controller is used for data gathering, processing, and communicating. We created a dataset for 36 gestures, which includes ten digits and 26 English alphabets. For each gesture, a dataset of 300 samples has been created from 5 subjects of age group between 21-30. Thus, the final dataset consists of a total of 10800 samples belonging to 36 gestures. A total of six features comprising linear accelerations and angular rotation in 3-dimensional axes are used for training and validation. The proposed model can segment 93.75% of data segments correctly using the adaptive threshold selection algorithm, and the CNN classification algorithm can classify 98.67% gestures correctly.
Designing a Forensic Investigation Framework for IoT Monitoring and Modelling

Securing a wireless digital scene for IoT based digital investigations has been problematic. Amongst the most pressing issues is the increasingly challenging task of identifying IoT devices. Practical difficulties arise from the rapid introduction, growing variety and likeness to ordinary physical objects of IoT devices. The mostly indistinguishable digital evidence sources are often overlooked during the forensic process of identification. The alternative means of IoT device identification, including from the Device Fingerprinting and Indoor Localisation areas, are lacking for the need of investigators to be able to accomplish the task of identification. To assist the search operations, including for IoT devices, a suitable approach is to reconstruct wireless sensing deployments ahead of the identification task. This, however, will require investigators to harness the communications of IoT devices. This paper apprises the salient features and capabilities desirable for an effective IoT monitoring and modelling system. A model of the envisaged system is light-weight and suited for both forensic and law enforcement purposes. The underlying principles of the contemporary model are three discrete aspects of IoT device communications, namely, monitorability, traceability and discoverability.
Investigating The Best Pre-Trained Object Detection Model for Flutter Framework

Object detection is a machine learning task that can detect objects in an image or video. With the rising demand for object detection features, a solution is needed to make it more accessible. This can be solved by integrating an object detection model into Flutter, a framework that can be compiled and used on popular platforms like iOS and Android. We investigated a total of 13 pre-trained models from PyTorch that will be integrated into Flutter. Through our investigation, we found that the YOLOv5 variants provided the best balance between accuracy and speed while boasting a significantly higher accuracy-to-speed ratio compared to the rest. We also found that quantizing the models can reduce their file size and execution time by up to 55% and 26% respectively while retaining comparable accuracies. However, we were not able to integrate them into flutter due to issues that we encountered.
An Algorithm to Decrease the Key Distribution Error Rate Using Pulsars

In this work we propose a new algorithm that decreases the error rate of the key distribution protocol that uses a pulsar as source of randomness. In the core of the proposed algorithm is the relative disentropy working as a measure of distance between probability distributions. The algorithm is described and simulations are performed.
IoT Sensor Data Consistency using Deep Learning

Sensor data consistency in Internet of Things (IoT) Applications is the problem of ensuring that large number of sensors in a system are providing mutually consistent values. Detection of data inconsistency can be used to detect unusual conditions like malicious intrusion and other anomalous situation. Machine learning-based anomaly detection approaches can be used to detect sensor data inconsistency. This paper studies the problem of sensor data consistency in the context of detecting hotspots in sensor data being generated in pairs of sensors embedded in a commercial IoT system deployed to monitor grain in large horizontal grain bins. The paper explores how well traditional anomaly detection machine learning algorithms like Location Factor, Isolation Forest, and One class support vector machine work in this environment. A memory efficient Long Short-Term Memory (LSTM) deep learning model was proposed that outperformed the traditional machine learning approaches.
Twitter Sentiment Analysis of Indonesian Airlines Using LSTM

Twitter is one of the social media that is currently a trend, where Twitter users can tweet as freely as possible about their opinions and even those opinions about airlines in Indonesia. Twitter sentiment analysis is a process to identify whether tweets on Twitter are included as positive tweets or negative tweets. In this research, the tweets will be divided into three categories: positive, neutral, and negative, using Lexicon and Long Short-Term Memory (LSTM). The data taken are tweets from Twitter in the form of text. One hundred positive, one hundred neutral, and one hundred negative tweets were taken. After going through the process using the Lexicon and LSTM method, the results obtained are 73% accuracy, where there are 130 positive tweets, 105 negative tweets, and 62 neutral tweets.