Reviewerly/exHarmony huggingface dataset:
Smart American Sign Language Recognition For Deaf

People who are deaf or dumb can usually communicate using Sign Languages . However, this is not feasible if one of the interlocutors doesn’t know Sign Language and hence the ease of communication provided by this language vanishes. In this proposed method we have developed a system that uses flex sensors to recognize hand movements related to alphanumeric signs used in American Sign Language. As a byproduct of our research efforts, we developed a new data set made up of depth images of letters and numbers in American Sign Language, carried out an empirical evaluation. In this paper we have introduced a novel method for predictions regarding the development of a comprehensive system using Arduino UNO for sign language transcription.
Comparison of Machine Learning Algorithms for Twitter Sentiment Analysis

In the era of social media, it's easier for people to express themselves. Every day, they share their views and ideas on numerous social media platforms about current events across the world. On hot issues, one can get a general sense of public opinion, whether favorable or unfavorable. Twitter is an example of a global social media platform where people discuss their opinions. By identifying these sentiments, machine learning provides us an advantage in analyzing and predicting them. Different machine learning models are employed to analyze sentiments in Twitter data in this study. Sentiment140, a Twitter dataset that is divided into positive and negative tweets, is used for training. The performance of several models such as Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN), as well as their variants, are compared in this Dataset.
Enforcing Safety in Cooperative Perception of Autonomous Driving Systems through Logistic Chaos Map-based End-to-End Encryption

Collaboration among multiple cyber-physical systems (CPSs) requires improved safety, reliability, and performance. Collaborative CPSs share common goals and collaborate to achieve them. Connected and autonomous vehicles (CAVs) are typical examples of collaborative CPSs. The Cooperative perception in CAVs is an emerging technology that enables the CAVs to share their local perception with others, thereby improving efficiency and road safety. However, in cooperative perception, malicious vehicles may send phantom vehicle information, and additionally, vehicles may unintentionally be malicious due to faulty sensors. These issues pose serious driving hazards as they can incur traffic accidents. Therefore, this article uses logistic chaos map-based end-to-end encryption techniques to avoid malicious vehicle information in cooperative perception. The cooperative perception is achieved via sharing the camera sensor image frames between two vehicles. Using the CARLA simulator, we demonstrated the real-time logistic chaos map-based encryption in the cooperative perception of CAVs. Unlike existing baseline approaches such as Cooper and F -Cooper, in our cooperative perception, we first encrypt the image frames before sharing and then decrypt the image frame at receiving end to avoid malicious information. The experimental results, such as the histogram, adjacent pixel correlation, and key sensitivity analysis, demonstrated that cooperative perception using logistic map-based encryption is safer and more secure than existing methods. In addition, our cooperative perception system increased the detection rate up to two times than the individual perception system of CAVs.
Coverage Path Planning for Unmanned Aerial Vehicles in Complex 3D Environments with Deep Reinforcement Learning

Coverage path planning (CPP) for unmanned aerial vehicles (UAVs) defines a vital role in the automation process of UAV-supported disaster management. While multiple algorithms exist to solve the CPP problem for planar areas, the proposed algorithm is the first to handle complex three-dimensional environments and also account for power constraints and changing environments. By applying proximal policy optimization to an advantage-based actor-critic deep reinforcement learning model, the proposed framework enables an agent to efficiently cover the target area (TA), considering the orientation of the observation sensor, avoiding collisions as well as no-flying zones (NFZ) and reacting to changing environments. Furthermore, a safe landing mechanism, based on the Dijkstra algorithm, expands the framework to guarantee a successful landing in the respective start and landing zone (SLZ) within the power constraints. The model is trained on real data to learn the optimal control policy. Additionally, the framework was tested and validated on hardware in a drone lab to confirm its effectiveness and capability to perform real-time path planning.
Automatic Production of Deep Learning Benchmark Dataset for Affine-Invariant Feature Matching

To promote the development of deep learning for feature matching, image registration, and three-dimensional reconstruction, we propose a method of constructing a deep learning benchmark dataset for affine-invariant feature matching. Existing images often have large viewpoint differences and areas with weak texture, which may cause difficulties for image matching, with respect to few matches, uneven distribution, and single matching texture. To solve this problem, we designed an algorithm for the automatic production of a benchmark dataset for affine-invariant feature matching. It combined two complementary algorithms, ASIFT (Affine-SIFT) and LoFTR (Local Feature Transformer), to significantly increase the types of matching patches and the number of matching features and generate quasi-dense matches. Optimized matches with uniform spatial distribution were obtained by the hybrid constraints of the neighborhood distance threshold and maximum information entropy. We applied this algorithm to the automatic construction of a dataset containing 20,000 images: 10,000 ground-based close-range images, 6000 satellite images, and 4000 aerial images. Each image had a resolution of 1024 × 1024 pixels and was composed of 128 pairs of corresponding patches, each with 64 × 64 pixels. Finally, we trained and tested the affine-invariant deep learning model, AffNet, separately on our dataset and the Brown dataset. The experimental results showed that the AffNet trained on our dataset had advantages, with respect to the number of matching points, match correct rate, and matching spatial distribution on stereo images with large viewpoint differences and weak texture. The results verified the effectiveness of the proposed algorithm and the superiority of our dataset. In the future, our dataset will continue to expand, and it is intended to become the most widely used benchmark dataset internationally for the deep learning of wide-baseline image matching.
CNN-Wavelet-Transform-Based Model for Solar Photovoltaic Power Prediction

Solar power is one of the abundant renewable energy sources. But the power generation capacity of photovoltaic power plants fluctuates significantly due to changes in weather conditions. In this paper, a new model is proposed, which consists of convolutional neural network, wavelet transform and support vector machine (CWS). Firstly, the features of the original data are expanded through the convolutional neural network (CNN). And then the wavelet transform is introduced to suppress the noise in the expanded data. Finally, the output power of the photovoltaic power station is predicted by the support vector regression (SVR) method. The experimental results show that the prediction accuracy and training time of the new model show obvious advantages compared with the previous BI-LSTM (Bidirectional Long Short Term Memory), LS-SPP (LSTM-Based Solar Power Prediction) and LSTM under different prediction time ranges.
Performance Comparison of Rabin-Karp Algorithm and Winnowing Algorithm for Document Abstraction Similarity Detection

Plagiarism is one of the actions that have the potential to occur in the academic environment. A real example is the plagiarism of assignments during the lecture process. One of the efforts to prevent plagiarism is to use a system that has been developed in recent years. However, the price of subscribing to the system for a period is quite high. Therefore, we need a plagiarism detection system that everyone can access for free. In developing the system, of course, modeling must be done. Two popular algorithms are often used to detect word similarity: Rabin-Karp and Winnowing. In this study, a performance comparison was made between Rabin-Karp and Winnowing. Comparisons were made using the same dataset of 30 abstraction data from scientific publications. Based on the research, the performance of the Rabin-Karp algorithm and the Winnowing algorithm is almost as good. However, based on the two evaluation parameters used by the Winnowing algorithm, it produces more similarity scores than Rabin-Karp. The same thing also applies to the evaluation of processing time. Winnowing is slightly longer than Rabin-Karp. With the results of this comparison, it can be concluded that if you want a model sensitive to similarity, then Winnowing is recommended. However, if processing time is the target, it is recommended to use Rabin-Karp. In future research, tuning the parameter values of k or n in the Rabin-Karp and Winnowing algorithms is recommended. In addition, it is also recommended to experiment with another distance scoring.
Effects of Intercept Point Placement On Clothoid Path Recovery Time After Collision Avoidance

Uncrewed vehicles require recovery routes to return autonomously to a mission after collision avoidance. The leading techniques for crewed aircraft such as ACAS do not directly translate to the explicit path definitions required for autonomous systems. Processes for automated route recovery such as the Path Recovery Automated Collision Avoidance System (PRACAS) are capable of returning a vehicle to an original mission, but has only been investigated for straight paths and the effects of interception point placement on recovery time has not been identified. Minimal deviation and recovery time is desired for maximum time and coverage in continuous monitoring tasks while ensuring safe operation in known airspace. The present work investigated the effects of intercept point placement on return time to mission routes in three simulation environments including a straight path, holding pattern, and a path generated from an exponential function to explore performance in common and curved paths and identified the first viable interception point. Clothoid return using the first viable interception point generated more cross track error and time to recover compared to non-linear guidance and pure pursuit, but had control on vehicle heading at path acquisition for smoother path following after interception. The general trend was that interception points for clothoid return farther along a path generated more cross track error and recover time, but had tighter control on acquisition heading. Impact of this work is an investigation of curved path interception within the PRACAS process to identify the first viable point meeting fixed wing turn rate limits to minimize path deviation so time spent in the desired operating region can be maximized where only straight paths had been previously considered.
Investigation on the Mobile Wheeled Robot in Terms of Energy Consumption, Travelling Time and Path Matching Accuracy

The task of controlling a wheeled mobile robot is an important element of navigation algorithms. The control algorithm manages the robot’s movement in accordance with the path determined by the planner module, where the accuracy of mapping the given route is very important. Most often, mobile robots are battery-powered, which makes minimizing energy consumption and shortening travel time an important issue. For this reason, in this work, the mobile robot control algorithm was tested in terms of energy consumption, travel time and path mapping accuracy. During the research, a criterion was developed, thanks to which it was possible to select the optimal parameters of the pure pursuit algorithm that controls the movement of the tested robot. The research was carried out in the Laboratory of Intelligent Mobile Robots using the QBot2e mobile robot operating on the basis of differential drive kinematics. As a result of the research, optimal values of the control parameters were obtained, minimizing the travel time, energy consumption and mapping error of the given paths.
Safe Policy Improvement Approaches and Their Limitations

Safe Policy Improvement (SPI) is an important technique for offline reinforcement learning in safety critical applications as it improves the behavior policy with a high probability. We classify various SPI approaches from the literature into two groups, based on how they utilize the uncertainty of state-action pairs. Focusing on the Soft-SPIBB (Safe Policy Improvement with Soft Baseline Bootstrapping) algorithms, we show that their claim of being provably safe does not hold. Based on this finding, we develop adaptations, the Adv-Soft-SPIBB algorithms, and show that they are provably safe. A heuristic adaptation, Lower-Approx-Soft-SPIBB, yields the best performance among all SPIBB algorithms in extensive experiments on two benchmarks. We also check the safety guarantees of the provably safe algorithms and show that huge amounts of data are necessary such that the safety bounds become useful in practice.
Identification of Scientific Texts with Similar Argumentation Complexity

The presented work describes the study of formally identifying texts that are similar in argumentation complexity. We analyze scientific articles in Russian language through the use of clustering algorithms (K-means, Ward, Spectral). The clustering features include the formally calculable characteristics of argumentation annotations for the dataset texts, so the method is applicable to texts in different genres and languages (after adapting the markers dictionary). The principal limitation consists in the requirement of inputting quantitative characteristics of argumentation structures of texts, which are constructed in accordance with the Argument Interchange Format (in form of rooted directed graphs) and Walton’s compendium of argumentation schemes. We analyze the performance of the clustering algorithms on different feature sets, which characterize the general properties of argumentation graphs, the specific argumentation patterns (common subgraphs for different texts), emotionality and authoritativeness of texts. Argumentation patterns are represented in two forms: standard (in accordance with Walton’s compendium) and generalized (based on functional similarity). We check the similarity of clustering results by different algorithms through using several quality measures (Jaccard-index-based, V-measure, FM-score), whose values belong to the 64±71 percent range. The employed dataset contains more than 1000 arguments from argumentation annotations (graphs) for 30 scientific texts in two thematic areas (linguistics and information technologies). Argumentation graphs are constructed by two annotators with the ArgNetBankStudio tool. The resulting clusters are distinguished by the general complexity of argumentation graphs, the usage of specific argumentation patterns, as well as by the difference in emotionality and authoritativeness.
Novel test method to determine shear properties of lattices: Test set-up and data analysis

In order to determine shear response for lattices, relevant standards for other similar materials were studied. A V-notched beam shear test set-up was developed, including design of a fixture and a V-notched lattice specimen as well as determining test procedure and data analysis method. Followings are some of the considerations and features. Sufficient number of cells are required to obtain representative properties for a given lattice. The free-end condition for installation and gripping of the specimen on fixture arms guarantees loading in pure shear. The overall geometry and presence of a V-notch smooth the shear stress variation across the notch section. It was demonstrated that using Digital Image Correlation (DIC) method improves the accuracy of measured shear strain by removing the deformation of cells adjacent to the notch, as well as the deflection of fixture components. Following the proposed method, shear properties including shear chord modulus of elasticity, offset shear strength, and shear ultimate strength/strain can be determined. These data can be used for material specification, quality assurance, research and development, and structural design and analysis of lattices.
Corrigendum: Artificial Intelligence Promotes the Evolution of English Writing Evaluation Model (2019 IOP Conf. Ser.: Mater. Sci. Eng. 646 012029)

Abstract Please see Article PDF for corrigendum text.
DC3D: A Video Action Recognition Network Based on Dense Connection

Efficiently extracting the temporal and spatial information of motion in the video, and how to obtain the spatiotemporal features with high degree of differentiation, is the key issue to improve the accuracy of action recognition classification. In this paper, a dense 3D convolutional block is designed as the basic unit to construct a dense convolutional 3D network, the spatiotemporal features existing in the video are extracted at the same time, and the transmission and reuse of the features in the network are strengthened, effectively fuse the shallow and deep spatiotemporal features of the network. At the same time, in order to make the features extracted by the network sufficiently discriminative, this paper proposes a joint loss function based on the Fisher discriminant regularization term, it can make the trained network have the ability to increase the inter-class dispersion and reduce the intra-class dispersion of the classified samples, and improve the classification accuracy. Experiments on the UCF-101 human actions classes dataset show that the network recognition accuracy rate proposed in this paper reaches 92.4%, which is higher than 85.2% of the C3D network, which proves the effectiveness of the method proposed in this paper.
Technology Integration in the Teaching of Human Resource Management by Practicing Lecturers

The study investigated factors affecting technology integration in teaching human resource management by public university lecturers.The authors adopted the descriptive survey design.The study's target population comprised all the one hundred and sixty members who teach human resource management in their respective universities.Data were collected through a questionnaire.It was established that the availability and accessibility of Information and Communication Technology (ICT) facilities significantly affect the integration of ICT facilities in teaching human resource management.Furthermore, ICT facilities were found to be inadequate and the extent of use by lecturers was not encouraging.The authors recommend that the government and management of public universities should not only organize further ICT training programs for lecturers but encourage and motivate them to use ICT facilities in their teaching.
Comprehensive evaluation of 5G+ smart distribution network based on combined weighting method-cloud model

With the large-scale application of 5G technology in smart distribution networks, the operation effects of distribution networks are not clear. Herein, we propose a comprehensive evaluation model of a 5G+ smart distribution network based on the combination weighting and cloud model of the improved Fuzzy Analytic Hierarchy-Entropy Weight Method (FAHP- EWM). First, we establish comprehensive evaluation indexes of a 5G+ smart distribution network from five dimensions: reliable operation, economic operation, efficient interaction, technological intelligence, and green emission reduction. Second, by introducing the principle of variance minimization, we propose a combined weighting method based on the improved FAHP-EWM to calculate the comprehensive weight, so as to reduce the defects of subjective arbitrariness and promote objectivity. Finally, a comprehensive evaluation model of 5G+ smart distribution network based on cloud model is proposed by considering the uncertainty of distribution network node information and equipment status information. The example analysis indicates that the overall operation of the 5G+ smart distribution network project is decent, and the weight value calculated by the combined weighting method is more reasonable and accurate than that calculated by the single weighting method, which verifies the effectiveness and rationality of the proposed evaluation method. Moreover, the proposed evaluation method has a certain guiding role for the large-scale application of 5G communication technology in smart distribution networks.
Improving the localisation of features for the calibration of cameras using EfficientNets

Camera-based methods for optical coordinate metrology, such as digital fringe projection, rely on accurate calibration of the cameras in the system. Camera calibration is the process of determining the intrinsic and distortion parameters which define the camera model and relies on the localisation of targets (in this case, circular dots) within a set of calibration images. Localising these features with sub-pixel accuracy is key to providing high quality calibration results which in turn allows for high quality measurement results. A popular solution to the localisation of calibration features is provided in the OpenCV library. In this paper, we adopt a hybrid machine learning approach where an initial localisation is given by OpenCV which is then refined through a convolutional neural network based on the EfficientNet architecture. Our proposed localisation method is then compared with the OpenCV locations without refinement, and to an alternative refinement method based on traditional image processing. We show that under ideal imaging conditions, both refinement methods provide a reduction in the mean residual reprojection error of approximately 50%. However, in adverse imaging conditions, with high noise levels and specular reflection, we show that the traditional refinement degrades the results given by pure OpenCV, increasing the mean residual magnitude by 34%, which corresponds to 0.2 pixels. In contrast, the EfficientNet refinement is shown to be robust to the unideal conditions and is still able to reduce the mean residual magnitude by 50% compared to OpenCV. The EfficientNet feature localisation refinement, therefore, enables a greater range of viable imaging positions across the measurement volume. leading to more robust camera parameter estimations.
Heuristics for Spatial Data Descriptions in a Multi-Agent System

Navigation and an agent’s map representation in a multi-agent system become problematic when agents are situated in complex environments such as the real world. Challenging modifiability of maps, long updating period, resource-demanding data collection makes it difficult for agents to keep pace with rather quickly expanding cities. This study presents the first steps to a possible solution by exploiting natural language processing and symbolic methods of supervised machine learning. An adjusted algorithm processes formalized descriptions of one’s journey to produce a description of the journey. The explication is represented employing Transparent Intensional Logic. A combination of several explications might be used as a representation of spatial data, which may help the agents to navigate. Results of the study showed that it is possible to obtain a topological representation of a map using natural language descriptions. Collecting spatial data from spoken language may accelerate updating and creation of maps, which would result in up-to-date information for the agents obtained at a rather low cost.