Reviewerly/exHarmony huggingface dataset:
Heuristic sparse sampling path planning algorithm for complex scenes

In this paper, a heuristic sparse sampling method is proposed to solve the path planning problems in complex scenarios such as narrow passages and zigzags. The proposed approach first obtains sampling points around the obstacles that obstruct the linear link between the start and end points, and expands the sampling points to ensure that there is a path connection between any two sampling points. Then use an improved ant colony algorithm to plan a path between these sampling points. Finally path compression is performed to shorten the path length. Experimental results show that this method is better than the Informed-RRT* algorithm in terms of planning efficiency and path length when performing path planning in complex scenes such as narrow passages and zigzags.
Crime Visualization using A Novel GIS-Based Framework

Past decades have experienced the rage of GIS technology in a never-ending scope. In today&#x2019;s era, visualization of high-dimensional hyperspectral data is an indispensable task and GIS is simply a platform to practically experience the visualization. Furthermore, crime is an unprecedented event and to analyze crime their exists many technologies but to visualize it we are limited to adopt a few technologies and GIS is one of those few. Since crime data which are location-specific acts as a better data for crime analysis, we prefer GIS technology for mapping and visualization. This paper focuses on visualization of crime data using GIS technology and proposes a framework for futuristic crime analysis with the aid of deep learning acting at the backend. The experimentation performed over crime dataset presents the visualization of crime hotspots over a specified region basing on the dataset. The entire workflow is mentioned as a consequence of GIS technology over crime hotspot detection. Furthermore, the essence of deep learning is proposed as a future research direction for the real-time visualization of crime so that it can be checked before it happens. Finally, this paper provides the crime hotspot mappings as the output for visualization and analysis through proper experimentation.
Blind Noisy Image Quality Assessment Using Variance, Kurtosis, and Noise Estimation Score

To meet image quality requirements in daily applications and the evaluation of image algorithm performance, the image quality assessment has been widely researched. The discrete cosine transform (DCT) does not effectively represent mutations or singularities, and the AWGN usually appears in the DWT sub-bands at high frequencies, so DWT has a better choice than DCT to respond to distinct high frequencies. Based on the above characteristics, the proposed algorithm extracted variance, kurtosis, and noise estimation score features to assess image quality, and trained the above features by SVR model, and proposed an improved method to assess the quality of the image. The proposed algorithm was simulated in the CSIQ, LIVE, and TID database. Then, the three databases were synthesized together to a new database and tested algorithm performance for improving generality. The results show that the improved model has better evaluation results compared to the existing no-reference noise evaluation model.
User Experience of PLM-Systems Analysis of the Current State of Research

This paper sets out to analyse the current state regarding the user experience (UX) provided by PLM environments. Our literature review sets focus on commonly used methods in UX research, and the identification of research gaps regarding UX in the field of PLM. The resulting list of research methods and gaps was allocated to previously defined research questions. This leads to an extensive list for further research, highlighting the gaps, describing the questions, and proposing methods on how to bridge these gaps.
Students' Perceptions of E-Learning Systems at the Jordanian Universities Through the Lens of E-Business Booming During the Coronavirus Pandemic

This paper investigates undergraduate students’ perception and acceptance of e-learning systems at Jordanian universities. The framework of this study is guided by the Unified Theory of Acceptance and Use of Technology (UTAUT) and, DeLone and McLean Information System Success Model. The online questionnaire is used to collect data from 411 undergraduate students at Jordanian public and private universities. Partial Least Squares Structural Equation Modeling (PLS-SEM) is used to analyze the data. The findings suggest that (i) performance expectancy, facilitating conditions, and information quality have a significant, positive effect on the actual usage of e-learning systems, whereas system quality did not, (ii) the usage of e-learning systems positively influenced educational performance and students' satisfaction, (iii) the impact of COVID-19 moderate the relationship between the use of e-learning systems and educational performance and (iv) face to face is the most favorable educational-learning approach, followed by blended, and e-learning.
Demand-side Response User Selection Based on Improved BPSO

Abstract In order to use the demand-side response activities to stabilize the peak load, ensure the balance of power supply and demand, and promote the quality and efficiency of the power grid, this paper constructs the demand-side response user competition models in different stages. The improved BPSO algorithm is used to optimize the subsidy cost weighted model in the saturated user competition model, and the algorithm is implemented automatically based on the blockchain intelligent contract. Simulation and experimental results show that, compared with other user selection schemes, these models can improve the actual user response and reduce the subsidy cost of the demand response centre. It can be seen that in the case of saturated user competition, the improved BPSO algorithm is feasible in the application of demand-side response to user selection and optimization of demand-side management.
On Mediating Space, Sound and Experience: Interviews with situated sound art practitioners

This article reports on an interview-based study with ten sound artists and composers, all engaged in situated sonic practices. We propose that these artists engage the ear and shape possible interactions with the artwork by altering the relationship between sound, the space in which it is heard and the people who hear it. Our interviews probe the creative process and explore how a sound artist’s methods and tools might influence the reception of their work. A thematic analysis of interview transcriptions leads us to characterise artist processes as mediatory, in the sense that they act in between site and audience experience and are guided by the non-human agencies of settings and material things. We propose that artists transfer their own situated and embodied listening to that of the audience and develop sonic and staging devices to direct perceptual activity and listening attention. Our findings also highlight a number of engagement challenges, in particular the difficulty artists face in understanding their audience’s experience and the specificity of an artwork’s effect not just to its location but also to the disposition, abilities and prior experiences of listeners.
Semi-Supervised with Partition Strategy and MS- G3D for Skeleton-Based Action Recognition

In the recognition of human behavior, skeleton-based behavior recognition has attracted much attention because of its excellent model performance and its ability to show good applicability on mobile devices. In recent years, there have been many improved algorithms for basic graph convolutional neural networks, all in order to improve the performance of the model. Long-distance and multi-scale feature extraction is the focus of these models. Among these models, Disentangling and Unifying Graph Convolutions for Skeleton-Based Action Recognition based on the partition strategy is the best performing model. Although the recognition rate has increased in all actions, the performance of the model has declined in the face of a data set with missing supervision information. Therefore, a separation and unified graph convolutional neural network based on a semi- supervised partition strategy is proposed, and a local first-order approximation is used to design the convolutional architecture. The model linearly expands the number on the edge of the graph and learns the hidden layer representation. This model effectively solves the problem of low recognition rate when the supervision information of the data set is missing, and at the same time improves the scalability of the model. Experiments on NTU RGB+D 60 and Kinetics Skeleton 400 datasets show that the algorithm effectively improves the accuracy of recognition.
Using Competitive Binary Particle Swarm Optimization Algorithm for Matching Sensor Ontologies

Developing sensor ontologies and using them to annotate the sensor data is a feasible way to address the data heterogeneity issue on Internet of Things (IoT). However, the heterogeneity issue exists between different sensor ontologies hampers their communications. Sensor ontology matching aims at finding all the heterogeneous entities in two ontologies, which is a feasible solution for aggregating heterogeneous sensor ontologies. This work investigates swarm intelligence (SI)-based sensor ontology matching techniques and further proposes a competitive binary particle swarm optimization algorithm (CBPSO)-based sensor ontology matching technique. In particular, a guiding matrix (GM) is proposed to ensure the population’s diversity and a competitive evolutionary framework is presented. The experiment uses ontology alignment evaluation initiative (OAEI)’s benchmark and three real sensor ontologies to test CBPSO’s performance. The experimental results show that the competitive evolutionary framework is able to help CBPSO effectively optimize the alignment’s quality, and it significantly outperforms other SIs at 5% significant level.
VNAnomaly: A novel Vietnam surveillance video dataset for anomaly detection

Surveillance systems have long been considered as an effective tool to capture various realistic abnormal actions or events in various domains such as traffic management or security. With the smart city development, thousand of installed surveillance cameras have played a vital role in detection and prevention of dangerous events. However, there is a lack of anomaly datasets for developing automatic anomaly detection systems in Vietnam. In this study, we introduce a new dataset named VNAnomaly for anomaly detection in Vietnam. Moreover, we also conduct a thorough evaluation of current state-of-the-art for unsupervised anomaly detection methods based on deep architectures including MLEP, Future frame prediction, MNAD, and MNAD with modified inference on benchmark datasets and our dataset. Experimental results indicate that the proposed method almost always outperforms the competitors and achieves the best performance in terms of Area Under the Curve (AUC) score at 61.14%.
Development of New Energy Vehicle Teaching System Based on Mixed Reality Technology

Abstract There are many high-pressure parts in new energy vehicles, and the structure of the vehicle is basically closed. These bring great trouble to the training and learning of new energy vehicles. With the rapid development of mixed reality and computer control technology, virtual reality teaching technology emerges at the historic moment. But the current system needs to be improved in performance, especially in the combination of virtual and real. This paper developed a new energy vehicle teaching system based on virtual mixed reality (MR) technology. In the system, there are system hardware basics, basic layer, management, system application layer, professional application layer and so on. The system achieves the effect of combining the virtual and the real, and operates the virtual model by Gesture movement. This system is very convenient and practical. Users can teach and train the structure and working principle of new energy vehicle parts, by operating the new energy vehicle model in this system.
An Integrated Framework Based on GAN and RBI for Learning with Insufficient Datasets

Generative adversarial networks are known as being capable of outputting data that can imitate the input well. This characteristic has led the previous research to propose the WGAN_MTD model, which joins the common version of Generative Adversarial Networks and Mega-Trend-Diffusion methods. To prevent the data-driven model from becoming susceptible to small datasets with insufficient information, we introduced a robust Bayesian inference to the process of virtual sample generation based on the previous version and proposed its refined version, WGAN_MTD2. The new version allows users to append subjective information to the contaminated estimation of the unknown population, at a certain level. It helps Mega-Trend-Diffusion methods take into account not only the information from original small datasets but also the user’s subjective information when generating virtual samples. The flexible model will not be subject to the information from the present datasets. To verify the performance and confirm whether a robust Bayesian inference benefits the effective generation of virtual samples, we applied the proposed model to the learning task with three open data and conducted corresponding experiments for the significance tests. As the experimental study revealed, the integrated framework based on GAN and RBI, WGAN_MTD2, can perform better and lead to higher learning accuracies than the previous one. The results also confirm that a robust Bayesian inference can improve the information capturing from insufficient datasets.
Style Transfer for Reconstruct 3D Human Pose from Single Image

In this paper, the image after the style transfer is used to train the neural network to learn texture information for 3D human shape reconstruction, and then the human pose which is extracted from Openpose is used as the ground truth for loss iteration. The auxiliary neural network can be obtained from the complex image Appropriate human parameters allow shape reconstruction through SMPL. the combination of iterative human parameters and training neural network to learn texture extraction information improves the accuracy of 3D human reconstruction. This method alleviates the low training speed and reduces the need for a large amount of supervision data to a certain extent. This work uses style transfer to enhance images and modify network structures that are validated on the dataset and robust.
FI-BRS: A Few-Interaction Backpropagating Refinement Scheme for Video Object Segmentation

As a backpropagating refinement scheme (BRS) is introduced as an interactive algorithm to combine deep neural network and user-specified context for object segmentation, we further propose a Few-Interaction BRS (FI-BRS) for video Object segmentation (VOS) task. With only few user&#x2019;s scribbles, FI-BRS automatically selects key regions (markers) inside and outside a target object in reference frame to play the role of scribbles. To form the distance maps in a BRS, the corresponding markers in test frame are located due to feature similarity with the ones in reference frame. For higher efficiency, the markers follow a parallel input order to form distance maps, which has a faster speed without obvious accuracy loss than the conventional way of serial input. The good performances on DAVIS and Youtube-Object benchmarks demonstrate that our proposed FI-BRS yields competitive results compared with state-of-the-art VOS methods.
Comparative Analysis of Social Media Hate Detection over Code Mixed Hindi-English Language

In current era, the social media like Facebook, Twitter and many other platform play very important role of information sharing and connecting the society in digital way. Even social media comes with great benefit and opportunity but there are also some serious and negative issues. Haters use social media as a weapon to targeting someone. Recently, many researches are being mainly done on English language, in this research we have tried to focus on code mixed languages. In this research, we took Hindi and English as code mixed languages, and we also focused on sentences which are not use abuse word but content hate. Machine learning and Deep learning provide some algorithms, in this research comparatively analysed these algorithms and proved that CNN (Convolutional Neural Network), LSTM (Long Short-Term Memory) and SVM (Support Vector Machine) are given better F1 score from rest. Natural language processing (NLP) is also found helpful for text processing in machine.
Research on high precision location algorithm for trajectory tracking of service robot

Aiming at the problem that the trajectory tracking of service robot deviates from the set trajectory due to the low positioning accuracy of single sensor, a combined information positioning method is proposed. The odometer and lidar are selected as the positioning sensor of the indoor service robot to study the high-precision positioning and tracking algorithm based on trajectory tracking. At the same time, in view of the reasonable selection of service robot landmark and the uncertainty of observed landmark data, the links of observation landmark selection and prediction observation matching are added to the positioning algorithm. The simulation results show that the combined information positioning method overcomes the defect of low positioning accuracy of a single sensor and has high positioning accuracy.
The issue of forming the competence of experiments realization in humanistic science

This article investigates the concept of competence, from its first attestations, delimiting the three generations of definitions previously proposed by researchers. The duality of the concept that will be worked on in the context of the formation and development of the competence to carry out experiments in the humanities is underlined. In the context of the last decades, in which there are more and more frequent attempts to use the humanities experiment method, the basic differences between the real science experiments and the humanities experiments are identified, detailing the essence of the experiment method. In the third part of the paper, a review of the competence of conducting experiments is made through the prism of educational policy documents and detailing the activities performed by students for the formation and development of this competence.
Comparative Analysis of IoT based Blockchain Secure framework for Various Applications

BlockChain (BC) has attracted a lot of attention, Because of its advanced immutability, security and special protection features. BC may overcome security and protection issues of the Internet of Things (IoT). In every event, BC is computationally expensive, limited flexibility also introduces crucial transfer speed overheads and delays too. Those are unsuitable for the IoT applications. IoT is still in its early stages, but it is expected to have a significant influence on the items we use in day today life. In addition with that the usage of IoT with the lessor security will lead to the malfunctioning of the operations from the external threats. The abusive operations attract every researcher to build up a mechanism to ensure the security of IoT platforms. Since the existing security methods are ineffective in the protection IoT systems, Blockchain is emerging as a potential solution for creating more secured IoT architectures in the future. In this paper, we presented comparative analysis among various applications in which the framework of IoT is based on Blockchain organization, and how they used BC with IoT innovation to achieve the security and other objectives.
Towards Pre-trained Language Model for Dynamic Disturbance

Disturbance token embedding can effectively prevent model overfitting and data augmentation. Simple injection disturbances, however, can adversely impact the model, making it challenging to learn the correct sample features. In this paper, we propose a novel idea, which is called Dynamic Disturbance Embedding (DDEM), to inject disturbances. When the model is trained to a certain extent, the token embedding is disturbed according to the training error change. The disturbance methods mainly include adversarial training and gradient penalty. To ver- ify the effectiveness of the proposed method, we conduct experi- ments in several natural language processing tasks. Experiments indicate that the DDEM method has a pronounced improvement effect than the simple injection disturbance, especially in the summary generation task, the ROUGE-1 index increases by 2.3%. Additionally, dynamic disturbance embedding helps avoid overfitting while improving model accuracy and learning the correct sample features.
Large-Range Reconstruction With Non-Pre-Calibrated Camera via MBM of Laser Referenced by Radial-Collinear-Features

The 3-D reconstruction is of great importance for measuring, inspecting, monitoring, or understanding surface characteristics. Previous active target-based vision methods provide the reconstruction with more than three images for monocular vision, usually using extra external monocular, stereo, or multiview vision to expand the measurement range. This article aims to achieve the reconstruction from the initial image of monocular vision without precalibration. The subpixel centers on the target with groups of three collinear concentric circles in the radial pattern are obtained in the region of interest (ROI) by histogram of oriented gradient (HOG)–support vector machine (SVM), grouped by electromagnetic (EM), and extracted by the Zernike moment (ZM). The intrinsic parameters of the projection of the single image are modeled by the two projections of the general-direction infinity points generated from the collinear centers of the radial pattern. Moreover, the ratio of the cross ratios of CSI and CST features with infinity points is proposed to determine moderate images with small errors for reconstruction. The mutual benchmark model is presented to enlarge the effective measurement range without extra external observation instruments. Moreover, the reconstruction is modeled and further refined in SE(3) to achieve the orthogonality, accuracy, and large measurement range, which are proved by simulation and experimental verification.
Blockchain-based distributed platform for accountable medical data sharing

In the recent years, blockchain has been widely studied and applied as a solution to address various healthcare challenges associated with the legacy systems. Availability of a trusted healthcare ecosystem for accountable medical data sharing still remains a problem. This paper discusses the potential applications of blockchain in healthcare and proposes a blockchain-based framework to facilitate health data availability and sharing. It identifies the implementation challenges of such a system and discusses their relationship with blockchain's intrinsic design and characteristics. In the end, this paper delineates the future research directions required to overcome the challenges in realizing a blockchain-based platform for accountable medical data management and sharing.