Reviewerly/exHarmony huggingface dataset:
Application of Artificial Intelligence in Modern Vocational Education Technology

Abstract In recent years, with the rapid development of network technology and information technology, artificial intelligence has become the development trend of modern technology. Over the past 50 years, scientists from various countries around the world have continuously explored artificial intelligence technology. Up to now, artificial intelligence has achieved great results. Compared with traditional modern technology, artificial intelligence has broken through the shackles of traditional technology, and has been highly sought after in various fields for its advantages such as being more convenient, more effective, saving time, labor and cost. Modern vocational education is an effective part of China’s higher education, mainly providing professional technical talents for the society. At present, vocational education has not been developing in China for a long time, and the application of computer technology in vocational education is even later. In recent years, although the rapid development of computer technology in China has promoted the development of vocational education to a certain extent, the actual application of computer technology in modern vocational education has lagged far behind the development of computer science itself. While many disciplines of artificial intelligence based on “knowledge engineering” have achieved certain results, modern vocational education still evolves traditional computer technology. Obviously, modern vocational education is far from artificial intelligence technology in computer application. How to effectively apply artificial intelligence technology to the summary of modern vocational education, make full use of the potential of computer technology itself, and bring new development opportunities for the development of modern vocational education has become an important issue that modern vocational education workers need to solve urgently. From the perspective of artificial intelligence technology, this paper deeply analyzes the current status and problems of the application of artificial intelligence technology in modern vocational education in China. And on this basis, this paper puts forward the feasibility suggestions for introducing artificial intelligence into modern vocational education technology. The main purpose of the research in this article is to urge our modern vocational education colleges to pay enough attention to artificial intelligence technology and to stimulate the interest of modern vocational education scholars on artificial intelligence. Furthermore, the artificial intelligence technology can be fully and efficiently implemented into modern vocational education, and more high-quality professional and technical personnel can be cultivated for the society.
Exploiting Out-of-Domain Datasets and Visual Representations for Image Sentiment Classification

Visual sentiment analysis has recently gained attention as an important means of opinion mining, with many applications. It involves a high level of abstraction and subjectivity, which makes it a challenging task. The most recent works are based on deep convolutional neural networks, and exploit transfer learning from other image classification tasks. However, transferring knowledge from tasks other than image classification has not been investigated in the literature. Motivated by this, in this work we examine the potential of transferring knowledge from several pre-trained networks, some of which are out-of-domain. We show that by simply concatenating these diverse feature vectors we construct a rich image representation that can be used to train a classifier with state of the art performance on image sentiment analysis. We also evaluate a Mixture of Experts approach, for learning from this combination of representations, and highlight its performance advantages. We compare against the top-performing recently-published methods on four popular benchmark datasets and report new SOTA results on three of the four.
Adaptive Spatial Augmented Reality for Industrial Site Assembly

Today, industrial assembly of large components is mostly organized as site assembly. The human-centered information provisioning for assembly tasks still bears significant optimization potential. The paper presents first results of the integration of a dynamic projection system with visual human position and gesture recognition. Work instructions and other relevant information is displayed relative to the humans’ position within a large workspace. A high-resolution RGB-camera as well as the real-time object recognition algorithm YOLOv3, have been integrated into a proof-of-concept demonstrator. An evaluation at the TU Wien Pilot Factory shows the potential in terms of efficiency, usability, and cognitive workload.
Numerical Simulations of Noisy Variational Quantum Eigensolver Ansatz Circuits

This is a case study of the variational quantum eigensolver (VQE) method using numerical simulations to test the influence of noise on the accuracy of the underlying circuit ansatz. We investigate a computational chemistry application of VQE to calculate the electronic ground state and its energy for Sodium Hydride (NaH), a prototypical two-electron problem. Using a one-parameter ansatz derived from unitary coupled cluster (UCC) theory, we simulate the effects of noise on the energy expectation value and variance with respect to the ansatz parameter. These numerical simulations provide insights into the accuracy of the prepared quantum state and the efficiency of the classical optimizer that iteratively refines the ansatz. We conduct a comparative study between analytical results derived for the UCC ansatz in the absence of noise and the noisy numerical simulation results obtained using an isotropic depolarizing noise model for each gate. We also compare the relative increase in noise on logically equivalent UCC ansatz circuits generated by randomized compiling. Notably, we observe that the intrinsic variance in the energy due to the simplicity of the ansatz itself compares with the noise induced by the bare circuit.
Research on Recognition and Location Method of Insulator in Infrared Image Based on Deep Learning

Abstract Infrared thermography technology is widely used in the thermal condition detection of insulators due to its advantages of non-contact, sensitive, online detection. To realize the automatic detection of the operating condition of insulators in complex environments, this paper proposes a method for the recognition and location of the insulator based on Region-based Fully Convolutional Networks (R-FCN). The model was trained and tested on the constructed insulator infrared data set, compared with the SSD model. The results showed that the R-FCN detecting insulators can not only accurately locate insulators, but have an AP (average precision) value as high as 89.2%. Therefore, the findings in this paper have verified that R-FCN has great advantages in the recognition and location of infrared images of insulators and has practical application value.
Recurrent Network Knowledge Distillation for Image Rain Removal

Single-image rain removal (SIRR) based on deep learning has long been a problem of great interest in low-level vision systems. However, traditional convolutional neural network (CNN)-based approaches fail to capture long-range location dependencies effectively and may cause the image background blurred. In this article, we propose a knowledge distilling deraining network (KDRN) to address the SIRR problem. In the proposed network, the teacher regards rain streaks as a linear combination of many residual networks. It is used for image reconstruction at different resolutions. With the aid of a teacher network, the proposed deraining network performs better. A spatial channel aggregation residual attention block (SCARAB) is designed to remove the rain streaks. The block not only concentrates on the rain streak features but also captures the spatial-channel information of the image. For the network structure, we used an end-to-end approach to design the teacher and student networks separately. The proposed KDRN obtains the predicted residual image by a combination of the stage-wise results and the original input image. Extensive experiments show that the proposed KDRN obtains better subjective quality than most of the compared methods, on both heavy and light rain data sets.
Efficient and Privacy-Preserving Similar Patients Query Scheme Over Outsourced Genomic Data

Over the past decade, genomic data has grown exponentially and is widely used in promising medical and health-related applications, which opens up new opportunities for the field of medicine. Similar patients query (SPQ), which can help physicians formulate an optimal therapy, is one of such popular applications. Despite its popularity, since human genomes are usually highly sensitive, a series of policies have been launched by the government to strictly control its acquisitions and utilization. Thus, how to prevent privacy disclosure becomes of great importance to the flourish of SPQ services. In this article, aiming at the above challenge, we first design a novel genetic BK-tree (GBK-tree) for a genomic database. Then, combined with a random sorting mechanism and some existing encryption techniques, we propose an efficient and privacy-preserving similar patients query scheme over encrypted cloud data, named CASPER. With CASPER, a medical institution can securely outsource its private genomic database to a cloud server, and physicians can request SPQ services from the cloud server while keeping her/his query secret. Detailed security analysis shows that CASPER can preserve privacy in the presence of different threats. Furthermore, extensive performance evaluations demonstrate the high accuracy and efficiency of our proposed scheme.
Dynamic boundary‐insensitive loss for magnetic resonance medical image segmentation

A deep learning method has achieved great success in MR medical image segmentation. One challenge in applying deep learning segmentation models to clinical practice is their poor generalization mainly due to limited labeled training samples, inter-site heterogeneity of different datasets, and ambiguous boundary definition, etc. The objective of this work is to develop a dynamic boundary-insensitive (DBI) loss to address this poor generalization caused by an uncertain boundary.The DBI loss is designed to assign higher penalties to misclassified voxels farther from the boundaries in each training iteration to reduce the sensitivity of the segmentation model to the uncertain boundary. The weighting factor of the DBI loss can be adjusted adaptively without any manual setting and adjustment. Extensive experiments were conducted to verify the performance of our DBI loss and its variant, DiceDBI, on four heterogeneous prostate MRI datasets for prostate zonal segmentation and whole prostate segmentation.Experimental results show that our DBI loss, when combined with Dice loss, outperforms all competing loss functions in dice similarity coefficient (DSC) and improves the segmentation performance across all datasets consistently, especially on unseen datasets and when segmenting small or narrow targets.The proposed DiceDBI loss will be valuable for enhancement of the generalization performance of the segmentation model.
An improved classification of mineralized zones using particle swarm optimization: A case study from Dagh-Dali Zn Pb (±Au) prospect, Northwest Iran

Classification of mineralized areas into different geochemical classes in terms of prospectivity is crucial in the optimal management of exploration risk and costs. Machine learning (ML) algorithms can be served as appropriate alternatives for separating ore-related anomalies due to avoiding the assumptions of statistical distribution and compatibility with the multivariate nature of geochemical features. By hybridizing the ML with a metaheuristic algorithm called particle swarm optimization (PSO), this contribution aims to provide an innovative approach to optimize the classification of geochemical anomalies within the study area. The algorithm, PSO, is inspired by simulating the social behavior of flocks of birds in search of food. The Dagh-Dali ZnPb (±Au) mineral prospect in northwest Iran was subjected as a case study to examine the integrity of the proposed method. Mineralization-related features were extracted by applying principal component analysis (PCA) on metallogenic elements analyzed in soil samples as PC1 and PC2 with elemental assemblages of AgAuPbZn and PbZn, respectively. The silhouette index was employed to estimate the number of underlying geochemical clusters within the adopted feature space. To constitute a comparative analysis, two k-means clustering and PSO-based learning (PSO-L) algorithms were implemented to classify the gridded data of PC1 and PC2 within the study area. The results indicated that the use of PSO has improved the cost function of the clustering problem (up to 4%). Adapting the mineralization classes with the metallogenic evidence demonstrated by boreholes drilled in the study area indicated that PSO-L was superior to the traditional k-means method, improving the accurate estimation of subsurface mineralization classes by 7%. By overcoming the drawbacks of conventional methods for trapping at the local optima, PSO-based learning possesses the potential to highlight weak mineralization signals that are numerically located in boundary conditions. The results show that the proposed approach can serve as an effective medium for optimal modeling of geochemical classes and management of detailed exploration operations.
Feasible Trajectories Generation for Autonomous Driving Vehicles

This study presents smooth and fast feasible trajectory generation for autonomous driving vehicles subject to the vehicle physical constraints on the vehicle power, speed, acceleration as well as the hard limitations of the vehicle steering angle and the steering angular speed. This is due to the fact the vehicle speed and the vehicle steering angle are always in a strict relationship for safety purposes, depending on the real vehicle driving constraints, the environmental conditions, and the surrounding obstacles. Three different methods of the position quintic polynomial, speed quartic polynomial, and symmetric polynomial function for generating the vehicle trajectories are presented and illustrated with simulations. The optimal trajectory is selected according to three criteria: Smoother curve, smaller tracking error, and shorter distance. The outcomes of this paper can be used for generating online trajectories for autonomous driving vehicles and auto-parking systems.
MaskOut: A Data Augmentation Method for Masked Face Recognition

Deep learning methods have achieved great performances in face recognition. However, the performances of deep learning methods deteriorate in case of wearing a mask. Recently, due to the world-wide COVID-19 pandemic, masked face recognition attracts more attention. It is non-trivial and urgent to improve the performances in masked face recognition. In this work, a simple and effective data augmentation method, named MaskOut, is proposed. MaskOut replaces a random region below the nose of a face with a random mask template to mask out original face features. Our method is computing and memory efficient and convenient to combine with other methods. The experimental results show that the performances in masked face recognition are improved by a large margin with MaskOut. Besides, we construct a real-life masked face dataset, named MCPRL-Mask, to evaluate the performance of masked face recognition models.
Construction of Military Knowledge Graph Based on Paper Bibliographic Data

In order to solve the problems of sparse data distribution, weak correlation between data, and difficulty in efficient use of data in the retrieval process of academic research content in the military industry, a method for constructing military knowledge graph based on bibliographic data of military disciplines is proposed. This method designs the structure of the knowledge graph according to the characteristics of the bibliographic information of the paper, which uses information-rich paper titles and keywords to study entity recognition, entity classification, knowledge storage and display. Finally, based on the idea of distributed storage, a knowledge graph service system is built, which improves the intuitiveness and relevance of knowledge, and provides certain significance for academic research in the military field.
Path Planning of Welding Robots Based on Developed Whale Optimization Algorithm

Aiming at the problem of optimal welding path of welding robots, an developed whale optimization algorithm is proposed. In this paper, the layout of 14 welding spots is analyzed and simplified as Traveling Salesman Problem (TSP). The mathematical models of welding path optimization are established. On the basis of analyzing the principle of whale optimization algorithm, WOA searching flow is redesigned and searching ability is enhanced. Experiments of 5 testing functions indicate that the developed WOA has advantages (GSWOA), such as greater global searching ability, higher optimizing accuracy and so on. The sequence of welding path points is taken as the search agent of WOA and the shortest path distance is taken as the fitness function so that the developed WOA has been used in path planning of robots. Matlab software is employed to realize the simulation experiment of path planning. The simulation results show that the developed WOA can significantly shorten the welding distance and enhance the production efficiency.
The Problem with Real-World Novelty Detection - Issues in Multivariate Probabilistic Models

Novelty and anomaly detection in real-world data streams are becoming more and more important for IoT, industry 4.0 and digital-twin applications. However, most of these algorithms are designed in-vitro and usually not very resilient against the failure behaviour of real-world systems, that is, minor system faults (e.g. a failing sensor, small damage, or firmware updates). In most scenarios, such a minor fault leads to a total failure of the detection engine, resulting either in the constant reporting of an anomaly or a total inability for further detection. In this article we investigate this problem in more detail and present simple approaches to circumvent them.
Integration of Mixed Reality in Teaching and Learning Effectiveness: A Systematic Literature Review of the Analyses

In this study, a literature review was conducted on the trends in applying mixed reality (MR) technologies to education over the past decade (2011–2021) and to examine the learning outcomes achieved. This in-depth research project and examination included 64 articles in total, and an integrated analysis was performed based on six dimensions: year of publication, research discipline, educational stage of subjects, location of experimental subjects, learning orientation, and experimental design. The research results indicated that MR was mostly applied to teaching, practical training, and auxiliary experiences in the fields of health and wellbeing and science took the form of using simulation experiences during teaching and practical training and assisting students in visualizing abstract concepts. To improve students’ learning cognition and enhance their practical abilities. In addition, relevant suggestions for mixed reality in education future research are proposed. The experimental data compiled in this study can be used for follow-up analysis, which will clarify the key moderating variables affecting the effectiveness of the application of MR technologies to teaching.
Task-Oriented API Usage Examples Prompting Powered By Programming Task Knowledge Graph

Programming tutorials demonstrate programming tasks with code examples. However, our study of Stack Overflow questions reveals the low utilization of high-quality programming tutorials, which is caused task description mismatch and code information overload. Neither document search nor recently proposed activity-centric search can address these two barriers. In this work, we enrich the programming task knowledge graph with actions extracted from comments in code examples and more forms of activity sentences. To overcome the task description mismatch problem, we use code matching based task search method to find relevant programming tasks and code examples to the code under development. We integrate our knowledge graph and task search method in the IDE, and develop an observe-push based tool to prompt developers with API usage examples in explicit task contexts. To alleviate the code information overload problem, our tool highlights programming task and API information in the prompted tutorial excerpts and code examples based on the underlying knowledge graph. Our evaluation confirms the high quality of the constructed knowledge graph, and show that our code matching based task search can recommend effective code solutions to programming issues asked on Stack Overflow. Through an user study, we demonstrate that our tool is useful for assisting developers in finding and using relevant programming tutorials in their programming tasks.
Automotive Mechanical Vehicle Starter

Abstract This research is used to crank start automotive vehicle. There are many different system used in order to start-up vehicles using electric starter, in the time of battery low-power or totally drained. The purpose of this research is to help the driver to get out of this difficulty. Nowadays there are many people that have experienced such a bad moment, where they are stranded at road side due to malfunction starter in their car because of battery problem. Most of the vehicle electric starter failure is because of battery corrosion or battery undercharged. The importance of this research is to solve this problem. Starter is a vital part of the vehicle, without it no automotive vehicles able to operate. These starters will rotate an internal-combustion engine to initiate the engine’s operation under its own power. Starters also can be malfunction too due to corroded electrical connections or an undercharged battery. This system can be used to solve this problem. This system used human energy by using mechanical parts in order to produce electrical power. In order to produce electrical current, workforce will be applied by rotating the wheel that already linked by belt and from that rotations will trigger a magnetic force and it will produce an electrical current and supply it into battery. This system is divided into two development; hardware development and software development. The hardware development involved, mechanical device which is used and electrical device such as monitor. For software development, Fritzing is used to construct circuit.
ResNet with Attention Mechanism and Deformable Convolution for Facial Expression Recognition

Due to the influence of face occlusion, side face and other factors, the recognition accuracy still needs to be improved in facial expression recognition(FER). This paper proposes a deep residual network incorporating an attention mechanism and deformable convolution to improve this phenomenon. The improved network is an attention mechanism embedded in the residual blocks with position information and a deformable convolution is added at the input to enhance the feature extraction capability for different locations of the image. This paper has been proved on the RAF-DB and FERPlus datasets, and the experimental results show that the accuracy of the improved network is 1.76% higher than the original network on the RAF-DB dataset and 2.76% higher than the original network on the FERPlus dataset.