Reviewerly/exHarmony huggingface dataset:
Immersive Visualization in Pilot Training: from Cockpit Panels to Drone Navigation

Training in aviation is a costly but needed activity. The sooner pilots become acquainted with flight instrumentation, procedures, and maneuvers, the earlier their performance becomes safe and effective and the more cost-saving the training is. The advantage of using flight simulators is well understood and their use widespread. The use of virtual reality (VR) technologies promises to further speed up the training process, allowing for more realistic and varying scenarios and a stronger sense of being there. There is however some reluctance towards the use of immersive VR systems in aviation training and lack of knowledge on its effectiveness. The result is a slow take-up of VR solutions while the use of 2Dscreen monitors remains dominant. This paper aim is to assess the “VR advantage” for pilot training. Users’ performance on VR headset is compared to that on 2D monitors. The focus is on two types of operations: flight deck training, i.e. the different operations pilots need to learn at cockpit; and skillful teleoperation practice, i.e. conducting aerial vehicle driving. We experiment the first type of operation for two sets of tasks on a virtual 3D representation of a Cessna cockpit, and the second type of operation on a quadcopter drone driven in a narrow tunnel. The aim is to gather insight on user’s performance and sensations when using immersive VR technologies. User performance is objectively measured in terms of accuracy of operation and timing, whereas users’ sensations are subjectively rated in terms of sense of presence, visual comfort and ease of use.
Research on Improved CLIQUE Partial Order Algorithm Weight

Aiming at the problem that the existing CLIQUE clustering algorithm does not consider the feature weight, which leads to the low accuracy, a weighted improvement method combined with POset idea is proposed. First, obtain the weight order of features. The original data are then weighted in partial order. Finally, the traditional CLIQUE algorithm is used to cluster the weighted data. This method can effectively integrate the weight information into the algorithm when only the feature weight order is obtained. The experimental results show that the clustering accuracy has been significantly improved, which fully reflects the role of feature weight. At the same time, the idea of POset can effectively integrate expert information. The representation of nearest neighbor elements in Hasse graph can enhance the robustness of clustering results. This is an effective method to improve CLIQUE clustering algorithm.
Causal Discovery of Medical Test Parameters Based on Improved PC Algorithm

Causal discovery from observational data is extremely challenging, especially in obtaining precise causal relationships in observational data. Existing methods for such issue can be roughly categorized into Constrained-based and Score-based causal discovery methods. A common independence test in PC algorithms is Fisher’s exact test, which can only cope with the complete data set. However, missing data is common in many application domains including the healthcare data analysis. When processing data set with missing values, the independence of observed data may differ from that of the corresponding full data generated by the underlying causal processes, and thus unsatisfactory results may occur if we simply applied the Fisher’s exact test-based PC causal discovery method to observational data. Medical test parameters are often used to reflect the patient’s physical condition, and mastering the causal relationship between medical test parameters can manage patients more efficiently. However, in most cases, medical test parameters have missing values. This paper, consequently, proposes an algorithm to first perform a testwise-deletion Fisher-z independence test to data sets with missing values, fill in missing data by generating virtual data to perform the CI relations test, and then use the rule of resolving conflicts between unshielded colliders confirmed as orient bi-directed. Finally, the K2 and Bayesian-Dirichlet equivalent uniform (BDeu) scoring functions were used to score the causal structure discovered by the PC algorithm and the causal structure found by the PC algorithm based on the Missing-value Fisher-z test with orient bi-directed, respectively. Experimental results demonstrate that the causal structure discovered by the proposed algorithm yields a more precise casual analysis.
A Hybrid State of Health Estimation Method for Lithium Ion Battery

In order to improve the accuracy and practicability of state of health estimation for lithium-ion batteries, the Improved Arithmetic Particle Swarm Optimization Algorithm (IAPSOA) is proposed in this work. Combined with Deterministically Constructed Cycle Reservoirs with Regular Jumps(CRJ), IAPSOA-CRJ estimation method is proposed. The constant current charging time of the battery is extracted as a health indicator to predict the real capacity series. Then, by improving the Arithmetic Optimization Algorithm(AOA),the search ability and stability of AOA algorithm are improved. This paper also studied the influence of different training set length on the model. Finally, the generalization performance is tested with the trained model on another set of battery data of the same type. IAPSOA algorithm is used to optimize the input matrix parameter, reservoir parameters and regularization coefficient of CRJ network, and compared with Radial Basis Function Neural Network, Elman Neural Network and Optimized Kernel Extreme Learning Machine. The results show that the proposed IAPSOA-CRJ estimation model performs best in all aspects, and has strong robustness and generalization ability.
Identity-Assisted Network for Pedestrian Attribute Recognition

Pedestrian attribute recognition aims to accurately locate and extract high-level semantic attributes of pedestrians, and provide important support for pedestrian re-identification. The existing pedestrian attribute recognition method has achieved good recognition results in indoor and other single scences. However, it does not perform well in complex background with changes of illumination, viewing position and occlusion. In this work, we introduce pedestrian identity (ID) as auxiliary information for attribute recognition, and propose an identity-assisted pedestrian attribute recognition network (IA). The IA network uses ResNet-50 as the backbone network, removes the last fully connected layer, and then connects to a multi-branch network, which contains re-identification branch and attribute branch. The re-identification branch is used to extract pedestrian features, then use hierarchical clustering to generate pseudo IDs, which finally assists pedestrian attribute identification. Besides, we construct a quintuple loss function. Firstly, it constructs a intra-triple loss within an attribute. And then, it constructs an inter-triple loss between attributes according to the pseudo ID information to fully optimize the attribute space. The average accuracy mA of the IA model for all attributes on the PETA dataset exceeds 85%. Through comparative experiments, it can be proved that the IA model gets a bettrer performance on attribute recognition.
Automated Categorization of Behavioral Quality Through Deep Neural Networks

The possibility to automatically judge the quality of the motor behavior of human subjects could permit to monitor elder people in their natural home environment. More specifically, it might allow to identify critical situations requiring medical intervention in real time and extract data supporting medical diagnosis. In this work, we present an AI system automatically evaluating the quality level of the walking behavior of human subjects. The system is composed of a depth camera, a skeleton tracking software, and a neural network. The network is trained by using a training set containing the behavioral data of behaviors of different quality displayed by different individuals. The quality of the behaviors included in the training set is manually evaluated by us. The preliminary results collected by using a small training set indicate that the system is able to judge the behavioral quality of new subjects, not included in the training set, reasonably well. In future works the system will be improved by collecting and using a larger training set and considering also behaviors of other classes.
Safeguarding Face-To-Face Communication in Augmented Reality: An Adaptive Interface

Recent advances in wearable augmented reality devices foster the vision of ubiquitous interaction in an immersive, digitally augmented, physical world. Assuming that such devices could one day replace smartphones for accessing information, creating interfaces safeguarding face-to-face communication is challenging. This work presents the design of an interface that adapts the information visualisation to the presence of a possible interlocutor while allowing a high level of user control. The aim was to define an interface for wearable devices adaptive to interactions coming from the surrounding environment and expressly thought for application domains in which it will be necessary to continuously monitor information. For instance, those applications that require monitoring patient data in medical applications or the progress of a production process in an industrial environment. We focused on human-to-human communication, minimising the use of mid-air interaction to hide the synthetic information that might interrupt the conversation flow. Two different visualisation modalities allowing the coexistence of real and virtual worlds are proposed and evaluated in a preliminary study with six participants who showed a generalised appreciation for the solution which maximises the display of information requiring less user intervention.
The influence of visual representation factors on bio signals and its relation to Presence in Virtual Reality Environments

We investigated visual representation factors influencing presence in virtual reality (VR) environments. The study consists of a VR experiment displaying four scenarios differing in style and lighting and corresponding questionnaires. In order to be able to make a valid conclusion about the presence in a VR environment objective and subjective responses of ten participants concerning their VR experience is performed with several questionnaires and bio signal recordings. Concretely we used the Igroup Presence Questionnaire (IPQ), the System Usability Scale (SUS) and the NASA TLX to measure presence, subjective assessment of usability and mental workload, respectively. To obtain more objective data of presence we additionally recorded the electrocardiogram (ECG) of the eight volunteers while interacting with the VR environment. Heart rate (HR) and heart rate variability (HRV) have been analyzed and correlated to the degree of the user’s presence. The user’s task was to search for four particular objects within each room, which are displayed near the entrance point. A game was implemented to encourage the participant to interact with the VR environment. We found strong inter-individual differences in heart rate (HR) and heart rate variability (HRV) indicating an influence of visual representation factors during presence. Furthermore, the results of the IPQ showed that the realistic dark version of the room has the highest value of the “Sense of Being There”, followed by the realistic light and the stylized dark room. No differences in the systems usability scores and mental workload scores are observed.
Structured-Light-Based 3D Scanning System for Industrial Manipulator in Bin Picking Application

This paper developed an industrial manipulator integrated with a structured-light-based 3D scanning system using Gray code and phase-shifting patterns, implementing the bin-picking problem. The procedure of the vision-based robotic system developed in this paper is described as follows: First, the object is reconstructed in point cloud format by projecting a series of Gray code patterns followed by four-phase shift patterns. Point clouds of the object from different views are filtered and concatenated to obtain the full cloud. The full clouds, altogether with partial clouds, then are fed to PointNet neural network as a training dataset, whose estimated grasping pose can be used to pick up the objects from a bin. By using the estimated grasping pose, the robotic arm can reach the desired position and pick the object. Experimental results indicate that the developed vision-based system can reconstruct objects and grasp different-sized objects in the bin.
The Immersion Advantage in Command &amp; Control: from Desktop Monitors to VR Headsets

Advances in virtual reality technologies, including display and camera systems, have gone through a tremendous development in recent years. We can now use them efficiently in a significant number of applications. In remote exploration, monitoring and intervention tasks, immersive technology can lead to greater comprehension and accuracy of observation, making the decision process faster and more effective. However, the spread of immersive technologies has shown slow take-up, with VR adoption limited to few applications. This appears due to the often-needed change of operating paradigm and some skepticism towards the “VR advantage”. There is, therefore, a need to assess the benefits a VR system can bring to decision-making within commercial applications and the contribution immersion brings to user performance. This knowledge will help system designers understand when immersive technologies can be proposed to replace or complement standard display systems. A desktop monitor may, for example, be more suitable for visual overviews, whereas a VR headset can serve best on focused observations and actions. This paper proposes an evaluation of the advantage of using a VR headset compared to a standard desktop monitor for identification, discovery, and positioning tasks within command-and-control operations. Our assessment compares desktop monitors and VR headsets, and includes a VR desktop (i.e. a flat monitor-like view within a VR headset). Testing involves the use of remote drones within military defense actions. The aim is to gather insights into the role of immersive elements, e.g. isolation and 360 views. We aim at building new knowledge by evaluating findings and identifying advantages in complex scenarios. We rely on analyzing both objective measurements (time and accuracy), and subjective ratings related to human factors (sense of presence and depth perception).
An Off-COMA Algorithm for Multi-UCAV Intelligent Combat Decision-Making

Unmanned Combat Aerial Vehicle (UCAV) has played an important role in modern military warfare, whose level of intelligent decision-making needs to be improved urgently. In this paper, a simplified multi-UCAV combat environment is established, which is modeled as a multi-agent Markov games. There are many difficulties in multi-UCAV combat problem, including strong randomness and complexity, sparse rewards, and no strong opponents for training. In order to solve the above problems, an algorithm called Off Conterfactual Multi-Agent (Off-COMA) is proposed. This algorithm extends the COMA algorithm to the off-policy version, and can reuse historical data for training, which improves data utilization. In addition, the proposed Off-COMA algorithm exploits an improved prioritized experience replay method to deal with the sparse reward. This paper presents an asymmetric policy replay self-play method, which provides a guarantee for the algorithm to generate a powerful policy. Finally, compared with several classical multi-agent reinforcement learning algorithms, the superiority of Off-COMA algorithm in solving the multi-UCAV combat problem is verified.
Knowledge transfer for Object Detection with Evolution architecture search

Deep learning has been proved to achieve excellent results in various fields, and appropriate network architecture and sufficient data play an important role. Due to the high cost of annotation for the task of object detection, domain adaptation methods have been introduced in this field. But these methods are based on the rigid network architecture and bound to the input dimension of the adaptive module, which is not only difficult to better balance accuracy and speed of detection model, but also can not use the multi-scale training method, resulting in the reduction of the application scenario of the model. Inspired by this problem, we propose a new object detection method based on multi-scale adversarial domain adaptation and network architecture search. An evolutionary algorithm is adopted to help search the network architecture to balance accuracy and speed. The ability of domain adaptation can also be effectively improved by the searched architecture. The experimental results have demonstrated the significant improvement that benefited from the framework in terms of its performance and computational efficiency in solving unlabeled object detection.
Wikipedia bi-linear link (WBLM) model: A new approach for measuring semantic similarity and relatedness between linguistic concepts using Wikipedia link structure

Wikipedia links its articles by manually defined semantic relations called the Wikipedia hyperlink (link) structure. The existing Wikipedia link-based semantic similarity (SS) and semantic relatedness (SR) computation models, such as Wikipedia one-way link (WOLM) model and Wikipedia two-way link (WTLM) model, do not assess the strengths of the relationships between a candidate concept and its links (out-links or in-links). These models treat all the links as equally important even though some links are semantically more influential than others and should be given more importance. This phenomenon reduces the accuracy of these models. This paper presents the Wikipedia bi-linear link (WBLM) model that extends the previously proposed WOLM and WTLM models. The WBLM model explores the Wikipedia link structure as a semantic graph and discovers the strongly (bi-linear links) and weakly (out-links or in-links) connected links of a candidate concept. It improves the link-based vector representations of concepts by assigning weights to their connected links according to the strengths of their semantic associations. The experimental results demonstrate that the proposed WBLM model significantly improves the SS and SR computation accuracy of the WOLM model (6.9%, 8%, 24%, 17.3%, 31.2%, 30.6%, 26.5%, and 35.4%) and WTLM model (1.2%, 3.9%, 7.1%, 9.9%, 11%, 6.3%, 12.7%, and 13%), in terms of linear correlations with human judgments on gold standard benchmarks, including MC30, RG65, WS203, SimLex, 353All, MTurk287, MTurk771, and MEN3000, respectively. Moreover, this research offers a deep insight into the Wikipedia link structure and provides an adequate base for understanding it as a semantic graph.
Using Modified YOLOv4 for Military Target Detection

We propose methods for object detection based on remote sensing images. This method further improves detection accuracy and decreases error rates. Modified YOLOv4 is an accelerated neural network model based on the YOLO (YouOnly-Look-Once) object detection method. It outperforms existing networks in terms of execution time and detection performance. The experimental results show improved mAP (mean average precision) performance of the proposed method for object detection in remote sensing images. We thus propose a novel system for automatic object detection for high-resolution remote sensing images.
Differences in Visibility of Students' Proficiency by Grading Methods in Energy Electronics-related Lectures Based on DX Format

In Japan, the Ministry of Education, Culture, Sports, Science and Technology (MEXT) decided in March 2021 to select institutions to implement the “Plan for Upgrading Education at Universities and Colleges of Technology Using Digital Technology” project. Universities implementing the plan will use the Internet, computers, artificial intelligence (AI), and other technologies to promote learning tailored to each student's needs. However, the method of grading, as well as the method of converting lectures to DX, is left to the lecture educators, and it is difficult to say that sufficient know-how has been accumulated. This paper discusses how the incorporation of DX into lectures and examinations to increase students' proficiency in lecture content has been reflected in grading results.
An Efficient Single Image Fog Removal using Improved Dark Channel Prior

Fog removal is one of the most challenging problem in image processing. In the most scenes, using dark channel prior (DCP) for single image fog removal achieves good results. However, DCP itself has weakness to poorly suffer scenes having extreme light source. We propose an improved single image fog removal algorithm that based on DCP algorithm-a very popular single image fog removal algorithm. As a result, the proposed method can improve the fog removal effectiveness up to 19.7%, which enhances the vision of camera system application.
Image Recognition by YoloV4 for Vehicle Type Distinction in Side of Road

At present, most of the domestic and foreign research on vehicle recognition focuses on real-time camera recognition in front of the vehicle while driving and real-time camera recognition over the road for overhead camera angles. For roads in mountainous areas or special terrain, due to the small hinterland on the left and right side of the road, it is not easy to set up overhead camera systems for the road. Therefore, this thesis proposes “Yolo_v4-based road vehicle recognition” to solve the above problem. Unlike the overhead camera system, this side camera detachable temporary road vehicle recognition system only needs a very small road hinterland and can reduce the cost of space construction. The Yolo_V4 deep learning algorithm can accurately identify different vehicle types to ensure road safety and traffic monitoring. In this study, the accuracy of the vehicle recognition model for the side camera angle is 96%, which is comparable to the accuracy of the vehicle recognition model for the top camera angle.
A Conditional Generative Adversarial Network Method for Image Segmentation in Blurry Image

Nowadays, the wide application of machine vision is developing rapidly, and image segmentation is indeed one of the indispensable technologies. When the input image is blurred, the outline of the target object will be unclear, resulting in inaccurate segmentation. This paper proposes an image segmentation method based on Generative Adversarial Network, combined with Concatentation and Summation to enhance the encoder and decoder and improve these problems without preprocessing data. This paper uses two experimental methods to verify the final image segmentation effect. It uses the Intersection over Union (IoU) score as the evaluation metric. The number of low IoU in this model does not increase drastically with the degree of blur. Moreover, without an overfitting issue in the testing dataset. It can prove the universality of the model.
Extraction of ROI of Graphical Attributes on the Image of the Document of Cooperation

For the needs of data collection, reporting, and synchronization of the large number of documents of cooperation, an automatic content exploration system is needed for the collection of documents of cooperation. The first step is to detect and extract the graphical attributes on the image of the document of cooperation. Document image analysis methods must be applied to detect and extract region of interest (ROI) containing graphical attributes in documents, separating them from document areas containing textual content. This paper presents a proposed method to detect and extract the graphical attributes on the image of the document of cooperation, such as logo, stamp, signature, and seal. The proposed method consists of binarization method, connected component analysis, and image morphology processing. The results show that the proposed method is performed well in extracting the ROI for logo, stamp, signature, and seal in the image of document of cooperation.