Reviewerly/exHarmony huggingface dataset:
A Mechanical CAD Drawings Retrieval Method Based on Text and Image Information

In the field of industrial production, a lot of engineering drawings are drawn every day. For the purpose of reducing processing costs and improving production efficiency, reusing the used engineering drawings is necessary. In order to effectively reuse engineering drawings, we propose a drawings retrieval method based on text and image information. Firstly, a text parser is used to extract the text information about the manufacturing process features in the drawing and to identify drawings with the same features in the database. Then, the histogram similarity algorithm is used to roughly filter the drawings. Finally, calculating the similarity of the image geometry information of mechanical parts based on the multi-hash algorithm. Through experimental tests, the average recall rate of our proposed drawing retrieval model is about 87% and the average error retrieval rate is less than 20%. The experimental results show that our method meets the actual market demand and has good practical application capability.
Virtual Hand-Gesture Interaction System based on SRHandNet with High Frame Rate

Hand gesture recognition plays an important role in virtual human-computer interaction systems. However, existing methods often rely on a variety of smart sensors or data gloves, which significantly increases the cost and threshold of research in this area. Therefore, this paper sets out to hand key points recognition from a low-cost monocular camera. Previous research on hand key points recognition with monocular cameras suffer from high latency and instability since they process each frame independently via a traditional deep learning backbone that cost much computing power. To alleviate this issue, we build a real-time interaction system with a monocular camera based on SRHandNet, which is a simplified and qualified deep neural network model on hand detection. We modify the original model with TensorRT and Kalman Filtering, intending to achieve a more stable and efficient application for both windows and edge platforms(e.g. NVIDIA Jetson), on which the capturing frame rate of the interaction system is doubled. With the estimation of camera position, we realized the mapping between 2D and 3D hand key points, achieving virtual human-machine interaction. A series of experiments validated the superiority of the proposed model over baselines, suggesting that this work may have great prospect in model deployment and hand pose recognition in augmented reality applications.
An End-to-end Learning-based Approach to 3D Novel View Style Transfer

3D novel view style transfer is a rising research topic. Recently developed methods aim to build globally optimized scene representations and stylize them directly on the scene. However, these methods are time-consuming because they need globally-consistent optimization or rendering fields reconstruction. In this paper, we introduce an end-to-end learning framework to handle the problem of stylized novel view synthesis, which can speed up the 3D style transfer by applying learning-based structure-of-motion (SfM) approaches. Experimental results show that our method can achieve comparable visual effects to the original style transfer module with higher efficiency.
Periodic Extrapolative Generalisation in Neural Networks

The learning of the simplest possible computational pattern - periodicity - is an open problem in the research of strong generalisation in neural networks. We formalise the problem of extrapolative generalisation for periodic signals and systematically investigate the generalisation abilities of classical, population-based, and recently proposed periodic architectures on a set of benchmarking tasks. We find that periodic and “snake” activation functions consistently fail at periodic extrapolation, regardless of the trainability of their periodicity parameters. Further, our results show that traditional sequential models still outperform the novel architectures designed specifically for extrapolation, and that these are in turn trumped by population-based training. We make our benchmarking and evaluation toolkit, Perkit <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> PERKIT: A toolkit for the study of periodicity in neural networks. Available at hups://github.com/pbelcakiperkit., available and easily accessible to facilitate future work in the area.
Security posture assessment of power information networks based on adversarial deep learning and D-S evidence theory

To address the shortcomings of over-reliance on expert experience in the construction of the basic probability assignment of traditional D-S evidence theory, a security posture assessment model for power information networks against deep learning and D-S evidence theory is proposed. Firstly, sample data are collected from different security detection devices and augmented using a generative adversarial network. Secondly, a genetic algorithm is used to optimize the back propagation neural network to obtain the basic probability assignments of different evidences. Finally, the D-S evidence theory is used to synthesize and calculate the trust degree matrices of different posture states according to the temporal order to successfully complete the posture assessment task. The proposed evaluation method is experimentally verified to be feasible and effective.
A Unique Approach to Efficient Fraudulent Signature Detection Using Deep Convolutional Neural Network, Xception, and EfficientNet

Signatures act as the key to the validation of important documents, both in offline and digitized forms. This also means that forged signatures are vastly used to commit fraud. With Machine Learning gaining importance in every field, manual techniques are becoming obsolete and inefficient in several technology-related areas, including fraudulent signature detection. Hence, this research focuses on the use of Deep Learning to construct a generalized yet accurate model, which can directly classify a given signature as genuine or forged instead of having a separate class for genuine and fraud for each of the signatures in the output layer. We train separate models with Deep Convolutional Neural Network, Xception, and EfficientNet respectively, then, we analyze and compare the results obtained by these models, as well as by the existing related works, inclusive of various performance metrics. After rigorous hyperparameter tuning and experimenting, we obtained the best results from Xception, with the highest training accuracy of 100% and validation accuracy of 98.02%.
Explainable AI Applied to the Analysis of the Climatic Behavior of 11 Years of Meteosat Water Vapor Images

Large stocks of meteorological satellite images contain important climatic information of the last decades. This paper explores the capabilities of three Explainable AI procedures (Occlusion, XRAI and SHAP) as tools to analyze and uncover patterns in the mid to upper troposphere Water Vapor dynamics, and their relationships with yearly seasons. The data consisted of 4140 daily Meteosat satellite images on the WV6.2 band between 2010 to 2020 and the base model was a convolutional neural network targeting season prediction. The results show that each explanation procedure highlights different aspects of the atmospheric processes and are appropriate for different purposes: Occlusion for studying the general traits of the 11 years time period covered by the images, XRAI for detecting and following dynamic patterns for short spans of time, and SHAP for detailed geographical expressions of atmospheric processes. All together Explainable AI exhibits an important potential for the study of atmospheric and climate change, which should be further investigated.
Multi-Sensor Automatic Planning Technology Based on Greedy Algorithm Combined with Flight Segment Migration

Heterogeneous sensor scheduling of unmanned aerial platforms is a problem that needs to consider both the detection target and the significance. So there will be situations where multiple targets compete for sensor resources. At present, the research on the scheduling of heterogeneous sensors for unmanned aerial platforms is not in-depth especially on the problem of resource contention. Therefore, this paper proposes a greedy algorithm combined with flight segment transfer (GAFST) to slove the heterogeneous sensor scheduling problem. First, a sensor preprocessing mechanism is designed to identify scarce individuals among multiple sensors. Second, a greedy algorithm combined with flight segment transfer is applied to the pairing of sensors and targets. Then, to solve the resource conflict problem, a new task migration mechanism is designed. Finally, the effectiveness of GA-FST in sensor scheduling is verified by comparing with other algorithms.
Touching the Explanations: Explaining Movie Recommendation Scores in Mobile Augmented Reality

While most studies are devoted to improving movie recommendation scores with progressively accurate algorithms, many users still do not understand their recommendations entirely. Consequently, scholars have started to design explanations for movie recommendation systems. In this context, information visualization can offer adequate explanations to improve user experience. Additionally, augmented reality (AR) can broaden those visualizations, particularly in public spaces. This study provides an initial exploration of the design space of mobile AR explanations for movie recommendation scores. We first explored how movie streaming platform users portray mobile AR explanations. We later conducted a mixed-method prototype evaluation uncovering specific information visualization strategies to provide explanations that improved the usability and explainability of the system. Based on both iterations, we propose suggestions for designing AR explanations for movie recommendations and present MovieAdvisor: a mobile AR movie recommendation system that explains personalized movie recommendation scores.
Image denoising technology of power equipment based on deep residual network

The continuous development and expansion of the power system puts forward higher and higher requirements for the reliability of the power equipment. Therefore, the early detection and elimination of power equipment faults is very important, which can not only reduce equipment losses, but also avoid accidents to a certain extent. With the development of artificial intelligence technology, computer vision has begun to be widely used in power systems. For computer vision technology, high-quality images determine whether the identification of power equipment and defect detection technology can have high performance. The image is noisy. In order to solve the problem of noise interference in the visible light image of power equipment, this paper proposes a denoising method for the visible light image of power equipment based on deep learning network. First, the text analyzes the existing problems and requirements in power equipment image denoising, and proposes the overall system architecture of the study. Secondly, on the basis of analyzing the basic principle of residual network, the paper proposes a deep residual network for denoising of visible light images of power equipment, in which the improved activation function is used to improve the performance of the network, and the denoising experiments of visible light images of power equipment in this paper, It is proved that the proposed denoising method is better than the traditional method.
Active Visualization of Visual Cues on Hand for Better User Interface Design Generalization in Mixed Reality

With the emergence of various unique augmented reality devices, researchers are exploring how mixed-reality applications can enhance user experience. We propose a working prototype emphasizing that mixed reality applications should consider incorporating visual cues on the user’s hand for better user experience, in our case representing selected color on the index fingertip. Such a design can assist users in being attentive regarding what color they are using. Eventually, reducing unintended errors occur when the active visual component is not visible in the field of view. Generally, we argue that representing visual cues on the user’s hand has major advantages, including defining a general platform for placing visual cues, immediate response, and preserving computational resources. Most importantly, developers can utilize the general platform to give or place visual feedback in mixed-reality applications. Moreover, we highlight the importance of interacting in mid-air compared to tactile feedback.
A Mobile Robot Path Planning Based on Modified Sparrow Search Algorithm

In order to overcome the shortcomings of sparrow search algorithm(SSA) in the path planning of mobile robots, such as initial population quality, search accuracy and convergence speed, a modified sparrow search algorithm(MSSA) is proposed in this paper. Firstly, the opposition-based learning(OBL) strategy is introduced in the initial population stage to optimize the fitness of the initial population. Second, a better producer and scrounger location update strategy is used to promote both exploration and exploitation of the search space, to improve the convergence and search precision. Finally, the local search strategy is used to locally search for the optimal fitness individuals to enhance the local search ability and speed up the convergence. The experimental results show that the MSSA has better accuracy in seeking and faster convergence, making it better able to accomplish the path planning task.
Behavioral Avoidance Test: Comparison between in vivo and virtual reality using questionnaires and psychophysiology

The field of virtual reality (VR) is no longer limited to the entertainment sector, and the use also increased in the sector of healthcare and therapy. This paper attempts to compare the effectiveness of a Behavioral Avoidance Test for spider phobia in virtual reality to in vivo. To do this, two different BAT approaches are designed for both in vivo and in virtuo to be compared by a group of phobic participants. In addition, physiological data of the participants were recorded to investigate this comparison on a physiological level.
Visualizing Difference between Bodily Movements of Athletes and Users Learning to Play Baseball

We propose a support method to instruct movements in sports by comparing the forms of a user and those of a model using their differences visualized on a large screen. Captured images of the model and user performance are scaled and displaced to match each other and the progress of both movements is adjusted by using the dynamic time warping (DTW) technique. We conducted a small usability study in a local museum with an implemented prototype of the proposed method. Although we only received qualitative evaluations and comments from the participants, we found several advantages in our method, such as ease of understanding. Our future work includes to employ three-dimensional estimation of human movements and to conduct a larger series of experiments to evaluate our method.
Design of Secure Communication Protocol in Energy Internet Scenario

We propose a security interaction protocol in the energy Internet scenario, which solves secure problems of business data, such as the lack of security protocol in the energy Internet, data plaintext transmission, easy tampering, lack of integrity verification, and lack of authorization mechanism of the business data. We use additive homomorphic encryption technology to encrypt sensitive data, the decryptor can get the aggregation result without knowing the single information of the encryptor. We protect data privacy and reduce the load of intermediate decryption. We use the multi-level hash algorithm to verify the integrity of data, and adopt the shadow identity and tracking serial number to realize synchronization, to avoid a replay attack. We use the attribute-based access control method to realize the fine-grained access control of the data. Finally, we give the structure field and interaction mechanism of the new protocol frame. The designed protocol can effectively prevent unauthorized users from decrypting information, unauthorized users from obtaining sensitive data, dictionary attacks, replay attacks, repudiation, and maintain the efficiency of the protocol.
Attention Score: Objective Measure of Attentiveness in Immersive Omnidirectional Videos

The application of Virtual Reality (VR) in education and entertainment has shown to increase the overall learning experience of the users and inspire them in unique ways. Identifying and analyzing the viewing and attention patterns of users is extremely important for improving their engagement with the content in VR. In this paper, we introduce two new objective metrics: Attention Score (ATNS) and Maximum Attention Span Score (MASS) to quantify the attention of a user to a Point of Interest (PoI) in a VR scenario. The metrics are computationally inexpensive and require no additional hardware. As part of our work, we present ReptilesVR, a VR-based edutainment and cinematic zoo experience developed using omnidirectional stereo videos to provide immersive close encounters with wildlife. We conducted a study with 25 participants to quantitatively measure the overall experience of users in ReptilesVR using the two newly defined metrics: ATNS and MASS. Also, the emotional engagement of the users was measured qualitatively using a questionnaire on a Likert scale of 1–10. We observed that scenarios with activity in close proximity to reptiles have positive correlation between the proposed metrics with the subjective scores of presence (μ = 8.58, σ = 1.28) and immersion (μ = 9.0, σ = 1.14) as reported by participants in the questionnaire. This work shows that the metrics can be useful to find the features that draw a user’s attention to certain points of interest (PoIs) in a scene. This will aid in the enhancement of the overall user experience and the development of more compelling immersive VR applications.
Improving Accessibility of Elevation Control in an Immersive Virtual Environment

Despite the advances made in Virtual Reality (VR) technology, the design of VR experiences lacks sufficient focus on accessibility and inclusion as the primary requirements. These are especially important for STEM education, where engaging in experiential activities is essential. This study was conducted to investigate accessibility considerations in the design and development of Immersive VR (IVR) learning spaces for wheelchair users. The specific research question is: How can we make a VR system easier to interact with for wheelchair users needing vertical movement? A user study with thirty (30) participants in three groups was conducted: Group A (the control group, non-wheelchair users) who used natural body movement to interact with the environment, Group B (verification group, non-wheelchair users) who used software controls for accessibility, and Group C (wheelchair users) who used the same software accessibility feature. The results indicate that the accessibility feature enabled wheelchair users to complete the tasks requiring raising or lowering of the body, with almost similar levels of completion rate and accuracy.
Research on Improved Chaotic Particle Swarm Optimization Algorithm

In order to solve the problem that traditional particle swarm optimization algorithm is easy to fall into local optimal and slow convergence, this paper uses improved Tent chaos map to initialize, and designs a new non-linear inertial weight adjustment and chaotic search strategy. A Improved Chaotic Particle Swarm optimization (ICPSO) is proposed. In order to better verify the optimization performance of the proposed algorithm, six standard test functions were compared with SPSO and CPSO algorithms. The results show that the proposed algorithm has better optimization accuracy, convergence speed and global search ability than SPSO and CPSO, which proves the feasibility and effectiveness of the proposed algorithm.
A camera calibration method based on scene and human semantics

Camera calibration is an important preliminary for many applications such as 3D reconstruction, motion capture and human-computer interactions. Although great achievements have been made in the past several years, it still remains two main problems. The one is that conventional calibration methods always depend on specific calibration tools (e.g., checkerboard). The other is that the calibration procedure is complex when these methods are used in multi-view large scenarios. In this paper, we propose a camera calibration method which can be applied to large-scale sports scenarios without a special calibration object. To tackle these challenges, our key-idea is to use scene and human semantics detected from RGB images to calibrate the intrinsic and extrinsic parameters of multi-view cameras. We first detect the scene and human keypoints, then the initial camera parameters are calculated with the static scene keypoints and a predefined sports field model. Finally, we combine the scene and human semantics to refine the camera intrinsic and extrinsic parameters. Experimental results demonstrate that our method successfully obtains accurate camera intrinsic and extrinsic parameters for large-scale sports scenarios.
Prototype Model of an IoT-based Digital and Smart Door Locking System with Enhanced Security

Biometric-based digital door locking system plays a significant role in providing authentication, reducing the workforce in smart homes, and building automation scenarios. This paper proposes a prototype model of an IoT-based Smart Door Locking System (SDLS) with reliable double-access authentication. The IoT and biometric technology authentication mechanism is adopted to make SDLS more secure and reachable with the alert mechanism on unauthorized access. The SDLS contains a biometric scanner, and a thumb impression is made either from a mobile or a biometric-mounted device on the door and both are connected to the centralized cloud-hosted real-time database. The proposed system was tested in real-time and has shown competitive results compared to other Radio-Frequency Identification (RFID) and password-based door locks. Hence, the overall performance efficiency of SDLS is satisfactory and provides biometric-based remote access to control and monitor door locks with the alert system on unauthorized access.
CCD Automobile Wheel Alignment System Based on Cloud Platform

The traditional automobile wheel alignment system has some drawbacks of large detection error and long waiting time of users. Based on the cloud platform technology and the Internet of Things technology, the project establishes an automobile alignment system with distinct features, convenience and speed, improves the alignment accuracy, optimizes the alignment process, greatly reduces the waiting time and improves the alignment efficiency, enhances the user's experience.
Search-based Trajectory Planning with Motion Primitives for Quadrotors Using Pruning A* Algorithm

In this paper, a trajectory planning method based on pruning A* with motion primitives is proposed for quadrotors. First, the motion planning problem for quadrotors is formulated as an optimization problem and the control space is discretized uniformly to obtain motion primitives with the consideration of quadrotor’s dynamics. Secondly, a Pruning A* algorithm with high search efficiency is designed to find the suboptimal and free-collision trajectory. Furthermore, the constraint of Field of View (FOV) is considered in order to be applied into practice. Finally, the simulation results show the effectiveness of the proposed methods.