Reviewerly/exHarmony huggingface dataset:
Study on The Dilemmas and Ability Improvement of University Experimental Teaching Staff under The Background of Double First-class

Experimental teaching staff is an integral part of colleges and universities under a “double first-class” background. This paper analyses the current situation of the number of undergraduates in Regular HEIs and postgraduates in China and the current situation of university experimental teaching staff under the “double first-class” background. Taking the School of Automation of the Wuhan University of Technology as an example, this paper analyzes the needs of experimental teaching staff, discusses the dilemmas and challenges faced by them, and gives corresponding suggestions based on existing policies.
JuzzyPy ― A Python Library to Create Type―1, Interval Type-2 and General Type-2 Fuzzy Logic Systems

We present JuzzyPy, a Python based fuzzy logic toolkit enabling the creation of type-1, interval type-2, and general type-2 fuzzy logic systems. Fuzzy logic systems are being applied in disciplines across engineering and sectors such as cyber-security and autonomous systems, where an increasing focus on interpretability and trust is re-emphasising a focus on rule-based systems. This highlights the value of broadly accessible software resources to support both experts and non-experts in efficiently designing fuzzy systems. Python is currently the most popular programming language in the world. JuzzyPy is the first library capable of supporting the development of general type-2 systems in Python, facilitating access to these systems across engineering disciplines. In this paper we delve into the ability of the toolkit and explore its implementation. We hope that this allows for further accessibility in the use of fuzzy logic systems and provides a basis for developers, practitioners and researchers to collaborate on scientific advancement of the field and the tackling of real-world problems.
OCR Enhanced Augmented Reality Indoor Navigation

We present an augmented reality system for indoor navigation. On the front-end, we have a mobile application named "Augmented Reality Navigation" that was developed in Unity using AR Foundation. The user first takes a photograph of a nearby nameplate or room number. Then the application sends that image to a server that performs text detection and Optical Character Recognition (OCR) to identify the user’s location within a building. It will send back to the app a location to be referenced in the virtual map. The virtual map is then used to construct a path made out of arrows that the user can follow to their destination. To develop the virtual maps used by the app, we have created a supporting MapMaker application that can scan the space and develop navigable maps rapidly and efficiently. Our contribution to this field of navigation is the ability to not require QR codes, Wi-Fi signals, or Bluetooth beacons to identify a user’s location. Instead, we introduce a new way of locating the user with OCR technology that uses the surroundings to see where the user is located. To improve tracking, we engineered and deployed additional efficiency techniques when guiding a user to their destination. To keep the virtual path aligned with where the user is walking, we use constant checks and micro-adjustments of augmented reality arrows to keep the highlighted path in front of the user. Based on the outstanding results of the survey, we hope that our changes and innovations are explored and reflected in the future development of augmented reality navigation applications.
Improving Topic Quality with Interactive Beta-Liouville Mixture Allocation Model

One of the major tasks in natural language processing is to categorize texts into different categories. Topic models are an important set of tools for categorizing texts and so are mixture models since both models learn patterns from data in an unsupervised manner. The introduction of latent Dirichlet allocation (LDA) triggered a lot of research in this domain. Recent research investigates the use of distributions other than Dirichlet for the topic proportions in LDA especially generalized Dirichlet and Beta-Liouville distributions in addition to adding useful attributes specific to the task at hand. Improving the quality of topics extracted from these models is important for accurate inference and unsupervised language tasks. Owing to this cause, in this paper, we propose interactive Beta-Liouville mixture allocation (iBLMA) model which combines the clustering capabilities of mixture models with interactive learning which helps the user modify the topic weights of irrelevant words within the topic. We show the efficiency of our model with experiments on two different text datasets.
Towards Casually Captured 6DoF VR Videos

In contrast to traditional media, content production tools and capturing for immersive experiences such as Virtual Reality (VR) headsets are still not widely accessible to casual users. Often expensive hardware or sophisticated software tools are involved in the capturing and content production pipeline. The main goal of our work is to address this gap and simplify content creation for virtual reality viewing to make it accessible to casual users.While traditional 360 panorama photographs and 360-degree videos can already be viewed on virtual reality devices, they do not provide a sense of depth to the user. Traditional methods for reconstructing depth or stereo information for 360 footage on the other hand involve specialized hardware. In this work, we investigate methods that allow computing 6-degree-of-freedom (6DoF) videos from standard 360-degree cameras. Thereby, we extract multiple layers of interest representing the background and the foreground. We also provide some first results on the results achieved.
Event-triggered sequential fusion filters for multi-sensor systems with multiplicative noises

This paper combines an event-triggered mechanism with a multiplicative noise system to reduce the communication congestion and the calculation workload of the networked fusion center, and proposes an event-triggered Sequence Covariance Intersection and Sequence Inverse Covariance Intersection fusion algorithm based on the above system. It is mentioned that the event-triggered fusion algorithms are robust with respect to the unknown cross-covariances, and have higher fault tolerance and stability than the previous fusers. Validity of the proposed algorithm is demonstrated by studying arithmetic examples.
Identification of Endometrial Cancer in Myometrium Using ANFIS and CNN Algorithms for Accuracy and Sensitivity Improvement

The main aim of this work is to measure the accuracy and sensitivity using the Adaptive Neuro Fuzzy Inference System in comparison with the Convolution Neural Network algorithm for identification of Endometrial Cancer. Materials and Methods: Endometrial cancer images from mini-MIAS database are used in this research work for experiment. For endometrial cancer detection a total of 20 samples are used out of which 10 samples are used for group 1 and 10 for group 2 used with 80 % of G power and maximum accepted error is fixed as 0.5 that is calculated from Clincalc. Results: The predictive model using ANFIS algorithm shows the Accuracy of 94.46 ± 0.39215 and Sensitivity of 91.04 ± 1.07207 followed by CNN algorithm provides an accuracy of 88.63 ± 1.02746 and sensitivity of 86.15 ± 1.34268 with the significance value of 0.002 for accuracy and 0.000 (2-tailed test) for sensitivity obtained from SPSS tool. Conclusion: Based on the outcome of the proposed work ANFIS classifier shows higher performance than CNN classifier to identify the cancerous cells in the beginning stage of the disease.
Fostering students’ engineering competence by adopting augmented reality: a proposed randomized controlled trial study

Developing students’ engineering competence is an essential objective in engineering education. However, delivering effective and just-in-time instruction is under pressure due to the need to offer personalized learning and the existence of trainer shortages in the engineering sector. Recent developments in educational technology allow alternative innovative sources, such as Augmented Reality (AR), to imitate real-life situations and to offer learning activities which supports students to practice their skills in a safe environment. However, limited experimental studies have been reported that provide insights into the impact of AR-based learning activities on students’ development of engineering competence in comparison to traditional teaching approaches. Therefore, this proposed experimental study aims to investigate the effect of an AR-based learning activity, which involves solving the malfunction of an air handling unit, on students’ development of engineering competence. This experimental condition will be compared to traditional teaching approaches, consisting of an identical learning task including books and written exercises. Additionally, cognitive load, intrinsic motivation and students’ perceptions towards the learning task and learning environment are included, seeing that these aspects are considered to be crucial intermediate variables in learning processes. Results of this study provide insights in the effectivity of AR-based learning environments in engineering education supporting students’ hands-on experience, while releasing the pressure on resources such as industrial devices and staffing.
Fast Candidate Region Extraction for SAR Ship Target

At present, deep learning technology is widely used in ship target detection in synthetic aperture radar (SAR) images. However, high-resolution remote sensing SAR images cover a larger area and have larger image sizes. To be able to use the deep learning model for training and testing, the image needs to be cropped to the appropriate size. In a high-resolution SAR ship image, the ship target usually takes only a small part of the whole image. As a result, only part of the cropped image contains the target, and most of the rest are background regions. This will cause a lot of computational redundancy in the model inference stage. To solve this problem, a fast candidate region extraction algorithm is proposed in this paper for ship target extraction. The algorithm consists of three parts: firstly, extract the saliency map of the SAR ship image, secondly, perform median filtering on the segmented image, and thirdly, extract candidate regions. The superiority of the algorithm was demonstrated by experiments on the AIR-SARShip-1.0 dataset.
Conditional Inpainting Generative Flow

Image inpainting tasks have attracted much attention in computer vision. Many existing methods propose to generate the corrupted images by employing generative adversarial networks (GAN) to sample from the data distribution conditioned on the observed regions. However, due to the slowly growing receptive field of the convolutional layers in GAN, it is difficult for these GAN-based methods to effectively model long-range interactions, which is critical for inpainting. Besides, existing approaches generally introduce joint losses to render visually realistic results, while the weights for different losses are fixed during training and may not adapt to the dynamical learning process. To address the above issues, this paper proposes a novel conditional inpainting generative flow (CIFlow). CIFlow is a flow-based conditional generative method with pixel-wise permutations and channel-wise permutations performed before traditional convolutional layers to effectively model long-range interactions. Moreover, the weights for the joint losses in CIFlow are dynamically adjusted based on the current learning state to provide gradients of the same scale for different losses. Experiments are conducted on real-world datasets and the results demonstrate the robustness and effectiveness of the proposed algorithm.
A SOC estimation method based on Improved Fuzzy Broad Learning System

In this paper, FBLS is used as the base model and Grasshopper optimization Algorithm algorithm(GOA) is used to find the optimal initial weight of Fuzzy Broad Learning System(FBLS). On the basis of finding the initial center of membership function, K-means algorithm is improved by using local density and local probability theory to find the optimal initial clustering center, so that the algorithm can be more accurate. According to the disadvantage that Grasshopper optimization Algorithm(GOA) is not easy to converge and jump out of the global optimization, the algorithm is improved by using the sine cosine theory, and the algorithm search is more comprehensive by improving the parameter Rl. The experimental simulation shows that the error of the improved SC-GOA-FBLS is significantly lower than that of GOA-FBLS,PSO-BP and FBLS.
Comparative experiment of attention prompting methods using VR driving simulator

This paper mainly introduces the methods of attracting the driver’s viewpoint to a specific position in the field of view in the simulation of automobile driving and carries out experiments on these methods. The use of a driving simulator can help drivers to become familiar with driving operations and traffic rules. However, because drivers often lack attention during driving and bear a lot of cognition, such as traffic signs, traffic lights, and other objects, it is necessary to provide visual guidance for drivers. Using virtual reality technology for this task is generally considered reasonable. The authors propose four attention prompt methods, namely "Point", "Arrow", "Blur" and "Dusk". To verify these four attention prompt methods’ feasibility, the authors use Unity3D to develop a virtual driving simulator. To improve the realism of the simulated driving, the authors also used a Logitech G29 steering wheel as an input device and uses the D2R-test method to conduct experiments and get the results. The experimental results show that, compared with not using any attention prompting method, turning on the attentional prompt method has a significant effect on the experimenter’s attention improvement.
Cement Quality Management and Analysis System Development

This paper provides a detailed description of the cement quality management and analysis system. The use of SQL database is more standardized to establish a unified platform for cement plant data, and the client system uses ASP.NET three-layer architecture in B/S mode. This paper proposes an effective method for data integration of quality management systems in cement enterprises and the analysis of data applications to solve the current problem of increasingly serious information silos in cement enterprises. The overall framework for the integrated management of cement management and analysis of intelligent manufacturing is introduced, and the integrated quality correlation analysis module is designed in detail.
Generative Adversarial Networks based image generation of insulator

In recent years, the combination of artificial intelligence and power systems has been increasing. However, the collection of image datasets of power equipment is limited by places and environments, so the number of collected datasets is relatively small, which makes it unable to provide sufficient data support for specific applications. We propose to use Generative Adversarial Networks to generate images of electrical equipment from existing sparse datasets, thereby increasing the size of the electrical equipment dataset. In this paper, three generative adversarial networks with different structures are used to generate images of insulator and to assess the caliber of images produced by the three models, the analysis of loss function image and FID score of generated images is performed.
Conservation of Felonious Records for Predicting Crime Rates by Using Random Forest Algorithm Comparing with KNN Algorithm

To calculate the novel time usage for the crime analysis using K-Nearest Neighbor algorithm compared with Random Forest algorithm on Data of Crimes. K-Nearest Neighbor with sample size = 247 and Random Forest with sample size = 154 with G power (value = 0.8) were calculated many times to predict the accuracy percentage for the various crime data. K-Nearest Neighbor has significantly done with better accuracy (86.75%) compared to Random Forest (84.53%) and the statistical significance difference (two-tailed) is 0.01 (P¡0.05). Based on Independent T-test analysis using SPSS statistical software, K-Nearest Neighbor achieved significantly better than Random Forest for novel time usage calculation.
Towards Improving Exploration in Self-Imitation Learning using Intrinsic Motivation

Reinforcement Learning has emerged as a strong alternative to solve optimization tasks efficiently. The use of these algorithms highly depends on the feedback signals provided by the environment in charge of informing about how good (or bad) the decisions made by the learned agent are. Unfortunately, in a broad range of problems the design of a good reward function is not trivial, so in such cases sparse reward signals are instead adopted. The lack of a dense reward function poses new challenges, mostly related to exploration. Imitation Learning has addressed those problems by leveraging demonstrations from experts. In the absence of an expert (and its subsequent demonstrations), an option is to prioritize well-suited exploration experiences collected by the agent in order to bootstrap its learning process with good exploration behaviors. However, this solution highly depends on the ability of the agent to discover such trajectories in the early stages of its learning process. To tackle this issue, we propose to combine imitation learning with intrinsic motivation, two of the most widely adopted techniques to address problems with sparse reward. In this work intrinsic motivation is used to encourage the agent to explore the environment based on its curiosity, whereas imitation learning allows repeating the most promising experiences to accelerate the learning process. This combination is shown to yield an improved performance and better generalization in procedurally-generated environments, outperforming previously reported self-imitation learning methods and achieving equal or better sample efficiency with respect to intrinsic motivation in isolation.
An Efficient Automatic Face Mask Detection System for Human Safety Based on Deep Learning using Novel YOLOv3 in Comparison of YOLO with Improved Accuracy

The objective is to build an efficient face mask detector using Novel YOLOv3. The algorithm used to detect face masks is Novel YOLOv3 in comparison with YOLO, the dataset used was (Facemask Detection Dataset, no date) with the sample size was 136. Novel YOLOv3 gets an accuracy of 92% and in YOLO it is 88% the increase in accuracy is due to the use of Darknet53 neural network model, the novel YOLOv3 and YOLO are statistically satisfied with the independent sample t-test value <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\mathrm{P}\unicode{x00A1}{0.001})$</tex> with confidence level of 95%. Face Mask detection in Novel Yolov3 has a significantly better accuracy than YOLO.
Variational Inference on Decomposition of Conceptual Knowledge for Few-Shot Learning

Few-shot learning (FSL) aims at learning a model to generalize well on new tasks with limited training examples. In this paper, we propose a new FSL model called Variational Conceptual Decomposition Network (VCDNet) that learns to decompose the image representation from the conceptual level for maintaining both generalization and discrimination capabilities when adapting to new data. Specifically, the decomposition is accomplished by variational inference on the disentanglement of a frame feature and a salient feature that represent the conceptual structure and the discriminative detailed information for generating the distribution over the images, respectively. In addition, we introduce an adversarial classification method to further guide the decomposition conducted on the conceptual knowledge level. Our experiments on the CIFAR-FS dataset demonstrate that the proposed VCDNet outperforms many competitive baselines.
Comparative studies of evolutionary methods and RL for learning behavior of virtual creatures

Simulation-to-reality is receiving more attention, as demonstrated by the evolution of virtual creatures such as Xenobot. Until now, researchers have conducted experiments on virtual creatures in their own environments. However, Evolution Gym, a benchmark for 2d soft robot simulations, has recently been proposed. Meanwhile, evolutionary methods are used to evolve the morphology of virtual creatures, and reinforcement learning (RL) is frequently used to learn their behavior. Despite the high performance of RL, there are cases where learning does not proceed well, and the evolutionary methods are expected to be effective in such tasks. In this study, we compare the evolutionary methods and RL method to learn the behavior, of the various robot structures and Evolution Gym tasks.
Selective Data Collection Method for Deep Reinforcement Learning

In deep reinforcement learning, reinforcement learning is responsible for interacting with the environment to produce data, and artificial neural networks are responsible for value function fitting. It is observed that artificial neural networks converged differently to different inputs, which, in our analysis, is due to imbalanced data. Therefore, we propose selective data collection to boost the quality of the data by then discarding the excess data. It has been proved experimentally that our method can significantly contribute to the convergence rate of the reinforcement learning algorithm.
Autonomous Navigation of UAVs in Resource Limited Environment Using Deep Reinforcement Learning

In recent years, we can see the number of applications of UAVs in various situations are growing, such as accomplishing missions in resource limited environments where the space and time to perform tasks are limited. In this paper, we propose a new framework for leveraging machine learning technologies such as deep reinforcement learning methods to let the UAV to accomplish navigation tasks in complex resource limited environments. The proposed framework adopt PID algorithm to control the UAV’s attitude and position during flight and use PPO algorithm to optimize the navigation planning. Technical details involve the use of domain specific knowledge and well-designed reward function and state representation. We make some general tests with a single quadcopter UAV in a simulated pybullet environment using the developed framework. This experimental results show that the proposed framework can achieve high performance in navigation tasks in resource limited environments. This will enable continuing research and active development on the deep reinforcement learning based frameworks for UAV autonomous navigation in more complex applications and environments.
Feature consistency anomaly detection method based on adversarial training deep autoencoder

Real-time and accurate fault diagnosis is essential to ensure the safe and stable operation of industrial systems. However, most of the monitoring data collected by sensors in actual industrial sites are obtained when the system is operating in a healthy state, so it is difficult to obtain abnormal data with fault labels. Therefore, it is of great practical significance to carry out research on unsupervised anomaly detection during equipment operation. This paper proposed an anomaly detection method based on the consistency of features of adversarial training deep autoencoder. This method fed two random training sets into two deep autoencoder network, and designed loss functions by the error of input and output consistency and the error defined by the degree of feature inconsistency. Then the network parameters are updated by back-propagation through adversarial training of the two deep autoencoder network, and then the anomaly score is obtained by using the weighted sum of the generated and discriminated losses, and finally unsupervised anomaly detection is performed by the discrepancy of the anomaly score. The efficiency of the proposed method is verified by the data set of gearboxes.