Reviewerly/exHarmony huggingface dataset:
Information Data Fusion Algorithm Based upon that Fractional Differential Operator and Its Application in Manufacturing System

In engineering practice, online measurement of manufacturing equipment production information faces high uncertainty and ambiguity, which directly has an impact on the dependability of manufacturing execution systems. In just this research, we study a multi-sensor detection information fusion method relied on a fractional differential operator and cultivate an online detection data fusion model based on it. We came to the conclusion that the proposed technique based on the fractional differential operator increased the operating dependability of the detection system by boosting the information strength and data detection accuracy after using it to fuse the data of measurement of robot spindle diameter. Thus, using this strategy can increase the manufacturing execution systems' dependability..
Research on the Application of Human-Computer Interaction

As modern technology advances and the computer's function becomes complete, along with the development of machine learning, virtual reality, and artificial intelligence, how humans interact with the device starts to be one of the decisive factors of productivity in the current society. In this paper, the authors will mainly look at five aspects of human-computer interaction (HCI), The first part introduces the development history and early research of HCI since the birth of the personal computer. Secondly, the applications of HCI in medical treatment is listed, which includes medical devices and psychotherapy through emotional analysis platforms and gameplay. Furthermore, the paper will demonstrate how HCI plays a more and more important role in video games with three parts: equipment, sound effect and visual effect. Then we will discuss the revolutionary changes HCI has brought to the virtual reality industry. Lastly, the paper predicts the future direction of HCI and emphasize its possible drawbacks.
Application of Virtual Reality in Different Fields

VR, also known as Virtual Reality technology, is a new hot technology, its research content is suitable for different fields in the construction of application, so it has a good prospect for development. VR is facilitating human life, and some industries including education, games, health care etc. This paper combining with some examples, combs VR's development history and summarizes the application of VR in games, education and health care. Specific, this paper analyzes the development status of VR and summarizes the impact of VR on these fields. Compare the differences between traditional games and VR games. In particular, we also focus on the advantages and disadvantages of VR in these three fields. These contents are of great significance for analyzing and discussing the development prospects of VR in these fields. At the end of all, this paper give summary and forecast the prospect of VR technology.
Automatic Collision Avoidance via Deep Reinforcement Learning for Mobile Robot

Collision avoidance is one of the most critical problems to be explored in the mobile robot field for finding an optimal path between source and destination. In this paper, we propose a novel DRL-based mapless collision avoidance algorithm for mobile robots, which maps the raw sensor data to control-level instructions and realizes robot navigation in an unknown environment without any complex numerical calculation. A game-like simulation environment enables the agent to interact with the surroundings and optimize its policy. The simulation environment is abstracted as walls and cylindrical obstacles, and it would be randomly initialized at the beginning of the training process to improve the generalization ability of DRL policy and obtain reliable test data for comprehensive analysis. We compare the performance of some state-of-the-art deep reinforcement learning algorithms with randomized environments, including DDPG, TD3, and SAC. The result shows that TD3 and SAC both gain a high passing rate when the obstacle density is from 0.0 to 0.10. The agent with DDPG fails to learn in the training environments and performs poorly. When obstacle density is 0.0 to 0.5, TD3 is more stable and performs better than SAC. However, SAC serves better when the obstacle density is 0.10 to 0.20. Both TD3 and SAC generate smooth trajectories in the test stage. The video and code are available: https://github.com/Vinson-sheep/turbot_rl.
Long-term Tracker with Adaptive Occlusion and Recovery Judgment

Compared with a short-term task, long-term tracking has received more attention and research in recent years. Long-term tracking is more challenging because it needs to solve two difficult problems: when to update and how to update our model. Many outstanding short-term tracking methods update frame by frame or manually set the threshold to judge if the tracker should be updated, but when the target is blocked or escapes from the field of view, it is easy to get and update wrong samples, resulting in model pollution and drift. Not only that, but due to the lack of a re-detection mechanism, it is difficult for these short-term tracking methods to recover once the target is lost (especially when the target reappears from another location). In this work, we propose a high-speed long-term tracker with adaptive occlusion and recovery judgment (LT-AOR), which comprehensively judge the update chance of the tracker through the discrimination information and appearance information, and re-detects the target in a simplified way to achieve stable tracking in the case of target occlusion and loss.
Computer-generated Humour Based on GPT-2

Humour generation has always been a huge challenge in the area of computational humour. In this paper, we explore how to generate jokes based on keywords by fine-tuning GPT-2 pre-training model based on keywords task and comparing them with the LSTM-based encoder-decoder model. We trained the model by the short jokes of Conan O'Brien and the puns dataset of Yang et al. with the help of Pos-Tagger. Then, we evaluate the humour by using human evaluation and the similarity with keywords and jokes by using automatic evaluation. In terms of the final average score, the performance of our model is better than the LSTM-based encoder-decoder model.
D3QHF: A Hybrid Double-deck Heuristic Reinforcement Learning Approach for UAV Path Planning

An Unmanned Aerial Vehicles (UAVs) related research hotspot appertains to the autonomy, and as a filed extension, the autonomous path planning is also of concern. In allusion to the demands of path planning in dynamic environments, a hybrid double-deck joint path planning strategy based on deep Q-network and Q-learning (D3Q) is proposed in this paper. In comparison to the single deep Q-network (DQN) for path planning in dynamic environments, the proposed D3Q utilizes two algorithms to handle static and dynamic obstacles, respectively, which refrains from the problem of poor network fitting when only employing DQN. Furthermore, a heuristic fish (HF) algorithm is presented as a prior strategy to assist D3Q in exploring the environments so as to speed up the training. Simulation results demonstrate that our advocated approach has an excellent performance in performing dynamic path planning in different scenarios and can generate relatively shorter and more dependable trajectories.
Track Matching Method of Sea Surface Targets Based on Improved Longest Common Subsequence Algorithm

Track similarity analysis is an important part of historical track big data mining analysis, which provides a basis for track clustering, target recognition, and target activity law analysis. The paper adopts the improved longest common subsequence method to measure the similarity between different tracks and complete track matching. Firstly, we establish a historical track database and establish a raster spatial index for the key sea areas of concern. Secondly, we preprocess the track data and unify the data of different sampling scales into one space-time dimension through sparse or interpolation, retrieve historical tracks that are geographically close to the current track, and use the improved longest common subsequence method to calculate the matching similarity probability between the current track and the historical track. Finally, the track clustering and activity rule mining analysis are completed according to the calculated maximum matching probability. The simulation experiment verifies that the algorithm can effectively calculate and complete the correlation matching of target tracks, which provides an effective basis for target track clustering and activity law mining and analysis.
Hidden Markov Model Based Traffic Identification Technology of Satellite Communication Network

In the satellite communication network, it is significant to realize real-time identification of specific application traffic for ensuring the Quality of Service (QoS). A traffic identification technology based on Hidden Markov model (HMM) is proposed, which establishes a specific application traffic identification model that relies on the characteristics of packet arrival order, arrival time gap and packet size. The sample traffic packets map to HMM states, and some discrete random variables are used to represent the fixed state characteristics of HMM. This technology can shorten the model establishment time, and improve the accuracy and real-time performance of identifying application traffic. This paper proposes an optimal state count selection algorithm and designs the Network Application Identification Architecture (SNAIA). Eventually, the testing envirnoment is built in the virtual satellite communication network simulation system. SNAIA is used to identify a variety of application traffic. Results show that SNAIA can quickly and accurately identify the packet traffic generated by specific applications, and provide support for their OoS.
Oriented Object Detection for Large Aspect Ratio Vehicles in Remote Sensing Images

Existing vehicle detection methods in remote sensing images encounter challenges when detecting vehicles with large aspect ratios. Due to the big scale gap between the long edge and the short edge, large aspect ratio vehicles are hard to extract fine features. In addition, large aspect ratio results in strong orientation information and the inconsistency between regression task and classification task is even more severe. To address these issues, this paper proposes a Large Aspect Ratio Vehicles Detector (LARDet). Aiming at the difficulty of feature extraction for objects with large aspect ratios, we adopt more data augmentation and introduce PAN structure to pass through the short edge feature from shallow layer to deep layer, so as to extract more discriminative features. A lightweight Boxes Quality Predication Module (BQPM) is designed to alleviate the inconsistency between classification score and location accuracy. To alleviate the feature inconsistency between regression and classification, we further design the Align Classification Module (ACM), change the regression branch and classification branch from parallel to serial, then apply AlignConv to extract rotation-invariance feature for classification. A Large Aspect Ratio Vehicles Dataset (LAR1024) is proposed to evaluate our method. Compared with other SOTA methods, LARDet gains 5.0% AP on LAR1024 with the fastest speed of 23.9 FPS, which achieves a better speed-accuracy trade-off in the detection of large aspect ratio vehicles.
Video Object Detection Based on 3D Convolution

There is context-related temporal and spatial information in video. How to make better use of these information is the key to improving the accuracy and speed of video object detection. The method of processing spatial and temporal information through 3D convolution is better than that of processing spatial information first and then processing time dimension information. There are two reasons for this assertion. One is that the 3D convolution can process spatial information and temporal information at the same time which will not divide them into two parts in feature extraction. The other is that the human visual derivation mechanism needs to memorize multi frames and process a video as a whole. After the training test on NFS dataset, it is verified that 3D convolution has stronger ability to extract temporal and spatial information. In addition, this paper also proposes a downward sampling method by connecting the low-resolution layer and the high-resolution layer, which can effectively improve the FPN model and enrich its high-level feature representation. After training and testing with different number of downward sample modules on MOT16 dataset, the improvement of model performance by down-sampling module is verified. Finally, this paper combines the downward sample module with the 3D network, and obtains the best test results on the NFS dataset.
Dataset for assessing the definition of underwater images

in recent years underwater image quality evaluation methods have been proposed. These methods aim to measure the performance of underwater image enhancement and recovery methods. Image datasets provide a basis for researchers to evaluate image processing algorithms and to adapt their structure. The work presented in this paper is different and aims to evaluate image algorithms in terms of both definition and overall perception. We propose a dataset in which we score our image datas in terms of definition and overall perception by subjective evaluating. Afterwards, we evaluate the performance of recent image quality evaluation methods by looking at these two aspects and find the superiority of using definition alone as index for evaluating.
Optimization and Processing of Helicopter Axis Trajectory Measurement Data

In order to improve the validity of the measured data of the axis trajectory, a data optimization algorithm combining multiple algorithms is proposed in this paper. Firstly, the box graph theory is used to detect the abnormal data, and then the use of median filter is improved. For the abnormal values detected, the improved strong tracking filter algorithm is inserted into the Kalman smoothing algorithm to realize the data optimization. At the same time, a strong tracking sequential fusion algorithm is used to realize the data fusion. Finally, through the algorithm to optimize the real axis trajectory measurement data, it is found that the algorithm can accurately detect and eliminate abnormal values, and can also smooth and optimize the data to achieve effective data fusion and improve the authenticity of the data.
Failure Risk Assessment of Functional Modules of Shipboard Equipment Based on Data Fusion

In view of the difficulties in assessing the failure risk of shipborne equipment, the focus of risk prevention is only determined by job experience, and the lack of quantitative decision support for equipment preventive maintenance and spare parts procurement and storage, a data fusion-based research on the failure risk assessment of shipborne equipment functional modules has been carried out. On the basis of comprehensive consideration of the signal transmission process of the shipboard equipment system, the proportion of spare parts reserve, historical failure conditions, condition monitoring methods and other factors, the genetic algorithm-based equipment function module failure risk assessment model was constructed, and the model was verified through examples. And the feasibility and rationality of the algorithm.
An Invisible Backdoor Attack based on DCT-Injection

In recent years, as deep learning models have been widely used, the research on the security of network models attracts more and more attention. As a novel type of attack method, backdoor attacks pose a great threat to the models due to their stealthiness. To improve the security of network models, possible backdoor attacks need to be investigated. The current mainstream backdoor attacks embed trigger patterns to images in the spatial domain, which makes their trigger patterns observable. To solve this problem, an invisible backdoor attack based on discrete cosine transform (DCT) injection is proposed in this paper, which injects backdoor information in the frequency domain by using DCT. Experiments on three different models with CIFAR-10 dataset demonstrate that the proposed method is more effective and stealthier than the spatial domain embedding backdoor attack. It is further demonstrated that the proposed method is resistant to Fine-Pruning defense by comparing it with mainstream backdoor attacks.
Research and Application of Cloud Computing and Big Data Technology in Intelligent Desktop Virtualization System

By analyzing the design requirements of a secure desktop virtualization information system, this paper proposes the security virtualization technology of “whitelist” security mechanism, the virtualization layer security technology of optimized design, and the virtual machine security technology of resource and network layer isolation. On this basis, this paper constructs the overall architecture of the secure desktop virtualization information system. This paper studies the desktop virtualization technology research based on VMware using VMware server virtualization solution to transform and upgrade the traditional intelligent desktop virtualization system, improve server resource utilization rate, and reduce operation and maintenance costs.
Multi-target Assignment Method of Multi-UAVs Based on Genetic Algorithm Sample Size-Limited Clustering

In order to solve the problem of multi-UAV multi-target assignment, the paper proposes a clustering method limited by the number of samples to solve such problems. The traditional K-means clustering method has the disadvantages of randomness in initial cluster center selection and easy to fall into local optimum. On the basis of constructing the fitness function reasonably, based on the genetic algorithm, by introducing the mutation mechanism to avoid the algorithm from falling into the local optimum, and having the advantages of strong search ability, a finite sample clustering method based on the genetic algorithm is proposed and simulated. The results show the effectiveness of the method.
Speed Control of Separately Excited DC Motor Using NARMA-L2 Controller

An Intelligent Neural Network (NN) based nonlinear Autoregressive-moving average (NARMA-L2) Controller is developed for speed control of separately excited D.C. Motor by performing the features of Artificial Neural Networks (ANN). The aim of the proposed approach is to improve tracking performance of separately excited D.C. motor as compared to the conventional (PI) control approach. Performance Comparison of SEDM for NARMA-L2 controller and the conventional PI controller is also discussed. The entire speed control mechanism for SEDM is modelled by using the MATLAB 8.0 toolbox. The intelligent NARMA-L2 controller is operated in two steps: - the first, the variations in external loads is performed to check the speed control performance of NARMA-L2. The second, the controller is operated at various reference speed. Simulation results shows the effectiveness, advantages and good performance of the NARMA-L2 which is described through the comparison of conventional PI controller and NARMA-L2 controller. Excellent results added to the simplicity of the drive system, makes the ANN based NARMA-L2 controller strategy very suitable for a wide range of applications such as industries, paper mills etc.
Blockchain Energy for Future Smart Grid in Indonesia: A Brief Review

Blockchain is a disruptive technology in this upcoming disruption era. There are ideas to apply this technology in the electricity industry and transform it in form of peer-to-peer energy transactions in the future power grid. Research and pilot projects from companies around the world are starting to implement it to analyze the effect on the electricity supply chain and its feasibility. This paper will briefly discuss the concept of blockchain in a peer-to-peer transaction and also its effect on the electricity industry as a smart grid. Next, by considering the electric grid and its supply chain in Indonesia that is operated by PLN, the paper will discuss the possibility of how blockchain will affect the electricity industry in Indonesia by using an approach from the existing research in another country. The discussion shows that in Indonesia, blockchain will impact the most on distribution sector and the electricity retail market. And by considering blockchain technology, private and consortium blockchains with BFT consensus are preferred in Indonesia. PLN must be prepared to face these changes by conducting more research, especially using the regulator sandbox approach. The government of Indonesia also needs to update its regulations to anticipate blockchain integration into the grid by participating in collaborative research with PLN.
SLIC Research and Implementation of a Parallel Optimization Algorithm

SLIC is an image segmentation method that clusters picture pixels into superpixel blocks. But it has many flaws, including large amount of computation and high data dependency. In this paper, the reference point of the main computation of the SLIC algorithm is moved from the cluster center to the ordinary pixel point by changing the structure of the algorithm. Although the computation time increased, the paralism of the algorithm improved, and the pixel dependency reduced too. At the same time, the traversal method is changed from a single pixel point to a cluster center when calculating the pixel sum of each cluster center. The parallel optimization method is a hybrid programming paradigm based on OpenMP and MPI. Compared with the standard SLIC method, in this parallel algorithm, the computation time largely reduced in the multi-core computer.
Automatic Mail Reception System for Patients to Identify Health Conditions

Predicting and detecting human health conditions in the early stages is a crucial thing to be considered in the medical field. Lung tumour has been one of the most often common malignancies in the past few decades, as well as the high incidence of melanoma-related fatality globally. As a cost-effective alternative, computer-aided diagnostic systems can combine complicated features and identify a patient's probability of getting a lung tumour, reducing the need for unneeded and costly medical treatments. The computing profession is completely digitizing everything, and the healthcare profession is following suit with image identification and data analytics. As a modern solution to predict the presence of tumours in scan images aConvolutional Neural Network (CNN),Deep Learning (DL) model is proposed in this study. The dataset required for this study is collected from the Kaggle website and pre-processed using various pre-processing methods to make it compatible with the DL model. After pre-processing the data, the dataset is divided into a training set, testing set and validation set. The CNN model is trained using the training set, validated using the validation set and tested for efficiency using the testing set. After training and testing the CNN model a user-friendly webpage is created to make this CNN model easily accessible to the individuals. The webpage is designed in such a way that when the patients upload their lung CT scan image and enter the necessary contact details requested by the webpage, the scan is analysed and evaluated by the trained CNN model and the result is sent as an email to the user.