Reviewerly/exHarmony huggingface dataset:
Research on Image Restoration in Remote Sensing Quick-View System

Quick-view system plays an indispensable row in space exploration and earth observation. Currently, the remote sensing quick-view system of our country only has quick display and store abilities. The motion-blurred remote sensing images would not be restored. This paper centers on image restoration in remote sensing quick-view systems. Through calculating the two-level screw target matching image, the ratio of power spectrum density could be estimated quickly. Then Wiener filtering method is implemented to restore blurred images. To restrain the ringing artifact in quick-view system, different weights are introduced on the edge of the target when implementing Wiener filter. Experiments indicate that the optimized image restoration method can obtain relatively gratifying remote sensing data restorative results and treatment period, which might satisfy the requirement of real time display in remote sensing quick-view system.
Caption generation on scenes with seen and unseen object categories

Image caption generation is one of the most challenging problems at the intersection of vision and language domains. In this work, we propose a realistic captioning task where the input scenes may incorporate visual objects with no corresponding visual or textual training examples. For this problem, we propose a detection-driven approach that consists of a single-stage generalized zero-shot detection model to recognize and localize instances of both seen and unseen classes, and a template-based captioning model that transforms detections into sentences. To improve the generalized zero-shot detection model, which provides essential information for captioning, we define effective class representations in terms of class-to-class semantic similarities, and leverage their special structure to construct an effective unseen/seen class confidence score calibration mechanism. We also propose a novel evaluation metric that provides additional insights for the captioning outputs by separately measuring the visual and non-visual contents of generated sentences. Our experiments highlight the importance of studying captioning in the proposed zero-shot setting, and verify the effectiveness of the proposed detection-driven zero-shot captioning approach.
The Application of Image Processing Technology in Camera Picture

The application scene of camera pictures in our life is very huge, and the effect of information conveyed by images is more intense than words and language, so it is very important for us to make good use of pictures obtained by photography or even pictures obtained by other means. Aiming at the processing problem of camera pictures, we have adopted image scaling method, camera picture color change method, gray degree processing method, image color space change method, image brightness processing method, and other methods to solve the processing problems of camera pictures. After reasonable processing, the presentation effect of camera pictures will be greatly improved, and the utilization efficiency will be greatly improved.
Study on IIoT-based Safety Platform of Industrial Enterprises

Production safety is an important guarantee for the high-quality development of industrial enterprises. Factors affecting the safety of industrial enterprises are increasing, and various potential hazards are intertwined and superimposed, resulting in frequent occurrence of accidents. In order to eliminate potential hazards timely, reduce risk of accidents, a safety management and control system based on Industrial Internet of Things (IIoT) for industrial enterprises is constructed, making full use of the advantages of IIoT in rapid perception, real-time monitoring, advanced early warning, dynamic optimization, intelligent decision-making and linkage disposal. The reference architecture of IIoT and application structure of IIoT-based safety platform are given. The core technologies and applications required for the safety management and control system of industrial enterprises based on IIoT are proposed. The main functions of the IIoT-based safety platform and its realization method are studied and designed in detail. Meanwhile, aiming at network information security, the network security structure of the IIoT-based safety platform is proposed. The test application of the system shows that it enhances the safety perception, monitoring, early warning, disposal and evaluation capabilities of industrial enterprises, improves intelligence of safety management and control, and reduces risk of accidents.
Safeen anticline: a complicated structure and its negative impact on oil exploration, Iraqi Kurdistan Region

Safeen anticline is one of the most complicated anticlines in Iraqi Kurdistan Region (IKR), with NW–SE trend. Structurally, the anticline is truncated by different types of faults; some of them are deep-seated faults, which have complicated the subsurface form of the anticline. The anticline includes two domes with two parasitic anticlines and synclines along both limbs. Moreover, two hanging synclines exist northeastwards and one southwestwards. Six possible deep seated transversal faults may occur along the anticline, dissecting the anticline in NE–SW trend and extending northeastwards and southwestwards. Moreover, the deep-seated faults dissect many anticlines on both sides of the Safeen anticline. The deep-seated faults have contributed to the complexity of the anticline, as compared to other anticlines in the Zagros Fold–Thrust Belt, especially inside IKR, and have negative impact to the oil exploration activities in the anticline. Therefore, the drilled Safeen Oil well 01 has missed the target. We have used satellite images from land viewer website + Terraincognita app to study the structural style of the anticline, and to recognize different structural features. Field investigation was carried out to check the interpreted data.
System for Robust Detection of Pedestrians in Dynamic Environments Based on 3D Range Data

The paper addresses the problem of detecting pedestrians using three dimensional data acquired by an autonomous mobile robot equipped with an on-board 3D laser scanner. Previous works in this field have dealt with various approaches for combining 2D and 3D range data features for the use in pedestrian classification. In this paper we propose an image processing pipeline for generating a depth image from point clouds data and then localizing object candidates from the depth image. It involves the image segmentation, feature extraction and human classification processes within unstructured dynamic environments. Three different approaches for the detection of pedestrians, vehicles and cyclists using only 3D range data were employed as a part of this system. We train and test the classifiers in an open environment, with presence of multiple pedestrians, cyclists and vehicles, using only point cloud data. The effectiveness and robustness of the proposed system are verified through experiments with real data. This system is also capable to deal with a real-time framerate (10Hz) with high accuracy.
Media Au-Vi Berbasis PowerPoint dalam Pembelajaran Daring Bahasa Indonesia

Learning Indonesian Language nowadays is a challenge for both teachers and students. This condition certainly requires a media to be an intermediary between teachers and students in online and offline learning. Especially, the media that functions as a messanger or delivers the materials from the teacher to students in learning Indonesian language. This media is needed to support Indonesian language learning so that the target is achieved as planned. The objectives of this research include (1) the procedure for making PowerPoint-based Au-Vi media, (2) the factors that influence the making of PowerPoint-based Au-Vi media. This research uses a qualitative method. The process of data analysis of this research is expressed descriptively. The results show that the process of making PowerPoint-based Au-Vi media tends to be easy if the teacher is proficient in using Microsoft PowerPoint. Teachers can create templates, insert learning materials, insert voice explanations for material, add back sounds, set automatic transitions and animations, and save them in MP4 video format. There are two factors that influence the making of this media, namely factors related to the learning components and technical factors outside the learning components.&#x0D;
KAGAN: A Chinese poetry style transfer method

Character style transfer is challenging, especially when working with Chinese characters. Compared with English characters, Chinese characters have a range of structures and font styles and have attracted a lot of attention in recent research. Some GAN-based methods were proposed for Chinese character style transfer; however, these methods were focused on a single character image ignoring Chinese sentences or multiple characters in one image. A Chinese poetry style transfer method is proposed to address the problem based on Chinese character style transfer. The proposed method includes Smooth L1 loss, which is used to generate superior images. A novel key-attention mechanism generative adversarial network (KAGAN) and a multi-channel discriminator are introduced to generate high-quality images of Chinese characters. The experiments demonstrate that our method is better than other transfer methods, and the proposed model has improved nearly 2% from the conventional methods according to the SSIM evaluation metric.
Conception and Prospects of Exploration and Mining Robots on Lunar Base

Today, the United States, Russia, Europe, and China have all put forward their own plans for lunar bases, and the prelude to the construction of lunar bases as a transit base for deep space exploration has been slowly opened. This article investigates and analyzes the construction plans of various national lunar bases and the results obtained in the exploration and sampling of extraterrestrial celestial bodies. On this basis, an exploration and mining robot for the construction of lunar bases is proposed and briefly explained, include its engineering goals, conceptual assumptions and key technologies, etc.
Freezing Sets for Arbitrary Digital Dimension

The study of freezing sets is part of the theory of fixed points in digital topology. Most of the previous work on freezing sets is for digital images in the digital plane Z2. In this paper, we show how to obtain freezing sets for digital images in Zn for arbitrary n, using the c1 and cn adjacencies.
Reinforcement Learning Based Dance Movement Generation

Generating genuinely creative and novel artifacts with machine learning is still a challenge in the world of computational science. A creative machine learning agent can be beneficial for applications where novel solutions are desired and may also optimize search. Reinforcement Learnings’ (RL) interactive properties can make it an effective tool to investigate these possibilities in creative contexts. This paper shows how a Reinforcement learning-based technique, in combination with Principal Component Analysis (PCA), can be utilized for generating varying movements based on a goal picking policy. The proposed model is trained on a data set of motion capture recordings of dance improvisation. Our study shows that the trained RL agent can learn to pick sequences of dance poses that are coherent, have compound movement, and can resemble dance.
Toward stable astronaut following of extravehicular activity assistant robots using deep reinforcement learning

The use of mobile robots for assisting astronauts in extravehicular activities could be an effective option for improving mission productivity and crew safety. It is thus critical that these robots follow the astronaut and maintain a stable distance to provide personalized and timely assistance. However, most extraterrestrial bodies exhibit rugged terrain that can impede a robot’s movements. As such, a novel predictive-guide following strategy is proposed to improve the stability of astronaut–robot distance in obstructive environments. This strategy combines a deep reinforcement learning navigator and a Kalman filter-based predictor to generate optimized motion sequences for safely following the astronaut and acquire predictive guidance concerning future astronaut movements. The proposed model achieved a success rate of 95.0% in simulated navigation tasks and adapted well to untrained complex environments and varied robot movement settings. Comparative tests indicated our strategy managed to stabilize the following distance to within ±1.0 m of the reference value in obstructed environments, significantly outperforming other following strategies. The feasibility and advantage of the proposed approach was validated with a physical robotic follower in a Mars-like environment. [Formula: see text]
”Every move you make in every game you play”: can game logs determine player’s engagement?

Context: Metrics and instruments available to measure player’s engagement usually involve the application of questionnaires or interviews. Problem: Despite their importance and validity, these approaches can affect the player experience by interrupting the interaction process. In addition, the literature reports a growing interest in the development of games capable of real-time self-adapting based on the player state. This type of game requires strategies and tools to allow an automatic and non-noticeable evaluation of the user’s experience, which is a challenging and open research field. Solution: Game analytics, which embraces techniques that can automatically collect and analyze data, can contribute in this context by providing data that can be interpreted in order to establish eventual relation with players’ experience. IS Theory: Flow theory. Methods: This study investigates whether there is a correlation between interaction logs with the self-assessed engagement. An experimental evaluation was performed with 37 volunteers interacting with two games. The correlations were analyzed considering each log individually and their classification in three categories: neutral, positive and negative. Results: The results of the experimental evaluation reveal that the correlation coefficients found are considered weak (about 0.44). On the other hand, it is interesting that the categorization of logs between neutral, positive and negative presented the main significant results in both games. Contributions and Impact in the IS area: The experiment showed weak correlation between game logs and engagement in the most cases. However, they suggest that the categorization of logs in neutral, positive and negative logs can be an interesting approach.
Health Assessment of Turnout System Based on Mahalanobis Distance

This paper presents a health evaluation of S700K turnout system based on Mahalanobis distance. The main implementation steps of this method consist of obtaining a time-domain statistical index data matrix of the normal-state turnout system, performing principal component analysis on the time-domain statistical index data matrix, establishing baseline Mahalanobis distance for the turnout system normal, determining the health threshold for the safe operation, evaluating the turnout system in an unknown health state. If the Mahalanobis distance of health assessment threshold is greater than the threshold, then it is suggested that condition-based maintenance be taken. An experiment case is demonstrated to verify the efficiency of the proposed method.
An Modified Earth Mover’s Distance for Few-Shot Image Classification

The problem of few-shot image classification has become a popular research area in the field of imaging. In order to improve the accuracy of few-shot image classification, many learning methods have been proposed, among which there are four main approaches: meta-based learning, data augmentation-based, migration-based learning and metric-based learning. In this paper, we propose a modified earth mover’s distance (MEMD) based on the metric learning approach, which has received much attention due to its simple structure and accurate classification. Similarly, MEMD constructs correlations between image regions, using such correlations to characterise the class of the image. MEMD generates a stream of best matches between image regions, and this stream of matches represents the similarity of the classified images. the MEMD algorithm requires the generation of feature weights for image regions, and EMD uses a mechanism of cross-referenced citations to generate uniform consultation weights. in contrast to EMD, MEMD generates inconsistent reference weights based on the similarity of regions. In dealing with the K-Shot problem, we used a learnable class prototype to characterise the class feature vectors. We conducted comprehensive experiments to validate our improved MEMD algorithm and tested it on four popular few-shot datasets. Namely: miniImageNet, tieredImageNet, Fewshot-CIFAR100 (FC100) and the CUB dataset.
SAM: Multi-turn Response Selection Based on Semantic Awareness Matching

Multi-turn response selection is a key issue in retrieval-based chatbots and has attracted considerable attention in the NLP (Natural Language processing) field. So far, researchers have developed many solutions that can select appropriate responses for multi-turn conversations. However, these works are still suffering from the semantic mismatch problem when responses and context share similar words with different meanings. In this article, we propose a novel chatbot model based on Semantic Awareness Matching, called SAM. SAM can capture both similarity and semantic features in the context by a two-layer matching network. Appropriate responses are selected according to the matching probability made through the aggregation of the two feature types. In the evaluation, we pick 4 widely used datasets and compare SAM’s performance to that of 12 other models. Experiment results show that SAM achieves substantial improvements, with up to 1.5% R 10 @1 on Ubuntu Dialogue Corpus V2, 0.5% R 10 @1 on Douban Conversation Corpus, and 1.3% R 10 @1 on E-commerce Corpus.
Accelerating Deep Reinforcement Learning Under the Guidance of Adaptive Fuzzy Logic Rules

While Deep Reinforcement Learning (DRL) has emerged as a prospective method to many tough tasks, it remains laborious to train DRL agents with a handful of data collection and high sample efficiency. In this paper, we present an Adaptive Fuzzy Reinforcement Learning framework (AFuRL) for accelerating the learning process by incorporating adaptive fuzzy logic rules, enabling DRL agents to improve the efficiency of exploring the state space. In AFuRL, the DRL agent first leverages prior fuzzy logic rules designed especially for the actor-critic framework to learn some near-optimal policies, then further improves these policies by automatically generating adaptive fuzzy rules from state-action pairs. Ultimately, the RL algorithm is applied to refine the rough policy obtained by a fuzzy controller. We demonstrate the validity of AFuRL in both discrete and continuous control tasks, where our method surpasses DRL algorithms by a substantial margin. The experiment results show that AFuRL can find superior policies in comparison with imitation-based and some prior knowledge-based approaches.
An Image Denoising Algorithm Combining Global Clustering and Low-Rank Theory

In recent years, image denoising algorithms based on non-local image prior information have been extensively researched. At present, most similar image blocks are gathered by neighborhood block matching, that is, a fixed number of similar image blocks are matched in the field of image blocks by a similarity measurement method, basing on Euclidean distance. Although this method is more efficient, there are some problems: when searching for similar image blocks locally, some special structures, such as image edges or corners that cannot match similar image patterns and existing similar structures in the global scope, are ignored.To solve the above problems, a global clustering method is proposed in this paper to match image blocks, which improves the flexibility of the algorithm. At the same time, to solve the problem of the loss of detailed information such as edge texture when the similar image block matrix is constrained by low-rank, a low-rank sparse image denoising model combined with global clustering is proposed to effectively filter out noise while preserving image details as much as possible.
College English Precise Teaching Model Using Combination Optimization Collaborative Filtering Algorithm

With the rapid development of the Internet and various learning platforms, the explosive growth of resources in the field of education has made the recommendation of learning resources increasingly important, and it has gradually become the research focus of academic circles. This article develops an English precision teaching platform based on the CF algorithm of combinatorial optimization to accurately recommend English learning materials that meet the learners’ personal preferences and achieve precision teaching. This optimization algorithm takes full advantage of the background information of users, calculates the similarity between users, and forms the best neighbor set, and then recommends the learning resources evaluated by neighbors with similar interests to the target users, addressing the problem of data sparseness and cold start, which leads to a decline in recommendation accuracy. On different data sets, experimental results show that this method’s recommendation accuracy is 94.6%, which is higher than the CF algorithm of multifeature fusion by 4.5% and the traditional CF algorithm by 9.7%. The findings show that when applied to English instructional resources, this method is accurate and practical and that it can effectively recommend resources that are appropriate for users to achieve accurate CET.
Two Layer Data Security Using 2-D Cellular Automata

Information or message security is very reducing nowadays where the web is known as an exceptionally quick and most advantageous route for information transmission. So it is critical to have an elevated level of security for concealing the secret data. Encryption is a general and compelling technique for concealing recovered data from illicit access. This strategy utilizes a secret key to cover up hidden data in a picture. Be that as it may, it gives just one degree of security over the transmission channel. The point of this proposal is to give 2 degrees of security. The first level includes concealing content behind some pictures utilizing secret phrases and the subsequent level includes encryption utilizing 2D Cellular Automata rules. Encryption is finished utilizing 2-D rules of Cellular Automata. In the primary degree of security, the secret data is inserted in the picture utilizing the Least Significant Bit (LSB) and Enhanced Least Significant Bit (ELSB) procedures with the assistance of a secret phrase. In the dim picture, the LSB method to shroud the data in the picture was utilized. While in RGB picture both LSB and ELSB strategies were used to shroud the data. LSB strategy for RGB pictures uses each of the 3 planes of the picture while the ELSB system uses just one plane of RGB picture. In the second degree of security, 2D cell automata rules were applied on the picture in which hidden data was at that point installed utilizing LSB or ELSB methods. 2D Cellular Automata rules 2, 8, 32, and 128 were utilized to give an additional degree of security to hidden data. Progressively Cellular Automata rules gives increasingly mind-boggling and make sure about encryption procedure. In this way, various principles were used to procure a lot of security. The unscrambling method was completed incomparable style yet in turn around the request.