Reviewerly/exHarmony huggingface dataset:
Research on Remote Sensing Images Clarification Algorithm Based on Grid Technology

In order to meet the needs of fast processing and image clarification of massive remote sensing images, I designed a grid computing-based remote sensing image clarification platform and developed the corresponding grid computing middleware. The image clarification process is carried out by using constrained power spectrum equalization filtering, and the effective chunking method is adopted to speed up the fast processing of massive data and solve the problems of boundary effects. The experimental results show that the grid platform can greatly improve the efficiency of processing massive amounts of data and obtain satisfactory clarification results.
The fuzzy objects recognition in scientific and technical papers by means of natural languages processing technologies

The article describes an approach to build a search index for fuzzy objects recognition in scientific and technical papers. A fuzzy object is explained as an entity described by some physical parameters. A physical parameter consists of two parts: a value, represented in various forms of digits, and a physical unit, represented as text with some digits. The approach presented in the article allows users to search not by ordinary substrings, but by physical parameters with range, less than or greater than operators. The current realisation of the approach is a web service that has functionality to perform all stages for extraction of physical parameters of a fuzzy object. These stages are the extraction of sentences, the extraction of physical parameters and the normalisation of physical parameters values. The web service can be exploited in the synchronous and asynchronous modes. The synchronous mode helps to troubleshoot the software. The asynchronous mode is designed to process papers using queues in the distributed computer systems. The current work is aimed to develop a search engine which will address the challenge of structuring information of a continuously growing number of scientific and technical papers. The search index will help to perform robust navigation among a huge number of documents for scientific workers and engineers.
Hyperspectral Image Restoration Based on Tensor Average Rank and Spatial-Spectral Total Variation

Hyperspectral images(HSIs) captured from actual observations are inevitably interfered by different types of degradation, but existing methods are difficult to recover from corruptions with extensive coverage. To surmount these challenges, a hyperspectral image restoration method based on tensor av-erage rank and spatial-spectral total variation is proposed in this paper, which can be solved by the alternating direction method of multipliers (ADMM) algorithm. Extensive experi-ments on both synthetic and real-world data demonstrate that the model has a better ability to restore hyperspectral images from corruptions, such as deadlines and stripe noises.
A Joint Loss based Deep Learning Model for Enhanced Image Inpainting

Research on Image inpainting has attracted growing attention as image data and applications become commonplace. Nevertheless, it is still a challenge to repair or restore missing parts of electronic images to its original state. In this paper, we develop a new deep learning based model by combining the two mainstream generative models: Auto-encoders and Generative Adversarial Networks to improve the effect of image inpainting. Specifically we design a novel joint loss function which take into consideration reconstruction loss in a Generator and adversarial loss in a Discriminator to improve the repaired effect of texture features and semantic information. The model and joint loss function were experimented and evaluated using the public face data set celebA. Initial results showed that our model and function has significantly improved the effect of repairment in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM) metrics.
Momentum Distillation Improves Multimodal Sentiment Analysis

With the development of computer technology, the Internet floods with abundant multimodal data. For better understanding users’ feelings, multimodal sentiment analysis and sarcasm detection have become popular research topics. However, previous studies did not take noise into account when designing models. In this paper, based on designing a novel architecture, we also introduce a momentum distillation method to improve the model’s performance from noisy data. Specifically, we propose the Transformer-Based Network with Momentum Distillation (TBNMD). For model architecture, we first encode different modalities to obtain hidden representations. Then we use a multimodal interaction module to obtain text-guided image features and image-guided text features. After that, we use a multimodal fusion module to obtain the fusion features. For momentum distillation, it is a self-distillation method. During the training process, the teacher model generates semantically similar samples as additional supervision of the student model. Experimental results on five publicly available datasets demonstrate the effectiveness of our method.
News Headline Generation Using Abstractive Text Summarization

People’s willingness to read is declining at an alarming rate as their attention spans decrease. Consequently, crafting a brief description of the most significant news story, as well as the most intuitive title that relates to the synopsis, is key concept. When individuals write summaries of articles, they do not only take words and concatenate them, they also make up new grammatical phrases or sentences that are grammatically comparable to the original piece and communicate original gist of text. Despite the fact that humans have a great ability to abstract information, automated summarization is a challenging task. To produce news headlines from a corpus of news articles, an abstractive text summarization-based approach has been developed. Abstractive text summarization is a suitable fit for this goal since it produces human-like summaries. The seq2seq approach is often employed in abstractive text summarization. The proposed method Seq2Seq model with LSTM for generation of news headlines. ROUGE and BLEU scores have been used to evaluate performance of the model. The proposed approach also performs better in terms of ROUGE and BLEU scores, when compared with other state of art news headline generation methods.
A Real Space Extension Approach Using a Turntable for Natural Walking in Virtual Reality

There are three main types of walking techniques for moving in a virtual reality (VR) space. However, these techniques are far from real walking and cause discomfort when walking. This study proposes a method for natural walking in VR space. The proposed method uses a turntable to rotate only the body in real space, thereby extending the walking range. Furthermore, we evaluate the efficiency of the proposed method by comparing the subjective evaluation of walking sensation with two other representative movement methods. In the experiment, we evaluated the sense of natural gait by comparing the scores from a questionnaire on the sense of gait. The experimental results suggest that the proposed method is the most natural way to walk in the VR space compared to other methods. The results also indicated that the perception of space differed depending on the means of movement in the VR space, suggesting the need for further investigation.
Low Light Video Enhancement Based on Temporal-Spatial Complementary Feature

Under low light conditions, the quality of video data is heavily affected by noise, artifacts, and weak contrast, leading to low signal-to-noise ratio. Therefore, enhancing low light video to obtain high-quality information expression is a challenging problem. Deep learning based methods have achieved good performance on low light enhancement tasks and a majority of them are based on Unet. However, the widely used Unet architecture may generate pseudo-detail textures, as the simple skip connections of Unet introduce feature inconsistency between encoding and decoding stages. To overcome these shortcomings, we propose a novel network 3D Swin Skip Unet (3DS $$^2$$ Unet) in this paper. Specifically, we design a novel feature extraction and reconstruction module based on Swin Transformer and a temporal-channel attention module. Temporal-spatial complementary feature is generated by two modules and then fed into the decoder. The experimental results show that our model can well restore the texture of objects in the video, and performs better in removing noise and maintaining object boundaries between frames under low light conditions.
Identification of Point Spread Function based on Turbulent Blur Model

To solve the complexity of blur in Aero-optics, we proposed a method of identification based on the turbulent blur model, namely an improved least-square point spread function identification method. Combined with the characteristics of the flow field, select the initial value in this method. Solve the second-order difference of the point spread function identified in the process of each two iterations, and observe its changes before and after. When the second-order difference tends to be stable, the iteration ends, and the iteration times at this time can be used as the iteration times of the least-square. The improved method can obtain the accurate result by improving the initial value and iteration times of conjugate gradient method. The proposed method is studied, and the experimental results illustrate the method's efficiency.
Gesture Control System for Desktop Robotic Arm

As an important human-machine interaction method, gesture control attracts lots of researchers’ concentration in recent years. Different from other literatures on industrial manipulator, this paper presents the possibility of using Leap motion gesture sensor to control a desktop robotic arm’s movement by means of gestures. It describes the system framework and control algorithm of robot control. A Leap motion senser, a desktop robotic arm and one computer are used to construct the system. In the control flow of the system. Coordinate transformation between the palm position detected by Leap motion gesture sensor and the tool center point (TCP) of the desktop robotic arm is calculated using the method of spatial mapping. The mapping data can be used to control the desktop robotic arm. To filter the noise and smooth the gesture signal acquired by the sensor, mean filter and Kalman filter are applied. The program for the system is developed in Python language with Leap motion Python SDK and xArm Python SDK. The experiments show that the system can operate stably and can control the movement of the desktop robotic arm accurately in real time by human palm’ gesture.
TextSRNet: Scene Text Super-Resolution Based on Contour Prior and Atrous Convolution

Low-resolution (LR) scene text images are often encountered in real application scenarios. It is difficult to recognize these scene texts due to the loss of their character details. Super-resolution (SR) is an intuitive alternative to solve this problem. Unlike generic SR, which mainly focuses on image quality, text SR focuses on the accuracy of the downstream recognition tasks. Although several scene text SR methods have been recently proposed, they ignore retaining character contours that are meaningful for character readability and poorly perform on long texts. Therefore, we proposes a new scene text SR network called TextSRNet. In order to get fine character details, we adopt the segmentation maps of scene text images as the prior knowledge of character contours and embed it into the proposed TextSRNet. In addition, we incorporate Sobel loss to enhance the shape boundaries of characters. To improve the robustness of the model for long texts, we adopt a text atrous spatial pyramid pooling module. The proposed TextSRNet is trained and evaluated on TextZoom, a real-world scene text SR dataset. Extensive experiments demonstrate that our TextSRNet outperforms existing methods in terms of both recognition accuracy and image quality.
Construction of Piano Live Broadcasting Platform Based on Wireless Network Communication Technology

Information disseminated on the Internet is stored in optical or magnetic storage media in digital form, disseminates at high speed through computer networks or wireless networks, and is read and used by computers or mobile phones. Live teaching is to use the advantages of Internet communication to solve the problem of normalized teaching in the post-epidemic era. The core work of this paper is to provide solutions for the construction of college piano teaching live broadcast platform, including streaming media transmission architecture design, live broadcast platform function design and video live broadcast process design. According to the characteristics of piano teaching in colleges, various methods of online teaching are optimized to meet the diverse learning needs of students, and the practical path of piano live teaching in colleges is proposed to help piano teaching in the post-epidemic era.
Visual Exploration of Human Motion Data

Human motion data are beginning to appear in many application domains, which brings a need to develop user-friendly motion processing applications. One of important open challenges is the presentation of high-dimensional spatio-temporal motion data to end users in a way that is easy to understand and allows fast browsing and exploration of the motion datasets. For many applications such as computer-assisted rehabilitation or motion learning, it is also very desirable to visualize the differences between two motion sequences. In this paper, we present a publicly available software tool that provides the visualization functionality for individual motion sequences, comparison of two motions, and exploration of large motion datasets.
Spectral analysis of signals by time-domain statistical characterization and neural network processing: Application to correction of spectral amplitude alterations in pulse-like waveforms

We present a time-domain method to detect and correct spectral alterations of signals by employing statistical characterization of waveforms and a pattern-recognition procedure using simple Artificial Neural Networks. The proposed strategy implements very-fast routines with a computational cost proportional to the number of signal samples, being convenient for applications in embedded environments with limited computational capabilities or fast real-time control tasks. We use the proposed algorithms to correct spectral amplitude attenuations in a pulse-like waveform with a sinc profile as an application example.
A Rule-Based Behaviour Planner for Autonomous Driving

Autonomous vehicles require highly sophisticated decision-making to determine their motion. This paper describes how such functionality can be achieved with a practical rule engine learned from expert driving decisions. We propose an algorithm to create and maintain a rule-based behaviour planner, using a two-layer rule-based theory. The first layer determines a set of feasible parametrized behaviours, given the perceived state of the environment. From these, a resolution function chooses the most conservative high-level maneuver. The second layer then reconciles the parameters into a single behaviour. To demonstrate the practicality of our approach, we report results of its implementation in a level-3 autonomous vehicle and its field test in an urban environment.
Real-time Hierarchical Soft Attention-based 3D Object Detection in Point Clouds

Deep neural network-based 3D object detection in LiDAR point clouds has achieved excellent performance in various applications including autonomous driving and robot vision. However, achieving high accuracy in real-time is paramount in time-critical applications. We propose a real-time Hierarchical Soft Attention Network (HSAN) to employ soft attention in the backbone of the original network to increase the detection accuracy without slowing down its inference speed. The proposed HSAN applies a hierarchical structure on the baseline network to combine features at different scales to obtain rich and fine-grained information and utilizes the characteristic of a layered attention structure to give more attention to the correct regions of target objects. Our proposed system improves the baseline network and achieves comparable detection results in terms of detection accuracy and inference speed when compared with peer state-of-the-art systems on the KITTI validation 3D detection benchmark.
The COVID-19 Crisis: A Case of Capitalism’s Dialectic of Failure

Acknowledging the multiple ways capitalism fails humanity and the planet, regularly triggering anticapitalist resistance, this essay employs the term “dialectic of failure” to examine how capitalist failures often boost profits and facilitate capitalism’s reproduction. This tendency for capitalism’s failures to create opportunities as well as risks for itself is analyzed with respect to the COVID-19 pandemic. After first analyzing the pandemic as a capitalist failure, and while acknowledging the social-justice and anticapitalist struggles the pandemic has given rise to, the essay examines how the pandemic may also be facilitating capital accumulation and the reproduction of the prevailing socioeconomic order.
Quantum Networks with Multiple Service Providers: Transport Layer Protocols and Research Opportunities

A quantum Internet for communicating information encoded in quantum systems over large distances would enable a host of new technologies to be deployed. While exciting progress has been made in small-scale quantum networks, a global quantum Internet will require - like the classical Internet - communicating in a multiple quantum Internet service provider (multi-qISP) setting, with protocols that are oblivious to the global network topology. Here we give a brief overview of quantum networks aimed at those new to the field, present two network-oblivious transport layer protocols for enabling high fidelity communication and performing fault diagnostics, and highlight a number of research opportunities in this nascent field of research.
Stegnograpghy Based Image Hiding Using Security Application

Steganography is the practice of covertly concealing sensitive information. In this paper, an innovative evidence method based on ASCII pattern matching and image data encryption is proposed. The American Standard Code of Encoding And decoding (ASCII) code elements are first read from the cover digital image as numerical, and the ASCII code locations are then saved in a database table. Similar to this, the encrypted image is converted to numerical and then checked to determine the placements of the characters. Before the hidden information was conveyed, constraint and encryption were also performed to the individual values. The main benefit is that neither the scale factor nor neither quantity of said pixels will alter; just the locations of the cryptic data will just be noted. Additionally, a number of field tests have been conducted to show the effectiveness and performance of the presented method and to assess it using the metrics that are most commonly used: Mean Square error Error (MSE), Peak Signal to Noise Ratio (PSNR), and Hiding Capacity (HC). Structured Similarity Index Measure (SSIM) and Histogram study have been carried out in terms of security. The presented approach is compared to more similar works. The efficiency of the presented strategy was confirmed by the experimental findings.
Guiding Field Exploration on Earth and Mars with Outlier Detection

Analyzing remote sensing datasets to identify sites of interest for in-situ investigation in robotic and human exploration is challenging with current methods. In this study, we investigated the utility of machine learning algorithms for outlier detection to identify samples of interest at field exploration sites based on remotely-sensed observations. In our analysis of two study sites, Cucomungo alluvial fan in Death Valley, CA and Jezero crater on Mars, we found that outliers identified by the isolation forest algorithm in satellite datasets seemed to correspond to unique compositions or surface properties. Our results show that outlier detection is a promising method for assisting scientists by guiding field exploration site selection.
Recurrent Interaction Network for Stereoscopic Image Super-Resolution

Recently, deep learning-based stereoscopic image super-resolution has attracted extensive attention and made great progress. However, existing methods have not adequately explored the inter-view dependency among two-view multi-level features. In this paper, a recurrent interaction network for stereoscopic image super-resolution (RISSRnet) is proposed to learn the inter-view dependency. To efficiently utilize the relationship between the two views, a recurrent interaction module is designed to achieve recurrent interaction among two-view multi-level features from the regrouped sequences, which are generated by a coupled queue-regroup mechanism. In addition, to recursively enhance features in the recurrent interaction module, an iterative propagation strategy is developed for sufficient interaction. Extensive experimental results demonstrate the effectiveness and superiority of the proposed RISSRnet.
What is the Role of Interaction Design of Triangulation Elements in Encouraging Citizens Towards Social Interaction?

Urban public spaces set the conditions for social life, becoming familiar with other inhabitants and the rhythms of the city, assigning meanings, everyday individual and collective experience and actions develop social connections. This PhD research questions the role of design as an interaction initiator between citizens in public settings. The objective is to explore how interaction design of ‘21st cc triangulation elements’ initiate social interaction and the role of body movements, through designing mediators that afford a redescribed body response. A corpus collection is developed and analysed using methods adopted from grounded theory. Analysed data provided further information to base the flow of interview and focus group studies. Findings from those studies will generate design parameters for the concept development and testing for the future of the study.
Automated leukocyte classification based on transfer learning for heterogeneous dataset

Leukocytes are the most important element of blood which protects us by retaining immunity, thereby providing the defense mechanism. There are several challenges such as structure, size change, time complexity, poor image quality, and varying angle of rotation in the classification of WBC which affect the accuracy. The purpose of this study was to use transfer learning based on a pre-trained model to detect and classify the leukocytes into four separate classes such as lymphocyte, neutrophil, eosinophil, and monocyte. We employed a blend of deep learning-based models and machine learning for the classification, and have compared the results using the features extracted using MobileNetv1 and MobileNetv2 learning model, which is trained and estimated using Logistic Regression. The results show an accuracy of 88.46% for a heterogeneous dataset of leukocytes while using MobileNetv2 and Logistic regression without data augmentation.