Reviewerly/exHarmony huggingface dataset:
Impact of US Dollar Appreciation on Cryptocurrency Market: Evidence from Time Series Model

Since March 16, 2022, when the first federal funds rate hike was announced, federal funds rate has been raised from 0.25% to a level between 0.25% and 0.5%. Until July 27, 2022, the Fed has raised federal funds rate four times, bringing the benchmark of federal funds rate from 0.25% to a range of 2.25% to 2.5%. Against the backdrop of a downward spiral in the US economy and increased inflationary pressure, monetary policy in the US is gradually tightening. This paper selects two typical cryptocurrencies, Bitcoin and Ethereum, and uses time-series model to analyze the impact of US dollar appreciation on cryptocurrency market under the background of Fed rate hike, to fill the research gap and make suggestions for investors and policymakers. This paper concludes that US dollar appreciation would not affect Bitcoin market significantly but would negatively affect Ethereum market. Investors are supposed to be more alert to the risks of cryptocurrency market and reduce the holdings of Ethereum when US dollar index increases. For policymakers, they are suggested to increase regulation of cryptocurrency markets and prevent some malicious speculators’ attacks on the unmatured market from happening.
Integrated System for Management of Food Supply Chain using Blockchain

According to WHO, over the past decade the South-East Asia Region has witnessed a staggering number of people affected by foodborne diseases. In 2010 alone, nearly 150 million people fell ill due to contaminated food, leading to a heartbreaking 175,000 deaths. The burden of this issue disproportionately affects young children, with 40% of foodborne disease cases occurring among children under the age of 5. It's worth nothing that malnutrition is also a significant problem in the region, and poor sanitation practices exacerbate this issue. Approximately 50% of malnutrition cases stem from unhygienic practices and life-threatening infections, such as diarrhea, rather than a poor diet. In these situations, a well structured integrated platform is required to monitor the food supply chain straight from the producer to consumer in a way that other government authorities can track the quality of the products of different producers. This paper is an attempt to create an SDK that can integrate with online marketplaces in order to trace the quality and quantity of the food products by storing the data of the entire supply chain using smart contracts and providing the data to the end consumers using ledger
Chat-Bot for Blind Student

The Chat-bot for blind students is a Software application design to assist visually Impaired students in accessing educational Materials and communicating with their peer and teachers. The chat-bot uses artificial intelligence and text to speech technology to provide audio feedback to the user it can answer questions and read text aloud. The purpose of these android application is to provide educational based chat-bot for visually impaired students
Intelligent robot for glass cleaning at high altitude based on rotating shaft connection and stm32 control

At present, there are few high-altitude glass cleaning equipment on the market that can cross obstacles, usually requiring manual transfer. Even with intelligent obstacle avoidance system, it is still difficult to climb over the floor, the frame between the glass and so on. The automation of the project is low. To solve the above problems, the glass cleaning robot designed in this paper can autonomic obstacle avoidance transfer through the stm32f407 core control board, and Raspberry PI to jointly complete the obstacle avoidance, navigation, movement and other control tasks. And the use of a new mechanical structure, through the quadcopter wing folding contraction, to complete the process of wiping glass push and pull. During the transition from flight to climbing, horizontal to vertical changes are achieved by folding the fuselage. Through many experiments, the design is reliable and practical.
WLSAN: Wavelet-Layer-Spatial Attention Network for Single Image Super Resolution

Single Image Super Resolution has been a hot topic in recent years, which has wide application prospects. Some recent works use attention in SR to capture the inter-channel and inter-spatial relationships of feature maps. How- ever, these works cannot focus on the image features at different frequency do- mains. At the same time, the loss function of some SR models will make the model more inclined to reconstruct the smooth part and cannot consider the de- tails in multiple directions of the image. To address these problems, we propose a Wavelet-Layer-Spatial Attention Network (WLSAN), which makes use of both essential global context and local information to improve accuracy and visual quality. Specifically, the proposed WLSAN consists of wavelet attention to model inter-dependencies by decomposing the image signals and non-local attention, including channel-spatial-layers. Meanwhile, we combine L1 with improved Sobel Loss, which enhances the stability during training and constrains different aspects of the HR image generation. Extensive experiments demonstrate that the proposed WLSAN exceeds many classical methods and states of the arts.
Deep Single-Exposure HDR Imaging Approaches

The purpose of single-exposure HDR image reconstruction is to recover the missing information in the saturated region of the LDR image, and use neural network to reconstruct the HDR image. In recent years, single-exposure HDR imaging using deep learning (DL) has made significant progress. In this study, the latest development of deep single-exposure HDR imaging method was comprehensively and deeply investigated and analyzed. At present, there are five main methods for deep single-exposure HDR imaging: direct learning from a single LDR image, generating bracketed LDR image stacks, computationally efficient learning, learning camera imaging pipeline, and learning neural sensors. Importantly, this study reviewed the limitations and future work of each category. In addition, this study also discussed the potential and future trends of each category constructively.
The Drone System based on Optimal Combination Algorithm

This paper help CFA design a drones use system that determine the number and the location of SSA drones and Radio Repeater drones to facilitate wildfifires fifighting. we establish a model to determine the optimal combination of SSA drones and Radio Repeater drones. The number of Radio Repeater drones needs to be calculated based on the distance from the EOC to the center of the fire field. For the number of SSA drones, we consider the factors of capability, safety, topography and so on. Through the genetic algorithm, we determine the shortest path of each SSA drones. By data sigmoid normalization and determining the weight through coefficient of variation, we build a comprehensive capability and safety evaluation index to choose the optimal number of SSA drones. Besides, we use DUDC Model to determine the location of hovering radio-repeater drones. We determine the straight line where the Radio Repeater drones are located, and then use the straight line obtained as the abscissa, uniformly determine the coordinate axis direction and use greedy algorithm to solve where should Radio Repeater drones be. Finally, we summarize the model and explain the strengths of the model.
Transformer fault text classification model based on double feature channel

The power transformer plays a key role in the power distribution and transmission of the power system of the equipment, if the transformer fault leads to unexpected outage of the power system, then it will have a great threat to the whole power system, through the analysis of the transformer fault text can better guide the procurement of the transformer and fault maintenance. This paper proposes a classification method of transformer fault text based on two feature channels. This method extracts useful text emotion information from different aspects and levels by using two feature vectors, namely word vector and pinyin vector, and inputs it into Bilstm network to extract local and global semantic features. Then the features are fused through the attention mechanism. Finally, the features output by softmax function are classified and predicted. Experimental results show that the proposed method greatly improves the accuracy of transformer fault text classification.
Research on Glass Classification and Recognition Based on Fisher's Linear Discriminant and Hierarchical Clustering

Buried in the ground, the glass cultural relics weathered, and a large number of internal elements were exchanged. In order to classify the types of glass, this paper first uses Fisher linear discriminant analysis to obtain a binary classification model of glass types, and finds that the prediction accuracy of the model has reached 90%. Considering the possible overfitting of the model, this paper also uses the principal component analysis method to reduce the dimension of the data, thus obtaining the model and determining that its accuracy rate is maintained above 90%. In order to identify the types of unknown glass cultural relics, the multi-category Fisher’s Linear Discriminant method based on principal component analysis was used to identify the subcategories they belonged to.
Research on No-reference Image Quality Assessment Algorithm Based on Generative Adversarial Networks

Currently, with the massive generation and transmission of digital images, especially the rapid development of the Internet and mobile Internet industries where images are widely used, the study of reference-free image quality assessment has attracted much attention in academia and practical applications, and is a popular research direction in the field of computer vision. In response to the low performance of existing reference-free image quality assessment algorithms in the face of real distorted images, a reference-free image quality assessment algorithm based on generative adversarial networks is proposed. Firstly, the generator structure is changed, the U-Net structure is improved, and the channel attention mechanism SeNet structure is introduced to update the feature map after down sampling. Secondly, a feature similarity measurement system is incorporated and a dual discriminator structure is used to discriminate multiple groups of images. The FPN structure is combined in the feature extractor to produce a multi-scale feature representation. Experiments are conducted on KonIQ-10k dataset and LIVEC dataset, and the experimental results show that the algorithm exhibits good prediction accuracy as well as good generalization performance in the face of real distorted images.
Polar federated search: New infrastructure to support the polar community

This paper reports how the World Data System International Technology Office (WDS-ITO) has contributed resources in the Polar Research Data Management community. The office has focused on federated search and data enhancement practices that bridge the gap between data holdings in the Arctic and Antarctic polar research communities and has built a shared catalog characterized by harmonized “dialects'' of metadata standards. The portal, referred to as the Polar Federated Search (PFS) site, is part of a larger set of WDS-ITO polar support activities and allows researchers to find data that has been published in multiple repositories serving both Arctic and Antarctic data (World Data System – International Technology Office, 2022). The portal provides federated search capability through a single interface. The PFS indexes repository landing pages that have been enriched with the addition of semantic markup marrying the best practices from the web publishing world with domain specific metadata. This technique leads to broader discovery, notably by Google Dataset Search, and better reuse of data repository holdings. Increasingly, the base ontology that has been adopted for use in these markup activities is Schema.org (SDO) a simple ontology developed in the commercial sector. This article outlines the steps and progressions made throughout the first year of PFS development, its highlights, and describes how these infrastructures move far beyond metadata discovery to include data integration, analysis, visualization and advanced, reproducible workflows.
A first look at bug report templates on GitHub

Bug reports which are written by different people have a variety of styles, and such various styles lead to difficulty in understanding bug reports. To enhance the comprehensibility of bug reports, GitHub has proposed a template mechanism to guide how to report the bugs. However, there is no study on the use of bug report templates on GitHub. In this paper, we conduct an empirical study on the bug report templates on GitHub, including the popularity, benefits, and content of the templates. Our empirical study finds that: (1) For popularity, more and more open source projects and bug reports are applying templates over time. (2) For benefits, bug reports written using templates will be resolved quicker and have a higher comment coverage. (3) For content, the most common items for templates are expected behavior, describe the bug and to reproduce etc. Additionally, we summarize a taxonomy of items for bug report templates. Finally, we propose an automatic templating approach for templating an un-templated bug report. Our approach achieves an accuracy of 0.718 and an F1-score of 0.717 on average, which shows that our approach can effectively templatize an un-templated bug report.
enemos-p: An enhanced emotion specific prediction for recommender systems

Recommender systems suggest relevant item(s) to a new user by analyzing the existing user/item data. In recommender systems, collaborative filtering (CF) is a widely used technique to understand user preferences. The CF technique predicts a new user preferences to suggest the items by employing existing users’ preferences expressed in purchase history, ratings, and reviews. We observed from the real-world data that the user-rating data and user-review data given by a user are not expressing the same intent (we call inconsistency). This inconsistency in user-rating data and user-review data influences the performance of the recommender system. In this paper, we address the problem of inconsistency, and propose a novel approach that provide true recommendations. In this approach, we propose a refined approach called Enhanced Emotion Specific Prediction to refine the user-rating data considering emotional features in the user-review data. In the proposed approach, we exploit the multi-polarity to compute the emotional features from the emotional words. The emotional words are extracted at sentence-level by proposing sentence-level emotion detection algorithm. To validate the efficiency of the proposed approach, we conducted experiments on real-world data sets, Amazon and Yelp. We have used Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), and Normalized RMSE (NRMSE) for comparing the performance. The results of the experiments show that the proposed approach significantly reduces MAE, MAPE, RMSE, and NRMSE compared to the existing approaches.
Design and application of a structure-varying coupled chaotic system with high security

Chaos-based security applications are facing great challenges with various data analysis and prediction techniques. Focusing on the security risks in chaotic applications, this paper proposes a structure-varying delay-coupled chaotic model (SVDCCM), which has attack immunity and is proven to be chaotic mathematically. To choose suitable coupling structures for variation, the effects of different structures on chaotic performance are investigated in detail. We recommend loop structures for better chaotic behaviors and the structures in which the same coupling terms exist should be avoided to produce synchronous behaviors. Moreover, the general changing principles including random and fast varying are proposed to be the guidance on designing structure-varying systems, and a simple state-driven changing mechanism is given as an example. Experiment results show that SVDCCM has complex chaotic behaviors, good statistical properties, and high unpredictability. Its security when facing various attack challenges is also demonstrated, such as time–frequency attacks, phase space reconstruction attacks, change-point detection, etc. To illustrate its feasibility in the practical security application, a pseudo-random number generator (PRNG) based on SVDCCM is designed and implemented on the hardware platform. Performance tests indicate that the proposed PRNG can produce binary sequences with high reliability of randomness and unpredictability, which can be used in cryptosystems and other potential applications.
Unpaired, unsupervised domain adaptation assumes your domains are already similar

Unsupervised domain adaptation is a popular method in medical image analysis, but it can be tricky to make it work: without labels to link the domains, domains must be matched using feature distributions. If there is no additional information, this often leaves a choice between multiple possibilities to map the data that may be equally likely but not equally correct. In this paper we explore the fundamental problems that may arise in unsupervised domain adaptation, and discuss conditions that might still make it work. Focusing on medical image analysis, we argue that images from different domains may have similar class balance, similar intensities, similar spatial structure, or similar textures. We demonstrate how these implicit conditions can affect domain adaptation performance in experiments with synthetic data, MNIST digits, and medical images. We observe that practical success of unsupervised domain adaptation relies on existing similarities in the data, and is anything but guaranteed in the general case. Understanding these implicit assumptions is a key step in identifying potential problems in domain adaptation and improving the reliability of the results.
Navigation of a compartmentalized robot system

After making progression in developing the fundamental problems related to single-robot control, many researchers swerved and diverged their focus to studying multi-robot coordination. This research aims to take the motion planning and control (MPC) problem of a multi-robot system into a new space by considering a compartmentalized robot. An efficient variant of globally rigid formation, in which multiple car-like units are adjoint and move in parallel without collisions. The motion is governed by one of the sub-units acting as a leader, while other units maintain the fixed distance amongst each other and the leader in a rigid formation. The minimum distance technique is an important input to facilitate collision avoidance, robot decision making, and robot navigation. In this study a novel method to analytically compute the minimum distance between the closest point on the line segments of rectangular protective region and the obstacle is presented. Utilizing the Lyapunov-based Control Scheme a set of autonomous controllers are designed. Computer simulations of the proposed Lyapunov-based controllers for the compartmentalized robot are presented in interesting scenarios to show the efficacy of the unique set of controllers. In these simulations, the compartmentalized robot shows strict maintenance of a rigid formation with efficient collision and obstacle avoidance. The results open up research in the design and implementation of controllers by considering multiple compartmentalized robots into swarm models, splitting and re-joining units, and applying rotational leadership ideas.
Co-attention enabled content-based image retrieval

Content-based image retrieval (CBIR) aims to provide the most similar images to a given query. Feature extraction plays an essential role in retrieval performance within a CBIR pipeline. Current CBIR studies would either uniformly extract feature information from the input image and use it directly or employ some trainable spatial weighting module which is then used for similarity comparison between pairs of query and candidate matching images. These spatial weighting modules are normally query non-sensitive and only based on the knowledge learned during the training stage. They may focus towards incorrect regions, especially when the target image is not salient or is surrounded by distractors. This paper proposes an efficient query sensitive co-attention1 mechanism for large-scale CBIR tasks. In order to reduce the extra computation cost required by the query sensitivity to the co-attention mechanism, the proposed method employs clustering of the selected local features. Experimental results indicate that the co-attention maps can provide the best retrieval results on benchmark datasets under challenging situations, such as having completely different image acquisition conditions between the query and its match image.
Analysis of displacement fields with large deformations using an improved spectral digital image correlation method

In this paper, the systematic study of the analysis and measurement of displacement fields with large deformations based on an improved spectral digital image correlation (SDIC) method is conducted. As a shape function including displacement gradients has been successfully introduced, the SDIC method can deal with deformation issues involving different strains. We use the large image Fast Fourier Transform (FFT) to calculate full-field gray gradients instead of the subset image FFT. Therefore, all the gray gradients on image grids can be computed before formation of the Hessian matrix and the iterative optimization process. This can improve the efficiency and accuracy of the SDIC method. To analyze deformation with large strain, an easy-to-use and effective initial displacement estimation method for the seed point is introduced based on the correlation searching technique. The final displacements and displacement gradients of the region of interest (ROI) are then calculated by the subsequent iterative optimization algorithm, which can converge when the initial displacement error is within the range of ± 3 pixels. A set of images with noise ranging from 0 to 4 standard deviations and strains ranging from 0 to 0.6 are simulated and analyzed. The error analysis confirmed that the improved SDIC method performs well in anti-noise and can accurately analyze the displacement field. Finally, the silicone rubber specimens were tested in compression, tension and bending and analyzed to further confirm the validity of the improved SDIC method in the measurement of large deformations.
Exploring Online education for Universities in the Post-Epidemic of COVID-19

The model of online teaching in universities the result with in the development of Internet Plus education. The outbreak of coronavirus Disease 2019 (COVID-19) is an opportunity to promote the development of online teaching. In the later stage when the Covid-19 epidemic is gradually controlled, the hybrid teaching mode with offline teaching as the main mode and online teaching as the supplement will become the future development trend. Through analyzing the current situation and problems of online teaching during the epidemic, this paper puts forward targeted suggestions and strategies to provide reference for the development of teaching reform.
Generalized Dirichlet Distribution Based on Confluent Hypergeometric Series

Dirichlet distribution is a kind of high-dimensional continuous probability distribution, which has important applications in the fields of statistics, machine learning and bioinformatics. In this paper, based on gamma distribution we study two two-dimensional random variables. Then we derive the properties of these two two-dimensional random variables by using the properties of non-central gamma distribution and confluent hypergeometric series. From these properties, we find the two random variables follow generalized Dirichlet distributions. Applying hypergeometric series to Dirichlet distribution broadens the research of Dirichlet distribution.