Reviewerly/exHarmony huggingface dataset:
Cyclic Learning-Based Lightweight Network for Inverse Tone Mapping

Recent studies on inverse tone mapping (iTM) have moved toward indirect mapping, which generates a stack of low dynamic range (LDR) images with multiple exposure values (multi-EV stack) and then merges them. In order to generate multi-EV stack(s), several large-scale networks with more than 20 M parameters have been proposed, but their high dynamic range (HDR) reconstruction and multi-EV stack generation performance were not acceptable. Also, some previous methods using cycle consistency should even have trained additional networks that are not used for multi-EV stack generation, which results in large memory for training. Thus, this paper proposes novel cyclic learning based on cycle consistency to reduce the memory burden in training. In detail, we eliminated networks used only for training, so the proposed method enables efficient learning in terms of training-purpose memory. In addition, this paper presents a lightweight iTM network that dramatically reduces the network sizes of the existing networks. Actually, the proposed lightweight network requires only a small parameter size of 1/100 compared to the state-of-the-art (SOTA) method. The lightweight network contributes to the practical use of iTM. Therefore, the proposed method based on a lightweight network reliably generates a multi-EV stack. Experimental results show that the proposed method achieves quantitatively SOTA performance and is qualitatively comparable to conventional indirect iTM methods.
Research on leg posture recognition of sprinters based on SVM classifier

In order to overcome the problems of low recognition rate, high time consumption and high misclassification rate caused by the difficulty in obtaining the global motion pattern information of sprinters in traditional posture recognition methods, a leg pose recognition method based on SVM classifier is proposed. Using multivariate statistical model to denoise the sprint image, the effective leg movement pattern information of sprinters is extracted. In the SVM classifier, the samples are divided by decision function to realise the recognition of sprinters' leg posture. In order to verify the effectiveness of the method in this paper, a comparative experiment is designed. Experimental results show that the recognition rate of the proposed method is more than 90%, the time consumption of recognition process is always less than 0.5 s and the misclassification rate of leg pose features is always below 5%, which fully demonstrates the high recognition performance of the method.
Dynamic prediction of mortality in COVID-19 patients in the intensive care unit: A retrospective multi-center cohort study

The COVID-19 pandemic continues to overwhelm intensive care units (ICUs) worldwide, and improved prediction of mortality among COVID-19 patients could assist decision making in the ICU setting. In this work, we report on the development and validation of a dynamic mortality model specifically for critically ill COVID-19 patients and discuss its potential utility in the ICU. We collected electronic medical record (EMR) data from 3222 ICU admissions with a COVID-19 infection from 25 different ICUs in the Netherlands. We extracted daily observations of each patient and fitted both a linear (logistic regression) and non-linear (random forest) model to predict mortality within 24 h from the moment of prediction. Isotonic regression was used to re-calibrate the predictions of the fitted models. We evaluated the models in a leave-one-ICU-out (LOIO) cross-validation procedure. The logistic regression and random forest model yielded an area under the receiver operating characteristic curve of 0·87 [0·85; 0·88] and 0·86 [0·84; 0·88], respectively. The recalibrated model predictions showed a calibration intercept of −0·04 [−0·12; 0·04] and slope of 0·90 [0·85; 0·95] for logistic regression model and a calibration intercept of −0·19 [−0·27; −0·10] and slope of 0·89 [0·84; 0·94] for the random forest model. We presented a model for dynamic mortality prediction, specifically for critically ill COVID-19 patients, which predicts near-term mortality rather than in-ICU mortality. The potential clinical utility of dynamic mortality models such as benchmarking, improving resource allocation and informing family members, as well as the development of models with more causal structure, should be topics for future research.
A Librarian’s Perspective on Sci-Hub’s Impact on Users and the Library

On December 19, 2019, The Washington Post reported that the U.S. Justice Department is investigating the founder and operator of Sci-Hub Alexandra Elbakyan on suspicion of working with Russian intelligence to steal U.S. military secrets from defense contractors. The article further discusses Sci-Hub’s methods for acquiring the login credentials of university students and faculty “to pilfer vast amounts of academic literature.” This has long been public knowledge. But the confirmation of Sci-Hub potentially working with Russian intelligence was major news. Both fronts of the Sci-Hub assault on stealing intellectual property are concerning. Since many academic researchers and their employers routinely receive defense contracts to perform sensitive research, the article helped posit that offering free access to academic research articles is perhaps a Trojan Horse strategy for Sci-Hub. To add to The Washington Post’s report, we sought out individuals at universities with a vantage point on Sci-Hub’s activities to see if there is independent evidence to support the report. We spoke to Dr. Jason Ensor who at the time of this interview was Manager, Engagement Strategy and Scholarly Communication, Library Systems at Western Sydney University Library in Australia. Ensor holds four degrees in related critical thinking fields and is an experienced business professional in software development, data scholarship and print publishing. He is also a distinguished speaker on digital humanities and linked fields, presenting regularly in national and international forums.
COSMOS: An Innovative, Graph-based Application for the Relational Presentation and Visualization of Greek Mythology and the Associated Art

The project "COSMOS.Cultural Osmosis -Mythology & Art" covers the development of an application for the presentation of Greek Mythology and its related Art, in an innovative and interactive way, using AI technology.The COSMOS application takes one step beyond the usual practice of the linear narration of myths and the depiction of related artwork images, as found in existing applications.COSMOS is developed in two interconnected levels, Myths, and Art, each organized in three data visualization, dynamic windows.In level Myths, these windows present a) the Stories in a Character-oriented organization (e.g., Zeus Thematic), b) the participating Characters, and c) the Places where the Stories unfold.Ιn level Art, respectively, they present a) the depictions of the myths in ancient Artworks, b) the depicted Characters, and c) the Artworks place of origin if known, as well as their present location.The user can discover relations that are not obvious, such as (i) the connections and influences between different Stories, (ii) the various stories that take place in a specific location or (iii) the various stories that a specific Character participates, (iv) the Artworks exhibited in the same location, etc.To reveal the aforementioned relations, the User Interface design adopts a node-based approach, where the nodes' connections, based on shared elements, are displayed.Regarding the implementation, two systems were developed: The Knowledge Organization System and the Knowledge Presentation System.Employing cutting-edge technologies in the fields of Machine Learning, Natural Language Processing, and 3D Graphics, COSMOS visualizes Myths, Artworks, and the connections between them, engagingly and coherently.The COSMOS application was tested and evaluated in two events.The evaluation results from both events reflect the overall positive response of the audience, and validate the fulfillment of the objectives set at the beginning of the project.The final product intends to address a vast audience and it can be used a) as a study aid for anyone interested, b) for educational purposes, by teachers and students, c) as a reference tool for the production of research projects, and d) as a scientific documentation tool and artworks database, for exhibition curating purposes.
Banking Bot

Abstract: Banking bot is an artificial intelligent development for bankingoperations, which can understand people queries and responds accordingly. The main aim of this project is to develop a banking bot using artificial intelligent algorithms which should be able to analyze and understand user’s queries and react accordingly. For any banking related querieswe have to go to the bank or call to customer care. It takes lot of time and effort and bank people are also very busy to attend our queries. On the other hand we don’t get complete information from the customer care executives. It will be more suitable ifwe can directly post our queries online or chat with the bank people and get the response within less time. To overcome this problem we proposed banking bot where people can directly chat with a bot and theycan integrate all of their bank accounts into same bot account and access them easily. The objective of this project is to contribute to the solution of the problem of direct communication between user and the bank. It provide 24/7 client support, so existing and potential customers can try and solve their banking problems after work hours and on weekends. This ultimately also leads to better customer experience. Customers can check account balance or check their transaction status using a botinterface. Banks can also analyze the usage of available schemes and generate feedback from diversified customers accordingly.
Forming meaningful connections between strangers in virtual reality: Comparing affiliative outcomes by interaction modality.

At present the extent to which close, meaningful connections can be formed in virtual reality (VR) between strangers is unknown, as is how the development of such connections compares to those formed in other established communication modalities.The present preregistered experiment examined the induction of closeness in VR using a validated social interaction task and how it compares with such induction in other interaction modalities with respect to affiliative outcomes (e.g., closeness toward and perceived responsiveness of interaction partner).Based on previous theory and research, we hypothesized that affiliative outcomes would be higher in interaction modalities featuring more versus less sensory input but would not differ significantly between the most sensoryrich modalities (video and VR).Two hundred seventy-two previously unacquainted undergraduate students were randomly assigned to interact with a partner (forming 136 dyads) using the "fast friends" procedure (Aron et al., 1997) via either text, audio chat, video chat, or VR.Results were consistent with hypotheses, suggesting that closeness and related outcomes can be generated and experienced in VR akin to levels experienced via other sensory-rich computer-mediated modalities.Exploratory analyses of possible individual difference moderator variables (Big Five personality dimensions and attachment orientations) yielded largely nonsignificant findings, suggesting the robustness of the obtained findings across individual differences.
Impact of employee motivation on work performance

This paper endeavors to understand how an employee work performance could be influenced by the motivation ,as the employee work performance excel or fail depends upon the motivation .The research aims to define and understand the role of motivation on employees performance. To determine the motivation's root cause in employee's efficiency various theories of motivations are studied and their correlation with employee performance are detailed. The findings justified the influence on employees psychology and its high influence on employee work efficiency and effectiveness. The research will prove beneficial for researchers to understand the concept of motivation in organization behavior.
Robust LiDAR-Camera Alignment With Modality Adapted Local-to-Global Representation

LiDAR-Camera alignment (LCA) is an important preprocessing procedure for fusing LiDAR and camera data. For it, one key issue is to extract unified cross-modality representation for characterizing the heterogeneous LiDAR and camera data effectively and robustly. The main challenge is to resist the modality gap and visual data degradation during feature learning, while still maintaining strong representative power. To address this, a novel modality adapted local-to-global representation learning method is proposed. The research efforts are paid in 2 main folders via modality adaptation and capturing global spatial context. First for modality gap resistance, LiDAR and camera data is projected into the same depth map domain for unified representation learning. Particularly, LiDAR data is converted to depth map according to pre-acquired extrinsic parameters. Thanks to the recent advantage of deep learning based monocular depth estimation, camera data is transformed into depth map in data driven manner, which is jointly optimized with LCA. Secondly to capture global spatial context, ViT (vision transformer) is introduced to LCA. The concept of LCA token is proposed for aggregating the local spatial patterns to form global spatial representation with transformer encoding. And, it is shared by all the samples. In this way, it can involve global sample-level information to leverage generalization ability. The experiments on KITTI dataset verify superiority of our proposition. Furthermore, the proposed approach is more robust to camera data degeneration (e.g., imaging blurring and noise) often faced by the practical applications. Under some challenging test cases, the performance advancement of our method is over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.9~cm$ </tex-math></inline-formula> /4.1° on translation / rotation error. While our model size (8.77M) is much smaller than existing methods (e.g., LCCNet of 66.75M). The source code will be released at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Zaf233/RLCA</uri> upon acceptance.
Morphological Difference of Closings Operator for No-Reference Quality Evaluation of DIBR-Synthesized Images

Images synthesized using Depth-Image-Based Rendering (DIBR) techniques are characterized by complex structural distortion. Multi-resolution multi-scale sparse image representation generated using morphological Difference of Closings operator (DoC) is used to efficiently capture structure-related distortion of synthesized images in the no-reference DoC-GRNN image quality assessment model. Non-linear morphological Difference of Closings operator (DoC) with an array of line-shaped structuring elements of increasing length is used to extract perceptually important details of object structure at different scales and resolutions. The sparsity of DoC band is calculated as scalar feature. The extracted features are mapped to the quality score by general regression neural network (GRNN). We have explored the influence of the direction of an array of line-shaped structuring elements on the model's performances. The DoC-GRNN model shows high agreement with perceptual quality scores, comparable to the state-of-the-art metrics, when evaluated on the stereoscopic DIBR-synthesized images of MCL-3D dataset.
The smoots Package in R for Semiparametric Modeling of Trend Stationary Time Series

This paper is an introduction to the new package in R called [smoots](https://CRAN.R-project.org/package=smoots) (smoothing time series), developed for data-driven local polynomial smoothing of trend-stationary time series. Functions for data-driven estimation of the first and second derivatives of the trend are also built-in. It is first applied to monthly changes of the global temperature. The quarterly US-GDP series shows that this package can also be well applied to a semiparametric multiplicative component model for non-negative time series via the log-transformation. Furthermore, we introduced a semiparametric Log-GARCH and a semiparametric Log-ACD model, which can be easily estimated by the smoots package. Of course, this package applies to suitable time series from any other research area. The smoots package also provides a useful tool for teaching time series analysis, because many practical time series follow an additive or a multiplicative component model.
Double-Balanced Loss for Imbalanced Colorectal Lesion Classification

Colorectal cancer has a high incidence rate in all countries around the world, and the survival rate of patients is improved by early detection. With the development of object detection technology based on deep learning, computer-aided diagnosis of colonoscopy medical images becomes a reality, which can effectively reduce the occurrence of missed diagnosis and misdiagnosis. In medical image recognition, the assumption that training samples follow independent identical distribution (IID) is the key to the high accuracy of deep learning. However, the classification of medical images is unbalanced in most cases. This paper proposes a new loss function named the double-balanced loss function for the deep learning model, to improve the impact of datasets on classification accuracy. It introduces the effects of sample size and sample difficulty to the loss calculation and deals with both sample size imbalance and sample difficulty imbalance. And it combines with deep learning to build the medical diagnosis model for colorectal cancer. Experimentally verified by three colorectal white-light endoscopic image datasets, the double-balanced loss function proposed in this paper has better performance on the imbalance classification problem of colorectal medical images.
Research progress of flexible sensor and its interaction technology in force feedback electronic clothing

&lt;p&gt;The sense in simulated reality is the key of human-computer interaction technology. Force feedback interaction technology is an important factor to realize simulated force sense in virtual reality. It can truly reproduce the physical information such as the mass, inertia and hardness of things in the virtual world. This paper summarizes the flexible sensors commonly used in force feedback technology and the development and research status of virtual reality wearable electronic clothing equipment based on force feedback technology, summarizes the principles of several force feedback structures, analyzes and compares their characteristics and main application fields. This paper briefly describes the prospect of force feedback technology, summarizes the trend of high-precision, multi-modal and multi-point interaction of force feedback equipment in the future, and puts forward some suggestions on miniaturization, softness and authenticity of force feedback technology in combination with the application characteristics of wearable electronic clothing.&lt;/p&gt;
Overview of intelligent clothing and accessories technology system for the disabled

&lt;p&gt;The purpose of this paper is to explore the application of intelligent technology in the industry of the disabled. Through the systematic review of databases (redib, doaj, redalyc, bmj, bvs, dialnet and pubmed), 11 articles were obtained, describing the intelligent technology in ves-tiles devices, which are designed to help patients recover. In an interdisciplinary field, especially in the field of social sciences, there is a new and underutilized phenomenon. This review shows that further research is needed to expand this topic.&lt;/p&gt;
Enhancing the Framework: Common Mobile Actions

In the previous chapter, we learned how to create Android and iOS drivers based on flags in the uitest.properties file. In this chapter, we will create a class to implement common page object actions, like wait, click, type, swipe, scroll, press, and so on, and to define variables that will be used in all page objects. This class will be extended by all page objects. Let’s begin.
Creating Android, iOS, and Web Drivers on Demand

In the previous chapter, we created properties files and learned how to read them using the Spring-Boot library, as well as in the traditional way. In this chapter, we will learn how to create a driver instance with the desired set of capabilities in the property file. Let’s dive in!
English Online Course Development Model and Course Content Recommendation

The process of education informatization has been accelerated in the Internet era. English course teaching in colleges and universities also gradually follows the development of the times and makes efforts to promote the reform of informatization teaching. This paper gives the design and application strategy of the English course development model in China’s colleges and universities and designs a diversified teaching model. In addition, aiming at the unreasonable selection of teaching resources and learning materials, this paper designs a multidimensional course recommendation algorithm with integrated features. The proposed algorithm uses a deep learning model for data feature extraction and extracts the information of course attributes and review texts. Then an improved association rule algorithm is conceived to perform association clustering analysis on the original course categories to increase the coverage of recommendations. Experimental results demonstrate that the proposed content recommendation algorithm can recommend high-quality course resources for English teachers, and its performance is better than other algorithms.
A Comparative Study of Machine Learning Models for Sentiment Analysis: Customer Reviews of E-Commerce Platforms

Understanding customers' preferences can be vital for companies to improve customer satisfaction. Reviews of products and services written by customers and published on various online platforms offer tremendous potential to gain important insights about customers' opinions. Sentiment classification with various machine learning models has been of great interest to academia and practice for a while, however, the emergence of language transformer models brings forth new avenues of research. In this article, we compare the performance of traditional machine learning models and recently introduced transformer-based techniques on a dataset of customer reviews published on the Trustpilot platform. We found that transformer-based models outperform traditional models, and one can achieve over 98% accuracy. The best performing model shows the same excellent performance independently of the store considered. We also illustrate why it can be sometimes more reliable to use the sentiment polarity assigned by the machine learning model, rather than a numeric rating that is provided by the customer.
Study of Traceability System of Renewable Energy Power Trading Based on Blockchain Technology

This article first introduced the process of renewable energy power trading, sorted out the core information and data required for the traceability of the entire chain from the power generation side to the power consumption side, and established the renewable energy power traceability mechanism of “sub-link traceability + overall calculation and verification”. Using blockchain encryption and anti-tampering features, a blockchain-based core data collection and traceability system was studied and designed, which realized the on-chain certification and traceability verification of key information to ensure the authenticity and credibility of traceable data, and improve traceability and supervision capabilities. The renewable energy traceability system has been verified in a power supply scenario.