Reviewerly/exHarmony huggingface dataset:
Contrast-Aware Color Consistency Correction for Multiple Images

Color consistency optimization for multiple images is a challenging task in remote sensing and computer vision. To ensure that the visual quality of corrected images is satisfactory, not only should the color discrepancies between multiple images be invisible, but also the contrast of individual images should be visually appealing. Most color correction approaches focus on eliminating drastic color discrepancies, but ignore the problem of image contrast enhancement. Some color correction approaches even tend to degrade the image contrast to ensure that the image tones are consistent especially when the contrast of input images is low. To solve this problem, we present a contrast-aware color consistency correction approach in this paper. We attempt to eliminate the drastic color differences and enhance the contrast of input images simultaneously. We creatively integrate the problems of color consistency correction and image contrast enhancement into the same global energy optimization framework, and we also design a special cost function to minimize the color discrepancies and enhance the image contrast using the original color information. Thus, although the contrast of input images is low, our approach can still generate the corrected images with consistent tones and visually appealing contrast. At last, we select several challenging datasets to evaluate our approach. The experimental results visually and quantitatively demonstrate the effectiveness and superiority of the proposed contrast-aware color consistency correction approach. The results also demonstrate that our approach significantly outperforms the existing approaches, especially when the contrast of the input images is low.
Optimal Deep Transfer Learning Model for Histopathological Breast Cancer燙lassification

Earlier recognition of breast cancer is crucial to decrease the severity and optimize the survival rate. One of the commonly utilized imaging modalities for breast cancer is histopathological images. Since manual inspection of histopathological images is a challenging task, automated tools using deep learning (DL) and artificial intelligence (AI) approaches need to be designed. The latest advances of DL models help in accomplishing maximum image classification performance in several application areas. In this view, this study develops a Deep Transfer Learning with Rider Optimization Algorithm for Histopathological Classification of Breast Cancer (DTLRO-HCBC) technique. The proposed DTLRO-HCBC technique aims to categorize the existence of breast cancer using histopathological images. To accomplish this, the DTLRO-HCBC technique undergoes pre-processing and data augmentation to increase quantitative analysis. Then, optimal SqueezeNet model is employed for feature extractor and the hyperparameter tuning process is carried out using the Adadelta optimizer. Finally, rider optimization with deep feed forward neural network (RO-DFFNN) technique was utilized employed for breast cancer classification. The RO algorithm is applied for optimally adjusting the weight and bias values of the DFFNN technique. For demonstrating the greater performance of the DTLRO-HCBC approach, a sequence of simulations were carried out and the outcomes reported its promising performance over the current state of art approaches.
Evaluation of Mixed Reality Technologies in Remote Teaching

The trend towards remote teaching is steadily increasing and intensified by the current situation of the global pandemic. This is a particular challenge for subjects with a high practical relevance, such as mining engineering education, as practical experiences and on-site excursions are an integral part of the curriculum. In face-to-face teaching settings, mixed reality technologies are already considered a promising medium for the implementation of e.g., virtual field trips. Based on this, the current study addresses the question to what extent the integration of mixed reality technologies is suitable for remote teaching and which strengths and challenges are perceived by students and teachers. For this purpose, two 60-min remote lectures in the field of mining engineering were conducted, in which the use of mixed reality was tested on the basis of shared 360° experiences and 3D models and evaluated by students and teachers. Results reveal that the use of mixed reality in remote teaching was perceived as useful, enabled a realistic experience and improved students' understanding of presented theory compared to traditional teaching methods. In this paper, we discuss possible potentials and risks of using mixed reality in remote teaching and derive directions for further research.
Encryption Algorithm for Securing Non-Disclosure Agreements in Outsourcing Offshore Software Maintenance

Properly created and securely communicated, non-disclosure agreement (NDA) can resolve most of the common disputes related to outsourcing of offshore software maintenance (OSMO). Occasionally, these NDAs are in the form of images. Since the work is done offshore, these agreements or images must be shared through the Internet or stored over the cloud. The breach of privacy, on the other hand, is a potential threat for the image owners as both the Internet and cloud servers are not void of danger. This article proposes a novel algorithm for securing the NDAs in the form of images. As an agreement is signed between the two parties, it will be encrypted before sending to the cloud server or travelling through the public network, the Internet. As the image is input to the algorithm, its pixels would be scrambled through the set of randomly generated rectangles for an arbitrary amount of time. The confusion effects have been realized through an XOR operation between the confused image, and chaotic data. Besides, 5D multi-wing hyperchaotic system has been employed to spawn the chaotic vectors due to good properties of chaoticity it has. The machine experimentation and the security analysis through a comprehensive set of validation metric vividly demonstrate the robustness, defiance to the multifarious threats and the prospects for some real-world application of the proposed encryption algorithm for the NDA images.
Cross-Lingual Text Image Recognition via Multi-Hierarchy Cross-Modal Mimic

Optical character recognition and machine translation are usually studied and applied separately. In this paper, we consider a new problem named cross-lingual text image recognition (CLTIR) that integrates these two tasks together. The core of this problem is to recognize source language texts shown in images and transcribe them to the target language in an end-to-end manner. Traditional cascaded systems perform text image recognition and text translation sequentially. This can lead to error accumulation and parameter redundancy problems. To overcome these problems, we propose a multihierarchy cross-modal mimic (MHCMM) framework for end-to-end CLTIR, which can be trained with a massive bilingual text corpus and a small number of bilingual annotated text images. In this framework, a plug-in machine translation model is used as a teacher to guide the CLTIR model for learning representations compatible with image and text modes. Via adversarial learning and attention mechanisms, the proposed mimic method can integrate both global and local information in the semantic space. Experiments on a newly collected dataset demonstrate the superiority of the proposed framework. Our method outperforms other pipelines while containing fewer parameters. Additionally, the MHCMM framework can utilize a large-scale bilingual corpus to further improve the performance efficiently. The visualization of attention scores indicates that the proposed model can read text images in a fashion similar to the machine translation model reading text tokens.
An Ensemble Approach for Dutch Cross-Domain Hate Speech Detection

Over the past years, the amount of online hate speech has been growing steadily. Among multiple approaches to automatically detect hateful content online, ensemble learning is considered one of the best strategies, as shown by several studies on English and other languages. In this paper, we evaluate state-of-the-art approaches for Dutch hate speech detection both under in-domain and cross-domain hate speech detection conditions, and introduce a new ensemble approach with additional features for detecting hateful content in Dutch social media. The ensemble consists of the gradient boosting classifier that incorporates state-of-the-art transformer-based pre-trained language models for Dutch (i.e., BERTje and RobBERT), a robust SVM approach, and additional input information such as the number of emotion-conveying and hateful words, the number of personal pronouns, and the length of the message. The ensemble significantly outperforms all the individual models both in the in-domain and cross-domain hate speech detection settings. We perform an in-depth error analysis focusing on the explicit and implicit hate speech instances, providing various insights into open challenges in Dutch hate speech detection and directions for future research.
Mutual Accountability Layer: Accountable Anonymity Within Accountable Trust

Anonymous cryptographic primitives reduce the traces left by users when they interact over a digital platform. But they also prevent a platform owner from holding users accountable for malicious behaviour. Revocable anonymity offers a compromise by allowing only the manager of the digital platform to de-anonymize a user's activities when necessary. However, a misbehaving manager can abuse their de-anonymization power by de-anonymizing activities without the user's awareness. Although previous works mitigate this issue by distributing the de-anonymization power across several entities, there is no comprehensive and formal treatment where both accountability and non-frameability (i.e., the inability to falsely accuse a party of misbehavior) for both the user and the manager are explicitly defined and provably achieved. In this paper we formally define mutual accountability: a user can be held accountable for her otherwise anonymous digital actions and a manager is held accountable for every de-anonymization attempt. Also, no honest party can be framed regardless of what malicious parties do. In contrast with previous work, we do not distribute the de-anonymization power across entities, instead, we decouple the power of de-anonymization from the power of monitoring de-anonymization attempts. This allows for greater flexibility, particularly in the choice of the monitoring entities. We show that our framework can be instantiated generically from threshold encryption schemes and succinct non-interactive zero-knowledge. We also show that the highly-efficient threshold group signature scheme by Camenisch et al. (SCN'20) can be modified and extended to instantiate our framework.
QUALITY OF HIGHER EDUCATION: INTERNATIONAL DISCOURSE

The article deals with the state of research of the concept of higher education quality in Ukraine and other countries of the world. The issue of assessment of university education quality has been defined as the insufficiently studied phenomenon in the domestic and foreign scientific literature and, therefore, it requires further research. It has been found out that the world community considers high-quality higher education as a tool for social, cultural and economic growth. The article stresses that nowadays there is no country which would be satisfied with its higher education system, and the search for innovations for provision tangible results continues. The views of the scientists on higher education quality have been revealed: higher education quality is considered as a set of qualities of a person with higher education that reflects his or her professional competence, value orientation, social orientation and determines the ability of higher education to satisfy both personal spiritual and material needs, as well as the needs of society.
Snipper: A Spatiotemporal Transformer for Simultaneous Multi-Person 3D Pose Estimation Tracking and Forecasting on a Video Snippet

Multi-person pose understanding from RGB videos involves three complex tasks: pose estimation, tracking and motion forecasting. Intuitively, accurate multi-person pose estimation facilitates robust tracking, and robust tracking builds crucial history for correct motion forecasting. Most existing works either focus on a single task or employ multi-stage approaches to solving multiple tasks separately, which tends to make sub-optimal decision at each stage and also fail to exploit correlations among the three tasks. In this paper, we propose Snipper, a unified framework to perform multi-person 3D pose estimation, tracking, and motion forecasting simultaneously in a single stage. We propose an efficient yet powerful deformable attention mechanism to aggregate spatiotemporal information from the video snippet. Building upon this deformable attention, a video transformer is learned to encode the spatiotemporal features from the multi-frame snippet and to decode informative pose features for multi-person pose queries. Finally, these pose queries are regressed to predict multi-person pose trajectories and future motions in a single shot. In the experiments, we show the effectiveness of Snipper on three challenging public datasets where our generic model rivals specialized state-of-art baselines for pose estimation, tracking, and forecasting. Code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> https://github.com/JimmyZou/Snipper</uri> .
SAR Despeckling Based on CNN and Bayesian Estimator in Complex Wavelet Domain

We propose a hybrid algorithm for despeckling the Synthetic Aperture Radar (SAR) images using the Convolutional Neural Network (CNN) denoising and complex wavelet shrinkage. In particular, we perform the speckle reduction process in the complex wavelet domain. We first despeckled the approximation complex wavelet coefficients using the MUltichannel LOgarithm with the Gaussian denoising algorithm (MuLoG) based on a pre-trained CNN model named FFDNet. Next, we despeckled the log-transformed details of the complex wavelet coefficients using the averaged version of the Maximum a Posteriori (AMAP) estimator. The experimental results on simulated and real SAR images showed that the proposed method achieved better speckle suppression in the homogeneous areas while preserving edges and point targets than other state-of-the-art methods.
Influence of Learning Presence of Non-Face-to-Face Class Experience in Nursing Students on Academic Achievement: Mediating Effect of Learning Flow and Moderated Mediation of Digital Literacy

This study aimed to identify the mediating effect of learning flow and the moderated mediation effect of digital literacy on the effect of the learning presence of non-face-to-face class experience in nursing students on academic achievement.Participants were 272 nursing students from six universities in two different cities. A self-report questionnaire was used to measure learning presence, learning flow, digital literacy, and academic achievement. Analysis was performed using SPSS 26.0 and SPSS PROCESS Macro (4.0).The mediating effect of learning flow on the effect of learning presence on academic achievement was 0.42, and the moderated mediation index of digital literacy was 0.17. Learning flow showed a mediating effect on the relationship between learning presence and academic achievement. Digital literacy had a moderated mediation effect on the relationship between learning presence and academic achievement that was mediated by learning flow.The intensity of the mediating effect of nursing students' learning presence on academic achievement through learning flow increases as the level of digital literacy increases. These results suggest that educational programs considering the level of learning presence, learning flow, and digital literacy are required to promote the academic achievement of nursing college students.
Toward Accurate Camouflaged Object Detection With In-Layer Information Enhancement and Cross-Layer Information Aggregation

According to the process of the human visual observation mechanism we propose a novel architecture for camouflaged object detection (COD), which is composed of two main components: 1) in-layer information enhancement module (IIE) and 2) cross-layer information aggregation module (CIA). Specifically, the IIE module is to simulate the first step of human eyes detecting the target, namely, judging whether the camouflaged object exists and roughly be located. Besides, the CIA module is leveraged to imitate the second process of the human visual observation mechanism, refining the edge of the camouflaged object and eliminating interference. Since the shallow texture information and the deep semantic information are complementary, we combine the in-layer information with the cross-layer information to more accurately locate the target object and avoid noise and interference at the same time. Our model outperforms 13 state-of-the-art deep learning-based methods upon three public data sets in terms of four widely used metrics.
Y-branch wavelength demultiplexer based on topological valley photonic crystals

• This topological structure can achieve frequency-dependent light splitting. • Excellent performance of Y-branch wavelength demultiplexer at telecom bands. • The defect has little effect on this device due to the topological protection. Inspired by topological insulators in the condensed matter physics, topological photonics is a new paradigm, which is free of light backscattering at sharp waveguide bends due to the topological protection, and shows unidirectional light propagation based on the valley-chiral locking. Herein, we propose a two-channel Y-branch wavelength demultiplexer operating at the telecom band using the topological photonic integrated circuits for the first time. The proposed Y-branch wavelength demultiplexer is designed as adjoining and separating of two different topological interfaces at its input and output ports, respectively. Topological edge states with spectral interleaving can be excited in the two different topological interfaces, and be split into two output ports of the demultiplexer in short and long wavelengths. The Y-branch topological waveguide can be used as a dichroic mirror or a wavelength filter for separating light at two different wavelengths. The proposed topological integrated circuit is immune to structural defects. It is promising for the practical applications of optical interconnects and signal processing on a silicon photonic chip.
Towards Response-able PD: Putting Feminist New Materialisms to Work in the Practices of Participatory Design

This paper contributes to recent discussions in the field of Participatory Design (PD) that have considered relationality, becoming, non-human, and more-than-human to invent alternative, more expansive, responsible, and accountable ways of understanding and doing PD. To add to these PD discussions of sociomaterial relationality and emergence, our aim is to bring PD practices into generative dialogue with feminist new materialist praxis. For this, we consider our past and ongoing participatory research and design engagements related to collaboration with union professionals and creative research-activism with children. Drawing methodological insights from the ways in which sociomaterial relationality and becoming informed these processes, we explore how feminist new materialism can inform PD 1) when setting up participatory practices, 2) when engaging during design events, and 3) when re-thinking designer-researchers’ responsibilities and accountabilities as unfolding from multiplicitous, multiscalar engagements. We tentatively propose the notion of response-able PD to bring these insights together.
Governing Open Science

In November 2021, Member States unanimously adopted the United Nations Educational Scientific and Cultural Organization (UNESCO) Recommendation on Open Science. As attention now turns to the implementation of the Recommendation, time is ripe for the science community to take an active role in governance of the evolving science system if a new era of open science is to serve the global public good. This paper discusses open science in terms of the paradigm shift needed, the potential and directions for the evolution of global science, and governance arrangements in the broader perspective of Anthropocene-related challenges, with the hydrological entry point at its core.
A novel trust recommendation model in online social networks using soft computing methods

Summary Social network (OSN) is an emerging platform through which people can connect with their friends, relatives, and other like‐minded people. On the other hand, users' personal information might be misused because of other users' biased and malicious behavior. Establishing a trusted environment in social networks is one of the current research problems. Some of the research papers proposed to trust computational methods, but still, there is a lack of methods to handle biased recommendations and loss of trust accuracy towards the target user. In this article, to address these open issues, “a novel trust recommendation model in online social networks using soft computing methods (TRMSC)” is proposed for the Twitter social networks. Here direct and indirect trust is computed for known and unknown users, respectively. The direct trust of a user is computed using clustering methods based on his social activities (posts, retweets received, mentions received, listed count, and follower count) with other users. In the computation of indirect trust, the impact of biased recommendations is suppressed using the Dempster Shafer theory(DST) method, and loss of trust is minimized using trust transitive matrices. The performance of the proposed method is analyzed theoretically and experimentally. Time and space complexities are measured using asymptotic notations. In experimental results, TRMSC is evaluated for different network sizes and for target users at different distances (2 to 4‐hops), where it could perform better than existing methods.
Analysis of sentiment changes in online messages of depression patients before and during the COVID-19 epidemic based on BERT+BiLSTM

With the development of the Internet, more and more people prefer to confide their sentiments in the virtual world, especially those with depression. The social media where people with depression collectively leave messages is called the "Tree Hole". The purpose of this article is to support the "Tree Hole" rescue volunteers to help patients with depression, especially after the outbreak of COVID-19 and other major events, to guide the crisis intervention of patients with depression. Based on the message data of "Tree Hole" named "Zou Fan", this paper used a deep learning model and sentiment scoring algorithm to analyze the fluctuation characteristics sentiment of user's message in different time dimensions. Through detailed investigation of the research results, we found that the number of "Tree Hole" messages in multiple time dimensions is positively correlated to emotion. The longer the "Tree Hole" is formed, the more negative the emotion is, and the outbreak of COVID-19 and other major events have obvious effects on the emotion of the messages. In order to improve the efficiency of "Tree Hole" rescue, volunteers should focus on the long-formed "Tree Hole" and the user groups that are active in the early morning. This research is of great significance for the emotional guidance of online mental health patients, especially the crisis intervention for depression patients after the outbreak of COVID-19 and other major events.