Reviewerly/exHarmony huggingface dataset:
<scp>FAEC‐GAN</scp>: An unsupervised face‐to‐anime translation based on edge enhancement and coordinate attention

Abstract Animation is a widely loved artistic form with high abstraction and powerful expression. The task of image translation from face to anime involves complex geometric and texture transformations, and requires the generated images with clear lines. The existing unsupervised image translation frameworks are often ineffective for this task. According to the characteristics of animation image, we propose an animation translation method based on edge enhancement and coordinate attention, which is called FAEC‐GAN. We design a novel edge discrimination network to identify the edge features of images, so that the generated anime images can present clear and coherent lines. And the coordinate attention module is introduced in the encoder to adapt the model to the geometric changes in translation, so as to produce more realistic animation images. In addition, our method combines the focal frequency loss and pixel loss, which can pay attention to both the frequency domain information and pixel information of the generated image to improve the visual effect of the image. The experimental results demonstrate that FAEC‐GAN is superior to the state‐of‐the‐art methods in the task of face‐to‐animation image translation.
Study and Analysis of Various Cloud Security, Authentication, and Data Storage Models

In recent days, widespread acceptance of cloud data storage applications increases various privacy problems and security problems. Outsourced data security is considered the main confrontation for cloud clients because of data control loss. This review presents a detailed survey of 50 research papers presenting privacy preservation approaches, namely authentication-based, cloud security-based, data storage-based, data security-based, and encryption-based techniques. The analysis is considered based on the categorization of approaches, dataset employed, utilized software tools, published year, and the performance metrics are discussed. Furthermore, problems raised in existing privacy preservation techniques are elucidated in the research gaps and problems section. The future work of this study is based on the research gaps and problems recognized from present research schemes. Additionally, JAVA software language is widely utilized for implementing privacy preservation models, and the Amazon access sample database is a commonly employed dataset for the privacy preservation approach.
Annotated Pedestrians: A Dataset for Soft Biometrics Estimation for Varying Distances

Following the significance of soft biometrics to facilitate seamless recognition or retrieval, the need for multi-modality annotated datasets is increasing - to evaluate any standalone soft biometrics system. Although, large-size datasets like PETA were annotated to evaluate soft biometrics systems, however, they were mainly annotated for global soft biometrics such as gender and age and for clothing modality. By looking at the usefulness of multiple modalities of the human body during recognition or retrieval, we designed, developed and annotated a new dataset called Annotated Pedestrians for the individuals. The images in the dataset were explicitly recorded for the individuals at four different distances from the camera and they incorporate annotations for four different modalities of the human body i.e., i) global soft biometrics, ii) extended facial region, iii) body including limbs, and iv) clothing with attachments. The annotation process was expert opinion and qualitative annotation types were used. There were a total of three global soft biometrics annotated and for remaining three modalities, categorical annotations for 46 soft biometrics were performed. In terms of comparative annotations, there were a total of 26 soft biometrics annotated for the same three modalities. To the best of our knowledge, Annotated Pedestrians is a unique dataset designed by incorporating the impact of distance during recognition or retrieval, where markers were placed on the surface at 4, 6, 8, and 10 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text{m}$</tex-math></inline-formula> distances from the camera, and approximately 300 frames were recorded for 50 distinct individuals in a 20 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text{m}$</tex-math></inline-formula> long corridor. Moreover, the usefulness of the dataset is annotation using four different modalities of the human body, and a total of 75 soft biometrics annotated using a qualitative approach - making Annotated Pedestrians a highly-diverse dataset to evaluate any soft biometrics system for recognition during short-term tracking and feature-based retrieval from the database.
Robust Image Population Based Stain Color Normalization: How Many Reference Slides Are Enough?

Histopathologic evaluation of Hematoxylin & Eosin (H&E) stained slides is essential for disease diagnosis, revealing tissue morphology, structure, and cellular composition. Variations in staining protocols and equipment result in images with color nonconformity. Although pathologists compensate for color variations, these disparities introduce inaccuracies in computational whole slide image (WSI) analysis, accentuating data domain shift and degrading generalization. Current state-of-the-art normalization methods employ a single WSI as reference, but selecting a single WSI representative of a complete WSI-cohort is infeasible, inadvertently introducing normalization bias. We seek the optimal number of slides to construct a more representative reference based on composite/aggregate of multiple H&E density histograms and stain-vectors, obtained from a randomly selected WSI population (WSI-Cohort-Subset). We utilized 1,864 IvyGAP WSIs as a WSI-cohort, and built 200 WSI-Cohort-Subsets varying in size (from 1 to 200 WSI-pairs) using randomly selected WSIs. The WSI-pairs' mean Wasserstein Distances and WSI-Cohort-Subsets' standard deviations were calculated. The Pareto Principle defined the optimal WSI-Cohort-Subset size. The WSI-cohort underwent structure-preserving color normalization using the optimal WSI-Cohort-Subset histogram and stain-vector aggregates. Numerous normalization permutations support WSI-Cohort-Subset aggregates as representative of a WSI-cohort through WSI-cohort CIELAB color space swift convergence, as a result of the law of large numbers and shown as a power law distribution. We show normalization at the optimal (Pareto Principle) WSI-Cohort-Subset size and corresponding CIELAB convergence: a) Quantitatively, using 500 WSI-cohorts; b) Quantitatively, using 8,100 WSI-regions; c) Qualitatively, using 30 cellular tumor normalization permutations. Aggregate-based stain normalization may contribute in increasing computational pathology robustness, reproducibility, and integrity.
From Front to Rear: 3D Semantic Scene Completion Through Planar Convolution and Attention-Based Network

Semantic Scene Completion (SSC) aims to reconstruct complete 3D scenes with precise voxel-wise semantics from the single-view incomplete input data, a crucial but highly challenging problem for scene understanding. Although SSC has seen significant progress due to the introduction of 2D semantic priors in recent years, the occluded parts, especially the rear-view of the scenes, are still poorly completed and segmented. To ameliorate this issue, we propose a novel deep learning framework for 3D SSC, named Planar Convolution and Attention-based Network (PCANet), to effectively extend high-precision predictions of the front-view surface to the rear-view occluded areas. Specifically, we decompose the traditional convolutional layer into three successive planar convolutions to form a Planar Convolution Residual (PCR) block, which maintains the planar features of the 3D scene. Afterward, the Planar Attention Module (PAM) is proposed to capture three different planar attentions and harvest the global context from the front surface to the rear occluded areas to improve the overall accuracy. Extensive experiments on the real NYU and NYUCAD datasets and the synthetic SUNCG-RGBD dataset demonstrate that our proposed framework can generate high-quality SSC results in both front and rear views and outperforms the state-of-the-art approaches trained in an end-to-end manner without additional data.
Development of an information and analytical system for assessing air pollution

One of the priority tasks in the field of meteorology is the search for solving problems related to the assessment and forecasting of the quality of the air environment. The article presents an analysis of existing information systems for air quality control. Thus, in many cities there are no instrumental and automatic controls that allow assessing the state of atmospheric air in real time, as well as around the clock. The presented automated air pollution control systems of the ANKOS-AM and ASKOS-A types require further improvement in order to expand the number of determined ingredients or install a large number of automatic stations in the city, which is not feasible on a regional scale. For automated control of the concentration of pollutants, programs have been developed, one of which is the "Ecolog" program, designed for use in local monitoring systems within the city. For regional monitoring, it is necessary to develop and adapt your own mathematical and simulation models of the transfer and dispersion of pollutants, taking into account the specifics of the region (relief, type of underlying surface, weather conditions, etc.). The mathematical model of the transport of pollutants on a regional scale proposed in the article allows taking into account the specifics of a given region. It is built on the basis of statistical (Gaussian) models. This mathematical model can take into account special processes (initial jet rise, chemical interactions, washing out and settling of particles), affecting the transfer of impurities on a regional scale.The developed software package allows you to build a unified regional system of analysis and control.
Monocular camera-based 3D human body pose estimation by Generative Adversarial Network considering joint range of motion represented by quaternion

This study aims to establish a monocular camera-based system that uses a generative adversarial network (GAN) to estimate a 3D pose of a human and his/her orientation relative to the camera from images by considering anatomical knowledge such as segment length and joint range of motion. The proposed network is trained by unsupervised learning using only 2D joint positions as training data, i.e., does not require the ground truth data on 3D joint positions and angles in real space for training. Unsupervised learning of the proposed network was performed based on a new loss function consisting of the typical GAN loss function and three new terms, which provide constraints on the quaternion norm, joint range of motion, and the similarity between the real and fake 2D poses, respectively. Numerical validation was performed using the Human3.6M human pose dataset, which includes real-space measured images, 2D pose, and 3D joint angles and positions. The results show that the proposed network is slightly less accurate than the depth estimation method obtained by supervised learning, but is as accurate as the depth estimation method obtained by unsupervised learning using GAN. However, qualitative comparisons in plots of 3D pose suggest that the joint range of motion constraints introduced in this paper are effective in estimating 3D pose without anatomical failures. Particularly in the scenes with large flexion of the upper and lower limbs, our network can avoid anatomical failures in the estimated 3D pose while the depth estimation methods could not. In addition, the proposed network can adjust itself adaptively for cameras with unknown external parameters.
Association between different sensory modalities based on concurrent time series data obtained by a collaborative reservoir computing model

Humans perceive the external world by integrating information from different modalities, obtained through the sensory organs. However, the aforementioned mechanism is still unclear and has been a subject of widespread interest in the fields of psychology and brain science. A model using two reservoir computing systems, i.e., a type of recurrent neural network trained to mimic each other's output, can detect stimulus patterns that repeatedly appear in a time series signal. We applied this model for identifying specific patterns that co-occur between information from different modalities. The model was self-organized by specific fluctuation patterns that co-occurred between different modalities, and could detect each fluctuation pattern. Additionally, similarly to the case where perception is influenced by synchronous/asynchronous presentation of multimodal stimuli, the model failed to work correctly for signals that did not co-occur with corresponding fluctuation patterns. Recent experimental studies have suggested that direct interaction between different sensory systems is important for multisensory integration, in addition to top-down control from higher brain regions such as the association cortex. Because several patterns of interaction between sensory modules can be incorporated into the employed model, we were able to compare the performance between them; the original version of the employed model incorporated such an interaction as the teaching signals for learning. The performance of the original and alternative models was evaluated, and the original model was found to perform the best. Thus, we demonstrated that feedback of the outputs of appropriately learned sensory modules performed the best when compared to the other examined patterns of interaction. The proposed model incorporated information encoded by the dynamic state of the neural population and the interactions between different sensory modules, both of which were based on recent experimental observations; this allowed us to study the influence of the temporal relationship and frequency of occurrence of multisensory signals on sensory integration, as well as the nature of interaction between different sensory signals.
Reinforcing personalized persuasion in task-oriented virtual sales assistant

Existing task-oriented virtual agents can assist users with simple tasks like ticket booking, hotel reservations, etc. effectively and with high confidence. These virtual assistants, however, assume specific, predictable end-user behavior, such as predefined/servable objectives, which results in conversation failures in challenging situations, such as when goals are unavailable.Inspired by the practice and its efficacy, we propose an end-to-end framework for task-oriented persuasive dialogue generation that combines pre-training and reinforcement learning for generating context-aware persuasive responses. We utilize four novel rewards to improve consistency and repetitiveness in generated responses. Additionally, a meta-learning strategy has also been utilized to make the model parameters better for domain adaptation. Furthermore, we also curate a personalized persuasive dialogue (PPD) corpus, which contains utterance-level intent, slot, sentiment, and persuasion strategy annotation.The obtained results and detailed analysis firmly establish the effectiveness of the proposed persuasive virtual assistant over traditional task-oriented virtual assistants. The proposed framework considerably increases the quality of dialogue generation in terms of consistency and repetitiveness. Additionally, our experiment with a few shot and zero-shot settings proves that our meta-learned model learns to quickly adopt new domains with a few or even zero no. of training epochs. It outperforms the non-meta-learning-based approaches keeping the base model constant.To the best of our knowledge, this is the first effort to improve a task-oriented virtual agent's persuasiveness and domain adaptation.
A dataset for plain language adaptation of biomedical abstracts

Though exponentially growing health-related literature has been made available to a broad audience online, the language of scientific articles can be difficult for the general public to understand. Therefore, adapting this expert-level language into plain language versions is necessary for the public to reliably comprehend the vast health-related literature. Deep Learning algorithms for automatic adaptation are a possible solution; however, gold standard datasets are needed for proper evaluation. Proposed datasets thus far consist of either pairs of comparable professional- and general public-facing documents or pairs of semantically similar sentences mined from such documents. This leads to a trade-off between imperfect alignments and small test sets. To address this issue, we created the Plain Language Adaptation of Biomedical Abstracts dataset. This dataset is the first manually adapted dataset that is both document- and sentence-aligned. The dataset contains 750 adapted abstracts, totaling 7643 sentence pairs. Along with describing the dataset, we benchmark automatic adaptation on the dataset with state-of-the-art Deep Learning approaches, setting baselines for future research.
Using Multinomial Naive Bayes Machine Learning Method To Classify, Detect, And Recognize Programming Language Source Code

Processing programming languages are very similar to processing natural languages, especially high-level languages such as Python, Java, C#, C, C++, and others. Therefore, the natural language processing concepts can be applied as one of the most important branches of artificial intelligence in detecting, recognizing, and classification scripts written in different programming languages. The programming language script classification can be counted as a classical machine learning problem. This research aims to present a model using Multinomial Naïve Bayes (MNB) artificial intelligence algorithm to identify and classify the programming language used in writing the source code file provided as an input for the proposed model. A set of categorized files containing source codes will be used in training the proposed model, and then the model will be able to automatically detect and classify a new script into one of the already trained categories. The machine learning method called NB Multinomial will be used to implement this matter. This work is very important for Mufti-programming language editors such as Visual Studio Code, Notepad+, and others, where the user can paste the source code, and the editor will recognize the programming language automatically.
A Stacked Generalization Ensemble Model for Help Desk Ticket Assignment

The assignment of Help Desk Support tickets (HDTAP) to programmers or developers is an important problem for Information Technology and software development companies. When the number of tickets and programmers are significant, the assignment becomes a time-consuming task. To handle this issue, companies develop software to manage the generation, the tracking, and the assignment of tickets to employees. Current advances in machine learning can automate the HDTAP, and make it more efficient. In this paper, we propose the use of an ensemble model based on Stacked Generalization that replicates the expert behavior by reducing the classification errors. The model is tested using data of a Chilean company that develops management software for hospitals. The results show that our proposal obtains the best accuracy and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$F_{1}$</tex> score outperforming, consistently, most state-of-the-art models.
Data Protection: Trust to Government and Willingness to Provide Information

The COVID-19 pandemic has seen many countries took their best measures to prevent the spread of the virus. Hence, the use of contact tracing apps to track infection and help to diagnose symptoms has become common. However, digital innovation for public health management has posed some challenges to the government and the society. There are trade-offs between the benefits of health protection and the risks of loss of data privacy. Therefore, the study aims to examine what data protection factors will predict users' trust to the government, and whether the trust will impact on how the users provide data to COVID-19 contact tracing apps. A self-administered survey was conducted, and 497 data was obtained. Analysis on structural equation modeling was done by using SmartPLS. The findings show trust to government is determined by perception of the users on ethics of data collection, regulation by the government, data protection policy, and information disclosure prevalence. Trust affects willingness to provide information in a different manner. The willingness to provide information is determined by cognitive trust. But affective trust increases people's willingness for falsification. The research contributes to data privacy field by demonstrating how different forms of trust to government during the pandemic influence cooperative behavior, and the identification of clear distinction of trust antecedents, which will be useful for the redesign of government relationship with the people.
Learning Local and Global Features for Optimized Multi-Label Text Classification

In multi-label text classification, the central aim is to associate an array of descriptive labels for a better understanding of the text. There are three main challenges in doing multi-label text classification (i) a large number of text (input) features, (ii) the underlying implicit relationship between input features and output labels, and (iii) an implicit inter-label dependency. In traditional approaches to multi-label classification, these problems are not being addressed collectively. A feature selection strategy that inherently uses local features to discriminate a class and similarly global features that can distinctly separate classes can be very effective for multi-label classification. In this research, we perform a feature selection and ranking strategy based on local and global features. A Naïve Bayes classifier is being used using a combination of these two -feature sets, it is compared with the baseline implemented with the term frequency-inverse document frequency (TF-IDF). A series of experiments have been carried out on standard multi-label text datasets, using evaluation metrics like Hamming loss, Subset Accuracy and Micro/Macro F1 scores, and encouraging results are obtained.
Maneuvering target oriented digital camouflage image generation method

Since the digital camouflage generation method based on fixed single background image cannot fully meet the hidden need of maneuvering targets. In this paper, a method of generating digital camouflage image based on several background images is proposed. Firstly, according to the active range of the maneuvering target, several background images are collected. Secondly, in order to resolve the problem that the clustering algorithm is sensitive to the initial clustering center and easy to fall into local optimum, K-harmonic means (KHM) clustering algorithm is introduced and initial clustering center is determined based on color histogram. Again, KHM clustering is used to extract color features from several background images, the first clustering is extracted from a single background image, and the second clustering is extracted with several color features sets extracted from the first clustering of background images as input. Finally, the regular hexagon is used as the basic unit of the digital camouflage image to construct the spot template, and the digital camouflage image is generated by the lowest horizontal line algorithm. Example verification and camouflage effect detection show that the digital camouflage image generated by this method can effectively realize the concealment of maneuvering targets and has good camouflage effect.