Reviewerly/exHarmony huggingface dataset:
Generative adversarial networks: A recent survey

The enormous potential of GANs has enabled their usage for a variety of diverse applications. In this survey, we highlight major applications that have been made throughout the years, including the creation of images for the visualization of stories, the repair of damaged or old photographs, the creation of 3D models, the translation of text into images, and more. The survey indicates several constraints and barriers to more consistent training as well as creating higher-quality pictures. For instance, some models may struggle to eliminate deformities like wave strikes. As GAN development goes on, there will be more chances for advancement and the emergence of new applications.
MMC: Multi-Modal Colorization of Images using Textual Descriptions

Handling various objects with different colors is a significant challenge for image colorization techniques. Thus, for complex real-world scenes, the existing image colorization algorithms often fail to maintain color consistency. In this work, we attempt to integrate textual descriptions as an auxiliary condition, along with the grayscale image that is to be colorized, to improve the fidelity of the colorization process. To do so, we have proposed a deep network that takes two inputs (grayscale image and the respective encoded text description) and tries to predict the relevant color components. Also, we have predicted each object in the image and have colorized them with their individual description to incorporate their specific attributes in the colorization process. After that, a fusion model fuses all the image objects (segments) to generate the final colorized image. As the respective textual descriptions contain color information of the objects present in the image, text encoding helps to improve the overall quality of predicted colors. In terms of performance, the proposed method outperforms existing colorization techniques in terms of LPIPS, PSNR and SSIM metrics.
Unsupervised Discovery of Extreme Weather Events Using Universal Representations of Emergent Organization

Spontaneous self-organization is ubiquitous in systems far from thermodynamic equilibrium. While organized structures that emerge dominate transport properties, universal representations that identify and describe these key objects remain elusive. Here, we introduce a theoretically-grounded framework for describing emergent organization that, via data-driven algorithms, is constructive in practice. Its building blocks are spacetime lightcones that embody how information propagates across a system through local interactions. We show that predictive equivalence classes of lightcones -- local causal states -- capture organized behaviors and coherent structures in complex spatiotemporal systems. Employing an unsupervised physics-informed machine learning algorithm and a high-performance computing implementation, we demonstrate automatically discovering coherent structures in two real world domain science problems. We show that local causal states identify vortices and track their power-law decay behavior in two-dimensional fluid turbulence. We then show how to detect and track familiar extreme weather events -- hurricanes and atmospheric rivers -- and discover other novel coherent structures associated with precipitation extremes in high-resolution climate data at the grid-cell level.
FLCC: Efficient Distributed Federated Learning on IoMT over CSMA/CA

Federated Learning (FL) has emerged as a promising approach for privacy preservation, allowing sharing of the model parameters between users and the cloud server rather than the raw local data. FL approaches have been adopted as a cornerstone of distributed machine learning (ML) to solve several complex use cases. FL presents an interesting interplay between communication and ML performance when implemented over distributed wireless nodes. Both the dynamics of networking and learning play an important role. In this article, we investigate the performance of FL on an application that might be used to improve a remote healthcare system over ad hoc networks which employ CSMA/CA to schedule its transmissions. Our FL over CSMA/CA (FLCC) model is designed to eliminate untrusted devices and harness frequency reuse and spatial clustering techniques to improve the throughput required for coordinating a distributed implementation of FL in the wireless network. In our proposed model, frequency allocation is performed on the basis of spatial clustering performed using virtual cells. Each cell assigns a FL server and dedicated carrier frequencies to exchange the updated model's parameters within the cell. We present two metrics to evaluate the network performance: 1) probability of successful transmission while minimizing the interference, and 2) performance of distributed FL model in terms of accuracy and loss while considering the networking dynamics. We benchmark the proposed approach using a well-known MNIST dataset for performance evaluation. We demonstrate that the proposed approach outperforms the baseline FL algorithms in terms of explicitly defining the chosen users' criteria and achieving high accuracy in a robust network.
Medición y evaluación de la alfabetización digital: una revisión de literatura y análisis bibliométrico

Information and Communication Technologies (ICT) have transformed practically all sectors of society due to the benefits that these tools bring with them. The education sector has been one of the ones that has had the greatest impact with these digital transformation processes and it is required that teachers and students improve their levels of digital literacy. However, it has been identified that there are different ways to carry out the process of measuring digital literacy, but a literary review on this topic has not been constructed. Based on the above, this article reviews the literature and bibliometric analysis on the measurement and evaluation of digital literacy. For this, a systematic search of bibliographic material was carried out in the Web of Science database and an analysis was carried out using the Biblioshiny tool. The results of this process show that, although this topic has been discussed since 2004, only until 2012 was relevant research carried out on the measurement of digital literacy, likewise, it was possible to identify that what is sought is to measure the skills and competencies of teachers and students for the use and exploitation of ICT. In practical terms, this document is expected to enable people interested in digital literacy measurement and evaluation to identify relevant areas and locate their own research.
A Cross-Media Retrieval System for Web-SNS-Map Using Suggested Keywords Generating and Ranking Method Based on Search Characteristics

The research on multimedia retrieval has lasted for several decades. However, past efforts generally focused on single-media retrieval, where the queries and retrieval results belong to the same media (platform) type, such as social media platforms or search engines. In single-media retrieval, users have to select search media or options based on search characteristics such as contents, time, or spatial distance, they might be unable to retrieve correct results mixed in other media if they carelessly forget to select. In this study, we propose a cross-media retrieval system using suggestion generation methods to integrate three search characteristics of the Web (textual content-based retrieval), SNS (timeliness), and map (spatial distance-aware retrieval). In our previous research, we attempted to improve search efficiency using clustering methods to provide search results to users through related terms, etc. In this paper, we focus on the search efficiency of multiple search media. We utilize Google search engine to obtain the retrieval content from the Web, Twitter to obtain timely information from SNSs, and Google Maps to get geographical information from maps. We apply the obtained retrieval results to analyze the similarities between them by clustering. Then, we generate relevant suggestions and provide them to users. Moreover, we validate the effectiveness of the search results generated by our proposed system.
Differential regional importance mapping for thyroid nodule malignancy prediction with potential to improve needle aspiration biopsy sampling reliability

Existing guidelines for ultrasound-guided fine-needle aspiration biopsy lack specifications on sampling sites, but the number of biopsies improves diagnostic reliability. We propose the use of class activation maps (CAMs) and our modified malignancy-specific heat maps that locate important deep representations of thyroid nodules for class predictions.We applied adversarial noise perturbations to the segmented concentric "hot" nodular regions of equal sizes to differentiate regional importance for the malignancy diagnostic performances of an accurate ultrasound-based artificial intelligence computer-aided diagnosis (AI-CADx) system using 2,602 retrospectively collected thyroid nodules with known histopathological diagnosis.The AI system demonstrated high diagnostic performance with an area under the curve (AUC) value of 0.9302 and good nodule identification capability with a median dice coefficient >0.9 when compared to radiologists' segmentations. Experiments confirmed that the CAM-based heat maps reflect the differentiable importance of different nodular regions for an AI-CADx system to make its predictions. No less importantly, the hot regions in malignancy heat maps of ultrasound images in comparison with the inactivated regions of the same 100 malignant nodules randomly selected from the dataset had higher summed frequency-weighted feature scores of 6.04 versus 4.96 rated by radiologists with more than 15 years of ultrasound examination experience according to widely used ultrasound-based risk stratification American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TI-RADS) in terms of nodule composition, echogenicity, and echogenic foci, excluding shape and margin attributes, which could only be evaluated on the whole rather than on the sub-nodular component levels. In addition, we show examples demonstrating good spatial correspondence of highlighted regions of malignancy heat map to malignant tumor cell-rich regions in hematoxylin and eosin-stained histopathological images.Our proposed CAM-based ultrasonographic malignancy heat map provides quantitative visualization of malignancy heterogeneity within a tumor, and it is of clinical interest to investigate in the future its usefulness to improve fine-needle aspiration biopsy (FNAB) sampling reliability by targeting potentially more suspicious sub-nodular regions.
Evaluating multi-agent reinforcement learning on heterogeneous platforms

Multi-Agent Reinforcement Learning (MARL) is a key technology in Artificial Intelligence Systems. The development of MARL is a “Training-in-Simulation” process and involves time-consuming computation and communication. Existing implementations of MARL using homogeneous architecture fail to satisfy the above requirements simultaneously. In this work, using a state-of-the-art MARL algorithm - Multi-Agent Deep Deterministic Policy Gradient (MADDPG) - as a benchmark, we provide a systematic analysis of the performance bottleneck on multi-core CPU platforms. We show that (a). the training throughput of large neural network models can benefit from data parallel architecture such as GPU; (b) Memory-intensive components including replay management and all-to-all communication leads to large memory overheads on multi-level cache memory hierarchy on CPU and GPU, thus should be accelerated using custom hardware coupled with large on-chip SRAM with specialized data layout optimizations on FPGA.
Processes of Our Mediated Spaces

Through art examples, I would like to show how real exhibiting space enters the dialogue with the visitor and his movement while using Virtual Reality and Artificial Intelligence. The analysis of the technological aspect is important, as well as the recognition of social and material conditions in the creation of new artistic practices, the production and later recognition of what happens to art. I would move within the paradigm shift of image, photography, film, installation, and Internet use, as well as within the emergence of artificial intelligence. The audience is seen as a participant in the creation and evaluation of artistic experience, they are invited to reveal their action views. Today, the exchange of experiences as well as the study of common experience in the context of artistic research is made easier through art systems. Artists incorporate evaluation into their practice, thus, establishing a new program of research in art and technology. The interactive experience in the digital age explores different ways of creating and evaluating interactive art. While the visitor of contemporary art exhibitions explores and examines what is happening, how fast contemporary art is changing, (s)he still does not understand what is happening in front of her/him. Various art examples presented in this paper show a new phenomenon that places science, technology, and art on the same side. This opens a new chapter in artistic practice and makes the contemporary scene livelier and more diverse. At this point, we come to the possible sequence of events in art, which brings a new way of expression and provides an interesting space for further research. Article received: May 20, 2022; Article accepted: July 15, 2022; Published online: October 15, 2022; Original scholarly paper
Improved Fusion of SVD and Relevance Weighted LDA Algorithms via Symmetric Sum‑Based Rules for Face Recognition

In this paper, we propose a new extension of the score level fusion of SVD (singular value decomposition) and RWLDA (relevance weighted linear discriminant analysis using QR decomposition) algorithms for face recognition called Improved DWT (SVD + RWLDA). The proposed extension exploits new techniques based on the symmetric sum using triangular norms. Experiments on two established data sets demonstrate that our approach enhances the face recognition rate compared to the original version.
Translation Tools and Techniques

This chapter introduces methods for translating text, websites, documents, videos, and images. It also introduces some translator communities of interest where the researcher may turn to ask for translation assistance. Translation is an art, not a science. The methods discussed in this chapter are the reason that foreign-language research is possible and necessary today. One need not speak every language to benefit from the knowledge shared in those languages. Translation also has varying goals, ranging from comprehension to publication-quality translation. Translation goals and methods should be a part of the researcher's plan from the beginning. The tools and methods discussed in this chapter are just some of the available resources online today, but they demonstrate the breadth of capabilities available for linguists and non-linguists alike.
Proposed Hybrid Model Recurrent Neural Network for Human Activity Recognition

It is evident that the field of Human Action Recognition (HAR), which uses time series of sensors, has become a trending field for the scientific community, thanks to the vast evolution and availability of smart devices in daily life. As well as the technological evolution of applications based on AI systems. The exploitation of HAR systems and applications in various sensitive areas such as the health sector and intelligent surveillance. After the tremendous relay of Deep Learning methods applied for natural language processing and speech recognition. The recurrent architectures LSTM and GRU have become the most popular architectures in the automatic extraction of temporal characteristics and the classification of human activities, but the efficiency of this extraction remains differentiated between various human activities carried out such as static activities, and dynamic activities. Our main objective in this work is to establish a hybrid RNN model tested with raw 3D accelerometer data from smartphones. Optimally, our model proposes to take advantage of the advantages of RNN architectures and respond in a more efficient and balanced way to the classification of the different human activities carried out. We used accuracy, precision, and other necessary measures to obtain well-classified and performing results on the comparison between our hybrid model and two other simple RNN models. The results revealed the efficiency of our hybrid model in properly classifying the different usual activities existing in the UCI Heterogeneity HAR dataset.
Surface Feature Detection Method of Optical Remote Sensing Images Based on YOLOX

A novel surface feature detection algorithm for optical remote sensing images is designed in this paper. First, the adaptively spatial feature fusion (ASFF) network is added to the feature extraction module PANET to address the issue of Multi-scale object detection. Second, a feature extraction module based on CBMA mixed domain convolutional block attention mechanism is designed, which is more concerned about the positive sample feature information in the feature map. In the end, EIoU loss and VariFocalLoss are introduced to exactly reflect the true distance error of the two predicted targets and elevate the performance of multi-target detection. Experiments are conducted on DOTA data sets. The optimal mechanism and validity of the improved method are verified via ablation experiments and comparison experiments. The mAP precision of the ameliorated network model is up to 70.75% on the premise of appending ASFF mechanism and CBAM attention mechanism, which is 9.08% higher than that of the original network. The comparison experiment with the mainstream target detection algorithm shows that the proposed method is valid.
FLAC: Fairness-Aware Representation Learning by Suppressing Attribute-Class Associations

Bias in computer vision systems can perpetuate or even amplify discrimination against certain populations. Considering that bias is often introduced by biased visual datasets, many recent research efforts focus on training fair models using such data. However, most of them heavily rely on the availability of protected attribute labels in the dataset, which limits their applicability, while label-unaware approaches, i.e., approaches operating without such labels, exhibit considerably lower performance. To overcome these limitations, this work introduces FLAC, a methodology that minimizes mutual information between the features extracted by the model and a protected attribute, without the use of attribute labels. To do that, FLAC proposes a sampling strategy that highlights underrepresented samples in the dataset, and casts the problem of learning fair representations as a probability matching problem that leverages representations extracted by a bias-capturing classifier. It is theoretically shown that FLAC can indeed lead to fair representations, that are independent of the protected attributes. FLAC surpasses the current state-of-the-art on Biased MNIST, CelebA, and UTKFace, by 29.1%, 18.1%, and 21.9%, respectively. Additionally, FLAC exhibits 2.2% increased accuracy on ImageNet-A consisting of the most challenging samples of ImageNet. Finally, in most experiments, FLAC even outperforms the bias label-aware state-of-the-art methods.
USMOTE: A Synthetic Data-Set-Based Method Improving Imbalanced Learning

Imbalanced learning plays an important role in our daily life, featuring large amounts of normal samples and small percentage of abnormal ones in its data set. To solve these imbalanced data cases, machine learning models like Decision Tree and Logistic Regression have been widely applied. However, performance of models is always negatively affected due to the massive imbalance. In order to fix this problem, sampling methods are used to balance the data sets. This work combines random undersampling with SMOTE (Synthetic Minority Over-sampling Technique) to synthetically modify data sets and train models, which achieves better recall_score performance in experiments. Additionally, we correct the mistake that other works about sampling methods always evaluate models on the transformed data set, which is against its original purpose. At last, we improve the Logistic Regression algorithm using this data-set-based technique, allowing it to perform better when handling imbalanced data cases.
Digital Dreams: Speculative Futures for Artificial Intelligence, Augmented Reality, and the Human Brain

This chapter explores possible future interactions between Artificial Intelligence (AI) and Augmented Reality (AR). Taking an interdisciplinary approach, the chapter considers dreaming in both humans and machines as a lens through which to investigate how future integrations of AI and AR may support both creativity and learning. It begins by exploring the creative potential for immersive world building with AI and AR, as well as its potential impact on human perception. It then examines how AR and AI could provide moments of revelatory learning for individuals by uncovering hidden data structures that influence our daily lives. Next, we consider possible negative impacts of combining AI and AR technology; how their vulnerabilities to bias, hacking and invasions of privacy, could be exploited for nightmarish ends. Finally, we offer some thoughts regarding speculative futures for AI and AR, as fields that compel on-going creative and critical engagement by artists, designers, and technologists.