Reviewerly/exHarmony huggingface dataset:
HiFT: Hierarchical Feature Transformer for Aerial Tracking

Most existing Siamese-based tracking methods execute the classification and regression of the target object based on the similarity maps. However, they either employ a single map from the last convolutional layer which degrades the localization accuracy in complex scenarios or separately use multiple maps for decision making, introducing intractable computations for aerial mobile platforms. Thus, in this work, we propose an efficient and effective hierarchical feature transformer (HiFT) for aerial tracking. Hierarchical similarity maps generated by multi-level convolutional layers are fed into the feature transformer to achieve the interactive fusion of spatial (shallow layers) and semantics cues (deep layers). Consequently, not only the global contextual information can be raised, facilitating the target search, but also our end-to-end architecture with the transformer can efficiently learn the interdependencies among multi-level features, thereby discovering a tracking-tailored feature space with strong discriminability. Comprehensive evaluations on four aerial benchmarks have proven the effectiveness of HiFT. Real-world tests on the aerial platform have strongly validated its practicability with a real-time speed. Our code is available at https://github.com/vision4robotics/HiFT.
HealthBlock: A secure blockchain-based healthcare data management system

The security and privacy of electronic healthcare records (EHRs) remain a critical issue for both healthcare services consumers and providers. Breaching a healthcare system causes the disclosure of sensitive health data. This data is usually saved into centralized databases, which creates vulnerabilities and gives rise to cyber attacks. This research focuses on enhancing the security and privacy of EHRs by using blockchain technology. This paper proposes a new architecture that takes advantage of decentralized databases to avoid centralized storage issues. The decentralized used database for storing patient electronic health records is the OrbitDB with Interplanetary File System (IPFS). Besides, we have deployed a blockchain network built on Hyperledger fabric by using Hyperledger composer to save hashes of stored data and control access when retrieving it. The proposed Blockchain-based architecture is designed to contribute to the healthcare management systems’ robustness and to avoid recorded security limitations in commonly used systems for smart healthcare. Performance evaluation results issued from Hyperledger Caliper and comparative analysis have proved the robustness and superiority of the proposed system in terms of security and privacy requirements, key features of blockchain-based healthcare systems, and performance metrics including various throughput and latency.
ACDC: The Adverse Conditions Dataset with Correspondences for Semantic Driving Scene Understanding

Level 5 autonomy for self-driving cars requires a robust visual perception system that can parse input images under any visual condition. However, existing semantic segmentation datasets are either dominated by images captured under normal conditions or are small in scale. To address this, we introduce ACDC, the Adverse Conditions Dataset with Correspondences for training and testing semantic segmentation methods on adverse visual conditions. ACDC consists of a large set of 4006 images which are equally distributed between four common adverse conditions: fog, nighttime, rain, and snow. Each adverse-condition image comes with a high-quality fine pixel-level semantic annotation, a corresponding image of the same scene taken under normal conditions, and a binary mask that distinguishes between intra-image regions of clear and uncertain semantic content. Thus, ACDC supports both standard semantic segmentation and the newly introduced uncertainty-aware semantic segmentation. A detailed empirical study demonstrates the challenges that the adverse domains of ACDC pose to state-of-the-art supervised and unsupervised approaches and indicates the value of our dataset in steering future progress in the field. Our dataset and benchmark are publicly available.
Utilizing the Metaverse for Learner-Centered Constructivist Education in the Post-Pandemic Era: An Analysis of Elementary School Students

Due to COVID-19, numerous new technologies are being implemented in education, with a growing interest in the metaverse. The term "metaverse" refers to an immersive digital environment where one can interact with virtual avatars. This study aims to analyze the experiences and attitudes of the metaverse for learner-centered education from a constructivist perspective to determine how closely related this virtual environment is to the lives of elementary school students. This study also examined how students are becoming the focal point of new educational technologies. After reviewing the literature on this topic, a survey of 336 elementary school students in Korea was conducted using 18 items for measuring each factor in the metaverse, followed by statistical analyses that included a difference of means and an independent sample t-test. The results revealed that, on average, 97.9% of elementary school students had experiences with the metaverse, with 95.5% of them considering it closely related to their everyday life. In addition, various conclusions according to each metaverse factor and each participant's gender are provided.
A random forests quantile classifier for class imbalanced data

Extending previous work on quantile classifiers (q-classifiers) we propose the q*-classifier for the class imbalance problem. The classifier assigns a sample to the minority class if the minority class conditional probability exceeds 0 < q* < 1, where q* equals the unconditional probability of observing a minority class sample. The motivation for q*-classification stems from a density-based approach and leads to the useful property that the q*-classifier maximizes the sum of the true positive and true negative rates. Moreover, because the procedure can be equivalently expressed as a cost-weighted Bayes classifier, it also minimizes weighted risk. Because of this dual optimization, the q*-classifier can achieve near zero risk in imbalance problems, while simultaneously optimizing true positive and true negative rates. We use random forests to apply q*-classification. This new method which we call RFQ is shown to outperform or is competitive with existing techniques with respect to tt-mean performance and variable selection. Extensions to the multiclass imbalanced setting are also considered.
A survey of teaching–learning-based optimization

Over past few decades, swarm intelligent algorithms based on the intelligent behaviors of social creatures have been extensively studied and applied for all kinds of optimization areas. Teaching–learning-based optimization (TLBO) algorithm which imitates the teaching–learning process in a classroom, is one of population-based heuristic stochastic swarm intelligent algorithms. TLBO executes through similar iterative evolution processes as utilized by a standard evolutionary algorithm. Unlike traditional evolutionary algorithms and swarm intelligent algorithms, the iterative computation process of TLBO is divided into two phases and each phase executes iterative learning operation. Since its introduction by Rao and his team in 2010, TLBO has attracted more and more researchers' attention because of some of its strengths such as simple concept, without algorithm-specific parameters, rapid convergence and easy implementation yet effectiveness. In this paper we attempt to provide a brief review of the basic concepts of TLBO and a comprehensive survey of its prominent variants and its typical application, and the theoretical analysis conducted on TLBO so far. We hope that this survey can be very beneficial for the researchers engaged in the study of TLBO.
Action-Stage Emphasized Spatiotemporal VLAD for Video Action Recognition

Despite outstanding performance in image recognition, convolutional neural networks (CNNs) do not yet achieve the same impressive results on action recognition in videos. This is partially due to the inability of CNN for modeling long-range temporal structures especially those involving individual action stages that are critical to human action recognition. In this paper, we propose a novel action-stage (ActionS) emphasized spatiotemporal Vector of Locally Aggregated Descriptors (ActionS-STVLAD) method to aggregate informative deep features across the entire video according to adaptive video feature segmentation and adaptive segment feature sampling (AVFS-ASFS). In our ActionSST- VLAD encoding approach, by using AVFS-ASFS, the key frame features are chosen and the corresponding deep features are automatically split into segments with the features in each segment belonging to a temporally coherent ActionS. Then, based on the extracted key frame feature in each segment, a flow-guided warping technique is introduced to detect and discard redundant feature maps, while the informative ones are aggregated by using our exploited similarity weight. Furthermore, we exploit an RGBF modality to capture motion salient regions in the RGB images corresponding to action activity. Extensive experiments are conducted on four public benchmarks - HMDB51, UCF101, Kinetics and ActivityNet for evaluation. Results show that our method is able to effectively pool useful deep features spatiotemporally, leading to state-of-the-art performance for videobased action recognition.
Fuzzy Clustering: A Historical Perspective

Fuzzy sets emerged in 1965 in a paper by Lotfi Zadeh. In 1969 Ruspini published a seminal paper that has become the basis of most fuzzy clustering algorithms. His ideas established the underlying structure for fuzzy partitioning, and also described and exemplified the first algorithm for accomplishing it. Bezdek developed the general case of the fuzzy c-means model in 1973. Many branches of this tree grew from 1969 to 1993. Then another watershed paper in fuzzy clustering appeared: Krishnapuram and Keller?s work on possibilistic clustering. This tree has also developed many branches, and together, these two topics comprise two thirds (of the conceptual field) of soft clustering (the other third belongs to probabilistic clustering in its many guises). Another important class of fuzzy and possibilistic methods, known as generalized clustering, were later developed, based on the idea of stressing the internal nature of clusters rather than solely on metric notions or on the sharing of some significant traits. This article reviews some of the key highlights of fuzzy and possibilistic clustering. This is not a comprehensive survey: that would require an article the size of an encyclopedia and an army of well-informed authors. The best we can do here is to give readers a small glimpse of the overall reach and span of Zadeh's idea in the vast jungle that is fuzzy cluster analysis.
Activity-Aware Fall Detection and Recognition Based on Wearable Sensors

Falling is a severe hazard among older adults. Fall treatment is considered to be one of the most costly treatments, which usually extends to a long time. One bad fall can cause severe injuries that may lead to permanent disability or even death. Therefore, an efficient and cost-effective fall monitoring system is exceptionally indispensable. With the advancement in technology, wearable sensors and systems provide a lucrative way to continuously monitor the elderly people for detecting any fall incident that may occur. Most of these wearable fall monitoring systems focus only on detecting a fall incident. However, to avoid the risk of any future fall, it is essential to be aware of the cause of a fall incident also. Therefore, to address this challenge, a wearable sensor-based continuous fall monitoring system is proposed in this paper, which is capable of detecting a fall and identifying the falling pattern and the activity associated with the fall incident. The performance of the proposed scheme is investigated with a series of experiments using three machine learning algorithms, namely, k-nearest neighbors (KNNs), support vector machine, and random forest (RF). The proposed methodology achieved the highest accuracy for fall detection, i.e., 99.80%, using KNNs classifier, whereas the highest accuracy achieved in recognizing different falling activities is 96.82% using RF classifier.
Deep Virtual Reality Image Quality Assessment With Human Perception Guider for Omnidirectional Image

In this paper, we propose a novel deep learning-based virtual reality image quality assessment method that automatically predicts the visual quality of an omnidirectional image. In order to assess the visual quality in viewing the omnidirectional image, we propose deep networks consisting of virtual reality (VR) quality score predictor and human perception guider. The proposed VR quality score predictor learns the positional and visual characteristics of the omnidirectional image by encoding the positional feature and visual feature of a patch on the omnidirectional image. With the encoded positional feature and visual feature, patch weight and patch quality score are estimated. Then, by aggregating all weights and scores of the patches, the image quality score is predicted. The proposed human perception guider evaluates the predicted quality score by referring to the human subjective score (i.e., ground-truth obtained by subjects) using an adversarial learning. With adversarial learning, the VR quality score predictor is trained to accurately predict the quality score in order to deceive the guider, while the proposed human perception guider is trained to precisely distinguish between the predictor score and the ground-truth subjective score. To verify the performance of the proposed method, we conducted comprehensive subjective experiments and evaluated the performance of the proposed method. The experimental results show that the proposed method outperforms the existing two-dimentional image quality models and the state-of-the-art image quality models for omnidirectional images.
Convolutional Graph Autoencoder: A Generative Deep Neural Network for Probabilistic Spatio-Temporal Solar Irradiance Forecasting

Machine learning on graphs is an important and omnipresent task for a vast variety of applications including anomaly detection and dynamic network analysis. In this paper, a deep generative model is introduced to capture continuous probability densities corresponding to the nodes of an arbitrary graph. In contrast to all learning formulations in the area of discriminative pattern recognition, we propose a scalable generative optimization/algorithm theoretically proved to capture distributions at the nodes of a graph. Our model is able to generate samples from the probability densities learned at each node. This probabilistic data generation model, i.e., convolutional graph autoencoder (CGAE), is devised based on the localized first-order approximation of spectral graph convolutions, deep learning, and the variational Bayesian inference. We apply the CGAE to anew problem, the spatio-temporal probabilistic solar irradiance prediction. Multiple solar radiation measurement sites in a wide area in northern states of the U.S. are modeled as an undirected graph. Using our proposed model, the distribution of future irradiance given historical radiation observations is estimated for every site/node. Numerical results on the national solar radiation database show state-of-the-art performance for probabilistic radiation prediction on geographically distributed irradiance data in terms of reliability, sharpness, and continuous ranked probability score.
Dynamic improved pixel value ordering reversible data hiding

The existing reversible data hiding (RDH) methods based on improved pixel value ordering (IPVO) use only a small portion of the pixels (e.g., at most six pixels) in a block for embedding data. Our proposed method is a dynamic IPVO RDH, which can flexibly modify the number of pixels in a block by classifying the local complexity into multiple levels. For a highly-correlated, smooth block, almost all of the pixels, except for one or two in the middle positions, can be involved in embedding data. For a lowly-correlated smooth block, only two pixels can be embedded with data, and no data can be embedded into a highly-textured block. Thus, it is apparent that the redundancy among pixels is fully exploited, and further, more data can be embedded into smooth blocks while maintaining lower distortion. However, when all of the levels are used for embedding data, the visual quality may not be maximized for a given payload. Hence, in our method, we traverse from the first level to the highest level to search for the optimal number of levels that can provide the best embedding performance. Extensive experiments have demonstrated that the proposed method outperforms the existing methods.
Deep Unified Model For Face Recognition Based on Convolution Neural Network and Edge Computing

Currently, data generated by smart devices connected through the Internet is increasing relentlessly. An effective and efficient paradigm is needed to deal with the bulk amount of data produced by the Internet of Things (IoT). Deep learning and edge computing are the emerging technologies, which are used for efficient processing of huge amount of data with distinct accuracy. In this world of advanced information systems, one of the major issues is authentication. Several techniques have been employed to solve this problem. Face recognition is considered as one of the most reliable solutions. Usually, for face recognition, scale-invariant feature transforms (SIFT) and speeded up robust features (SURF) have been used by the research community. This paper proposes an algorithm for face detection and recognition based on convolution neural networks (CNN), which outperform the traditional techniques. In order to validate the efficiency of the proposed algorithm, a smart classroom for the student’s attendance using face recognition has been proposed. The face recognition system is trained on publically available labeled faces in the wild (LFW) dataset. The system can detect approximately 35 faces and recognizes 30 out of them from the single image of 40 students. The proposed system achieved 97.9% accuracy on the testing data. Moreover, generated data by smart classrooms is computed and transmitted through an IoT-based architecture using edge computing. A comparative performance study shows that our architecture outperforms in terms of data latency and real-time response.
Human augmentation: Past, present and future

Human augmentation is a field of research that aims to enhance human abilities through medicine or technology. This has historically been achieved by consuming chemical substances that improve a selected ability or by installing implants which require medical operations. Both of these methods of augmentation can be invasive. Augmented abilities have also been achieved with external tools, such as eyeglasses, binoculars, microscopes or highly sensitive microphones. Lately, augmented reality and multimodal interaction technologies have enabled non-invasive ways to augment human. In this article, we first discuss the field and related terms. We provide relevant definitions based on the present understanding of the field. This is followed by a summary of existing work in augmented senses, action, and cognition. Our contribution to the future includes a model for wearable augmentation. In addition, we present a call for research to realize this vision. Then, we discuss future human abilities. Wearable technologies may act as mediators for human augmentation, in the same manner as eyeglasses once revolutionized human vision. Non-invasive and easy-to-use wearable extensions will enable lengthening the active life for aging citizens or supporting the full inclusion of people with special needs in society, but there are also potential problems. Therefore, we conclude by discussing ethical and societal issues: privacy, social manipulation, autonomy and side effects, accessibility, safety and balance, and unpredictable future.
Exploring Misogyny across the Manosphere in Reddit

The 'manosphere' has been a recent subject of feminist scholarship on the web. Serious accusations have been levied against it for its role in encouraging misogyny and violent threats towards women online, as well as for potentially radicalising lonely or disenfranchised men. Feminist scholars evidence this through a shift in the language and interests of some men's rights activists on the manosphere, away from traditional subjects of family law or mental health and towards more sexually explicit, violent, racist and homophobic language. In this paper, we study this phenomenon by investigating the flow of extreme language across seven online communities on Reddit, with openly misogynistic members (e.g., Men Going Their Own Way, Involuntarily Celibates), and investigate if and how misogynistic ideas spread within and across these communities. Grounded on feminist critiques of language, we created nine lexicons capturing specific misogynistic rhetoric (Physical Violence, Sexual Violence, Hostility, Patriarchy, Stoicism, Racism, Homophobia, Belittling, and Flipped Narrative) and used these lexicons to explore how language evolves within and across misogynistic groups. This analysis was conducted on 6 million posts, from 300K conversations created between 2011 and December 2018. Our results shows increasing patterns on misogynistic content and users as well as violent attitudes, corroborating existing theories of feminist studies that the amount of misogyny, hostility and violence is steadily increasing in the manosphere.
Online hatred of women in the Incels.me forum

Abstract This paper presents a study of the (now suspended) online discussion forum Incels.me and its users, involuntary celibates or incels , a virtual community of isolated men without a sexual life, who see women as the cause of their problems and often use the forum for misogynistic hate speech and other forms of incitement. Involuntary celibates have attracted media attention and concern, after a killing spree in April 2018 in Toronto, Canada. The aim of this study is to shed light on the group dynamics of the incel community, by applying mixed-methods quantitative and qualitative approaches to analyze how the users of the forum create in-group identity and how they construct major out-groups, particularly women. We investigate the vernacular used by incels, apply automatic profiling techniques to determine who they are, discuss the hate speech posted in the forum, and propose a Deep Learning system that is able to detect instances of misogyny, homophobia, and racism, with approximately 95% accuracy.