Reviewerly/exHarmony huggingface dataset:
Towards Better Quality Assessment of High-Quality Videos

In recent times, video content encoded at High-Definition (HD) and Ultra-High-Definition (UHD) resolution dominates internet traffic. The significantly increased data rate and growing expectations of video quality from users create great challenges in video compression and quality assessment, especially for higher-resolution, higher-quality content. The development of robust video quality assessment metrics relies on the collection of subjective ground truths. As high-quality video content is more ambiguous and difficult for a human observer to rate, a more distinguishable subjective protocol/methodology should be considered. In this study, towards better quality assessment of high-quality videos, a subjective study was conducted focusing on high-quality HD and UHD content with the Degradation Category Rating (DCR) protocol. Commonly used video quality metrics were benchmarked in two quality ranges.
Immersive media practices in the classroom: models of the teaching research nexus in an Australian context

In recent years, a new wave of virtual reality (VR) and immersive media forms have gained popularity with both viewers and creators around the globe. This article presents the argument that the emerging nature of this current wave of practices makes this a site of particular importance for research by both academics and students. We explore the nature of the teaching-research nexus around immersivemedia practices by reporting on six case studies from three Australian Universities. Academic experience is varied and complex, with avariety of Inquiry-based learning approaches in place at undergraduate and postgraduate level, spanning the areas of writing, production,post-production and spectatorship. A study of these aspects allows students to seek knowledge and new understandings of media forms, an experience that we believe is vital for the development of higher-order skills. By exploring this area, we pose the question: How canimmersive media researchers use Inquiry-based learning to conceptually engage students with emerging formats through course workdesign? Furthermore, we seek to examine how Inquiry-based learning in the context of immersive media connects to the more widely used Problem-Based Learning models that have traditionally been employed in media practice courses.
Improved RetinaNet model for the application of small target detection in the aerial images

Abstract The target detection algorithm for accurately locating the location of objects in the image and their categories is widely used in intelligent traffic, forest fire prevention, electric power patrol and other fields. Existing target detection algorithms detect objects in head-up images, and their accuracy is not high when used in aerial image detection. To solve this problem, a small target detection algorithm based on improved RetinaNet model is proposed. First, replace the ResNet101 deep residual network in the original network with ResNet152, Independent of image features, The model is accelerated by merging ScaleNet network structure; Besides, in order to increase the feeling of the small target detection, to upgrade the existing FPN network output, increase the P2 feature layer, improved the range makes the network overall receptive field and the robustness of small target detection. Through the experimental result shows that compared with the original RetinaNet algorithm, The performance index of the improved algorithm is obviously improved. the average detection accuracy of the improved algorithm for small target detection is quite high 6.21%, it has a good detection effect in many scenarios.
JukeBox: A Multilingual Singer Recognition Dataset

A text-independent speaker recognition system relies on successfully encoding speech factors such as vocal pitch, intensity, and timbre to achieve good performance. A majority of such systems are trained and evaluated using spoken voice or everyday conversational voice data. Spoken voice, however, exhibits a limited range of possible speaker dynamics, thus constraining the utility of the derived speaker recognition models. Singing voice, on the other hand, covers a broader range of vocal and ambient factors and can, therefore, be used to evaluate the robustness of a speaker recognition system. However, a majority of existing speaker recognition datasets only focus on the spoken voice. In comparison, there is a significant shortage of labeled singing voice data suitable for speaker recognition research. To address this issue, we assemble \textit{JukeBox} - a speaker recognition dataset with multilingual singing voice audio annotated with singer identity, gender, and language labels. We use the current state-of-the-art methods to demonstrate the difficulty of performing speaker recognition on singing voice using models trained on spoken voice alone. We also evaluate the effect of gender and language on speaker recognition performance, both in spoken and singing voice data. The complete \textit{JukeBox} dataset can be accessed at this http URL.
3D Cylindrical Obstacle Avoidance Using the Minimum Distance Technique

In this article, we address the motion planning and control problem of a mobile robot, herein considered as a navigating in a workspace with obstacle. We use Lyapunov’s second method to control the motion of the mobile robot. The minimum distance technique, incorporated for the avoidance of the cylindrical obstacle in a 3D space, used for the first time. Here, the minimum distance between the center of the point mass, representing a mobile robot, and the surface of the cylinder is calculated; thus, only the avoidance of a point on the surface of the cylinder is considered. We propose a set of artificial potential field functions that can be used for the avoidance of the cylindrical obstacle, and for the attraction to the assigned target. The effectiveness of the suggested robust, continuous, nonlinear control inputs is verified numerically via a computer simulation.
Young children monitor the fidelity of visual working memory.

The ability to concurrently maintain representations of multiple objects and their locations in visual working memory is severely limited. Thus, making optimal use of visual working memory requires continual, moment-to-moment monitoring of its fidelity: High-fidelity representations can be relied upon, whereas incomplete or fuzzy representations must be refreshed or ignored. Previous work showed that adults track the fidelity of their visual working memory. Here, we asked whether children, whose capacities for visual working memory are undergoing protracted development, also can do so. We showed 5- and 6-year-olds sets of 2-5 single-feature (Experiment 1) or multifeature (Experiment 2) objects hidden simultaneously in separate locations. We asked children to recall the location of one of the objects, then bet 0-3 resources on whether they were correct. In both experiments, we found that children's confidence in their visual working memory, as indexed by their bets, was correlated with their accuracy on each trial, controlling for task difficulty: Children bet higher when they were correct and lower when they were incorrect. Our results suggest that metacognitive awareness of the representational limits of visual working memory may emerge before working memory reaches stable capacity. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
Optimization of Learning Strategies for ARTM-Based Topic Models

Topic modelling is a popular unsupervised method for text processing which provides interpretable document representation. One of the most high-level approaches, considering its capability of imitating the behaviour of various methods such as LDA or PLSA, is based on additive regularization technique. However, due to its flexibility and advanced regularization abilities, it is challenging to find optimal learning strategy to create high-quality topics, because a user needs to select the regularizers with their values and determine the order of application. At the same time, there is a lack of research on parameters optimization of topic models, especially for ARTM-based models. Our work proposes an approach that formalizes the learning strategy into a vector of parameters which can be solved with an evolutionary or Bayesian approach. An experimental study conducted on English and Russian datasets indicates that the proposed learning strategy can be successfully optimized even in the presence of strong constrains.
WITHDRAWN: A predictive model of machine learning against phishing attacks and effective defense mechanisms

In recent years, online discontent has become a major threat to the security of information. A phishing email begins the common form of on-line deception, then sends targets to a phishing website where the attacker tries goals to share their credentials. General emails from the phishing targets and the defender are relatively easy to understand. Usually they come from odd addresses, the content is very general and they occur in big quantities. Online deception, however, usually leads to a spear phishing which has significant consequences. Spear-phishing e-mails are sent in a very small volume, target small audiences, sometimes impersonate a trustworthy entity, and redirect targets to a phishing website where an attacker tricks targets to share their identity. Attackers mostly use domain spoof techniques to convince the phishing site even more. The whole process we evaluate. Beginning with phishing mail, we discuss anti-spleen protocols, evaluate the policy and alert of email services and assess the environment for email monitoring. For phishing websites, we use dynamic and static analyses to detect the impersonation of domain names and identify phishing pages. On phishing websites we researched the exchange of credentials. We calculate what happens following the goals. Finally, on modern sites such as Alexa and Google Assistant, we discuss possible questions about phishing and privacy.
Sarcasm Detection Approaches Survey

Sarcasm is a special way of expressing opinion most commonly on social media websites like Twitter and product review platforms like Amazon, Flipkart, Snapdeal, etc., in which the actual meaning and the implied meanings differ. Generally, sarcasm is aimed at insulting someone or something in an indirect way or expressing irony. Detecting sarcasm is crucial for many natural language processing (NLP) applications like opinion analysis and sentiment analysis which in turn play an important role in analysing product reviews and comments. There are a lot of challenges proposed when it comes to detecting sarcasm and it is not as simple and straightforward as sentiment analysis. In this survey, we focus on the various approaches that have been used to detect sarcasm primarily on social media websites and categorise them according to the technique used for extracting features. We have also elucidated the challenges encountered while detecting sarcasm and have considered a particular use case for detecting sarcasm in Hindi-English mixed tweets. An approach has been suggested to detect the same.
Towards Crowdsourced Training of Large Neural Networks using Decentralized Mixture-of-Experts

Many recent breakthroughs in deep learning were achieved by training increasingly larger models on massive datasets. However, training such models can be prohibitively expensive. For instance, the cluster used to train GPT-3 costs over \$250 million. As a result, most researchers cannot afford to train state of the art models and contribute to their development. Hypothetically, a researcher could crowdsource the training of large neural networks with thousands of regular PCs provided by volunteers. The raw computing power of a hundred thousand \$2500 desktops dwarfs that of a \$250M server pod, but one cannot utilize that power efficiently with conventional distributed training methods. In this work, we propose Learning@home: a novel neural network training paradigm designed to handle large amounts of poorly connected participants. We analyze the performance, reliability, and architectural constraints of this paradigm and compare it against existing distributed training techniques.
Fourier Sparse Leverage Scores and Approximate Kernel Learning

We prove new explicit upper bounds on the leverage scores of Fourier sparse functions under both the Gaussian and Laplace measures. In particular, we study $s$-sparse functions of the form $f(x) = \sum_{j=1}^s a_j e^{i \lambda_j x}$ for coefficients $a_j \in \mathbb{C}$ and frequencies $\lambda_j \in \mathbb{R}$. Bounding Fourier sparse leverage scores under various measures is of pure mathematical interest in approximation theory, and our work extends existing results for the uniform measure [Erd17,CP19a]. Practically, our bounds are motivated by two important applications in machine learning: 1. Kernel Approximation. They yield a new random Fourier features algorithm for approximating Gaussian and Cauchy (rational quadratic) kernel matrices. For low-dimensional data, our uses a near optimal number of features, and its runtime is polynomial in the $statistical dimension$ of the approximated kernel matrix. It is the first oblivious sketching method with this property for any kernel besides the polynomial kernel, resolving an open question of [AKM+17,AKK+20b]. 2. Active Learning. They can be used as non-uniform sampling distributions for robust active learning when data follows a Gaussian or Laplace distribution. Using the framework of [AKM+19], we provide essentially optimal results for bandlimited and multiband interpolation, and Gaussian process regression. These results generalize existing work that only applies to uniformly distributed data.
Self-Supervised Out-of-Distribution Detection in Brain CT Scans.

Medical imaging data suffers from the limited availability of annotation because annotating 3D medical data is a time-consuming and expensive task. Moreover, even if the annotation is available, supervised learning-based approaches suffer highly imbalanced data. Most of the scans during the screening are from normal subjects, but there are also large variations in abnormal cases. To address these issues, recently, unsupervised deep anomaly detection methods that train the model on large-sized normal scans and detect abnormal scans by calculating reconstruction error have been reported. In this paper, we propose a novel self-supervised learning technique for anomaly detection. Our architecture largely consists of two parts: 1) Reconstruction and 2) predicting geometric transformations. By training the network to predict geometric transformations, the model could learn better image features and distribution of normal scans. In the test time, the geometric transformation predictor can assign the anomaly score by calculating the error between geometric transformation and prediction. Moreover, we further use self-supervised learning with context restoration for pretraining our model. By comparative experiments on clinical brain CT scans, the effectiveness of the proposed method has been verified.
Targeted Adversarial Perturbations for Monocular Depth Prediction

We study the effect of adversarial perturbations on the task of monocular depth prediction. Specifically, we explore the ability of small, imperceptible additive perturbations to selectively alter the perceived geometry of the scene. We show that such perturbations can not only globally re-scale the predicted distances from the camera, but also alter the prediction to match a different target scene. We also show that, when given semantic or instance information, perturbations can fool the network to alter the depth of specific categories or instances in the scene, and even remove them while preserving the rest of the scene. To understand the effect of targeted perturbations, we conduct experiments on state-of-the-art monocular depth prediction methods. Our experiments reveal vulnerabilities in monocular depth prediction networks, and shed light on the biases and context learned by them.
Unveiling Usability and UX Relationships for Different Gender, Users Habits and Contexts of Use

Despite the abundance of research into usability and user experience (UX), there are few works approaching how the context influences the relationships between these two concepts. The actual experience of a user highly depends on personal characteristics, like the social and cultural background. In this work, we address user-related and contextual factors for a better comprehension of usability UX correlations. We conducted a study with 160 participants in entertainment, e-bank, education and e-commerce websites. As a result, for both sex and usage time user classes, a good usability was positively correlated to positive emotions. Despite the correlations between usability and female users negative and positive emotional reactions are greater than for male users, no significant difference was observed. Also, there was no significant difference involving correlations between usability and emotional reactions, when comparing less and more frequent users. As another result, there are differences of the degree of the correlations between perceived usability and emotional responses in different contexts of use, when dividing the users on sex and usage time classes, but the correlations between usability and positive emotions remained positive. In spite of the particularities of user classes, similarities on the usability for different user classes and the agreement of patterns of correlations with previous literature, our results reinforces the usability as a determinant aspect for UX.
Uncertainty Reduction in the Neural Network’s Weather Forecast for the Andean City of Quito Through the Adjustment of the Posterior Predictive Distribution Based on Estimators

The weather forecast in cities as Quito is highly complicated due to its proximity to Latitude 0° and because it is located in the Andes mountains range. A statistical post-processing is compulsory in order to improve the output from the physical model and to improve the weather forecast in the city. A neural network can be applied in order to carry out this task but it is necessary first to reduce its uncertainty. The Bayesian Neural Networks (BNN) have been studied deeply thanks to its probability analysis, the uncertainty can be approximated. In this paper an analysis founded on the adjustment of the posterior predictive distribution based on estimators is carried out in order to reduce the prediction error variation (implicitly the uncertainty) in a Short-Term Weather Forecast for the Andean city of Quito. From the analysis it is obtained a maximum error forecast of 12% and it is proven that for Long Short Term Memory (LSTM) structures, the variation of the error reduces almost to the half with weight-decays of $$ 2.04 \times 10^{ - 7} $$ and $$ 2.23 \times 10^{ - 7} $$ .
Eye Movements Biometrics: A Bibliometric Analysis from 2004 to 2019

Person identification based on eye movements is getting more and more attention, as it is anti-spoofing resistant and can be useful for continuous authentication. Therefore, it is noteworthy for researchers to know who and what is relevant in the field, including authors, journals, conferences, and institutions. This paper presents a comprehensive quantitative overview of the field of eye movement biometrics using a bibliometric approach. All data and analyses are based on documents written in English published between 2004 and 2019. Scopus was used to perform information retrieval. This research focused on temporal evolution, leading authors, most cited papers, leading journals, competitions and collaboration networks.
Variance Reduction via Accelerated Dual Averaging for Finite-Sum Optimization

In this paper, we introduce a simplified and unified method for finite-sum convex optimization, named \emph{Variance Reduction via Accelerated Dual Averaging (VRADA)}. In both general convex and strongly convex settings, VRADA can attain an $O\big(\frac{1}{n}\big)$-accurate solution in $O(n\log\log n)$ number of stochastic gradient evaluations which improves the best-known result $O(n\log n)$, where $n$ is the number of samples. Meanwhile, VRADA matches the lower bound of the general convex setting up to a $\log\log n$ factor and matches the lower bounds in both regimes $n\le \Theta(\kappa)$ and $n\gg \kappa$ of the strongly convex setting, where $\kappa$ denotes the condition number. Besides improving the best-known results and matching all the above lower bounds simultaneously, VRADA has more unified and simplified algorithmic implementation and convergence analysis for both the general convex and strongly convex settings. The underlying novel approaches such as the novel initialization strategy in VRADA may be of independent interest. Through experiments on real datasets, we show the good performance of VRADA over existing methods for large-scale machine learning problems.
PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization

We present a new framework for Patch Distribution Modeling, PaDiM, to concurrently detect and localize anomalies in images in a one-class learning setting. PaDiM makes use of a pretrained convolutional neural network (CNN) for patch embedding, and of multivariate Gaussian distributions to get a probabilistic representation of the normal class. It also exploits correlations between the different semantic levels of CNN to better localize anomalies. PaDiM outperforms current state-of-the-art approaches for both anomaly detection and localization on the MVTec AD and STC datasets. To match real-world visual industrial inspection, we extend the evaluation protocol to assess performance of anomaly localization algorithms on non-aligned dataset. The state-of-the-art performance and low complexity of PaDiM make it a good candidate for many industrial applications.
Challenges of Simulating Uncertainty of Information

Human information interaction including human computer interaction is complex given the variability of the human and now the ever-increasing amount of information available. While more information seems to be ideal, most would agree that having the needed information at the time required maybe the ideal. This is particularly true for decision-making. At each step along the decision-making process, different information may be needed so that the goal of generating the best decision is reached. However, information is not perfect and comes with uncertainties. Our approach has been to explore what we have termed uncertainty of information (UoI) and its impact on decision-making. We have selected Gershon’s taxonomy of imperfect information as variable descriptors to represent potential sources of uncertainty. Using terms from the taxonomy we have created an algorithm that can capture the uncertainty from this point of view. In addition, we have developed simulations to begin to explore the how UoI from various sources can influence decision-making. Consequently, we have been faced with the difficulties of building a general comprehensive model for simulating UoI. These difficulties range from how to express the behavior of the human to how to express the behavior of the computer and devices that may be providing the information. The difficulties also include how to incorporate the different factors and underlying dependencies that contribute to the UoI value.
On Convergence and Generalization of Dropout Training

We study dropout in two-layer neural networks with rectified linear unit (ReLU) activations. Under mild overparametrization and assuming that the limiting kernel can separate the data distribution with a positive margin, we show that dropout training with logistic loss achieves $\epsilon$-suboptimality in test error in $O(1/\epsilon)$ iterations.