Reviewerly/exHarmony huggingface dataset:
Analysis of COVID-19 Impact using Data Visualization

The world has suffered from many crises and pandemics in the past but it's the creativity and inventiveness of its people and their rigorous efforts with the capacity to think out of the box which has made them combat and overcome those situations. The world is facing a similar situation with the inception of COVID-19. The deadly new coronavirus first detected in Wuhan; the capital of China's Hubei province has sat its foot across the globe by infecting millions of people worldwide. Nevertheless, tough times ask for draconian measures and smart solutions, so the objective of our research work is to analyse the data with the help of visualization using Python to delineate and bring out a result by comparing the COVID-19 outbreak in different continents and countries like the USA, China, India, Italy, and Taiwan. For differentiation, we have used the total number of confirmed cases, the total number of casualties, and the total number of recovered cases of the COVID-19. Additionally, we have also compared the total number of tests conducted by the countries mentioned above. Our research will also concentrate on what makes COVID-19 pandemic different from other epidemics like SARS (2003), MERS (2012), Ebola (2014) by comparing their mortality rate, contagiousness, and symptoms. For all the comparison we have used data visualization. This research provides a comprehensive understanding of COVID-19 and compares its impact on different regions of the world with the help of Data Visualization andit will also help to derive a better solution for future emergencies.
Audiovisual spaces

Echoing the spatial turns across the humanities, this chapter applies post-Bergsonian conceptions of space via Deleuze and Guttari, Massumi and Massey, to explore how a spatial conception might provide novel perspectives to conceive and understand the audiovisual. Spatial concepts embrace subjectivity, multiplicity, movement, haptics and tactility, and afford readings of audiovisual practice that frame sound and image associations, not in abstract conceptual terms – synchronisation, difference or contrast – but in terms of embodied experience, movement and potentiality. Critiqued via practice research, this chapter brings together spatial ideas with established concepts from electroacoustic music and audiovisual composition, to build a novel framework for representing the audiovisual. One which has the potential to be applied to a wide range of sound and image media, from experimental audiovisual film, to sound design and narrative film soundtracks. The primacy of temporal sync is challenged, established concepts of gesture and texture reframed in light of the spatial; and potentiality and anticipation positioned as more representative terminologies for reflecting the unity of sound and image within audiovisual practices. Through spatiality we have the potential to dissolve the binarism between sound and image and access an understanding of audiovisual affect, which underpins both audience experience and composition of sound and image works.
The effect of measurement error on clustering algorithms

Clustering consists of a popular set of techniques used to separate data into interesting groups for further analysis. Many data sources on which clustering is performed are well-known to contain random and systematic measurement errors. Such errors may adversely affect clustering. While several techniques have been developed to deal with this problem, little is known about the effectiveness of these solutions. Moreover, no work to-date has examined the effect of systematic errors on clustering solutions. In this paper, we perform a Monte Carlo study to investigate the sensitivity of two common clustering algorithms, GMMs with merging and DBSCAN, to random and systematic error. We find that measurement error is particularly problematic when it is systematic and when it affects all variables in the dataset. For the conditions considered here, we also find that the partition-based GMM with merged components is less sensitive to measurement error than the density-based DBSCAN procedure.
COVID 19 Data Visualization through Automatic Phase Detection

This paper presents a new interactive visualization system to represent and compare rate of spread of COVID 19 pandemic across different countries over time. We surveyed existing visualization techniques used in various websites and media outlets and introduced use of a knee detection algorithm that divides the exponential spread in multiple linear components. A set of use cases demonstrates the utility of the system in comparing the spread across different countries for both analysis and prediction.
Still Simpler Static Level Ancestors

A level-ancestor or LA query about a rooted tree $T$ takes as arguments a node $v$ in $T$, of depth $d_v$, say, and an integer $d$ with $0\le d\le d_v$ and returns the ancestor of $v$ in $T$ of depth $d$. The static LA problem is to process a given rooted tree $T$ so as to support efficient subsequent processing of LA queries about $T$. All previous efficient solutions to the static LA problem work by reducing a given instance of the problem to a smaller instance of the same or a related problem, solved with a less efficient data structure, and a collection of small micro-instances for which a different solution is provided. We indicate the first efficient solution to the static LA problem that works directly, without resorting to reductions or micro-instances.
S3VAE: Self-Supervised Sequential VAE for Representation Disentanglement and Data Generation

We propose a sequential variational autoencoder to learn disentangled representations of sequential data (e.g., videos and audios) under self-supervision. Specifically, we exploit the benefits of some readily accessible supervisory signals from input data itself or some off-the-shelf functional models and accordingly design auxiliary tasks for our model to utilize these signals. With the supervision of the signals, our model can easily disentangle the representation of an input sequence into static factors and dynamic factors (i.e., time-invariant and time-varying parts). Comprehensive experiments across videos and audios verify the effectiveness of our model on representation disentanglement and generation of sequential data, and demonstrate that, our model with self-supervision performs comparable to, if not better than, the fully-supervised model with ground truth labels, and outperforms state-of-the-art unsupervised models by a large margin.
InfoScrub: Towards Attribute Privacy by Targeted Obfuscation

Personal photos of individuals when shared online, apart from exhibiting a myriad of memorable details, also reveals a wide range of private information and potentially entails privacy risks (e.g., online harassment, tracking). To mitigate such risks, it is crucial to study techniques that allow individuals to limit the private information leaked in visual data. We tackle this problem in a novel image obfuscation framework: to maximize entropy on inferences over targeted privacy attributes, while retaining image fidelity. We approach the problem based on an encoder-decoder style architecture, with two key novelties: (a) introducing a discriminator to perform bi-directional translation simultaneously from multiple unpaired domains; (b) predicting an image interpolation which maximizes uncertainty over a target set of attributes. We find our approach generates obfuscated images faithful to the original input images, and additionally increase uncertainty by 6.2$\times$ (or up to 0.85 bits) over the non-obfuscated counterparts.
Unsupervised Text Feature Selection Using Memetic Dichotomous Differential Evolution

Feature Selection (FS) methods have been studied extensively in the literature, and there are a crucial component in machine learning techniques. However, unsupervised text feature selection has not been well studied in document clustering problems. Feature selection could be modelled as an optimization problem due to the large number of possible solutions that might be valid. In this paper, a memetic method that combines Differential Evolution (DE) with Simulated Annealing (SA) for unsupervised FS was proposed. Due to the use of only two values indicating the existence or absence of the feature, a binary version of differential evolution is used. A dichotomous DE was used for the purpose of the binary version, and the proposed method is named Dichotomous Differential Evolution Simulated Annealing (DDESA). This method uses dichotomous mutation instead of using the standard mutation DE to be more effective for binary purposes. The Mean Absolute Distance (MAD) filter was used as the feature subset internal evaluation measure in this paper. The proposed method was compared with other state-of-the-art methods including the standard DE combined with SA, which is named DESA in this paper, using five benchmark datasets. The F-micro, F-macro (F-scores) and Average Distance of Document to Cluster (ADDC) measures were utilized as the evaluation measures. The Reduction Rate (RR) was also used as an evaluation measure. Test results showed that the proposed DDESA outperformed the other tested methods in performing the unsupervised text feature selection.
Theoretical study of Maxwell's equations in nonlinear optics

The translation of Maxwell's equations and the motion of electrons in a medium that always been known to use potential depends on time and position. Maxwell's equations in a vacuum result in the completion of a linear electromagnetic field. If the non-linear medium Maxwell's equation will create a non-linear electromagnetic field solution.
High-Fidelity Illumination Normalization for Face Recognition Based on Auto-Encoder

Nonuniform illumination is one of the main issues that hinder the accuracy of face recognition because it makes the intra-person variation more complicated. To minimize the intra-person differences caused by varying illuminations, this paper presents a normalization method based on Convolutional Auto-encoder (CAE). The CAE is employed to map the face images under various illumination conditions to a normalized one, generating preliminary results with blurry and insufficient facial details, which are tricky for recognition. To recover these details, a restoration method based on re-blurring strategy and frequency analysis is proposed to preserve the facial features lying in high-frequency components based on discrete cosine transform (DCT). Therefore, in our method, these components are extracted and re-introduced into the outputs of CAE to enhance the fidelity of outputs. Thus, the facial details are preserved to the largest degree and the following works such as recognition tasks are benefited. Experiments conducted on the AR, extended Yale B, and CAS-PEAL database demonstrate the effectiveness of our method.
On Improving Primary Teachers' Assessment Literacy Based on Artificial Intelligence – Focused on Ongoing Assessment Aligned with English Classroom Activities

The purpose of this study is to suggest the direction of improving teachers’ assessment literacy based on Artificial Intelligence that focuses on ongoing assessment aligned with classroom activities. To achieve the purpose, two lesson plans presented to demonstrate a class for Teaching English in English (TEE)-Ace were analyzed in the aspect of ongoing assessment. It was found that 1) the objective, one of the important factors in assessments, was not set up based on the key competencies established in the 2015 national curriculum, 2) the assessment plan was not set up in advance before teaching-learning activities were implemented, 3) teachers did not implement assessments aligned with classroom activities, and 4) there were no assessment methods and tools based on technology in classroom activities. From the findings, it was suggested that teachers need to be trained in the program of designing and developing assessment methods and tools aligned with classroom activities for learners to improve key competencies emphasized in the curriculum.
A Hybrid Deep Learning Architecture Using 3D CNNs and GRUs for Human Action Recognition

Video contents have variations in temporal and spatial dimensions, and recognizing human actions requires considering the changes in both directions. To this end, convolutional neural networks (CNNs) and recurrent neural networks (RNNs) and their combinations have been used to tackle the video dynamics. However, a hybrid architecture usually results in a more complex model and hence a greater number of parameters to be optimized. In this study, we propose to use a stack of gated recurrent unit (GRU) layers on top of a two-stream inflated convolutional neural network. Raw frames and optical flow of the video are processed in the first and second streams, respectively. We first segment the video frames in order to be able to track the video contents in more details and by using 3D CNNs extract spatial-temporal features, called local features. We then import the sequence of local features to the GRU‌ network, and use a weighted averaging operator to aggregate the outcome of the two processing flows, called global features. The evaluations confirm acceptable results for the two HMDB51 and UCF101 datasets. The proposed method resulted in a 1.6% improvement in the classification accuracy of the HMDB51 challenging dataset compared to the best reported results.
PyChain: A Fully Parallelized PyTorch Implementation of LF-MMI for End-to-End ASR

We present PyChain, a fully parallelized PyTorch implementation of end-to-end lattice-free maximum mutual information (LF-MMI) training for the so-called \emph{chain models} in the Kaldi automatic speech recognition (ASR) toolkit. Unlike other PyTorch and Kaldi based ASR toolkits, PyChain is designed to be as flexible and light-weight as possible so that it can be easily plugged into new ASR projects, or other existing PyTorch-based ASR tools, as exemplified respectively by a new project PyChain-example, and Espresso, an existing end-to-end ASR toolkit. PyChain's efficiency and flexibility is demonstrated through such novel features as full GPU training on numerator/denominator graphs, and support for unequal length sequences. Experiments on the WSJ dataset show that with simple neural networks and commonly used machine learning techniques, PyChain can achieve competitive results that are comparable to Kaldi and better than other end-to-end ASR systems.
Dynamic mixtures of finite mixtures and telescoping sampling

Within a Bayesian framework, a comprehensive investigation of the model class of mixtures of finite mixtures (MFMs) where a prior on the number of components is specified is performed. This model class has applications in model-based clustering as well as for semi-parametric density approximation, but requires suitable prior specifications and inference methods to exploit its full potential. We contribute to the Bayesian analysis of MFMs by (1) considering static and dynamic MFMs where the Dirichlet parameter of the component weights either is fixed or depends on the number of components, (2) proposing a flexible prior distribution class for the number of components $K$, (3) characterizing the implicit prior on the number of clusters $K_+$ as well as partitions by deriving computationally feasible formulas, (4) linking MFMs to Bayesian non-parametric mixtures, and (5) finally proposing a novel sampling scheme for MFMs called the telescoping sampler which allows Bayesian inference for mixtures with arbitrary component distributions. The telescoping sampler explicitly samples the number of components, but otherwise requires only the usual MCMC steps for estimating a finite mixture model. The ease of its application using different component distributions is demonstrated on real data sets.
Deep R-Learning for Continual Area Sweeping

Coverage path planning is a well-studied problem in robotics in which a robot must plan a path that passes through every point in a given area repeatedly, usually with a uniform frequency. To address the scenario in which some points need to be visited more frequently than others, this problem has been extended to non-uniform coverage planning. This paper considers the variant of non-uniform coverage in which the robot does not know the distribution of relevant events beforehand and must nevertheless learn to maximize the rate of detecting events of interest. This continual area sweeping problem has been previously formalized in a way that makes strong assumptions about the environment, and to date only a greedy approach has been proposed. We generalize the continual area sweeping formulation to include fewer environmental constraints, and propose a novel approach based on reinforcement learning in a Semi-Markov Decision Process. This approach is evaluated in an abstract simulation and in a high fidelity Gazebo simulation. These evaluations show significant improvement upon the existing approach in general settings, which is especially relevant in the growing area of service robotics.
Methodical techniques and exercise system for creative learning in preparation for abstracting foreign texts in a non-linguistic university

The article is devoted to the development of methodological techniques and exercise system in accordance with the specifics of creative training in foreign language abstracting of foreign texts in foreign language classes at a non-linguistic university. The author gives detailed analysis of the theoretical and practical foundations of the creative training proposed by the author, represented by a set of exercises. The author proposed an algorithm for sequential actions on the part of the teacher and students, presented methodological techniques that the teacher should apply when using these exercises. The aim of the article is to optimize the process of teaching foreign languages at university in the form of developing methodological techniques and exercises for a creative teaching format in order to materialize an innovative methodological approach from the perspective of a creative existential format for teaching foreign languages at university.
Samples Are Useful? Not Always: denoising policy gradient updates using variance explained

Policy gradient algorithms in reinforcement learning optimize the policy directly and rely on efficiently sampling an environment. Most sampling procedures are based solely on the sampling of the agent's policy. However, other measures directly available through these algorithms could be used to improve sampling before each policy update. Following this line of thoughts, we propose the use of SAUNA, a method where a transition is rejected from the gradient update if it does not meet a particular criterion, and kept otherwise. This criterion is the fraction of variance explained $\mathcal{V}^{ex}$, a measure of the discrepancy between a model and actual samples. $\mathcal{V}^{ex}$ can be used to evaluate the impact each transition will have on the learning. This criterion refines sampling and improves the policy gradient algorithm. In this paper: (a) We introduce and explore $\mathcal{V}^{ex}$, the selection criterion used to improve the sampling procedure. (b) We conduct experiments across a variety of benchmark environments, including standard continuous control problems. Our results show better performance than if we did not use the $\mathcal{V}^{ex}$ criterion for the policy gradient update. (c) We investigate why $\mathcal{V}^{ex}$ provides a reliable assessment for the selection of samples that will have a positive impact on learning. (d) We show how to interpret this criterion as a dynamic way to adjust the ratio between exploration and exploitation.
On the Influence of the Coding Rate and SFN Gain on DAB+ Coverage

The ubiquitous digitization which also affects the audio broadcasting technologies, has spawned multiple standards, such as IBOC/HD Radio, ISDB-TSB, DRM, DBM or DAB+ , out of which the latter has gained the wideset endorsement, especially among European countries. Despite requiring a ten-fold wider channel, it offers multiple advantages over the traditional analog (FM) broadcasting counterpart. These are enumerated and evaluated in terms of financial benefits, enhanced/new technological functionalities or even ecological aspects. Some of the highlights include the possibility of attaching synchronized images (even video clips), website broadcasting, journaline service, traffic messaging etc. from multiple broadcasters within a single multiplex (MUX) occupying a single frequency channel emitted from multiple sites simultaneously, forming so called a Single Frequency Network (SFN). The research part of the paper regards simulations obtained for a DAB+ SFN network, with a purpose of demonstrating how coverage will differ the coding rate which is the only variable parameter at an operator’s disposal, by means of which they can scale the audibility and data transmission range in digital audio broadcasting systems. A separate analysis is devoted to the SFN gain; an effect observed by users caused by the transmit diversity from multiple (three in the analyzed case) transmitters.
The application of scaffolding augmented reality (AR) media in the sharing task learning of electrolyte and non-electrolyte solutions

Abstract This research is based on the difficulty of students in understanding the abstract concept of electrolyte and non-electrolyte solutions. The aim of this study is to evaluate the improvement of students’ understanding concepts with the scaffolding of augmented reality (AR) media in the sharing task learning of electrolyte and non-electrolyte solutions. This research was conducted at one of the high schools in Bandung, Indonesia. Data were obtained through questionare understanding the concept of electrolyte and non-electrolyte solutions and student’s worksheets. Data were analysed quantitatively and qualitatively. The results define students’ conceptual understanding from each question indicator obtaining positive results. It can be concluded that the application of scaffolding augmented reality (AR) media in the sharing task learning of electrolyte and non-electrolyte solutions enhances understanding of student’s concepts.
Eye Gaze Controlled Wheelchair

Eye gaze wheelchair is a unique technique executed mainly for the disabled persons who are fully paralyzed.In this system manual control of wheelchair is being replaced be automatic control i.e. controlled by the eyeball movement, so that the patients feel free and less or no difficulty in their movements.Continuous image is captured with the help of webcam which further undergoes several image processing techniques, to detect the position of eye pupil Haar cascade algorithm is being implemented with the resultant of the image processing technique wheelchair moves accordingly.DC motor is mounted to the wheels for easy motion of the wheelchair.The ultrasonic sensor is mounted to the wheelchair so that it detects any obstacles in the path of its movements and wheelchair stops movement as per sensor command.
Bayesian neural networks at scale: a performance analysis and pruning study

Bayesian neural Networks (BNNs) are a promising method of obtaining statistical uncertainties for neural network predictions but with a higher computational overhead which can limit their practical usage. This work explores the use of high performance computing with distributed training to address the challenges of training BNNs at scale. We present a performance and scalability comparison of training the VGG-16 and Resnet-18 models on a Cray-XC40 cluster. We demonstrate that network pruning can speed up inference without accuracy loss and provide an open source software package, {\it{BPrune}} to automate this pruning. For certain models we find that pruning up to 80\% of the network results in only a 7.0\% loss in accuracy. With the development of new hardware accelerators for Deep Learning, BNNs are of considerable interest for benchmarking performance. This analysis of training a BNN at scale outlines the limitations and benefits compared to a conventional neural network.