Reviewerly/exHarmony huggingface dataset:
The Use of Ambisonic Audio to Improve Presence, Focus, and Noticing While Viewing 360 Video

Research has provided evidence of the value of producing multiple representations of content for learners (e.g., verbal, visual, etc.). However, much of the research has acknowledged changes in visual technologies while not recognizing or utilizing related audio innovations. For instance, teacher education students who were once taught through two-dimensional video are now being presented with interactive, three-dimensional content (e.g., simulations or 360 video). Users in old and new formats, however, still typically receive monophonic sound. A limited number of research studies exist that have examined the impact of combining three-dimensional sound to match three-dimensional video in learning environments. The purpose of this study was to respond to this gap by comparing the outcomes of watching 360 video with either monophonic or ambisonic audio. Results provided evidence that ambisonic audio increased perceived presence for those familiar with the content being taught, led to differentiation in what ambisonic viewers noticed compared to monophonic groups, and improved participant focus in watching the 360 video. Implications for the development and implementation into virtual worlds are discussed.
Discriminative learning of bayesian network parameters by differential evolution

This work proposes Differential Evolution (DE) to train parameters of Bayesian Networks (BN) for optimizing the Conditional Log-Likelihood (Discriminative Learning) instead of the log-likelihood (Generative Learning). Any given BN structure encodes assumptions about conditional independencies among the attributes and will result in error if they do not hold in the data. Such an error includes the classification dimension. The main goal of Discriminative learning is minimize this type of error. In this sense, although BN Discriminative Parameter Learning algorithms have been proposed, to the best of the authors' knowledge, a meta-heuristic approach has not been devised yet. Thus, this is our main contribution: to come up with this kind of solution and evaluate its behavior so that its feasibility in this domain can be determined. Regarding the proposed method, the bias provided by the best solution in the population improves DE's performance. DE variants based on JADE, such as L-SHADE and C-JADE, are especially recommended when introducing adaptation mechanisms of mutation and crossover parameters thus reducing the dependence on their calibration. L-SHADE is computationally recommended over any other DE variant. DE approach works well in essentially every standard situation, so DE approach is robust and at least as good, and often better, than the state-of-art method for Discriminative Learning called WANBIA. Our results suggest a potential benefit for Discriminative parameter learning with strong independence assumptions among attributes and that it typically produces more accurate classifiers than generative learning.
Differentiable Product Quantization for End-to-End Embedding Compression

Embedding layers are commonly used to map discrete symbols into continuous embedding vectors that reflect their semantic meanings. Despite their effectiveness, the number of parameters in an embedding layer increases linearly with the number of symbols and poses a critical challenge on memory and storage constraints. In this work, we propose a generic and end-to-end learnable compression framework termed differentiable product quantization (DPQ). We present two instantiations of DPQ that leverage different approximation techniques to enable differentiability in end-to-end learning. Our method can readily serve as a drop-in alternative for any existing embedding layer. Empirically, DPQ offers significant compression ratios (14-238$\times$) at negligible or no performance cost on 10 datasets across three different language tasks.
Generating Diverse Corrections with Local Beam Search for Grammatical Error Correction

In this study, we propose a beam search method to obtain diverse outputs in a local sequence transduction task where most of the tokens in the source and target sentences overlap, such as in grammatical error correction (GEC).In GEC, it is advisable to rewrite only the local sequences that must be rewritten while leaving the correct sequences unchanged.However, existing methods of acquiring various outputs focus on revising all tokens of a sentence.Therefore, existing methods may either generate ungrammatical sentences because they force the entire sentence to be changed or produce non-diversified sentences by weakening the constraints to avoid generating ungrammatical sentences.Considering these issues, we propose a method that does not rewrite all the tokens in a text, but only rewrites those parts that need to be diversely corrected.Our beam search method adjusts the search token in the beam according to the probability that the prediction is copied from the source sentence.The experimental results show that our proposed method generates more diverse corrections than existing methods without losing accuracy in the GEC task.
Quantum Computational Techniques for Prediction of Cognitive State of Human Mind from EEG Signals

The utilization of quantum states for the representation of information and the advances in machine learning is considered as an efficient way of modeling the working of complex systems. The states of mind or judgment outcomes are highly complex phenomena that happen inside the human body. Decoding these states is significant for improving the quality of technology and providing an impetus to scientific research aimed at understanding the functioning of the human mind. One of the key advantages of quantum wave-functions over conventional classical models is the existence of configurable hidden variables, which provide more data density due to its exponential state-space growth. These hidden variables correspond to the amplitudes of each probable state of the system and allow for the modeling of various intricate aspects of measurable and observable physical quantities. This makes the quantum wave-functions powerful and felicitous to model cognitive states of the human mind, as it inherits the ability to efficiently couple the current context with past experiences temporally and spatially to approach an appropriate future cognitive state. This paper implements and compares some techniques like Variational Quantum Classifiers (VQC), quantum annealing classifiers, and hybrid quantum-classical neural networks, to harness the power of quantum computing for processing cognitive states of the mind by making use of EEG data. It also introduces a novel pipeline by logically combining some of the aforementioned techniques, to predict future cognitive responses. The preliminary results of these approaches are presented and are very encouraging with upto 61.53% validation accuracy.
Findings of the WMT 2020 Shared Task on Machine Translation Robustness.

We report the findings of the second edition of the shared task on improving robustness in Machine Translation (MT). The task aims to test current machine translation systems in their ability to handle challenges facing MT models to be deployed in the real world, including domain diversity and non-standard texts common in user generated content, especially in social media. We cover two language pairs – English-German and English-Japanese and provide test sets in zero-shot and few-shot variants. Participating systems are evaluated both automatically and manually, with an additional human evaluation for ”catastrophic errors”. We received 59 submissions by 11 participating teams from a variety of types of institutions.
The Use of Electronic Educational Resources of The University as A Means of Increasing The Educational Motivation of Students

The relevance of using electronic educational resources as a means of developing educational motivation is determined by a detailed study of issues related to the use of this educational technology in practice in the modern educational process. The aim of our research work was to study the relationship of educational motivation of students and their relationship to the use of electronic educational resources for the subsequent improvement of the quality of education at a university. Based on a survey and questionnaire of 100 respondents, the authors analyzed topical issues of using electronic educational resources and types of educational motivation of students with a pedagogical profile. The authors studied the impact of electronic educational resources on the effectiveness of the educational process and increasing student motivation. Features of the organization of this process are considered on the example of the Elabuga Institute of Kazan Federal University.
Witches' Brew: Industrial Scale Data Poisoning via Gradient Matching

Data Poisoning attacks modify training data to maliciously control a model trained on such data. In this work, we focus on targeted poisoning attacks which cause a reclassification of an unmodified test image and as such breach model integrity. We consider a particularly malicious poisoning attack that is both ``from and ``clean label, meaning we analyze an attack that successfully works against new, randomly initialized models, and is nearly imperceptible to humans, all while perturbing only a small fraction of the training data. Previous poisoning attacks against deep neural networks in this setting have been limited in scope and success, working only in simplified settings or being prohibitively expensive for large datasets. The central mechanism of the new attack is matching the gradient direction of malicious examples. We analyze why this works, supplement with practical considerations. and show its threat to real-world practitioners, finding that it is the first poisoning method to cause targeted misclassification in modern deep networks trained from scratch on a full-sized, poisoned ImageNet dataset. Finally we demonstrate the limitations of existing defensive strategies against such an attack, concluding that data poisoning is a credible threat, even for large-scale deep learning systems.
Image Haze Removal Algorithm Using a Logarithmic Guide Filtering and Multi-Channel Prior

In reality, the quality of an image is generally affected by haze. To obtain a well-quality image, removing haze is a hot issue on theory and application. This paper proposes a new algorithm to remove haze of hazy images. In the algorithm, first, the ambient illumination is estimated by a logarithmic guide filtering that can reserve the characteristics of the bright source areas and improve the dark source areas of the hazy image. Second, to overcome the defect of dark channel prior (DCP) and the over-brightness of the bright channel prior (BCP), two models with two parameters are introduced to improve the DCP and BCP, called multi-channel prior method. At the same time, a self-adaptive method is presented to compute the values of the two parameters. At last, based on the multi-channel prior, a self-adaptive method is proposed to compute the transmission mapping value. Further, four classes hazy images are employed to test the proposed method. The experimental results carried out on the public databases demonstrate that the proposed algorithm can outperform the current state-of-the-arts, including more effective defogging, clearer visibility and richer details.
Quality of Histopathological Reporting in Breast Cancer: Results From Four South African Breast Units

High-quality histopathology reporting forms the basis for treatment decisions. The quality indicator for pathology reports from the European Society of Breast Cancer Specialists was applied to a cohort from four South African breast units.The study included 1,850 patients with invasive breast cancer and evaluated 1,850 core biopsies and 1,158 surgical specimen reports with cross-center comparisons. A core biopsy report required histologic type; tumor grade; and estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2 (HER2) status, with a confirmatory test for equivocal HER2 results. Ki-67 was regarded as optional. Pathologic stage, tumor size, lymphovascular invasion, and distance to nearest invasive margin were mandatory for surgical specimens. Specimen turnaround time (TAT) was added as a locally relevant indicator.Seventy-five percent of core biopsy and 74.3% of surgical specimen reports were complete but showed large variability across study sites. The most common reason for an incomplete core biopsy report was missing tumor grade (17.9%). Half of the equivocal HER2 results lacked confirmatory testing (50.6%). Ki-67 was reported in 89.3%. For surgical specimens, the closest surgical margin was reported in 78.1% and lymphovascular invasion in 84.8% of patients. Mean TAT was 11.9 days (standard deviation [SD], 10.8 days) for core biopsies and 16.1 days (SD, 11.3) for surgical specimens.Histopathology reporting is at a high level but can be improved, especially for tumor grade, HER2, and Ki-67, as is reporting of margins and lymphovascular invasion. A South African pathology consensus will reduce variability among laboratories. Routine use of standardized data sheets with synoptic reports and ongoing audits will improve completeness of reports over time.
A Dataset of State-Censored Tweets

Many governments impose traditional censorship methods on social media platforms. Instead of removing it completely, many social media companies, including Twitter, only withhold the content from the requesting country. This makes such content still accessible outside of the censored region, allowing for an excellent setting in which to study government censorship on social media. We mine such content using the Internet Archive's Twitter Stream Grab. We release a dataset of 583,437 tweets by 155,715 users that were censored between 2012-2020 July. We also release 4,301 accounts that were censored in their entirety. Additionally, we release a set of 22,083,759 supplemental tweets made up of all tweets by users with at least one censored tweet as well as instances of other users retweeting the censored user. We provide an exploratory analysis of this dataset. Our dataset will not only aid in the study of government censorship but will also aid in studying hate speech detection and the effect of censorship on social media users. The dataset is publicly available at https://doi.org/10.5281/zenodo.4439509
Low Complexity Trellis-Coded Quantization in Versatile Video Coding

The forthcoming Versatile Video Coding (VVC) standard adopts the trellis-coded quantization, which leverages the delicate trellis graph to map the quantization candidates within one block into the optimal path. Despite the high compression efficiency, the complex trellis search with soft decision quantization may hinder the applications due to high complexity and low throughput capacity. To reduce the complexity, in this paper, we propose a low complexity trellis-coded quantization scheme in a scientifically sound way with theoretical modeling of the rate and distortion. As such, the trellis departure point can be adaptively adjusted, and unnecessarily visited branches are accordingly pruned, leading to the shrink of total trellis stages and simplification of transition branches. Extensive experimental results on the VVC test model show that the proposed scheme is effective in reducing the encoding complexity by 11% and 5% with all intra and random access configurations, respectively, at the cost of only 0.11% and 0.05% BD-Rate increase. Meanwhile, on average 24% and 27% quantization time savings can be achieved under all intra and random access configurations. Due to the excellent performance, the VVC test model has adopted one implementation of the proposed scheme.
Dissipative state transfer and Maxwell's demon in single quantum trajectories: Excitation transfer between two noninteracting qubits via unbalanced dissipation rates

We introduce a protocol to transfer excitations between two noninteracting qubits via purely dissipative processes (i.e., in the Lindblad master equation there is no coherent interaction between the qubits). The fundamental ingredients are the presence of collective (i.e., nonlocal) dissipation and unbalanced local dissipation rates (the qubits dissipate at different rates). The resulting quantum trajectories show that the measurement back-action changes the system wave function and induces a passage of the excitation from one qubit to the other. While similar phenomena have been witnessed for a non-Markovian environment, here the dissipative quantum state transfer is induced by an update of the observer knowledge of the wave function in the presence of a Markovian (memoryless) environment---this is a single quantum trajectory effect. That is, a postselection of a jumpless trajectory allows a transfer even for a non-Markovian environment where no quantum jumps have taken place. Beyond single quantum trajectories and postselection, such an effect can be observed by histogramming the ratio of quantum jumps at different times along several realizations. By investigating the effect of the temperature in the presence of unbalanced local dissipation, we demonstrate that, if appropriately switched on and off, the collective dissipator can act as a Maxwell's demon. These effects are a generalized measure equivalent to the standard projective measure description of quantum teleportation and Maxwell's demon. They can be witnessed in state-of-the-art setups given the extreme experimental control in, e.g., superconducting qubits, Rydberg atoms, and nitrogen-vacancy (NV) centers.
Face Detection to Recognize Students’ Emotion and Their Engagement: A Systematic Review

Following the Coronavirus COVID pandemic 19, many countries have adopted distance education to ensure pedagogical continuity for which they have not been ready yet to deal with. So, to reduce the spread of the virus, since March 2020, Morocco has called for state health emergency and imposed strict confinement on the population. In response to this, all training establishments have been locked down. In such a context of crisis and disability, teachers, students, and their families have found themselves in a delicate situation to manage, where new distance learning needs and practical training have been imposed without adequate measures for conducting an education as an integral part of a normative academic curriculum. In this article we will discuss the teachers' inability to asses the students' emotional state and determine their engagement which can be clearly read from their faces in normal teaching situation. It aims at analyzing and responding to problems related to the detection of faces to automatically deduce emotions and study student engagement while exploring and analyzing the references available on the some databases cited in the article. This literature review is supplemented by thematic analysis and a meta-analysis of the corpus. This study has identified both quantitative and qualitative and/or experimental work.
Computer Vision applied to improve interaction and communication of people with motor disabilities: A systematic mapping

BACKGROUND: The use of computers as a communication tool by people with disabilities can serve as an alternative effective to promote social interactions and the more inclusive and active participation of people in society. OBJECTIVE: This paper presents a systematic mapping of the literature that provides a survey of scientific contributions where Computer Vision is applied to enable users with motor and speech impairments to access computers easily, allowing them to exert their communicative abilities. METHODS: The mapping was conducted employing searches that identified 221 potentially eligible scientific articles published between 2009 and 2019, indexed by ACM, IEEE, Science Direct, and Springer databases. RESULTS: From the retrieved papers, 33 were selected and categorized into themes of this research interest: Human-Computer Interaction, Human-Machine Interaction, Human-Robot Interaction, Recreation, and surveys. Most of the chosen studies use sets of predefined gestures, low-cost cameras, and tracking a specific body region for gestural interaction. CONCLUSION: The results offer an overview of the Computer Vision techniques used in applied research on Assistive Technology for people with motor and speech disabilities, pointing out opportunities and challenges in this research domain.
Fractional Superlets

The Continuous Wavelet Transform (CWT) provides a multi-resolution representation of a signal by scaling a mother wavelet and convolving it with the signal. The scalogram (squared modulus of the CWT) then represents the spread of the signal's energy as a function of time and scale. The scalogram has constant relative temporal resolution but, as the scale is compressed (frequency increased), it loses frequency resolution. To compensate for this, the recently-introduced superlets geometrically combine a set of wavelets with increasing frequency resolution to achieve time-frequency super-resolution. The number of wavelets in the set is called the order of the superlet and was initially defined as an integer number. This creates a series of issues when adaptive superlets are implemented, i.e. superlets whose order depends on frequency. In particular, adaptive superlets generate representations that suffer from "banding" because the order is adjusted in discrete steps as the frequency increases. Here, by relying on the weighted geometric mean, we introduce fractional superlets, which allow the order to be a fractional number. We show that fractional adaptive superlets provide high-resolution representations that are smooth across the entire spectrum and are clearly superior to representations based on the discrete adaptive superlets.
Iterative multi‐scale residual network for deblurring

In dynamic scene deblurring, recent neural network–based methods have been very successful. But with the improvement of deep deblurring performance, network structure and learning become more complicated. Compared with large-scale network parameters and complex network structures, an iterative multi-scale residual network to achieve a more effective parameter sharing scheme is proposed. In each iterative unit, fast multi-scale residual blocks to replace superimposed convolutional layers or classic residual blocks are used. On the basis of preventing model overfitting, the receptive field of the network is increased. At the same time, the gated recurrent unit is introduced to connect modules of different stages. The model does not rely on the estimation of the blur kernel and directly generates sharp images in an end-to-end manner. The experimental structure on the benchmark dataset and real-world images showed that this method has better quality than the existing methods in terms of large-scale blur and subjective perception effects, both in quantitative and qualitative terms.