Reviewerly/exHarmony huggingface dataset:
The Impact of Strategic Leadership on Crisis Management

The crisis is an unusual and stressful event. It is highlighted by the strategic gap due to the lack of adaptation to the changes that occur in the organization's environment. This study aims to identify the reality of strategic leadership represented by (investing strategic capabilities, defining strategic direction, improving and developing human capital, strengthening organizational culture with ethical practices, implementing balanced regulatory supervision) and crisis management represented by (detection of warning signals, preparedness and prevention, containing and limiting damages, restoration of activity, and learning) at OOREDOO Q.S.C. (Ooredoo), Qatar - Company, in addition to highlighting the role of strategic leadership in Crisis management in the Company. The study found that there is a statistically significant effect of Strategic leadership practices of all dimensions on Crisis management. It recommended that OOREDOO Q.S.C. (Ooredoo), Qatar - Company focus more on studying and analyzing its internal environment to identify the strengths and weaknesses in it and identify possible indicators of the occurrence of the crisis, and thus help it to enhance its strengths and take appropriate solutions to remedy its weaknesses, along with taking the necessary measures to prevent crises from occurring and reducing their impacts and its negative repercussions.
Generating Behavior-Diverse Game AIs with Evolutionary Multi-Objective Deep Reinforcement Learning

Generating diverse behaviors for game artificial intelligence (Game AI) has been long recognized as a challenging task in the game industry. Designing a Game AI with a satisfying behavioral characteristic (style) heavily depends on the domain knowledge and is hard to achieve manually. Deep reinforcement learning sheds light on advancing the automatic Game AI design. However, most of them focus on creating a superhuman Game AI, ignoring the importance of behavioral diversity in games. To bridge the gap, we introduce a new framework, named EMOGI, which can automatically generate desirable styles with almost no domain knowledge. More importantly, EMOGI succeeds in creating a range of diverse styles, providing behavior-diverse Game AIs. Evaluations on the Atari and real commercial games indicate that, compared to existing algorithms, EMOGI performs better in generating diverse behaviors and significantly improves the efficiency of Game AI design.
Gauge principle and gauge invariance in two-level systems

The quantum Rabi model is a widespread description of the coupling between a two-level system and a quantized single mode of an electromagnetic resonator. Issues about this model's gauge invariance have been raised. These issues become evident when the light-matter interaction reaches the so-called ultrastrong coupling regime. Recently, a modified quantum Rabi model able to provide gauge-invariant physical results (e.g., energy levels, expectation values of observables, quantum probabilities) in any interaction regime was introduced [O. Di Stefano, A. Settineri, V. Macr\`{\i}, L. Garziano, R. Stassi, S. Savasta, and F. Nori, Nat. Phys. 15, 803 (2019)]. Here we provide an alternative derivation of this result, based on the implementation in two-state systems of the gauge principle, which is the principle from which all the fundamental interactions in quantum field theory are derived. The adopted procedure can be regarded as the two-site version of the general method used to implement the gauge principle in lattice gauge theories. Applying this method, we also obtain the gauge-invariant quantum Rabi model for asymmetric two-state systems, and the multimode gauge-invariant quantum Rabi model beyond the dipole approximation.
SOS: Selective Objective Switch for Rapid Immunofluorescence Whole Slide Image Classification

The difficulty of processing gigapixel whole slide images (WSIs) in clinical microscopy has been a long-standing barrier to implementing computer aided diagnostic systems.Since modern computing resources are unable to perform computations at this extremely large scale, current state of the art methods utilize patch-based processing to preserve the resolution of WSIs.However, these methods are often resource intensive and make significant compromises on processing time.In this paper, we demonstrate that conventional patch-based processing is redundant for certain WSI classification tasks where high resolution is only required in a minority of cases.This reflects what is observed in clinical practice; where a pathologist may screen slides using a low power objective and only switch to a high power in cases where they are uncertain about their findings.To eliminate these redundancies, we propose a method for the selective use of high resolution processing based on the confidence of predictions on downscaled WSIs -we call this the Selective Objective Switch (SOS).Our method is validated on a novel dataset of 684 Liver-Kidney-Stomach immunofluorescence WSIs routinely used in the investigation of autoimmune liver disease.By limiting high resolution processing to cases which cannot be classified confidently at low resolution, we maintain the accuracy of patch-level analysis whilst reducing the inference time by a factor of 7.74.
Postdigital Living and Algorithms of Desire

This article-commentary introduces the concept of living. It reflects on the power of the media, collectively speaking across various genres whose boundaries are ever more blurred, and shifts in general media machinery in relation to algorithms. Media (news, film, social media, advertising) hold a powerful grip over human life, influencing its various aspects such as(un)democratic decision-making ), adolescents’life decisions, and adoption of worldviews that put entire groups of people under the banner of mortal enemies. Many people are increasingly‘relying on their devices during this pandemic to inform and distract more than ever before. In our postdigital times of blurred boundaries between virtual and real, we also experience an increased blurring of boundaries between‘natural’and‘artificial’forms of life. In 2003, Steve Fuller and Bruno Latour debated whether‘a strong distinction between humans and non-humans is no longer required for research purposes. Two decades later, their debate has become‘embodied’in various artificial intelligences. Algorithmic online celebrities, such as Miquela Sousa aka Lil Miquela with over 2 million followers on Instagram, are examples of uncanny postdigital developments, targeting children and teen audiences.
Training (Overparametrized) Neural Networks in Near-Linear Time

The slow convergence rate and pathological curvature issues of first-order gradient methods for training deep neural networks, initiated an ongoing effort for developing faster $\mathit{second}$-$\mathit{order}$ optimization algorithms beyond SGD, without compromising the generalization error. Despite their remarkable convergence rate ($\mathit{independent}$ of the training batch size $n$), second-order algorithms incur a daunting slowdown in the $\mathit{cost}$ $\mathit{per}$ $\mathit{iteration}$ (inverting the Hessian matrix of the loss function), which renders them impractical. Very recently, this computational overhead was mitigated by the works of [ZMG19,CGH+19}, yielding an $O(mn^2)$-time second-order algorithm for training two-layer overparametrized neural networks of polynomial width $m$. We show how to speed up the algorithm of [CGH+19], achieving an $\tilde{O}(mn)$-time backpropagation algorithm for training (mildly overparametrized) ReLU networks, which is near-linear in the dimension ($mn$) of the full gradient (Jacobian) matrix. The centerpiece of our algorithm is to reformulate the Gauss-Newton iteration as an $\ell_2$-regression problem, and then use a Fast-JL type dimension reduction to $\mathit{precondition}$ the underlying Gram matrix in time independent of $M$, allowing to find a sufficiently good approximate solution via $\mathit{first}$-$\mathit{order}$ conjugate gradient. Our result provides a proof-of-concept that advanced machinery from randomized linear algebra -- which led to recent breakthroughs in $\mathit{convex}$ $\mathit{optimization}$ (ERM, LPs, Regression) -- can be carried over to the realm of deep learning as well.
Frustratingly Simple Domain Generalization via Image Stylization

Convolutional Neural Networks (CNNs) show impressive performance in the standard classification setting where training and testing data are drawn i.i.d. from a given domain. However, CNNs do not readily generalize to new domains with different statistics, a setting that is simple for humans. In this work, we address the Domain Generalization problem, where the classifier must generalize to an unknown target domain. Inspired by recent works that have shown a difference in biases between CNNs and humans, we demonstrate an extremely simple yet effective method, namely correcting this bias by augmenting the dataset with stylized images. In contrast with existing stylization works, which use external data sources such as art, we further introduce a method that is entirely in-domain using no such extra sources of data. We provide a detailed analysis as to the mechanism by which the method works, verifying our claim that it changes the shape/texture bias, and demonstrate results surpassing or comparable to the state of the arts that utilize much more complex methods.
Image noise reduction by denoising autoencoder

Neural networks are used in many tasks today. One of them is the images processing. Autoencoder is very popular neural networks for such problems. Denoising autoencoder is an important autoencoder because some tasks we need a preprocessed image to get less noisy result. This research describes ways to analyze noisy images produced by a physically-based render engine and how to reduce that noise. The results showed that the algorithms are logarithmic.
One-Size-Fits-All Multilingual Models

This paper presents DeepSPIN's submissions to Tasks 0 and 1 of the SIGMORPHON 2020 Shared Task.For both tasks, we present multilingual models, training jointly on data in all languages.We perform no languagespecific hyperparameter tuning -each of our submissions uses the same model for all languages.Our basic architecture is the sparse sequence-to-sequence model with entmax attention and loss, which allows our models to learn sparse, local alignments while still being trainable with gradient-based techniques.For Task 1, we achieve strong performance with both RNN-and transformer-based sparse models.For Task 0, we extend our RNN-based model to a multi-encoder set-up in which separate modules encode the lemma and inflection sequences.Despite our models' lack of language-specific tuning, they tie for first in Task 0 and place third in Task 1.
An english language learning study with rural chinese children using an augmented reality app

Augmented reality (AR) apps have the potential to support early English learning for children. However, few studies have investigated how children from rural low socio-economic status (SES) schools, who learn English as a foreign language (EFL) used and perceived an AR app in language learning. In this paper, we present an exploratory case study of 11 EFL children and four school teachers from a Chinese rural county who used an AR app (called AR PhonoBlocks), for one week. The goal of the app is to support children to learn the alphabetic principle of English. The key features are overlaid dynamic colour cues on 3D physical letters. We present the results including themes related to children's interactional behaviours and motivations, and rural teachers' feedback on the opportunities and concerns around using an AR app in a rural school context. We suggest design implications and future research directions for designing AR apps to support EFL children from low SES schools in early English learning.
Deep Knowledge Tracing with Transformers

In this work, we propose a Transformer-based model to trace students’ knowledge acquisition. We modified the Transformer structure to utilize 1) the association between questions and skills and 2) the elapsed time between question steps. The use of question-skill associations allows the model to learn specific representation for frequently encountered questions while representing rare questions with their underline skill representations. The inclusion of elapsed time opens the opportunity to address forgetting. Our approach outperforms the state-of-the-art methods in the literature by roughly 10% in AUC with frequently used public datasets.
Can co-creation in extended reality technologies facilitate the design process?

Purpose The purpose of this paper is to explore the benefits of co-creation/co-design using extended reality (XR) technologies during the initial stages of the design process. A review of the emerging co-creation tools within XR will be examined along with whether they offer the potential to improve the design process; this will also highlight the gaps on where further research is required. Design/methodology/approach The paper draws on professional and academic experiences of the authors in creative practices within the realm of XR technology, co-creation and co-design. In addition, a review of the current literature on emerging technologies and work-based learning will offer further insight on the themes covered. Findings To design, collaborate, iterate and amend with colleagues and peers in a virtual space gives a wide range of obvious benefits. Creative practitioners both in education and employment are working more collaboratively with the advancement of technology. However, there is a need to find a space where collaboration can also offer the opportunity for co-creation that improves the initial stages of the design process. This technology also offers solutions on the constraints of distance and ameliorates creative expression. Research limitations/implications There is an opportunity to test the ideas expressed in this paper empirically; this can be done through testing co-creation tools with professionals, work-based learners and students. Originality/value The paper will add to the existing literature on emerging technologies as a unique environment to improve co-create/co-design the visuals created during the fuzzy front end of the design process and offer a potential framework for future empirical work.
Super-resolution of SDO/HMI Magnetograms Using Novel Deep Learning Methods

Abstract Image super-resolution is a technique of enhancing the resolution of an image where a high-resolution (HR) image is reconstructed from a low-resolution (LR) image. In this Letter, we apply two novel deep learning models (residual attention model and progressive GAN model) for enhancing Solar Dynamics Observatory (SDO)/Helioseismic and Magnetic Imager (HMI) magnetograms. For this, we consider line-of-sight (LOS) magnetograms taken by SDO/HMI as output and their degraded ones with 4 × 4 binning as input. Deep learning networks try to find internal relationships between LR and HR images from the given input and the corresponding output image. We consider SDO/HMI magnetograms from 2014 May to August for training, from 2014 October to December for validation, and 2015 January to March for test. We find that the deep learning models generate higher-quality results than the bicubic interpolation in terms of visual aspects and metrics. We apply this model to a full-resolution SDO/HMI magnetogram and then compare the generated magnetogram with the corresponding Hinode/The Solar Optical Telescope Narrowband Filtergrams (NFI) magnetogram. This comparison shows that the generated magnetogram is consistent with the Hinode one with a high correlation (CC: 0.94) and a high similarity (SSIM: 0.93), which are better than the bicubic method.
Synthesis of Super-Optimized Smart Contracts Using Max-SMT

With the advent of smart contracts that execute on the blockchain ecosystem, a new mode of reasoning is required for developers that must pay meticulous attention to the gas spent by their smart contracts, as well as for optimization tools that must be capable of effectively reducing the gas required by the smart contracts. Super-optimization is a technique which attempts to find the best translation of a block of code by trying all possible sequences of instructions that produce the same result. This paper presents a novel approach for super-optimization of smart contracts based on Max-SMT which is split into two main phases: (i) the extraction of a stack functional specification from the basic blocks of the smart contract, which is simplified using rules that capture the semantics of the arithmetic, bit-wise, relational operations, etc. (ii) the synthesis of optimized blocks which, by means of an efficient Max-SMT encoding, finds the bytecode blocks with minimal gas cost whose stack functional specification is equal (modulo commutativity) to the extracted one. Our experimental results are very promising: we are able to optimize 55.41 % of the blocks, and prove that 34.28 % were already optimal, for more than 61000 blocks from the most called 2500 Ethereum contracts.
Multiphoton pulses interacting with multiple emitters in a one-dimensional waveguide

We derive a generalized master equation for multiphoton pulses interacting with multiple emitters in a waveguide-quantum electrodynamics system where the emitter frequency can be modulated and the effects of nonguided modes can also be considered. Based on this theory, we can calculate the real-time dynamics of an array of interacting emitters driven by an incident photon pulse which can be vacuum, a coherent state, a Fock state, or their superpositions. Moreover, we also derive generalized input-output relations to calculate the reflectivity and transmissivity of this system. We can also calculate the output photon pulse shapes. Our theory can find important applications in the study of waveguide-based quantum systems.
Chakuri-Bazaar: A Mobile Application for Illiterate and Semi-Literate People for Searching Employment

The purpose of this paper is to explore the design principles to develop mobile applications for illiterate and semi-literate people and to design, develop, and evaluate a mobile application for illiterate and semi-literate people in Bangladesh using the revealed design principles and following a design science research approach. The authors first conducted a requirement elicitation study to reveal a set of design principals to make the user interface (UI) intuitive for illiterate and semi-literate people. Then, a mobile application (Chakuri-Bazaar) was developed following these design principals. Finally, the application was evaluated with 40 illiterate and semi-literate people through a field study. As outcome, a set of design principles was revealed for designing usable mobile application for illiterate and semi-literate people. The findings of the evaluation study suggest that the application was effective, efficient, and the users were satisfied in terms of its ease of use, ease of learning, willingness to use it in future, and willingness to recommend it to others.
Augmentation adversarial training for unsupervised speaker recognition.

The goal of this work is to train robust speaker recognition models without speaker labels. Recent works on unsupervised speaker representations are based on contrastive learning in which they encourage within-utterance embeddings to be similar and across-utterance embeddings to be dissimilar. However, since the within-utterance segments share the same acoustic characteristics, it is difficult to separate the speaker information from the channel information. To this end, we propose augmentation adversarial training strategy that trains the network to be discriminative for the speaker information, while invariant to the augmentation applied. Since the augmentation simulates the acoustic characteristics, training the network to be invariant to augmentation also encourages the network to be invariant to the channel information in general. Extensive experiments on the VoxCeleb and VOiCES datasets show significant improvements over previous works using self-supervision, and the performance of our self-supervised models far exceed that of humans.
Fusion Mechanisms for Human Activity Recognition Using Automated Machine Learning

Human activity recognition has been a branch of interest in the field of computer vision for decades, due to its numerous applications in different domains, such as medicine, surveillance, entertainment or human-computer interaction. We propose an intuitive, effective, quickly trainable and customizable system for recognizing human activities designed with an automated machine learning method based on Neural Architecture Search. Information from all channels of a 3D video (RGB and depth data, skeleton and context objects) is merged by independently passing these data streams through 2D convolutional neural networks. The outputs of all networks are combined in a summarizing array of class scores using fusion mechanisms that are not computationally intensive but reflect the meaningful information from a video. The proposed system is tested using three public datasets and a new dataset-PRECIS HAR-that was created in our laboratory. In all our experiments, the system is proven to be highly accurate: 98.43% on MSRDailyActivity3D, 91.41% on UTD-MHAD, 90.95% on NTU RGB+D, and 94.38% on our dataset.
Pruning multi-view stereo net for efficient 3D reconstruction

How can we perform an efficient 3D reconstruction with high accuracy and completeness, in the presence of non-Lambertian surface and low textured regions? This paper aims at fast quality 3D reconstruction, best near real time. While deep learning approaches perform very well in multi-view stereo (MVS), the high complexity of models makes them inapplicable in practical applications. Few works were explored to accelerate deep learning-based 3D reconstruction approaches. In this paper, we take an unprecedented attempt to compress and accelerate these models via pruning their redundant parameters. We introduce an efficient channel pruning method for 2D convolutional neural networks (CNNs) based on a mixed back propagation process, where a soft mask is learned to prune the channels using a fast iterative shrinkage-thresholding algorithm. While in 3D CNNs, we train a large multi-scale CNNs architecture and observe that only utilizing one module enough for the 3D reconstruction, which can still maintain the performance of the full-precision model. We achieve an efficient MVS reconstruction system up to 2 times faster, in contrast to the state-of-the-arts, while maintaining comparable model accuracy and even better completeness.