Reviewerly/exHarmony huggingface dataset:
LeuFeatx: Deep learning–based feature extractor for the diagnosis of acute leukemia from microscopic images of peripheral blood smear

The abnormal growth of leukocytes causes hematologic malignancies such as leukemia. The clinical assessment methods for the diagnosis of the disease are labor-intensive and time-consuming. Image-based automated diagnostic systems can be of great help in the decision-making process for leukemia detection. A feature-dependent, intrinsic, reliable classifier is a critical component in building such a diagnostic system. However, the identification of vital and relevant features is a challenging task in the classification workflow. The proposed work presents a novel two-step methodology for the robust classification of leukocytes for leukemia diagnosis by building a VGG16-adapted fine-tuned feature-extractor model, termed as "LeuFeatx," which plays a critical role in the accurate classification of leukocytes. LeuFeatx was found to be capable of extracting notable leukocyte features using microscopic single-cell leukocyte images. The filters and learned features are visualized and compared with base VGG16 model features. Independent classification experiments using three public benchmark leukocyte datasets were conducted to assess the effectiveness of extracted features with the proposed LeuFeatx model. Multiclass classifiers trained using LeuFeatx deep features achieved higher precision and sensitivity for seven leukocyte subtypes compared to the latest research on the AML Morphological dataset, and it achieved higher sensitivity for all cell types vis-à-vis recent work on peripheral blood cells dataset from the Hospital Clinic of Barcelona. In a binary classification experiment using the ALL_IDB2 dataset, classifiers trained using LeuFeatx deep features achieved an accuracy of 96.15%, which is better than the other state-of-the-art methods reported in the literature. Thus, the higher performance of the classifiers across observed comparison metrics establishes the relevance of the extracted features and the overall robustness of the proposed model.
Blockchain Convergence: Analysis of Issues Affecting IoT, AI and Blockchain

The purpose of this study is to appraise the integration or convergence issues influencing the mutual functioning of blockchain, AI, and IoT. The study argued that the recent developments in the field of IoT and blockchain prediction have involved the integration of innumerable classification schemes to establish a hybrid model. The introduction of the hybrid technique relies on the prediction performance that strives to override the limitations of any available architectural scheme. This study offers a comprehensive exploratory appraisal of the issues influencing the successful integration of IoT and blockchain in regards to functionality and effectiveness of security, trust, and flawless communication issues. The exploratory research methodology was used in analyzing the issues affecting the integration of blockchain, artificial intelligence (AI), and the internet of things (IoT). The findings indicated that the integration challenges influencing the effective operations of blockchain, AI, and IoT as a single system involve security, scalability, accountability, and trust of communications. The study recommends that successful and effective integration will enhance the development of new business models as well as the digital transformation of market corporations. Accordingly, new approaches to convergence should ensure that executives address the new technology demands to obtain significant gains in efficiency.
Coherent control of a multi-qubit dark state in waveguide quantum electrodynamics

Superconducting qubits in a waveguide have long-range interactions mediated by photons that cause the emergence of collective states. Destructive interference between the qubits decouples the collective dark states from the waveguide environment. Their inability to emit photons into the waveguide render dark states a valuable resource for preparing long-lived quantum many-body states and realizing quantum information protocols in open quantum systems. However, they also decouple from fields that drive the waveguide, making manipulation a challenge. Here we show the coherent control of a collective dark state that is realized by controlling the interactions between four superconducting transmon qubits and local drives. The dark state's protection against decoherence results in decay times that exceed those of the waveguide-limited single qubits by more than two orders of magnitude. Moreover, we perform a phase-sensitive spectroscopy of the two-excitation manifold and reveal bosonic many-body statistics in the transmon array. Our dark-state qubit provides a starting point for implementing quantum information protocols with collective states. Dark states of quantum systems do not absorb or emit light, removing a major source of decoherence. Four superconducting qubits in a waveguide can be combined to make a coherently controlled dark-state qubit with a long lifetime.
The mediating role of operational Flexibility on the relationship between quality of health information technology and management capability

The purpose of this study is to investigate the mediating role of operational flexibility between the quality of health information technology and management capability. A cross-sectional study was conducted; data was obtained based on 365 medical staff (medical doctors, nurses, and medical technologists) from public hospitals in the northern region of Jordan; a covenant sample was used. The authors used structural equation modeling to verify the hypotheses. The results reveal that the quality of health information technology has a significant impact on management capability and operational flexibility. Operational flexibility as a partial mediating variable also showed a significant impact on management capability. The study suggests that increasing the quality of health information technology will enhance the management capability and operational flexibility in healthcare organizations and will increase employee loyalty as a vehicle to trigger positive work-related attitudes. This study is one of the very limited studies in the Middle East that examined the role of operational flexibility in the quality of health information technology and employee management capability.
Photon Blockade in Weakly Driven Cavity Quantum Electrodynamics Systems with Many Emitters

We use the scattering matrix formalism to analyze photon blockade in coherently driven cavity quantum electrodynamics systems with a weak drive. By approximating the weak coherent drive by an input single- and two-photon Fock state, we reduce the computational complexity of the transmission and the two-photon correlation function from exponential to polynomial in the number of emitters. This enables us to easily analyze cavity-based systems containing ∼50 quantum emitters with modest computational resources. Using this approach we study the coherence statistics of photon blockade while increasing the number of emitters for resonant and detuned multiemitter cavity quantum electrodynamics systems-we find that increasing the number of emitters worsens photon blockade in resonant systems, and improves it in detuned systems. We also analyze the impact of inhomogeneous broadening in the emitter frequencies on the photon blockade through this system.
Gender-based eye movement differences in passive indoor picture viewing: An eye-tracking study

Male and female brains have different structures, which can make genders produce various eye- movement patterns. This study presents the results of an eye tracking experiment in which we analyzed the eye movements of 25 male and 20 female participants during passive indoor picture viewing. We examined eye-movement parameters, namely fixation duration, scan path length, number of saccades, spatial density, saccade amplitude, and the ratio of total fixation duration to total saccade duration so as to investigate gender differences in eye-movement patterns while indoor picture viewing. We found significant differences in eye-movement patterns between genders. The results of eye-movement analysis also indicated that females showed more explorative gaze behavior, indicated by larger saccade amplitudes, and by longer scan paths. Furthermore, owing to shorter ratio of fixation durations to saccade duration in females as compared to male, we speculate that females inspect the images faster than males. In addition, we classified the genders into two subgroups—males and females—based on their eye-movement parameters by using a support vector machine classifier achieving an accuracy of 70%. We have come to the result males and females – with same culture – see the environment differently. Our findings have profound implications for researches employing gaze-based models.
Cell Nuclei Segmentation in Cytological Images Using Convolutional Neural Network and Seeded Watershed Algorithm

Morphometric analysis of nuclei is crucial in cytological examinations. Unfortunately, nuclei segmentation presents many challenges because they usually create complex clusters in cytological samples. To deal with this problem, we are proposing an approach, which combines convolutional neural network and watershed transform to segment nuclei in cytological images of breast cancer. The method initially is preprocessing images using color deconvolution to highlight hematoxylin-stained objects (nuclei). Next, convolutional neural network is applied to perform semantic segmentation of preprocessed image. It finds nuclei areas, cytoplasm areas, edges of nuclei, and background. All connected components in the binary mask of nuclei are treated as potential nuclei. However, some objects actually are clusters of overlapping nuclei. They are detected by their outlying values of morphometric features. Then an attempt is made to separate them using the seeded watershed segmentation. If the attempt is successful, they are included in the nuclei set. The accuracy of this approach is evaluated with the help of referenced, manually segmented images. The degree of matching between reference nuclei and discovered objects is measured with the help of Jaccard distance and Hausdorff distance. As part of the study, we verified how the use of a convolutional neural network instead of the intensity thresholding to generate a topographical map for the watershed improves segmentation outcomes. Our results show that convolutional neural network outperforms Otsu thresholding and adaptive thresholding in most cases, especially in scenarios with many overlapping nuclei.
Analyzing the Limitations of Cross-lingual Word Embedding Mappings

Recent research in cross-lingual word embeddings has almost exclusively focused on offline methods, which independently train word embeddings in different languages and map them to a shared space through linear transformations.While several authors have questioned the underlying isomorphism assumption, which states that word embeddings in different languages have approximately the same structure, it is not clear whether this is an inherent limitation of mapping approaches or a more general issue when learning crosslingual embeddings.So as to answer this question, we experiment with parallel corpora, which allows us to compare offline mapping to an extension of skip-gram that jointly learns both embedding spaces.We observe that, under these ideal conditions, joint learning yields to more isomorphic embeddings, is less sensitive to hubness, and obtains stronger results in bilingual lexicon induction.We thus conclude that current mapping methods do have strong limitations, calling for further research to jointly learn cross-lingual embeddings with a weaker cross-lingual signal.
Chebyshev polynomials, moment matching, and optimal estimation of the unseen

We consider the problem of estimating the support size of a discrete distribution whose minimum nonzero mass is at least 1 k .Under the independent sampling model, we show that the sample complexity, that is, the minimal sample size to achieve an additive error of εk with probability at least 0.1 is within universal constant factors of k log k log 2 1 ε , which improves the stateof-the-art result of k ε 2 log k in [In Advances in Neural Information Processing Systems (2013) 2157-2165].Similar characterization of the minimax risk is also obtained.Our procedure is a linear estimator based on the Chebyshev polynomial and its approximation-theoretic properties, which can be evaluated in O(n + log 2 k) time and attains the sample complexity within constant factors.The superiority of the proposed estimator in terms of accuracy, computational efficiency and scalability is demonstrated in a variety of synthetic and real datasets.
Resisting Adversarial Attacks Using Gaussian Mixture Variational Autoencoders

Susceptibility of deep neural networks to adversarial attacks poses a major theoretical and practical challenge. All efforts to harden classifiers against such attacks have seen limited success till now. Two distinct categories of samples against which deep neural networks are vulnerable, “adversarial samples” and “fooling samples”, have been tackled separately so far due to the difficulty posed when considered together. In this work, we show how one can defend against them both under a unified framework. Our model has the form of a variational autoencoder with a Gaussian mixture prior on the latent variable, such that each mixture component corresponds to a single class. We show how selective classification can be performed using this model, thereby causing the adversarial objective to entail a conflict. The proposed method leads to the rejection of adversarial samples instead of misclassification, while maintaining high precision and recall on test data. It also inherently provides a way of learning a selective classifier in a semi-supervised scenario, which can similarly resist adversarial attacks. We further show how one can reclassify the detected adversarial samples by iterative optimization.1
Nikolov-Radivchev at SemEval-2019 Task 6: Offensive Tweet Classification with BERT and Ensembles

This paper examines different approaches and models towards offensive tweet classification which were used as a part of the OffensEval 2019 competition.It reviews Tweet preprocessing, techniques for overcoming unbalanced class distribution in the provided test data, and comparison of multiple attempted machine learning models.
Supervised and Unsupervised Neural Approaches to Text Readability

Abstract We present a set of novel neural supervised and unsupervised approaches for determining the readability of documents. In the unsupervised setting, we leverage neural language models, whereas in the supervised setting, three different neural classification architectures are tested. We show that the proposed neural unsupervised approach is robust, transferable across languages, and allows adaptation to a specific readability task and data set. By systematic comparison of several neural architectures on a number of benchmark and new labeled readability data sets in two languages, this study also offers a comprehensive analysis of different neural approaches to readability classification. We expose their strengths and weaknesses, compare their performance to current state-of-the-art classification approaches to readability, which in most cases still rely on extensive feature engineering, and propose possibilities for improvements.
Artificial Intelligence Implementations on the Blockchain. Use Cases and Future Applications

An exemplary paradigm of how an AI can be a disruptive technological paragon via the utilization of blockchain comes straight from the world of deep learning. Data scientists have long struggled to maintain the quality of a dataset for machine learning by an AI entity. Datasets can be very expensive to purchase, as, depending on both the proper selection of the elements and the homogeneity of the data contained within, constructing and maintaining the integrity of a dataset is difficult. Blockchain as a highly secure storage medium presents a technological quantum leap in maintaining data integrity. Furthermore, blockchain’s immutability constructs a fruitful environment for creating high quality, permanent and growing datasets for deep learning. The combination of AI and blockchain could impact fields like Internet of things (IoT), identity, financial markets, civil governance, smart cities, small communities, supply chains, personalized medicine and other fields, and thereby deliver benefits to many people.
Immersive Analytics: Theory and Research Agenda

Advances in a variety of computing fields, including "big data", machine learning, visualization, and augmented/mixed/virtual reality, have combined to give rise to the emerging field of immersive analytics, which investigates how these new technologies support analysis and decision making. Thus far, we feel that immersive analytics research has been somewhat ad hoc, possibly owing to the fact that there is not yet an organizing framework for immersive analytics research. In this paper, we address this lack by proposing a definition for immersive analytics, and identifying some general research areas and specific research questions that we feel will be important for the development of this field. We also present three case studies that, while all being examples of what we would consider immersive analytics, present different challenges and opportunities. These serve to demonstrate the breadth of immersive analytics and illustrate how the framework proposed in this paper applies to practical research.
Unbabel’s Participation in the WMT19 Translation Quality Estimation Shared Task

We present the contribution of the Unbabel team to the WMT 2019 Shared Task on Quality Estimation.We participated on the word, sentence, and document-level tracks, encompassing 3 language pairs: English-German, English-Russian, and English-French.Our submissions build upon the recent OpenKiwi framework: we combine linear, neural, and predictor-estimator systems with new transfer learning approaches using BERT and XLM pre-trained models.We compare systems individually and propose new ensemble techniques for word and sentence-level predictions.We also propose a simple technique for converting word labels into document-level predictions.Overall, our submitted systems achieve the best results on all tracks and language pairs by a considerable margin.
Adversarial Video Generation on Complex Datasets

Generative models of natural images have progressed towards high fidelity samples by the strong leveraging of scale. We attempt to carry this success to the field of video modeling by showing that large Generative Adversarial Networks trained on the complex Kinetics-600 dataset are able to produce video samples of substantially higher complexity and fidelity than previous work. Our proposed model, Dual Video Discriminator GAN (DVD-GAN), scales to longer and higher resolution videos by leveraging a computationally efficient decomposition of its discriminator. We evaluate on the related tasks of video synthesis and video prediction, and achieve new state-of-the-art Fréchet Inception Distance for prediction for Kinetics-600, as well as state-of-the-art Inception Score for synthesis on the UCF-101 dataset, alongside establishing a strong baseline for synthesis on Kinetics-600.
ImaGINator: Conditional Spatio-Temporal GAN for Video Generation

Generating human videos based on single images entails the challenging simultaneous generation of realistic and visual appealing appearance and motion. In this context, we propose a novel conditional GAN architecture, namely ImaGINator, which given a single image, a condition (label of a facial expression or action) and noise, decomposes appearance and motion in both latent and high level feature spaces, generating realistic videos. This is achieved by (i) a novel spatio-temporal fusion scheme, which generates dynamic motion, while retaining appearance throughout the full video sequence by transmitting appearance (originating from the single image) through all layers of the network. In addition, we propose (ii) a novel transposed (1+2)D convolution, factorizing the transposed 3D convolutional filters into separate transposed temporal and spatial components, which yields significantly gains in video quality and speed. We extensively evaluate our approach on the facial expression datasets MUG and UvA-NEMO, as well as on the action datasets NATOPS and Weizmann. We show that our approach achieves significantly better quantitative and qualitative results than the state-of-the-art. The source code and models are available under https://github.com/wyhsirius/ImaGINator.
Your Classifier is Secretly an Energy Based Model and You Should Treat it Like One

We propose to reinterpret a standard discriminative classifier of p(y|x) as an energy based model for the joint distribution p(x,y). In this setting, the standard class probabilities can be easily computed as well as unnormalized values of p(x) and p(x|y). Within this framework, standard discriminative architectures may beused and the model can also be trained on unlabeled data. We demonstrate that energy based training of the joint distribution improves calibration, robustness, andout-of-distribution detection while also enabling our models to generate samplesrivaling the quality of recent GAN approaches. We improve upon recently proposed techniques for scaling up the training of energy based models and presentan approach which adds little overhead compared to standard classification training. Our approach is the first to achieve performance rivaling the state-of-the-artin both generative and discriminative learning within one hybrid model.
A Signal Propagation Perspective for Pruning Neural Networks at Initialization

Network pruning is a promising avenue for compressing deep neural networks. A typical approach to pruning starts by training a model and removing redundant parameters while minimizing the impact on what is learned. Alternatively, a recent approach shows that pruning can be done at initialization prior to training, based on a pruning criterion called connection sensitivity. However, it remains unclear exactly why pruning an untrained, randomly initialized neural network is effective. In this work, by noting connection sensitivity as a form of gradients, we formally characterize initialization conditions to ensure reliable connection sensitivity measurements, which in turn yields effective pruning results. Moreover, we analyze the signal propagation properties of the resulting pruned networks and introduce a simple, data-free method to improve their trainability. Our modifications to the existing pruning at initialization method lead to improved results on all tested network models for image classification tasks. Furthermore, we empirically study the effect of supervision for pruning and demonstrate that our signal propagation perspective, combined with unsupervised pruning, can be useful in various scenarios where pruning is applied to non-standard arbitrarily-designed architectures.