Reviewerly/exHarmony huggingface dataset:
Histopathological imaging database for oral cancer analysis

The repository is composed of 1224 images divided into two sets of images with two different resolutions. First set consists of 89 histopathological images with the normal epithelium of the oral cavity and 439 images of Oral Squamous Cell Carcinoma (OSCC) in 100x magnification. The second set consists of 201 images with the normal epithelium of the oral cavity and 495 histopathological images of OSCC in 400x magnification. The images were captured using a Leica ICC50 HD microscope from Hematoxyline and Eosin (H&E) stained tissue slides collected, prepared and catalogued by medical experts from 230 patients. A subset of 269 images from the second data set was used to detect OSCC based on textural features [1]. Histopathology plays a very important role in diagnosing a disease. It is the investigation of biological tissues to detect the presence of diseased cells in microscopic detail. It usually involves a biopsy. Till date biopsy is the gold-standard test to diagnose cancer. The biopsy slides are examined based on various cytological criteria under a microscope. Therefore, there is a high possibility of not retaining uniformity and ensuring reproducibility in outcomes [2, 3]. Computational diagnostic tools, on the other hand, facilitate objective judgments by making the use of the quantitative measure. This dataset can be utilized in establishing automated diagnostic tool using Artificial Intelligence approaches.
Target specific mining of COVID-19 scholarly articles using one-class approach

In recent years, several research articles have been published in the field of corona-virus caused diseases like severe acute respiratory syndrome (SARS), middle east respiratory syndrome (MERS) and COVID-19. In the presence of numerous research articles, extracting best-suited articles is time-consuming and manually impractical. The objective of this paper is to extract the activity and trends of corona-virus related research articles using machine learning approaches. The COVID-19 open research dataset (CORD-19) is used for experiments, whereas several target-tasks along with explanations are defined for classification, based on domain knowledge. Clustering techniques are used to create the different clusters of available articles, and later the task assignment is performed using parallel one-class support vector machines (OCSVMs). Experiments with original and reduced features validate the performance of the approach. It is evident that the k-means clustering algorithm, followed by parallel OCSVMs, outperforms other methods for both original and reduced feature space.
Industry 4.0 and its Implications for the Financial Sector

Rather than a necessity with technological development, digitalization has become an unavoidable requirement in all sectors. Spreading between the end of the 18th and the beginning of the 19th century the first industrial revolution was marked by mechanical production driven by hydraulic and steam engines, mass production based on the division of labor and driven by electrical energy, and the shift to automated production supported by computer technologies. Today the world is witnessing a new and deeper transformation based on virtualization and the interconnection of intelligent industrial objects called Industry 4.0. First uttered in 2013, this new era aims to define "the transition from a time when people worked with computers to a time when computers work without humans". Thus, the development of information technology, the widespread use of computers, the internet and mobile phones have had an impact on the financial sector like all other sectors, leading to the emergence of new companies, new financial instruments and products. Indeed, Fintech innovations, digital banking and Blockchain technologies can be counted among the examples. In this article we try to examine the impact of the digitalization process on the financial sector. In the same sense, business activities have changed with the emergence of Industry 4.0, the evolution of financial markets in Morocco and around the world, and new next generation technologies in the field of finance will be covered.
Boosting Decision-Based Black-Box Adversarial Attacks with Random Sign Flip

Decision-based black-box adversarial attacks (decision-based attack) pose a severe threat to current deep neural networks, as they only need the predicted label of the target model to craft adversarial examples. However, existing decision-based attacks perform poorly on the $$ l_\infty $$ setting and the required enormous queries cast a shadow over the practicality. In this paper, we show that just randomly flipping the signs of a small number of entries in adversarial perturbations can significantly boost the attack performance. We name this simple and highly efficient decision-based $$ l_\infty $$ attack as Sign Flip Attack. Extensive experiments on CIFAR-10 and ImageNet show that the proposed method outperforms existing decision-based attacks by large margins and can serve as a strong baseline to evaluate the robustness of defensive models. We further demonstrate the applicability of the proposed method on real-world systems.
Adaptive Federated Optimization

Federated learning is a distributed machine learning paradigm in which a large number of clients coordinate with a central server to learn a model without sharing their own training data. Standard federated optimization methods such as Federated Averaging (FedAvg) are often difficult to tune and exhibit unfavorable convergence behavior. In non-federated settings, adaptive optimization methods have had notable success in combating such issues. In this work, we propose federated versions of adaptive optimizers, including Adagrad, Adam, and Yogi, and analyze their convergence in the presence of heterogeneous data for general non-convex settings. Our results highlight the interplay between client heterogeneity and communication efficiency. We also perform extensive experiments on these methods and show that the use of adaptive optimizers can significantly improve the performance of federated learning.
PGNet: Real-time Arbitrarily-Shaped Text Spotting with Point Gathering Network

The reading of arbitrarily-shaped text has received increasing research attention. However, existing text spotters are mostly built on two-stage frameworks or character-based methods, which suffer from either Non-Maximum Suppression (NMS), Region-of-Interest (RoI) operations, or character-level annotations. In this paper, to address the above problems, we propose a novel fully convolutional Point Gathering Network (PGNet) for reading arbitrarily-shaped text in real-time. The PGNet is a single-shot text spotter, where the pixel-level character classification map is learned with proposed PG-CTC loss avoiding the usage of character-level annotations. With a PG-CTC decoder, we gather high-level character classification vectors from two-dimensional space and decode them into text symbols without NMS and RoI operations involved, which guarantees high efficiency. Additionally, reasoning the relations between each character and its neighbors, a graph refinement module (GRM) is proposed to optimize the coarse recognition and improve the end-to-end performance. Experiments prove that the proposed method achieves competitive accuracy, meanwhile significantly improving the running speed. In particular, in Total-Text, it runs at 46.7 FPS, surpassing the previous spotters with a large margin.
Ensuring data integrity of healthcare information in the era of digital health

Data integrity continues to be a persistent problem in the current healthcare sector. It ensures that the data is correct and has not even in any manner been improperly changed. Incorrect data might become significant health threats for patients and a big responsibility for clinicians, resulting in problems such as scam, misconduct, inadequate treatment and data theft. This sort of endangering scenario causes tremendous difficulty in handling healthcare data. This research intends to describe the threat plot of data integrity in healthcare through numerous attack statistics from around the world and Saudi Arabia and identify the criticality in Saudi Arabia in particular. A literature review by descriptive analysis, unit analysis and rating analysis to achieve the planned systematic literature review goal is outlined. The outcome of ranking analysis using a fuzzy analytical hierarchy process methodology offers a route for Saudi Arabian researchers to promote medical records or data security in Arabic healthcare. It is suggested that blockchain is the most prioritized method for regular use and adaptation across Saudi Arabia in all data integrity management techniques. To address the challenges of data integrity and future path, the authors critically examine the challenges posed by data integrity in the healthcare sector.
Improved Consistency Regularization for GANs

Recent work has increased the performance of Generative Adversarial Networks (GANs) by enforcing a consistency cost on the discriminator. We improve on this technique in several ways. We first show that consistency regularization can introduce artifacts into the GAN samples and explain how to fix this issue. We then propose several modifications to the consistency regularization procedure designed to improve its performance. We carry out extensive experiments quantifying the benefit of our improvements. For unconditional image synthesis on CIFAR-10 and CelebA, our modifications yield the best known FID scores on various GAN architectures. For conditional image synthesis on CIFAR-10, we improve the state-of-the-art FID score from 11.48 to 9.21. Finally, on ImageNet-2012, we apply our technique to the original BigGAN model and improve the FID from 6.66 to 5.38, which is the best score at that model size.
Visually asymmetric image encryption algorithm based on SHA-3 and compressive sensing by embedding encrypted image

This paper presents a new asymmetric image encryption and hiding algorithm based on SHA-3 and compressive sensing. First, to resist the chosen-plaintext attack (CPA) and the known-plaintext attack (KPA), SHA-3 is employed to calculate the hash values of the plain image, and they are transformed into the initial values of the chaotic map. Second, the plain image is divided into blocks which are processed by sparse transform, scrambling, compressive measurement, and merged. Then they are quantized and scrambled again to get an encrypted image. Third, the encrypted image is filled with zeros to the same size as the plain image. Then three numbers from the hundreds digit, tens digit, and single digit of each pixel are extracted respectively. Finally, the carrier image is transformed by integer wavelet transform (IWT) to get four coefficients, and then the above three numbers are embedded into low-frequency coefficients respectively. By inverse IWT, the carrier image containing secrets (CICS) can be obtained. The experimental results show that the algorithm has strong imperceptibility and key sensitivity. Especially, when the embedding coefficient is set to 1.0, the NC value between the carrier image (CI) and the CICS can reach 0.9999, and the PSNR value can reach about 43 dB.
Introducing Urdu Digits Dataset with Demonstration of an Efficient and Robust Noisy Decoder-Based Pseudo Example Generator

In the present work, we propose a novel method utilizing only a decoder for generation of pseudo-examples, which has shown great success in image classification tasks. The proposed method is particularly constructive when the data are in a limited quantity used for semi-supervised learning (SSL) or few-shot learning (FSL). While most of the previous works have used an autoencoder to improve the classification performance for SSL, using a single autoencoder may generate confusing pseudo-examples that could degrade the classifier’s performance. On the other hand, various models that utilize encoder–decoder architecture for sample generation can significantly increase computational overhead. To address the issues mentioned above, we propose an efficient means of generating pseudo-examples by using only the generator (decoder) network separately for each class that has shown to be effective for both SSL and FSL. In our approach, the decoder is trained for each class sample using random noise, and multiple samples are generated using the trained decoder. Our generator-based approach outperforms previous state-of-the-art SSL and FSL approaches. In addition, we released the Urdu digits dataset consisting of 10,000 images, including 8000 training and 2000 test images collected through three different methods for purposes of diversity. Furthermore, we explored the effectiveness of our proposed method on the Urdu digits dataset by using both SSL and FSL, which demonstrated improvement of 3.04% and 1.50% in terms of average accuracy, respectively, illustrating the superiority of the proposed method compared to the current state-of-the-art models.
Photonic machine learning with on-chip diffractive optics

Machine learning technologies have been extensively applied in high-performance information-processing fields. However, the computation rate of existing hardware is severely circumscribed by conventional Von Neumann architecture. Photonic approaches have demonstrated extraordinary potential for executing deep learning processes that involve complex calculations. In this work, an on-chip diffractive optical neural network (DONN) based on a silicon-on-insulator platform is proposed to perform machine learning tasks with high integration and low power consumption characteristics. To validate the proposed DONN, we fabricated 1-hidden-layer and 3-hidden-layer on-chip DONNs with footprints of 0.15 mm2 and 0.3 mm2 and experimentally verified their performance on the classification task of the Iris plants dataset, yielding accuracies of 86.7% and 90%, respectively. Furthermore, a 3-hidden-layer on-chip DONN is fabricated to classify the Modified National Institute of Standards and Technology handwritten digit images. The proposed passive on-chip DONN provides a potential solution for accelerating future artificial intelligence hardware with enhanced performance.
Flexible Architectures for Cryptographic Algorithms — A Systematic Literature Review

Symmetric and asymmetric cryptographic algorithms are used for a secure transmission of data over an unsecured public channel. In order to use these algorithms in real-time applications, many flexible hardware architectures have been proposed and implemented with multiple design constraints. Therefore, a systematic study is required to analyze various implementation approaches. This paper has focused on the identification and classification of recent research practices pertaining to the flexible hardware implementation of cryptographic algorithms. We have used Systematic Literature Review (SLR) process to identify 51 research articles, published during 2008–2017. The identified researches have been classified according to three design approaches: (1) crypto processor, (2) crypto coprocessor and (3) multicore crypto processor. Consequently, a comparative analysis of various cryptographic algorithms in terms of flexibility, throughput, area, power and implementation technology has been presented. A comprehensive investigation of flexible architectures for implementing cryptographic algorithms facilitates researchers and designers of the domain to select an appropriate design approach for a particular algorithm and/or application according to their needs.
High-dimensional robust mean estimation in nearly-linear time

We study the fundamental problem of high-dimensional mean estimation in a robust model where a constant fraction of the samples are adversarially corrupted. Recent work gave the first polynomial time algorithms for this problem with dimension-independent error guarantees for several families of structured distributions.In this work, we give the first nearly-linear time algorithms for high-dimensional robust mean estimation. Specifically, we focus on distributions with (i) known covariance and subgaussian tails, and (ii) unknown bounded covariance. Given N samples on Rd, an e-fraction of which may be arbitrarily corrupted, our algorithms run in time O(Nd)/poly(e) and approximate the true mean within the information-theoretically optimal error, up to constant factors. Previous robust algorithms with comparable error guarantees have running times [MATH HERE], for e = Ω(1).Our algorithms rely on a natural family of SDPs parameterized by our current guess v for the unknown mean μ*. We give a win-win analysis establishing the following: either a near-optimal solution to the primal SDP yields a good candidate for μ* --- independent of our current guess v --- or a near-optimal solution to the dual SDP yields a new guess v' whose distance from μ* is smaller by a constant factor. We exploit the special structure of the corresponding SDPs to show that they are approximately solvable in nearly-linear time. Our approach is quite general, and we believe it can also be applied to obtain nearly-linear time algorithms for other high-dimensional robust learning problems.
Understanding Individual Decisions of CNNs via Contrastive Backpropagation

A number of backpropagation-based approaches such as DeConvNets, vanilla Gradient Visualization and Guided Backpropagation have been proposed to better understand individual decisions of deep convolutional neural networks. The saliency maps produced by them are proven to be non-discriminative. Recently, the Layer-wise Relevance Propagation (LRP) approach was proposed to explain the classification decisions of rectifier neural networks. In this work, we evaluate the discriminativeness of the generated explanations and analyze the theoretical foundation of LRP, i.e. Deep Taylor Decomposition. The experiments and analysis conclude that the explanations generated by LRP are not class-discriminative. Based on LRP, we propose Contrastive Layer-wise Relevance Propagation (CLRP), which is capable of producing instance-specific, class-discriminative, pixel-wise explanations. In the experiments, we use the CLRP to explain the decisions and understand the difference between neurons in individual classification decisions. We also evaluate the explanations quantitatively with a Pointing Game and an ablation study. Both qualitative and quantitative evaluations show that the CLRP generates better explanations than the LRP.
GANs-based Clothes Design

Machine learning have been recently applied to multiple areas, including fashion. Fashion design by generated images makes it possible to inherit design without fashion designer and get inspiration, however, little research has been done on usage of machine learning for creation of designer clothing. The state-of-the-art works aim for high-definition output images. However in fashion design image generation, it has not been thoroughly investigated to what extent the quality of the generated image should be provided to the pattern makers that draw the costume pattern from the design images. Therefore, in this paper we propose a method of generation of clothing images for pattern makers using Progressive Growing of GANs (P-GANs) and conduct a user study to investigate whether the different image quality factors such as epoch and resolution affect the participants' confidence score. We discuss the results and possible applications of the developed method.
Assessment of Employee Susceptibility to Phishing Attacks at US Health Care Institutions

Cybersecurity is an increasingly important threat to health care delivery, and email phishing is a major attack vector against hospital employees.To describe the practice of phishing simulation and the extent to which health care employees are vulnerable to phishing simulations.Retrospective, multicenter quality improvement study of a convenience sample of 6 geographically dispersed US health care institutions that ran phishing simulations from August 1, 2011, through April 10, 2018. The specific institutions are anonymized herein for security and privacy concerns.Simulated phishing emails received by employees at US health care institutions.Date of phishing campaign, campaign number, number of emails sent, number of emails clicked, and email content. Emails were classified into 3 categories (office related, personal, or information technology related).The final study sample included 6 anonymized US health care institutions, 95 simulated phishing campaigns, and 2 971 945 emails, 422 062 of which were clicked (14.2%). The median institutional click rates for campaigns ranged from 7.4% (interquartile range [IQR], 5.8%-9.6%) to 30.7% (IQR, 25.2%-34.4%), with an overall median click rate of 16.7% (IQR, 8.3%-24.2%) across all campaigns and institutions. In the regression model, repeated phishing campaigns were associated with decreased odds of clicking on a subsequent phishing email (adjusted OR, 0.511; 95% CI, 0.382-0.685 for 6-10 campaigns; adjusted OR, 0.335; 95% CI, 0.282-0.398 for >10 campaigns).Among a sample of US health care institutions that sent phishing simulations, almost 1 in 7 simulated emails sent were clicked on by employees. Increasing campaigns were associated with decreased odds of clicking on a phishing email, suggesting a potential benefit of phishing simulation and awareness. With cyberattacks increasing against US health care systems, these click rates represent a major cybersecurity risk for hospitals.
Discovering Neural Wirings

The success of neural networks has driven a shift in focus from feature engineering to architecture engineering. However, successful networks today are constructed using a small and manually defined set of building blocks. Even in methods of neural architecture search (NAS) the network connectivity patterns are largely constrained. In this work we propose a method for discovering neural wirings. We relax the typical notion of layers and instead enable channels to form connections independent of each other. This allows for a much larger space of possible networks. The wiring of our network is not fixed during training -- as we learn the network parameters we also learn the structure itself. Our experiments demonstrate that our learned connectivity outperforms hand engineered and randomly wired networks. By learning the connectivity of MobileNetV1we boost the ImageNet accuracy by 10% at ~41M FLOPs. Moreover, we show that our method generalizes to recurrent and continuous time networks. Our work may also be regarded as unifying core aspects of the neural architecture search problem with sparse neural network learning. As NAS becomes more fine grained, finding a good architecture is akin to finding a sparse subnetwork of the complete graph. Accordingly, DNW provides an effective mechanism for discovering sparse subnetworks of predefined architectures in a single training run. Though we only ever use a small percentage of the weights during the forward pass, we still play the so-called initialization lottery with a combinatorial number of subnetworks. Code and pretrained models are available at https://github.com/allenai/dnw while additional visualizations may be found at https://mitchellnw.github.io/blog/2019/dnw/.
Deep latent factor model for collaborative filtering

Latent factor models have been used widely in collaborative filtering based recommender systems. In recent years, deep learning has been successful in solving a wide variety of machine learning problems. Motivated by the success of deep learning, we propose a deeper version of latent factor model. Experiments on benchmark datasets shows that our proposed technique significantly outperforms all state-of-the-art collaborative filtering techniques.