Reviewerly/exHarmony huggingface dataset:
Learning to Clean: A GAN Perspective

In the big data era, the impetus to digitize the vast reservoirs of data trapped in unstructured scanned documents such as invoices, bank documents, courier receipts and contracts has gained fresh momentum. The scanning process often results in the introduction of artifacts such as salt-and-pepper/background noise, blur due to camera motion or shake, watermarkings, coffee stains, wrinkles, or faded text. These artifacts pose many readability challenges to current text recognition algorithms and significantly degrade their performance. Existing learning based denoising techniques require a dataset comprising of noisy documents paired with cleaned versions of the same document. In such scenarios, a model can be trained to generate clean documents from noisy versions. However, very often in the real world such a paired dataset is not available, and all we have for training our denoising model are unpaired sets of noisy and clean images. This paper explores the use of Generative Adversarial Networks (GAN) to generate denoised versions of the noisy documents. In particular, where paired information is available, we formulate the problem as an image-to-image translation task i.e, translating a document from noisy domain (i.e., background noise, blurred, faded, watermarked) to a target clean document using Generative Adversarial Networks (GAN). However, in the absence of paired images for training, we employed CycleGAN which is known to learn a mapping between the distributions of the noisy images to the denoised images using unpaired data to achieve image-to-image translation for cleaning the noisy documents. We compare the performance of CycleGAN for document cleaning tasks using unpaired images with a Conditional GAN trained on paired data from the same dataset. Experiments were performed on a public document dataset on which different types of noise were artificially induced, results demonstrate that CycleGAN learns a more robust mapping from the space of noisy to clean documents.
Enhanced double random phase encryption of quantum images

We improve the double random phase encryption (DRPE) algorithm to make the outcome of DRPE algorithm as uniformly mixed as possible via applying scrambling transform. So, the effectiveness of this method is reduced to prove the credibility of the scrambling transform. In doing so, our contributions include: (1) A transform set Set is constructed with some foundation transforms which have been proved to be numerous, to have lower complexity, and to be specified using rules. (2) A scrambling transform P constructed based on Set verifies the existence of the trend of the scrambling effect, and this trend means there exists an optimal scrambling transform that can be constructed with Set to uniformly mix the pixel information. The double-randomness of scrambling transform, the theoretic analysis and experimental results show the enhanced algorithm is effective.
Shorter Quadratic QA-NIZK Proofs

Despite recent advances in the area of pairing-friendly Non-Interactive Zero-Knowledge proofs, there have not been many efficiency improvements in constructing arguments of satisfiability of quadratic (and larger degree) equations since the publication of the Groth-Sahai proof system (JoC'12). In this work, we address the problem of aggregating such proofs using techniques derived from the interactive setting and recent constructions of SNARKs. For certain types of quadratic equations, this problem was investigated before by González et al. (ASIACRYPT'15). Compared to their result, we reduce the proof size by approximately 50% and the common reference string from quadratic to linear, at the price of using less standard computational assumptions. A theoretical motivation for our work is to investigate how efficient NIZK proofs based on falsifiable assumptions can be. On the practical side, quadratic equations appear naturally in several cryptographic schemes like shuffle and range arguments.
Illusions With Hands, but Not With Balloons – Comparing Ownership and Referral of Touch for a Corporal and Noncorporal Object After Visuotactile Stimulation

The rubber hand illusion is a perceptual illusion of perceiving an object like a model hand as part of the own body. The question whether the illusion can be induced with noncorporal objects that do not look like a human body part is not perfectly resolved yet. In this study, we directly assessed the subjective experience of two different components within the illusion (i.e., ownership and referral of touch) when a model hand and a balloon are stimulated. We observed significantly stronger illusion ratings for the hand as compared with the balloon, and only the hand ratings showed a clear affirmation of the illusion. We further conclude that (a) a significant difference between synchronous and asynchronous conditions may not be sufficient to argue for the successful induction of the illusion and (b) the subcomponents show a different pattern in the different conditions, which may lead to alternative interpretations. These observations call for a more fine-grained interpretation of questionnaire data in rubber hand illusion studies.
A class of linear codes of length 2 over finite chain rings

Let [Formula: see text] be a finite field of cardinality [Formula: see text], where [Formula: see text] is an odd prime, [Formula: see text] be positive integers satisfying [Formula: see text], and denote [Formula: see text], where [Formula: see text] is an irreducible polynomial in [Formula: see text]. In this note, for any fixed invertible element [Formula: see text], we present all distinct linear codes [Formula: see text] over [Formula: see text] of length [Formula: see text] satisfying the condition: [Formula: see text] for all [Formula: see text]. This conclusion can be used to determine the structure of [Formula: see text]-constacyclic codes over the finite chain ring [Formula: see text] of length [Formula: see text] for any positive integer [Formula: see text] satisfying [Formula: see text].
Big but Imperceptible Adversarial Perturbations via Semantic Manipulation.

Machine learning models, especially deep neural networks (DNNs), have been shown to be vulnerable against adversarial examples which are carefully crafted samples with a small magnitude of the perturbation. Such adversarial perturbations are usually restricted by bounding their $\mathcal{L}_p$ norm such that they are imperceptible, and thus many current defenses can exploit this property to reduce their adversarial impact. In this paper, we instead introduce unrestricted perturbations that manipulate semantically meaningful image-based visual descriptors - color and texture - in order to generate effective and photorealistic adversarial examples. We show that these semantically aware perturbations are effective against JPEG compression, feature squeezing and adversarially trained model. We also show that the proposed methods can effectively be applied to both image classification and image captioning tasks on complex datasets such as ImageNet and MSCOCO. In addition, we conduct comprehensive user studies to show that our generated semantic adversarial examples are photorealistic to humans despite large magnitude perturbations when compared to other attacks.
Semi-Cyclic Stochastic Gradient Descent

We consider convex SGD updates with a block-cyclic structure, i.e. where each cycle consists of a small number of blocks, each with many samples from a possibly different, block-specific, distribution. This situation arises, e.g., in Federated Learning where the mobile devices available for updates at different times during the day have different characteristics. We show that such block-cyclic structure can significantly deteriorate the performance of SGD, but propose a simple approach that allows prediction with the same performance guarantees as for i.i.d., non-cyclic, sampling.
On the Connection Between Adversarial Robustness and Saliency Map Interpretability

Recent studies on the adversarial vulnerability of neural networks have shown that models trained to be more robust to adversarial attacks exhibit more interpretable saliency maps than their non-robust counterparts. We aim to quantify this behavior by considering the alignment between input image and saliency map. We hypothesize that as the distance to the decision boundary grows,so does the alignment. This connection is strictly true in the case of linear models. We confirm these theoretical findings with experiments based on models trained with a local Lipschitz regularization and identify where the non-linear nature of neural networks weakens the relation.
A calibration method of ultra-short baseline installation error with large misalignment based on variational Bayesian unscented Kalman filter

The ultrashort baseline system is widely used in ships and underwater navigation and positioning. The misalignment and level arm with the inertial measurement unit are the two sources of positioning inaccuracy. The accuracy of calibration is usually affected by measurement noise and linearization of the observation equation. In order to improve the calibration accuracy, the variational Bayesian unscented Kalman filter (VBUKF) method is proposed for the calibration of the ultrashort baseline installation error in the paper. The detailed derivation of VBUKF for the calibration is presented in the paper. Simulation experiments and field experiments were carried out, respectively, to verify the algorithm. The simulation results show that the proposed method can calibrate the installation error of the sensors in real time on line. The field experiment verified that the algorithm improves the calibration accuracy of the installation error under the large misalignment. The positioning accuracy is also improved compared with the traditional method.
Stochastic Tokenization with a Language Model for Neural Text Classification

For unsegmented languages such as Japanese and Chinese, tokenization of a sentence has a significant impact on the performance of text classification. Sentences are usually segmented with words or subwords by a morphological analyzer or byte pair encoding and then encoded with word (or subword) representations for neural networks. However, segmentation is potentially ambiguous, and it is unclear whether the segmented tokens achieve the best performance for the target task. In this paper, we propose a method to simultaneously learn tokenization and text classification to address these problems. Our model incorporates a language model for unsupervised tokenization into a text classifier and then trains both models simultaneously. To make the model robust against infrequent tokens, we sampled segmentation for each sentence stochastically during training, which resulted in improved performance of text classification. We conducted experiments on sentiment analysis as a text classification task and show that our method achieves better performance than previous methods.
Active Learning for Patch-Based Digital Pathology Using Convolutional Neural Networks to Reduce Annotation Costs

Methods to reduce the need for costly data annotations become increasingly important as deep learning gains popularity in medical image analysis and digital pathology. Active learning is an appealing approach that can reduce the amount of annotated data needed to train machine learning models but traditional active learning strategies do not always work well with deep learning. In patch-based machine learning systems, active learning methods typically request annotations for small individual patches which can be tedious and costly for the annotator who needs to rely on visual context for the patches. We propose an active learning framework that selects regions for annotation that are built up of several patches, which should increase annotation throughput. The framework was evaluated with several query strategies on the task of nuclei classification. Convolutional neural networks were trained on small patches, each containing a single nucleus. Traditional query strategies performed worse than random sampling. A K-centre sampling strategy showed a modest gain. Further investigation is needed in order to achieve significant performance gains using deep active learning for this task.
Body-positive computing as a means to counteract normative biases in fitness trackers

Fitness trackers are rigidly defining what it means to be healthy and who "counts" as healthy. Body-positive computing can provide an alternative that allows people to engage with technologies more on their terms.
Anisotropic Diffusion Based Multiplicative Speckle Noise Removal

Multiplicative speckle noise removal is a challenging task in image processing. Motivated by the performance of anisotropic diffusion in additive noise removal and the structure of the standard deviation of a compressed speckle noisy image, we address this problem with anisotropic diffusion theories. Firstly, an anisotropic diffusion model based on image statistics, including information on the gradient of the image, gray levels, and noise standard deviation of the image, is proposed. Although the proposed model can effectively remove multiplicative speckle noise, it does not consider the noise at the edge during the denoising process. Hence, we decompose the divergence term in order to make the diffusion at the edge occur along the boundaries rather than perpendicular to the boundaries, and improve the model to meet our requirements. Secondly, the iteration stopping criteria based on kurtosis and correlation in view of the lack of ground truth in real image experiments, is proposed. The optimal values of the parameters in the model are obtained by learning. To improve the denoising effect, post-processing is performed. Finally, the simulation results show that the proposed model can effectively remove the speckle noise and retain minute details of the images for the real ultrasound and RGB color images.
Lipschitz Generative Adversarial Nets.

In this paper, we study the convergence of generative adversarial networks (GANs) from the perspective of the informativeness of the gradient of the optimal discriminative function. We show that GANs without restriction on the discriminative function space commonly suffer from the problem that the gradient produced by the discriminator is uninformative to guide the generator. By contrast, Wasserstein GAN (WGAN), where the discriminative function is restricted to 1-Lipschitz, does not suffer from such a gradient uninformativeness problem. We further show in the paper that the model with a compact dual form of Wasserstein distance, where the Lipschitz condition is relaxed, may also theoretically suffer from this issue. This implies the importance of Lipschitz condition and motivates us to study the general formulation of GANs with Lipschitz constraint, which leads to a new family of GANs that we call Lipschitz GANs (LGANs). We show that LGANs guarantee the existence and uniqueness of the optimal discriminative function as well as the existence of a unique Nash equilibrium. We prove that LGANs are generally capable of eliminating the gradient uninformativeness problem. According to our empirical analysis, LGANs are more stable and generate consistently higher quality samples compared with WGAN.
The ethical and privacy implications of mixed reality

The spatial computing affordances of virtual and augmented reality introduce new ethical and privacy dilemmas. This panel will explore the many implications of biometric data (eye tracking, facial tracking, gait detection, emotional sentiment analysis, galvanic skin response, EEG, EMG, and ECG) to contextually-aware computing that can scan and identify your immediate surroundings. There are many unknown ethical thresholds with immersive computing, and this panel will discuss our own moral intuitions on the topic while inviting the audience to share their own questions and insights for how to navigate this landscape.
Attentive Normalization

In state-of-the-art deep neural networks, both feature normalization and feature attention have become ubiquitous. They are usually studied as separate modules, however. In this paper, we propose a light-weight integration between the two schema and present Attentive Normalization (AN). Instead of learning a single affine transformation, AN learns a mixture of affine transformations and utilizes their weighted-sum as the final affine transformation applied to re-calibrate features in an instance-specific way. The weights are learned by leveraging channel-wise feature attention. In experiments, we test the proposed AN using four representative neural architectures in the ImageNet-1000 classification benchmark and the MS-COCO 2017 object detection and instance segmentation benchmark. AN obtains consistent performance improvement for different neural architectures in both benchmarks with absolute increase of top-1 accuracy in ImageNet-1000 between 0.5% and 2.7%, and absolute increase up to 1.8% and 2.2% for bounding box and mask AP in MS-COCO respectively. We observe that the proposed AN provides a strong alternative to the widely used Squeeze-and-Excitation (SE) module. The source codes are publicly available at the ImageNet Classification Repo and the MS-COCO Detection and Segmentation Repo .
Invertible Convolutional Flow

Normalizing flows can be used to construct high quality generative probabilistic models, but training and sample generation require repeated evaluation of Jacobian determinants and function inverses. To make such computations feasible, current approaches employ highly constrained architectures that produce diagonal, triangular, or low rank Jacobian matrices. As an alternative, we investigate a set of novel normalizing flows based on the circular and symmetric convolutions. We show that these transforms admit efficient Jacobian determinant computation and inverse mapping (deconvolution) in O(N log N) time. Additionally, element-wise multiplication, widely used in normalizing flow architectures, can be combined with these transforms to increase modeling flexibility. We further propose an analytic approach to designing nonlinear elementwise bijectors that induce special properties in the intermediate layers, by implicitly introducing specific regularizers in the loss. We show that these transforms allow more effective normalizing flow models to be developed for generative image models.
Fusion In Breast Cancer Histology Classification

Breast cancer is a deadly disease that affects millions of women worldwide. The International Conference on Image Analysis and Recognition in 2018 presents the BreAst Cancer Histology (ICIAR2018 BACH) image data challenge that calls for computer tools to assist pathologists and doctors in the clinical diagnosis of breast cancer subtypes. Using the BACH dataset, we have developed an image classification pipeline that combines both a shallow learner (support vector machine) and a deep learner (convolutional neural network). The shallow learner and deep learners achieved moderate accuracies of 79% and 81% individually. When being integrated by fusion algorithms, the system outperformed any individual learner with the highest accuracy as 92%. The fusion presents big potential for improving clinical design support. KEYWORDS
Introduction to Blockchain and IoT

The blockchain is emerging rapidly as a current area of research these days. The blockchain is a technology used to run bitcoin. It is distributed database maintaining a list of record growing continuously called blocks in order to ensure the security of those blocks from revision and tampering. Every block is connected to other blocks by maintaining the hash of the previous block in the chain. This chapter discusses the technical aspects of blockchain and IoT. The IoT is merely not a concept these days. It is the necessity of time in everyday life. The “smartphone” is the most familiar application of IoT in the day-to-day life. The application of IoT is not limited to smart homes. It is ranging from industrial and commercial sectors to agriculture, public safety, and the health sector. The IoT can also be considered as “Internet of Everything (IoE)” because of a wide range of real-life applications of IoT.
A Deep Attention based Framework for Image Caption Generation in Hindi Language

Image captioning refers to the process of generating a textual description for an image which defines the object and activity within the image. It is an intersection of computer vision and natural language processing where computer vision is used to understand the content of an image and language modelling from natural language processing is used to convert an image into words in the right order. A large number of works exist for generating image captioning in English language, but no work exists for generating image captioning in Hindi language. Hindiis the official language of India, and it is the fourth most-spoken language in the world, after Mandarin, Spanish and English. The current paper attempts to bridge this gap. Here an attention-based novel architecture for generating image captioning in Hindi language is proposed. Convolution neural network isused as an encoder to extract features from an input image and gated recurrent unit based neural network isused as a decoder to perform language modelling up to the word level. In between, we have used the attention mechanism which helps the decoder to look into the important portions of the image. In order to show theefficacy of the proposed model, we have first created a manually annotated image captioning training corpusin Hindi corresponding to popular MS COCO English dataset having around 80000 images. Experimental results show that our proposed model attains a BLEU1score of 0.5706 on this data set.
No pixel-level annotations needed

A deep-learning model for cancer detection trained on a large number of scanned pathology slides and associated diagnosis labels enables model development without the need for pixel-level annotations.
Asynchronous Online Federated Learning for Edge Devices.

Federated learning (FL) is a machine learning paradigm where a shared centralmodel is learned across multiple distributed client devices while the trainingdata remains on edge devices or local clients. Most prior work on federatedlearning uses Federated Averaging (FedAvg) as an optimization method fortraining in a synchronized fashion. This involves independent training atmultiple edge devices with synchronous aggregation steps. However, theassumptions made by FedAvg are not realistic given the heterogeneity ofdevices. In particular, the volume and distribution of collected data vary inthe training process due to different sampling rates of edge devices. The edgedevices themselves also vary in their available communication bandwidth andsystem configurations, such as memory, processor speed, and power requirements.This leads to vastly different training times as well as model/data transfertimes. Furthermore, availability issues at edge devices can lead to a lack ofcontribution from specific edge devices to the federated model. In this paper,we present an Asynchronous Online Federated Learning (ASO- fed) framework,where the edge devices perform online learning with continuous streaming localdata and a central server aggregates model parameters from local clients. Ourframework updates the central model in an asynchronous manner to tackle thechallenges associated with both varying computational loads at heterogeneousedge devices and edge devices that lag behind or dropout. Experiments on threereal-world datasets show the effectiveness of ASO-fed on lowering the overalltraining cost and maintaining good prediction performance.