Reviewerly/exHarmony huggingface dataset:
Mammographic Mass Detection by Bilateral Analysis Based on Convolution Neural Network

In this paper, a bilateral mass detection method is proposed for mammogram combining self-supervised learning network and Siamese-Faster-RCNN. The breast region is first identified by threshold segmentation and morphological filter. Then self-supervised learning network is built to learn the spatial transformation between the bilateral breast regions. Following bilateral mammograms are registered, a Siamese-Faster-RCNN consisting of the Region Proposal Network (RPN) and a Siamese fully connected (Siamese-FC) network is designed and employed for mass detection. The proposed method is estimated on two datasets (publicly available dataset INbreast and private dataset BCPKUPH). Experimental results show that the proposed method performs better than the previous state of art methods, which demonstrates the promise of the proposed method.
Czech Speech Synthesis with Generative Neural Vocoder

In recent years, new neural architectures for generating high-quality synthetic speech on a per-sample basis were introduced. We describe our application of statistical parametric speech synthesis based on LSTM neural networks combined with a generative neural vocoder for the Czech language. We used a traditional LSTM architecture for generating vocoder parametrization from linguistic features. We replaced a standard vocoder with a WaveRNN neural network. We conducted a MUSHRA listening test to compare the proposed approach with the unit selection and LSTM-based parametric speech synthesis utilizing a standard vocoder. In contrast with our previous work, we managed to outperform a well-tuned unit selection TTS system by a great margin on both professional and amateur voices.
Optical Flow Based Line Drawing Frame Interpolation Using Distance Transform to Support Inbetweenings

Two-dimensional (2D) hand-drawn animation is still being produced in Japan, and the market is growing each year against a backdrop of worldwide popularity. Inbetweening, the process of creating interpolated frames between key frames, is an important but laborious task in 2D hand-drawn animation. With this process, we aim to avoid the need for a special interface or vectorization. We therefore propose an optical-flow based line drawing frame interpolation method using a distance transform. In general, an optical flow is not applicable to line drawings owing to a lack of intensity in the gradients and colors. Therefore, we use a distance transform to add intensity gradients to line drawings, and it is possible to estimate an optical flow between them. We evaluated our method quantitatively using commercial hand-drawn animation inbetweens. Our method significantly outperforms a baseline method that does not use a distance transform. In addition, we also compared our method to an existing image-based method qualitatively and showed that our method generates better results.
Egocentric Action Anticipation by Disentangling Encoding and Inference

Egocentric action anticipation consists in predicting future actions from videos collected by means of a wearable camera. Action anticipation methods should be able to continuously 1) summarize the past and 2) predict possible future actions. We observe that action anticipation benefits from explicitly disentangling the two tasks. To this aim, we introduce a learning architecture which makes use of a "rolling" LSTM to continuously summarize the past and an "unrolling" LSTM to anticipate future actions at multiple temporal scales. The model includes a spatial and a temporal branch which process RGB images and optical flow fields independently. The predictions performed by the two branches are fused using a novel modality attention mechanism which leverages the complementary nature of the modalities. Experiments on the EPIC-KITCHENS dataset show that the proposed method surpasses the state-of-the-art by +4.02% and +6.39% when considering Top-1 and Top-5 accuracy respectively. Please see the project webpage at http://iplab.dmi.unict.it/rulstm/.
Generation of Natural Traffic Sign Images Using Domain Translation with Cycle-Consistent Generative Adversarial Networks

Video-based traffic sign recognition poses a highly challenging problem due to the significant number of possible classes and large variances of recording conditions in natural environments. Gathering an appropriate amount of data to solve this task with machine learning techniques remains an overall issue. In this study, we assess the suitability of automatically generated traffic sign images for training corresponding image classifiers. To this end, we adapt the recently proposed cycle-consistent generative adversarial networks in order to transfer automatically rendered prototypical traffic sign images for which we control type, pose, and-to a degree-background into their true-to-life counterparts. We test the proposed system by extensive experiments on the German Traffic Sign Recognition Benchmark dataset [1] and learn that both a HOG-feature-based SVM classifier and a state-of-the-art CNN exhibit reasonable performance when solely trained on artificial data. Consequently, it is well suited as data augmentation method and allows for covering uncommon cases and classes.
Blockchain - Can It Solve the Security Issues and Fraud Expenses for Credit Card Commerce?

We live in a world where we attempt to keep credit card numbers as a secret. However we have seen large scale hacking into companies such as Target, Equifax, Home Depot, PayPal, Sak's, Lord & Taylors, and many others. Financial credit card companies spend millions of dollars each year replacing consumers' cards because of risk of fraudulent charges. Businesses are spending millions of dollars ensuring these numbers are protected within their IT infrastructure. Businesses are losing millions of dollars in stolen merchandise due to fraudulent use of credit cards. The Blockchain technology can be used to prevent these losses from being incurred. This paper analyzes how the Blockchain technology could be used, how it can be secure, and how it can alleviate the risk to credit card data. In addition, this paper identifies and defines a system that can be developed using existing technologies, such as, two factor identification, SMS text messaging, and biometrics to prevent credit card breaches.
An Interpretable Generative Model for Handwritten Digits Synthesis

An interpretable generative model for handwritten digits synthesis is proposed in this work. Modern image generative models such as the variational autoencoder (VAE) are trained by backpropagation (BP). The training process is complex, and its underlying mechanism is not transparent. Here, we present an explainable generative model using a feedforward design methodology without BP. Being similar to VAEs, it has an encoder and a decoder. For the encoder design, we derive principal-component-analysis-based (PCA-based) transform kernels using the covariance of its inputs. This process converts input images of correlated pixels to uncorrelated spectral components, which play the same role as latent variables in a VAE system. For the decoder design, we convert randomly generated spectral components to synthesized images through the inverse PCA transform. A subject test is conducted to compare the quality of digits generated using the proposed method and the VAE method. They offer comparable perceptual quality yet our model can be obtained at much lower complexity.
Distributed Model Predictive Pose Control of Multiple Nonholonomic Vehicles

This paper investigates a method for pose control of multiple nonholonomic vehicles using time-optimal control in a Model Predictive Control (MPC) framework. The vehicles are driving on a limited space considering field boundaries to build-up a formation. Distributed Model Predictive Control (DMPC) in a priority-based manner reduces computation time and avoids vehicle collisions. Priorities are automatically set by evaluating the target poses of the vehicles. We demonstrate our method in simulations. It produces near-optimal trajectories and reduces the compuation time in comparison to centralized MPC.
SNR analysis with speckle noise in interferometry using monochromatic expanded source and fringe localization

As a strongly coherent light source, laser is widely used in various high-precision interferometry techniques. However, the coherence of laser also produces coherence noise. Laser coherence noise degrades the signal-to-noise (SNR) ratio and the interferogram resolution, leading to difficulties in data classification and information restoration. Here, we aim to derive the expressions of laser speckle contrast and SNR of interferograms through the theory of laser speckle noise. The factors affecting the speckle contrast and SNR of images are analyzed theoretically. The problem of fringe localization caused by dynamic scatter is discussed. We verify the relevant expression by using an experimental system and find that it can serve as a basis of laser interferometry to analyze measurement accuracy and suppress speckle noise.
Executing Collaborative Decisions Confidentially on Blockchains

Decisions are an important aspect of enterprise operations. Decisions cross the boundary of a single enterprise, if multiple business partners collaborate in the decision making. To ascertain that all the participants behave as expected, blockchains can support collaborative decision making by storing relevant data and executing crucial decision logic in a tamper-proof and transparent manner. However, current blockchain technologies require the participants to publish the decision logic and are, therefore, not suited for sensitive data. This paper addresses this issue by proposing an approach that does not need to reveal sensitive data for supporting decision making. However, in case of a conflict, any participant can call for a blockchain-based conflict resolution at the cost of revealing the decision. To counter false claims that purposely reveal the decision, we provide a blockchain-enforced mechanism that discourages malicious behavior. We implement the approach using the Ethereum blockchain and evaluate the costs of resolving conflicts on a large set of decision models.
Generating a Question Answering System from Text Causal Relations

The aim of this paper is to present a methodology for creating expert systems by processing texts in order to respond to the queries of a question answering system. In previous work, we have shown several algorithms that were able to extract causal information from text documents and to summarize it. These approaches extracted knowledge from unstructured information, but the performed representation could not be processed automatically to infer new knowledge. Generated summaries only present the information in natural language, and hence cannot be processed in order to generate complex implications. In this paper, we introduce a procedure capable of using this knowledge in order to infer new causal relations between concepts automatically by creating expert systems from the processed texts. These expert systems will contain the causal relations presented in the processed texts. In this representation, by using logic programming, we can infer new concepts that are implied by causal relations. We describe the methodology, technical details of the implementation of our question answering system and a full example where its usefulness is described.
Cube Cryptanalysis of Round-Reduced ACORN

The cube attack is one of the most powerful techniques in cryptanalysis of symmetric cryptographic primitives. The basic idea of cube attack is to determine the value of a polynomial in key bits by summing over a cube (a subset of public variables, e.g., plaintext bits or IV bits). If the degree of the polynomial is relatively low, then we can obtain a low-degree equation in key bits, thus may contribute to reducing the complexity of key recovery. In this paper, we use cube cryptanalysis to analyze the authenticated stream cipher ACORN (one of the 6 algorithms in the final portfolio of the CAESAR competition), and give some new results in both distinguishing attacks and key recovery attacks. Firstly, we give a new method of finding cube testers, which is based on the greedy algorithm of finding cubes, and the numeric mapping method for estimating the algebraic degree of NFSR-based cryptosystems. We apply it to ACORN, and obtain the best practical distinguishing attacks for its 690-round variant using a cube of size 38, and its 706-round variant using a cube of size 46. Then we theoretically analyze the security bound of ACORN via the division property based cube attack. By exploiting the embedded property, we find some new distinguishers for ACORN, so the zero-sum property of the output of its 775-round variant can be observed with a complexity of $$2^{127}$$ . Finally, we propose a key recovery attack on ACORN reduced to 772 rounds. The time complexity to recover the linear superpoly of the 123-dimensional cube is $$2^{127.46}$$ . As far as we know, this is the best key recovery attack on round-reduced ACORN. It is also worth noting that this work does not threaten the security of ACORN.
Deep Enhancement for 3D HDR Brain Image Compression

The rapid advancement of high-speed whole-brain imaging techniques poses great challenge for the existing image compression schemes. To accommodate the huge amount of data on limited storage and also facilitate transmission, high compression rates are required, leading to noticeable image degradations that hinder the subsequent scientific observation. In this paper, we propose a deep learning framework for the enhancement of compressed brain images, which learns an efficient post-processing network for the reduction of compression artifacts. This framework is designed for the 3D volumetric brain image with high dynamic range by introducing novel network components, yet it is also applicable to other types of biomedical images. Comprehensive experiments demonstrate the superiority of the proposed method over existing solutions. To the best of our knowledge, this is the first framework for 3D HDR brain image compression with deep enhancement.
Instance Segmentation by Learning Deep Feature in Embedding Space

The proposal-based framework is the mainstream architecture for instance segmentation. However, such architecture typically ignores the interference between objects, which fails to correctly segment overlapping objects with same category or appearance. In this paper, we propose a new instance segmentation network named Instance Discrimination Network (ID-Net) to consider the interference between objects by mapping pixels into an embedding space so that the pixels from different objects can be distinguished more accurately. To identify the foreground object in RoI, we learn a discriminative deep feature that can represent the embedding vectors corresponding to the foreground. Then, we get the foreground confidence map by calculating the similarities between the deep feature and embeddings. The experiments on PASCAL VOC and COCO datasets demonstrate the effectiveness of our method.
High Dimensional Restrictive Federated Model Selection with Multi-objective Bayesian Optimization over Shifted Distributions

A novel machine learning optimization process coined Restrictive Federated Model Selection (RFMS) is proposed under the scenario, for example, when data from healthcare units can not leave the site it is situated on and it is forbidden to carry out training algorithms on remote data sites due to either technical or privacy and trust concerns. To carry out a clinical research in this scenario, an analyst could train a machine learning model only on local data site, but it is still possible to execute a statistical query at a certain cost in the form of sending a machine learning model to some of the remote data sites and get the performance measures as feedback, maybe due to prediction being usually much cheaper. Compared to federated learning, which is optimizing the model parameters directly by carrying out training across all data sites, RFMS trains model parameters only on one local data site but optimizes hyper parameters across other data sites jointly since hyper-parameters play an important role in machine learning performance. The aim is to get a Pareto optimal model with respective to both local and remote unseen prediction losses, which could generalize well across data sites. In this work, we specifically consider high dimensional data with different distributions over data sites. As an initial investigation, Bayesian Optimization especially multi-objective Bayesian Optimization is used to guide an adaptive hyper-parameter optimization process to select models under the RFMS scenario. Empirical results shows that solely using the local data site to tune hyper-parameters generalizes poorly across data sites, compared to methods that utilize the local and remote performances. Furthermore, in terms of hypervolumes, multi-objective Bayesian Optimization algorithms show increased performance across multiple data sites among other candidates.
A Robust Methodology for Building an Artificial Intelligent (AI) Virtual Assistant for Payment Processing

The evolution of business interactions with customers has defined a new paradigm of human-machine interaction through AI enabled conversations. In such scenarios, the machine interacts with the human (also referred to as customers) by applying NLP, NLU and NLG techniques that are used to process, understand and generate. dynamic and rapidly changing course of conversations to keep it meaningful and within context. With the changing landscape, today's NLU and NLP components are supported by the latest deep learning algorithms, making them highly palatable of understanding customers intent, forming a cogent response, or taking an appropriate action than was possible in the recent past. But such sophisticated techniques require good quality of sample examples to learn. In this work, we discuss a step-by-step approach to enhance and enable the NLU behind the AI virtual assistant to robustly interpret customers utterances. As a system for interaction, we developed an AI virtual assistant for a payment process to help customer process the due amount in invoice, which is an integral part of several industries, like Banking, Utilities, Telcos, Retailers, etc. We tested our model by processing sample interactions of which a significant percentage of interactions (approximately 75%) went through finishing the intended task of completing the payment.
QoE Evaluation for Live Broadcasting Video

The great variations of videographic skills in shot environment, photographic apparatus, compression and processing protocols give rise to very complicated impairments in the live broadcasting videos, which can adversely impact the quality of experience (QoE) of end users. Evaluating QoE of these videos is of great significance. Given the fact that there is still no publicly available database that studies the combined effects of the distortions in the live broadcasting videos, we have built the Live Broadcasting Video Database (LBVD) with the associated QoE scores. Towards depicting the distortions in live broadcasting videos, totally 1013 videos were included in the database, with diversified, authentic distortions. A subjective evaluation of these videos is conducted, and the correlation results between the tested state-of-the-art objective metrics and the subjective QoE scores on this database reveal that further studies are in urgent need for a better objective QoE metric dedicated to the live broadcasting videos.
A new methodology to create valid time-dependent correlation matrices <i>via</i> isospectral flows

In many areas of finance and of risk management it is interesting to know how to specify time-dependent correlation matrices. In this work we propose a new methodology to create valid time-dependent instantaneous correlation matrices, which we called correlation flows. In our methodology one needs only an initial correlation matrix to create these correlation flows based on isospectral flows. The tendency of the time-dependent matrices can be controlled by requirements. An application example is presented to illustrate our methodology.
Dual-Domain Single Image De-Raining Using Conditional Generative Adversarial Network

This paper presents a novel method for a single image rain streak removal problem which exploits the spatial as well as wavelet transformed coefficients of the rainy images. The proposed method adopts the Conditional Generative Adversarial Network [1] framework and consists of two following networks: Generator and Discriminator. The generator model receives the input from both spatial, frequency domain of the rainy image and yields five de-rained image candidates. A Deep Residual Network [2] has been used to merge these derained candidates and predict a single de-rained image. To ensure the visual quality of the de-rained image, Perceptual loss function [3] in addition to adversarial training has been incorporated. Extensive experiments on the synthetic and realworld rainy images dataset reveal an improvement over the existing state-of-the-art methods [4], [5] by ~ 1.08%, 2.57% in Structural Similarity Index [6] and ~ 7.39%, 9.95% in Peak signal-to-noise ratio respectively.
An IND-CCA2 secure post-quantum encryption scheme and a secure cloud storage use case

Abstract Code-based public key encryption (PKE) is a popular choice to achieve post-quantum security, partly due to its capability to achieve fast encryption/decryption. However, code-based PKE has larger ciphertext and public key sizes in comparison to conventional PKE schemes (e.g., those based on RSA). In 2018, Lau and Tan proposed a new rank metric code-based PKE scheme, which has smaller public key and ciphertext sizes compared to other code-based PKE schemes. They also proved that their scheme achieves IND-CPA security, assuming the intractability of the decisional rank syndrome decoding problem. It is known that IND-CCA2 security is the strongest and most popular security assurance for PKE schemes. Therefore, in this paper, we obtain a new code-based PKE scheme from Lau and Tan’s scheme, in order to inherit the underlying small public key and ciphertext sizes. However, our new scheme is shown to achieve IND-CCA2 security, instead of the weaker IND-CPA security. Specifically, the respective public key size and ciphertext size in our new scheme are 15.06 KB and 1.37 KB under 141-bit security level, and 16.76 KB and 1.76 KB under 154-bit security level. We then present a use case for the proposed scheme, that is for secure cloud storage.