Reviewerly/exHarmony huggingface dataset:
ESPnet-ST IWSLT 2021 Offline Speech Translation System

This paper describes the ESPnet-ST group's IWSLT 2021 submission in the offline speech translation track.This year we made various efforts on training data, architecture, and audio segmentation.On the data side, we investigated sequence-level knowledge distillation (SeqKD) for end-to-end (E2E) speech translation.Specifically, we used multi-referenced SeqKD from multiple teachers trained on different amounts of bitext.On the architecture side, we adopted the Conformer encoder and the Multi-Decoder architecture, which equips dedicated decoders for speech recognition and translation tasks in a unified encoder-decoder model and enables search in both source and target language spaces during inference.We also significantly improved audio segmentation by using the pyannote.audiotoolkit and merging multiple short segments for long context modeling.Experimental evaluations showed that each of them contributed to large improvements in translation performance.Our best E2E system combined all the above techniques with model ensembling and achieved 31.4BLEU on the 2-ref of tst2021 and 21.2 BLEU and 19.3 BLEU on the two single references of tst2021.
Doping Tests for Cyber-Physical Systems

The software running in embedded or cyber-physical systems (CPS) is typically of proprietary nature, so users do not know precisely what the systems they own are (in)capable of doing. Most malfunctionings of such systems are not intended by the manufacturer, but some are, which means these cannot be classified as bugs or security loopholes. The most prominent examples have become public in the diesel emissions scandal, where millions of cars were found to be equipped with software violating the law, altogether polluting the environment and putting human health at risk. The behaviour of the software embedded in these cars was intended by the manufacturer, but it was not in the interest of society, a phenomenon that has been called software doping. Doped software is significantly different from buggy or insecure software and hence it is not possible to use classical verification and testing techniques to discover and mitigate software doping. The work presented in this paper builds on existing definitions of software doping and lays the theoretical foundations for conducting software doping tests, so as to enable attacking evil manufacturers. The complex nature of software doping makes it very hard to effectuate doping tests in practice. We explain the biggest challenges and provide efficient solutions to realise doping tests despite this complexity.
Recoverability for optimized quantum f-divergences

The optimized quantum $f$-divergences form a family of distinguishability measures that includes the quantum relative entropy and the sandwiched R\'enyi relative quasi-entropy as special cases. In this paper, we establish physically meaningful refinements of the data-processing inequality for the optimized $f$-divergence. In particular, the refinements state that the absolute difference between the optimized $f$-divergence and its channel-processed version is an upper bound on how well one can recover a quantum state acted upon by a quantum channel, whenever the recovery channel is taken to be a rotated Petz recovery channel. Not only do these results lead to physically meaningful refinements of the data-processing inequality for the sandwiched R\'enyi relative entropy, but they also have implications for perfect reversibility (i.e., quantum sufficiency) of the optimized $f$-divergences. Along the way, we improve upon previous physically meaningful refinements of the data-processing inequality for the standard $f$-divergence, as established in recent work of Carlen and Vershynina [arXiv:1710.02409, arXiv:1710.08080]. Finally, we extend the definition of the optimized $f$-divergence, its data-processing inequality, and all of our recoverability results to the general von Neumann algebraic setting, so that all of our results can be employed in physical settings beyond those confined to the most common finite-dimensional setting of interest in quantum information theory.
Corrigendum: Development and Validation of a Nomogram for the Prediction of Hospital Mortality of Patients With Encephalopathy Caused by Microbial Infection: A Retrospective Cohort Study

[This corrects the article DOI: 10.3389/fmicb.2021.737066.].
Experimental Masking of Real Quantum States

Masking of quantum information is a way of hiding information in correlations such that no information is accessible to any local observer. Although the set of all quantum states as a whole cannot be masked into bipartite correlations according to the no-masking theorem, the set of real states is maskable and is a maximal maskable set. In this work, we experimentally realize a masking protocol of the real ququart by virtue of a photonic quantum walk. Our experiment clearly demonstrates that quantum information of the real ququart can be completely hidden in bipartite correlations of two-qubit hybrid entangled states, which are encoded in two different degrees of freedom of a single photon. The hidden information is not accessible from each qubit alone, but can be faithfully retrieved with a fidelity of about 99% from correlation measurements. By contrast, any superset of the set of real density matrices cannot be masked.
The binary Gold function and its c-boomerang connectivity table

Here, we give a complete description of the entire c-Boomerang Connectivity Table for the Gold function over finite fields of even characteristic, by using double Weil sums. As a by-product, we generalize a result of Boura and Canteaut (IACR Trans. Symmetric Cryptol. 2018(3) : 290–310, 2018) for the classical boomerang uniformity (see also the extended abstract by Eddahmani and Mesnager at the Boolean Functions and their Applications (BFA 2021) conference).
Dataset of shell commands used by participants of hands-on cybersecurity training

We present a dataset of 13446 shell commands from 175 participants who attended cybersecurity training and solved assignments in the Linux terminal. Each acquired data record contains a command with its arguments and metadata, such as a timestamp, working directory, and host identification in the emulated training infrastructure. The commands were captured in Bash, ZSH, and Metasploit shells. The data are stored as JSON records, enabling vast possibilities for their further use in research and development. These include educational data mining, learning analytics, student modeling, and evaluating machine learning models for intrusion detection. The data were collected from 27 cybersecurity training sessions using an open-source logging toolset and two open-source interactive learning environments. Researchers and developers may use the dataset or deploy the learning environments with the logging toolset to generate their own data in the same format. Moreover, we provide a set of common analytical queries to facilitate the exploratory analysis of the dataset.
Reverse engineering gene regulatory network based on complex-valued ordinary differential equation model

Abstract Background The growing researches of molecular biology reveal that complex life phenomena have the ability to demonstrating various types of interactions in the level of genomics. To establish the interactions between genes or proteins and understand the intrinsic mechanisms of biological systems have become an urgent need and study hotspot. Results In order to forecast gene expression data and identify more accurate gene regulatory network, complex-valued version of ordinary differential equation (CVODE) is proposed in this paper. In order to optimize CVODE model, a complex-valued hybrid evolutionary method based on Grammar-guided genetic programming and complex-valued firefly algorithm is presented. Conclusions When tested on three real gene expression datasets from E. coli and Human Cell, the experiment results suggest that CVODE model could improve 20–50% prediction accuracy of gene expression data, which could also infer more true-positive regulatory relationships and less false-positive regulations than ordinary differential equation.
Using sound to understand protein sequence data: new sonification algorithms for protein sequences and multiple sequence alignments

The use of sound to represent sequence data-sonification-has great potential as an alternative and complement to visual representation, exploiting features of human psychoacoustic intuitions to convey nuance more effectively. We have created five parameter-mapping sonification algorithms that aim to improve knowledge discovery from protein sequences and small protein multiple sequence alignments. For two of these algorithms, we investigated their effectiveness at conveying information. To do this we focussed on subjective assessments of user experience. This entailed a focus group session and survey research by questionnaire of individuals engaged in bioinformatics research.For single protein sequences, the success of our sonifications for conveying features was supported by both the survey and focus group findings. For protein multiple sequence alignments, there was limited evidence that the sonifications successfully conveyed information. Additional work is required to identify effective algorithms to render multiple sequence alignment sonification useful to researchers. Feedback from both our survey and focus groups suggests future directions for sonification of multiple alignments: animated visualisation indicating the column in the multiple alignment as the sonification progresses, user control of sequence navigation, and customisation of the sound parameters.Sonification approaches undertaken in this work have shown some success in conveying information from protein sequence data. Feedback points out future directions to build on the sonification approaches outlined in this paper. The effectiveness assessment process implemented in this work proved useful, giving detailed feedback and key approaches for improvement based on end-user input. The uptake of similar user experience focussed effectiveness assessments could also help with other areas of bioinformatics, for example in visualisation.
Improving 360 Monocular Depth Estimation via Non-local Dense Prediction Transformer and Joint Supervised and Self-Supervised Learning

Due to difficulties in acquiring ground truth depth of equirectangular (360) images, the quality and quantity of equirectangular depth data today is insufficient to represent the various scenes in the world. Therefore, 360 depth estimation studies, which relied solely on supervised learning, are destined to produce unsatisfactory results. Although self-supervised learning methods focusing on equirectangular images (EIs) are introduced, they often have incorrect or non-unique solutions, causing unstable performance. In this paper, we propose 360 monocular depth estimation methods which improve on the areas that limited previous studies. First, we introduce a self-supervised 360 depth learning method that only utilizes gravity-aligned videos, which has the potential to eliminate the needs for depth data during the training procedure. Second, we propose a joint learning scheme realized by combining supervised and self-supervised learning. The weakness of each learning is compensated, thus leading to more accurate depth estimation. Third, we propose a non-local fusion block, which can further retain the global information encoded by vision transformer when reconstructing the depths. With the proposed methods, we successfully apply the transformer to 360 depth estimations, to the best of our knowledge, which has not been tried before. On several benchmarks, our approach achieves significant improvements over previous works and establishes a state of the art.
Detecting Central Nodes From Low-Rank Excited Graph Signals via Structured Factor Analysis

This paper treats a blind detection problem to identify the central nodes in a graph from filtered graph signals. Unlike prior works which impose strong restrictions on the data model, we only require the underlying graph filter to satisfy a low pass property with a generic low-rank excitation model. We treat two cases depending on the low pass graph filter's strength. When the graph filter is strong low pass, i.e., it has a frequency response that drops sharply at the high frequencies, we show that the principal component analysis (PCA) method detects central nodes with high accuracy. For general low pass graph filter, we show that the graph signals can be described by a structured factor model featuring the product between a low-rank plus sparse factor and an unstructured factor. We propose a two-stage decomposition algorithm to learn the structured factor model via a judicious combination of the non-negative matrix factorization and robust PCA algorithms. We analyze the identifiability conditions for the model which lead to accurate central nodes detection. Numerical experiments on synthetic and real data are provided to support our findings. We demonstrate significant performance gains over prior works.
Learning online visual invariances for novel objects via supervised and self-supervised training

Humans can identify objects following various spatial transformations such as scale and viewpoint. This extends to novel objects, after a single presentation at a single pose, sometimes referred to as online invariance. CNNs have been proposed as a compelling model of human vision, but their ability to identify objects across transformations is typically tested on held-out samples of trained categories after extensive data augmentation. This paper assesses whether standard CNNs can support human-like online invariance by training models to recognize images of synthetic 3D objects that undergo several transformations: rotation, scaling, translation, brightness, contrast, and viewpoint. Through the analysis of models' internal representations, we show that standard supervised CNNs trained on transformed objects can acquire strong invariances on novel classes even when trained with as few as 50 objects taken from 10 classes. This extended to a different dataset of photographs of real objects. We also show that these invariances can be acquired in a self-supervised way, through solving the same/different task. We suggest that this latter approach may be similar to how humans acquire invariances.
Previewable Contract-Based On-Chain X-Ray Image Sharing Framework for Clinical Research

An image sharing framework is important to support downstream data analysis especially for pandemics like Coronavirus Disease 2019 (COVID-19). Current centralized image sharing frameworks become dysfunctional if any part of the framework fails. Existing decentralized image sharing frameworks do not store the images on the blockchain, thus the data themselves are not highly available, immutable, and provable. Meanwhile, storing images on the blockchain provides availability/immutability/provenance to the images, yet produces challenges such as large-image handling, high viewing latency while viewing images, and software inconsistency while storing/loading images. This study aims to store chest x-ray images using a blockchain-based framework to handle large images, improve viewing latency, and enhance software consistency. We developed a splitting and merging function to handle large images, a feature that allows previewing an image earlier to improve viewing latency, and a smart contract to enhance software consistency. We used 920 publicly available images to evaluate the storing and loading methods through time measurements. The blockchain network successfully shares large images up to 18 MB and supports smart contracts to provide code immutability, availability, and provenance. Applying the preview feature successfully shared images 93% faster than sharing images without the preview feature. The findings of this study can guide future studies to generalize our framework to other forms of data to improve sharing and interoperability.
A Correlation Attack on Full SNOW-V and SNOW-Vi

In this paper, a method for searching correlations between the binary stream of Linear Feedback Shift Register (LFSR) and the keystream of SNOW-V and SNOW-Vi is presented based on the technique of approximation to composite functions. With the aid of the linear relationship between the four taps of LFSR input into Finite State Machine (FSM) at three consecutive clocks, we present an automatic search model based on the SAT/SMT technique and search out a series of linear approximation trails with high correlation. By exhausting the intermediate masks, we find a binary linear approximation with a correlation $$-2^{-47.76}$$ . Using such approximation, we propose a correlation attack on SNOW-V with an expected time complexity $$2^{246.53}$$ , a memory complexity $$2^{238.77}$$ and $$2^{237.5}$$ keystream words generated by the same key and Initial Vector (IV). For SNOW-Vi, we provide a binary linear approximation with the same correlation and mount a correlation attack with the same complexity as that of SNOW-V. To the best of our knowledge, this is the first known attack on full SNOW-V and SNOW-Vi, which is better than the exhaustive key search with respect to time complexity. The results indicate that neither SNOW-V nor SNOW-Vi can guarantee the 256-bit security level if we ignore the design constraint that the maximum length of keystream for a single pair of key and IV is less than $$2^{64}$$ .
Deep state-space Gaussian processes

This paper is concerned with a state-space approach to deep Gaussian process (DGP) regression. We construct the DGP by hierarchically putting transformed Gaussian process (GP) priors on the length scales and magnitudes of the next level of Gaussian processes in the hierarchy. The idea of the state-space approach is to represent the DGP as a non-linear hierarchical system of linear stochastic differential equations (SDEs), where each SDE corresponds to a conditional GP. The DGP regression problem then becomes a state estimation problem, and we can estimate the state efficiently with sequential methods by using the Markov property of the state-space DGP. The computational complexity scales linearly with respect to the number of measurements. Based on this, we formulate state-space MAP as well as Bayesian filtering and smoothing solutions to the DGP regression problem. We demonstrate the performance of the proposed models and methods on synthetic non-stationary signals and apply the state-space DGP to detection of the gravitational waves from LIGO measurements.
A Novel Ultra-Short-Term PV Power Forecasting Method Based on DBN-Based Takagi-Sugeno Fuzzy Model

Forecasting uncertainties limit the development of photovoltaic (PV) power generation. New forecasting technologies are urgently needed to improve the accuracy of power generation forecasting. In this paper, a novel ultra-short-term PV power forecasting method is proposed based on a deep belief network (DBN)-based Takagi-Sugeno (T-S) fuzzy model. Firstly, the correlation analysis is used to filter redundant information. Furthermore, a T-S fuzzy model, which integrates fuzzy c-means (FCM) for the fuzzy division of input variables and DBN for fuzzy subsets forecasting, is developed. Finally, the proposed method is compared to a benchmark DBN method and the T-S fuzzy model in case studies. The numerical results show the feasibility and flexibility of the proposed ultra-short-term PV power forecasting approach.
A Master-Slave Binary Grey Wolf Optimizer for Optimal Feature Selection in Biomedical Data Classification

A new master-slave binary grey wolf optimizer (MSBGWO) is introduced. A master-slave learning scheme is introduced to the grey wolf optimizer (GWO) to improve its ability to explore and get better solutions in a search space. Five high-dimensional biomedical datasets are used to test the ability of MSBGWO in feature selection. The experimental results of MSBGWO are superior in terms of classification accuracy, precision, recall, <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>F</mi> </math> -measure, and number of features selected when compared to those of the binary grey wolf optimizer version 2 (BGWO2), binary genetic algorithm (BGA), binary particle swarm optimization (BPSO), differential evolution (DE) algorithm, and sine-cosine algorithm (SCA).
Accurate Pupil Center Detection in Off-the-Shelf Eye Tracking Systems Using Convolutional Neural Networks

Remote eye tracking technology has suffered an increasing growth in recent years due to its applicability in many research areas. In this paper, a video-oculography method based on convolutional neural networks (CNNs) for pupil center detection over webcam images is proposed. As the first contribution of this work and in order to train the model, a pupil center manual labeling procedure of a facial landmark dataset has been performed. The model has been tested over both real and synthetic databases and outperforms state-of-the-art methods, achieving pupil center estimation errors below the size of a constricted pupil in more than 95% of the images, while reducing computing time by a 8 factor. Results show the importance of use high quality training data and well-known architectures to achieve an outstanding performance.
Hyperspectral Image Super-Resolution Based on Multiscale Mixed Attention Network Fusion

Hyperspectral images (HSIs) contain rich spectral information and have great application value. However, due to various hardware limitations, the spatial resolution of HSIs acquired by the sensor is low. HSI super-resolution (SR) attracts much attention to improve spatial quality. In this letter, a single HSI SR method based on network fusion is proposed. Our method includes the SR network part and fusion part. In the SR network part, we construct 3-D multiscale mixed attention networks (3-D-MSMANs) by cascading 3-D multiscale mixed attention block (3-D-MSMAB) to restore high-resolution HSIs. 3-D-MSMAB consists of the 3-D Res2net module and the mixed attention module. 3-D Res2net module is a simple and effective multiscale method. The mixed attention module is proposed by combining the first- and second-order statistics of features. In addition, we use the mutual learning loss between 3-D-MSMAN so that they can learn from each other. In the fusion part, the fusion module is designed to merge the output of each 3-D-MSMAN. Our method can achieve good results in both simulated and real SR experiments. Code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/LYT-max/Mixed-Attention-for-HSI-SR</uri> .