Reviewerly/exHarmony huggingface dataset:
A Geometric Approach to Obtain a Bird's Eye View from an Image

The objective of this paper is to rectify any monocular image by computing a homography matrix that transforms it to a bird's eye (overhead) view. We make the following contributions: (i) we show that the homography matrix can be parameterised with only four parameters that specify the horizon line and the vertical vanishing point, or only two if the field of view or focal length is known; (ii) We introduce a novel representation for the geometry of a line or point (which can be at infinity) that is suitable for regression with a convolutional neural network (CNN); (iii) We introduce a large synthetic image dataset with ground truth for the orthogonal vanishing points, that can be used for training a CNN to predict these geometric entities; and finally (iv) We achieve state-of-the-art results on horizon detection, with 74.52% AUC on the Horizon Lines in the Wild dataset. Our method is fast and robust, and can be used to remove perspective distortion from videos in real time.
Tight Lower Bounds on the VC-dimension of Geometric Set Systems

The VC-dimension of a set system is a way to capture its complexity and has been a key parameter studied extensively in machine learning and geometry communities. In this paper, we resolve two longstanding open problems on bounding the VC-dimension of two fundamental set systems: k-fold unions/intersections of half-spaces and the simplices set system. Among other implications, it settles an open question in machine learning that was first studied in the foundational paper of Blumer et al. (1989) as well as by Eisenstat and Angluin (2007) and Johnson (2008).
Named Entity Recognition of Chinese Electronic Medical Records Based on Cascaded Conditional Random Field

Medical information carried by electronic medical records has high clinical application value, and named entity recognition is the key task to extract valuable information from a large number of electronic medical records. In order to realize the intelligent identification of named entities in Chinese electronic medical record texts, this paper analyzes the characteristics that affect the recognition performance. The feature set is composed of language symbol features, part of speech features, context features, word boundary features and identifier feature. The feature template is designed and the Cascaded Conditional Random Field model is established. Based on the data selection strategy, we trained the Cascaded Conditional Random Field to identify the disease names, drug names and symptom names in Chinese electronic medical records. This method reduced the scale of training data, reduced the artificial markers and improved the recognition performance of disease names, drug names and symptom names, and the F value reached 68.18%, 90.91% and 87.13%, respectively.
State-of-the-Arts Person Re-Identification Using Deep Learning

Person Re-Identification has become prominent because of various reasons majorly due to its high-performance methods based on deep-learning. It is the process of person recognition from various images captured by different cameras. Provided two set of images the purpose is to find that the given set of images are identical or not. Person Re-Id is often a challenging task due to the similarity in nature like people with identical features, color or clothes. Images are taken from various angles and distances of a given subject in order to achieve high accuracy, so it identifies correctly. Re-Identification has broadly two major categories: i) Image Re-ID and ii) Video Re-ID. Based on the category Re-ID has numerous applications like robotics, automated video surveillance, forensics and multimedia that are deployed using various public datasets like Market1501, VIPeR, MARS, CUHK01, CUHK02, CUHK03, DukeMTMC-reID, MSMT17 etc. In this paper we aim to briefly discuss the process, datasets, recent work on Re-ID, challenges, its approaches and techniques that has been implemented using deep learning systems.
Enhancing Opinion Role Labeling with Semantic-Aware Word Representations from Semantic Role Labeling

Meishan Zhang, Peili Liang, Guohong Fu. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
On Transfer Learning for Detecting Abusive Language Online

Abusive language online has become a growing social issue in our age of social media. Given the massive amounts of data being generated daily on social platforms, manually detecting and regulating such behavior has become unfeasible, so automatic solutions are necessary, and tasks related to identifying abusive language, in its various forms, from hate speech to bullying, have come into the focus of the natural language processing research community. In this paper, we focus on two subtypes of abusive language: aggressive language and offensive language, for which we implement a deep learning model based on convolutional neural networks. We further propose a new approach using transfer learning to boost performance of abusive language detection by leveraging data annotated with a different type of label, related to sentiment. We show how transferring knowledge between these tasks affects performance of detecting abusive language, offering insights into how these tasks are related, and how the more traditional task of sentiment analysis can be leveraged to help with solving the newer and less data rich task of abusive language detection.
BayesSim: adaptive domain randomization via probabilistic inference for robotics simulators

We introduce BayesSim, a framework for robotics simulations allowing a full Bayesian treatment for the parameters of the simulator. As simulators become more sophisticated and able to represent the dynamics more accurately, fundamental problems in robotics such as motion planning and perception can be solved in simulation and solutions transferred to the physical robot. However, even the most complex simulator might still not be able to represent reality in all its details either due to inaccurate parametrization or simplistic assumptions in the dynamic models. BayesSim provides a principled framework to reason about the uncertainty of simulation parameters. Given a black box simulator (or generative model) that outputs trajectories of state and action pairs from unknown simulation parameters, followed by trajectories obtained with a physical robot, we develop a likelihood-free inference method that computes the posterior distribution of simulation parameters. This posterior can then be used in problems where Sim2Real is critical, for example in policy search. We compare the performance of BayesSim in obtaining accurate posteriors in a number of classical control and robotics problems. Results show that the posterior computed from BayesSim can be used for domain randomization outperforming alternative methods that randomize based on uniform priors.
Fidelity deviation in quantum teleportation with a two-qubit state

Quantum teleportation with an arbitrary two-qubit state can be appropriately characterized in terms of maximal fidelity and fidelity deviation.The former quantifies optimality of the process and is defined as the maximal average fidelity achievable within the standard protocol and local unitary strategies, whereas the latter, defined as the standard deviation of fidelity over all input states, is a measure of fidelity fluctuations.The maximal fidelity for a two-qubit state is known and is given by a simple formula that can be exactly computed, but no such formula is known for the fidelity deviation.In this paper, we derive an exact computable formula for the fidelity deviation in optimal quantum teleportation with an arbitrary state of two qubits.From this formula, we obtain the dispersion-free condition, also known as the universality condition: the condition that all input states are teleported equally well and provide a necessary and sufficient condition for a state to be both useful (maximal fidelity larger than the classical bound) and universal (zero fidelity deviation).We also show that for any given maximal fidelity, larger than the classical bound, there always exist dispersion-free or universal states and argue that such states are the most desirable ones within the set of useful states.We illustrate these results with well-known families of two-qubit states: pure entangled states, Bell-diagonal states, and subsets of X states.
Bayesian Generative Active Deep Learning

Deep learning models have demonstrated outstanding performance in several problems, but their training process tends to require immense amounts of computational and human resources for training and labeling, constraining the types of problems that can be tackled. Therefore, the design of effective training methods that require small labeled training sets is an important research direction that will allow a more effective use of resources.Among current approaches designed to address this issue, two are particularly interesting: data augmentation and active learning. Data augmentation achieves this goal by artificially generating new training points, while active learning relies on the selection of the most subset of unlabeled training samples to be labelled by an oracle. Although successful in practice, data augmentation can waste computational resources because it indiscriminately generates samples that are not guaranteed to be informative, and active learning selects a small subset of informative samples (from a large un-annotated set) that may be insufficient for the training process. In this paper, we propose a Bayesian generative active deep learning approach that combines active learning with data augmentation -- we provide theoretical and empirical evidence (MNIST, CIFAR-$\{10,100\}$, and SVHN) that our approach has more efficient training and better classification results than data augmentation and active learning.
MixedUX

In this paper, we rethink and challenge conventional ways of prototyping. We present a new perspective on how usability testings of digital products may benefit from emerging augmented reality (AR) technologies. We demonstrate a conceptual prototype of an innovative framework that makes it possible to combine 3D models of complex devices, represented by holograms, with 2D user interfaces (UIs) opened in a web browser on a physical touch display. The framework aims to facilitate the processes of UI design and interactions with complex, costly, or even not-yet-existing systems. For demonstration purposes we use the Microsoft HoloLens Development Edition and a conventional touch display of a smartphone.
Predicting Election Result with Sentimental Analysis Using Twitter Data for Candidate Selection

The aim of this paper is to provide a well and effective solution to distribute political party’s tickets during the election. The sole parameter of the current distribution of political party’s tickets is based on the money power and person’s references from their superior leaders. Our proposal is to highlight the discrepancy between the real candidate who is predicted to win the election based on their popularity with other parameters and those who have only references with money power. We will choose the deserving candidate by analyzing parameters such as social work, criminal records, educational qualification, and his/her popularity on social media (twitter).
Adversarial Robustness via Adversarial Label-Smoothing.

We study Label-Smoothing as a means for improving adversarial robustness of supervised deep-learning models. After establishing a thorough and unified framework, we propose several novel Label-Smoothing methods: adversarial, Boltzmann and second-best Label-Smoothing methods. On various datasets (MNIST, CIFAR10, SVHN) and models (linear models, MLPs, LeNet, ResNet), we show that these methods improve adversarial robustness against a variety of attacks (FGSM, BIM, DeepFool, Carlini-Wagner) by better taking account of the dataset geometry. These proposed Label-Smoothing methods have two main advantages: they can be implemented as a modified cross-entropy loss, thus do not require any modifications of the network architecture nor do they lead to increased training times, and they improve both standard and adversarial accuracy.
Characterization of the non-stationary nature of steady-state visual evoked potentials using echo state networks

State Visual Evoked Potentials (SSVEPs) arise from a resonance phenomenon in the visual cortex that is produced by a repetitive visual stimulus. SSVEPs have long been considered a steady-state response resulting from purely oscillatory components phase locked with the stimulation source, matching the stimulation frequency and its harmonics. Here we explore the dynamical character of the SSVEP response by proposing a novel non-stationary methodology for SSVEP detection based on an ensemble of Echo State Networks (ESN). The performance of this dynamical approach is compared to stationary canonical correlation analysis (CCA) for the detection of 6 visual stimulation frequencies ranging from 12 to 22 Hz. ESN-based methodology outperformed CCA, achieving an average information transfer rate of 47 bits/minute when simulating a BCI system of 6 degrees of freedom. However, for some subjects and stimulation frequencies the detection accuracy of CCA exceeds that of ESN. The comparison suggests that each methodology captures different features of the SSVEP response: while CCA captures purely stationary patterns, the ESN-based approach presented here is capable of detecting the non-stationary nature of the SSVEP.
Blockchain Storage Analysis and Optimization of Bitcoin Miner Node

Aiming at the storage problem of blockchain, a space storage optimization scheme based on the InterPlanetary File System (IPFS) is designed. The scheme mainly achieves the purpose of space optimization by summarizing the blocks regularly, excluding the expired Transaction Output (TXO) that has no effect on verification. The Unspent Transaction Output (UTXO) will be saved as a file on the IPFS network during optimization. In this way, the miner node can quickly get the file and start mining. The ultimate goal is to optimize the storage space for the node and to increase the synchronization efficiency of the newly added miner node. The simulation experiment of the proposed optimization scheme is carried out. The experimental results show that the scheme can summarize the historical transaction data regularly, eliminate the Spent Transaction Output (STXO) which has no effect on the verification transaction, so as to effectively reduce the node storage space.
DNA-SeAl: Sensitivity Levels to Optimize the Performance of Privacy-Preserving DNA Alignment

The advent of next-generation sequencing (NGS) machines made DNA sequencing cheaper, but also put pressure on the genomic life-cycle, which includes aligning millions of short DNA sequences, called reads, to a reference genome.On the performance side, efficient algorithms have been developed, and parallelized on public clouds.On the privacy side, since genomic data are utterly sensitive, several cryptographic mechanisms have been proposed to align reads more securely than the former, but with a lower performance.This paper presents DNA-SeAl a novel contribution to improving the privacy × performance product in current genomic workflows.First, building on recent works that argue that genomic data needs to be treated according to a threat-risk analysis, we introduce a multi-level sensitivity classification of genomic variations designed to prevent the amplification of possible privacy attacks.We show that the usage of sensitivity levels reduces future re-identification risks, and that their partitioning helps prevent linkage attacks.Second, after extending this classification to reads, we show how to align and store reads using different security levels.To do so, DNA-SeAl extends a recent reads filter to classify unaligned reads into sensitivity levels, and adapts existing alignment algorithms to the reads sensitivity.We show that using DNA-SeAl allows high performance gains whilst enforcing high privacy levels in hybrid cloud environments.
Blockchain as a Platform for Fog Computing

In this article, we observe the prospective approaches and techniques in the modern system of automatic control of a group of robots based on distributed ledger technology, using modern methods and development tools. The architecture of this solution is based on the junction of two rapidly developing technologies, such as the Internet of Things (IoT) and distributed ledgers technologies (DLT). We focused on the advantages of the built-in architecture, chose the platforms on which each step will work properly, and looked up at the problems that may arise when implementing individual module of this architecture.
AMBAS: an autonomous multimodal biometric authentication system

The traditional authentication techniques based on single factors such as passwords and tokens suffer from problems related to their robustness. Moreover, multi-factor authentication based on multi...
Scene Recognition in User Preference Prediction Based on Classification of Deep Embeddings and Object Detection

In this paper we consider general scene recognition problem for analysis of user preferences based on his or her photos on mobile phone. Special attention is paid to out-of-class detections and efficient processing using MobileNet-based architectures. We propose the three stage procedure. At first, pre-trained convolutional neural network (CNN) is used extraction of input image embeddings at one of the last layers, which are used for training a classifier, e.g., support vector machine or random forest. Secondly, we fine-tune the pre-trained network on the given training set and compute the predictions (scores) at the output of the resulted CNN. Finally, we perform object detection in the input image, and the resulted sparse vector of detected objects is classified. The decision is made based on a computation of a weighted sum of the class posterior probabilities estimated by all three classifiers. Experimental results with a subset of ImageNet dataset demonstrate that the proposed approach is up to 5% more accurate when compared to conventional fine-tuned models.
SampleFix: Learning to Correct Programs by Sampling Diverse Fixes

Automatic program correction is an active topic of research, which holds the potential of dramatically improving productivity of programmers during the software development process and correctness of software in general. Recent advances in machine learning, deep learning and NLP have rekindled the hope to eventually fully automate the process of repairing programs. A key challenge is ambiguity, as multiple codes -- or fixes -- can implement the same functionality. In addition, datasets by nature fail to capture the variance introduced by such ambiguities. Therefore, we propose a deep generative model to automatically correct programming errors by learning a distribution of potential fixes. Our model is formulated as a deep conditional variational autoencoder that samples diverse fixes for the given erroneous programs. In order to account for ambiguity and inherent lack of representative datasets, we propose a novel regularizer to encourage the model to generate diverse fixes. Our evaluations on common programming errors show for the first time the generation of diverse fixes and strong improvements over the state-of-the-art approaches by fixing up to 65% of the mistakes.
Augmented Reality Museum Visiting Application based on the Microsoft HoloLens

Abstract Immersive technologies such as the Augmented Reality (AR), which attaches the virtual information to the real environment, are extending the research space and attracting various domain experts including museum experts. Applying the AR technology in the 3D relic information visualization and helping visitors to visit the museum are also hot research issues. Current museum solutions are mainly based on the website, smart phone and Virtual Reality (VR) technology, AR museum applications are also mainly deployed on the smart phone. In this paper, an AR museum application based on the Microsoft HoloLens is presented, and the Microsoft HoloLens is a kind of head mounted device (HMD). Besides, multiple user interaction forms are supported in this solution, such as gaze, voice command, voice speech and hand gesture. Evaluation about this solution has also done, and the conclusion can be drawn that most participants appreciated this solution in clear interface and completion aspects.
Golden codes: quantum LDPC codes from regular tessellations of hyperbolic 4-manifolds

We adapt a construction of Guth and Lubotzky \cite{GL14} to obtain a family of quantum LDPC codes with non-vanishing rate and minimum distance scaling like n^{0.1} where n is the number of physical qubits. Similarly as in Ref.~\cite{GL14}, our homological code family stems from hyperbolic 4-manifolds equipped with tessellations. The main novelty of this work is that we consider a regular tessellation consisting of hypercubes. We exploit this strong local structure to design and analyze an efficient decoding algorithm.
Glyph: Symbolic Regression Tools

We present Glyph – a Python package for genetic programming based symbolic regression. Glyph is designed for usage in numerical simulations as well as real world experiments. For experimentalists, glyph-remote provides a separation of tasks: a ZeroMQ interface splits the genetic programming optimization task from the evaluation of an experimental (or numerical) run. Glyph can be accessed at https://github.com/Ambrosys/glyph. Domain experts are able to employ symbolic regression in their experiments with ease, even if they are not expert programmers. The reuse potential is kept high by a generic interface design. Glyph is available on PyPI and Github. Funding statement: This work has been partially supported by the German Science Foundation via SFB 880. MQ was supported by a fellowship within the FITweltweit program of the German Academic Exchange Service (DAAD).
Accumulation bit-width scaling for ultra-low precision training of deep networks

Efforts to reduce the numerical precision of computations in deep learning training have yielded systems that aggressively quantize weights and activations, yet employ wide high-precision accumulators for partial sums in inner-product operations to preserve the quality of convergence. The absence of any framework to analyze the precision requirements of partial sum accumulations results in conservative design choices. This imposes an upper-bound on the reduction of complexity of multiply-accumulate units. We present a statistical approach to analyze the impact of reduced accumulation precision on deep learning training. Observing that a bad choice for accumulation precision results in loss of information that manifests itself as a reduction in variance in an ensemble of partial sums, we derive a set of equations that relate this variance to the length of accumulation and the minimum number of bits needed for accumulation. We apply our analysis to three benchmark networks: CIFAR-10 ResNet 32, ImageNet ResNet 18 and ImageNet AlexNet. In each case, with accumulation precision set in accordance with our proposed equations, the networks successfully converge to the single precision floating-point baseline. We also show that reducing accumulation precision further degrades the quality of the trained network, proving that our equations produce tight bounds. Overall this analysis enables precise tailoring of computation hardware to the application, yielding area- and power-optimal systems.