Reviewerly/exHarmony huggingface dataset:
Machine Learning of Time Series Using Time-Delay Embedding and Precision Annealing

Tasking machine learning to predict segments of a time series requires estimating the parameters of a ML model with input/output pairs from the time series. We borrow two techniques used in statistical data assimilation in order to accomplish this task: time-delay embedding to prepare our input data and precision annealing as a training method. The precision annealing approach identifies the global minimum of the action ([Formula: see text]). In this way, we are able to identify the number of training pairs required to produce good generalizations (predictions) for the time series. We proceed from a scalar time series [Formula: see text] and, using methods of nonlinear time series analysis, show how to produce a [Formula: see text]-dimensional time-delay embedding space in which the time series has no false neighbors as does the observed [Formula: see text] time series. In that [Formula: see text]-dimensional space, we explore the use of feedforward multilayer perceptrons as network models operating on [Formula: see text]-dimensional input and producing [Formula: see text]-dimensional outputs.
DISH Algorithm Solving the CEC 2019 100-Digit Challenge

This paper presents the results of the "Distance Based Parameter Adaptation for Success-History Based Differential Evolution Algorithm" (DISH) on the CEC 2019 100-Digit Challenge. The algorithm has shown satisfying performance when solving 8 out of 10 benchmark functions with a perfect score of 10. This success is further supported by the fact that allowed parameter tuning was used only for three of the test functions and just for one parameter. Thus, the DISH algorithm can be labeled as robust.
LN-SNE: Log-Normal Distributed Stochastic Neighbor Embedding for Anomaly Detection

We present a new unsupervised dimensionality reduction technique, called LN-SNE, for anomaly detection. LN-SNE generates a parametric embedding by means of Restricted Boltzmann Machines and uses a heavy-tail distribution to project data to a lower dimensional space such that dissimilarities between normal data and anomalies are preserved or strengthened. We compare LN-SNE to several benchmark dimensionality reduction methods on real datasets. The results suggest that LN-SNE for anomaly detection is less sensitive to the dimension of the latent space than the other methods and outperforms them in terms of accuracy. We empirically show that our technique scales near-linearly with respect to the number of dimensions and data size.
Smart Insurance Contracts Based on Virtual Currency

It this paper it has been analyzed whether and under what conditions an insurance company can accept insurance premiums and claims payments in Bitcoins or other crypto currency based on a smart insurance contract. On the one hand the question has been explored with regard to private law, especially insurance contract law; on the other with regard to public law, in particular financial supervisory law. The research for the present paper has been conducted primarily by taking into consideration the relevant legal frameworks in the European Union, Switzerland, UK and Liechtenstein, while referring at the same time to general legal principles of public and private law, which have their validity in most other jurisdictions of common or civil law, such as for example in the US, Hong Kong, Singapore or Japan.
Augmented Reality Floods and Smoke Smartphone App Disaster Scope utilizing Real-time Occlusion

Natural disasters occur frequently in Japan. In the Great East Japan Earthquake in 2011 and the heavy rain disaster at western Japan in 2018, many people didn't have the crisis consciousness enough to evacuate safely. We developed the augmented reality smartphone-application Disaster Scope that enables immersive experience in order to improve the crisis awareness of disasters in peacetime. The application can superimpose the occurrence situation of disasters such as CG floods and debris and fire smoke in the actual scenery, using only a smartphone and paper headset. By using a smartphone equipped with a 3D depth sensor, it is possible to sense the height from the ground and recognize surrounding objects. The real-time occlusion processing enabled using only by a smartphone. The collision detection of the real world's objects and CG debris is possible. The floods height and flow speed can be changed by each user's setting. As a result, it has become possible to understand more realistically the dangerous of floods and a fire smoke charge. We utilized this system in evacuation drills organized by elementary schools and municipalities. As a result of the survey and verification, it was very useful for improving crisis awareness of students and citizens.
Enriching Threat Intelligence Platforms Capabilities

One of the weakest points in actual security detection and monitoring systems is the data retrieval from Open Source Intelligence (OSINT), as well as how this kind of information should be processed and normalized, considering their unstructured nature. This cybersecurity related information (e.g., Indicator of Compromise - IoC) is obtained from diverse and different sources and collected by Threat Intelligence Platforms (TIPs). In order to improve its quality, such information should be correlated with real-time data coming from the monitored infrastructure, before being further analyzed and shared. In this way, it could be prioritized, allowing a faster incident detection and response. This paper presents an Enriched Threat Intelligence Platform as a way to extend import, quality assessment processes, and information sharing capabilities in current TIPs. The platform receives structured cyber threat information from multiple sources, and performs the correlation among them with both static and dynamic data coming from the monitored infrastructure. This allows the evaluation of a threat score through heuristic-based analysis, used for enriching the information received from OSINT and other sources. The final result, expressed in a well defined format, is sent to external entities, which is further used for monitoring and detecting incidents (e.g., SIEMs), or for more in-depth analysis, and shared with trusted organizations.
Comparing Unsupervised Word Translation Methods Step by Step

Cross-lingual word vector space alignment is the task of mapping the vocabularies of two languages into a shared semantic space, which can be used for dictionary induction, unsupervised machine translation, and transfer learning. In the unsupervised regime, an initial seed dictionary is learned in the absence of any known correspondences between words, through {\bf distribution matching}, and the seed dictionary is then used to supervise the induction of the final alignment in what is typically referred to as a (possibly iterative) {\bf refinement} step. We focus on the first step and compare distribution matching techniques in the context of language pairs for which mixed training stability and evaluation scores have been reported. We show that, surprisingly, when looking at this initial step in isolation, vanilla GANs are superior to more recent methods, both in terms of precision and robustness. The improvements reported by more recent methods thus stem from the refinement techniques, and we show that we can obtain state-of-the-art performance combining vanilla GANs with such refinement techniques.
Sequence Modeling with Unconstrained Generation Order

The dominant approach to sequence generation is to produce a sequence in some predefined order, e.g. left to right. In contrast, we propose a more general model that can generate the output sequence by inserting tokens in any arbitrary order. Our model learns decoding order as a result of its training procedure. Our experiments show that this model is superior to fixed order models on a number of sequence generation tasks, such as Machine Translation, Image-to-LaTeX and Image Captioning.
Judge the Judges: A Large-Scale Evaluation Study of Neural Language Models for Online Review Generation

Cristina Garbacea, Samuel Carton, Shiyan Yan, Qiaozhu Mei. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.
Chirality Nets for Human Pose Regression

We propose Chirality Nets, a family of deep nets that is equivariant to the “chirality transform,” i.e., the transformation to create a chiral pair. Through parameter sharing, odd and even symmetry, we propose and prove variants of standard building blocks of deep nets that satisfy the equivariance property, including fully connected layers, convolutional layers, batch-normalization, and LSTM/GRU cells. The proposed layers lead to a more data efficient representation and a reduction in computation by exploiting symmetry. We evaluate chirality nets on the task of human pose regression, which naturally exploits the left/right mirroring of the human body. We study three pose regression tasks: 3D pose estimation from video, 2D pose forecasting, and skeleton based activity recognition. Our approach achieves/matches state-of-the-art results, with more significant gains on small datasets and limited-data settings.
Nearly Tight Bounds for Robust Proper Learning of Halfspaces with a Margin

We study the problem of {\em properly} learning large margin halfspaces in the agnostic PAC model. In more detail, we study the complexity of properly learning $d$-dimensional halfspaces on the unit ball within misclassification error $\alpha \cdot \opt_{\gamma} + \eps$, where $\opt_{\gamma}$ is the optimal $\gamma$-margin error rate and $\alpha \geq 1$ is the approximation ratio. We give learning algorithms and computational hardness results for this problem, for all values of the approximation ratio $\alpha \geq 1$, that are nearly-matching for a range of parameters. Specifically, for the natural setting that $\alpha$ is any constant bigger than one, we provide an essentially tight complexity characterization. On the positive side, we give an $\alpha = 1.01$-approximate proper learner that uses $O(1/(\eps^2\gamma^2))$ samples (which is optimal) and runs in time $\poly(d/\eps) \cdot 2^{\tilde{O}(1/\gamma^2)}$. On the negative side, we show that {\em any} constant factor approximate proper learner has runtime $\poly(d/\eps) \cdot 2^{(1/\gamma)^{2-o(1)}}$, assuming the Exponential Time Hypothesis.
Visualizing the PHATE of Neural Networks

Understanding why and how certain neural networks outperform others is key to guiding future development of network architectures and optimization methods. To this end, we introduce a novel visualization algorithm that reveals the internal geometry of such networks: Multislice PHATE (M-PHATE), the first method designed explicitly to visualize how a neural network's hidden representations of data evolve throughout the course of training. We demonstrate that our visualization provides intuitive, detailed summaries of the learning dynamics beyond simple global measures (i.e., validation loss and accuracy), without the need to access validation data. Furthermore, M-PHATE better captures both the dynamics and community structure of the hidden units as compared to visualization based on standard dimensionality reduction methods (e.g., ISOMAP, t-SNE). We demonstrate M-PHATE with two vignettes: continual learning and generalization. In the former, the M-PHATE visualizations display the mechanism of catastrophic forgetting which is a major challenge for learning in task-switching contexts. In the latter, our visualizations reveal how increased heterogeneity among hidden units correlates with improved generalization performance. An implementation of M-PHATE, along with scripts to reproduce the figures in this paper, is available at https://github.com/scottgigante/M-PHATE.
Universal Boosting Variational Inference

Boosting variational inference (BVI) approximates an intractable probability density by iteratively building up a mixture of simple component distributions one at a time, using techniques from sparse convex optimization to provide both computational scalability and approximation error guarantees. But the guarantees have strong conditions that do not often hold in practice, resulting in degenerate component optimization problems; and we show that the ad-hoc regularization used to prevent degeneracy in practice can cause BVI to fail in unintuitive ways. We thus develop universal boosting variational inference (UBVI), a BVI scheme that exploits the simple geometry of probability densities under the Hellinger metric to prevent the degeneracy of other gradient-based BVI methods, avoid difficult joint optimizations of both component and weight, and simplify fully-corrective weight optimizations. We show that for any target density and any mixture component family, the output of UBVI converges to the best possible approximation in the mixture family, even when the mixture family is misspecified. We develop a scalable implementation based on exponential family mixture components and standard stochastic optimization techniques. Finally, we discuss statistical benefits of the Hellinger distance as a variational objective through bounds on posterior probability, moment, and importance sampling errors. Experiments on multiple datasets and models show that UBVI provides reliable, accurate posterior approximations.
SMILe: Scalable Meta Inverse Reinforcement Learning through Context-Conditional Policies

Imitation Learning (IL) has been successfully applied to complex sequential decision-making problems where standard Reinforcement Learning (RL) algorithms fail. A number of recent methods extend IL to few-shot learning scenarios, where a meta-trained policy learns to quickly master new tasks using limited demonstrations. However, although Inverse Reinforcement Learning (IRL) often outperforms Behavioral Cloning (BC) in terms of imitation quality, most of these approaches build on BC due to its simple optimization objective. In this work, we propose SMILe, a scalable framework for Meta Inverse Reinforcement Learning (Meta-IRL) based on maximum entropy IRL, which can learn high-quality policies from few demonstrations. We examine the efficacy of our method on a variety of high-dimensional simulated continuous control tasks and observe that SMILe significantly outperforms Meta-BC. Furthermore, we observe that SMILe performs comparably or outperforms Meta-DAgger, while being applicable in the state-only setting and not requiring online experts. To our knowledge, our approach is the first efficient method for Meta-IRL that scales to the function approximator setting. For datasets and reproducing results please refer to https://github.com/KamyarGh/rl_swiss/blob/master/reproducing/smile_paper.md .
An efficient, font independent word and character segmentation algorithm for printed Arabic text

Characters segmentation is a necessity and the most critical stage in Arabic OCR system. It has attracted the interest of a wide range of researchers. However, the nature of the Arabic cursive script poses extra challenges that need further investigation. Therefore, having a reliable and efficient Arabic OCR system that is independent of font variations is highly required. In this paper, an indirect, font-in dependent word and character segmentation algorithm for printed Arabic text investigated. The proposed algorithm takes a binary line image as an input and produces a set of binary images consisting of one character or ligature as an output. The segmentation performed at two levels: a word segmentation performed in the first level, by employing a vertical projection at the input line image along with using Interquartile Range (IQR) method to differentiate between word gaps and within word gaps. A projection profile method used as a second level of segmentation along with a set of statistical and topological features, which are font-independent, to identify the correct segmentation points from all potential points. The APTI dataset used to test the proposed algorithm with a variety of font type, size, and style. The algorithm experimented on 1800 lines (approximately 24,816 words) with an average accuracy of 97.7% for words segmentation and 97.51% for characters segmentation.
Better Rewards Yield Better Summaries: Learning to Summarise Without References

Reinforcement Learning (RL) based document summarisation systems yield state-of-the-art performance in terms of ROUGE scores, because they directly use ROUGE as the rewards during training. However, summaries with high ROUGE scores often receive low human judgement. To find a better reward function that can guide RL to generate human-appealing summaries, we learn a reward function from human ratings on 2,500 summaries. Our reward function only takes the document and system summary as input. Hence, once trained, it can be used to train RL-based summarisation systems without using any reference summaries. We show that our learned rewards have significantly higher correlation with human ratings than previous approaches. Human evaluation experiments show that, compared to the state-of-the-art supervised-learning systems and ROUGE-as-rewards RL summarisation systems, the RL systems using our learned rewards during training generate summarieswith higher human ratings. The learned reward function and our source code are available at this https URL.
Applying Resilience to Hybrid Threats

Where hybrid threats represent potential disruptions to interlaced and interdependent systems within national and local governments, companies, and even individual households, methodologies are needed to help identify and understand 1) the source of the disruptions; 2) the system vulnerabilities they exploit with varying degrees of success; and 3) the potential for cascading harms to infrastructure, information systems, and social harmony and cohesion should a hybrid threat be successful. The need for such methodologies is particularly salient given the rise of cyberhacking, data theft, and fake news events triggered by state and nonstate actors. To address these challenges, a multifactorial, multitemporal approach is needed that allows for a whole -of -system view of how disruptions can percolate throughout the nested interconnections of hybrid -threat targets.
Problem-Agnostic Speech Embeddings for Multi-Speaker Text-to-Speech with SampleRNN

Text-to-speech (TTS) acoustic models map linguistic features into an acoustic representation out of which an audible waveform is generated. The latest and most natural TTS systems build a direct mapping between linguistic and waveform domains, like SampleRNN. This way, possible signal naturalness losses are avoided as intermediate acoustic representations are discarded. Another important dimension of study apart from naturalness is their adaptability to generate voice from new speakers that were unseen during training. In this paper we first propose the use of problem-agnostic speech embeddings in a multi-speaker acoustic model for TTS based on SampleRNN. This way we feed the acoustic model with speaker acoustically dependent representations that enrich the waveform generation more than discrete embeddings unrelated to these factors. Our first results suggest that the proposed embeddings lead to better quality voices than those obtained with discrete embeddings. Furthermore, as we can use any speech segment as an encoded representation during inference, the model is capable to generalize to new speaker identities without retraining the network. We finally show that, with a small increase of speech duration in the embedding extractor, we dramatically reduce the spectral distortion to close the gap towards the target identities.
Deep Learning for Visual Recognition of Environmental Enteropathy and Celiac Disease

Physicians use biopsies to distinguish between different but histologically similar enteropathies. The range of syndromes and pathologies that could cause different gastrointestinal conditions makes this a difficult problem. Recently, deep learning has been used successfully in helping diagnose cancerous tissues in histopathological images. These successes motivated the research presented in this paper, which describes a deep learning approach that distinguishes between Celiac Disease (CD) and Environmental Enteropathy (EE) and normal tissue from digitized duodenal biopsies. Experimental results show accuracies of over 90% for this approach. We also look into interpreting the neural network model using Gradient-weighted Class Activation Mappings and filter activations on input images to understand the visual explanations for the decisions made by the model.
Optimal phase point for SU(1,1) interferometer

Optimal phase point, where the best phase sensitivity can be achieved, plays an important role in the operation of an interferometer. In this paper, we theoretically investigate the optimal phase point of an SU(1,1) interferometer in joint intensity detection and joint homodyne detection, respectively. We demonstrate that for joint intensity detection, the optimal phase point of the SU(1,1) interferometer happens nearby the dark fringe with realistic factors considered. The divergence of the optimal phase point from the dark fringe can be dependent on the initial injection scheme of the SU(1,1) interferometer, the strength of the parametric amplifier inside the interferometer, and the internal losses of the system. In addition, we find that for joint homodyne detection, the optimal phase point of the SU(1,1) interferometer can happen only at the bright or dark fringe. Our results may pave the way to real applications of the SU(1,1) interferometer.
Phase and polarization autocompensating N-dimensional quantum cryptography in multicore optical fibers

Space division multiplexing based on multicore fibers (MCFs) is an optimum candidate to further increase the data bandwidth in optical communications, and its interest for spatial high-dimensional quantum cryptography has increased in the last few years. However, the main shortcoming lies in the appearance of phases and polarization acquired independently in each spatial mode after a long propagation, which prevents implementing efficient quantum key distribution (QKD) protocols with spatial optical modes. In this work, we propose a method for achieving phase, polarization, and time delay autocompensating for N-dimensional QKD in MCFs by using strong coherent states coming from the Bob system. These coherent states make N trips between Alice and Bob and undergo N suitable optical transformations before Alice attenuates such states for producing 1-qudit states. Likewise, passive random-projective-measurement integrated devices are presented as well, in order to measure 1-qudit states. Finally, we analyze the behavior of the system under a cryptoghraphic attack, such as, e.g., the phase-remapping attack, in order to assess the strength of our system.