OpenRaiser/Intern-Atlas huggingface dataset:
TLGAN: document Text Localization using Generative Adversarial Nets
Venue: arXiv cs.CV 2020
Year: 2020

Text localization from the digital image is the first step for the optical character recognition task. Conventional image processing based text localization performs adequately for specific examples. Yet, a general text localization are only archived by recent deep-learning based modalities. Here we present document Text Localization Generative Adversarial Nets (TLGAN) which are deep neural networks to perform the text localization from digital image. TLGAN is an versatile and easy-train text localization model requiring a small amount of data. Training only ten labeled receipt images from Robust Reading Challenge on Scanned Receipts OCR and Information Extraction (SROIE), TLGAN achieved 99.83% precision and 99.64% recall for SROIE test data. Our TLGAN is a practical text localization solution requiring minimal effort for data labeling and model training and producing a state-of-art performance.
Method of noun phrase detection in Ukrainian texts
Venue: arXiv cs.CL 2020
Year: 2020

Introduction. The area of natural language processing considers AI-complete tasks that cannot be solved using traditional algorithmic actions. Such tasks are commonly implemented with the usage of machine learning methodology and means of computer linguistics. One of the preprocessing tasks of a text is the search of noun phrases. The accuracy of this task has implications for the effectiveness of many other tasks in the area of natural language processing. In spite of the active development of research in the area of natural language processing, the investigation of the search for noun phrases within Ukrainian texts are still at an early stage. Results. The different methods of noun phrases detection have been analyzed. The expediency of the representation of sentences as a tree structure has been justified. The key disadvantage of many methods of noun phrase detection is the severe dependence of the effectiveness of their detection from the features of a certain language. Taking into account the unified format of sentence processing and the availability of the trained model for the building of sentence trees for Ukrainian texts, the Universal Dependency model has been chosen. The complex method of noun phrases detection in Ukrainian texts utilizing Universal Dependencies means and named-entity recognition model has been suggested. Experimental verification of the effectiveness of the suggested method on the corpus of Ukrainian news has been performed. Different metrics of method accuracy have been calculated. Conclusions. The results obtained can indicate that the suggested method can be used to find noun phrases in Ukrainian texts. An accuracy increase of the method can be made with the usage of appropriate named-entity recognition models according to a subject area.
A 4-Approximation of the $\frac{2π}{3}$-MST
Venue: arXiv cs.CG 2020
Year: 2020

Bounded-angle (minimum) spanning trees were first introduced in the context of wireless networks with directional antennas. They are reminiscent of bounded-degree spanning trees, which have received significant attention. Let $P = \{p_1,\ldots,p_n\}$ be a set of $n$ points in the plane, let $Π$ be the polygonal path $(p_1,\ldots,p_n)$, and let $0 < α< 2π$ be an angle. An $α$-spanning tree ($α$-ST) of $P$ is a spanning tree of the complete Euclidean graph over $P$, with the following property: For each vertex $p_i \in P$, the (smallest) angle that is spanned by all the edges incident to $p_i$ is at most $α$. An $α$-minimum spanning tree ($α$-MST) is an $α$-ST of $P$ of minimum weight, where the weight of an $α$-ST is the sum of the lengths of its edges. In this paper, we consider the problem of computing an $α$-MST, for the important case where $α= \frac{2π}{3}$. We present a simple 4-approximation algorithm, thus improving upon the previous results of Aschner and Katz and Biniaz et al., who presented algorithms with approximation ratios 6 and $\frac{16}{3}$, respectively. In order to obtain this result, we devise a simple $O(n)$-time algorithm for constructing a $\frac{2π}{3}$-ST\, ${\cal T}$ of $P$, such that ${\cal T}$'s weight is at most twice that of $Π$ and, moreover, ${\cal T}$ is a 3-hop spanner of $Π$. This latter result is optimal in the sense that for any $\varepsilon > 0$ there exists a polygonal path for which every $\frac{2π}{3}$-ST has weight greater than $2-\varepsilon$ times the weight of the path.
Automata and Fixpoints for Asynchronous Hyperproperties
Venue: arXiv cs.LO 2020
Year: 2020

Hyperproperties have received increasing attention in the last decade due to their importance e.g. for security analyses. Past approaches have focussed on synchronous analyses, i.e. techniques in which different paths are compared lockstepwise. In this paper, we systematically study asynchronous analyses for hyperproperties by introducing both a novel automata model (Alternating Asynchronous Parity Automata) and the temporal fixpoint calculus $\Hmu$, the first fixpoint calculus that can systematically express hyperproperties in an asynchronous manner and at the same time subsumes the existing logic HyperLTL. We show that the expressive power of both models coincides over fixed path assignments. The high expressive power of both models is evidenced by the fact that decision problems of interest are highly undecidable, i.e. not even arithmetical. As a remedy, we propose approximative analyses for both models that also induce natural decidable fragments.
Towards Fully Bilingual Deep Language Modeling
Venue: arXiv cs.CL 2020
Year: 2020

Language models based on deep neural networks have facilitated great advances in natural language processing and understanding tasks in recent years. While models covering a large number of languages have been introduced, their multilinguality has come at a cost in terms of monolingual performance, and the best-performing models at most tasks not involving cross-lingual transfer remain monolingual. In this paper, we consider the question of whether it is possible to pre-train a bilingual model for two remotely related languages without compromising performance at either language. We collect pre-training data, create a Finnish-English bilingual BERT model and evaluate its performance on datasets used to evaluate the corresponding monolingual models. Our bilingual model performs on par with Google's original English BERT on GLUE and nearly matches the performance of monolingual Finnish BERT on a range of Finnish NLP tasks, clearly outperforming multilingual BERT. We find that when the model vocabulary size is increased, the BERT-Base architecture has sufficient capacity to learn two remotely related languages to a level where it achieves comparable performance with monolingual models, demonstrating the feasibility of training fully bilingual deep language models. The model and all tools involved in its creation are freely available at https://github.com/TurkuNLP/biBERT
Input-Shaping for Feed-Forward Control of Cable-Driven Parallel Robots
Venue: arXiv cs.RO 2020
Year: 2020

This paper deals with the use of input-shaping filters in conjunction with a feed-forward control of Cable-Driven Parallel Robots (CDPRs), while integrating cable tension calculation to satisfy positive cable tensions along the prescribed trajectory of the moving-platform. This method aims to attenuate the oscillatory motions of the moving-platform. Thus, the input signal is modified to make it self-cancel residual vibrations. 5 The effectiveness, in terms of moving-platform oscillation attenuation, of the proposed closed-loop control method combined with shaping inputs is experimentally studied on a suspended and non-redundant CDPR prototype. This confirms residual vibration reduction improvement with respect to the unshaped control in terms of Peak-to-Peak amplitude of velocity error, which can achieve 72 % while using input-shaping filters.
Second layer data governance for permissioned blockchains: the privacy management challenge
Venue: arXiv cs.CY 2020
Year: 2020

Data privacy is a trending topic in the internet era. Given such importance, many challenges emerged in order to collect, manage, process, and publish data. In this sense, personal data have got attention, and many regulations emerged, such as GDPR in the European Union and LGPD in Brazil. This regulation model aims to protect users' data from misusage and leakage and allow users to request an explanation from companies when needed. In pandemic situations, such as the COVID-19 and Ebola outbreak, the action related to sharing health data between different organizations is/ was crucial to develop a significant movement to avoid the massive infection and decrease the number of deaths. However, the data subject, i.e., the users, should have the right to request the purpose of data use, anonymization, and data deletion. In this sense, permissioned blockchain technology emerges to empower users to get their rights providing data ownership, transparency, and security through an immutable, unified, and distributed database ruled by smart contracts. The governance model discussed in blockchain applications is usually regarding the first layer governance, i.e., public and permissioned models. However, this discussion is too superficial, and they do not cover compliance with the data regulations. Therefore, in order to organize the relationship between data owners and the stakeholders, i.e., companies and governmental entities, we developed a second layer data governance model for permissioned blockchains based on the Governance Analytical Framework principles applied in pandemic situations preserving the users' privacy and their duties. From the law perspective, we based our model on the UE GDPR in regard to data privacy concerns.
Joint Power Allocation and User Association Optimization for IRS-Assisted mmWave Systems
Venue: arXiv cs.IT 2020
Year: 2020

Intelligent reflect surface (IRS) is a potential technology to build programmable wireless environment in future communication systems. In this paper, we consider an IRS-assisted multi-base station (multi-BS) multi-user millimeter wave (mmWave) downlink communication system, exploiting IRS to extend mmWave signal coverage to blind spots. Considering the impact of IRS on user association in multi-BS mmWave systems, we formulate a sum rate maximization problem by jointly optimizing passive beamforming at IRS, power allocation and user association. This leads to an intractable nonconvex problem, for which to tackle we propose a computationally affordable iterative algorithm, capitalizing on alternating optimization, sequential fractional programming (SFP) and forward-reverse auction (FRA). In particular, passive beamforming at IRS is optimized by utilizing the SFP method, power allocation is solved through means of standard convex optimization method, and user association is handled by the network optimization based FRA algorithm. Simulation results demonstrate that the proposed algorithm can achieve significant performance gains, e.g., it can provide up to 175% higher sum rate compared with the benchmark and 140% higher energy efficiency compared with amplify-and-forward relay.
A study of the Multicriteria decision analysis based on the time-series features and a TOPSIS method proposal for a tensorial approach
Venue: arXiv cs.AI 2020
Year: 2020

A number of Multiple Criteria Decision Analysis (MCDA) methods have been developed to rank alternatives based on several decision criteria. Usually, MCDA methods deal with the criteria value at the time the decision is made without considering their evolution over time. However, it may be relevant to consider the criteria' time series since providing essential information for decision-making (e.g., an improvement of the criteria). To deal with this issue, we propose a new approach to rank the alternatives based on the criteria time-series features (tendency, variance, etc.). In this novel approach, the data is structured in three dimensions, which require a more complex data structure, as the \textit{tensors}, instead of the classical matrix representation used in MCDA. Consequently, we propose an extension for the TOPSIS method to handle a tensor rather than a matrix. Computational results reveal that it is possible to rank the alternatives from a new perspective by considering meaningful decision-making information.
TurboLoRa: enhancing LoRaWAN data rate via device synchronization
Venue: arXiv cs.NI 2020
Year: 2020

Over the last few years we have witnessed an exponential growth in the adoption of LoRaWAN as LPWAN technology for IoT. While LoRaWAN offers many advantages, one of its limitations is the paltry data rate. Most IoT applications don't require a high throughput but there are some that would benefit from a higher data rate. In this paper, we present TurboLoRa, a system that combines the strengths of LoRaWAN while providing a higher data rate by synchronizing the transmission of multiple LoRaWAN devices. Our proposal allows to combine cheap devices making it a frugal solution to this kind of problems. We present some preliminary results obtained using a real prototype of TurboLoRa.
A Comparative Analysis of Industry Human-AI Interaction Guidelines
Venue: arXiv cs.HC 2020
Year: 2020

With the recent release of AI interaction guidelines from Apple, Google, and Microsoft, there is clearly interest in understanding the best practices in human-AI interaction. However, industry standards are not determined by a single company, but rather by the synthesis of knowledge from the whole community. We have surveyed all of the design guidelines from each of these major companies and developed a single, unified structure of guidelines, giving developers a centralized reference. We have then used this framework to compare each of the surveyed companies to find differences in areas of emphasis. Finally, we encourage people to contribute additional guidelines from other companies, academia, or individuals, to provide an open and extensible reference of AI design guidelines at https://ai-open-guidelines.readthedocs.io/.
Resource allocation for task-level speculative scientific applications: a proof of concept using Parallel Trajectory Splicing
Venue: arXiv cs.DC 2020
Year: 2020

The constant increase in parallelism available on large-scale distributed computers poses major scalability challenges to many scientific applications. A common strategy to improve scalability is to express the algorithm in terms of independent tasks that can be executed concurrently on a runtime system. In this manuscript, we consider a generalization of this approach where task-level speculation is allowed. In this context, a probability is attached to each task which corresponds to the likelihood that the product of the task will be consumed as part of the calculation. We consider the problem of optimal resource allocation to each of the possible tasks so as too maximize the expected overall computational throughput. The power of this approach is demonstrated by analyzing its application to Parallel Trajectory Splicing, a massively-parallel long-time-dynamics method for atomistic simulations.
Castle in the Sky: Dynamic Sky Replacement and Harmonization in Videos
Venue: arXiv cs.CV 2020
Year: 2020

This paper proposes a vision-based method for video sky replacement and harmonization, which can automatically generate realistic and dramatic sky backgrounds in videos with controllable styles. Different from previous sky editing methods that either focus on static photos or require inertial measurement units integrated in smartphones on shooting videos, our method is purely vision-based, without any requirements on the capturing devices, and can be well applied to either online or offline processing scenarios. Our method runs in real-time and is free of user interactions. We decompose this artistic creation process into a couple of proxy tasks including sky matting, motion estimation, and image blending. Experiments are conducted on videos diversely captured in the wild by handheld smartphones and dash cameras, and show high fidelity and good generalization of our method in both visual quality and lighting/motion dynamics. Our code and animated results are available at \url{https://jiupinjia.github.io/skyar/}.
Learning Invariances in Neural Networks
Venue: arXiv cs.LG 2020
Year: 2020

Invariances to translations have imbued convolutional neural networks with powerful generalization properties. However, we often do not know a priori what invariances are present in the data, or to what extent a model should be invariant to a given symmetry group. We show how to \emph{learn} invariances and equivariances by parameterizing a distribution over augmentations and optimizing the training loss simultaneously with respect to the network parameters and augmentation parameters. With this simple procedure we can recover the correct set and extent of invariances on image classification, regression, segmentation, and molecular property prediction from a large space of augmentations, on training data alone.
AEGIS: A real-time multimodal augmented reality computer vision based system to assist facial expression recognition for individuals with autism spectrum disorder
Venue: arXiv cs.CV 2020
Year: 2020

The ability to interpret social cues comes naturally for most people, but for those living with Autism Spectrum Disorder (ASD), some experience a deficiency in this area. This paper presents the development of a multimodal augmented reality (AR) system which combines the use of computer vision and deep convolutional neural networks (CNN) in order to assist individuals with the detection and interpretation of facial expressions in social settings. The proposed system, which we call AEGIS (Augmented-reality Expression Guided Interpretation System), is an assistive technology deployable on a variety of user devices including tablets, smartphones, video conference systems, or smartglasses, showcasing its extreme flexibility and wide range of use cases, to allow integration into daily life with ease. Given a streaming video camera source, each real-world frame is passed into AEGIS, processed for facial bounding boxes, and then fed into our novel deep convolutional time windowed neural network (TimeConvNet). We leverage both spatial and temporal information in order to provide an accurate expression prediction, which is then converted into its corresponding visualization and drawn on top of the original video frame. The system runs in real-time, requires minimal set up and is simple to use. With the use of AEGIS, we can assist individuals living with ASD to learn to better identify expressions and thus improve their social experiences.
Few-Shot Adaptation of Generative Adversarial Networks
Venue: arXiv cs.CV 2020
Year: 2020

Generative Adversarial Networks (GANs) have shown remarkable performance in image synthesis tasks, but typically require a large number of training samples to achieve high-quality synthesis. This paper proposes a simple and effective method, Few-Shot GAN (FSGAN), for adapting GANs in few-shot settings (less than 100 images). FSGAN repurposes component analysis techniques and learns to adapt the singular values of the pre-trained weights while freezing the corresponding singular vectors. This provides a highly expressive parameter space for adaptation while constraining changes to the pretrained weights. We validate our method in a challenging few-shot setting of 5-100 images in the target domain. We show that our method has significant visual quality gains compared with existing GAN adaptation methods. We report qualitative and quantitative results showing the effectiveness of our method. We additionally highlight a problem for few-shot synthesis in the standard quantitative metric used by data-efficient image synthesis works. Code and additional results are available at http://e-271.github.io/few-shot-gan.
Accelerating Reinforcement Learning with Learned Skill Priors
Venue: arXiv cs.LG 2020
Year: 2020

Intelligent agents rely heavily on prior experience when learning a new task, yet most modern reinforcement learning (RL) approaches learn every task from scratch. One approach for leveraging prior knowledge is to transfer skills learned on prior tasks to the new task. However, as the amount of prior experience increases, the number of transferable skills grows too, making it challenging to explore the full set of available skills during downstream learning. Yet, intuitively, not all skills should be explored with equal probability; for example information about the current state can hint which skills are promising to explore. In this work, we propose to implement this intuition by learning a prior over skills. We propose a deep latent variable model that jointly learns an embedding space of skills and the skill prior from offline agent experience. We then extend common maximum-entropy RL approaches to use skill priors to guide downstream learning. We validate our approach, SPiRL (Skill-Prior RL), on complex navigation and robotic manipulation tasks and show that learned skill priors are essential for effective skill transfer from rich datasets. Videos and code are available at https://clvrai.com/spirl.
A Differentially Private Text Perturbation Method Using a Regularized Mahalanobis Metric
Venue: arXiv cs.CL 2020
Year: 2020

Balancing the privacy-utility tradeoff is a crucial requirement of many practical machine learning systems that deal with sensitive customer data. A popular approach for privacy-preserving text analysis is noise injection, in which text data is first mapped into a continuous embedding space, perturbed by sampling a spherical noise from an appropriate distribution, and then projected back to the discrete vocabulary space. While this allows the perturbation to admit the required metric differential privacy, often the utility of downstream tasks modeled on this perturbed data is low because the spherical noise does not account for the variability in the density around different words in the embedding space. In particular, words in a sparse region are likely unchanged even when the noise scale is large. %Using the global sensitivity of the mechanism can potentially add too much noise to the words in the dense regions of the embedding space, causing a high utility loss, whereas using local sensitivity can leak information through the scale of the noise added. In this paper, we propose a text perturbation mechanism based on a carefully designed regularized variant of the Mahalanobis metric to overcome this problem. For any given noise scale, this metric adds an elliptical noise to account for the covariance structure in the embedding space. This heterogeneity in the noise scale along different directions helps ensure that the words in the sparse region have sufficient likelihood of replacement without sacrificing the overall utility. We provide a text-perturbation algorithm based on this metric and formally prove its privacy guarantees. Additionally, we empirically show that our mechanism improves the privacy statistics to achieve the same level of utility as compared to the state-of-the-art Laplace mechanism.