Reviewerly/exHarmony huggingface dataset:
Stateless Authentication with JSON Web Tokens using RSA-512 Algorithm

Today's technology needs are getting higher, one of the technologies that continues to grow now is Web Service (WS). WS can increase service flexibility on a system. However, security at WS is one of the things that needs attention. One effort to overcome this problem is JWT (JSON Web Token). JWT is one of the authentication mechanisms in WS, with a standard signature algorithm, HMAC SHA256, RSA-256 or ECDSA. In this research we will discuss the performance of JWT RSA-512 which is implemented on SOAP and RESTful. Because based on previous research the speed performance of the 512-bit algorithm is better, but it is not yet known if applied to JWT. The test results show that the speed of the JWT RSA-512 token on the RESTful process is superior to 24.69% compared to SOAP. Then the speed of the authentication of JWT RSA-512 tokens, RESTful is superior to 11.64% compared to SOAP. Whereas in testing the size of JWT RSA-512 generated tokens, RESTful is only 1.25% superior to SOAP.
HO-3D: A Multi-User, Multi-Object Dataset for Joint 3D Hand-Object Pose Estimation.

We propose a new dataset for 3D hand+object pose estimation from color images, together with a method for efficiently annotating this dataset, and a 3D pose prediction method based on this dataset. The current lack of training data makes the 3D hand+object pose estimation very challenging. This lack is due to the complexity of labeling many real images with both 3D poses and of generating synthetic images with various realistic interaction. Moreover, even if synthetic images could be used for training, annotated real images are still needed for validation. To tackle this challenge, we capture sequences with a simple setup made of a single RGB-D camera. We also use a color camera imaging the sequences from a side view, but only for validation. We introduce a novel method based on global optimization that exploits depth, color, and temporal constraints for efficiently annotating the sequences, which we use to train another novel method that predicts both the 3D poses of the hand and the object from a single color image. Our hope is to encourage other researchers to develop better annotation methods for our dataset: One can then apply such method to capture and easily annotate sequences captured with a single RGB-D camera to easily create additional training data thus solving one of the main problems of 3D hand+object pose estimation.
Learning Description Logic Concepts: When can Positive and Negative Examples be Separated?

Learning description logic (DL) concepts from positive and negative examples given in the form of labeled data items in a KB has received significant attention in the literature. We study the fundamental question of when a separating DL concept exists and provide useful model-theoretic characterizations as well as complexity results for the associated decision problem. For expressive DLs such as ALC and ALCQI, our characterizations show a surprising link to the evaluation of ontology-mediated conjunctive queries. We exploit this to determine the combined complexity (between ExpTime and NExpTime) and data complexity (second level of the polynomial hierarchy) of separability. For the Horn DL EL, separability is ExpTime-complete both in combined and in data complexity while for its modest extension ELI it is even undecidable. Separability is also undecidable when the KB is formulated in ALC and the separating concept is required to be in EL or ELI.
Cograph Regularized Collective Nonnegative Matrix Factorization for Multilabel Image Annotation

Automatic image annotation is an effective and straightforward way to facilitate many applications in computer vision.However, manually annotating images is a computation-expensive and labor-intensive task.To address these problems, this paper proposes a novel approach by using a cograph regularized collective nonnegative matrix factorization method to annotate images, which is referred to as CG-CNMF; CG-CNMF maximizes the annotation consistency for each image and minimizes the semantic gap for good annotation performance.To reduce the computation cost, this method formulates the annotation problem as a recommending issue and uses nonnegative matrix factorization (NMF) to recover the image-tolabel relation for the testing images.Moreover, to find the most similar latent image features and latent label features during the matrix factorization, it exploits the image-to-image relation and label-to-label relation by utilizing the visual content information of images and the semantic cooccurrence information of labels, respectively.To reduce the semantic gap between the image visual content and semantic concepts, both the semantic features and convolutional neural networks (CNNs)-based visual features are considered.Moreover, to address the label-imbalance and incomplete-label problems, the visual-based label cooccurrence information is also considered.In this way, visually similar images are highly correlated with the true semantics of the test images.The experimental results for three multilabel image datasets demonstrate the effectiveness and the efficiency of the proposed method.
Interactive Drinking Gadget for the Elderly and Alzheimer Patients

With this contribution we want to give insights into the development of a drinking gadget which is useable by the elderly and dementia patients with the goal to create an automated drinking protocol. Through the literature and available smart cups, we deduced important strategies for the design of a drinking aid. Our natural solution is integrated into the daily workflow, hence enables the caregivers to react to the needs of the elderly without additional burden. Therefore, we identified some key requirements for both user groups. Furthermore, we integrated aspects of expandability with software, a vision of rich interaction between user/gadget and focused on the convenience during the daily usage to reach the elderly and caregivers alike.
Interdependencies, Conflicts and Trade-Offs Between Security and Usability: Why and How Should We Engineer Them?

Security and usability are considered as conflicting goals. Despite the recognition that security and usability conflicts pose a serious challenge to achieve effective security, the review of the state of art identifies many gaps in today’s practices including, (1) failure of security specialists to address usability, as perceived and defined by the human computer interaction (HCI) community, (2) industry’s behavior is being more driven by bug fixing, rather than trying to examine and consider the context and the human experiences in which the bugs occurs, and (3) the lack of HCI skills required for conducting effective user studies. Furthermore, analysis of the existing literature identifies different perceptions concerning the relationship between security and usability. Some researchers have identified existence of trade-offs when it comes to the security and usability conflicts, however, others refer to the trade-offs as mere myths. A four staged process oriented framework to address the security and usability conflict is presented in this paper. The framework governs aspects from identification of the conflicts to elicitation of suitable trade-offs. To support re-use, the outcomes of employing the framework are documented in form of design patterns. A template to standardize documentation of the patterns is also presented along with one example of the usable security patterns.
Convolutional Regression Network for Multi-Oriented Text Detection

Multi-oriented text detection in the wild is a challenging task due to the variations of scales, orientations, illumination, and languages. The traditional anchor mechanism on generic object detection can only generate horizontal proposals, which cannot be applied to detecting multi-oriented text regions. Considering this, in this paper, we propose a novel convolutional regression network (CRN) to localize multi-oriented text in natural images, which consists of two components: region proposal extractor and text locator. To be specific, we first present a hierarchical deconvolution module (HDM), a text-line and geometry segmentation module (TGM) to segment the multi-oriented proposals accurately, both of which are fully convolutional networks. Then, a classification and regression module (CRM) is adopted to process the proposals and obtain the final localization results. The whole framework can be trained in an end-to-end mechanism which is suitable for detecting multi-oriented texts. The extensive experiments are conducted on three mainstream scene-text datasets, and the experimental results evidence the proposed CRN achieves competitive performance.
Hierarchical Reinforcement Learning via Advantage-Weighted Information Maximization.

Real-world tasks are often highly structured. Hierarchical reinforcement learning (HRL) has attracted research interest as an approach for leveraging the hierarchical structure of a given task in reinforcement learning (RL). However, identifying the hierarchical policy structure that enhances the performance of RL is not a trivial task. In this paper, we propose an HRL method that learns a latent variable of a hierarchical policy using mutual information maximization. Our approach can be interpreted as a way to learn a discrete and latent representation of the state-action space. To learn option policies that correspond to modes of the advantage function, we introduce advantage-weighted importance sampling. In our HRL method, the gating policy learns to select option policies based on an option-value function, and these option policies are optimized based on the deterministic policy gradient method. This framework is derived by leveraging the analogy between a monolithic policy in standard RL and a hierarchical policy in HRL by using a deterministic option policy. Experimental results indicate that our HRL approach can learn a diversity of options and that it can enhance the performance of RL in continuous control tasks.
Redundancy Techniques for Straggler Mitigation in Distributed Optimization and Learning

Performance of distributed optimization and learning systems is bottlenecked by straggler nodes and slow communication links, which significantly delay computation. We propose a distributed optimization framework where the dataset is to have an over-complete representation with built-in redundancy, and the straggling nodes in the system are dynamically left out of the computation at every iteration, whose loss is compensated by the embedded redundancy. We show that oblivious application of several popular optimization algorithms on encoded data, including gradient descent, L-BFGS, proximal gradient under data parallelism, and coordinate descent under model parallelism, converge to either approximate or exact solutions of the original problem when stragglers are treated as erasures. These convergence results are deterministic, i.e., they establish sample path convergence for arbitrary sequences of delay patterns or distributions on the nodes, and are independent of the tail behavior of the delay distribution. We demonstrate that equiangular tight frames have desirable properties as encoding matrices, and propose efficient mechanisms for encoding large-scale data. We implement the proposed technique on Amazon EC2 clusters, and demonstrate its performance over several learning problems, including matrix factorization, LASSO, ridge regression and logistic regression, and compare the proposed method with uncoded, asynchronous, and data replication strategies.
Policy Optimization with Model-Based Explorations

Model-free reinforcement learning methods such as the Proximal Policy Optimization algorithm (PPO) have successfully applied in complex decision-making problems such as Atari games. However, these methods suffer from high variances and high sample complexity. On the other hand, model-based reinforcement learning methods that learn the transition dynamics are more sample efficient, but they often suffer from the bias of the transition estimation. How to make use of both model-based and model-free learning is a central problem in reinforcement learning.In this paper, we present a new technique to address the tradeoff between exploration and exploitation, which regards the difference between model-free and model-based estimations as a measure of exploration value. We apply this new technique to the PPO algorithm and arrive at a new policy optimization method, named Policy Optimization with Modelbased Explorations (POME). POME uses two components to predict the actions’ target values: a model-free one estimated by Monte-Carlo sampling and a model-based one which learns a transition model and predicts the value of the next state. POME adds the error of these two target estimations as the additional exploration value for each state-action pair, i.e, encourages the algorithm to explore the states with larger target errors which are hard to estimate. We compare POME with PPO on Atari 2600 games, and it shows that POME outperforms PPO on 33 games out of 49 games.
Asymptotically optimal Boolean functions

The largest Hamming distance between a Boolean function in n variables and the set of all affine Boolean functions in n variables is known as the covering radius ρn of the [2n,n+1] Reed–Muller code. This number determines how well Boolean functions can be approximated by linear Boolean functions. We prove thatlimn→∞⁡2n/2−ρn/2n/2−1=1, which resolves a conjecture due to Patterson and Wiedemann from 1983.
Recognition of Anomalously Deformed Kana Sequences in Japanese Historical Documents

This paper presents recognition of anomalously deformed Kana sequences in Japanese historical documents, for which a contest was held by IEICE PRMU 2017. The contest was divided into three levels in accordance with the number of characters to be recognized: level 1: single characters, level 2: sequences of three vertically written Kana characters, and level 3: unrestricted sets of characters composed of three or more characters possibly in multiple lines. This paper focuses on the methods for levels 2 and 3 that won the contest. We basically follow the segmentation-free approach and employ the hierarchy of a Convolutional Neural Network (CNN) for feature extraction, Bidirectional Long Short-Term Memory (BLSTM) for frame prediction, and Connectionist Temporal Classification (CTC) for text recognition, which is named a Deep Convolutional Recurrent Network (DCRN). We compare the pretrained CNN approach and the end-to-end approach with more detailed variations for level 2. Then, we propose a method of vertical text line segmentation and multiple line concatenation before applying DCRN for level 3. We also examine a two-dimensional BLSTM (2DBLSTM) based method for level 3. We present the evaluation of the best methods by cross validation. We achieved an accuracy of 89.10% for the three-Kana-character sequence recognition and an accuracy of 87.70% for the unrestricted Kana recognition without employing linguistic context. These results prove the performances of the proposed models on the level 2 and 3 tasks.
IoT-Based Improved Human Motion Estimations Method Under Cyber Attacks

Human beings and control centers are usually far away from each other, so cyber attacks on the sensor measurements can lead to loss of user privacy, information, and trust. Driven by this motivation, this letter proposes an Internet of Things (IoT)-based human motion estimations algorithm under cyber attacks. The sensing measurements are transmitted to the control center over an unreliable communication channel where cyber attack occurs. Based on the mean squared error, the optimal state estimation algorithm is derived to estimate human motions. Simulation results show that the proposed method provides significant performance improvement compared with the existing approach.
Assessing the Effect of Image Quality on SSD and Faster R-CNN Networks for Face Detection

Face detection is one of the most challenging and long-studied areas in computer vision. In real-world, images are exposed to the noise and degradation. In this paper, we investigate the robustness of two networks namely SSD and Faster R-CNN in confrontation with salt and pepper noise, Gaussian blur, as well as JPEG compression. Our experiments are conducted on the well-known Wider Face dataset. These experiments show that the Faster R-CNN is more robust against Gaussian blur, while SSD is much more sensitive to the edges. On the other hand, SSD is more robust against reduced-quality JPEG compressed images. The reason should be due to the sensitivity of Faster R-CNN to the texture of the objects. Moreover, our experiments demonstrated that both networks have a relatively similar resistance under salt and pepper noise.
Assessment of monthly global solar irradiation estimates using air temperature in different climates of the state of Rio de Janeiro, Southeastern Brazil

The number of weather stations that measure solar global irradiance (Ig) is scarce, and when it is available, it does not present long-time series, without gaps and high quality. When Ig is unavailable, it is possible to estimate its integral over time—solar global irradiation (Hg)—using empirical methods. However, for a better performance these methods need to be fitted to the local climatic conditions. The aim of this study was to assess the Hargreaves–Samani (HS) and Bristow–Campbell (BC) methods to estimate monthly average daily Hg in the state of Rio de Janeiro, Southeastern Brazil, and to propose a simple approach to determine the empirical coefficients in function of the climate. The methods are based on maximum and minimum air temperature and on extraterrestrial solar irradiation. Series of air temperature extremes and Hg from 15 automatic weather stations between 2000 until 2013 were used. The methods were evaluated by the statistical indexes: determination coefficient (r2) of the linear regression between observed and estimated monthly Hg, root mean square error (RMSE), Willmott’s index (d) and performance index (c). The methods (BC—r2 > 0.60, d > 0.85 and RMSE < 2.99 MJ m−2 d−1 and HS—r2 > 0.55, d > 0.75 and RMSE < 3.85 MJ m−2 d−1) had satisfactory performance in the estimation of monthly Hg for the state of Rio de Janeiro, when their coefficients were fitted to local climatic conditions. The BC presented performance classified as “optimal” (c > 0.85) in approximately 80% of the stations analyzed, while for Hargreaves–Samani, only 55% of the stations were classified as “optimal.” The highest HS coefficients (kr) occurred in Semiarid (0.246 ± 0,023) and Dry Sub-humid (0.181 ± 0.011) climates and were associated with coastal regions (< 20 km), while the stations in Humid (0.146 ± 0.008), Sub-humid (0.1524 ± 0.003) and Dry Sub-humid (0.162 ± 0.011) climates located in interior regions presented the lowest kr. Thus, it is possible to determine the kr coefficient based only on the climatic classification of the site and distance of the coastal environment. In general, the highest atmospheric transmittance (β0—BC method) was observed in Semiarid and Dry Sub-humid climate regions. β1 and β2 coefficients did not present a distribution pattern with the local climatology and with the proximity of large water bodies. The methods presented a better performance in Dry Sub-humid and Semiarid climates, due to the lower variability of cloudiness and greater thermal amplitude.
Orientation-Aware Motion Planning in Complex Workspaces using Adaptive Harmonic Potential Fields

In this work, a hybrid control scheme is presented in order to address the navigation problem for a planar robotic platform of arbitrary shape that is moving inside an obstacle cluttered workspace. Given an initial and desired robot configuration, we propose a methodology based on approximate configuration space decomposition techniques that makes use of heuristics to adaptively refine a partition of the configuration space into non-overlapping, adjacent slices. Furthermore, we employ appropriate workspace transformations and adaptive potential field based control laws that integrate elegantly with the type of configuration space representation used, in order to safely navigate within a given cell and successfully cross over to the next, for almost all initial configurations, until the desired configuration is reached. Finally, we present simulation results that demonstrate the efficacy of the proposed control scheme.
Flexible Regression Models for Count Data Based on Renewal Processes: The Countr Package

A new alternative to the standard Poisson regression model for count data is suggested.This new family of models is based on discrete distributions derived from renewal processes, i.e., distributions of the number of events by some time t.Unlike the Poisson model, these models have, in general, time-dependent hazard functions.Any survival distribution can be used to describe the inter-arrival times between events, which gives a rich class of count processes with great flexibility for modelling both underdispersed and overdispersed data.The R package Countr provides a function, renewalCount(), for fitting renewal count regression models and methods for working with the fitted models.The interface is designed to mimic the glm() interface and standard methods for model exploration, diagnosis and prediction are implemented.Package Countr implements stateof-the-art recently developed methods for fast computation of the count probabilities.The package functionalities are illustrated using several datasets.
Enhancement of Weakly Illuminated Images by Deep Fusion Networks

We propose an end-to-end deep fusion-based approach to enhance the quality of images acquired in weak illumination environment. The proposed deep fusion network (DFN), without estimating illumination explicitly, uses a convolutional neural network (CNN) to generate confidence maps as spatial weighting factors to fuse images created by multiple base image enhancement techniques that complement each other in a content-dependent manner. Our tests on both synthetic and real weakly illuminated images show that the proposed DFN approach delivers superior performance in terms of both subjective visual perception and objective quality assessment.
NTT’s Machine Translation Systems for WMT19 Robustness Task

This paper describes NTT's submission to the WMT19 robustness task.This task mainly focuses on translating noisy text (e.g., posts on Twitter), which presents different difficulties from typical translation tasks such as news.Our submission combined techniques including utilization of a synthetic corpus, domain adaptation, and a placeholder mechanism, which significantly improved over the previous baseline.Experimental results revealed the placeholder mechanism, which temporarily replaces the non-standard tokens including emojis and emoticons with special placeholder tokens during translation, improves translation accuracy even with noisy texts.
Complexity of Highly Parallel Non-Smooth Convex Optimization

A landmark result of non-smooth convex optimization is that gradient descent is an optimal algorithm whenever the number of computed gradients is smaller than the dimension $d$. In this paper we study the extension of this result to the parallel optimization setting. Namely we consider optimization algorithms interacting with a highly parallel gradient oracle, that is one that can answer $\mathrm{poly}(d)$ gradient queries in parallel. We show that in this case gradient descent is optimal only up to $\tilde{O}(\sqrt{d})$ rounds of interactions with the oracle. The lower bound improves upon a decades old construction by Nemirovski which proves optimality only up to $d^{1/3}$ rounds (as recently observed by Balkanski and Singer), and the suboptimality of gradient descent after $\sqrt{d}$ rounds was already observed by Duchi, Bartlett and Wainwright. In the latter regime we propose a new method with improved complexity, which we conjecture to be optimal. The analysis of this new method is based upon a generalized version of the recent results on optimal acceleration for highly smooth convex optimization.