Reviewerly/exHarmony huggingface dataset:
3D Hand Pose Estimation from RGB Using Privileged Learning with Depth Data

This paper proposes a method for 3D hand pose estimation given a large dataset of depth images with joint annotations, and a smaller dataset of depth and RGB image pairs with joint annotations. We explore different ways of using the depth data at the training stage to improve the pose estimation accuracy of a network that only takes RGB images as input. By using paired RGB and depth images, we are able to supervise the RGB-based network to learn middle layer features that mimic that of a network trained on largescale, accurately annotated depth data. Further, depth data provides accurate foreground masks, which are employed to learn better feature activations in the RGB network. During testing, when only RGB images are available, our method produces accurate 3D hand pose predictions. The method is also shown to perform well on the 2D hand pose estimation task. We validate the approach on three public datasets, and compare it to other published methods.
Optimizing Performance of Convolutional Neural Network Using Computing Technique

Based on the deep learning algorithms, there are many recent rapid growths of applications. A deep learning algorithm which is expanded from Artificial Neural Networks, and it is extensively used for picture categorization and identification. Still there is no Neural Network based computing technique, i.e. pipelining has been signified in order to assist the existence of deep neural network in terms of accuracy. In this paper, Computing-based convolutional neural network system produced highly precise and productive system is used. The proposed convolutional neural network will compare with previous convolutional neural network executed on an FPGA using a computing technique to optimize the time delay and power consumption of the system, with high accuracy as compared to previous conventional neural network implementations.
DAPA: A Decentralized, Accountable, and Privacy-Preserving Architecture for Car Sharing Services

Car sharing offers a flexible peer-to-peer or station based car rental service to customers. On one hand, it requires customers to expose identifications (e.g., valid driving licenses) to car sharing service providers (CSSPs) for accountability, i.e., the driving qualification of customers can be verified and misbehaving customers can be traced by CSSPs. On the other hand, privacy concerns arise when customers identities are exposed as honest-but-curious CSSPs may secretly extract customers privacy information by linking their car rental records to their identities. To resolve this contradiction, we propose a decentralized, accountable, and privacy-preserving architecture for car sharing services, named DAPA. In specific, to overcome the limitation of the single point of failure, multiple dynamic validation servers are employed to substitute a single trusted third-party authority and assist in building decentralized trust for customers. In addition, to protect customers' privacy and achieve accountability simultaneously under the decentralized architecture, a new privacy-preserving identity management (PPIM) scheme is introduced as a basic module for DAPA. Customers' identities are protected in a distributed and dynamic manner but publicly verified based on a well-designed zero-knowledge proof protocol. Only the misbehaving customers' identities can be recovered by a majority of validation servers using adaptive verifiable secret sharing/redistribution techniques. Detailed security analysis shows that DAPA can minimize privacy breaches and guarantee the accountability. Performance evaluations via extensive simulations demonstrate that DAPA is efficient in terms of computational costs and communication overheads.
SpecAugment Impact on Automatic Speaker Verification System

An automatic speaker verification (ASV) is one of the challenging problem in speech processing since there are so many models of machine learnings those capable of synthesizing a fake speech from a given text. This paper discusses the impact of SpecAugment to methods such as Gaussian Mixture Models (GMM) and Deep Neural Networks (DNNs). Some experiments on a speech dataset sampled from the ASVSpoof2019, which is specially made to tackle the threat of spoofing, show that DNNs produces an Equal Error Rate (EER) of 18.1% that is better than the GMM system with EER of 19.0%. And after combining with a traditional augmentation technique, the DNNs also gives a better EER of 15.3% than GMM with EER of 15.7%.
EyeNet: A Multi-Task Deep Network for Off-Axis Eye Gaze Estimation

Eye gaze estimation is a crucial component in Virtual and Mixed Reality. In head-mounted VR/MR devices the eyes are imaged off-axis to avoid blocking the user's gaze, this view-point makes drawing eye related inferences very challenging. In this work, we present EyeNet, the first single deep neural network which solves multiple heterogeneous tasks related to eye gaze estimation for an off-axis camera setting. The tasks include eye segmentation, IR LED glints detection, pupil and cornea center estimation. We benchmark all tasks on MagicEyes, a large and new dataset of 587 subjects with varying morphology, gender, skin-color, make-up and imaging conditions.
PLANET: Improved Convolutional Neural Networks with Image Enhancement for Image Classification

In the past few years, deep learning has become a research hotspot and has had a profound impact on computer vision. Deep CNN has been proven to be the most important and effective model for image processing, but due to the lack of training samples and huge number of learning parameters, it is easy to tend to overfit. In this work, we propose a new two-stage CNN image classification network, named “Improved Convolutional Neural Networks with Image Enhancement for Image Classification” and PLANET in abbreviation, which uses a new image data enhancement method called InnerMove to enhance images and augment the number of training samples. InnerMove is inspired by the “object movement” scene in computer vision and can improve the generalization ability of deep CNN models for image classification tasks. Sufficient experiment results show that PLANET utilizing InnerMove for image enhancement outperforms the comparative algorithms, and InnerMove has a more significant effect than the comparative data enhancement methods for image classification tasks.
The application of unsupervised deep learning in predictive models using electronic health records

Abstract Background The main goal of this study is to explore the use of features representing patient-level electronic health record (EHR) data, generated by the unsupervised deep learning algorithm autoencoder, in predictive modeling. Since autoencoder features are unsupervised, this paper focuses on their general lower-dimensional representation of EHR information in a wide variety of predictive tasks. Methods We compare the model with autoencoder features to traditional models: logistic model with least absolute shrinkage and selection operator (LASSO) and Random Forest algorithm. In addition, we include a predictive model using a small subset of response-specific variables (Simple Reg) and a model combining these variables with features from autoencoder (Enhanced Reg). We performed the study first on simulated data that mimics real world EHR data and then on actual EHR data from eight Advocate hospitals. Results On simulated data with incorrect categories and missing data, the precision for autoencoder is 24.16% when fixing recall at 0.7, which is higher than Random Forest (23.61%) and lower than LASSO (25.32%). The precision is 20.92% in Simple Reg and improves to 24.89% in Enhanced Reg. When using real EHR data to predict the 30-day readmission rate, the precision of autoencoder is 19.04%, which again is higher than Random Forest (18.48%) and lower than LASSO (19.70%). The precisions for Simple Reg and Enhanced Reg are 18.70 and 19.69% respectively. That is, Enhanced Reg can have competitive prediction performance compared to LASSO. In addition, results show that Enhanced Reg usually relies on fewer features under the setting of simulations of this paper. Conclusions We conclude that autoencoder can create useful features representing the entire space of EHR data and which are applicable to a wide array of predictive tasks. Together with important response-specific predictors, we can derive efficient and robust predictive models with less labor in data extraction and model training.
Distant Supervision for Multi-Stage Fine-Tuning in Retrieval-Based Question Answering

We tackle the problem of question answering directly on a large document collection, combining simple “bag of words” passage retrieval with a BERT-based reader for extracting answer spans. In the context of this architecture, we present a data augmentation technique using distant supervision to automatically annotate paragraphs as either positive or negative examples to supplement existing training data, which are then used together to fine-tune BERT. We explore a number of details that are critical to achieving high accuracy in this setup: the proper sequencing of different datasets during fine-tuning, the balance between “difficult” vs. “easy” examples, and different approaches to gathering negative examples. Experimental results show that, with the appropriate settings, we can achieve large gains in effectiveness on two English and two Chinese QA datasets. We are able to achieve results at or near the state of the art without any modeling advances, which once again affirms the cliché “there’s no data like more data”.
Keywords Generation Improves E-Commerce Session-based Recommendation

By exploring fine-grained user behaviors, session-based recommendation predicts a user's next action from short-term behavior sessions. Most of previous work learns about a user's implicit behavior by merely taking the last click action as the supervision signal. However, in e-commerce scenarios, large-scale products with elusive click behaviors make such task challenging because of the low inclusiveness problem, i.e., many relevant products that satisfy the user's shopping intention are neglected by recommenders. Since similar products with different IDs may share the same intention, we argue that the textual information (e.g., keywords of product titles) from sessions can be used as additional supervision signals to tackle above problem through learning more shared intention within similar products. Therefore, to improve the performance of e-commerce session-based recommendation, we explicitly infer the user's intention by generating keywords entirely from the click sequence in the current session.
Visual Question Answering for Cultural Heritage

Technology and the fruition of cultural heritage are becoming increasingly more entwined, especially with the advent of smart audio guides, virtual and augmented reality, and interactive installations. Machine learning and computer vision are important components of this ongoing integration, enabling new interaction modalities between user and museum. Nonetheless, the most frequent way of interacting with paintings and statues still remains taking pictures. Yet images alone can only convey the aesthetics of the artwork, lacking is information which is often required to fully understand and appreciate it. Usually this additional knowledge comes both from the artwork itself (and therefore the image depicting it) and from an external source of knowledge, such as an information sheet. While the former can be inferred by computer vision algorithms, the latter needs more structured data to pair visual content with relevant information. Regardless of its source, this information still must be be effectively transmitted to the user. A popular emerging trend in computer vision is Visual Question Answering (VQA), in which users can interact with a neural network by posing questions in natural language and receiving answers about the visual content. We believe that this will be the evolution of smart audio guides for museum visits and simple image browsing on personal smartphones. This will turn the classic audio guide into a smart personal instructor with which the visitor can interact by asking for explanations focused on specific interests. The advantages are twofold: on the one hand the cognitive burden of the visitor will decrease, limiting the flow of information to what the user actually wants to hear; and on the other hand it proposes the most natural way of interacting with a guide, favoring engagement.
Sub-linear RACE Sketches for Approximate Kernel Density Estimation on Streaming Data

Kernel density estimation is a simple and effective method that lies at the heart of many important machine learning applications. Unfortunately, kernel methods scale poorly for large, high dimensional datasets. Approximate kernel density estimation has a prohibitively high memory and computation cost, especially in the streaming setting. Recent sampling algorithms for high dimensional densities can reduce the computation cost but cannot operate online, while streaming algorithms cannot handle high dimensional datasets due to the curse of dimensionality. We propose RACE, an efficient sketching algorithm for kernel density estimation on high-dimensional streaming data. RACE compresses a set of N high dimensional vectors into tiny arrays of integer counters. These arrays are sufficient to estimate the kernel density for a large class of kernels. Our one-pass sketch is simple to implement and comes with strong theoretical guarantees. We evaluate our method on real-world high-dimensional datasets and show that our sketch achieves 10x better compression compared to existing methods.1
A Cooperative Coevolutionary Approach to Maximise Surveillance Coverage of UAV Swarms

This paper presents the parameterisation and optimisation of the CACOC (Chaotic Ant Colony Optimisation for Coverage) mobility model used by an Unmanned Aerial Vehicle (UAV) swarm to perform surveillance tasks. CACOC uses chaotic solutions of a dynamical system and pheromones for optimising area coverage. Consequently, several parameters of CACOC are to be optimised with the aim of improving its coverage performance. We propose a Genetic Algorithm (GA) and two Cooperative Coevolutionary Genetic Algorithms (CCGA) to tackle this problem. After testing our proposals on four case studies we performed a comparative analysis to conclude that the cooperative approaches allow a better exploration of the search space by optimising each UAV parameters independently.
Unsupervised Word Translation with Adversarial Autoencoder

Crosslingual word embeddings learned from monolingual embeddings have a crucial role in many downstream tasks, ranging from machine translation to transfer learning. Adversarial training has shown impressive success in learning crosslingual embeddings and the associated word translation task without any parallel data by mapping monolingual embeddings to a shared space. However, recent work has shown superior performance for non-adversarial methods in more challenging language pairs. In this article, we investigate adversarial autoencoder for unsupervised word translation and propose two novel extensions to it that yield more stable training and improved results. Our method includes regularization terms to enforce cycle consistency and input reconstruction, and puts the target encoders as an adversary against the corresponding discriminator. We use two types of refinement procedures sequentially after obtaining the trained encoders and mappings from the adversarial training, namely, refinement with Procrustes solution and refinement with symmetric re-weighting. Extensive experimentations with high- and low-resource languages from two different data sets show that our method achieves better performance than existing adversarial and non-adversarial approaches and is also competitive with the supervised system. Along with performing comprehensive ablation studies to understand the contribution of different components of our adversarial model, we also conduct a thorough analysis of the refinement procedures to understand their effects.
Quantum coherence spectrum and quantum phase transitions

We review the internal connection between quantum coherence (QC) and quantum phase transitions (QPTs). We propose two quantities ${L}_{\text{QC}}$ and ${S}_{\text{QC}}$ to detect QPTs of different orders by analyzing the spectrum of QC. Our results show that the two quantities not only can detect different types of QPT but can also exhibit a remarkable robustness against thermal fluctuations. In comparison with other detectors, such as quantum entanglement and QD, ${L}_{\text{QC}}$ and ${S}_{\text{QC}}$ can show more effective and reliable behaviors in characterizing a QPT at zero and finite temperatures. In light of this property and the fundamental role of QC in quantum information processing, QC spectrum-related ${L}_{\text{QC}}$ and ${S}_{\text{QC}}$ have considerable importance in detecting QPTs at a real finite-temperature experimental environment.
LegRoast: Efficient Post-quantum Signatures from the Legendre PRF

We introduce an efficient post-quantum signature scheme that relies on the one-wayness of the Legendre PRF. This "LEGendRe One-wAyness SignaTure" (LegRoast) builds upon the MPC-in-the-head technique to construct an efficient zero-knowledge proof, which is then turned into a signature scheme with the Fiat-Shamir transform. Unlike many other Fiat-Shamir signatures, the security of LegRoast can be proven without using the forking lemma, and this leads to a tight (classical) ROM proof. We also introduce a generalization that relies on the one-wayness of higher-power residue characters; the "POwer Residue ChaRacter One-wAyness SignaTure" (PorcRoast). LegRoast outperforms existing MPC-in-the-head-based signatures (most notably Picnic/Picnic2) in terms of signature size and speed. Moreover, PorcRoast outperforms LegRoast by a factor of 2 in both signature size and signing time. For example, one of our parameter sets targeting NIST security level I results in a signature size of 7.2 KB and a signing time of 2.8ms. This makes PorcRoast the most efficient signature scheme based on symmetric primitives in terms of signature size and signing time.
Representing Data Visualization Goals and Tasks through Meta-Modeling to Tailor Information Dashboards

Information dashboards are everywhere. They support knowledge discovery in a huge variety of contexts and domains. Although powerful, these tools can be complex, not only for the end-users but also for developers and designers. Information dashboards encode complex datasets into different visual marks to ease knowledge discovery. Choosing a wrong design could compromise the entire dashboard’s effectiveness, selecting the appropriate encoding or configuration for each potential context, user, or data domain is a crucial task. For these reasons, there is a necessity to automatize the recommendation of visualizations and dashboard configurations to deliver tools adapted to their context. Recommendations can be based on different aspects, such as user characteristics, the data domain, or the goals and tasks that will be achieved or carried out through the visualizations. This work presents a dashboard meta-model that abstracts all these factors and the integration of a visualization task taxonomy to account for the different actions that can be performed with information dashboards. This meta-model has been used to design a domain specific language to specify dashboards requirements in a structured way. The ultimate goal is to obtain a dashboard generation pipeline to deliver dashboards adapted to any context, such as the educational context, in which a lot of data are generated, and there are several actors involved (students, teachers, managers, etc.) that would want to reach different insights regarding their learning performance or learning methodologies.
Joint Progressive Network and Datacenter Recovery After Large-Scale Disasters

Large-scale disasters affecting both network and datacenter (DC) infrastructures can cause severe disruptions in cloud-based services. During post-disaster recovery, repairs are usually carried out in stages in a progressive manner due to limited repair resource availability. The order in which network elements and DCs are repaired can significantly impact users' reachability to important contents/services. We investigate joint progressive network and DC recovery in which network recovery and DC recovery are conducted in a coordinated manner such that users have access to the maximum possible amount of contents/services at each repair stage. We first solve the optimization problem of joint progressive recovery to find the optimal sequence of network element and DC repairs with the objective to maximize cumulative weighted content reachability in the network. We then propose a scalable heuristic for scheduling the sequential repair of network nodes/links and DCs. Our model assumes that, at each repair stage, one network node with adjacent links and one DC can be fully repaired; however, full recovery may not be guaranteed due to limited resource availability. Hence, we also propose a “resource-aware” approach (with two resource-allocation strategies, namely “selective allocation” and “adaptive allocation”), which considers both full and partial recovery of elements based on available resources at each stage. We show that, compared to disjoint progressive recovery approach, in which network recovery and DC recovery plans are independent, our joint progressive recovery approach provides significantly higher per-stage content reachability in the network.
Analysis of biased stochastic gradient descent using sequential semidefinite programs

We present a convergence rate analysis for biased stochastic gradient descent (SGD), where individual gradient updates are corrupted by computation errors. We develop stochastic quadratic constraints to formulate a small linear matrix inequality (LMI) whose feasible points lead to convergence bounds of biased SGD. Based on this LMI condition, we develop a sequential minimization approach to analyze the intricate trade-offs that couple stepsize selection, convergence rate, optimization accuracy, and robustness to gradient inaccuracy. We also provide feasible points for this LMI and obtain theoretical formulas that quantify the convergence properties of biased SGD under various assumptions on the loss functions.