Reviewerly/exHarmony huggingface dataset:
SignSpeaker

Sign language is a natural and fully-formed communication method for deaf or hearing-impaired people. Unfortunately, most of the state-of-the-art sign recognition technologies are limited by either high energy consumption or expensive device costs and have a difficult time providing a real-time service in a daily-life environment. Inspired by previous works on motion detection with wearable devices, we propose Sign Speaker - a real-time, robust, and user-friendly American sign language recognition (ASLR) system with affordable and portable commodity mobile devices. SignSpeaker is deployed on a smartwatch along with a smartphone; the smartwatch collects the sign signals and the smartphone outputs translation through an inbuilt loudspeaker. We implement a prototype system and run a series of experiments that demonstrate the promising performance of our system. For example, the average translation time is approximately $1.1$ seconds for a sentence with eleven words. The average detection ratio and reliability of sign recognition are 99.2% and 99.5%, respectively. The average word error rate of continuous sentence recognition is 1.04% on average.
A Novel Distributed and Self-Organized Swarm Control Framework for Underactuated Unmanned Marine Vehicles

This paper presents a novel swarm control framework for path following of multiple underactuated unmanned marine vehicles (UMVs) with uncertain dynamics and unmeasured velocities.Main contributions are as follows: (1) unlike previous master-slave formation control, a swarm system function with distributed and self-organized capability is designed; (2) a center-of-swarm (COS) guidance scheme without vehicle number constraints is proposed for swarm path following, where an improved artificial potential field (APF) using ring-shaped repulsion is further employed for collision avoidance and obstacle avoidance; (3) a nonlinear velocity observer is incorporated into the proposed swarm control framework to estimate the unmeasured velocities, thereby contributing to robust controllers based on fuzzy sliding mode against uncertain dynamics and time-varying disturbances.Simulations are carried out to illustrate the universal applicability and effectiveness of the proposed swarm control framework.
QuaRTz: An Open-Domain Dataset of Qualitative Relationship Questions

We introduce the first open-domain dataset, called QUARTZ, for reasoning about textual qualitative relationships.QUARTZ contains general qualitative statements, e.g., "A sunscreen with a higher SPF protects the skin longer.",twinned with 3864 crowdsourced situated questions, e.g., "Billy is wearing sunscreen with a lower SPF than Lucy.Who will be best protected from the sun?", plus annotations of the properties being compared.Unlike previous datasets, the general knowledge is textual and not tied to a fixed set of relationships, and tests a system's ability to comprehend and apply textual qualitative knowledge in a novel setting.We find state-of-the-art results are substantially (20%) below human performance, presenting an open challenge to the NLP community.
Assessing Optimizer Impact on DNN Model Sensitivity to Adversarial Examples

Deep Neural Networks (DNNs) have been gaining state-of-the-art achievement compared with many traditional Machine Learning (ML) models in diverse fields. However, adversarial examples challenge the further deployment and application of DNNs. Analysis has been carried out for studying the reasons of DNNs' vulnerability to adversarial perturbation and focused on model architecture. No research has been done on investigating the impact of optimization algorithms (namely, optimizers in DNNs) employed in training DNN models on models' sensitivity to adversarial examples. This paper aims to study this impact from an experimental perspective. We analyze the sensitivity of a model not only from the aspect of white-box and black-box attack setups, but also from the aspect of different types of datasets. Four common optimizers, SGD, RMSprop, Adadelta, and Adam, are investigated on structured and unstructured datasets. Extensive experiment results indicate that an optimization algorithm does pose effects on the DNN model sensitivity to adversarial examples. That is, when training models and generating adversarial examples, Adam optimizer can generate better quality adversarial examples for structured datasets, and Adadelta optimizer can generate better quality adversarial examples for unstructured datasets. In addition, the choice of optimizers does not affect the transferability of adversarial examples.
Exploring the feasibility of blockchain technology as an infrastructure for improving built asset sustainability

Purpose The purpose of this paper is to show the feasibility of blockchain technology to perform as an infrastructure for improving built asset sustainability by providing all the necessary information for better decision making at all the stages of its life cycle. Design/methodology/approach Blockchain technology can be used as a tool to build a reliable and secure decentralized information system to capture and disseminate all the data required for different sustainability assessment models. A model is designed and tested through a synthetic scenario to substantiate the research objective with empirical work. Findings It is shown that blockchain can revolutionize the current state of knowledge for long-term sustainability thinking and provide necessary information in different stages of the life cycle of a built asset. With the proposed decentralized, transparent and comprehensive database using blockchain, the life cycle assessment methods can become much more inclusive and reliable. The new holistic analysis of the built asset sustainability enables better decision making in design, build, operation and demolition of each asset. Originality/value This paper proposes and tests a model for using blockchain as an infrastructure to support built asset sustainability. Practitioners from different backgrounds at different stages of a built asset life cycle can use such a network to make better decisions and better assess the sustainability of their built assets.
A Communication Efficient Vertical Federated Learning Framework.

One critical challenge for applying today's Artificial Intelligence (AI)technologies to real-world applications is the common existence of data silosacross different organizations. Due to legal, privacy and other practicalconstraints, data from different organizations cannot be easily integrated.Federated learning (FL), especially the vertical FL (VFL), allows multipleparties having different sets of attributes about the same user collaborativelybuild models while preserving user privacy. However, communication overhead isa principal bottleneck since the existing VFL protocols require per-iterationcommunications among all parties. In this paper, we propose the FederatedStochastic Block Coordinate Descent (FedBCD) to effectively reduce thecommunication rounds for VFL. We show that when the batch size, sample size,and the local iterations are selected appropriately, the algorithm requires$\mathcal{O}(\sqrt{T})$ communication rounds to achieve$\mathcal{O}(1/\sqrt{T})$ accuracy. Finally, we demonstrate the performance ofFedBCD on several models and datasets, and on a large-scale industrial platformfor VFL.
Temporal Interlacing Network

For a long time, the vision community tries to learn the spatio-temporal representation by combining convolutional neural network together with various temporal models, such as the families of Markov chain, optical flow, RNN and temporal convolution. However, these pipelines consume enormous computing resources due to the alternately learning process for spatial and temporal information. One natural question is whether we can embed the temporal information into the spatial one so the information in the two domains can be jointly learned once-only. In this work, we answer this question by presenting a simple yet powerful operator – temporal interlacing network (TIN). Instead of learning the temporal features, TIN fuses the two kinds of information by interlacing spatial representations from the past to the future, and vice versa. A differentiable interlacing target can be learned to control the interlacing process. In this way, a heavy temporal model is replaced by a simple interlacing operator. We theoretically prove that with a learnable interlacing target, TIN performs equivalently to the regularized temporal convolution network (r-TCN), but gains 4% more accuracy with 6x less latency on 6 challenging benchmarks. These results push the state-of-the-art performances of video understanding by a considerable margin. Not surprising, the ensemble model of the proposed TIN won the 1st place in the ICCV19 - Multi Moments in Time challenge. Code is made available to facilitate further research.1
Distilling Portable Generative Adversarial Networks for Image Translation

Despite Generative Adversarial Networks (GANs) have been widely used in various image-to-image translation tasks, they can be hardly applied on mobile devices due to their heavy computation and storage cost. Traditional network compression methods focus on visually recognition tasks, but never deal with generation tasks. Inspired by knowledge distillation, a student generator of fewer parameters is trained by inheriting the low-level and high-level information from the original heavy teacher generator. To promote the capability of student generator, we include a student discriminator to measure the distances between real images, and images generated by student and teacher generators. An adversarial learning process is therefore established to optimize student generator and student discriminator. Qualitative and quantitative analysis by conducting experiments on benchmark datasets demonstrate that the proposed method can learn portable generative models with strong performance.
Bi-Directional Generation for Unsupervised Domain Adaptation

Unsupervised domain adaptation facilitates the unlabeled target domain relying on well-established source domain information. The conventional methods forcefully reducing the domain discrepancy in the latent space will result in the destruction of intrinsic data structure. To balance the mitigation of domain gap and the preservation of the inherent structure, we propose a Bi-Directional Generation domain adaptation model with consistent classifiers interpolating two intermediate domains to bridge source and target domains. Specifically, two cross-domain generators are employed to synthesize one domain conditioned on the other. The performance of our proposed method can be further enhanced by the consistent classifiers and the cross-domain alignment constraints. We also design two classifiers which are jointly optimized to maximize the consistency on target sample prediction. Extensive experiments verify that our proposed model outperforms the state-of-the-art on standard cross domain visual benchmarks.
JSI-GAN: GAN-Based Joint Super-Resolution and Inverse Tone-Mapping with Pixel-Wise Task-Specific Filters for UHD HDR Video

Joint learning of super-resolution (SR) and inverse tone-mapping (ITM) has been explored recently, to convert legacy low resolution (LR) standard dynamic range (SDR) videos to high resolution (HR) high dynamic range (HDR) videos for the growing need of UHD HDR TV/broadcasting applications. However, previous CNN-based methods directly reconstruct the HR HDR frames from LR SDR frames, and are only trained with a simple L2 loss. In this paper, we take a divide-and-conquer approach in designing a novel GAN-based joint SR-ITM network, called JSI-GAN, which is composed of three task-specific subnets: an image reconstruction subnet, a detail restoration (DR) subnet and a local contrast enhancement (LCE) subnet. We delicately design these subnets so that they are appropriately trained for the intended purpose, learning a pair of pixel-wise 1D separable filters via the DR subnet for detail restoration and a pixel-wise 2D local filter by the LCE subnet for contrast enhancement. Moreover, to train the JSI-GAN effectively, we propose a novel detail GAN loss alongside the conventional GAN loss, which helps enhancing both local details and contrasts to reconstruct high quality HR HDR results. When all subnets are jointly trained well, the predicted HR HDR results of higher quality are obtained with at least 0.41 dB gain in PSNR over those generated by the previous methods. The official Tensorflow code is available at https://github.com/JihyongOh/JSI-GAN.
Analysis of Interpersonal Competences in the Use of ICT in the Spanish University Context

This article analyzes Higher Education students’ development of interpersonal competences when using Information and Communication Technologies. The participating sample was made up of 1490 students from three Spanish universities: Complutense University of Madrid (Spain), Pablo de Olavide University (Spain), and National Distance Education University (UNED). The data were collected through a questionnaire called “Basic digital skills 2.0 of university students” COBADI® (Registered trademark: 2970648). A factorial analysis was performed to determine possible groupings of representative factors and subsequently the trees technique was applied by running the CHAID (Chi-squared Automatic Interaction Detector) algorithm. This made it possible to develop a map of possible differences between universities, ages, and gender of students. The results showed that university students have higher competences in communicating through interactive presentations and video-images, as well as in collaborating and working with documents online through mobile devices.
A secure and scalable data integrity auditing scheme based on hyperledger fabric

As our society becomes smarter and more interconnected, more data such as those generated by Internet of Things (IoT) devices are stored remotely. These devices and services are generally externally owned and operated (e.g., commercial cloud servers). Hence, there has been interest in verifying the integrity of outsourced data, such as those stored in the remote cloud server, for example using schemes involving a third-party auditor (TPA). However, existing solutions involving TPA do not generally consider credibility and centralization, and such solutions may not be easily scalable. Thus, in this paper, we propose an efficient decentralized data integrity auditing scheme based on Hyperledger Fabric (HF-Audit), a popular consortium blockchain. Specifically, we use Hyperledger Fabric as a communication platform, where TPA can be dynamically selected for each auditing task. In order to improve the scalability of TPA, we design an efficient auditing protocol for data integrity based on bilinear pairing and commitments. Also, to improve auditing efficiency, we design two TPA selection algorithms under complete and incomplete information. Finally, we prove the security of the proposed approach, and evaluate its performance to demonstrate the utility of our proposed approach.
On-chip deterministic operation of quantum dots in dual-mode waveguides for a plug-and-play single-photon source

Abstract A deterministic source of coherent single photons is an enabling device for quantum information processing. Quantum dots in nanophotonic structures have been employed as excellent sources of single photons with the promise of scaling up towards multiple photons and emitters. It remains a challenge to implement deterministic resonant optical excitation of the quantum dot required for generating coherent single photons, since residual light from the excitation laser should be suppressed without compromising source efficiency and scalability. Here, we present a planar nanophotonic circuit that enables deterministic pulsed resonant excitation of quantum dots using two orthogonal waveguide modes for separating the laser and the emitted photons. We report a coherent and stable single-photon source that simultaneously achieves high-purity ( g (2) (0) = 0.020 ± 0.005), high-indistinguishability ( V = 96 ± 2%), and &gt;80% coupling efficiency into the waveguide. Such ‘plug-and-play’ single-photon source can be integrated with on-chip optical networks implementing photonic quantum processors.
Ellipse R-CNN: Learning to Infer Elliptical Object From Clustering and Occlusion

Images of heavily occluded objects in cluttered scenes, such as fruit clusters in trees, are hard to segment. To further retrieve the 3D size and 6D pose of each individual object in such cases, bounding boxes are not reliable from multiple views since only a little portion of the object's geometry is captured. We introduce the first CNN-based ellipse detector, called Ellipse R-CNN, to represent and infer occluded objects as ellipses. We first propose a robust and compact ellipse regression based on the Mask R-CNN architecture for elliptical object detection. Our method can infer the parameters of multiple elliptical objects even they are occluded by other neighboring objects. For better occlusion handling, we exploit refined feature regions for the regression stage, and integrate the U-Net structure for learning different occlusion patterns to compute the final detection score. The correctness of ellipse regression is validated through experiments performed on synthetic data of clustered ellipses. We further quantitatively and qualitatively demonstrate that our approach outperforms the state-of-the-art model (i.e., Mask R-CNN followed by ellipse fitting) and its three variants on both synthetic and real datasets of occluded and clustered elliptical objects.
Density-Aware Multi-Task Learning for Crowd Counting

In this paper, we present a method called density-aware convolutional neural network (DensityCNN) to perform the crowd counting task in various crowded scenes. The key idea of the DensityCNN is to utilize high-level semantic information to provide guidance and constraint when generating density maps. To this end, we implement the DensityCNN by adopting a multi-task CNN structure to jointly learn density-level classification and density map estimation. The density-level classification task learns multi-channel semantic features that are aware of the density distributions of the input image. This task is accomplished via our specially designed group-based convolutional structure in a supervised learning manner. In the density map estimation task, these semantic features are deployed together with high-dimension convolutional features to generate density maps with lower count errors. Extensive experiments on four challenging crowd datasets (ShanghaiTech, UCF_CC_50, UCF-QNCF, and WorldExpo'10) and one vehicle dataset TRANCOS demonstrate the effectiveness of the proposed method.
Intuitionistic fuzzy time series functions approach for time series forecasting

Abstract Fuzzy inference systems have been commonly used for time series forecasting in the literature. Adaptive network fuzzy inference system, fuzzy time series approaches and fuzzy regression functions approaches are popular among fuzzy inference systems. In recent years, intuitionistic fuzzy sets have been preferred in the fuzzy modeling and new fuzzy inference systems have been proposed based on intuitionistic fuzzy sets. In this paper, a new intuitionistic fuzzy regression functions approach is proposed based on intuitionistic fuzzy sets for forecasting purpose. This new inference system is called an intuitionistic fuzzy time series functions approach. The contribution of the paper is proposing a new intuitionistic fuzzy inference system. To evaluate the performance of intuitionistic fuzzy time series functions, twenty-three real-world time series data sets are analyzed. The results obtained from the intuitionistic fuzzy time series functions approach are compared with some other methods according to a root mean square error and mean absolute percentage error criteria. The proposed method has superior forecasting performance among all methods.
NPENAS: Neural Predictor Guided Evolution for Neural Architecture Search

Neural architecture search (NAS) adopts a search strategy to explore the predefined search space to find superior architecture with the minimum searching costs. Bayesian optimization (BO) and evolutionary algorithms (EA) are two commonly used search strategies, but they suffer from being computationally expensive, challenging to implement, and exhibiting inefficient exploration ability. In this article, we propose a neural predictor guided EA to enhance the exploration ability of EA for NAS (NPENAS) and design two kinds of neural predictors. The first predictor is a BO acquisition function for which we design a graph-based uncertainty estimation network as the surrogate model. The second predictor is a graph-based neural network that directly predicts the performance of the input neural architecture. The NPENAS using the two neural predictors are denoted as NPENAS-BO and NPENAS-NP, respectively. In addition, we introduce a new random architecture sampling method to overcome the drawbacks of the existing sampling method. Experimental results on five NAS search spaces indicate that NPENAS-BO and NPENAS-NP outperform most existing NAS algorithms, with NPENAS-NP achieving state-of-the-art performance on four of the five search spaces.
Time-aware domain-based social influence prediction

Abstract Online social networks have established virtual platforms enabling people to express their opinions, interests and thoughts in a variety of contexts and domains, allowing legitimate users as well as spammers and other untrustworthy users to publish and spread their content. Hence, it is vital to have an accurate understanding of the contextual content of social users, thus establishing grounds for measuring their social influence accordingly. In particular, there is the need for a better understanding of domain-based social trust to improve and expand the analysis process and determining the credibility of Social Big Data. The aim of this paper is to determine domain-based social influencers by means of a framework that incorporates semantic analysis and machine learning modules to measure and predict users’ credibility in numerous domains at different time periods. The evaluation of the experiment conducted herein validates the applicability of semantic analysis and machine learning techniques in detecting highly trustworthy domain-based influencers.
ACO Resampling: Enhancing the performance of oversampling methods for class imbalance classification

Many sampling-based preprocessing methods have been proposed to solve the problem of unbalanced dataset classification. The fundamental principle of these methods is rebalancing an unbalanced dataset by a concrete strategy. Herein, we introduce a novel hybrid proposal named ant colony optimization resampling (ACOR) to overcome class imbalance classification. ACOR primarily includes two steps: first, it rebalances an imbalanced dataset by a specific oversampling algorithm; next, it finds an (sub)optimal subset from the balanced dataset by ant colony optimization. Unlike other oversampling techniques, ACOR does not focus on the mechanics of generating new samples. The main advantage of ACOR is that existing oversampling algorithms can be fully utilized and an ideal training set can be obtained by ant colony optimization. Therefore, ACOR can enhance the performance of existing oversampling algorithms. Experimental results on 18 real imbalanced datasets prove that ACOR yields significantly better results compared with four popular oversampling methods in terms of various assessment metrics, such as AUC, G-mean, and BACC.