Reviewerly/exHarmony huggingface dataset:
A Large-Scale, Time-Synchronized Visible and Thermal Face Dataset

Thermal face imagery, which captures the naturally emitted heat from the face, is limited in availability compared to face imagery in the visible spectrum. To help address this scarcity of thermal face imagery for research and algorithm development, we present the DEVCOM Army Research Laboratory Visible-Thermal Face Dataset (ARL-VTF). With over 500,000 images from 395 subjects, the ARL-VTF dataset represents, to the best of our knowledge, the largest collection of paired visible and thermal face images to date. The data was captured using a modern long wave infrared (LWIR) camera mounted alongside a stereo setup of three visible spectrum cameras. Variability in expressions, pose, and eyewear has been systematically recorded. The dataset has been curated with extensive annotations, metadata, and standardized protocols for evaluation. Furthermore, this paper presents extensive benchmark results and analysis on thermal face landmark detection and thermal-to-visible face verification by evaluating state-of-the-art models on the ARL-VTF dataset.
Optimization using the firefly algorithm of ensemble neural networks with type-2 fuzzy integration for COVID-19 time series prediction

In this paper, the latest global COVID-19 pandemic prediction is addressed. Each country worldwide has faced this pandemic differently, reflected in its statistical number of confirmed and death cases. Predicting the number of confirmed and death cases could allow us to know the future number of cases and provide each country with the necessary information to make decisions based on the predictions. Recent works are focused only on confirmed COVID-19 cases or a specific country. In this work, the firefly algorithm designs an ensemble neural network architecture for each one of 26 countries. In this work, we propose the firefly algorithm for ensemble neural network optimization applied to COVID-19 time series prediction with type-2 fuzzy logic in a weighted average integration method. The proposed method finds the number of artificial neural networks needed to form an ensemble neural network and their architecture using a type-2 fuzzy inference system to combine the responses of individual artificial neural networks to perform a final prediction. The advantages of the type-2 fuzzy weighted average integration (FWA) method over the conventional average method and type-1 fuzzy weighted average integration are shown.
Biodigital technologies and the bioeconomy: The Global New Green Deal?

Presented in December 2019, the European Green Deal has established the legislative framework for achieving the European Union’s climate neutrality by 2050. The Explanatory Memorandum puts the obje...
Saliency Driven Perceptual Image Compression

This paper proposes a new end-to-end trainable model for lossy image compression, which includes several novel components. The method incorporates 1) an adequate perceptual similarity metric; 2) saliency in the images; 3) a hierarchical auto-regressive model. This paper demonstrates that the popularly used evaluations metrics such as MS-SSIM and PSNR are inadequate for judging the performance of image compression techniques as they do not align with the human perception of similarity. Alternatively, a new metric is proposed, which is learned on perceptual similarity data specific to image compression. The proposed compression model incorporates the salient regions and optimizes on the proposed perceptual similarity metric. The model not only generates images which are visually better but also gives superior performance for subsequent computer vision tasks such as object detection and segmentation when compared to existing engineered or learned compression techniques.
Attentive multi-view deep subspace clustering net

In this paper, we propose a novel Attentive Multi-View Deep Subspace Nets (AMVDSN), which deeply explores underlying consistent and view-specific information from multiple views and fuse them by considering each view’s dynamic contribution obtained by attention mechanism. Unlike most multi-view subspace learning methods that they directly reconstruct data points on raw data or only consider consistency or complementarity when learning representation in deep or shallow space, our proposed method seeks to find a joint latent representation that explicitly considers both consensus and view-specific information among multiple views, and then performs subspace clustering on learned joint latent representation. Besides, different views contribute differently to representation learning, we therefore introduce attention mechanism to derive dynamic weight for each view, which performs much better than previous fusion methods in the field of multi-view subspace clustering. The proposed algorithm is intuitive and can be easily optimized just by using Stochastic Gradient Descent (SGD) because of the neural network framework, which also provides strong non-linear characterization capability compared with traditional subspace clustering approaches. The experimental results on seven real-world data sets have demonstrated the effectiveness of our proposed algorithm against some state-of-the-art subspace learning approaches.
Certificateless multi-replica public integrity auditing scheme for dynamic shared data in cloud storage

Cloud storage enables data users to store and access their data anytime, from any location, using any device, under a pay-per-use policy. However, cloud storage raises various security concerns such as integrity and availability of data. To address these problems simultaneously, recently, several multi-replica integrity auditing schemes have been proposed in the literature. However, most of the existing schemes are designed based on either the public key infrastructure (PKI) or identity-based cryptography (IBC), which suffers from a big burden of certificate management or key escrow problems. In this paper, we propose a Certficateless Multi-Replica Dynamic Public Auditing Scheme for Shared data in cloud storage to solve these problems. Our scheme supports shared data dynamics through a novel data structure, namely, the replica version table. Further, our scheme also supports secure user revocation from the group. Through the security analysis, we prove that our scheme is provably secure against type I/II/III adversaries under the hardness assumption of the standard CDH and DL problems in the random oracle model (ROM). The performance and experimental evaluation show that our scheme is efficient and practical.
Quantum inspired Particle Swarm Optimization with guided exploration for function optimization

The complex function optimization problems, which have properties like multimodality, high dimensionality, non-differentiability, non-linear parameter interactions, are challenging and hard to solve. A number of meta-heuristic algorithms are proposed to find near optimal solution to these complex problems. However, most of them suffer from poor exploration and get caught in local optima. In order to overcome this problem, we present an enhanced Quantum behaved Particle Swarm Optimization (e-QPSO) algorithm, which improves the exploration and the exploitation properties of the original QPSO for function optimization. We introduce the adaptive balance among the personal best and the global best positions using the parameter alpha and achieve the balance between the diversification and the intensification using the parameter gamma. Further, we re-initialize a percentage of the worst performing population to help escape the particle from the local optima. These three modifications in the e-QPSO play crucial role to enhance the performance of the original QPSO algorithm. The e-QPSO is validated on 59 well-known challenging benchmark problems including 5 engineering problems. The results of e-QPSO outperform those of existing twelve QPSO variants, two adaptive variants of PSO, as well as nine well-known evolutionary algorithms. Statistical tests also demonstrate the statistically significant performance of the e-QPSO compared to the other meta-heuristic methods.
Deep neural network for fringe pattern filtering and normalization

We propose a new framework for processing fringe patterns (FPs). Our novel, to the best of our knowledge, approach builds upon the hypothesis that the denoising and normalization of FPs can be learned by a deep neural network if enough pairs of corrupted and ideal FPs are provided. The main contributions of this paper are the following: (1) we propose the use of the U-net neural network architecture for FP normalization tasks; (2) we propose a modification for the distribution of weights in the U-net, called here the V-net model, which is more convenient for reconstruction tasks, and we conduct extensive experimental evidence in which the V-net produces high-quality results for FP filtering and normalization; (3) we also propose two modifications of the V-net scheme, namely, a residual version called ResV-net and a fast operating version of the V-net, to evaluate potential improvements when modifying our proposal. We evaluate the performance of our methods in various scenarios: FPs corrupted with different degrees of noise, and corrupted with different noise distributions. We compare our methodology versus other state-of-the-art methods. The experimental results (on both synthetic and real data) demonstrate the capabilities and potential of this new paradigm for processing interferograms.
AMPNet: Average- and Max-Pool Networks for Salient Object Detection

Salient Object Detection aims to detect the most visually distinctive objects in an image. We solve this problem by introducing the average pool to explore the multi-level deep average pool convolution features different from the max pool information. Based on the U-net structure, we propose an Average- and Max-Pool Network (AMPNet) that leverages the average- and max-pool modules to integrate the multi-level complementary contextual features in the spatial and channel-wise dimensions, respectively. The complementary contextual features generated by our network can improve the completeness of detected objects. It has been observed that the non-salient regions are misrecognized as the salient objects because of the redundant information contained in the multi-level convolution features. To address the problem, two top-down feedback paths are introduced based on the above two modules, and their top-level semantic guidance information is fully utilized to improve the accuracy of salient objects detection. Finally, we apply the Feature Fusion Module and Deep Supervision Mechanism to further improve the performance of the network over different datasets. Experimental results on six benchmark datasets show that our network is on par with state-of-the-art approaches. Our method runs at more than 45 FPS (based on VGG) and 35 FPS (based on ResNet) on a single GPU and meets real-time requirements.
Cross adversarial consistency self-prediction learning for unsupervised domain adaptation person re-identification

Domain-invariant feature-extraction has become very popular for unsupervised domain adaptation (UDA) person re-identification (Re-ID). However, most methods using it are limited by weak discrimination of learned domain-invariant features. To solve this problem, we develop a new approach: cross-adversarial consistency self-prediction learning. Cross-adversarial consistency is used to endow the learned feature with domain invariance and discrimination; consistency self-prediction fine-tunes the pre-trained model by selecting non-paired samples from target data. First, the camera views of source domain are randomly divided into two groups with their samples. Then, the two identifiers are used crosswise on both groups, forcing consistent results through adversarial learning between the identifiers and the feature encoder. To refine the model, a self-prediction mechanism is introduced that conservatively selects target domain samples with high identity similarities to labeled source domain samples. This practical design helps to alleviate domain bias between the source and target domains. The results of experiments conducted on five benchmark datasets verify that the proposed method is effective and outperforms state-of-the-art competitors. The source code of our method is available at https://github.com/PangJian123/CAC-CSP.
What's Next in Blockchain Research?

Distributed ledger technology, frequently designated as 'blockchain,' is evolving from its hype phase toward greater maturity and long-term value creation. Although many academic communities were initially slow to grasp the technology's numerous potential implications, meanwhile a substantial amount of research is dedicated to investigating the development and impact of blockchain and related technologies. As undertaken, most research projects take a specific homogenous perspective, such as a technical or business viewpoint. To date, blockchain research studies are largely missing a bridge between and across academic disciplines. Given the manifold implications of blockchain technology, a fruitful crossdisciplinary exchange is therefore needed. In this paper, we bring together researchers with varying expertise to provide a vision into what may be next in terms of concepts, applications, and research agendas. We consider business, economic, societal, legal, technical, and philosophical viewpoints and propose multiple research questions as well as hypotheses arising from these diverse viewpoints. Simultaneously, we challenge various academic communities to tackle some of the most crucial issues of current blockchain research and to develop a solid foundation for future exploration.
Measurement and key influencing factors of the economic benefits for China’s photovoltaic power generation: A LCOE-based hybrid model

2020 may be redefining China’s photovoltaic power generation (PPG) development. This research is an attempt to extract the key influencing factors and analyze the main driving forces to improve the economic benefits of China’s PPG and thus a lower-cost access to the grid as soon as possible. In this research, the relationship analysis model of PPG economic benefits based on decision lab analysis method (DEMATEL) and interpretative structural model (ISM) is constructed, and thereby accumulated research and development (R&D) funds and accumulated installed capacity are extracted from the set as the key factors. Then, the levelized cost of energy (LCOE) model based on the two-factor learning curve is established, and a measurement and calculation on the basis of the assumption of the East China Power Grid is conducted. Recommendations are offered on the grounds of the comparisons in the development status of PPG economic benefits between different regions of China. Finally, through the sensitivity analysis of key influencing factors with LCOE as a variable, it can be found that a 5% more investment in vital influencing factors could produce the average decrease of about 0.0184 yuan/kWh in LCOE.
A lightweight and provable secure identity-based generalized proxy signcryption (IBGPS) scheme for Industrial Internet of Things (IIoT)

Recently, the Industrial Internet of Things (IIoT) has become increasingly important for applications in the industry. IIoT has essentially become a prime security focus for implementing secure communication. Among the available cryptographic tools, identity-based signcryption provide a sound solution to fulfill the security requirement of IIoT. On the other hand a generalized proxy signcryption can work adaptively as proxy signature and proxy signcryption using a single algorithm. In order to accomplish effective and on-time communication whenever the manager of the company went out for a usual business trip or an IIoT device which has lack of resources, then it must need to delegate her/his signcryption rights to their subordinates (proxy agents/device). For this purpose, to make the delegation procedure more efficient and reliable, we proposed a lightweight identity-based generalized proxy signcryption (IBGPS) scheme for IIoT. The IBGPS scheme is provably secure in terms of indistinguishability against adaptive chosen ciphertext attack (IND − IBGPS − CCA) and existential unforgeable against a possible adaptive chosen message attack (EUF − IBGPS − CMA) under Hyperelliptic Curve Decisional Diffie-Hellman problem (HEDHP) and Hyperelliptic Curve Discrete Logarithm problem (HECDLP) in the random oracle model. Furthermore the performance evaluation in terms of computation and communication costs shows that the IBGPS scheme is more effective than the existing schemes.
Enhanced network optimized generative adversarial network for image enhancement

Abstract With the development of image recognition technology, face, body shape, and other factors have been widely used as identification labels, which provide a lot of convenience for our daily life. However, image recognition has much higher requirements for image conditions than traditional identification methods like a password. Therefore, image enhancement plays an important role in the process of image analysis for images with noise, among which the image of low-light is the top priority of our research. In this paper, a low-light image enhancement method based on the enhanced network module optimized Generative Adversarial Networks(GAN) is proposed. The proposed method first applied the enhancement network to input the image into the generator to generate a similar image in the new space, Then constructed a loss function and minimized it to train the discriminator, which is used to compare the image generated by the generator with the real image. We implemented the proposed method on two image datasets (DPED, LOL), and compared it with both the traditional image enhancement method and the deep learning approach. Experiments showed that our proposed network enhanced images have higher PNSR and SSIM, the overall perception of relatively good quality, demonstrating the effectiveness of the method in the aspect of low illumination image enhancement.
Country Image in COVID-19 Pandemic: A Case Study of China

Country image has a profound influence on international relations and economic development. In the worldwide outbreak of COVID-19, countries and their people display different reactions, resulting in diverse perceived images among foreign public. Therefore, in this study, we take China as a specific and typical case and investigate its image with aspect-based sentiment analysis on a large-scale Twitter dataset. To our knowledge, this is the first study to explore country image in such a fine-grained way. To perform the analysis, we first build a manually-labeled Twitter dataset with aspect-level sentiment annotations. Afterward, we conduct the aspect-based sentiment analysis with BERT to explore the image of China. We discover an overall sentiment change from non-negative to negative in the general public, and explain it with the increasing mentions of negative ideology-related aspects and decreasing mentions of non-negative fact-based aspects. Further investigations into different groups of Twitter users, including U.S. Congress members, English media, and social bots, reveal different patterns in their attitudes toward China. This study provides a deeper understanding of the changing image of China in COVID-19 pandemic. Our research also demonstrates how aspect-based sentiment analysis can be applied in social science researches to deliver valuable insights.
Benchmarking Metric Ground Navigation

Metric ground navigation addresses the problem of autonomously moving a robot from one point to another in an obstacle-occupied planar environment in a collision-free manner. It is one of the most fundamental capabilities of intelligent mobile robots. This paper presents a standardized testbed with a set of environments and metrics to benchmark difficulty of different scenarios and performance of different systems of metric ground navigation. Current benchmarks focus on individual components of mobile robot navigation, such as perception and state estimation, but the navigation performance as a whole is rarely measured in a systematic and standardized fashion. As a result, navigation systems are usually tested and compared in an ad hoc manner, such as in one or two manually chosen environments. The introduced benchmark provides a general testbed for ground robot navigation in a metric world. The Benchmark for Autonomous Robot Navigation (BARN) dataset includes 300 navigation environments, which are ordered by a set of difficulty metrics. Navigation performance can be tested and compared in those environments in a systematic and objective fashion. This benchmark can be used to predict navigation difficulty of a new environment, compare navigation systems, and potentially serve as a cost function and a curriculum for planning-based and learning-based navigation systems. We have published our dataset and the source code to generate datasets for different robot footprints at www.cs.utexas.edu/~attruong/metric_dataset.html.
UI-GAN: Generative Adversarial Network-Based Anomaly Detection Using User Initial Information for Wearable Devices

This article proposes an automatic fall detection method for a wearable device that can promptly alert caregivers when a fall is detected, which could reduce the injuries of elder people. To do this, we propose a novel generative adversarial network (GAN-) based fall detection method using a heart rate sensor and an accelerometer. Acquiring fall data compared with normal behavioral data can be an arduous process. Instead, we introduce a compelling GAN-based anomaly detection partially surrounded with User Initial information features (UI-GAN). Although GAN-based anomaly detection methods have been previously proposed, each model has its adequate suitability for each anomaly detection application. Therefore, this study firstly evaluates suitable GAN-based anomaly detection models for fall detection from among nine recently proposed GAN-based models. From UI-GAN, performance improvements are observed in the fall detection when using the UI information. To objectively demonstrate the competitive performance of UI-GAN, we compare it with eight other recently presented fall detection studies and have observed that it leads to better results. Lastly, since the target application in this study is the use of UI-GAN with a wearable device, the sufficiently satisfied latency of UI-GAN on the smartwatch is estimated. This study is the first attempt to use the initial information of users in GAN, and we hope that the effectiveness of the UI information is expected to be seen in other applications.
Thresholds of External Flows for Inclusive Human Development in Sub-Saharan Africa

This research investigates the incidence of enhancing external flows on inclusive human development in a panel of 48 countries in sub-Saharan Africa. It complements the literature by examining the relevance of enhancing three types of external flows, namely: development assistance, foreign investment and remittances. Ordinary Least Squares, Tobit, Fixed effects, Generalised Method of Moments and Quantile regressions are used as empirical strategies. The following main results are apparent: (i) between 60 and 150 (% of GDP) is the threshold of foreign aid; (ii) 33.333 (% of GDP) is the foreign investment threshold and (iii) 25 (% of GDP) is the critical mass of remittances. At the established critical masses or thresholds, external flows start having positive effects on inclusive human development. Countries characterized by inclusive development levels that are low need more investment in foreign aid for inclusive human development compared to their counterparts characterized by inclusive human development levels that are high.