Reviewerly/exHarmony huggingface dataset:
Deep Perceptual Image Enhancement Network for Exposure Restoration

Image restoration techniques process degraded images to highlight obscure details or enhance the scene with good contrast and vivid color for the best possible visibility. Poor illumination condition causes issues, such as high-level noise, unlikely color or texture distortions, nonuniform exposure, halo artifacts, and lack of sharpness in the images. This article presents a novel end-to-end trainable deep convolutional neural network called the deep perceptual image enhancement network (DPIENet) to address these challenges. The novel contributions of the proposed work are: 1) a framework to synthesize multiple exposures from a single image and utilizing the exposure variation to restore the image and 2) a loss function based on the approximation of the logarithmic response of the human eye. Extensive computer simulations on the benchmark MIT-Adobe FiveK and user studies performed using Google high dynamic range, DIV2K, and low light image datasets show that DPIENet has clear advantages over state-of-the-art techniques. It has the potential to be useful for many everyday applications such as modernizing traditional camera technologies that currently capture images/videos with under/overexposed regions due to their sensors limitations, to be used in consumer photography to help the users capture appealing images, or for a variety of intelligent systems, including automated driving and video surveillance applications.
A Smart Wearable Fall Detection System for Firefighters Using V-RNN

Falling is one of the leading causes of death among firefighters in China. Fall detection systems (FDS) have yet to be deployed in firefighting applications in China, negatively impacting the safety of firefighters in the fireground. Despite many studies exploring FDSs, few have explored the application of multiple sensors, or applications outside of geriatric healthcare. This study proposes a smart wearable FDS for detecting firefighter falls by incorporating motion sensors in nine different positions on the firefighter’s personal protective clothing (PPC). The firefighter’s fall activities are detected by a proposed RNN model combined with a Boyer-Moore Voting (BMV) Algorithm (V-RNN) and a fall alert can be issued at an early phase. The results indicated that the proposed FDS with optimized parameters achieves 97.86% and 98.20% in sensitivity and accuracy, respectively.
ReDetect: Reentrancy Vulnerability Detection in Smart Contracts with High Accuracy

Smart contracts are a landmark achievement of blockchain technology 2.0 and are widely adopted in various applications. However, smart contracts are not always secure and there are various vulnerabilities. The reentrancy vulnerability is one of most serious vulnerabilities, and it has caused huge economic losses. Although many methods have been proposed to detect reentrancy vulnerabilities, they all have high false positives. To deal with this problem, we propose a symbolic execution-based detection tool for reentrancy vulnerabilities of smart contracts at the EVM bytecode level. By analyzing a large number of real-world smart contracts, we conclude main patterns of false positives and design five effective path filters to eliminate false positives. We evaluate its performance on real-world datasets in comparison with the state-of-the-art works, and the results show that our tool is more effective in the detection of reentrancy vulnerabilities.
State feedback based on grey wolf optimizer controller for two-wheeled self-balancing robot

Abstract The two-wheeled self-balancing robot (TWSBR) is based on the axletree and inverted pendulum. Its balancing problem requires a control action. To speed up the response of the robot and minimize the steady state error, in this article, a grey wolf optimizer (GWO) method is proposed for TWSBR control based on state space feedback control technique. The controller stabilizes the balancing robot and minimizes the overshoot value of the system. The dynamic model of the system is derived based on Euler formula and linearized to state space representation to enhance the control technique. Then, the GWO optimizes the state feedback controller parameters. Simulation results show that the system reaches the zero steady-state error with less than 2 ms, which proves the effectiveness of the proposed controller over the classical state feedback controller in terms of fast response, very small overall error, and minimum overshoot.
An embodied conversational agent coach to support societal participation learning by low-literate users

Abstract People of low literacy could benefit from automated support when learning about societal participation. We design an Embodied Conversational Agent (ECA) ‘coach’ that can provide effective learning support to low-literate learners, develop a prototype virtual learning environment, and evaluate this prototype with low-literate end users. First, we inventory the learning support benefits of ECA coaching. Second, we update existing requirements to better specify functional demands for the coach ECA. Third, we write use cases and develop the prototype. Finally, we evaluate the prototype with low-literate users in a mixed-method within-subjects experiment. Results show that the coach influences the subjective learning experience: Participants report higher positive affect, higher user-system engagement, and increased self-efficacy regarding online banking. These results particularly apply to the domain of challenging information skills exercises. Caveats apply: One of four exercises was significantly more difficult than the other three; and coach support rules were not clearly formalized.
Special Conditions for the Formation of Large Exogenic Epigenetic Uranium Deposits

Abstract —For uranium deposits to form from oxidizing waters of surface origin, it is necessary to combine several conditions and processes of different ranks that make up a specific mineral system. Analysis of exogenic deposits of different scales and ore quality from different regions of the world showed that, among all the critical processes necessary for the formation of large deposits and vast provinces, global tectonic movements play a particular role. A geodynamic regime with moderate vertical differentiation of crustal blocks is most favorable for development of a hydrodynamic infiltration ore-forming system under near-surface conditions. An infiltration artesian system is capable of functioning for millions and tens of millions of years, which makes it possible to accumulate large uranium reserves at the geochemical reduction barrier. The required regime is typical of regions with a certain geotectonic position. For example, the peripheral regions of orogenic belts along tectonic plate boundaries, as well as the frames of some long-lived intraplate deep faults. In the same regions, the proximity of infiltration and expulsion hydrodynamic systems is possible. In local areas where groundwater of various origin and composition interact, conditions occur for particularly efficient uranium concentration in ores. Limitations on the productivity of a hydrodynamic infiltration ore-forming system are imposed by historical factors that predetermine the formation of the uranium source and lithological conditions for ore localization, as well as the climatic conditions of the time in which the system functioned and postore conditions determining the preservation of mineralization accumulated in the system. The revealed patterns be used to predict ancient provinces of exogenic uranium deposits.
A Simple and Effective Way to Detect DeepFakes: Using 2D and 3D CNN

In the age of technologies, a brand-new technology in the field of deep neural networks and artificial intelligence has been developed. This technology of creating counter-fit realistic videos is known as Deepfake and is born lately. It's troublesome to discriminate between fake and real videos which are daily uploaded on the web across different websites. There are several Deepfake open-source creation methods available online, which results in a growing number of fake videos on the web. There are many quick and efficient ways and techniques which are made to spot, observe, and detect these developments. To define a few, comparison of backgrounds, facial artifacts, blinking of eyes, pattern analysis, pose, and likewise features of the face and surroundings are used to help in detecting a Deepfake video. In this paper, a simple but effective approach for detecting fakes using 2D Convolutional Neural Network (Conv2D) is followed and the use of 3D Convolutional Neural Network (Conv3D) is introduced in Deepfake Detection with the addition of Multi-Task Cascade Convolutional Neural Network (MTCNN) in preprocessing. The significance and importance of this work are that a different approach is taken in preprocessing which in return gives better results and the introduction for the use of Conv3D. The proposed models are trained and tested on celeb-df v2 and Deepfake-TIMIT both low-quality (LQ) and high-quality videos (HQ) with gaining the highest accuracy of 99.81%. Also, compared to the state-of-the-art techniques, the proposed approach helped in gaining a good accuracy on low-quality videos, and the difference between both HQ and LQ is observed to be less than 1%.
QINR: A Quantum-Inspired Network for Interpretable Review-based Recommendation

Recent studies in recommender systems have managed to achieve significantly improved performance by leveraging reviews for rating prediction. However, convolutional neural networks (CNNs) have been widely adopted in recent methods, which are difficult to intuitively understand and interpret. To make the recommendation process more transparent and interpretable, we seek to model reviews by the mathematical framework of quantum physics rather than general deep learning methods. In this paper, we propose a Quantum-Inspired Network for interpretable review-based Recommendation (QINR), which models reviews with the well-designed mathematical formulations in quantum physics and further predicts user ratings for items. Specifically, reviews are represented by density matrices and a set of measurement operators are applied to the density matrices. Experiments on the Amazon datasets demonstrate that QINR outperforms recent recommendation methods in terms of mean square errors (MSE).
Deep Reinforced Attention Regression for Partial Sketch Based Image Retrieval

Fine-Grained Sketch-Based Image Retrieval (FG-SBIR) aims at finding a specific image from a large gallery given a query sketch. Despite the widespread applicability of FG-SBIR in many critical domains (e.g., crime activity tracking), existing approaches still suffer from a low accuracy while being sensitive to external noises such as unnecessary strokes in the sketch. The retrieval performance will further deteriorate under a more practical on-the-fly setting, where only a partially complete sketch with only a few (noisy) strokes are available to retrieve corresponding images. We propose a novel framework that leverages a uniquely designed deep reinforcement learning model that performs a dual-level exploration to deal with partial sketch training and attention region selection. By enforcing the model’s attention on the important regions of the original sketches, it remains robust to unnecessary stroke noises and improve the retrieval accuracy by a large margin. To sufficiently explore partial sketches and locate the important regions to attend, the model performs bootstrapped policy gradient for global exploration while adjusting a standard deviation term that governs a locator network for local exploration. The training process is guided by a hybrid loss that integrates a reinforcement loss and a supervised loss. A dynamic ranking reward is developed to fit the on-the-fly image retrieval process using partial sketches. The extensive experimentation performed on three public datasets shows that our proposed approach achieves the state-of-the-art performance on partial sketch based image retrieval.
A Practical Algorithm Design and Evaluation for Heterogeneous Elastic Computing with Stragglers

Our extensive real measurements over Amazon EC2 show that the virtual instances often have different computing speeds even if they share the same configurations. This motivates us to study heterogeneous Coded Storage Elastic Computing (CSEC) systems where machines, with different computing speeds, join and leave the network arbitrarily over different computing steps. In CSEC systems, a Maximum Distance Separable (MDS) code is used for coded storage such that the file placement does not have to be re-defined with each elastic event. Computation assignment algorithms are used to minimize the computation time given computation speeds of different machines. While previous studies of heterogeneous CSEC do not include stragglers - the slow machines during the computation, we develop a new framework in heterogeneous CSEC that introduces straggler tolerance. Based on this framework, we design a novel algorithm using our previously proposed approach for heterogeneous CSEC such that the system can handle any subset of stragglers of a specified size while minimizing the computation time. Furthermore, we establish a trade-off in computation time and straggler tolerance. Another major limitation of existing CSEC designs is the lack of practical evaluations using real applications. In this paper, we evaluate the performance of our designs on Amazon EC2 for applications of the power iteration and linear regression. Evaluation results show that the proposed heterogeneous CSEC algorithms outperform the state-of-the-art designs by more than 30%.
Research on the Application of Artificial Intelligence Technology in Cross-Cultural English Teaching

Language and culture are inextricably linked. Language is the carrier of culture, and culture is the base of language. The purpose of college English teaching is not only to help students grasp the language system but to disseminate human civilization and cultural knowledge so that they can have the awareness of cultural differences and cross-cultural communicative competence. Under the background of “Internet+” development, artificial intelligence brings new direction and momentum to cross-cultural English teaching. This paper tries to combine artificial intelligence technology with cross-cultural English teaching and explores methods of applying artificial intelligence in teaching process, teaching materials and assessment system to effectively improve college students’ cross-cultural communication skills.
Towards the Urban Future: A Novel Trash Segregation Algorithm Based on Improved YOLOV4

The ecological imbalance caused by environmental pollution has gradually become a thought-provoking topic. In addition, the development of global smart cities urgently requires the emergence of a powerful smart waste management system which can realize trash segregation. Therefore we focus on the this issue and proposed a novel method for trash classification based on deep learning strategy. In order to realize the efficiency of real-time outdoor trash detection by robot, we adopt a lightweight GhostNet as backbone of detection network. The network was trained in our self-made dataset which includes 4 kinds of object(namely : plastic bottle, glass bottle, metal can, carton). Moreover, for comparison and evaluation, we set up a comparative experiment. The experimental results show that the improved version of the YOLOv4 method proposed has improved detection performance compared to the original YOLOV4 algorithm, and has produced satisfactory Generalization performance in different types of trash.
Data Management Plans in Horizon 2020: what beneficiaries think and what we can learn from their experience

Background: Data Management Plans (DMPs) are at the heart of many research funder requirements for data management and open data, including the EU's Framework Programme for Research and Innovation, Horizon 2020. This article provides a summary of the findings of the DMP Use Case study, conducted as part of OpenAIRE Advance. Methods: As part of the study we created a vetted collection of over 800 Horizon 2020 DMPs. Primarily, however, we report the results of qualitative interviews and a quantitative survey on the experience of Horizon 2020 projects with DMPs. Results & Conclusions: We find that a significant number of projects had to develop a DMP for the first time in the context of Horizon 2020, which points to the importance of funder requirements in spreading good data management practices. In total, 82% of survey respondents found DMPs useful or partially useful, beyond them being "just" an European Commission (EC) requirement. DMPs are most prominently developed within a project's Management Work Package. Templates were considered important, with 40% of respondents using the EC/European Research Council template. However, some argue for a more tailor-made approach. The most frequent source for support with DMPs were other project partners, but many beneficiaries did not receive any support at all. A number of survey respondents and interviewees therefore ask for a dedicated contact point at the EC, which could take the form of an EC Data Management Helpdesk, akin to the IP helpdesk. If DMPs are published, they are most often made available on the project website, which, however, is often taken offline after the project ends. There is therefore a need to further raise awareness on the importance of using repositories to ensure preservation and curation of DMPs. The study identifies IP and licensing arrangements for DMPs as promising areas for further research.
Enhanced processing of aversive stimuli on embodied artificial limbs by the human amygdala

Body perception has been extensively investigated, with one particular focus being the integration of vision and touch within a neuronal body representation. Previous studies have implicated a distributed network comprising the extrastriate body area (EBA), posterior parietal cortex (PPC) and ventral premotor cortex (PMv) during illusory self-attribution of a rubber hand. Here, we set up an fMRI paradigm in virtual reality (VR) to study whether and how the self-attribution of (artificial) body parts is altered if these body parts are somehow threatened. Participants (N = 30) saw a spider (aversive stimulus) or a toy-car (neutral stimulus) moving along a 3D-rendered virtual forearm positioned like their real forearm, while tactile stimulation was applied on the real arm in the same (congruent) or opposite (incongruent) direction. We found that the PPC was more activated during congruent stimulation; higher visual areas and the anterior insula (aIns) showed increased activation during aversive stimulus presentation; and the amygdala was more strongly activated for aversive stimuli when there was stronger multisensory integration of body-related information (interaction of aversiveness and congruency). Together, these findings suggest an enhanced processing of aversive stimuli within the amygdala when they represent a bodily threat.
To Talk or to Work: Delay Efficient Federated Learning over Mobile Edge Devices

Federated learning (FL), an emerging distributed machine learning paradigm, in conflux with edge computing is a promising area with novel applications over mobile edge devices. In FL, since mobile devices collaborate to train a model based on their own data under the coordination of a central server by sharing just the model updates, training data is maintained private. However, without the central availability of data, computing nodes need to communicate the model updates often to attain convergence. Hence, the local computation time to create local model updates along with the time taken for transmitting them to and from the server result in a delay in the overall time. Furthermore, unreliable network connections may obstruct an efficient communication of these updates. To address these, in this paper, we propose a delay-efficient FL mechanism that reduces the overall time (consisting of both the computation and communication latencies) and communication rounds required for the model to converge. Exploring the impact of various parameters contributing to delay, we seek to balance the trade-off between wireless communication (to talk) and local computation (to work). We formulate a relation with overall time as an optimization problem and demonstrate the efficacy of our approach through extensive simulations.
AWGAN: Unsupervised Spectrum Anomaly Detection with Wasserstein Generative Adversarial Network along with Random Reverse Mapping

Automatic wireless spectrum anomaly detection is vital to intelligent management of electromagnetic spectrum, which aims to detect various jamming and anomalous working states, especially intentional jamming. The intentional jamming has evolved in a variety of ways, but the existing spectrum anomaly detection efforts give little consideration to the diverse intentional jamming. Here, we firstly generate a rich dataset consisting of five types of normal signals and four types of intentional jamming. In order to effectively detect anomalies, we propose AWGAN, a novel anomaly detection method based on Wasserstein generative adversarial network. AWGAN can not only learn the distribution of normal time-frequency waterfall images in a latent space, but also remember the detailed features of normal images, and generate same images as the normal images by adversarial training. To detect anomalies, we propose a random reverse mapping (RRM) method based on backpropagation, to map a new time-frequency waterfall image into the latent space, so as to find the vector closest to the distribution of the new image in the latent space. We also define a scoring criterion to score images indicating their fit into the learned distribution. The experimental results show that the comprehensive detection ability of our method is superior to other methods for detecting the four types of anomalies.
Att-PyNet: An Attention Pyramidal Feature Network for Hand Gesture Recognition

This paper proposes a novel deep network Att-PyNet: an attention pyramidal feature network for hand gesture recognition. The proposed Att-PyNet comprises three feature streams: multi-scale feature extractor (MSFE), attention pyramid module, and locally connected (LC) layer. The MSFE is introduced to enrich the proposed Att-PyNet model with features of macro- and micro-edges by learning the complementary features of multi-receptive fields. The attention pyramid module is designed to carry forward the high-level features to the lower layer by maintaining the distinctive quality. The locally connected layer is adopted to enhance the discriminative capability of the proposed network by preserving the pertinent context information. The Att-PyNet is a computationally effective model as it holds very less 565K parameters than state-of-the-art models and can be easily deployed in a resource-constrained platform. The effectiveness of the proposed Att-PyNet is evaluated on three standard datasets: MUGD, Triesch, and ASL FingerSpelling. The quantitative and qualitative results validate that Att-PyNet outperforms the state-of-the-art hand gesture approaches.