Reviewerly/exHarmony huggingface dataset:
Intelligent Monitoring and Inspection of Power Line Components Powered by UAVs and Deep Learning

In this paper, we present a novel automatic autonomous vision-based power line inspection system that uses unmanned aerial vehicle inspection as the main inspection method, optical images as the primary data source, and deep learning as the backbone of the data analysis. To facilitate the implementation of the system, we address three main challenges of deep learning in vision-based power line inspection: (i) the lack of training data; (ii) class imbalance; and (iii) the detection of small components and faults. First, we create four medium-sized datasets for training component detection and classification models. Furthermore, we apply a series of effective data augmentation techniques to balance out the imbalanced classes. Finally, we propose the multi-stage component detection and classification based on the Single Shot Multibox detector and deep Residual Networks to detect small components and faults. The results show that the proposed system is fast and accurate in detecting common faults on power line components, including missing top caps, cracks in poles and cross arms, woodpecker damage on poles, and rot damage on cross arms. The field tests suggest that our system has a promising role in the intelligent monitoring and inspection of power line components and as a valuable addition to smart grids.
Development of fire safety behavioral skills via virtual reality

In recent years, virtual reality has become prevalent in many educational settings. In this study, virtual reality-based behavioral skills training (VR-BST) approach is proposed to teach basic behavioral skills for fire safety. A virtual reality-based environment was designed and implemented in the context of the design-based research. A group of ten primary school students received a basic fire safety training package through this environment and in situ training was implemented when needed. The results indicated that students’ fire safety behavioral skills significantly improved with the use of virtual reality based training and the majority of the students could transfer their behavioral skills to real environments. The way of modelling the behaviors in this study and integrating in situ training into the learning environment positively contributed to the development of behavioral skills. The study concludes with suggestions for practitioners and researchers in the field of virtual reality for behavioral skills training.
Batching Techniques for Accumulators with Applications to IOPs and Stateless Blockchains

We present batching techniques for cryptographic accumulators and vector commitments in groups of unknown order. Our techniques are tailored for distributed settings where no trusted accumulator manager exists and updates to the accumulator are processed in batches. We develop techniques for non-interactively aggregating membership proofs that can be verified with a constant number of group operations. We also provide a constant sized batch non-membership proof for a large number of elements. These proofs can be used to build the first positional vector commitment (VC) with constant sized openings and constant sized public parameters. As a core building block for our batching techniques we develop several succinct proof systems in groups of unknown order. These extend a recent construction of a succinct proof of correct exponentiation, and include a succinct proof of knowledge of an integer discrete logarithm between two group elements. We circumvent an impossibility result for Sigma-protocols in these groups by using a short trapdoor-free CRS. We use these new accumulator and vector commitment constructions to design a stateless blockchain, where nodes only need a constant amount of storage in order to participate in consensus. Further, we show how to use these techniques to reduce the size of IOP instantiations, such as STARKs. The full version of the paper is available online [BBF18b].
Maintenance in aeronautics in an Industry 4.0 context: The role of Augmented Reality and Additive Manufacturing

Abstract The paper broadly addresses how Industry 4.0 program drivers will impact maintenance in aviation. Specifically, Industry 4.0 practices most suitable to aeronautical maintenance are selected, and a detailed exposure is provided. Advantages and open issues are widely discussed and case studies dealing with realistic scenarios are illustrated to support what has been proposed by authors. The attention has been oriented towards Augmented Reality and Additive Manufacturing technologies, which can support maintenance tasks and spare parts production, respectively. The intention is to demonstrate that Augmented Reality and Additive Manufacturing are viable tools in aviation maintenance, and while a strong effort is necessary to develop an appropriate regulatory framework, mandatory before the wide-spread introduction of these technologies in the aerospace systems maintenance process, there has been a great interest and pull from the industry sector. Highlights Industry 4.0 practices most suitable to aeronautical maintenance are selected. Advantages and open issues are widely discussed and case studies are illustrated. Augmented Reality can support maintenance tasks. Additive Manufacturing can be useful to produce spare parts. A strong effort is necessary to develop an appropriate aeronautical regulatory framework.
Comparison of traditional image processing and deep learning approaches for classification of white blood cells in peripheral blood smear images

Automated classification and morphological analysis of white blood cells has been addressed since last four decades, but there is no optimal method which can be used as decision support system in laboratories due to biologically complex nature of the cells. Automated blood cell analysis facilitates quick and objective results and can also handle massive amount of data without compromising with efficiency. In the present study, we demonstrate classification of white blood cells into six types namely lymphocytes, monocytes, neutrophils, eosinophils, basophils and abnormal cells. We provide the comparison of traditional image processing approach and deep learning methods for classification of white blood cells. We evaluated neural network classifier results for hand-crafted features and obtained the average accuracy of 99.8%. We also used full training and transfer learning approaches of convolutional neural network for the classification. An accuracy around 99% was obtained for full training CNN.
Hybrid task scheduling strategy for cloud computing by modified particle swarm optimization and fuzzy theory

Abstract As the world is progressing towards more efficient computing and faster approaches, cloud computing is a popular computing model to such increasing requirements. In order to provide cost-effective executions in cloud environment, appropriate task scheduling strategy is necessary. This paper proposes a hybrid task scheduling algorithm named FMPSO that is based on Fuzzy system and Modified Particle Swarm Optimization technique to enhance load balancing and cloud throughput. FMPSO strategy at first considers four modified velocity updating methods and roulette wheel selection technique to enhance the global search capability. Then, it uses crossover and mutation operators to overcome some drawbacks of PSO such as local optima. Finally, this schema applies fuzzy inference system for fitness calculations. The input parameters for the proposed fuzzy system are length of tasks, speed of CPU, size of RAM, and total execution time. By adding these fuzzy systems, FMPSO strategy achieves the goal of minimizing the execution time and resource usage. We evaluate FMPSO algorithm using the CloudSim toolkit and simulation results demonstrate that the proposed strategy has a better performance in terms of makespan, improvement ratio, imbalance degree, efficiency, and total execution time comparing to other approaches.
An all-optical neuron with sigmoid activation function

We present an all-optical neuron that utilizes a logistic sigmoid activation function, using a Wavelength-Division Multiplexing (WDM) input & weighting scheme. The activation function is realized by means of a deeply-saturated differentially-biased Semiconductor Optical Amplifier-Mach-Zehnder Interferometer (SOA-MZI) followed by a SOA-Cross-Gain-Modulation (XGM) gate. Its transfer function is both experimentally and theoretically analyzed, showing excellent agreement between theory and experiment and an almost perfect fitting with a logistic sigmoid function. The optical sigmoid transfer function is then exploited in the experimental demonstration of a photonic neuron, demonstrating successful thresholding over a 100psec-long pulse sequence with 4 different weighted-and-summed power levels.
Sustainable siting of solar power installations in Mediterranean using a GIS/AHP approach

This study aims to present and test a methodology for clarifying and prioritizing, the most suitable locations for siting solar power installations. By employing Geographical Information Systems and the Analytical Hierarchy Process (AHP), the recommended sustainable siting areas for solar power generation farms siting are identified, allowing the application of a spatial multi-criteria decision-making approach. The methodology identifies the available areas for solar farms siting, based on exclusion criteria defined from the current legislation. Furthermore, the available siting areas are evaluated and ranked through a multi-criteria analysis, considering also the relative importance of the selected criteria, through the AHP implementation performed by involved energy-related groups. The developed methodology employs different types of criteria, such as techno-economic and socio-environmental, it takes into account the distinct opinions of different stakeholders and it enables the evaluation of different scenarios. Finally, the methodology was applied for the case study of the Regional Unit of Rethymno, where the results, in terms of the potential maximum power that can be produced from the highest priority areas are 530 MWp for photovoltaics and 30 MW for Concentrated Solar Power systems.
Three convolutional neural network models for facial expression recognition in the wild

Facial expression recognition (FER) in the wild is a novel and challenging topic in the field of human emotion perception. Different kinds of convolutional neural network (CNN) approaches have been applied to this topic, but few of them ever considered what kind of architecture was better for the FER research. In this paper, we proposed three novel CNN models with different architectures. The first one is a shallow network, named the Light-CNN, which is a fully convolutional neural network consisting of six depthwise separable residual convolution modules to solve the problem of complex topology and over-fitting. The second one is a dual-branch CNN which extracts traditional LBP features and deep learning features in parallel. The third one is a pre-trained CNN which is designed by transfer learning technique to overcome the shortage of training samples. Extensive evaluations on three popular datasets (public CK+, multi-view BU-3DEF and FER2013 datasets) demonstrated that our models were competitive and representative in the field of FER in the wild research. We achieved significant better results with comparisons to plenty of state-of-the-art approaches. Moreover, we provided discussions on the effectiveness and practicability of CNNs with different feature types and architectures for FER in the wild as well.
Personalizing Dialogue Agents via Meta-Learning

Existing personalized dialogue models use human designed persona descriptions to improve dialogue consistency.Collecting such descriptions from existing dialogues is expensive and requires hand-crafted feature designs.In this paper, we propose to extend Model-Agnostic Meta-Learning (MAML) (Finn et al., 2017) to personalized dialogue learning without using any persona descriptions.Our model learns to quickly adapt to new personas by leveraging only a few dialogue samples collected from the same user, which is fundamentally different from conditioning the response on the persona descriptions.Empirical results on Persona-chat dataset (Zhang et al., 2018) indicate that our solution outperforms non-metalearning baselines using automatic evaluation metrics, and in terms of human-evaluated fluency and consistency.
The Effects of Super-Resolution on Object Detection Performance in Satellite Imagery

We explore the application of super-resolution techniques to satellite imagery, and the effects of these techniques on object detection algorithm performance. Specifically, we enhance satellite imagery beyond its native resolution, and test if we can identify various types of vehicles, planes, and boats with greater accuracy than native resolution. Using the Very Deep Super-Resolution (VDSR) framework and a custom Random Forest Super-Resolution (RFSR) framework we generate enhancement levels of 2×, 4×, and 8× over five distinct resolutions ranging from 30 cm to 4.8 meters. Using both native and super-resolved data, we then train several custom detection models using the SIMRDWN object detection framework. SIMRDWN combines a number of popular object detection algorithms (e.g. SSD, YOLO) into a unified framework that is designed to rapidly detect objects in large satellite images. This approach allows us to quantify the effects of super-resolution techniques on object detection performance across multiple classes and resolutions. We also quantify the performance of object detection as a function of native resolution and object pixel size. For our test set we note that performance degrades from mean average precision (mAP) = 0.53 at 30 cm resolution, down to mAP = 0.11 at 4.8 m resolution. Super-resolving native 30 cm imagery to 15 cm yields the greatest benefit; a 13-36% improvement in mAP. Super-resolution is less beneficial at coarser resolutions, though still provides a small improvement in performance.
A Deep Learning Model to Triage Screening Mammograms: A Simulation Study

Background Recent deep learning (DL) approaches have shown promise in improving sensitivity but have not addressed limitations in radiologist specificity or efficiency. Purpose To develop a DL model to triage a portion of mammograms as cancer free, improving performance and workflow efficiency. Materials and Methods In this retrospective study, 223 109 consecutive screening mammograms performed in 66 661 women from January 2009 to December 2016 were collected with cancer outcomes obtained through linkage to a regional tumor registry. This cohort was split by patient into 212 272, 25 999, and 26 540 mammograms from 56 831, 7021, and 7176 patients for training, validation, and testing, respectively. A DL model was developed to triage mammograms as cancer free and evaluated on the test set. A DL-triage workflow was simulated in which radiologists skipped mammograms triaged as cancer free (interpreting them as negative for cancer) and read mammograms not triaged as cancer free by using the original interpreting radiologists' assessments. Sensitivities, specificities, and percentage of mammograms read were calculated, with and without the DL-triage-simulated workflow. Statistics were computed across 5000 bootstrap samples to assess confidence intervals (CIs). Specificities were compared by using a two-tailed t test (P < .05) and sensitivities were compared by using a one-sided t test with a noninferiority margin of 5% (P < .05). Results The test set included 7176 women (mean age, 57.8 years ± 10.9 [standard deviation]). When reading all mammograms, radiologists obtained a sensitivity and specificity of 90.6% (173 of 191; 95% CI: 86.6%, 94.7%) and 93.5% (24 625 of 26 349; 95% CI: 93.3%, 93.9%). In the DL-simulated workflow, the radiologists obtained a sensitivity and specificity of 90.1% (172 of 191; 95% CI: 86.0%, 94.3%) and 94.2% (24 814 of 26 349; 95% CI: 94.0%, 94.6%) while reading 80.7% (21 420 of 26 540) of the mammograms. The simulated workflow improved specificity (P = .002) and obtained a noninferior sensitivity with a margin of 5% (P < .001). Conclusion This deep learning model has the potential to reduce radiologist workload and significantly improve specificity without harming sensitivity. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Kontos and Conant in this issue.
Semantic-Emotion Neural Network for Emotion Recognition From Text

Emotion detection and recognition from text is a recent essential research area in Natural Language Processing (NLP) which may reveal some valuable input to a variety of purposes. Nowadays, writings take many forms of social media posts, micro-blogs, news articles, customer review, etc., and the content of these short-texts can be a useful resource for text mining to discover an unhide various aspects, including emotions. The previously presented models mainly adopted word embedding vectors that represent rich semantic/syntactic information and those models cannot capture the emotional relationship between words. Recently, some emotional word embeddings are proposed but it requires semantic and syntactic information vice versa. To address this issue, we proposed a novel neural network architecture, called SENN (Semantic-Emotion Neural Network) which can utilize both semantic/syntactic and emotional information by adopting pre-trained word representations. SENN model has mainly two sub-networks, the first sub-network uses bidirectional Long-Short Term Memory (BiLSTM) to capture contextual information and focuses on semantic relationship, the second sub-network uses the convolutional neural network (CNN) to extract emotional features and focuses on the emotional relationship between words from the text. We conducted a comprehensive performance evaluation for the proposed model using standard real-world datasets. We adopted the notion of Ekman's six basic emotions. The experimental results show that the proposed model achieves a significantly superior quality of emotion recognition with various state-of-the-art approaches and further can be improved by other emotional word embeddings.
FML: Face Model Learning From Videos

Monocular image-based 3D reconstruction of faces is a long-standing problem in computer vision. Since image data is a 2D projection of a 3D face, the resulting depth ambiguity makes the problem ill-posed. Most existing methods rely on data-driven priors that are built from limited 3D face scans. In contrast, we propose multi-frame video-based self-supervised training of a deep network that (i) learns a face identity model both in shape and appearance while (ii) jointly learning to reconstruct 3D faces. Our face model is learned using only corpora of in-the-wild video clips collected from the Internet. This virtually endless source of training data enables learning of a highly general 3D face model. In order to achieve this, we propose a novel multi-frame consistency loss that ensures consistent shape and appearance across multiple frames of a subject's face, thus minimizing depth ambiguity. At test time we can use an arbitrary number of frames, so that we can perform both monocular as well as multi-frame reconstruction.
Evolving Deep Echo State Networks for Intelligent Fault Diagnosis

Echo state network (ESN) is a fast recurrent neural network with remarkable generalization performance for intelligent diagnosis of machinery faults. When dealing with high-dimensional signals mixed with much noise, however, the performance of a deep ESN is still highly affected by the random selection of input weights and reservoir weights, resulting in the optimal design of the deep ESN architecture, which is an open issue. For this reason, a hybrid evolutionary algorithm featuring a competitive swarm optimizer combined with a local search is proposed in this article. An indirect encoding method is designed based on the network characteristics of ESN to make the evolutionary process computationally economical. A layerwise optimization strategy is subsequently introduced for evolving deep ESNs. The results of two experimental cases show that the proposed approach has promising performance in identifying different faults reliably and accurately by comparing with other intelligent fault diagnosis approaches.
NutBaaS: A Blockchain-as-a-Service Platform

Blockchain, originated from Bitcoin system, has drawn intense attention from the academic community because of its decentralization, persistency, anonymity and auditability. In the past decade, the blockchain technology has evolved and became viable for various applications beyond the domain of finance. However, due to the complexity of blockchain technology, it is usually difficult and costly for most developers or teams to build, maintain and monitor a blockchain network that supports their applications. Most common developers or teams are unable to ensure the reliability and security of the blockchain system, which to a certain extent affects the quality of their applications. In this paper, we develop a BaaS platform called NutBaaS, which provides blockchain service over cloud computing environments, such as network deployment and system monitoring, smart contracts analysis and testing. Based on these services, developers can focus on the business code to explore how to apply blockchain technology more appropriately to their business scenarios, without bothering to maintain and monitor the system.
Selective Feature Aggregation Network with Area-Boundary Constraints for Polyp Segmentation

Automatic polyp segmentation is considered indispensable in modern polyp screening systems. It can help the clinicians accurately locate polyp areas for further diagnosis or surgeries. Benefit from the advancement of deep learning techniques, various neural networks are developed for handling the polyp segmentation problem. However, most of these methods neither aggregate multi-scale or multi-receptive-field features nor consider the area-boundary constraints. To address these issues, we propose a novel selective feature aggregation network with the area and boundary constraints. The network contains a shared encoder and two mutually constrained decoders for predicting polyp areas and boundaries, respectively. Feature aggregation is achieved by (1) introducing three up-concatenations between encoder and decoders and (2) embedding Selective Kernel Modules into convolutional layers which can adaptively extract features from different size of kernels. We call these two operations the Selective Feature Aggregation. Furthermore, a new boundary-sensitive loss function is proposed to take into account the dependency between the area and boundary branch, thus two branches can be reciprocally influenced and enable more accurate area predictions. We evaluate our method on the EndoScene dataset and achieve the state-of-the-art results with a Dice of 83.08% and a Accuracy of 96.68%.