Reviewerly/exHarmony huggingface dataset:
Development of empirical models for estimation of global solar radiation exergy in India

In this work, exergy of incoming solar radiation is estimated using empirical models. Long-term meteorological data (1986–2000) consisting of monthly mean diffuse and global solar radiation on a horizontal plane, ambient temperature and sunshine hours for 23 selected stations were available from Indian Meteorological Department (Pune), India. Exergy of direct and diffuse radiations was calculated using Petela model and summed to obtain exergy of global solar radiation. Beam and diffuse exergy efficiencies were in the range of 0.9286–0.9365 and 0.7189–0.7499 respectively. Estimated global exergy efficiencies were found in the range of 0.7700–0.8912. Empirical models were developed by correlating global solar exergy (in the form of global exergy efficiency and global exergy efficiency factor) in terms of clearness index and relative sunshine period. Four categories of single predictor models were defined with each consisting of eight models. Statistical analysis was used to compare and evaluate the accuracy of the models. Further, Global Performance Indicator was calculated and used to rank the models within the individual categories of models as well as the overall group of the 32 developed models. Overall assessment proved that power model with the clearness index is the best one for estimation of global exergy efficiency factor.
Complex-Valued Neural Networks With Nonparametric Activation Functions

Complex-valued neural networks (CVNNs) are a powerful modeling tool for domains where data can be naturally interpreted in terms of complex numbers. However, several analytical properties of the complex domain (such as holomorphicity) make the design of CVNNs a more challenging task than their real counterpart. In this paper, we consider the problem of flexible activation functions (AFs) in the complex domain, i.e., AFs endowed with sufficient degrees of freedom to adapt their shape given the training data. While this problem has received considerable attention in the real case, very limited literature exists for CVNNs, where most activation functions are generally developed in a split fashion (i.e., by considering the real and imaginary parts of the activation separately) or with simple phase-amplitude techniques. Leveraging over the recently proposed kernel activation functions, and related advances in the design of complex-valued kernels, we propose the first fully complex, nonparametric activation function for CVNNs, which is based on a kernel expansion with a fixed dictionary that can be implemented efficiently on vectorized hardware. Several experiments on common use cases, including prediction and channel equalization, validate our proposal when compared to real-valued neural networks and CVNNs with fixed activation functions.
Automatic Modulation Classification Using Cyclic Correntropy Spectrum in Impulsive Noise

Automatic modulation classification (AMC) plays an important role in many military and civilian communication applications. However, it remains a challenging task to support such AMC mechanisms under impulsive noise environments. Aiming at improving the classification performance in impulsive noise, in this letter, a novel modulation classification method is proposed by using the cyclic correntropy spectrum (CCES). In the proposed method, CCES is introduced into AMC for effectively suppressing impulsive noise. Specifically, it is verified that modulation types can be distinguished through CCES. Then, multi-slices are extracted at different cycle-frequencies from CCES as the original features for AMC. Following the extraction, the principal component analysis is applied to these slices to further optimize the original features. Finally, the radial basis function neural network is used as a classifier to perform modulation classification. Monte Carlo simulations demonstrate that the proposed algorithm outperforms other existing schemes in impulsive noise cases, especially with a low generalized signal to noise ratio.
Deep Neural Networks with Intersection over Union Loss for Binary Image Segmentation

In semantic segmentation tasks the Jaccard Index, or Intersection over Union (IoU), is often used as a measure of success.While this measure is more representative than per-pixel accuracy, state-of-the-art deep neural networks are still trained on accuracy by using Binary Cross Entropy loss.In this research, an alternative is used where deep neural networks are trained for a segmentation task of human faces by optimizing directly an approximation of IoU.When using this approximation, IoU becomes differentiable and can be used as a loss function.The comparison between IoU loss and Binary Cross Entropy loss is made by testing two deep neural network models on multiple datasets and data splits.The results show that training directly on IoU significantly increases performance for both models compared to training on conventional Binary Cross Entropy loss.
NLProlog: Reasoning with Weak Unification for Question Answering in Natural Language

Rule-based models are attractive for various tasks because they inherently lead to interpretable and explainable decisions and can easily incorporate prior knowledge.However, such systems are difficult to apply to problems involving natural language, due to its linguistic variability.In contrast, neural models can cope very well with ambiguity by learning distributed representations of words and their composition from data, but lead to models that are difficult to interpret.In this paper, we describe a model combining neural networks with logic programming in a novel manner for solving multi-hop reasoning tasks over natural language.Specifically, we propose to use a Prolog prover which we extend to utilize a similarity function over pretrained sentence encoders.We fine-tune the representations for the similarity function via backpropagation.This leads to a system that can apply rulebased reasoning to natural language, and induce domain-specific rules from training data.We evaluate the proposed system on two different question answering tasks, showing that it outperforms two baselines -BIDAF (Seo et al., 2016a) and FASTQA (Weissenborn et al., 2017b) on a subset of the WIKIHOP corpus and achieves competitive results on the MEDHOP data set (Welbl et al., 2017).
Navigation in Unknown Dynamic Environments Based on Deep Reinforcement Learning

In this paper, we propose a novel Deep Reinforcement Learning (DRL) algorithm which can navigate non-holonomic robots with continuous control in an unknown dynamic environment with moving obstacles. We call the approach MK-A3C (Memory and Knowledge-based Asynchronous Advantage Actor-Critic) for short. As its first component, MK-A3C builds a GRU-based memory neural network to enhance the robot's capability for temporal reasoning. Robots without it tend to suffer from a lack of rationality in face of incomplete and noisy estimations for complex environments. Additionally, robots with certain memory ability endowed by MK-A3C can avoid local minima traps by estimating the environmental model. Secondly, MK-A3C combines the domain knowledge-based reward function and the transfer learning-based training task architecture, which can solve the non-convergence policies problems caused by sparse reward. These improvements of MK-A3C can efficiently navigate robots in unknown dynamic environments, and satisfy kinetic constraints while handling moving objects. Simulation experiments show that compared with existing methods, MK-A3C can realize successful robotic navigation in unknown and challenging environments by outputting continuous acceleration commands.
FnSched

An imminent challenge in the serverless computing landscape is the escalating cost of infrastructure needed to handle the growing traffic at scale. This work presents FnSched, a function-level scheduler designed to minimize provider resource costs while meeting customer performance requirements. FnSched works by carefully regulating the resource usage of colocated functions on each invoker, and autoscaling capacity by concentrating load on few invokers in response to varying traffic. We implement a prototype of FnSched and show that, compared to existing baselines, FnSched significantly improves resource efficiency, by as much as 36%-55%, while providing acceptable application latency.
Time-Aware Smart Object Recommendation in Social Internet of Things

With a large number of possible smart objects in Social Internet of Things (SIoT), a recommendation system is of great necessity to help users find smart objects they need. However, traditional recommendation techniques usually exploit user's rating or feedback information, which are impractical as such kind of user preference information is difficult to collect in the SIoT environment. In addition, temporal context plays an important role in smart object recommendation since most users tend to utilize different objects at different time slots in a day, e.g., making coffee at morning and playing games on weekends. In this article, we propose a time-aware smart object recommendation model by jointly considering user's preference over time and smart object's social similarity. We first learn user's preference over time from his/her object usage events with a latent probabilistic model. Then, we estimate the smart object's social similarity by embedding their heterogeneous social relationships into a shared lower dimensional space. Finally, we generate the recommendation list with an item-based collaborative filtering. We conduct a comprehensive experimental study based on two real-world data sets, and the experimental results show our method outperforms all baselines significantly in terms of recommendation effectiveness.
Combining Blockchain and IoT: Food-Chain Traceability and Beyond

Recently, the interest around the Blockchain concept has grown faster and, as a consequence, several studies about the possibility of exploiting such technology in different application domains have been conducted. Most of these studies highlighted the benefits that the use of the blockchain could bring in those contexts where integrity and authenticity of the data are important, e.g., for reasons linked to regulations about consumers’ healthcare. In such cases, it would be important to collect data, coming in real-time through sensors, and then store them in the blockchain, so that they can become immutable and tamper-proof. In this paper, the design and development of a software framework that allows Internet-of-Things (IoT) devices to interact directly with an Ethereum-based blockchain are reported. The proposed solution represents an alternative way for integrating a wide category of IoT devices without relying on a centralized intermediary and third-party services. The main application scenario for which the project has been conceived regards food-chain traceability in the Industry 4.0 domain. Indeed, the designed system has been integrated into the depiction of a use case for monitoring the temperature of fish products within a warehouse and during the delivery process.
Hate and offensive speech detection on Arabic social media

We are witnessing an increasing proliferation of hate speech on social media targeting individuals for their protected characteristics. Our study aims to devise an effective Arabic hate and offensive speech detection framework to address this serious issue. First, we built a reliable Arabic textual corpus by crawling data from Twitter using four robust extraction strategies that we implement based on four types of hate: religion, ethnicity, nationality, and gender. Next, we label the corpus based on a three-hierarchical annotation scheme in which we verify the inter annotation agreement to ensure ground truth at each level. Based on machine and deep learning techniques, we develop numerous two-class, three-class, and six-class classification models that we combine with a variety of feature extraction techniques, such as contextual word embeddings. Finally, we conduct an intensive experiment to assess the performance of the different learned models and to examine the misclassification errors. The performance results are very encouraging compared to prior hate and offensive speech studies carried out on Arabic and other languages.
Zendoo: a zk-SNARK Verifiable Cross-Chain Transfer Protocol Enabling Decoupled and Decentralized Sidechains

We introduce Zendoo, a construction for Bitcoin-like blockchain systems that allows the creation and communication with sidechains of different types without knowing their internal structure. We consider a parent-child relationship between the mainchain and sidechains, where sidechain nodes directly observe the mainchain while mainchain nodes only observe cryptographically authenticated certificates from sidechain maintainers. We use zk-SNARKs to construct a universal verifiable transfer mechanism that is used by sidechains.
Precision–recall curve (PRC) classification trees

The classification of imbalanced data has presented a significant challenge for most well-known classification algorithms that were often designed for data with relatively balanced class distributions. Nevertheless skewed class distribution is a common feature in real world problems. It is especially prevalent in certain application domains with great need for machine learning and better predictive analysis such as disease diagnosis, fraud detection, bankruptcy prediction, and suspect identification. In this paper, we propose a novel tree-based algorithm based on the area under the precision–recall curve for variable selection in the classification context. Our algorithm, named as the “Precision–Recall curve classification tree”, or simply the “PRC classification tree” modifies two crucial stages in tree building. The first stage is to maximize the area under the precision–recall curve in node variable selection. The second stage is to maximize the harmonic mean of recall and precision (F-measure) for threshold selection. We found the proposed PRC classification tree, and its subsequent extension, the PRC random forest, work well especially for class-imbalanced data sets. We have demonstrated that our methods outperform their classic counterparts, the usual CART and random forest for both synthetic and real data. Furthermore, the ROC classification tree proposed by our group previously, based on the area under the ROC curve, has shown good performance in imbalanced data. Their combination, the PRC–ROC tree, has also shows great promise in identifying the minority class.
Does corporate social responsibility (CSR) impact on development of women in small-scale fisheries of sub-Saharan Africa? Evidence from coastal communities of Niger Delta in Nigeria

While women are often key actors in fisheries, they are commonly excluded from making fisheries management decisions, often due to cultural norms. The objective of this investigation is to assess the impact of a new CSR model of multinational oil companies (MOCs) on development of women in small-scale fisheries in the Niger Delta region of Nigeria. A total of eight hundred artisanal fisherwomen were sampled across the coastal communities of Niger Delta. Results from the use of logit model indicate that artisanal fisherwomen have remained widely excluded from the General Memorandum of Understandings (GMoUs) interventions in small-scale fisheries due to cultural norms of the people. This implies that if the cultural norms of the Niger Delta communities continue to restrain direct participation of the artisanal fisherwomen from GMoUs’ intervention, achieving gender equity and cultural change would be limited in the region. The findings suggest that since fisheries, which is the traditional source of livelihood of the people are no longer viable and have significantly declined due to environmental oil degradation, GMoUs’ intervention structure could focus on women playing key roles in fisheries management and conservation decision in Niger Delta region of Nigeria.
Delay-dependent stability analysis of the QUAD vector field fractional order quaternion-valued memristive uncertain neutral type leaky integrator echo state neural networks

This paper studies the robust stability analysis for a class of memristive-based neural networks (NN). The NN consists of a fractional order neutral type quaternion-valued leaky integrator echo state with parameter uncertainties and time-varying delays. First, the quaternion-valued leaky integrator echo state NN with QUAD vector field activation function is transformed into a real-valued system using a linear mapping function. Then, the Lyapunov–Krasovskii functional is adopted to derive the sufficient conditions on the existence and uniqueness of Filippov solution of the NN equilibrium point. The delay-dependent robust stability analysis of such NN is provided with the help of linear matrix inequality technique. Finally, the theoretical results are validated by means of a numerical example.
Review of augmented reality in aerospace industry

Purpose The purpose of this paper is to assess and determine the potential of augmented reality (AR) in aerospace applications through a survey of published sources. Design/methodology/approach This paper reviews a database of AR applications developed for the aerospace sector in academic research or industrial training and operations. The review process begins with the classification of these applications, followed by a brief discussion on the implications of AR technology in each category. Findings AR is abundantly applied in engineering, navigation, training and simulation. There is potential for application in in-flight entertainment and communication, crew support and airport operations monitoring. Originality/value This paper is a general review introducing existing and potential AR applications in various fields of the aerospace industry. Unlike previous publications, this article summarizes existing and emerging applications to familiarize readers with AR use in all of aerospace. The paper outlines example projects and creates a single comprehensive reference of AR advancements and its use in the aerospace industry. The paper provides individuals with a quick guide to available and emerging technology.
Towards Explaining the Regularization Effect of Initial Large Learning Rate in Training Neural Networks

Stochastic gradient descent with a large initial learning rate is widely used for training modern neural net architectures. Although a small initial learning rate allows for faster training and better test performance initially, the large learning rate achieves better generalization soon after the learning rate is annealed. Towards explaining this phenomenon, we devise a setting in which we can prove that a two layer network trained with large initial learning rate and annealing provably generalizes better than the same network trained with a small learning rate from the start. The key insight in our analysis is that the order of learning different types of patterns is crucial: because the small learning rate model first memorizes low-noise, hard-to-fit patterns, it generalizes worse on hard-to-generalize, easier-to-fit patterns than its large learning rate counterpart. This concept translates to a larger-scale setting: we demonstrate that one can add a small patch to CIFAR-10 images that is immediately memorizable by a model with small initial learning rate, but ignored by the model with large learning rate until after annealing. Our experiments show that this causes the small learning rate model's accuracy on unmodified images to suffer, as it relies too much on the patch early on.
A Real-Time Approach for Chance-Constrained Motion Planning With Dynamic Obstacles

Uncertain dynamic obstacles, such as pedestrians or vehicles, pose a major challenge for optimal robot navigation with safety guarantees. Previous work on motion planning has followed two main strategies to provide a safe bound on an obstacle's space: a polyhedron, such as a cuboid, or a nonlinear differentiable surface, such as an ellipsoid. The former approach relies on disjunctive programming, which has a relatively high computational cost that grows exponentially with the number of obstacles. The latter approach needs to be linearized locally to find a tractable evaluation of the chance constraints, which dramatically reduces the remaining free space and leads to over-conservative trajectories or even unfeasibility. In this work, we present a hybrid approach that eludes the pitfalls of both strategies while maintaining the original safety guarantees. The key idea consists in obtaining a safe differentiable approximation for the disjunctive chance constraints bounding the obstacles. The resulting nonlinear optimization problem is free of chance constraint linearization and disjunctive programming, and therefore, it can be efficiently solved to meet fast real-time requirements with multiple obstacles. We validate our approach through mathematical proof, simulation and real experiments with an aerial robot using nonlinear model predictive control to avoid pedestrians.
Flexible Lossy Compression for Selective Encrypted Image With Image Inpainting

In this paper, a novel lossy compression scheme for encrypted image based on image inpainting is proposed. In order to maintain confidentiality, the content owner encrypts the original image through a modulo-256 addition encryption and block permutation to mask image content. Then, the third party, such as a cloud server, can compress the selective encrypted image before transmitting to the receiver. During compression, encrypted blocks are categorized into four sets corresponding to different complexity degrees in plaintext domain without the loss of security. By allocating various bit rates to the encrypted blocks from different sets, flexible compression can be achieved with difference quantization. After parsing and decoding the compressed bit stream, the receiver first recovers partial encrypted pixels and then decrypts them. The other missing pixels are further recovered with the assistance of image inpainting based on a total variation model, and the final reconstructed image can be produced. Experimental results demonstrate that the proposed scheme achieves better rate-distortion performance than some of the state-of-the-art schemes.
SPAGAN: Shortest Path Graph Attention Network

Graph convolutional networks (GCN) have recently demonstrated their potential in analyzing non-grid structure data that can be represented as graphs. The core idea is to encode the local topology of a graph, via convolutions, into the feature of a center node. In this paper, we propose a novel GCN model, which we term as Shortest Path Graph Attention Network (SPAGAN). Unlike conventional GCN models that carry out node-based attentions, on either first-order neighbors or random higher-order ones, the proposed SPAGAN conducts path-based attention that explicitly accounts for the influence of a sequence of nodes yielding the minimum cost, or shortest path, between the center node and its higher-order neighbors. SPAGAN therefore allows for a more informative and intact exploration of the graph structure and further the more effective aggregation of information from distant neighbors, as compared to node-based GCN methods. We test SPAGAN for the downstream classification task on several standard datasets, and achieve performances superior to the state of the art.