Reviewerly/exHarmony huggingface dataset:
Single image resolution enhancement by efficient dilated densely connected residual network

Convolution Neural Networks have been widely applied in single image super-resolution (SR). Recent works have shown the superior performance of deep networks for SR tasks. With just an increase in the model's depth, more features and parameters (which lead to high computational cost) can be practically extracted. In this paper, we leverage the ground-truth high-resolution (HR) image as a useful guide for learning and present an effective model based on progressive dilated densely connected and a novel activation function, which is appropriate for image SR problems. Different to the common per-pixel activation functions, like Sigmoids and ReLUs, the proposed activation unit has a nonlinear learnable function with some short connections. These strategies help the network to obtain deep and complex features, consequently, the network demanding a much smaller number of layers to have similar performance for image SR, which supports the exponential growth of the receptive field, parallel by increasing the filter size. The dense connectivity facilitates feature extraction in the network and residual connections facilitate feature re-use that both are required to improve the performance of the network. Based on the experimental results, the proposed model accelerates 2 times faster than the current deep network approaches; the proposed network also achieves higher SR performance as compared to state-of-the-art results.
Improved Bat Algorithm Based on Multipopulation Strategy of Island Model for Solving Global Function Optimization Problem

The bat algorithm (BA) is a heuristic algorithm that globally optimizes by simulating the bat echolocation behavior. In order to improve the search performance and further improve the convergence speed and optimization precision of the bat algorithm, an improved algorithm based on chaotic map is introduced, and the improved bat algorithm of Levy flight search strategy and contraction factor is proposed. The optimal chaotic map operator is selected based on the simulation experiments results. Then, a multipopulation parallel bat algorithm based on the island model is proposed. Finally, the typical test functions are used to carry out the simulation experiments. The simulation results show that the proposed improved algorithm can effectively improve the convergence speed and optimization accuracy.
Towards Effective Mutation for Knowledge Transfer in Multifactorial Differential Evolution

Differential evolution (DE) is a simple yet powerful evolutionary algorithm for the solving of continuous optimization problems. In the last decades, a plethora of DE variants have been proposed in the literature for enhanced optimization performance. However, most of these DE variants are designed to solve a single problem in a single run. Recently, a multifactorial DE (MFDE) has been proposed to conduct evolutionary search on multiple tasks simultaneously. Benefitting from the implicit knowledge transfer among different tasks, MFDE has demonstrated a superior performance against the single-task DE in terms of convergence speed and solution quality. In MFDE, the knowledge transfer is realized via the mutation operation conducted on solutions with different skill factors. However, despite a lot of mutation strategies suggested in the literature, the current MFDE takes DE/rand/1 as the only strategy for knowledge transfer. The impacts of different mutation strategies on the performance of MFDE is still unexplored. Taking this cue, in this paper, we embark a study to investigate how different mutation strategies for knowledge transfer affect the performance of MFDE. In particular, besides DE/rand/1, another four commonly-used mutation strategies are adapted for the purpose of multitask optimization. Further, towards effective mutation for knowledge transfer in MFDE, a new mutation strategy called DE/best/1+ρ, which is able to adjust its behavior along the search process is proposed. Lastly, comprehensive empirical studies are conducted to investigate the performance of existing and the new proposed mutation strategies on the 9 single-objective multitasking benchmarks.
Inferring Latent Domains for Unsupervised Deep Domain Adaptation

Unsupervised Domain Adaptation (UDA) refers to the problem of learning a model in a target domain where labeled data are not available by leveraging information from annotated data in a source domain. Most deep UDA approaches operate in a single-source, single-target scenario, i.e. they assume that the source and the target samples arise from a single distribution. However, in practice most datasets can be regarded as mixtures of multiple domains. In these cases, exploiting traditional single-source, single-target methods for learning classification models may lead to poor results. Furthermore, it is often difficult to provide the domain labels for all data points, i.e. latent domains should be automatically discovered. This paper introduces a novel deep architecture which addresses the problem of UDA by automatically discovering latent domains in visual datasets and exploiting this information to learn robust target classifiers. Specifically, our architecture is based on two main components, i.e. a side branch that automatically computes the assignment of each sample to its latent domain and novel layers that exploit domain membership information to appropriately align the distribution of the CNN internal feature representations to a reference distribution. We evaluate our approach on publicly available benchmarks, showing that it outperforms state-of-the-art domain adaptation methods.
Improving Walking in Place Methods with Individualization and Deep Networks

Walking in place is a standard method for moving through large virtual environments when physical space or positional tracking is limited. This technique has become increasingly prominent with the advent of mobile virtual reality in which external tracking may not be present. In this paper, we revisit walking in place algorithms to address some of their technical challenges. Namely, our solutions attend to improving starting, stopping, and speed control for individual users. From a hand-tuned threshold based algorithm, we provide a new, fast method for individualizing the walking in place algorithm based on biomechanic measures of step rate. In addition, we introduce a new walking in place model based on a convolutional neural network trained to differentiate walking and standing. Over two experiments we assess these methods against a traditional threshold based algorithm on two mobile virtual reality platforms. The assessments are based on controllability, scale, and presence. Our results suggest that an adequately trained convolutional neural network can be an effective way of implementing walking in place.
Identification of Self-Admitted Technical Debt Using Enhanced Feature Selection Based on Word Embedding

Self-admitted technical debt (SATD) is annotated in source code comments by developers and has been recognized as a great source of discovering flawed software.To reduce manual effort, some recent studies have focused on automated detection of SATD using text classification methods.To train their classifier, these methods need labeled samples, which also require a lot of effort to obtain.We developed a new SATD identification method, which takes advantage of a large corpus of unlabeled code comments, extracted from open source projects, to train a word embedding model.After applying feature selection, the pre-trained word embedding is used for discovering semantically similar features in source code comments to enhance the original feature set.By using such enhanced feature set for classification, our goal was to improve the identification of SATD when compared to existing methods.The proposed feature enhancement method was used with the three most common feature selection methods (CHI, IG, and MI), and three well-known text classification algorithms (NB, SVM, and ME) and was tested on ten open source projects.The experimental results show a significant improvement in SATD identification over the compared methods.With an achieved 82% of correct predictions of SATD, the proposed method seems to be a good candidate to be adopted in practice.
Blockchain in Smart Cities: Exploring Possibilities in Terms of Opportunities and Challenges

The ultimate aim of a smart city is to enhance the quality of life for its residents and businesses through modern technologies in order to reduce resource deterioration and maintain overall costs.From this perspective, blockchain is one of these technologies that has received much attention during the recent years as it offers new alternatives for individuals and institutions in the smart city context.This study aims to explore the potential and contribution of blockchain in smart cities by studying and reviewing the literature of scientific research on the concept and fundamentals of blockchain, involving its most practical applications.In addition, it summarizes worldwide examples of success in using blockchain as well as exploring the challenges and opportunities related to this technology in smart cities.Thus, this study provides a useful reference for researchers to review all about the new blockchain technology.
Identification of the Hydrogeochemical Processes and Assessment of Groundwater Quality, Using Multivariate Statistical Approaches and Water Quality Index in a Wastewater Irrigated Region

Groundwater quality and availability are essential for human consumption and social and economic activities in arid and semiarid regions. Many developing countries use wastewater for irrigation, which has in most cases led to groundwater pollution. The Mezquital Valley, a semiarid region in central Mexico, is the largest agricultural irrigation region in the world, and it has relied on wastewater from Mexico City for over 100 years. Limited research has been conducted on the impact of irrigation practices on groundwater quality on the Mezquital Valley. In this study, 31 drinking water wells were sampled. Groundwater quality was determined using the water quality index (WQI) for drinking purposes. The hydrogeochemical process and the spatial variability of groundwater quality were analyzed using principal component analysis (PCA) and K-means clustering multivariate geostatistical tools. This study highlights the value of combining various approaches, such as multivariate geostatistical methods and WQI, for the identification of hydrogeochemical processes in the evolution of groundwater in a wastewater irrigated region. The PCA results revealed that salinization and pollution (wastewater irrigation and fertilizers) followed by geogenic sources (dissolution of carbonates) have a significant effect on groundwater quality. Groundwater quality evolution was grouped into cluster 1 and cluster 2, which were classified as unsuitable (low quality) and suitable (acceptable quality) for drinking purposes, respectively. Cluster 1 is located in wastewater irrigated zones, urban areas, and the surroundings of the Tula River. Cluster 2 locations are found in recharge zones, rural settlements, and seasonal agricultural fields. The results of this study strongly suggest that water management strategies that include a groundwater monitoring plan, as well as research-based wastewater irrigation regulations, in the Mezquital Valley are warranted.
How Effective is the Salp Swarm Algorithm in Data Classification

In this paper, the Salp Swam Algorithm (SSA) is deployed in training the Multilayer Perceptron (MLP) for the task of data classification. The UCI machine learning repository standard datasets are used for evaluation of the proposed SSA based MLP. The performance of the proposed method is verified by considering various standard classification measures over the considered benchmark datasets. The result obtained by the proposed method is compared with other evolutionary algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), Grey Wolf Optimization (GWO), etc. From the simulation study, it has been verified that the proposed method shows supremacy in results as compared with other evolutionary algorithm based MLP.
ECC Based Lightweight Cybersecurity Solution For IoT Networks Utilising Multi-Access Mobile Edge Computing

The Security of the Internet of Things (IoT) has gained great attention recently due to the increased cyber-threats in those systems. IoT technologies have many use cases and real-life applications and the IoT devices have been widely produced by industries. However, the security issue of IoT devices has not been fully resolved, leaving those devices exposing to cyber-threats. IoT end devices are normally resource constraint, which makes the implementation of traditional security solutions extremely hard if not impossible. The emergence of IoT-Edge computing technologies can grant the IoT devices the ability to overcome the local resource constraint problem. The current industrial revolution (Industry 4.0) requires IoT devices to be secured in its data transmission, and protected from network Intrusions. Since many IoT devices reply on the unsecured Internet as a means for data transmission, there is an urgent need to provide a public key cryptographic technique to protect the end-to-end transmission between those IoT devices. In this paper, based on the Elliptic Curve Cryptographic (ECC) technique, we have proposed a lightweight asymmetric security solution to enhance secured transmission of data through utilising the IoT-Edge computing architecture. We also have successfully embedded our lightweight cryptography on the edge, and the IoT device. The experimental results has shown that the proposed lightweight ECC security system can provide better practice when implemented in the IoT-Edge environment.
SSEM: A Novel Self-Adaptive Stacking Ensemble Model for Classification

In the past decades, the ensemble systems have been shown as an efficient method to increase the accuracy and stability of classification algorithms. However, how to get a valid combination of multiple base-classifiers is still an open question to be solved. In this paper, based on the genetic algorithm, a new self-adaptive stacking ensemble model (called SSEM) is proposed. Different from other ensemble learning classification algorithms, SSEM selectively integrates different base-classifiers, and automatically selects the optimal base-classifier combination and hyper-parameters of base-classifiers via the genetic algorithm. It is noted that all of machine learning methods can be the components of SSEM. In this work, based on two base-classifier selection principles (low complexity of base-classifier and high diversity between different base-classifiers), we select five state-of-art classifiers including Naïve Bayes (NB), Extremely Randomized trees (ERT), Logistic, Random Forest (RF) and Classification and Regression Tree (CART) as the base-classifiers of SSEM. To demonstrate the efficiency of SSEM, we have applied it to nine different datasets. Compared with other 11 state-of-art classifiers (NB, ERT, Logistic, RF, CART, Back Propagation Network (BPN), Support Vector Machine (SVM), AdaBoost, Bagging, Convolutional Neural Networks (CNN) and Deep neural network (DNN)), SSEM always performs the best under the five evaluation indexes (Accuracy, Recall, AUC, F1-score and Matthews correlation coefficient (MCC)). Moreover, the significance test result shows that SSEM can achieve highly competitive performance against the other 11 state-of-art classifiers. Altogether, it is evident that SSEM can be a useful framework for classification.
Entropy-Based Block Scrambling Image Encryption Using DES Structure and Chaotic Systems

Traditional encryption algorithms are inefficient when applied to image encryption because image data have the characteristics of large data sizes and strong correlations between adjacent pixels. The shortcomings of the traditional Data encryption standard (DES) encryption algorithm when applied to image encryption are analyzed, and a new image encryption algorithm based on the traditional DES encryption algorithm model, chaotic systems, DNA computing, and select cipher-text output is proposed. Select cipher-text output selects cipher image with the biggest entropy, and it can increase the randomness of cipher image and reduce the risk of encryption system being broken down. This algorithm overcomes the shortcomings of high computational complexity and inconvenient key management that the traditional text encryption algorithm has when applied to image encryption. The experimental results show that the security of this algorithm is verified by analyzing the information entropy, image correlation of adjacent pixels and other indexes. At the same time, this algorithm passes the noise attack test and the occlusion attack test, so it can resist common attacks.
Sim-to-Real Transfer Learning using Robustified Controllers in Robotic Tasks involving Complex Dynamics

Learning robot tasks or controllers using deep reinforcement learning has been proven effective in simulations. Learning in simulation has several advantages. For example, one can fully control the simulated environment, including halting motions while performing computations. Another advantage when robots are involved, is that the amount of time a robot is occupied learning a task-rather than being productive-can be reduced by transferring the learned task to the real robot. Transfer learning requires some amount of fine-tuning on the real robot. For tasks which involve complex (non-linear) dynamics, the fine-tuning itself may take a substantial amount of time. In order to reduce the amount of fine-tuning we propose to learn robustified controllers in simulation. Robustified controllers are learned by exploiting the ability to change simulation parameters (both appearance and dynamics) for successive training episodes. An additional benefit for this approach is that it alleviates the precise determination of physics parameters for the simulator, which is a non-trivial task. We demonstrate our proposed approach on a real setup in which a robot aims to solve a maze game, which involves complex dynamics due to static friction and potentially large accelerations. We show that the amount of fine-tuning in transfer learning for a robustified controller is substantially reduced compared to a non-robustified controller.
Nowcasting of Surface Solar Irradiance Using FengYun-4 Satellite Observations over China

The accurate prediction of surface solar irradiance is of great significance for the generation of photovoltaic power. Surface solar irradiance is affected by many random mutation factors, which means that there are great challenges faced in short-term prediction. In Northwest China, there are abundant solar energy resources and large desert areas, which have broad prospects for the development of photovoltaic (PV) systems. For the desert areas in Northwest China, where meteorological stations are scarce, satellite remote sensing data are extremely precious exploration data. In this paper, we present a model using FY-4A satellite images to forecast (up to 15–180 min ahead) global horizontal solar irradiance (GHI), at a 15 min temporal resolution in desert areas under different sky conditions, and compare it with the persistence model (SP). The spatial resolution of the FY-4A satellite images we used was 1 km × 1 km. Particle image velocimetry (PIV) was used to derive the cloud motion vector (CMV) field from the satellite cloud images. The accuracy of the forecast model was evaluated by the ground observed GHI data. The results showed that the normalized root mean square error (nRMSE) ranged from 18.9% to 21.6% and the normalized mean bias error (nMBE) ranged from 3.2% to 4.9% for time horizons from 15 to 180 min under all sky conditions. Compared with the SP model, the nRMSE value was reduced by about 6%, 8%, and 14% with the time horizons of 60, 120, and 180 min, respectively.
Explaining Deep Learning Models with Constrained Adversarial Examples

Machine learning algorithms generally suffer from a problem of explainability. Given a classification result from a model, it is typically hard to determine what caused the decision to be made, and to give an informative explanation. We explore a new method of generating counterfactual explanations, which instead of explaining why a particular classification was made explain how a different outcome can be achieved. This gives the recipients of the explanation a better way to understand the outcome, and provides an actionable suggestion. We show that the introduced method of Constrained Adversarial Examples (CADEX) can be used in real world applications, and yields explanations which incorporate business or domain constraints such as handling categorical attributes and range constraints.
Arabic Handwritten Characters Recognition using Convolutional Neural Network

The following topics are dealt with: learning (artificial intelligence); feature extraction; pattern classification; optimisation; cloud computing; image classification; convolutional neural nets; cryptography; particle swarm optimisation; computer aided instruction.
Evaluation of Machine Learning Algorithms for Anomaly Detection in Industrial Networks

The cyber-physical security of Industrial Control Systems (ICSs) represents an actual and worthwhile research topic. In this paper, we compare and evaluate different Machine Learning (ML) algorithms for anomaly detection in industrial control networks. We analyze supervised and unsupervised ML-based anomaly detection approaches using datasets extracted from the Secure Water Treatment (SWaT), a testbed developed to emulate a scaled-down real industrial plant. Our experiments show strengths and limitations of the two ML-based anomaly detection approaches for industrial networks.
State-Aware Re-Identification Feature for Multi-Target Multi-Camera Tracking

Multi-target Multi-camera Tracking (MTMCT) aims to extract the trajectories from videos captured by a set of cameras. Recently, the tracking performance of MTMCT is significantly enhanced with the employment of re-identification (Re-ID) model. However, the appearance feature usually becomes unreliable due to the occlusion and orientation variance of the targets. Directly applying Re-ID model in MTMCT will encounter the problem of identity switches (IDS) and tracklet fragment caused by occlusion. To solve these problems, we propose a novel tracking framework in this paper. In this framework, the occlusion status and orientation information are utilized in Re-ID model with human pose information considered. In addition, the tracklet association using the proposed fused tracking feature is adopted to handle the fragment problem. The proposed tracker achieves 81.3% IDF1 on the multiple-camera hard sequence, which outperforms all other reference methods by a large margin.
A comprehensive review on the variants of support vector machines

Machine learning (ML) represents the automated extraction of models (or patterns) from data. All ML techniques start with data. These data describe the desired relationship between the ML model inputs and outputs, the latter of which may be implicit for unsupervised approaches. Equivalently, these data encode the requirements we wish to be embodied in our ML model. Thereafter, the model selection comes in action, to select an efficient ML model. In this paper, we have focused on various ML models which are the extensions of the well-known ML model, i.e. Support vector machines (SVMs). The main objective of this paper is to compare the existing ML models with the variants of SVM. Limitations of the existing techniques including the variants of SVM are then drawn. Finally, future directions are presented.