Reviewerly/exHarmony huggingface dataset:
Effect of dust concentration, wind speed, and relative humidity on the performance of photovoltaic panels in Tehran

Impact of dust concentration and weather conditions – wind speed and relative humidity – on power generation of photovoltaic (PV) panels investigated experimentally in Tehran, Iran, for the period of April 10–September 22, 2018. Two Photovoltaic panels together with data-logging equipment were applied in this experiment. One panel was cleaned every day, while the other was remained unclean after 45 days. A performance ratio called “cleanness index” (CI) which is a temperature-corrected performance ratio of a soiled PV panel to that of a clean one was also used to indicate the dust deposition effect on PV panel performance. The uncertainty range of experimental results was between 5.27% and 5.55%. The results show that cleanness index had positive correlation with wind speed and negative correlations with relative humidity and dust concentration. Thus, in comparison to the clean PV panel, the output-power of the soiled panel reduced, approximately 8–12% per month. Furthermore, in order to numerically predict daily change of cleanness index (ΔCIPre), a linear regression model was developed and the outcome revealed that dust concentration had the most impact, with 21.56%.
Hyperplane Division in Fuzzy C-Means: Clustering Big Data

Big data with a large number of observations (samples) have posed genuine challenges for fuzzy clustering algorithms and fuzzy C-means (FCM), in particular. In this article, we propose an original algorithm referred to as a hyperplane division method to split the entire data set into disjoint subsets. By disjoint subsets, we mean that the data subspaces (parts of the entire data space), each of which is supported or spanned by the data points in the corresponding subset, do not overlap each other. The disjoint subsets turned out to be beneficial to the improvement of the quality of the clusters formed by the clustering algorithms. Moreover, considering that either a large number (say, thousands) or a small number (say, a few) of clusters may be pursued in the clustering task, we propose corresponding strategies (based on the hyperplane division method) to make clustering processes feasible, efficient, and effective. By validating the proposed strategies on both synthetic and publicly available data, we show their superiority (in terms of both efficiency and effectiveness) manifested in a visible way over the method of clustering the entire data and over some representative big data clustering methods.
Early warning system: From face recognition by surveillance cameras to social media analysis to detecting suspicious people

Surveillance security cameras are increasingly deployed in almost every location for monitoring purposes, including watching people and their actions for security purposes. For criminology, images collected from these cameras are usually used after an incident occurs to analyze who could be the people involved. While this usage of the cameras is important for a post crime action, there exists the need for real time monitoring to act as an early warning to prevent or avoid an incident before it occurs. In this paper, we describe the development and implementation of an early warning system that recognizes people automatically in a surveillance camera environment and then use data from various sources to identify these people and build their profile and network. The current literature is still missing a complete workflow from identifying people/criminals from a video surveillance to building a criminal information extraction framework and identifying those people and their interactions with others We train a feature extraction model for face recognition using convolutional neural networks to get a good recognition rate on the Chokepoint dataset collected using surveillance cameras. The system also provides the function to record people appearance in a location, such that unknown people passing through a scene excessive number of times (above a threshold decided by a security expert) will then be further analyzed to collect information about them. We implemented a queue based system to record people entrance. We try to avoid missing relevant individuals passing through as in some cases it is not possible to add every passing person to the queue which is maintained using some cache handling techniques. We collect and analyze information about unknown people by comparing their images from the cameras to a list of social media profiles collected from Facebook and intelligent services archives. After locating the profile of a person, traditional news and other social media platforms are crawled to collect and analyze more information about the identified person. The analyzed information is then presented to the analyst where a list of keywords and verb phrases are shown. We also construct the person's network from individuals mentioned with him/her in the text. Further analysis will allow security experts to mark this person as a suspect or safe. This work shows that building a complete early warning system is feasible to tackle and identify criminals so that authorities can take the required actions on the spot.
Bayesian neural multi-source transfer learning

Although the use of deep learning and neural networks techniques are gaining popularity, there remain a number of challenges when multiple sources of information and data need to be combined. Although transfer learning and data fusion methodologies try to address this challenge, they lack robust uncertainty quantification which is crucial for decision making. Bayesian inference provides a rigorous approach for uncertainty quantification in decision making. Uncertainty quantification using Bayesian inference takes into consideration uncertainty associated with model parameters, as well as, the uncertainty in combining multiple sources of data. In this paper, we present a Bayesian framework for transfer learning using neural networks that considers single and multiple sources of data. We use existence of prior distributions to define the dependency between different data sources in a multi-source Bayesian transfer learning framework. We use Markov Chain Monte-Carlo method to obtain samples from the posterior distribution that consider the knowledge from the source datasets as priors. The results show that the framework provides a robust probabilistic approach for decision making with accuracy that is similar to gradient-based learning methods. Moreover, the results are comparable to related machine learning methods used for transfer learning in the literature.
A Generative Adversarial Learning-Based Approach for Cell Outage Detection in Self-Organizing Cellular Networks

For enabling automatic deployment and management of cellular networks, Self-Organizing Network (SON) was boosted to enhance network performance, to improve service quality, and to reduce operational and capital expenditure. Cell outage detection is an essential functionality of SON to autonomously detect cells that fail to provide services, due to either software or hardware faults. Machine learning represents an effective tool for such a task. However, traditional classification algorithms for cell outage detection are likely to construct a biased classifier when training samples in one class significantly outnumber other classes. To counter this problem, in this letter, we present a novel method that is able to learn from imbalanced cell outage data in cellular networks, through combining Generative Adversarial Network (GAN) and Adaboost. Specifically, the proposed approach utilizes GAN to change distribution of imbalanced dataset by synthesizing more samples for minority class, and then uses Adaboost to classify the calibrated dataset. Experimental results show significant improvement of classification performance for imbalanced cell outage data, on the basis of several metrics including Receiver Operating Characteristic (ROC), precision, recall rate, and F-value.
Noise-boosted bidirectional backpropagation and adversarial learning

Bidirectional backpropagation trains a neural network with backpropagation in both the backward and forward directions using the same synaptic weights. Special injected noise can then improve the algorithm's training time and accuracy because backpropagation has a likelihood structure. Training in each direction is a form of generalized expectation-maximization because backpropagation itself is a form of generalized expectation-maximization. This requires backpropagation invariance in each direction: The gradient log-likelihood in each direction must give back the original update equations of the backpropagation algorithm. The special noise makes the current training signal more probable as bidirectional backpropagation climbs the nearest hill of joint probability or log-likelihood. The noise for injection differs for classification and regression even in the same network because of the constraint of backpropagation invariance. The backward pass in a bidirectionally trained classifier estimates the centroid of the input pattern class. So the feedback signal that arrives back at the input layer of a classifier tends to estimate the local pattern-class centroid. Simulations show that noise speeded convergence and improved the accuracy of bidirectional backpropagation on both the MNIST test set of hand-written digits and the CIFAR-10 test set of images. The noise boost further applies to regular and Wasserstein bidirectionally trained adversarial networks. Bidirectionality also greatly reduced the problem of mode collapse in regular adversarial networks.
Characterizing Attacker Behavior in a Cybersecurity Penetration Testing Competition

Background: Inculcating an attacker mindset (i.e. learning to think like an attacker) is an essential skill for engineers and administrators to improve the overall security of software. Describing the approach that adversaries use to discover and exploit vulnerabilities to infiltrate software systems can help inform such an attacker mindset. Aims: Our goal is to assist developers and administrators in inculcating an attacker mindset by proposing an approach to codify attacker behavior in cybersecurity penetration testing competition. Method: We use an existing multimodal dataset of events captured during the 2018 National Collegiate Penetration Testing Competition (CPTC'18) to characterize the approach a team of attackers used to discover and exploit vulnerabilities. Results: We collected 44 events to characterize the approach that one of the participating teams took to discover and exploit seven vulnerabilities. We used the MITRE ATT&CK <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">™</sup> framework to codify the approach in terms of tactics and techniques. Conclusions: We show that characterizing attackers' campaign as a chronological sequence of MITRE ATT&CK <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">™</sup> tactics and techniques is feasible. We hope that such a characterization can inform the attacker mindset of engineers and administrators in their pursuit of engineering secure software systems.
SCARL: Attentive Reinforcement Learning-Based Scheduling in a Multi-Resource Heterogeneous Cluster

Advanced reinforcement learning (RL) technologies have recently increased the opportunity for automating several tasks in cluster management at scale by exploiting repetitive logs of cluster operation and building a learning model for resource allocation and job scheduling. Yet, this trend of adopting RL in the domain of cluster management has not fully addressed the diversity and heterogeneity of jobs and machines in modern cluster environments. In this paper, we present an RL-based scheduler for a multi-resource cluster, namely SCARL (SCheduler with Attentive Reinforcement Learning), concentrating on intricate cluster operating conditions with different resource requirements and capabilities. Specifically, we employ attentive embedding and factored-action scheduling that together efficiently incorporate time-varying interdependency of jobs and machines in RL processing; they enable an end-to-end scalable policy for scheduling diverse jobs on heterogeneous machines. To the best of our knowledge, we are the first to employ attention mechanism in RL-based cluster resource management. Through experiments, we demonstrate that our approach is competitive with existing heuristic methods under various cluster simulation configurations, e.g., an average 9.2 % enhancement in slowdown over the shortest job first algorithm. Additionally, the approach yields stable performance with our test cluster for running synthetic workloads based on real traces.
On the benefit of adversarial training for monocular depth estimation

In this paper we address the benefit of adding adversarial training to the task of monocular depth estimation. A model can be trained in a self-supervised setting on stereo pairs of images, where depth (disparities) are an intermediate result in a right-to-left image reconstruction pipeline. For the quality of the image reconstruction and disparity prediction, a combination of different losses is used, including L1 image reconstruction losses and left-right disparity smoothness. These are local pixel-wise losses, while depth prediction requires global consistency. Therefore, we extend the self-supervised network to become a Generative Adversarial Network (GAN), by including a discriminator which should tell apart reconstructed (fake) images from real images. We evaluate Vanilla GANs, LSGANs and Wasserstein GANs in combination with different pixel-wise reconstruction losses. Based on extensive experimental evaluation, we conclude that adversarial training is beneficial if and only if the reconstruction loss is not too constrained. Even though adversarial training seems promising because it promotes global consistency, non-adversarial training outperforms (or is on par with) any method trained with a GAN when a constrained reconstruction loss is used in combination with batch normalisation. Based on the insights of our experimental evaluation we obtain state-of-the art monocular depth estimation results by using batch normalisation and different output scales.
Visual Attention Analysis and Prediction on Human Faces for Children with Autism Spectrum Disorder

The focus of this article is to analyze and predict the visual attention of children with Autism Spectrum Disorder (ASD) when looking at human faces. Social difficulties are the hallmark features of ASD and will lead to atypical visual attention toward various stimuli more or less, especially on human faces. Learning the visual attention of children with ASD could contribute to related research in the field of medical science, psychology, and education. We first construct a Visual Attention on Faces for Autism Spectrum Disorder (VAFA) database, which consists of 300 natural scene images with human faces and corresponding eye movement data collected from 13 children with ASD. Compared with matched typically developing (TD) controls, we quantify atypical visual attention on human faces in ASD. Statistics show that some high-level factors such as face size, facial features, face pose, and facial emotions have different impacts on the visual attention of children with ASD. Combining the feature maps extracted from the state-of-the-art saliency models, we get the visual attention model on human faces for individuals with ASD. The proposed model shows the best performance among all competitors. With the help of our proposed model, researchers in related fields could design specialized education contents containing human faces for the children with ASD or produce the specific model for rapidly screening ASD using their eye movement data.
Robustness of Optimal Investment Decisions in Mixed Insurance/Investment Cyber Risk Management

Abstract An integrated risk management strategy, combining insurance and security investments, where the latter contribute to reduce the insurance premium, is investigated to assess whether it can lead to reduced overall security expenses. The optimal investment for this mixed strategy is derived under three insurance policies, covering, respectively, all the losses (total coverage), just those below the limit of maximum liability (partial coverage), and those above a threshold but below the maximum liability (partial coverage with deductibles). Under certain conditions (e.g., low potential loss, or either very low or very high vulnerability), the mixed strategy reverts however to insurance alone, because investments do not provide an additional benefit. When the mixed strategy is the best choice, the dominant component in the overall security expenses is the insurance premium in most cases. Optimal investment decisions require an accurate estimate of the vulnerability, whereas larger estimation errors may be tolerated for the investment‐effectiveness coefficient.
Evolution-algorithm-based unmanned aerial vehicles path planning in complex environment

With the wide application of Unmanned Aerial Vehicles (UAVs) in production and life, more and more attention has been paid to the autonomous track planning of UAVs. When UAV path planning algorithm is dealing with flying in an unknown complex environment, there are some problems, such as inability to dynamically plan the track and slow speed to calculate the path. This paper proposes a dynamic path planning based on an improved evolutionary optimization algorithm. The experimental results show that the evolutionary optimization algorithm based on improved t-distribution can effectively deal with the problems of high computational complexity and low search efficiency encountered in UAV dynamic track planning. It has strong robustness and can dynamically plan the appropriate track.
Technical and economical evaluation of the photovoltaic system in Brazilian public buildings: A case study for peak and off-peak hours

An analysis of Brazilian public costs indicates that, although a significant part of its financial resources is destined for electricity consumption, only a small portion of the distributed generation belongs to public institutions. A technical and economic analysis of a PV power plant in the Brazilian public sector was proposed and various scenarios were evaluated, indicating that a change in the Brazilian tariff policy could be an alternative for boosting PV generation in the public sector. Risk analysis demonstrated that the minimum acceptable rate of return is the variable that most affects the system’s viability. In addition, a Monte Carlo Simulation (MCS) showed that the proposed PV system has a viability probability higher than 92%. A statistical analysis indicated that the system has a probability of at least 80% for supplying more than 25% of the building’s peak demand for all seasons. Finally, such systems have also been found to represent an alternative for improving the electricity grid’s stability, while supplying part of the building’s energy demand.
A Deep Learning Based Distributed Smart Surveillance Architecture using Edge and Cloud Computing

Smart surveillance is getting increasingly popular as technologies become easier to use and cheaper. Traditional surveillance records video footage to a storage device continuously. However, this generates enormous amount of data and reduces the life of the hard drive. Newer devices with Internet connection save footage to the Cloud. This feature comes with bandwidth requirements and extra Cloud costs. In this paper, we propose a deep learning based, distributed, and scalable surveillance architecture using Edge and Cloud computing. Our design reduces both the bandwidth and as well as the Cloud costs significantly by processing footage prior sending to the Cloud.
Classification of Local Seismic Events in the Utah Region: A Comparison of Amplitude Ratio Methods with a Spectrogram‐Based Machine Learning Approach

Abstract The capability to discriminate low‐magnitude earthquakes from low‐yield anthropogenic sources, both detectable only at local distances, is of increasing interest to the event monitoring community. We used a dataset of seismic events in Utah recorded during a 14‐day period (1–14 January 2011) by the University of Utah Seismic Stations network to perform a comparative study of event classification at local scale using amplitude ratio (AR) methods and a machine learning (ML) approach. The event catalog consists of 7377 events with magnitudes MC ranging from −2 and lower up to 5.8. Events were subdivided into six populations based on location and source type: tectonic earthquakes (TEs), mining‐induced events (MIEs), and mining blasts from four known mines (WMB, SMB, LMB, and CQB). The AR approach jointly exploits Pg‐to‐Sg phase ARs and Rg‐to‐Sg spectral ARs in multivariate quadratic discriminant functions and was able to classify 370 events with high signal quality from the three groups with sufficient size (TE, MIE, and SMB). For that subset of the events, the method achieved success rates between about 80% and 90%. The ML approach used trained convolutional neural network (CNN) models to classify the populations. The CNN approach was able to classify the subset of events with accuracies between about 91% and 98%. Because the neural network approach does not have a minimum signal quality requirement, we applied it to the entire event catalog, including the abundant extremely low-magnitude events, and achieved accuracies of about 94%–100%. We compare the AR and ML methodologies using a broad set of criteria and conclude that a major advantage to ML methods is their robustness to low signal‐to‐noise ratio data, allowing them to classify significantly smaller events.
Mobile Device Training Strategies in Federated Learning: An Evolutionary Game Approach

With the tremendous success of machine learning and increasingly powerful mobile devices, federated learning has gained growing attention from both academia and industry. It capitalizes on vast number of distributed data to support machine learning based applications while maintaining data privacy. In this paper, we consider a federated learning system, in which the mobile devices allocate their data and computation resources among the machine learning applications, i.e., model owners. Specifically, we formulate an evolutionary game mode for the mobile devices with bounded rationality to adapt their training strategies aiming to maximize the device's individual utility. The uniqueness and stability of the equilibrium of the game are analysed theoretically. Besides, Extensive experiments are conducted to determine the functions fitted for the accuracy and energy consumption metrics.