Reviewerly/exHarmony huggingface dataset:
Estimating Subhourly Inverter Clipping Loss From Satellite-Derived Irradiance Data

Photovoltaic system production simulations are conventionally run using hourly weather datasets. Hourly simulations are sufficiently accurate to predict the majority of long-term system behavior but cannot resolve high-frequency effects like inverter clipping caused by short-duration irradiance variability. Direct modeling of this subhourly clipping error is only possible for the few locations with high-resolution irradiance datasets. This paper describes a method of predicting the magnitude of this error using a machine learning regressor ensemble model, comprised of a random forest and an XGBoost model, and 30-minute satellite irradiance data. The method predicts a correction for each 30-minute interval with the potential to roll up into 60-minute corrections to match an hourly energy model. The model is trained and validated at locations where the error can be directly simulated from 1-minute ground data. The validation shows low bias at most ground station locations. The model is also applied to gridded satellite irradiance to produce a heatmap of the estimated clipping error across the United States. Finally, the relative importance of each predictor satellite variable is retrieved from the model and discussed.
Hybrid multi‐objective optimization algorithm using Taylor series model and Spider Monkey Optimization

Abstract Multi‐objective optimization is used for optimizing a number of objectives simultaneously. Mostly, the optimization algorithms considered the previous iterative position to find the next position updates. The main intention of this research is to design and develop a new model to solve the computational complexity, and the resource allocation problem. Based on this perspective, the Taylor series model and its predictive theory are applied to Spider Monkey Optimization (SMO), and a new optimization, named Taylor‐Spider Monkey Optimization (TaySMO) is developed. The proposed TaySMO computes the updated position of the swarm using the local leader phase and the global leader phase. However, a new position update equation is derived to enhance the searching process of the SMO. Here, multiple objectives such as, throughput, power, and fairness index are considered to solve the resource allocation problem. However, the performance of the proposed algorithm is evaluated using the conventional optimization function in terms of fitness function and convergence criteria as the mean square error (MSE) with the neural network learning is 0.3747, congestion rate of the resource allocation problem is 8.736E‐23, and MSE of the spectrum sensing is 8.74E‐23, respectively.
Vision-based Vehicle Detection and Distance Estimation

Real-time vehicle detection is one of the most important topics under the Autonomous Vehicles (AVs) research paradigm and traffic surveillance. Detecting vehicles and estimating their distances are essential to ensure that the vehicles can keep a safe distance and run safely on the roads. The technology can also be utilized to determine traffic flow and estimate vehicle speed. In this paper, we apply two different deep learning models and compare their performances in detecting vehicles such as cars and trucks for deployment on the self-driving cars to ensure road safety. Our models are based on YOLOv4 and Faster R-CNN which are efficient and accurate in object detection within a given distance. We also propose a vision-based distance estimation algorithm to estimate other vehicles' distances. In detecting vehicles within 100 meters, the two variations of our models, YOLOv4 and Faster R-CNN, achieved 99.16% and 95.47% mean precision, and 79.36% and 85.54% Fl-measure respectively on a two-way road. The detection speed is 68 fps and 14 fps for YOLOv4 and Faster R-CNN respectively.
DeepCFL: Deep Contextual Features Learning from a Single Image

Recently, there is a vast interest in developing image feature learning methods that are independent of the training data, such as deep image prior [35], InGAN [28], [29], SinGAN [27], and DCIL [8]. These methods perform various tasks, such as image restoration, image editing, and image synthesis. In this work, we proposed a new training data-independent framework, called Deep Contextual Features Learning (DeepCFL), to perform image synthesis and image restoration based on the semantics of the input image. The contextual features are simply the high dimensional vectors representing the semantics of the given image. DeepCFL is a single image GAN framework that learns the distribution of the context vectors from the input image. We show the performance of contextual learning in various challenging scenarios: outpainting, inpainting, and restoration of randomly removed pixels. DeepCFL is applicable when the input source image and the generated target image are not aligned. We illustrate image synthesis using DeepCFL for the task of image resizing.
Design Procedure for Dynamic Controllers based on LWE-based Homomorphic Encryption to Operate for Infinite Time Horizon

The design of encrypted controllers, which perform control operation directly over encrypted signals via homomorphic cryptosystems, should consider both security of the control data and performance of the controller. Considering the use of Learning With Errors (LWE) based cryptosystem, in this paper, we present a design procedure for encrypted linear dynamic controllers. Providing a guideline for choosing the parameters of the cryptosystem as well as quantization, the procedure guarantees both the desired level of security and performance. Receiving the encrypted signals of the plant input as well as the output, the proposed controller is able to perform the dynamic operation over encrypted data for infinite time horizon, without the use of decryption or reset of the state. Thanks to additively and multiplicatively homomorphic property of the LWE-based cryptosystem, information of both control signals and gain matrices is protected by encryption.
Automating the Design of Efficient Distributed Behaviours for a Swarm of UAVs

The usage of Unmanned Aerial Vehicles (UAVs) is gradually gaining momentum for commercial applications. The vast majority considers a single UAV, which comes with several constraints such as its range of operations or the number of sensors it can carry. Using multiple autonomous UAVs simultaneously as a swarm makes it possible to overcome these limitations. However, manually designing complex emerging behaviours like swarming is a difficult and tedious task especially for such distributed systems which performance is hardly predictable. This article therefore proposes to automate the design of UAV swarming behaviours by defining a multi-objective optimisation problem, so called Coverage of a Connected-UAV Swarm (CCUS), and designing a Q-Learning based Hyper-Heuristic (QLHH) for generating distributed CCUS heuristics. Experimental results demonstrate the capacity of QLHH to generate efficient heuristics for any instance from a given class.
Using deep belief network to demote web spam

Many score propagation based Web Spam Demotion Algorithms (WSDAs) have been proposed in last decade. There are two major challenges those algorithms suffer from. First, the non-incremental property of score propagation based WSDAs restricted their applications in real world since Web changes rapidly and running algorithm on the entire Web graph is computation consuming. Second, the score propagation based WSDAs adopt only link structure of the web graph to demote Web spam, so that they are vulnerable to some other kind of spamming techniques, such as content spam. In this paper, we propose a preference-based learning to rank method to address the above-mentioned issues confronted by score propagation based WSDAs. Our proposal consists of two components, a preference function and an ordering algorithm. The preference function is modeled by Deep Belief Network (DBN), which can benefit from unlabeled data for better generalization. The proposed Incremental Probabilistic Ordering Algorithm (IPOA) uses the trained preference function to calculate top-ranking probabilities of Web pages, and then uses those probabilities for final ranking. Therefore, the complex object (i.e. Web page) ranking problem is reduced to real number ranking problem, which can be solved efficiently by classical sorting algorithm. We conduct experiments to compare our proposal with conventional score propagation based WSDAs as well as some popular preference based learning to rank algorithms on two public available datasets, WEBSPAM-UK2006 and WEBSPAM-UK2007. Our experimental results demonstrate the superiority of our proposed method. Specifically, compared with score propagation based WSDAs, we obtain 0.0074 absolute improvement (0.7% relative improvement) on WEBSPAM-UK2006 and 0.065 absolute improvement (7.3% relative improvement) on WEBSPAM-UK2007 in terms of spam demotion score.
RETRACTED: Computer simulation of urban garden landscape design based on FPGA and neural network

Digital Landscape is a combination of the system and the computer software and hardware system of a high simulation model. The author analyzes the application of computer simulation in landscape design and value analysis of a city garden. In the computer-aided design, the importance of digitizing information in the landscape design process, mainly human and the interaction of computer, is reflected in the digital model's creation and multimedia performance, becoming more and more evident. To form a two-dimensional or three-dimensional spatial data, to realize real-time, statistical Analysis, using the human living environment, multi-dimensional, efficient, and humane, and environmental landscape plan to more rational and practical, used the computer simulation techniques. Effective use of urban rainwater, to reduce the flooding of urban areas, it is possible to alleviate the water crisis, the organic combination of rainwater can be used in the course of the construction of the urban landscape as well as make-up landscape, visual beautification has optimized the ecosystem, and from many rainwater utilization functions; These functions in landscape design, rainwater garden, It can be realized the rooftop garden, and the city's green. The construction and sustainable economy and the promotion of the ecological park's social development will positively sign. Suitable for rainwater regulation, water (recovery) is stored—Computer-Aided Design (CAD) green space. Technical measures save of suggestions for practical application of the square: innovation and new of space design, new artificial wetland system, and garden rainwater in the application of the regulation (population) storage system design of the water-saving of these to the sustainable development of such new square of rainwater adjustment (group) storage system design and urban landscape environment. It is useful for the application of technology.
Optimal Resource Allocation and Quality of Service Prediction in Cloud

In the present scenario, cloud computing service provides on-request access to a collection of resources available in remote system that can be shared by numerous clients.Resources are in self-administration; consequently, clients can adjust their usage according to their requirements.Resource usage is estimated and clients can pay according to their utilization.In literature, the existing method describes the usage of various hardware assets.Quality of Service (QoS) needs to be considered for ascertaining the schedule and the access of resources.Adhering with the security arrangement, any additional code is forbidden to ensure the usage of resources complying with QoS.Thus, all monitoring must be done from the hypervisor.To overcome the issues, Robust Resource Allocation and Utilization (RRAU) approach is developed for optimizing the management of its cloud resources.The work hosts a numerous virtual assets which could be expected under the circumstances and it enforces a controlled degree of QoS.The asset assignment calculation is heuristic, which is based on experimental evaluations, RRAU approach with J48 prediction model reduces Job Completion Time (JCT) by 4.75 s, Make Span (MS) 6.25, and Monetary Cost (MC) 4.25 for 15, 25, 35 and 45 resources are compared to the conventional methodologies in cloud environment.
Extended invariant-EKF designs for state and additive disturbance estimation

In this paper, we consider an estimation problem of invariant nonlinear systems subject to dynamic additive disturbances. We identify two sets of sufficient conditions that preserve the invariant properties of the systems under the disturbances. We apply the conditions to a unicycle model under linear dynamic disturbances and design two different Invariant Extended Kalman filters (IEKFs). Both IEKFs estimate the state of the unicycle and the disturbances based on position measurements. We also propose a correction to the IEKF covariances to better represent uncertainties in the invariant frame. The benefit of including the covariance correction and the performances of the two IEKF designs are demonstrated through Monte-Carlo simulations.
Revolutionising Higher Education by Adopting Blockchain Technology in the Certification Process

Distributed ledger technologies such as blockchain have recently gained prominence as one of the latest technological revolutions. Therefore, many studies are currently exploring blockchain adoption in various fields, including the higher education sector. Recently, the education sector has emerged as one of the fields in which investments for blockchain-based systems and services are desirable. However, the extant literature lacks a guiding framework for the integration of blockchain and other relevant technologies in the use of certificating systems that issue authentic and sharable student credentials. Existing credentialing systems use analogue operations to manage certificate generation. These systems are slow and unreliable and, in some cases, may raise other cultural and social issues depending on the context of the education system. Consequently, this paper presents an analysis of blockchain adoption in this field, specifically with regard to the process of generating and sharing higher education student certificates. The paper outlines the first phase of an ongoing research project by proposing a validating and sharing framework for certificates that will guarantee the authenticity of shared higher education certificates by providing high privacy and security aspects in a blockchain network. It includes the design of a blockchain-based certificating system architecture to address issues and solutions in higher education systems. Thus, deploying blockchain in the higher education sector is expected to be beneficial as it solves some existing issues with the certification process.
Towards Enhancing Fine-grained Details for Image Matting

In recent years, deep natural image matting has been rapidly evolved by extracting high-level contextual features into the model. However, most current methods still have difficulties with handling tiny details, like hairs or furs. In this paper, we argue that recovering these microscopic de-tails relies on low-level but high-definition texture features. However, these features are downsampled in a very early stage in current encoder-decoder-based models, resulting in the loss of microscopic details. To address this issue, we design a deep image matting model to enhance fine-grained details. Our model consists of two parallel paths: a conventional encoder-decoder Semantic Path and an independent downsampling-free Textural Compensate Path (TCP). The TCP is proposed to extract fine-grained details such as lines and edges in the original image size, which greatly enhances the fineness of prediction. Meanwhile, to lever-age the benefits of high-level context, we propose a feature fusion unit(FFU) to fuse multi-scale features from the se-mantic path and inject them into the TCP. In addition, we have observed that poorly annotated trimaps severely affect the performance of the model. Thus we further propose a novel term in loss function and a trimap generation method to improve our model's robustness to the trimaps. The experiments show that our method outperforms previous start-of-the-art methods on the Composition-1k dataset.
Joint distortion rectification and super-resolution for self-driving scene perception

Fisheye lenses are widely applied in the self-driving system due to its large field of view (FOV), which also induces the strong radial distortion in captured data. Distortion rectification is a crucial preprocessing step for scene perception since the non-linear projection heavily destroys the original geometric distribution of objects. Previous methods rectify the distorted image with a plausible global distribution, but they perform poorly on the local appearance such as the detailed texture of vehicles and pedestrians, which are hard to meet the fine requirements of object detection and semantic recognition. To solve this problem, we propose a joint distortion rectification (DR) and super-resolution (SR) end-to-end framework to generate the realistic rectification result with a visually pleasing local appearance. To the best of our knowledge, this is the first parametric framework that combines the challenging techniques DR and SR in the field of computer vision, which learns the accurate mapping function from the distorted and low-resolution domain into the corrected and high-resolution domain simultaneously. Moreover, the devised network architecture using the channel attention mechanism and object-aware loss function enables better-reconstructed scenes compared with the state-of-the-art methods. Experimental results show that our approach achieves superior rectification and enhancement performance in both quantitative measure and visual qualitative appearance, contributing a comprehensive and promising vision algorithm to the perception of the self-driving scene.
COVID-19 Outbreak Prediction Using Quantum Neural Networks

Artificial intelligence has become an important tool in fight against COVID-19. Machine learning models for COVID-19 global pandemic predictions have shown a higher accuracy than the previously used statistical models used by epidemiologists. With the advent of quantum machine learning, we present a comparative analysis of continuous variable quantum neural networks (variational circuits) and quantum backpropagation multilayer perceptron (QBMLP). We analyze the convoluted and sporadic data of two affected countries, and hope that our study will help in effective modeling of outbreak while throwing a light on bright future of quantum machine learning.
Identifying Health Insurance Claim Frauds Using Mixture of Clinical Concepts

Patients depend on health insurance provided by the government systems, private systems, or both to utilize the high-priced healthcare expenses. This dependency on health insurance draws some healthcare service providers to commit insurance frauds. Although the number of such service providers is small, it is reported that the insurance providers lose billions of dollars every year due to frauds. In this article, we formulate the fraud detection problem over a minimal, definitive claim data consisting of medical diagnosis and procedure codes. We present a solution to the fraudulent claim detection problem using a novel representation learning approach, which translates diagnosis and procedure codes into Mixtures of Clinical Codes (MCC). We also investigate extensions of MCC using Long Short Term Memory networks and Robust Principal Component Analysis. Our experimental results demonstrate promising outcomes in identifying fraudulent records.
Intelligent Agents Diagnostics—Enhancing Cyber‐Physical Systems with Self‐Diagnostic Capabilities

Allowing intelligent agents to deal with unforeseen situations that have not been considered during development in a smart way is a first step for increasing their autonomy. This requires diagnostic capabilities to detect the unforeseen situation and to identify a root cause that can be used afterward for carrying out repair and other compensating actions. Herein, foundations for diagnostic reasoning based on models of the system are provided. In particular, a diagnostic solution is presented that utilizes answer set solvers, which allow implementing non‐monotonic reasoning. The underlying ideas are introduced, an algorithm is discussed, and experimental results are obtained to clarify the question whether the approach can be used in practical applications. The obtained results indicate that answer set solving provides similar and sometimes even better results than specialized diagnosis algorithms, and can be used in practice.
Further Results on Efficient Implementations of Block Cipher Linear Layers

At the FSE conference of ToSC 2018, Kranz et al. presented their results on shortest linear programs for the linear layers of several well known block ciphers in literature. Shortest linear programs are essentially the minimum number of 2-input xor gates required to completely describe a linear system of equations. In the above paper the authors showed that the commonly used metrics like d-xor/s-xor count that are used to judge the “lightweightedness” do not represent the minimum number of xor gates required to describe a given MDS matrix. In fact they used heuristic based algorithms of Boyar-Peralta and Paar to find implementations of MDS matrices with even fewer xor gates than was previously known. They proved that the AES mixcolumn matrix can be implemented with as little as 97 xor gates. In this paper we show that the values reported in the above paper are not optimal. By suitably including random bits in the instances of the above algorithms we can achieve implementations of almost all matrices with lesser number of gates than were reported in the above paper. As a result we report an implementation of the AES mixcolumn matrix that uses only 95 xor gates. In FSE conference of ToSC 2019, Li et al. had tweaked the Boyar-Peralta algorithm to get low depth implementations of many matrices. We show that by introducing randomness in the tweaked algorithm, it is again possible to get low depth implementations with lesser number of gates than the above paper. As a result, we report a depth implementation of the AES mixcolumn matrix that uses only 103 xor gates, which is 2 gates less than the previous implementation. In the second part of the paper, we observe that most standard cell libraries contain both 2 and 3-input xor gates, with the silicon area of the 3-input xor gate being smaller than the sum of the areas of two 2-input xor gates. Hence when linear circuits are synthesized by logic compilers (with specific instructions to optimize for area), most of them would return a solution circuit containing both 2 and 3-input xor gates. Thus from a practical point of view, reducing circuit size in presence of these gates is no longer equivalent to solving the shortest linear program. In this paper we show that by adopting a graph based heuristic it is possible to convert a circuit constructed with 2-input xor gates to another functionally equivalent circuit that utilizes both 2 and 3-input xor gates and occupies less hardware area. As a result we obtain more lightweight implementations of all the matrices listed in the ToSC paper.