Reviewerly/exHarmony huggingface dataset:
Improving the prediction of DNI with physics-based representation of all-sky circumsolar radiation

Direct normal irradiance (DNI) is often interpreted differently in ground measurements and forecasts using numerical weather prediction (NWP) models, leading to substantial bias in DNI forecasting especially under cloudy-sky conditions. To mitigate the bias, we use the Fast All-sky Radiation Model for Solar applications with DNI (FARMS-DNI) to conduct physics-based simulations of solar radiation in the circumsolar region. The DNI is quantified using the sum of the radiation along the sun direction and the scattered radiation within the circumsolar region. FARMS-DNI is coupled with the Weather Research and Forecasting model with solar extensions (WRF-Solar) to predict day-ahead DNI in 9-km pixels over an extended domain covering the contiguous United States. The DNI forecasts for the entire year of 2018 are validated using surface-based observations at the Atmospheric Radiation Measurement (ARM) and Surface Radiation Budget Network (SURFRAD) sites and a satellite-based solar radiation product developed by the procedure of the National Solar Radiation Data Base (NSRDB). The results show that the WRF-Solar coupled with the conventional Beer-Bouguer-Lambert law underestimes the overall DNI forecasts by 30%-60%, which is a joint effect of the uncertainties in computing the circumsolar radiation and forecasting cloud properties. This bias is significantly reduced by using FARMS-DNI.
Ensemble of Key-Based Models: Defense Against Black-Box Adversarial Attacks

We propose a voting ensemble of models trained by using block-wise transformed images with secret keys against black-box attacks. Although key-based adversarial defenses were effective against gradient-based (white-box) attacks, they cannot defend against gradient-free (black-box) attacks without requiring any secret keys. In the proposed ensemble, a number of models are trained by using images transformed with different keys and block sizes, and then a voting ensemble is applied to the models. Experimental results show that the proposed defense achieves a clean accuracy of 95.56 % and an attack success rate of less than 9 % under attacks with a noise distance of 8/255 on the CIFAR-10 dataset.
Optimization of the FP-Growth Algorithm in Data Mining Techniques to Get the Electric Power Theft Pattern for the Development of Smart City

In general, people's daily lives cannot be separated from energy resources, namely electricity. Where electricity is one of the basic needs of society for survival. The amount of electricity consumption is now increasing, because people almost every day and every home uses electronic devices. Not only the needs of homes that use electricity, even companies, schools, shopping centers also cause an increase in the use of electric current. The increasing use of electric current makes many people commit violations in the use of electricity. This makes the Electric Power Company in managing the distribution of electrical energy to the public to check for violations in the use of electricity. For this reason, data mining techniques are needed to get patterns in energy use violations by optimizing the FP-Growth algorithm in getting a good pattern where the 1300Kwh meter in the optimized pattern is recognized as having more frequent violations such as breaking electric current. So that the optimization pattern can be applied to the electricity meter to develop smart city concepts such as the smart grid. In this paper, we apply data mining with the FP-Growth algorithm in analyzing the pattern of electricity theft. The existing pattern describes the cause and effect of the theft of electric power. In the future smart electricity grid, also known as the "smart grid", network stability is expected to be achieved through different energy sources, among others. If the results of renewable energy that depend on weather and time are not adjusted to the latest electricity usage, there can be a network imbalance that ends in a blackout or blackout.
Deep surrogate assisted MAP-elites for automated hearthstone deckbuilding

We study the problem of efficiently generating high-quality and diverse content in games. Previous work on automated deckbuilding in Hearthstone shows that the quality diversity algorithm MAP-Elites can generate a collection of high-performing decks with diverse strategic gameplay. However, MAP-Elites requires a large number of expensive evaluations to discover a diverse collection of decks. We propose assisting MAP-Elites with a deep surrogate model trained online to predict game outcomes with respect to candidate decks. MAP-Elites discovers a diverse dataset to improve the surrogate model accuracy while the surrogate model helps guide MAP-Elites towards promising new content. In a Hearthstone deck-building case study, we show that our approach improves the sample efficiency of MAP-Elites and outperforms a model trained offline with random decks, as well as a linear surrogate model baseline, setting a new state-of-the-art for quality diversity approaches in automated Hearthstone deckbuilding. We include the source code for all the experiments at: https://github.com/icaros-usc/EvoStone2.
Evaluating generic AutoML tools for computational pathology

Image analysis tasks in computational pathology are commonly solved using convolutional neural networks (CNNs). The selection of a suitable CNN architecture and hyperparameters is usually done through exploratory iterative optimization, which is computationally expensive and requires substantial manual work. The goal of this article is to evaluate how generic tools for neural network architecture search and hyperparameter optimization perform for common use cases in computational pathology. For this purpose, we evaluated one on-premises and one cloud-based tool for three different classification tasks for histological images: tissue classification, mutation prediction, and grading. We found that the default CNN architectures and parameterizations of the evaluated AutoML tools already yielded classification performance on par with the original publications. Hyperparameter optimization for these tasks did not substantially improve performance, despite the additional computational effort. However, performance varied substantially between classifiers obtained from individual AutoML runs due to non-deterministic effects. Generic CNN architectures and AutoML tools could thus be a viable alternative to manually optimizing CNN architectures and parametrizations. This would allow developers of software solutions for computational pathology to focus efforts on harder-to-automate tasks such as data curation.
A Bayesian Treatment of Real-to-Sim for Deformable Object Manipulation

We consider the problem of inferring simulation parameters such that the behavior of an object in simulation and the real world look similar. This <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">real-to-sim</i> problem is particularly challenging for deformable objects, where conventional techniques fall short as they often rely on precise state estimation and accurate dynamics models. In this letter, we formulate the real-to-sim problem as probabilistic inference over simulation parameters of deformable objects. Our key idea is in how we define the state space of a deformable object. We view noisy keypoints extracted from an image of the object as samples from the distribution that captures object geometry. We then embed this distribution into a reproducing kernel Hilbert space. A sequence of images of a moving object can then be represented by a trajectory of distribution embeddings in this novel state space for deformables. This allows for a principled way to incorporate noisy state observations into modern Bayesian tools for simulation parameter inference. Despite only using a small set of real-world trajectories, we show how the proposed approach can estimate posterior distributions over simulation parameters, such as elasticity, friction and scale, even for highly deformable objects, such as cloth and ropes.
Growth Monitoring System and Automatic Watering for Chili Plants Based on Internet of Things

The development of Internet of Things (IoT) technology in this all-digital era is increasingly advanced. The need in the form of automatic plant watering equipment in the community is starting to be needed given the increasingly sophisticated technology. Fulfillment of this increasing need must be accompanied by an increase in the productivity of chili cultivation. In this study, a monitoring system has been developed to observe the growth of chili plants integrated with the Internet of Things (IoT) with a power source from solar panels. In this system, a soil moisture sensor is added which functions as a sensor for detecting soil moisture, whose data will be used as a variable for the automatic watering system. Chili growth monitoring is carried out using a height sensor that is stored above the chili plant. Based on the test results, the results obtained for the growth of plants are very good and the soil moisture is below 60% which causes the water pump to turn on and then the data is sent to the Blynk application. And the voltage obtained from the solar cell is 12.95 volts.
Multi-Robot Hybrid Coverage Path Planning for 3D Reconstruction of Large Structures

Coverage Path Planning (CPP) is an essential capability for autonomous robots operating in various critical applications such as fire fighting, and inspection.Performing autonomous coverage using a single robot system consumes time and energy.In particular, 3D large structures might contain some complex and occluded areas that shall be scanned rapidly in certain application domains.In this paper, a new Hybrid Coverage Path Planning (HCPP) approach is proposed to explore and cover unknown 3D large structures using a decentralized multi-robot system.The HCPP approach combines a guided Next Best View (NBV) approach with a developed Long Short Term Memory (LSTM) waypoint prediction approach to decrease the CPP exploration time at each iteration and simultaneously achieve high coverage.The hybrid approach is the new ML paradigm which fosters intelligence by balancing between data efficiency and generality allowing the exchange of some CPP parts with a learned model.The HCPP uses a stateful LSTM network architecture which is trained based on collected paths that cover different 3D structures to predict the next viewpoint.This architecture captures the dynamic dependencies of adjacent viewpoints in the long term sequences like the coverage paths.The HCPP switches between these methods triggered by either the number of iterations or an entropy threshold.In the decentralized multi-robot system, the proposed HCPP is embedded in each robot where each one of them shares its global 3D map ensuring robustness.The results performed in a realistic Gazebo robotic simulator confirmed the advantage of the proposed HCPP approach by achieving high coverage on different 3D unknown structures in a shorter time compared to conventional NBV.
Quantum Resource Estimation for FSR Based Symmetric Ciphers and Related Grover’s Attacks

Several studies on the resource estimation of quantum key search attack exploiting Grover on different symmetric ciphers have been studied in state-of-the-art cryptology research. In this paper, we consider the popular Feedback Shift Register (FSR) based ciphers like Grain-128-AEAD, TinyJAMBU, LIZARD, Grain-v1 and study their implementations in different quantum environments. To evaluate with respect to NIST's depth restriction (MAXDEPTH), we design reversible quantum circuits for these ciphers and provide the QISKIT implementations with total gate counts. Our results show that quantum cryptanalysis is possible with gate counts less than \( 2^{170} /\mathsf{MAXDEPTH}\). Our results provide a clear view of the exact status of quantum cryptanalysis against FSR-based symmetric ciphers.
Securing IoT Devices using Blockchain Concept

The internet of things (IoT) implementation leads to an extended attack surface that requires continuous mitigation of security. Mission-critical situations (e.g., Smart Grid, Intelligent Transportation Systems, video surveillance, e-health) to business-oriented applications are all examples of IoT applications (e.g., banking, logistics, insurance, and contract law). IoT has made human existence considerably easier and more efficient. Because IoT devices are less expensive to build due to the use of various software, sensors, and the use of Artificial Intelligence, they violate several security regulations. That&#x0027;s why, in the IoT systems; extensive security support is required, particularly for mission-critical applications, but also for downstream commercial applications. There have been a variety of security strategies and approaches suggested and/or used. Blockchain is an amazing concept that manages a distributed ecosystem without the need for a third party. Researchers discovered that it has the ability to solve IoT software-related security problems. The biggest obstacle is combining blockchain with resource-constrained IoT computers. An experimental setup to incorporate blockchain on a smart laboratory IoT -based was completed, and then tested against Wireshark sniffing attacks, the system was more vulnerable to cyber-crimes attacks as we found out that when we employed blockchain technology as the communication medium of IoT devices, the no. of bytes needed for packet transmission was more than the bytes needed without applying Blockchain with about 959 bytes on average.
Challenges in the Evaluation of Observational Data Trustworthiness From a Data Producers Viewpoint (FAIR+)

Recent discussions in many scientific disciplines stress the necessity of “FAIR” data. FAIR data, however, does not necessarily include information on data trustworthiness, where trustworthiness comprises reliability, validity and provenience/provenance. This opens up the risk of misinterpreting scientific data, even though all criteria of “FAIR” are fulfilled. Especially applications such as secondary data processing, data blending, and joint interpretation or visualization efforts are affected. This paper intends to start a discussion in the scientific community about how to evaluate, describe, and implement trustworthiness in a standardized data evaluation approach and in its metadata description following the FAIR principles. It discusses exemplarily different assessment tools regarding soil moisture measurements, data processing and visualization and elaborates on which additional (metadata) information is required to increase the trustworthiness of data for secondary usage. Taking into account the perspectives of data collectors, providers and users, the authors identify three aspects of data trustworthiness that promote efficient data sharing: 1) trustworthiness of the measurement 2) trustworthiness of the data processing and 3) trustworthiness of the data integration and visualization. The paper should be seen as the basis for a community discussion on data trustworthiness for a scientifically correct secondary use of the data. We do not have the intention to replace existing procedures and do not claim completeness of reliable tools and approaches described. Our intention is to discuss several important aspects to assess data trustworthiness based on the data life cycle of soil moisture data as an example .
Algorithmic Formalization Of User Access To The Ecological Monitoring Information System

Currently, economic science and practice are increasingly using the achievements of applied mathematics, turning them from a weapon of scientific research into an important tool for effectively solving complex agricultural problems. The paper describes the use of digital technologies in monitoring the environmental situation, groundwater salinity in observation wells located in the Aral Sea regions, using information security methods for transmitted data. In the course of the study, algorithmic formalization of user access to the information system of environmental monitoring was carried out when monitoring changes in levels.
Multi-keywords fuzzy search encryption supporting dynamic update in an intelligent edge network

Formulae display:?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to zoom.
Learning classifier systems

tutorial Share on Learning classifier systems: from principles to modern systems Authors: Anthony Stein University of Hohenheim, Germany University of Hohenheim, GermanyView Profile , Masaya Nakata Yokohama National University, Japan Yokohama National University, JapanView Profile Authors Info & Claims GECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference CompanionJuly 2021 Pages 498–527https://doi.org/10.1145/3449726.3461414Published:08 July 2021Publication History 2citation135DownloadsMetricsTotal Citations2Total Downloads135Last 12 Months65Last 6 weeks4 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Self-Supervised Light Field Depth Estimation Using Epipolar Plane Images

Exploiting light field data makes it possible to obtain dense and accurate depth map. However, synthetic scenes with limited disparity range cannot contain the diversity of real scenes. By training in synthetic data, current learning based methods do not perform well in real scenes. In this paper, we propose a self-supervised learning framework for light field depth estimation. Different from the existing end-to-end training methods using disparity label per pixel, our approach implements network training by estimating EPI disparity shift after refocusing, which extends the disparity range of epipolar lines. To reduce the sensitivity of EPI to noise, we propose a new input mode called EPI-Stack, which stacks EPIs in the view dimension. This method is less sensitive to noise scenes than traditional input mode and improves the efficiency of estimation. Compared with other state-of-the-art methods, the proposed method can also obtain higher quality results in real-world scenarios, especially in the complex occlusion and depth discontinuity.
Tracking and Classification of Head Movement for Augmentative and Alternative Communication Systems

The use of assistive technologies can mitigate or reduce the challenges faced by individuals with motor disabilities to use computer systems. However, those who feature severe involuntary movements often have fewer options at hand. This work describes an application that can recognize the user's head using a conventional webcam, track its motion, model the desired functional movement, and recognize it to enable the use of a virtual keyboard. The proposed classifier features a flexible structure and may be personalized for different user need. Experimental results obtained with participants with no neurological disorders have shown that classifiers based on Hidden Markov Models provided similar or better performance than a classifier based on position threshold. However, motion segmentation and interpretation modules were sensitive to involuntary movements featured by participants with cerebral palsy that took part in the study.
Bioinclusive Collaborative and Participatory Design: A Conceptual Framework and a Research Agenda

Addressing the current unsustainability of socio-ecological-technological systems is urgently required. Unsustainability has been associated with the anthropocentric cultures of human societies that focus on their own needs while ignoring and belittling the needs of other species and natural systems. Design, including collaborative and participatory design (C&PD), is one of the multiple ways through which humans build solutions to satisfy their needs and wants in an anthropocentric manner. Through an integrative literature review of C&PD and a bioinclusive ethic (one of the non-anthropocentric frameworks in environmental ethics), this paper – a revised and expanded version of a conference paper previously published in Design Journal (2019) – develops a conceptual framework for bioinclusive C&PD rooted in a non-anthropocentric values base. It outlines twelve key principles for bioinclusive C&PD and proposes six avenues for further research.
Sensor Data-Driven UAV Anomaly Detection using Deep Learning Approach

Thanks to the high mobility and rich sensing capabilities of unmanned aerial vehicles (UAVs), or drones, they are increasingly leveraged to perform a series of military and civilian tasks today. Meanwhile, UAVs are also facing various security and safety concerns raised by both external attacks and internal hardware/software failures. Therefore, detecting the abnormal status of a UAV is a critical task to protect it against malicious adversaries and prevent potential crashes. In this paper, we propose an anomaly detection system for UAVs by monitoring and analyzing their sensor data in real-time using deep learning approaches. The proposed system leverages the convolutional neural network (CNN) to extract and learn features automatically from raw sensor data and then process them to support anomaly detection. We construct a data set of UAV IMU sensor data using our UAV cybersecurity simulation platform to support the training of our CNN model. Different deep learning models are also evaluated and compared in this paper. We validate the performance of the proposed detection system using extensive experimental evaluation, which demonstrates that our system achieves high detection accuracy under different conditions.
SRM-FPN: A Small Target Detection Method Based on FPN Optimized Feature

With the development of network detection models, researchers have achieved good results in general target detection, but there is still no good solution for small target detection in images, especially the feature processing of small targets. At present, the most suitable feature processing method is FPN, but this fusion method will cause the feature redundancy, ambiguity and inaccuracy of small targets, and has little effect on the general large targets, but it will cause great interference and errors in the detection of small targets. For the above problems, this paper improves FPN and proposes a new SRM-FPN feature fusion method. Specifically, SRM is a spatial refinement model that learns the location of future feature points according to the context features between adjacent layers and content, while borrowing the adaptive channel merging method of the attention mechanism to optimize feature fusion. Compared with other methods, the optimized scheme combined with the existing model can effectively improve the detection effect of small targets in the image.
Quality Monitoring and Assessment of Deployed Deep Learning Models for Network AIOps

Artificial intelligence (AI) has recently attracted a lot of attention, transitioning from research labs to a wide range of successful deployments in many fields, which is particularly true for deep learning (DL) techniques. Ultimately, DL models, being software artifacts, need to be regularly maintained and updated: AIOps is the logical extension of the DevOps software development practices to AI software applied to network operation and management. In the life cycle of a DL model deployment, it is important to assess the quality of deployed models, to detect &#x201C;stale&#x201D; models and prioritize their update. In this article, we cover the issue in the context of network management, proposing simple but effective techniques for quality assessment of individual inference, and for overall model quality tracking over multiple inferences, that we apply to two use cases, representative of the network management and image recognition fields.