Reviewerly/exHarmony huggingface dataset:
İHA Tarafından Elde Edilen Termal Görüntüler Kullanılarak Fotovoltaik Sistemde Derin Öğrenme Tabanlı Arıza Tespiti ve Teşhisi

Solar power is one of the largest renewable energy sources in the world. With photovoltaic systems, electrical energy can be generated wherever the sun is located. To prevent efficiency losses in photovoltaic systems, these systems should be tested at regular intervals. In this study, it is discussed to detect cell, module and panel faults in panels using thermal images obtained from solar panels. Within the scope of the study, a four-rotor unmanned aerial vehicle (drone) was designed and a thermal camera was placed on the vehicle. Thus, thermal images of the solar panels on the roof of Karabuk University buildings were taken. A thermal data set with cell fault, module fault and panel fault were created using the resulting thermal images. The YOLOv3 deep learning-based convolutional neural network was trained with the created dataset. This training was conducted on Nvidia Jetson TX2, an embedded AI (Artificial Intelligence) computing device. After the completion of the training of the YOLOv3 network, it was concluded that the faults mentioned in the tests were successfully detected.
Understanding the deterrence effect of punishment for marine information security policies non-compliance

In the organizational setting of marine engineering, a significant number of information security incidents have been arised from the employees’ failure to comply with the information security policies (ISPs). This may be treated as a principal-agent problem with moral hazard between the employer and the employee for the practical compliance effort of an employee is not observable without high cost-. On the other hand, according to the deterrence theory, the employer and the employee are inherently self-interested beings.It is worth examining to what extent the employee is self-interested in the marine ISPs compliance context. Moreover, it is important to clarify the proper degree of severity of punishment in terms of the deterrent effect. In this study, a marine ISPs compliance game model has been proposed to evaluate the deterrence effect of punishment on the non-compliance behavior of employee individuals. It is found that in a non-punishment contract, the employee will decline to comply with the marine ISPs; but in a punishment contract, appropriate punishment will lead her to select the marine ISPs compliance effort level expected by the employer, and cause no potential backfire effect.
EneX: An Energy-Aware Execution Scheduler for Serverless Computing

The emerging serverless computing paradigm has recently attracted huge attention from both academia and industry. It brings benefits such as less operational complexity, high scalability, and availability, and lower costs. Serverless applications are usually partitioned into several chains of functions. The serverless provider should schedule functions for execution per customers' requests considering their chained nature. Also, the existing scheduling mechanisms for serverless platforms pay little attention to the reduction of energy consumption during functions' execution. To fill this gap, we present an energy-aware execution scheduler for serverless service providers named EneX. To do so, we formulate the minimization of energy consumption for executing the incoming chains of functions with specified computational loads and deadlines. Due to the intractability of the problem, we introduce a linear programming reformulation based on which, we propose an online scheduler. Finally, our experiments demonstrate the significant improvement of EneX in terms of energy efficiency.
ECC + +: An algorithm family based on ensemble of classifier chains for classifying imbalanced multi-label data

Multi-label learning has attracted a great deal of research interests as it has a wide range of real-world applications. Although many multi-label learning methods have been proposed, very few of them have addressed the problem of class imbalance distribution in multi-label data. Moreover, most of the existing class imbalance multi-label learning algorithms only focus on solving the class imbalance problem, without taking into account the correlations among labels. To address these issues simultaneously, we propose to combine the well-known ensemble of classifier chain (ECC) algorithm with various binary-class imbalance learning techniques such as sampling, cost-sensitive learning, and threshold moving. This approach creates a new algorithm family called ECC++, designed specifically for class imbalance multi-label learning. ECC is already an excellent ensemble high-order binary relevance multi-label learning algorithm that is well-suited to exploiting correlations among labels. Combining it with binary-class imbalance learning techniques enables each link in a classifier chain (CC) to overcome the negative effect of skewed data distribution. ECC++ is a dynamic algorithm family that can be extended arbitrarily by applying any new binary-class imbalance learning techniques. To demonstrate the effectiveness and superiority of the proposed ECC++ algorithm family, we developed several ECC++ family members using some popular binary-class imbalance learning techniques. We then compared them with several state-of-the-art class imbalance multi-label learning algorithms on twelve benchmark and four real-world multi-label datasets. Our experimental results showed the effectiveness and superiority of the proposed ECC++ algorithm family over existing class imbalance multi-label learning algorithms. In conclusion, the proposed ECC++ algorithm family combines the strengths of the well-established ECC algorithm and binary-class imbalance learning techniques, resulting in a superior methodology for class imbalance multi-label learning.
Multiple partition structures and harmonic functions on branching graphs

We introduce and study multiple partition structures which are sequences of probability measures on families of Young diagrams subjected to a consistency condition. The multiple partition structures are generalizations of Kingman's partition structures, and are motivated by a problem of population genetics. They are related to harmonic functions and coherent systems of probability measures on a certain branching graph. The vertices of this graph are multiple Young diagrams (or multiple partitions), and the edges depend on the Jack parameter. If the value of the Jack parameter is equal to one the branching graph under considerations reflects the branching rule for the irreducible representations of the wreath product of a finite group with the symmetric group. If the value of the Jack parameter is zero then the coherent systems of probability measures are precisely the multiple partition structures. We establish a bijective correspondence between the set of harmonic functions on the graph and probability measures on the generalized Thoma set. The correspondence is determined by a canonical integral representation of harmonic functions. As a consequence we obtain a representation theorem for multiple partition structures. We give an example of a multiple partition structure which is expected to be relevant for a model of population genetics for the genetic variation of a sample of gametes from a large population. Namely, we construct a probability measure on the wreath product of a finite group with the symmetric group. If the finite group contains the identity element only then it coincides with the well-known Ewens probability measure on the symmetric group. The constructed probability measure defines a multiple partition structure which is a generalization of the Ewens partition structure studied by Kingman. We show that this multiple partition structure can be represented in terms of a multiple analogue of the Poisson-Dirichlet distribution called the multiple Poisson-Dirichlet distribution in the paper.
Scanning interferometric phase-calculation formula for simultaneous topographic profiling of thickness and surface of optical flats

With the rapid development of industrial technologies, both the thickness irregularity and surface shape of transparent optical flats should be profiled precisely to maintain the performance of industrial equipment. When measuring the optical flat thickness using an interferometer, the profiling uncertainty can deteriorate owing to a constant phase error. In this study, a new 3N phase-extraction formula that can compensate for the Zernike piston error was developed for the simultaneous measurement of thickness and surface. Using Fourier representation, the characteristics of the 3N formula were shown in the frequency domain. Subsequently, the combined and Zernike piston error suppression ability of the 3N formula was verified by comparison with the other formulas. Finally, the thickness and surface of the optical flats were measured using the 3N formula and a wavelength-scanning Fizeau interferometer. The standard deviations of the thickness and surface profiling measurements were 4.435 nm and 9.654 nm, respectively, which were much smaller than those calculated using other phase-calculation formulas.
Quantised and Simulated Max–min Fairness in Blockchain Ecosystems

Blockchain systems heavily rely on user incentive and participation for the success and scope of their operation. For this end, fairness is a key precondition to attract users to join the ecosystem. In order to assure users of the fairness of the ecosystem, the distribution of shared resources must be done according to a fair and transparent policy, and it must be handled by a robust mechanism that implements this policy. The present study addresses this problem and contributes four decentralised and autonomous algorithms that may serve for the fair distribution of shared intrinsic resources. These are the adaptations of Max–min Fairness (MF), a distribution scheme which is well established in the computer science literature as fair, and its weighted version (WMF). Blockchain faucets are shared resource allocation mechanisms which accomplish this task by providing users with fixed amounts of free cryptocurrency. Faucets are employed in academic networks such as Bloxberg, and also in test networks such as Ropsten and Rinkeby We implemented both MF and WMF as blockchain faucets for the demonstration of their operation and measurement of their performance. The first algorithm, we call quantised Max–min Fairness (QMF), operates under the restriction on demand volumes but is scalable to large numbers of users, and the second, we call Simulated Max–min Fairness (SMF), operates under the restriction on the number of users but allows for various weighting policies. The other two are the weighted versions of these algorithms.
Video-Based Depth Estimation Autoencoder With Weighted Temporal Feature and Spatial Edge Guided Modules

Convolutional neural networks with encoder and decoder structures, generally referred to as autoencoders, are used in many pixel- wise transformation, detection, segmentation and estimation applications, for example, which can be applied for face swapping, lane detection, semantic segmentation, and depth estimation, respectively. However, traditional autoencoders, which are based on single-frame inputs, ignore the temporal consistency between consecutive frames, and may hence produce unsatisfactory results. Accordingly, in this paper, a video-based depth estimation (VDE) autoencoder is proposed to improve the quality of depth estimation through the inclusion of two weighted temporal feature (WTF) modules in the encoder and a single spatial edge guided (SEG) module in the decoder. The WTF modules designed with channel weighted block (CWB) submodule effectively extract the temporal similarities in consecutive frames, while the SEG module provides spatial edge guidance of the object contours. Through the collaboration of the proposed modules, the accuracy of the depth estimation is greatly improved. The experimental results confirm that the proposed VDE autoencoder achieves a better monocular depth estimation performance than the existing autoencoders with only a slight increase in the computational cost.
The (Limited) Power of Blockchain Networks for Information Provision

We investigate the potential and limits of privacy-preserving corporate blockchain applications for information provision. We provide a theoretical model in which heterogeneous firms choose between adopting a blockchain application or relying on traditional third-party intermediaries to inform the capital market. The blockchain’s ability to generate information depends on each firm’s data profile and all firms’ endogenous adoption decisions. We show that blockchain technology can improve the information environment and outperform traditional institutions with firms’ adoption decisions serving as a credible value signal and the application uncovering firm values by analyzing all participating firms’ data. However, we also characterize an adverse mixed-adoption equilibrium in which neither of the two channels realizes its full potential and information provision declines not only for individual firms, but also in aggregate. The equilibrium is a warning sign that has broad implications for policymakers’ regulatory effort and investors’ assessment of corporate blockchain applications. This paper was accepted by Suraj Srinivasan, accounting. Funding: B. Franke and Q. Gao Fritz gratefully acknowledge financial support from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) Project-ID 403041268–TRR 266 Accounting for Transparency. A. Stenzel gratefully acknowledges financial support from the DFG through CRC TR 224 (Project C03) during prior employment at the University of Mannheim. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2023.4718 .
Captionomaly: A Deep Learning Toolbox for Anomaly Captioning in Social Surveillance Systems

Real-time video stream monitoring is gaining huge attention lately with an effort to fully automate this process. On the other hand, reporting can be a tedious task, requiring manual inspection of several hours of daily clippings. Errors are likely to occur because of the repetitive nature of the task causing mental strain on operators. There is a need for an automated system that is capable of real-time video stream monitoring in social systems and reporting them. In this article, we provide a tool aiming to automate the process of anomaly detection and reporting. We combine anomaly detection and video captioning models to create a pipeline for anomaly reporting in descriptive form. A new set of labels by creating descriptive captions for the videos collected from the UCF-Crime (University of Central Florida-Crime) dataset has been formulated. The anomaly detection model is trained on the UCF-Crime, and the captioning model is trained with the newly created labeled set UCF-Crime video description (UCFC-VD). The tool will be used for performing the combined task of anomaly detection and captioning. Automated anomaly captioning would be useful in the efficient reporting of video surveillance data in different social scenarios. Several testing and evaluation techniques were performed. Source code and dataset: https://github.com/Adit31/Captionomaly-Deep-Learning-Toolbox-for-Anomaly-Captioning.
Photosynthetically Active Radiation Separation Model for High-Latitude Regions in Agrivoltaic Systems Modelling

Photosynthetically active radiation is a key parameter for modelling the photosynthetic behaviour of plants in response to sunlight and, subsequently, for determining crop yield. Separating photosynthetically active radiation into direct and diffuse components is of significance to agrivoltaic systems, which combine solar energy conversion and agricultural farming on the same portion of land. Placing photovoltaic on agricultural land results in varying shading conditions throughout the day and seasons, producing a higher contribution of incident diffuse radiation to the crops beneath the system in these shaded regions. Additionally, photosynthesis is more efficient under conditions of diffuse radiation than direct radiation per unit of total photosynthetically active radiation. This study introduces a new separation model capable of accurately estimating the diffuse component from the global photosynthetically active radiation and conveniently retrievable meteorological parameters. The model builds upon one of the currently most performing separation models for decomposing global solar radiation into diffuse radiation, the YANG2 model. Four new predictors, found relevant in the literature to influence diffuse photosynthetically active radiation, are added: optical thickness, vapour pressure deficit, aerosol optical depth, and surface albedo. The proposed model has been calibrated, tested, and validated at three sites in Sweden with latitudes above 58° N, outperforming four other models in all examined locations, with R2 values greater than 0.90. To alleviate the scarcity of photosynthetically active radiation studies in high-latitude regions, the seasonal trends and variation of the various radiation components are analysed. To demonstrate the applicability of the developed model, the location of Sweden's first agrivoltaic system is used to assess a variety of cases that take into account the various data availability that is representative of current and future agrivoltaic systems. In cases where in-situ measurements of diffuse photosynthetically active radiation are not available, the results indicate that the model can be utilised and adjusted for adjacent stations. Utilising predictor values derived from satellite data is an alternative method, but the spatial resolution must be considered with caution.
Generalize Deep Neural Networks With Adaptive Regularization for Classifying

Regularization is a crucial technology to improve the generalization of deep neural networks. However, traditional regularization method approaches are all scenario-specific, because they are generally with ingeniously designed feature representations from input layer, hidden layer, and output layer, which increase the difficulty of model development and interpretation. To this end, a novel practical and flexible regularization method is presented to obtain higher generalization and interpretability. Specifically, the feature maps are decoupled by global suppression and partial suppression from various scales and locate the salient feature with strong low-resolution semantic information. Moreover, the guided discarding specification for feature decoupling by measuring the feature contributions to network decisions, leads to the logics with better interpretability. Subsequently, the max values of the feature map are suppressed by discarding the corresponding salient features. Comprehensive experiments demonstrate that the proposed adaptive regularization outperforms the state-of-the-art performance in image classification accuracy, generalization, and interpretability on several widely used datasets. And adaptive regularization helps the network to mine the connection between salient features, nonsalient features, and ground truth, encouraging the network to construct multiple layers of feature associations.
MinDet1: A Deep Learning-enabled Approach for Plagioclase Textural Studies

Textural information, such as crystal size distributions (CSDs) or crystal aspect ratios are powerful tools in igneous petrography for interrogating the thermal history of rocks. They facilitate the investigation of crystal nucleation, growth and mixing as well as the cooling rate of the rock. However, they require large volumes of crystal segmentations and measurements that are often obtained with manual methods. Here a deep learning-based computer vision technique, termed instance segmentation, is proposed to automate the pixel-by-pixel detection of each plagioclase crystal in thin section images. Using predictions from a re-trained model the physical properties of the detected crystals, such as size and aspect ratio, can be rapidly generated to provide textural insights. The present segmentations are validated against published results from manual approaches to prove the method's accuracy. The power and efficiency of this automated approach is showcased by analysing an entire sample suite, segmenting over 48,000 crystals in only a matter of days. Widescale use of this method is expected to drive significant developments in the igneous petrography and related fields
Attacking Distance-aware Attack: Semi-targeted Model Poisoning on Federated Learning

Existing model poisoning attacks on federated learning (FL) assume that an adversary has access to the full data distribution. In reality, an adversary usually has limited prior knowledge about clients' data. A poorly chosen target class renders an attack less effective. This work considers a semi-targeted situation where the source class is predetermined but the target class is not. The goal is to cause the misclassification of the global classifier on data from the source class. Approaches such as label flipping have been used to inject malicious parameters into FL. Nevertheless, it has shown that their performances are usually class-sensitive, varying with different target classes. Typically, an attack becomes less effective when shifting to a different target class. To overcome this challenge, we propose the Attacking Distance-aware Attack (ADA) that enhances model poisoning in FL by finding the optimized target class in the feature space. ADA deduces pair-wise class attacking distances using a Fast LAyer gradient MEthod (FLAME). Extensive evaluations were performed on five benchmark image classification tasks and three model architectures using varying attacking frequencies. Furthermore, ADA's robustness to conventional defenses of Byzantine-robust aggregation and differential privacy was validated. The results showed that ADA succeeded in increasing attack performance to 2.8 times in the most challenging case with an attacking frequency of 0.01 and bypassed existing defenses, where differential privacy that was the most effective defense still could not reduce the attack performance to below 50%.
Privacy-Preserving Federated Learning for Value-Added Service Model in Advanced Metering Infrastructure

Advanced metering infrastructure (AMI) is the backbone of the next generation smart city and smart grid; it not only provides near real-time two-way communication between the consumers and the energy systems, but also enables the third parties (TPs) to provide relevant value-added services to the consumers to improve user satisfaction. However, the existing services are implemented in a centralized manner, which has potential and associated security and privacy risks also increased with Internet-of-Things (IoT) devices. To better balance the quality of the services and ensure users’ privacy, a TP AMI service model based on differentially private federated learning (FL) is proposed in this article. Instead of sending the private energy data to the cloud server, the proposed service model trains the neural network models locally, and only model parameters are shared with the central server. Moreover, the identity of individuals is eliminated by adding random Gaussian noise during the secure aggregation. Furthermore, an attention-based bidirectional long short-term memory (ATT-BLSTM) neural network model is adopted to solve the long-range dependency problem of conventional neural networks. In the case study, a residential short-term load forecasting (STLF) task is implemented to evaluate the performance of the proposed model. Compared with other state-of-the-art energy service models, the proposed one can achieve similar accuracy as the typical centralized model and balances the trade-off between privacy loss and prediction accuracy flexibly.