Reviewerly/exHarmony huggingface dataset:
Exploring Distributed PV Power Measurements for Real-Time Potential Power Estimation in Utility-Scale PV Plants

We explore the use of distributed PV power measurements for real-time short-term forecasting of the maximum potential power output of a utility-scale PV power plant, to support future incorporation of PV plants in automatic generation control (AGC) systems. PV plants operating under AGC may run in a curtailed state but must be capable of accurately forecasting their maximum potential non-curtailed power output, or potential high limit (PHL), 10–20 seconds in advance - despite variable environmental conditions. One forecast approach estimates PHL from a subset of inverters, designated reference inverters, which are never curtailed. We propose an alternative approach using in-situ I-V tracers to measure maximum power point of reference modules distributed throughout a PV plant. This would allow for greater sampling of variations in irradiance, module temperature, soiling, and albedo, permitting better PHL prediction accuracy. To study potential benefits of this method, we analyzed a one-year data set from an approximately 100 MW PV power plant in the US Southeast, using non-curtailed string combiner outputs as a proxy for distributed module-level power measurements. We compare the reference inverter and distributed power measurement approaches. Results indicate that for highly flexible operation, substantial improvement in prediction accuracy can be achieved using distributed power measurements compared to using a low fraction of reference inverters. We review the study methodology, conclusions, and shortcomings and discuss prospective future work.
Cross-Situational Word Learning in Disentangled Latent Space

Cross-situational word learning (CSL) is a fast and efficient method for humans to acquire word meanings. Many studies have replicated human CSL using computational models. Among these, cross-situational learning with Bayesian probabilistic generative model (CSL-PGM) can estimate word meanings from observations that include multiple attributes, such as color and shape. However, as CSL-PGM receives observations for each attribute on a separate channel, it cannot perform CSL for images with multiple attributes. Therefore, we introduce a disentangled representation that captures the attributes within an image. Additionally, we propose CSL+VAE, which integrates CSL-PGM and a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\beta$</tex> -VAE to obtain a disentangled representation in an unsupervised manner. CSL+VAE can discover attributes hidden in images and word sequences and infer the meanings of words. Additionally, it can obtain a more disentangled representation using a learning framework wherein both models share parameters. During experiments, the model was trained on a set of images comprising five attributes and one to five words describing them. The results showed that 99.9% of the words correctly estimated the attributes of the words and correctly estimated the correspondence between the image features and the words. The proposed model also outperformed existing multimodal models in inferring images from word sequences, achieving an accuracy of 0.870.
Machine Learning, Unmanned Vehicles, and Energy: A Review

Automation has recently increased with the rise of machine learning models. Tasks that are inherently dangerous or laborious can be automated to reduce costs and potential loss of life. Machine learning has also improved the effectiveness of unmanned vehicles for these tasks. However, these improvements necessitate increased energy to sustain them. This review aims to understand basic machine learning models and their effectiveness in unmanned vehicle and energy generation applications.
Optimizing a Distributed Graph Data Structure for K - Path Centrality Estimation on HPC

K-Path centrality is based on the flow of information in a graph along simple paths of length at most K. This work addresses the computational cost of estimating K-path centrality in large-scale graphs by introducing the random neighbor traversal graph (RaNT-Graph). The distributed graph data structure employs a combination of vertex delegation partitioning and rejection sampling, enabling it to sample massive amounts of random paths on large scale-free graphs. We evaluate our approach by running experiments which demonstrate weak scaling on R-MAT graphs and strong scaling on large real-world graphs. The RaNT-Graph approach achieved a 56,544x speedup over the baseline 1D partition implementation when estimating K-path centrality on a graph with 89 million vertices and 1.9 billion edges.
EdgeSA: Secure Aggregation for Privacy-Preserving Federated Learning in Edge Computing

Due to the increased adoption of edge computing, and growing concern about data privacy, Federated Learning (FL) frameworks have gained significant attention as a promising approach to leverage distributed data while protecting user privacy. However, ensuring the privacy of model updates from multiple edge devices is a significant challenge due to the potential privacy breaches and security vulnerabilities. In this paper, we propose EdgeSA a novel approach that leverages secure aggregation for privacy-preserving federated learning in edge computing. EdgeSA ensures that local model updates are gathered from edge devices in a way that respects privacy and integrity. Our comprehensive security analysis and experiments, demonstrate the effectiveness and efficiency of EdgeSA. The implementation results show that our scheme reduces the computational overhead on edge devices while achieving the same training accuracy as traditional federated learning schemes.
Secure IIoT networks with hybrid CNN-GRU model using Edge-IIoTset

Industrial Internet of Things (IIoT), or Industry 4.0, is an application of IoT in the industrial sector. Its main objective is to enhance product quality and optimize production costs by leveraging advanced technologies such as edge/fog/cloud computing, 5G/6G, and artificial intelligence. In the context of Industry 4.0, numerous devices and systems are interconnected to provide seamless services to users. However, with this interconnection comes the need to protect these devices and the information they transmit from cyberthreats and intrusions. In order to tackle this challenge, our proposed solution involves the utilization of deep learning (DL) models to develop an anomaly-based detection system. Our approach involves two powerful DL models, namely Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU). The proposed model’s performance is studied within binary and multiclass classification using a new real-world industrial traffic dataset called Edge-IIoTset. The outcomes of our experiments showcased the efficacy of the CNN-GRU model that we proposed, surpassing the performance of recent related works in terms of performance metrics, including accuracy, precision, false positive rate, and detection cost. The combination of the two models CNN and GRU outperforms the GRU model with 88% of detection cost in multiclass classification for one traffic flow.
Quantification of Explainability in Black Box Models using Complexity Measures

As a result in the rapid growth of explainability methods, there is a significant interest, driven by industry to develop methods for quantitative evaluation of such explanations. The availability of standard explainability evaluation methods would result in the ability to develop models that suit different stakeholders in different use cases. To address this issue, we propose three measures of the complexity of explanations based on Linear correlation, Monotonicity and $\phi_{K}$. We evaluate these measures on three tabular datasets (Ames House Price, Auto Price, and Wind). We investigate how these complexity measures vary with model accuracy. Our results show that model accuracy varies with complexity measures across the datasets. These variations indicate that models can be developed with the same accuracy but with of models less complex explanations as a result of varying the hyperparameters. We observe a trade-off between complexity measures and model accuracy which is evidenced in Pareto-fronts. We suggest that our metrics could be used for the development of multi-objective optimisation methods for machine learning models with tunable accuracy and simplicity of explanation.
New Cryptographic Bent Functions from Permutations and Linear Translator in Cyber Security

Cryptographic bent functions are maximally nonlinear Boolean functions with an even number of variables. They are not only closely related to some interesting combinatorial objects, but also have important applications in coding, sequence design, cryptography and cyber security. In this paper, we firstly investigate the Walsh transform of two families of functions via Maiorana-MacFarland's class. Secondly, we present new infinite families of permutations. We show that those families have nice property that one can select three elements among them which can be used to construct bent functions. Finally, by using two linear translators, we construct bent functions from the class of Maiorana-MacFarland.
Deep Rule Visualizer: A Novel Explainable Deep Neural Network Visualization Architecture

This paper introduces the Deep Rule Visualizer, an architecture designed to enhance the interpretability of deep neural networks (DNNs). By integrating xDNN model, saliency maps, and IF … THEN rules, the architecture provides users with insights into DNN decision-making. We evaluate the visualizer on benchmark datasets, showcasing its effectiveness in explaining predictions. The user interface displays rule images, regions of interest, and a novel Deep Rule Score (DRS), enabling users to assess prediction reliability. Quantitative evaluation confirms that higher Deep Rule Scores align with correct predictions. The visualizer enhances trust in DNN models, aids error identification, and holds potential across diverse domains.
Self-Supervised Embodied Learning for Semantic Segmentation

Embodied learning methods enhance perception ability using observations. The existing techniques can be divided into two categories: investigating how to gather samples to improve the perceptual ability as much as possible and improving the perceptual ability with less human cost by self-supervised mechanism. For an embodied learning task, it is important to use fewer human costs to improve the perception ability as much as possible. We design the FC(Familiar-Curious) reward based on the consistency of the current and previous frames, and the policy based on this reward encourages the agent to explore the region with curious and familiar samples. We trained a vision-based agent to verify the effectiveness of our method. Experimental results demonstrate that our approach can improve perceptual ability without manual annotations.
Review of Fundamental, Security, and Privacy in Metaverse

The Metaverse, a living environment, and cyberspace are used to virtualize and digitize the real world. It utilizes a variety of existing technologies to map the physical world and potentially realms beyond it. Future forecasts imply that Metaverse will be used in a variety of applications. The creation of numerous related technologies serves as the foundation for the Metaverse's upkeep. It follows that the security threats associated with the growth of the Metaverse may be increasingly obvious and complicated. Metaverse is the name of the most recent Internet-based platform. In order to establish a completely engross extremely spatiotemporal, self-sustaining common virtual world that allows individuals to play, work, and socialize, the Metaverse-a concept for the future version of the Internet-was developed. In order to provide security for the metaverse, it may be difficult given its inherent characteristics, such as sensory reality, hyper-spatial duration, long-term viability and variety. We present in this paper some of the technologies used in the Metaverse as well as some potential security and privacy issues.
A Privacy Preserving Service-Oriented Approach for Data Anonymization Through Deep Learning

In the realm of safeguarding data privacy, particularly when it comes to sensitive personal information acquired via sensors, anonymization plays a crucial role. Within this paper, we present a novel architecture that operates on a service-oriented basis, enabling the real-time anonymization of such data. Our primary objective is to ensure that authorized users can access the data while upholding privacy standards. To achieve this, our architecture involves annotating the data upon ingestion, assigning privacy levels to groups of columns. The anonymization process relies on an autoencoder model which returns a lower-dimensional encoding of the original data. In our architecture, pretrained models are fetched from a library and updated according to a set of policies, or a new model is trained if the user requests a new set of columns. Our solution can manage any situation where anonymization policies must be managed on the fly, and provides a flexible approach to balancing privacy protection and data access. The proposed architecture can be a valuable tool for data infrastructures providers, researchers, and other organizations dealing with sensitive personal information.
Image Segmentation with Topological Priors

Solving segmentation tasks with topological priors proved to make fewer errors in fine-scale structures. In this work, we use topological priors before and during the deep neural network training procedure. We compared the results of the two approaches on a simple segmentation task using various accuracy metrics and the Betti number error metric, which is directly related to topological correctness. It was found that incorporating topological information into the classical U - Net model performed significantly better. We conducted experiments on the ISBI EM segmentation dataset to confirm the effectiveness of the proposed approaches.
Pioneering Efficient Blockchain in IoT: A Review of Tailored Protocols for Modern Devices

The dawn of the digital age has transformed our world, with the Internet of Things (IoT) at its heart, where billions of devices are seamlessly connected. Essential to the vision of smart cities these devices deliver convenience, though not without drawbacks, and also pose a serious security risk. This paper dives deeper into the potential of blockchain - a decentralized ledger system - as a solution to these challenges, specifically focusing on its adaptability to IoT constraints. Through detailed analysis we conduct few key experiments to map the landscape that blockchain protocols are optimized for IoT and we discuss their features and shortcomings. These reviews cover policies from managing secure transactions to verifying payments, approving channels, and more. Despite the promise these protocols show, the use of blockchain in the IoT is fraught with challenges, mainly due to the limited features of many IoT devices, and, while blockchain promises security communicatively, it does not protect against all possible threats such as physical changes. The purpose of the paper is to compare these protocols and give the readers a panoramic view on the convergence of IoT and blockchain. Looking ahead, the research will focus on how to adapt to the ever-evolving world of IoT and address existing challenges, ensuring a future where our devices are better connected and it is safe.
Automatic Medical Report Generation via Latent Space Conditioning and Transformers

This paper presents a comprehensive exploration of integrating artificial intelligence (AI) in the healthcare sector, focusing on the development and implementation of a novel framework called VAE-GPT. Our architecture combines Variational Autoencoder (VAE) and Generative Pre-trained Transformer (GPT), to generate high-quality medical reports. The VAE component enables the model to learn a latent space representation of the images, capturing the underlying patterns and structures. The GPT component leverages the power of transformer-based language models to generate coherent and contextually relevant text. Additionally, a novel metric, Medical Embeddings Attention Distance (MEAD), is proposed in order to capture the semantic similarity between the generated and training medical reports, taking into account the importance of specific words determined by the attention module. Experiments on real dataset demonstrate that our framework achieves state-of-the-art comparable performances in generating accurate and informative medical reports.
Simulated Model for Preventing IoT Fake Clients over the Smart Cities Environment

The advent of the Internet of Things (IoT) has ushered in the concept of smart cities – urban environments where everything from traffic lights to waste management is interconnected and digitally managed. While this transformation offers unparalleled efficiency and innovation, it opens the door to myriad cyber-attacks. Threats range from data breaches to infrastructure disruptions, with one subtle yet potent risk emerging: fake clients. These seemingly benign entities have the potential to carry out a multitude of cyber attacks, leveraging their deceptive appearance to infiltrate and compromise systems. This research presents a novel simulation model for a smart city based on the Internet of Things using the Netsim program. This city consists of several sectors, each of which consists of several clients that connect to produce the best performance, comfort and energy savings for this city. Fake clients are added to this simulation, who are they disguise themselves as benign clients while, in reality, they are exploiting this trust to carry out cyber attacks on these cities, then after preparing the simulation perfectly, the data flow of this system is captured and stored in a CSV file and classified into fake and normal, then this data set is subjected to several experiments using the Machine Learning using the MATLAB program. Each of them shows good results, based on the detection results shown by Model Machine Learning. The highest detection accuracy was in the third experiment using the k-nearest neighbors classifier and was 98.77%. Concluding, the research unveils a robust prevention model.
Flying Carpets: Assessing Artificial Intelligence as an Entertainment Service

Artificial Intelligence (AI) is a scientific field, which has reached a significant impact on human activity. Applying AI to Unmanned Aerial Vehicles (UAVs) technology leads to astonishing services, which are of high popularity in humans' daily life. An area of application, which reshapes human habits, is the entertainment industry. This article it is proposed a system, which uses AI potentiality to provide special services in the area of entertainment such as in thematic parks for children. Specifically, it introduced the concept of the flying carpet stemming from fairy tales, which are able to carry kids through UAV technology. Such a toy system takes advantage of AI operations, which make feasible dynamic routing of the carpets within the playground area. Selection of a certain route is supported by applying customized options to children with regard to adults' supervision. Special focus is given to safety measures incorporated to assure a pleasant innovative activity by youths during their playing activities. Such an entertainment area is unobtrusively monitored by a context awareness system to provide a safe and unique experience to youth users.
Deepfake Detection Using XceptionNet

This paper presents a precise and highly efficient method for detecting deepfakes, which have become increasingly accessible via mobile applications, posing significant threats to society. Leveraging the Xception network, a state-of-the-art convolutional neural network (CNN), we address the challenge of identifying and categorizing deepfakes in both images and videos. With deepfakes achieving unprece-dented levels of visual fidelity, traditional detection methods are inadequate. The Xception network excels in capturing intricate visual patterns and anomalies indicative of deepfake manipulation. Trained on extensive datasets encompassing both real and deepfake content, it offers exceptional generalization capabilities, enabling accurate classification of previously unseen instances. This research emphasizes the critical need for robust deep fake detection mechanisms to protect against malicious use, ensuring compliance with official rules and ethical standards, and preserving public trust. The proposed Xception- based approach holds promise in addressing this pressing challenge, providing a reliable means to distinguish deepfakes from authentic content, ultimately safeguarding the integrity of digital media and information dissemination.
Towards Cybersecurity Risk Assessment Automation: an Ontological Approach

In the past decade, cyber attacks have been escalating, affecting both large organizations and smaller businesses, as well as individuals and a proactive approach to cybersecurity risk assessment has become indispensable. The risk assessment process is composed of different activities, from vulnerability management to the identification of threats (threat modeling). The main challenge lies in the manual nature of these activities facing complexities due to evolving threats and the growth of ICT infrastructures. Hence, providing automation to the risk assessment process is becoming crucial. The work presented in this paper aims to automate risk assessment using an ontology-based approach; a structured and formal representation of data is provided for ICT infrastructures description and related security information leveraging a defined ontology. The proposed solution follows an asset-oriented approach in defining the ontology; this allows to tightly link together infrastructure components and security data, enhancing automation's effectiveness and precision. The ontology is automatically populated with the required security information of the infrastructure under analysis, threat modeling is performed resorting to defined inference rules and all this information items are used to assess the risk of identified threats. The risk assessment process automation, as well as the supporting models and knowledge-base, proposed allows to easily adapt to changes in both the ICT infrastructure under analysis and the threat land scape as new information are integrated seamlessly in the evaluation process and support analyst in gather, combine and analyse the information required for evaluating risk.
Measurement and Analysis of Annual Solar Spectra at Different Installation Angles in Central Europe

Real world solar spectra are rarely equal to those under standard test conditions, which points to the importance of measuring, analyzing and understanding the implications of solar spectra variations in photovoltaic performance. This work presents and analyzes one year of solar spectra measured with installed spectrometers at optimum and vertical (building-integrated-photovoltaics relevant) angles in central Europe, reporting for the first time experimental datasets measured simultaneously over such timespan. In addition, we quantify the differences between these datasets with known key performance indicators and discuss the implications of spectral changes on the maximum current density of ideal single-junction and tandem devices for both installation angles.