Reviewerly/exHarmony huggingface dataset:
A Comparison of the Performance of Supervised Learning Algorithms for Solar Power Prediction

Science seeks strategies to mitigate global warming and reduce the negative impacts of the long-term use of fossil fuels for power generation. In this sense, implementing and promoting renewable energy in different ways becomes one of the most effective solutions. The inaccuracy in the prediction of power generation from photovoltaic (PV) systems is a significant concern for the planning and operational stages of interconnected electric networks and the promotion of large-scale PV installations. This study proposes the use of Machine Learning techniques to model the photovoltaic power production for a system in Medellín, Colombia. Four forecasting models were generated from techniques compatible with Machine Learning and Artificial Intelligence methods: K-Nearest Neighbors (KNN), Linear Regression (LR), Artificial Neural Networks (ANN) and Support Vector Machines (SVM). The results obtained indicate that the four methods produced adequate estimations of photovoltaic energy generation. However, the best estimate according to RMSE and MAE is the ANN forecasting model. The proposed Machine Learning-based models were demonstrated to be practical and effective solutions to forecast PV power generation in Medellin.
Can blockchain link the future?

Recently, decentralization has been extensively explored by researchers. Blockchain, as a representation of decentralized technology, has attracted attention with its unique characteristics, such as irrevocability and security. Consequently, herein, we introduce cutting-edge blockchain technologies from four directions: blockchain system, consensus algorithms, smart contract, and scalability. Subsequently, we analyze the current lack of consensus mechanism, fault tolerance, and block capacity of the blockchain, and the integration of blockchain into 5G/6G mobile communication. Furthermore, we discuss the possible applications of blockchain in intellectual property protection, the Internet of Things, digital twins, standardization, and epidemic prevention and control. Finally, explore the impacts and solutions of blockchain on human society beyond technology.
Towards Designing a Trusted Routing Scheme in Wireless Sensor Networks: A New Deep Blockchain Approach

Routing is a critical process in Wireless Sensor Networks (WSNs) since it is responsible for data transmission to base stations. Routing attacks are capable of completely destroying and degrading the function of WSNs. A trustworthy routing system is critical for ensuring routing security and WSN efficiency. Numerous studies have been conducted to increase trust between routing nodes, including cryptographic techniques, and centralized routing decisions. Nonetheless, the majority of routing methods are impractical in practice, since it is difficult to identify untrusted activities of routing nodes effectively. Meanwhile, there is no efficient method of preventing malicious node attacks. As a result of these issues, this article offers a trusted routing method that combines deep blockchain and Markov Decision Processes (MDPs) in order to enhance the routing security and efficiency of WSNs. To authenticate the process of transmitting the node, the proposed approach utilizes a Proof of Authority (PoA) method inside the blockchain network. The validation group necessary for proofing is selected using a deep learning methodology that focuses on the properties of each node. MDPs are then used to choose the appropriate next hop as a forwarding node capable of transferring messages simply and securely. According to testing data, our routing system still performs well in a 50% malicious node routing environment when compared to existing routing algorithms.
Privacy preserving in blockchain-based government data sharing: A Service-On-Chain (SOC) approach

Sharing government data is of great significance to social development, but insecure and inappropriate sharing may lead to privacy breaches. Data sharing in consortium blockchain has provided a promising direction for efficient privacy preserving government data sharing. However, since government data is not owned by a single person or any government employers, it is hard to attribute participants' responsibility for motivating data sharing behavior in blockchain systems. Furthermore, the scope and scale of government data to be shared among departments is unclear, as the purposes for retrieving shared data are dynamically changing. In order to solve these problems, we propose a Service-On-Chain (or simply, SOC) approach. The SOC approach can effectively identify different departments' data retrieving requirements while efficiently sharing government data with trustworthiness in data content and controllability in data ownership. In particular, we utilize smart contracts to provide an onchain service to define data sharing agreements between government departments, which can identify ambiguous data retrieving requirements and formalize the process logic. We apply the SOC approach to a real world scenario and demonstrate that the SOC approach provides a feasible solution for secure and efficient sharing of data among government departments.
Stereo priori RCNN based car detection on point level for autonomous driving

Binocular vision target detection algorithms generally require selection of a large number of keypoints, which result in a heavy computational effort for online calculation and the lack of ability to utilize spatial semantic information to full advantage. This paper proposed a stereo priori RCNN based car detection method on point level for autonomous driving. The algorithm combines traditional Region Proposal Network (RPN) with a Mask-branch mechanism, which improves the accuracy of 3D target detection through minimizing luminosity errors, and employs RGB images to provide semantic information for spatial point-clouds. Firstly, the proposed algorithm obtains bounding boxes of left and right images through RPN and classification networks. Then, the prior information of vehicles and branched convolutional neural networks are used to extract wheel features from a feature map. Therefore, the coordinates and orientation of vehicles on bird eye map can be fitted. Afterward, by predicting the keypoints, a 3D bounding box of each vehicle is roughly restored. Then Region of Interest (ROI) is applied on both sides of left and right cameras to minimize the photometric error, so that the precise position and the size of the detection frame are obtained. In the meantime, a Mask-branch mechanism is adopted to achieve a precise semantic segmentation of each ROI. Finally, a 3D bounding box of vehicles is used for point-cloud segmentation, and the semantic information provided by the Mask-branch is exploited to improve the segmentation accuracy. Extensive numerical experiments are conducted on the Kitti dataset and nuScenes dataset to demonstrate the efficiency and effectiveness of the proposed algorithm.
Three Halves Make a Whole? Beating the Half-Gates Lower Bound for Garbled Circuits

We describe a garbling scheme for boolean circuits, in which XOR gates are free and AND gates require communication of $$1.5\kappa + 5$$ bits. This improves over the state-of-the-art "half-gates" scheme of Zahur, Rosulek, and Evans (Eurocrypt 2015), in which XOR gates are free and AND gates cost $$2\kappa $$ bits. The half-gates paper proved a lower bound of $$2\kappa $$ bits per AND gate, in a model that captured all known garbling techniques at the time. We bypass this lower bound with a novel technique that we call slicing and dicing, which involves slicing wire labels in half and operating separately on those halves. Ours is the first to bypass the lower bound while being fully compatible with free-XOR, making it a drop-in replacement for half-gates. Our construction is proven secure from a similar assumption to prior free-XOR garbling (circular correlation-robust hash), and uses only slightly more computation than half-gates.
A robust multiclass <scp>3D</scp> object recognition based on modern <scp>YOLO</scp> deep learning algorithms

Abstract A multiclass 3D object recognition has perceived a numerous evolution with respect to both accuracy and speed. This study introduces the implementation of modern YOLO algorithms (YOLOv3, YOLOv4, and YOLOv5) for multiclass 3D object detection and recognition. All YOLO algorithms have been tested according to a very large scaled dataset (Pascal VOC dataset). Performance evaluation has targeted the calculation of the following metrics; mAP (mean average precision), recall, F 1‐score, IOU (intersection over union), and the running time. Experimental results demonstrate that the YOLOv3 has targeted mAP of 77%, IOU of 0.41 and the total running time was almost 8 h. Moreover, in YOLOv4, it has targeted mAP of 55%, IOU of 0.035 and the total running time nearly 7 h. In addition, YOLOv5 has established the mAP of 48%, IOU of 0.045, and the total running time was about 3 h. Finally, a modified version of YOLOv5 has been proposed in the state‐of‐the‐art of optimizing its hyperparameters and layering system. Accordingly, the mAP scored about 55% with 3 h running time. The final conclusions of this study have demonstrated that YOLOv3 has scored the highest recognition accuracy, however, the proposed modified YOLOv5 has scored the least processing time.
Quantum generators of random numbers

Abstract Generation of random numbers is a central problem for many applications in the field of information processing, including, e.g., cryptography, in classical and quantum regime, but also mathematical modeling, Monte Carlo methods, gambling and many others. Both, the quality of the randomness and efficiency of the random numbers generation process are crucial for the most of these applications. Software produced pseudorandom bit sequences, though sufficiently quick, do not fulfill required randomness quality demands. Hence, the physical hardware methods are intensively developed to generate truly random number sequences for information processing and electronic security application. In the present paper we discuss the idea of the quantum random number generators. We also present a variety of tests utilized to assess the quality of randomness of generated bit sequences. In the experimental part we apply such tests to assess and compare two quantum random number generators, PQ4000KSI (of company ComScire US) and JUR01 (constructed in Wroclaw University of Science and Technology upon the project of The National Center for Research and Development) as well as a pseudorandom generator from the Mathematica Wolfram package. Finally, we present our new prototype of fully operative miniaturized quantum random generator JUR02 producing a random bit sequence with velocity of 1 Mb/s, which successfully passed standard tests of randomness quality (like NIST and Dieharder tests). We also shortly discuss our former concept of an entanglement-based quantum random number generator protocol with unconditionally secure public randomness verification.
Theorising on covid-19 educational emergency: magnifying glasses for the field of educational technology

The Covid-19 pandemic has brought challenges to the educational system on micro, meso or macro levels. In this article, we posit that the current emergency augmented, what we call the meso space, while at the same time representing a sort of magnifying glass to observe and examine how distance education tools, practices and new forms of collaboration can emerge within the patterns of re-organising. This article focuses on the meaning of the Covid-19 emergency-related changes in the field of educational technologies from a theoretical and practical perspective. While the article mainly represents a theoretical contribution, the main conclusions are derived from, illustrated and substantiated with an ethnographic case study based on Italian teacher online communities. As a result, while this article proposes much-needed theorising in the field of educational technologies during the Covid-19 crisis, it also brings insights into the practice with EdTech tools and teacher professional development.
Digital Image Security Based on the Hybrid Model of Image Hiding and Encryption

In today's world's internet is a major communication method to communicate from one end to other, and image is mostly used digital content which are distributed on the internet but securely data communication through the internet is major issue today. To secure data or image many techniques are used in which hiding and encryption is the most popular techniques for image or data security. In this paper proposed a hybrid model of digital image security technique based on the image hiding and encryption. In this proposed technique, 2-level of security is to be provided to a secret image. The secret image is first hidden within a cover image using LSB hiding technique and after that got the stego image. Now after getting the stego image, the stego image is encrypted using AES encryption algorithm.
Leveraging graph neural networks for point-of-interest recommendations

Point-of-Interest (POI) recommendation, i.e., suggesting POIs that a user is likely to visit, is a key task to improve user experience in location based social networks (LBSNs). Existing models either focus on geographical influence without considering other factors such as social influence and temporal influence or rely on linear methods to combine different modeling factors, lacking a sophisticated and systematical way to learn representations for users and POIs for recommendation. To remedy these issues, in this work we propose GNN-POI, a generic POI recommendation framework that leverages Graph Neural Networks (GNNs), which demonstrate powerful modeling capacity to learn node representations from node information and topological structure to improve POI recommendation. Specifically, we construct a LBSN graph comprising of two types of nodes, i.e., user node and POI node. For a target user, her preference representation is learned by combining (1) representations of her social connection nodes and (2) representations of the visited POI nodes. For social connection nodes integration, in order to model the complicated and multifaceted social influence, an attention mechanism is applied to learn strengths of heterogeneous social relations; for location nodes integration, we utilize Bi-directional Long Short-Term Memory (Bi-LSTM) to model users’ sequential check-in behavior, taking into account geographical and temporal features. Extensive experiments conducted over three real LBSN datasets show that the proposed GNN based framework significantly outperforms the state-of-the-art POI recommendation models in terms of precision, recall and Normalized Discounted Cumulative Gain (NDCG).
Offensive Security: Towards Proactive Threat Hunting via Adversary Emulation

Attackers increasingly seek to compromise organizations' systems and data with advanced methods, often utilising legitimate tools.In the main, organisations employ reactive approaches to cyber security, focused on rectifying immediate incidents and preventing repeat attacks, through protections such as Security Information and Event Management (SIEM), firewalls, anti-spam/anti-malware solutions and system patches have been demonstrated to have significant weaknesses in addressing modern attacks.Proactive approaches, have been seen as part of the solution to this problem.However, techniques such as vulnerability and penetration testing (VAPT) are recognised as having limited scope and only working with threats that have already been discovered.Promising methods such as threat hunting are gaining momentum, enabling organisations to identify and rapidly respond to any potential attacks, though they have been criticised for their significant cost.In this paper, we present a novel model for uncovering tactics, techniques, and procedures (TTPs) through offensive security, specifically threat hunting via adversary emulation.The proposed technique is based on a novel approach of inducing adversary emulation (mapping each respective phase) model inside the threat hunting approach.The experimental results show that the proposed approach exploits the threat hunting via adversary emulation and has countervailing effects on hunting advance level threats.Moreover, the threat detection ability of the proposed approach utilizes minimum resources.The proposed approach can be exploited to develop the offensive security-aware environment for organizations to uncover advanced attack mechanisms and test their ability for attack detection.
Coarse-to-Fine: Progressive Knowledge Transfer-Based Multitask Convolutional Neural Network for Intelligent Large-Scale Fault Diagnosis

In modern industry, large-scale fault diagnosis of complex systems is emerging and becoming increasingly important. Most deep learning-based methods perform well on small number of fault diagnosis, but cannot converge to satisfactory results when handling large-scale fault diagnosis because the huge number of fault types will lead to the problems of intra/inter-class distance unbalance and poor local minima in neural networks. To address the above problems, a progressive knowledge transfer-based multitask convolutional neural network (PKT-MCNN) is proposed. First, to construct the coarse-to-fine knowledge structure intelligently, a structure learning algorithm is proposed via clustering fault types in different coarse-grained nodes. Thus, the intra/inter-class distance unbalance problem can be mitigated by spreading similar tasks into different nodes. Then, an MCNN architecture is designed to learn the coarse and fine-grained task simultaneously and extract more general fault information, thereby pushing the algorithm away from poor local minima. Last but not least, a PKT algorithm is proposed, which can not only transfer the coarse-grained knowledge to the fine-grained task and further alleviate the intra/inter-class distance unbalance in feature space, but also regulate different learning stages by adjusting the attention weight to each task progressively. To verify the effectiveness of the proposed method, a dataset of a nuclear power system with 66 fault types was collected and analyzed. The results demonstrate that the proposed method can be a promising tool for large-scale fault diagnosis.
TransForensics: Image Forgery Localization with Dense Self-Attention

Nowadays advanced image editing tools and technical skills produce tampered images more realistically, which can easily evade image forensic systems and make authenticity verification of images more difficult. To tackle this challenging problem, we introduce TransForensics, a novel image forgery localization method inspired by Transformers. The two major components in our framework are dense self-attention encoders and dense correction modules. The former is to model global context and all pairwise inter-actions between local patches at different scales, while the latter is used for improving the transparency of the hidden layers and correcting the outputs from different branches. Compared to previous traditional and deep learning methods, TransForensics not only can capture discriminative representations and obtain high-quality mask predictions but is also not limited by tampering types and patch sequence orders. By conducting experiments on main bench-marks, we show that TransForensics outperforms the state-of-the-art methods by a large margin.
Towards Question-Answering as an Automatic Metric for Evaluating the Content Quality of a Summary

Abstract A desirable property of a reference-based evaluation metric that measures the content quality of a summary is that it should estimate how much information that summary has in common with a reference. Traditional text overlap based metrics such as ROUGE fail to achieve this because they are limited to matching tokens, either lexically or via embeddings. In this work, we propose a metric to evaluate the content quality of a summary using question-answering (QA). QA-based methods directly measure a summary’s information overlap with a reference, making them fundamentally different than text overlap metrics. We demonstrate the experimental benefits of QA-based metrics through an analysis of our proposed metric, QAEval. QAEval outperforms current state-of-the-art metrics on most evaluations using benchmark datasets, while being competitive on others due to limitations of state-of-the-art models. Through a careful analysis of each component of QAEval, we identify its performance bottlenecks and estimate that its potential upper-bound performance surpasses all other automatic metrics, approaching that of the gold-standard Pyramid Method.1
An efficient optimization based microstructure reconstruction approach with multiple loss functions

Stochastic microstructure reconstruction involves digital generation of microstructures that match key statistics and characteristics of a (set of) target microstructure(s). This process enables computational analyses on ensembles of microstructures without having to perform exhaustive and costly experimental characterizations. Statistical functions-based and deep learning-based methods are among the stochastic microstructure reconstruction approaches applicable to a wide range of material systems. In this paper, we integrate statistical descriptors as well as feature maps from a pre-trained deep neural network into an overall loss function for an optimization based reconstruction procedure. This helps us to achieve significant computational efficiency in reconstructing microstructures that retain the critically important physical properties of the target microstructure. A numerical example for the microstructure reconstruction of bi-phase random porous ceramic material demonstrates the efficiency of the proposed methodology. We further perform a detailed finite element analysis (FEA) of the reconstructed microstructures to calculate effective elastic modulus, effective thermal conductivity and effective hydraulic conductivity, in order to analyse the algorithm's capacity to capture the variability of these material properties with respect to those of the target microstructure. This method provides an economic, efficient and easy-to-use approach for reconstructing random multiphase materials in 2D which has the potential to be extended to 3D structures.
Content-Aware GAN Compression

Generative adversarial networks (GANs), e.g., StyleGAN2, play a vital role in various image generation and synthesis tasks, yet their notoriously high computational cost hinders their efficient deployment on edge devices. Directly applying generic compression approaches yields poor results on GANs, which motivates a number of recent GAN compression works. While prior works mainly accelerate conditional GANs, e.g., pix2pix and Cycle-GAN, compressing state-of-the-art unconditional GANs has rarely been explored and is more challenging. In this paper, we propose novel approaches for unconditional GAN compression. We first introduce effective channel pruning and knowledge distillation schemes specialized for unconditional GANs. We then propose a novel content-aware method to guide the processes of both pruning and distillation. With content-awareness, we can effectively prune channels that are unimportant to the contents of interest, e.g., human faces, and focus our distillation on these regions, which significantly enhances the distillation quality. On StyleGAN2 and SN-GAN, we achieve a substantial improvement over the state-of-the-art compression method. Notably, we reduce the FLOPs of StyleGAN2 by 11× with visually negligible image quality loss compared to the full-size model. More interestingly, when applied to various image manipulation tasks, our compressed model forms a smoother and better disentangled latent manifold, making it more effective for image editing.