Reviewerly/exHarmony huggingface dataset:
DEAL: Differentially Private Auction for Blockchain based Microgrids Energy Trading

Modern smart homes are being equipped with certain renewable energy resources that can produce their own electric energy. From time to time, these smart homes or microgrids are also capable of supplying energy to other houses, buildings, or energy grid in the time of available self-produced renewable energy. Therefore, researches have been carried out to develop optimal trading strategies, and many recent technologies are also being used in combination with microgrids. One such technology is blockchain, which works over decentralized distributed ledger. In this paper, we develop a blockchain based approach for microgrid energy auction. To make this auction more secure and private, we use differential privacy technique, which ensures that no adversary will be able to infer private information of any participant with confidence. Furthermore, to reduce computational complexity at every trading node, we use consortium blockchain, in which selected nodes are given authority to add a new block in the blockchain. Finally, we develop differentially private Energy Auction for bLockchain-based microgrid systems (DEAL). We compare DEAL with Vickrey-Clarke-Groves (VCG) auction scenario and experimental results demonstrates that DEAL outperforms VCG mechanism by maximizing sellers' revenue along with maintaining overall network benefit and social welfare.
Achieving Personalized $k$-Anonymity-Based Content Privacy for Autonomous Vehicles in CPS

Enabled by the industrial Internet, intelligent transportation has made remarkable achievements such as autonomous vehicles by carnegie mellon university (CMU) Navlab, Google Cars, Tesla, etc. Autonomous vehicles benefit, in various aspects, from the cooperation of the industrial Internet and cyber-physical systems. In this process, users in autonomous vehicles submit query contents, such as service interests or user locations, to service providers. However, privacy concerns arise since the query contents are exposed when the users are enjoying the services queried. Existing works on privacy preservation of query contents rely on location perturbation or k-anonymity, and they suffer from insufficient protection of privacy or low query utility incurred by processing multiple queries for a single query content. To achieve sufficient privacy preservation and satisfactory query utility for autonomous vehicles querying services in cyber-physical systems, this article proposes a novel privacy notion of client-based personalized k-anonymity (CPkA). To measure the performance of CPkA, we present a privacy metric and a utility metric, based on which, we formulate two problems to achieve the optimal CPkA in term of privacy and utility. An approach, including two modules, to establish mechanisms which achieve the optimal CPkA is presented. The first module is to build in-group mechanisms for achieving the optimal privacy within each content group. The second module includes linear programming-based methods to compute the optimal grouping strategies. The in-group mechanisms and the grouping strategies are combined to establish optimal CPkA mechanisms, which achieve the optimal privacy or the optimal utility. We employ real-life datasets and synthetic prior distributions to evaluate the CPkA mechanisms established by our approach. The evaluation results illustrate the effectiveness and efficiency of the established mechanisms.
Adversarial Deep Learning for Over-the-Air Spectrum Poisoning Attacks

An adversarial deep learning approach is presented to launch over-the-air spectrum poisoning attacks. A transmitter applies deep learning on its spectrum sensing results to predict idle time slots for data transmission. In the meantime, an adversary learns the transmitter's behavior (exploratory attack) by building another deep neural network to predict when transmissions will succeed. The adversary falsifies (poisons) the transmitter's spectrum sensing data over the air by transmitting during the short spectrum sensing period of the transmitter. Depending on whether the transmitter uses the sensing results as test data to make transmit decisions or as training data to retrain its deep neural network, either it is fooled into making incorrect decisions (evasion attack) or the transmitter's algorithm is retrained incorrectly for future decisions (causative attack). Both attacks are energy efficient and hard to detect (stealth) compared to jamming the long data transmission period, and substantially reduce the throughput. A dynamic defense is designed for the transmitter that deliberately makes a small number of incorrect transmissions (selected by the confidence score on channel classification) to manipulate the adversary's training data. This defense effectively fools the adversary (if any) and helps the transmitter sustain its throughput with or without an adversary present.
A Survey of Belief Rule-Base Expert System

The belief rule-base (BRB) model is a new intelligent expert system with the characteristics of both expert system and data-driven model. In BRB there are many if-then rules which use belief degrees to express various types of uncertain information, including fuzziness, randomness, and ignorance. As a semi-quantitative modeling tool for complex systems, BRB has the superiorities of dealing both numerical quantitative data and linguistic qualitative knowledge that are derived from heterogeneous sources. Moreover, it is also a white box approach which can provide direct access and transparency to decision makers and stakeholders. Currently, BRB has been widely applied in many fields, such as decision making, reliability evaluation, network security situation awareness, fault diagnosis, and so on. To fully demonstrate the progress of BRB, the original BRB, and some evolution forms are introduced in this article.
Deep Spatial–Spectral Representation Learning for Hyperspectral Image Denoising

Deep learning has found successful applications in restoration of two-dimensional (2-D) images including denoising, dehazing, and superresolution. However, existing deep convolutional neural network (DCNN) architecture cannot fully exploit spatial-spectral correlations in three-dimensional (3-D) hyperspectral images (HSIs) (directly extending 2-D DCNN into 3-D will significantly increase computational complexity); meantime, unlike 2-D images, there is an obstacle caused by the shortage of training data for HSIs. To meet those challenges, we present a novel, deep-learning framework for 3-D HSI denoising with the following contributions. First, inspired by the success of U-net in low-dose current-transformer denoising, we propose a novel approach of encoding rich multi-scale information of HSIs by a modified 3-D U-net. Second, we present a computationally efficient implementation of 3-D U-net based on the strategy of separable filtering. By decomposing 3-D filtering into 2-D spatial filtering and 1-D spectral filtering, we can achieve substantial savings on the number of network parameters to keep the computational complexity low. Third, we have developed a transfer learning approach of synthetically generating HSIs from RGB images as supplementary training data. The synthesized HSIs are used for the initial training of the modified 3-D U-net denoising network, which will be fine-tuned on real HSI images. Experimental results have shown that the proposed 3-D U-net denoising method significantly outperforms existing model-based HSI denoising methods.
The impact of macroeconomic news on Bitcoin returns

This paper examines the relationship between news coverage and Bitcoin returns. Previous studies have provided evidence to suggest that macroeconomic news affects stock returns, commodity prices and interest rates. We construct a sentiment index based on news stories that follow the announcements of four macroeconomic indicators: GDP, unemployment, Consumer Price Index (CPI) and durable goods. By controlling for a number of potential biases we determine as to whether each of the series' have a significant impact on Bitcoin returns. While an increase in positive news surrounding unemployment rates and durable goods would typically result in a corresponding increase in equity returns, we observe the opposite to be true in the case of Bitcoin. Increases in positive news after unemployment and durable goods announcements result in a decrease in Bitcoin returns. Conversely, an increase in the percentage of negative news surrounding these announcements is linked with an increase in Bitcoin returns. News relating to GDP and CPI are found not to have any statistically significant relationships with Bitcoin returns. Our results indicate that this developing cryptocurrency market is further maturing through interactions with macroeconomic news.
A High-Performance CNN Processor Based on FPGA for MobileNets

Convolution neural networks (CNNs) have been widely applied in the fields of computer vision tasks. However, it is hard to deploy those standard neural networks into embedded devices because of their large amount of operations and parameters. MobileNet, the state-of-the-art CNN which adopts depthwise separable convolution to replace the standard convolution has significantly reduced operations and parameters with only limited loss in accuracy. A high-performance CNN processor based on FPGA is proposed in this paper. To improve the efficiency, two dedicated computing engines named Conv Engine and Dwcv Engine were designed for pointwise convolution and depthwise convolution respectively. The schedule for Conv Engine and Dwcv Engine has significantly improved the efficiency of our accelerator. Furthermore, we designed a special architecture called Channel Augmentation to improve the efficiency in the first layer of MobileNets. The accelerator can be flexibly deployed to various devices with different configurations to balance hardware resources and computational performance. We implemented our accelerator on ZU2 and ZU9 MPSoC FPGAs. The classification on ImageNet achieved 205.3 frames per second(fps) on ZU2 and 809.8 fps on ZU9, which is 15.4x speedup on ZU2 and 60.7x speedup on ZU9 compared to CPU. We also deployed MobileNet + SSD network on our accelerator for object detection, and achieved 31.0 fps on ZU2 and 124.3 fps on ZU9.
Application of functional deep belief network for estimating daily global solar radiation: A case study in China

Solar energy plays an essential role in environment governance and resource protection, as it is totally pollution-free and extensively accessed. An accurate knowledge of solar radiation is beneficial to the deployments of solar energy constructions, photovoltaic and thermal solar systems. In this study, a deep learning method is proposed for estimating daily global solar radiation, which is constituted by embedding clustering (EC) and functional deep belief network (DBN). Based on the curve shapes of daily solar radiation, EC divides the overall dataset into different subsets, which can be modeled separately. Knowledge from empirical radiation models is also merged as the input of functional DBN. The model can be directly applied to solar estimation in various stations due to its strong nonlinear representation. The case study in China is adopted that involves radiation data from a total of 30 stations to validate the practicability and accuracy of the proposed method. From the results, the method obtains better estimation precision with empirical knowledge, achieving 1.706 MJ/m2 of mean absolute error (MAE), 2.352 MJ/m2 of root mean square error (RMSE) and 13.71% of mean absolute percentage error (MAPE) according to the average values at the 30 stations.
EduRSS: A Blockchain-Based Educational Records Secure Storage and Sharing Scheme

Accurate and complete educational records are a valuable asset for people.In recent years, educational records have been digitized.However, there are still two key challenges that have not been resolved.One is to achieve secure and privacy-preserving storage of educational records, while another concern how to realize the sharing of educational records and ensure the security of the sharing process.In this paper, we propose EduRSS, a blockchain-based storage and sharing scheme for educational records is proposed, which combines blockchain, storage servers, and cryptography techniques to create a reliable and safe environment.In our proposal, the blockchain technology is used to ensure the security and reliability of data storage, while the smart contracts on the blockchain are used to regulate the process of storage and sharing.More precisely, the off-chain storage servers store the original educational records in encrypted form, while the hash information of the records is stored on the blockchain.The off-chain records are anchored periodically with the hash information on the blockchain to ensure the security of data storage.Cryptography techniques are utilized to handle records encryption and messages digital signature.To assess the effectiveness of EduRSS, we designed and tested a proof of concept of this scheme.The relative security analysis shows that EduRSS is safe and has a lower computational cost than that of the CP-ABE and the MA-CPABE schemes.
Scalable Verified Training for Provably Robust Image Classification

Recent work has shown that it is possible to train deep neural networks that are provably robust to norm-bounded adversarial perturbations. Most of these methods are based on minimizing an upper bound on the worst-case loss over all possible adversarial perturbations. While these techniques show promise, they often result in difficult optimization procedures that remain hard to scale to larger networks. Through a comprehensive analysis, we show how a simple bounding technique, interval bound propagation (IBP), can be exploited to train large provably robust neural networks that beat the state-of-the-art in verified accuracy. While the upper bound computed by IBP can be quite weak for general networks, we demonstrate that an appropriate loss and clever hyper-parameter schedule allow the network to adapt such that the IBP bound is tight. This results in a fast and stable learning algorithm that outperforms more sophisticated methods and achieves state-of-the-art results on MNIST, CIFAR-10 and SVHN. It also allows us to train the largest model to be verified beyond vacuous bounds on a downscaled version of IMAGENET.
Pixel-Based Image Encryption Without Key Management for Privacy-Preserving Deep Neural Networks

We present a novel privacy-preserving scheme for deep neural networks (DNNs) that enables us not to only apply images without visual information to DNNs but to also consider the use of independent encryption keys for both training and testing images for the first time.In this paper, a novel pixel-based image encryption method that maintains important features of original images is proposed for privacy-preserving DNNs.For training, a DNN model is trained with images encrypted by using the proposed method with independent encryption keys.For testing, the model enables us to apply both encrypted images and plain images for image classification.Therefore, there is no need to manage keys.In addition, the proposed method allows us to perform data augmentation in the encrypted domain.In an experiment, the proposed method is applied to well-known networks, that is, deep residual networks and densely connected convolutional networks, for image classification.The experimental results demonstrate that the proposed method, under the use of independent encryption keys, can maintain a high classification performance, and it is robust against ciphertext-only attacks (COAs).Moreover, the results confirm that the proposed scheme is able to classify plain images as well as encrypted images, even when data augmentation is carried out in the encrypted domain.
Algorithm 1000

SuiteSparse:GraphBLAS is a full implementation of the GraphBLAS standard, which defines a set of sparse matrix operations on an extended algebra of semirings using an almost unlimited variety of operators and types. When applied to sparse adjacency matrices, these algebraic operations are equivalent to computations on graphs. GraphBLAS provides a powerful and expressive framework for creating graph algorithms based on the elegant mathematics of sparse matrix operations on a semiring. An overview of the GraphBLAS specification is given, followed by a description of the key features and performance of its implementation in the SuiteSparse:GraphBLAS package.
A PLS-SEM Neural Network Approach for Understanding Cryptocurrency Adoption

The majority of previous research on new technology acceptance has been conducted with single-step Structural Equation Modeling (SEM) based methods. The primary purpose of the study is to enhance the new technology acceptance based research with the Artificial Neural Network (ANN) method to enable more precise and in-depth research results as compared to the single-step SEM method. This study measures the relation between technology readiness dimension (optimism, innovativeness, discomfort, insecurity) and the technology acceptance (perceived ease of use and perceived usefulness) - and the intention to use cryptocurrency, such as bitcoin. The contribution of this study include the use of a multi-analytical approach by combining Partial Least Squares- Structural Equation Modeling (PLS-SEM) and Artificial Neural Network (ANN) analysis. First, PLS-SEM was applied to assess which factor has significant influence toward intention to use cryptocurrency. Second, an ANN was employed to rank the relative influence of the significant predictor variables attained from the PLS-SEM. The findings of the two-step PLS-SEM and ANN approach confirm that the use of ANN further verifies the results obtained by the PLS-SEM analysis. Also, ANN is capable of modelling complex linear and non-linear relationships with high predictive accuracy compared to SEM methods. Also, an Importance-Performance Map Analysis (IPMA) of the PLS-SEM results provides a more specific understanding of each factor's importance-performance.
Offline Signature Verification using Deep Learning Convolutional Neural Network (CNN) Architectures GoogLeNet Inception-v1 and Inception-v3

Biometric systems such as signature verification are highly viable in order to identify individuals in organizations or in finance divisions. Advancement in classification of images using deep learning networks has opened an opportunity for this problem. In this study, the largest available handwritten signature dataset, namely, the GPDS Synthetic Signature Database, was employed for the classification of signatures of 1000 users, each of which having 24 original (or genuine) signatures, and 30 forged (or fake) signatures. Moreover, two popular GoogLeNet architecture versions of CNN, namely, Inception-v1 and Inception-v3, were used. Firstly, algorithms were trained on samples from 20 users, and achieved a validation accuracy of 83% for Inception-v1 and 75% for Inception-v3. In terms of Equal Error Rates (EER), Inception-v1 managed to obtain an EER as low as 17 for 20 users; while EER for Inception-v3 with 20 users obtained 24, which is a good measure compared to prior works in the literature. Although Inception-v3 has performed better in the ImageNet image classification challenge, in the case of 2D images of signatures, Inception-v1 has performed the classification task better than Inception-v3 It is also acknowledged in this study that Inception-v1 spent less time training, as it had a lower number of operations compared to Inception-v3.
A Simple Local Minimal Intensity Prior and an Improved Algorithm for Blind Image Deblurring

Blind image deblurring is a long standing challenging problem in image processing and low-level vision. Recently, sophisticated priors such as dark channel prior, extreme channel prior, and local maximum gradient prior, have shown promising effectiveness. However, these methods are computationally expensive. Meanwhile, since these priors involved subproblems cannot be solved explicitly, approximate solution is commonly used, which limits the best exploitation of their capability. To address these problems, this work firstly proposes a simplified sparsity prior of local minimal pixels, namely patch-wise minimal pixels (PMP). The PMP of clear images is much more sparse than that of blurred ones, and hence is very effective in discriminating between clear and blurred images. Then, a novel algorithm is designed to efficiently exploit the sparsity of PMP in deblurring. The new algorithm flexibly imposes sparsity inducing on the PMP under the MAP framework rather than directly uses the half quadratic splitting algorithm. By this, it avoids non-rigorous approximation solution in existing algorithms, while being much more computationally efficient. Extensive experiments demonstrate that the proposed algorithm can achieve better practical stability compared with state-of-the-arts. In terms of deblurring quality, robustness and computational efficiency, the new algorithm is superior to state-of-the-arts. Code for reproducing the results of the new method is available at https://github.com/FWen/deblur-pmp.git.
Gated Graph Recurrent Neural Networks

Graph processes exhibit a temporal structure determined by the sequence index and and a spatial structure determined by the graph support. To learn from graph processes, an information processing architecture must then be able to exploit both underlying structures. We introduce Graph Recurrent Neural Networks (GRNNs) as a general learning framework that achieves this goal by leveraging the notion of a recurrent hidden state together with graph signal processing (GSP). In the GRNN, the number of learnable parameters is independent of the length of the sequence and of the size of the graph, guaranteeing scalability. We prove that GRNNs are permutation equivariant and that they are stable to perturbations of the underlying graph support. To address the problem of vanishing gradients, we also put forward gated GRNNs with three different gating mechanisms: time, node and edge gates. In numerical experiments involving both synthetic and real datasets, time-gated GRNNs are shown to improve upon GRNNs in problems with long term dependencies, while node and edge gates help encode long range dependencies present in the graph. The numerical results also show that GRNNs outperform GNNs and RNNs, highlighting the importance of taking both the temporal and graph structures of a graph process into account.
Sampling in software engineering research: a critical review and guidelines

Representative sampling appears rare in empirical software engineering research. Not all studies need representative samples, but a general lack of representative sampling undermines a scientific field. This article therefore reports a critical review of the state of sampling in recent, high-quality software engineering research. The key findings are: (1) random sampling is rare; (2) sophisticated sampling strategies are very rare; (3) sampling, representativeness and randomness often appear misunderstood. These findings suggest that software engineering research has a generalizability crisis. To address these problems, this paper synthesizes existing knowledge of sampling into a succinct primer and proposes extensive guidelines for improving the conduct, presentation and evaluation of sampling in software engineering research. It is further recommended that while researchers should strive for more representative samples, disparaging non-probability sampling is generally capricious and particularly misguided for predominately qualitative research.